論文 / 著書情報
Article / Book Information
題目(和文)

連携・知識構造特性に基づく科学運営のためのデータ計装

Title(English)

Data instrumentation for management of science based oncollaboration and knowledge-structural features

著者(和文)

宮下修人

Author(English)

Shuto Miyashita

出典(和文)

学位:博士（技術経営）,
学位授与機関:東京工業大学,
報告番号:甲第12139号,
授与年月日:2021年9月24日,
学位の種別:課程博士,
審査員:仙石 愼太郎,梶川 裕矢,橋本 正洋,笹原 和俊,西山 伸宏

Citation(English)

Degree:Doctor (Management of Technology),
Conferring organization: Tokyo Institute of Technology,
Report number:甲第12139号,
Conferred date:2021/9/24,
Degree Type:Course doctor,
Examiner:,,,,

学位種別(和文)

博士論文

Type(English)

Doctoral Thesis

Powered by T2R2 (Tokyo Institute Research Repository)

PhD Thesis

Data instrumentation for management of science based oncollaboration and knowledge-structural features

Shuto MIYASHITA

Department of Innovation Science
School of Environment and Society
Tokyo Institute of Technology

ABSTRACTThis thesis, entitled ‘data instrumentation for management of science based on collaboration andknowledge-structural features’, aims at the instrumentation to enable data-driven decision-making forthe management of scientific research, which consists of five Chapters.
Scientific research, particularly publicly-funded research and development (R&D) for
innovations and solving multifaced complex societal issues, is significantly increasing in scale andcomplexity. However, the literature on strategic and organizational management of scientific research(so-called ‘the management of science’) at universities or public research institutes is still
underdeveloped compared to the literature of management of technology that mainly includes
industrial R&D. Furthermore, methodology that enables the acquisition of prescriptive knowledge inthe management of science need to be developed alongside the descriptive knowledge that has been
nurtured based on conventional scientometrics. To address these outstanding issues, it is essential todevelop a methodology for instrumentation of structural features in basic and scientific research byfocusing on the collaboration and knowledge structures in large-scale R&D programs and theirdescendant projects. Based on the understanding, a methodology for capturing the real-time status ofa scientific research project was developed to support data-driven decision-making by defining activity
indicators for scientific research based on network science and conventional scientometrics.
Chapter 1 provides a background of the research including the concept of the managementof science, its relation to scientometrics and the significance of instrumentation. The research objective
is disseminated in this chapter as to develop a methodology for instrumentation of structural featuresof research activities by focusing on the collaboration and knowledge structures in large-scale R&D
projects centered on science research. Further, a general framework is provided as the micro-mesomacro architecture that corresponds to phenomena at the individual (or team), project and programlevels, respectively.
Chapter 2 focuses on the system structure of interdisciplinary R&D program and its
descendant projects, based on empirical observation of the FIRST Program and its descendant
NanoBio First project. In the program-level analysis, the linkage of research funds and humanresources to the outcomes is examined using a structural equation model, which suggest that different
mechanisms work in the creation of academic outcomes and intellectual properties. In the projectlevel analysis, the factors that promote or impede the creation of intellectual properties are explored.
The analyses adopt mixed-methods approach combining quantitative network analysis and qualitativeinterview. It was found that the collaboration between a central university and a startup firm (which
has a complementary role) established a knowledge logistics system to realize not only social
implementation of research results through joint patent applications but also cultivated a reciprocal
mindset and flexible resource management according to the R&D stage.
Chapter 3 explores the interactive dynamics of collaboration and knowledge structures ininterdisciplinary R&D projects by visualizing the co-author and the co-word network, respectively,
2based on bibliographic analyses of publications. By introducing graph entropy as a measure of thecomplexity of these weighted network structures, the instrumentation of individual and institutional
R&D activities can be visualized. Co-author and co-word network analyses constructed upon
bibliographic information from publications in the COINS project confirmed that that these networks
have distinctive structural features and different modes of time-course evolution.
Chapter 4 examines a qualitative case study on how changes in the collaboration andknowledge structures at the team and individual levels can influence those at the project level inachieving R&D project goals based on the COINS project. Based on the information from the key
participants in the COINS project and the qualitative and time-course observation of the co-author andco-word networks at the individual level, an R&D strategy taken by each observed individual was
assigned to one of four quadrants comprised with two independent axes: the production/consumptionof core/peripheral knowledge, which was in line with the result of the graph entropy-based
collaboration and knowledge structures in Chapter 3 subtheme. Furthermore, based on theobservations and analysis of the COINS project, these results were verified to be consistent with the
R&D strategy and organization management in the real world.
Finally, in Chapter 5, the insights obtained from the previous chapters were summarized.
The instrumentation of the collaboration and knowledge structures in R&D activities at the program,project, team, and individual levels, respectively, enable the observation of organizational andmanagerial phenomena at each level and the identification of mechanisms implemented across levels,
which can lead to a structural understanding of large-scale R&D programs and projects.
Methodological developments in the instrumentation of organizational and managerial phenomena inscientific research will contribute to the systematization of management of science and will help
practitioners to support evidence-based management and data-driven decision-making.

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CONTENT
CHAPTER 1. INTRODUCTION ................................................................................................ 7
1. 1. BACKGROUND ....................................................................................................................... 7
1.2. MANAGEMENT OF SCIENCE AND SCIENTOMETRICS ...................................................................... 8
1.3. INSTRUMENTATION FOR MANAGEMENT OF SCIENCE: HOW TO OBTAIN EVIDENCE FOR MANAGEMENT 91.4. OBJECTIVE ............................................................................................................................. 9
1.5. RESEARCH APPROACH ............................................................................................................. 9
1.6. CHAPTER STRUCTURE ........................................................................................................... 10
CHAPTER 2. STRUCTURAL RELATIONSHIP AND INTELLECTUAL PROPERTY CREATION
IN PUBLICLY FUNDED R&D PROGRAM AND PROJECT MANAGEMENT ......................... 13
2.1. INTRODUCTION..................................................................................................................... 13
2.2. LITERATURE REVIEW ............................................................................................................. 14
2.3. FOCUS OF THE CASE STUDY .................................................................................................... 16
2.3.1 FIRST Program .............................................................................................................. 16
2.3.2 NanoBio First ................................................................................................................ 16
2.4. METHOD .............................................................................................................................. 17
2.4.1 Data collection for quantitative analyses .......................................................................... 17
2.4.2 Quantitative program-level statistical modeling and project-level network analysis ............. 18
2.4.3 Qualitative Analysis: Semi-structured interview with Project Leader .................................. 19
2.5. RESULTS .............................................................................................................................. 19
2.5.1 Results of program-level statistical modeling .................................................................... 19
2.5.2 Result of network analysis based on co-author network ..................................................... 28
2.5.3 Result of network analysis based on co-inventor network ................................................... 30
2.5.2 Results of qualitative semi-structured interview ................................................................ 34
2.6. DISCUSSION ......................................................................................................................... 37
2.6.1 Management at the program level .................................................................................... 37
2.6.2 Core/periphery structure in a co-author network of papers and co-inventor network of patents............................................................................................................................................. 37
2.6.3 Building a knowledge logistics system and the contribution of a startup firm ....................... 37
2.6.4 Information asymmetry in intellectual property management ............................................. 38
2.6.5 Limitations and future perspectives .................................................................................. 40
2.7 CONCLUSION ........................................................................................................................ 41
CHAPTER 3. COMPLEXITY OF COLLABORATION AND KNOWLEDGE STRUCTURE IN A
TRANSDISCIPLINARY R&D PROJECT ................................................................................ 46
3.1 INTRODUCTION ..................................................................................................................... 46

4

3.2 CASE STUDY AND HYPOTHESES ............................................................................................... 48
3.3 METHOD............................................................................................................................... 50
3.3.1 The method to compute graph entropy .............................................................................. 50
3.3.2 Data collection .............................................................................................................. 52
3.3.3 Software ........................................................................................................................ 52
3.3.4 Analytical procedures ..................................................................................................... 52
3.4 RESULTS ............................................................................................................................... 53
3.4.1 Overview of the co-author and co-word networks.............................................................. 53
3.4.2 Cross-sectional analysis of the complexity of collaboration and knowledge structures .......... 69
3.4.3 Longitudinal analysis of the complexity of collaboration and knowledge structures ............. 72
3.5 DISCUSSION .......................................................................................................................... 82
3.5.1 Interpretation of the structure of co-author and co-word networks ...................................... 82
3.5.2 Changes caused by structural decomposition in the complexity of collaboration and knowledge
structures ............................................................................................................................... 82
3.5.3 Changes caused by time evolution in the complexity of collaboration and knowledge structures............................................................................................................................................. 83
3.5.4 Contributions and limitations .......................................................................................... 84
3.6 CONCLUSIONS ....................................................................................................................... 87
CHAPTER 4. CONFIGURATIONAL APPROACH TO GENERATE PRESCRIPTIVEKNOWLEDGE BASED ON QUALITATIVE DESCRIPTION OF CONTEXTS,
INTERVENTIONS, MECHANISMS, AND OUTCOMES ......................................................... 90
4.1 INTRODUCTION ..................................................................................................................... 90
4.2 FRAMEWORK TO GENERATE PRESCRIPTIVE KNOWLEDGE THROUGH CONFIGURATIONAL APPROACH . 91
4.2.1 Problems related to the research synthesis of case studies on the management of a large-scale
R&D program and project ....................................................................................................... 91
4.2.2 Arrangement framework for configuration of contexts, interventions, mechanisms, andoutcomes ............................................................................................................................... 92
4.3 DESCRIPTIONS OF CONTEXTS, INTERVENTIONS, MECHANISMS, AND OUTCOMES FOR THE ENTIRE
PROJECT AND SUBTHEME LEADERS BASED ON QUALITATIVE OBSERVATIONS ..................................... 93

4.3.1 Context, interventions, mechanisms, and outcomes of the entire project .............................. 93
4.3.2 Context, interventions, mechanisms, and outcomes of subtheme leader 1 ............................ 95
4.3.3 Context, interventions, mechanisms, and outcomes of subtheme leader 2 .......................... 100
4.3.4 Context, interventions, mechanisms, and outcomes of subtheme leader 3 .......................... 105
4.3.5 Context, interventions, mechanisms, and outcomes of subtheme leader 4 .......................... 110
4.3.6 Context, interventions, mechanisms, and outcomes of subtheme leader 5 .......................... 115
4.4 VALIDATION BASED ON THE R&D STRATEGY ......................................................................... 120

5

4.4.1 Classification based on type of knowledge of knowledge producers and knowledge consumers........................................................................................................................................... 120
4.4.2 Validation based on real-world R&D strategies: Quadrant of core knowledge producers andcore knowledge consumers .................................................................................................... 120
4.4.3 Validation based on real-world R&D strategies: Quadrant of core knowledge producers andperipheral knowledge consumers ........................................................................................... 120
4.4.5 Validation based on real-world R&D strategies: Quadrant of peripheral knowledge producers
and peripheral knowledge consumers ..................................................................................... 121
CHAPTER 5. CONCLUSIONS .............................................................................................. 127
5.1 SUMMARY OF KEY FINDINGS ................................................................................................. 127
5.2 IMPLICATIONS ..................................................................................................................... 129
5.2.1 Theoretical implications................................................................................................ 129
5.2.2 Practical implications ................................................................................................... 129
5.3 LIMITATIONS AND FUTURE PERSPECTIVES ............................................................................... 129
ACKNOWLEDGEMENT ...................................................................................................... 130
APPENDIX A ........................................................................................................................ 131
APPENDIX B ........................................................................................................................ 136

6

CHAPTER 1. Introduction
1. 1. BackgroundResearch and development (R&D) in advanced science and technology is expected to lead to new
discoveries and inventions that will provide innovative solutions to various social issues. The
sophistication of basic and academic scientific research in universities and public research institutes
as well as technological development in industry are imperative in pursuing sustainable development,
as referred in the sustainable development goals (SDGs). This is because R&D is a source ofinnovation that impacts the creation of economic value, especially in science-intensive industries suchas pharmaceuticals (Cohen, Nelson, and Walsh, 2002; Grigss et al., 2013). Put differently, in recent
years, scientific research has not been just a purely fundamental and academic pursuit of results; it isalso expected to contribute to industrialization and commercialization (Etzkowitz, 1998; Etzkowitz
and Leydesdorff,2000).
Mission-oriented R&D projects, which are funded by government-sponsored programs
aimed to solve social problems, are an important part of recent science, technology, and innovation
policy. Such projects emphasize transdisciplinarity and a key issue to be addressed is the open
innovation through cooperation between academia, industry, and government, that is, industryuniversity-government collaboration (Bogers, Chesbrough, and Moedas, 2018; Perkmann and Walsh,2007). Subsequently, basic science research activities in universities and public research institutes aretransitioning from small group activities to large-scale projects involving groups with different
specialties; simultaneously, encouragement of the integration of different disciplines to create
interdisciplinary knowledge broadens the complexity of R&D (Anzai et al., 2012). Furthermore, basic
science research should achieve high cost-effectiveness and perform appropriate risk management justlike corporate investment activities because it is usually subsidized by public fund. Thus, it ischallenging to organize a body of knowledge for the strategic and organizational management ofscientific research to realize sustainable R&D against such increasing size and complexity.
Management of technology (MoT) is a well-established discipline that deals with thestrategic and organizational management of innovation processes. It largely deals with strategic andorganizational management theories for R&D practiced in companies, and has contributed to new
product development or intellectual property management. However, the methodology for the
management of basic and academic scientific research in universities and public research institutes,
the so-called “management of science” is less developed than the established MoT (Anzai et al., 2012;
Kodama, Watatani, and Sengoku, 2013). Strategic and organizational management is essential for theoperation of modern basic science research, such as the increasingly large and complex
interdisciplinary R&D projects. Therefore, this study focuses on how to systematize the managementof science, which is the theoretical basis for dealing with the strategic and organizational managementof basic science research.
7

1.2. Management of science and scientometrics
Existing disciplines closely related to management of science include scientometrics, science of
science (Fortunato et al., 2018; Wang and Barabasi, 2021), or research on research (RoR) (Ioannidis,
2018). In response to the aforementioned environment change surrounding scientific research, theknowledge for conducting scientific research as a team, or “the science of team science” is increasingly
expected due to particularly close area in management of science (Börner et al., 2010; Stokols et al.,2008a, b). Scientometrics focuses on quantitative methods, especially bibliometric methods, tomeasure and evaluate various activities in science, and also to explain the observed phenomena. On
the contrary, management of science is a type of management science, which differs from the naturalsciences, such as physics and biology, because it not only describes phenomena but also prescribes
means of problem-solving (Van Aken, 2004; Van Aken, 2005). Thus, it focuses on strategic planningand organizational design for scientific research, employing scientometric methodology.
In scientometrics, quantitative indicators are usually used to assess the impact andinterdisciplinarity of scientific research. Some of these indicators are calculated based on networks
built from bibliographic data of academic publications. In this regard, science can be viewed as an
ecosystem—a complex network of ideas, researchers, and papers as elements. These indicators,
derived from networks that represent the structure of the system of science, can be described as asummarized quantity, because the networks’ adjacency matrix corresponds to the design structure
matrix (DSM) (Browning, 2001; Browning, 2016) representing the relationship between the elementsof science.
Scientometric methodologies allow us to quantitatively assess the outcomes of scientificresearch. However, several issues must be overcome before the insights can be applied to the ongoing
scientific research practice. Although the utilization of scientometric indicator in decision-making
process has advantages in terms of objectivity and quantitative evaluation, overestimating technicalaspect of scientometric indicators creates a gap between the reality of scientific research and practice(Rafols, 2019). Furthermore, some of the traditional scientometrics are not suitable for ongoing
research activities because of the time frames. For example, citation analysis, which is often used toevaluate the impact of scientific research, has a time lag between publication and citation, making it
difficult to apply the results to ongoing processes (Kodama, Watatani, and Sengoku, 2013).
In this regard, the methodological development for management of science is expected,
especially for decision-making in ongoing R&D. The present study focuses on a network representing
the system structure of scientific research, which can be built by information obtained frombibliographic data of published articles, and explores the means for facilitating real-time decisionmaking in scientific research.

8

1.3. Instrumentation for management of science: How to obtain evidence for management
To achieve the objectives of large-scale interdisciplinary R&D programs and projects, selection ofdesirable actions based on scientific procedures, that is, practice of evidence-based management(Rousseau, 2006), is necessitated. The insights for organizational and managerial phenomena will be
extracted from the underlying data and data-driven decision-making will be executed using these
insights. However, a means for measurement of the data that quantitatively represent the characteristicsof these phenomena is essential to generate relevant insights. Without access to such data, theobservation of phenomena and the interpretation of their results become impossible.
The main focus of this thesis is the instrumentation to enable data-driven decision-making
in transdisciplinary R&D projects. The history of science is also the history of the development of
instruments for scientific research: for example, advances in analytical chemistry were brought about
by the progress of measuring instruments (Baird, 1993). Similarly, the motivation of the present study
is to provide a methodology for the instrumentation of organizational and managerial phenomena thatare observed in the activity of scientific research in interdisciplinary R&D projects, by developing an
instrument, which is an application of the methodology for network science and scientometrics. In thisthesis, the term “measurement” and “instrumentation” are clearly distinguished in the process ofdecision-making for project management. The word “measurement” refers to the assignment of
concrete numerical values as (quantitative) data that reflect the characteristics of the phenomenon.
Meanwhile, the word “instrumentation” is intended to develop techniques or methods forcharacterization and quantification to enable data measurement of the phenomenon. Furthermore, as
instrumentation implies that it allows measurement to achieve automatic control (Trimmer, 1949;
Wildhack, 1954), the intention is providing a methodology for prescribing solutions to organizationaland managerial problems, which is characteristic of management science. Therefore, measurement is
an activity in the explanatory science paradigm, while instrumentation is an activity in the design
science paradigm (Van Aken, 2004; Van Aken, 2005) in this thesis.

1.4. Objective
The present study aims to develop a methodology for instrumentation of structural features of research
activities by focusing on the structure of collaboration and knowledge in large-scale R&D projects
centered on basic science research. Through the effective use of assets available to research institutions,such as scholarly literature databases, ex ante activity indicators which are required in the strategicand organizational management of scientific research to capture real-time status and to execute datadriven decision-making are constructed and their usefulness are examined, rather than ex post
performance indicators such as the number of citations.

1.5. Research approach
As the target of the present study is large-scale R&D projects funded by government programs, it is9necessary to decompose the system into appropriate granularity to reduce its complexity for analysis.
Here, a micro–meso–macro architecture (Börner et al., 2010) is adopted to focus on the phenomena
observed at the micro (individual), meso (team), and macro (project) levels, and the mechanisms inwhich such phenomena appear have been discussed.
Furthermore, this study assumes that there are two types of system structures in scientificresearch: collaboration structures and knowledge structures. The collaboration structure represents thecooperation of the actors involved in scientific research, while the knowledge structure represents thelinkage between the knowledge possessed by these actors. Considering the purpose of interdisciplinary
R&D, improving interdisciplinarity entails the manipulation of knowledge structures. However,intentionally or directly manipulating knowledge structures would be difficult and the usuallyobserved phenomenon involves organizing specific research teams. Here, the relationship between
collaboration and knowledge structures is investigated.

1.6. Chapter structure
In this chapter, as a background to establish management of science, the increasing size and complexityof publicly-funded R&D and the similarities or differences between management of science andscientometrics are explained. In the subsequent chapters, the observations and interpretations of thephenomenon at the micro, meso, and macro levels in large-scale and publicly-funded R&D programs
and projects have been discussed.
In Chapter 2, focus is directed on the system structure of interdisciplinary R&D programs
and projects and the program- and project-level analyses conducted, respectively. In the program-level
analysis, the manner in which the funds and human resources subsidized by the programs are linked
to the outcomes is statistically modeled. In project-level analysis, the creation of intellectual property,
is analyzed from the perspective of knowledge logistics at the individual level. By adopting a mixedmethods approach, which combines quantitative network analysis and qualitative interview analysis,
the factors that promote and impede the creation of intellectual property are explored.
In Chapter 3, graph entropy, a measure of the complexity of network structure, is introduced.
Collaboration and knowledge structures are represented by co-author and co-word networks of paper,
respectively. The complexity of these network structures is measured and cross-sectional andlongitudinal analyses are conducted.
In Chapter 4, how the generative mechanism of interventions for research collaboration is
implemented from project level to team level and from team level to individual level, as a researchstrategy to achieve the project objectives is discussed through the arrangement of the configuration
among the context, interventions, mechanisms, and outcomes of individual R&D activities in a largescale project. Using the insights obtained from the previous chapters and the information accumulated
through the participant observation, a qualitative analysis of the generative mechanism of interventions
to realize research collaboration is conducted.
10In the final chapter, Chapter 5, the insights obtained from the previous chapters are
summarized and how the insights in management of science can be used for decision-making inscientific research are discussed.

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CHAPTER 2. Structural relationship and intellectual property creation in
publicly funded R&D program and project management
2.1. Introduction
The promotion of innovation based on cutting-edge science and technology is a driving force for
economic growth and industrial development as well as a powerful tool for solving the various societal
challenges facing our world. In addition, the significance and benefits of innovation have recently
been emphasized in the sustainable development goals (SDGs) (Grigss et al., 2013; Voegtlin andScherer, 2017). In particular, the promotion of open innovation (Bogers, Chesbrough, and Moedas,
2018) based on cooperation against academia, industry, and government — the so-called university–
industry–government collaboration — is a key agenda in science, technology, and innovation policy,
and governmental research and development (R&D) programs are continuously being implemented(Beck et al., 2020; Etzkowitz, 2004). At the same time, the increasing size and complexity of R&D
projects pose a managerial issue (Anzai et al., 2012; Lauto and Valentin, 2013; Milojevic,2014;
Wuchty, Jones, and Uzzi, 2007). Specifically, construction of a management system to promotesmooth cooperation among actors with different specialties and sectors is a pressing issue in R&D
management (Katoh et al.,2018; Lauto and Sengoku, 2015; Sauermann et al., 2020).
In innovation-oriented R&D projects, strategic creation of intellectual property, especiallypatent management, has been emphasized in recent years (D’Este and Patel, 2007; Perkmann et al.,2013). Such projects not only call for publication of research results (which many scientists prefer)
but also simultaneous pursuit of patents. Pursuit of both patents and publication must be strategic, as
patent applications must be filed prior to publication of the paper (Gans, Murray, and Stern, 2017).
Creation of intellectual property is essential for sustainable science, technology, and innovation;transforming R&D outcomes into transferable goods such as patents promotes knowledge
dissemination and further innovation in society (Lee and Lin, 2020; Siegel, Veugelers, and Wright,
2007).
In fact, it has been pointed out that intellectual property can influence many SDGs andcontribute to solving many societal issues, such as food, health, energy, and climate change (AlosSimo, Verdu-Jover, and Gomez-Gras, 2020; Bannerman, 2020). Bridging the technologies created byuniversities and public research institutions for industry is an essential part of sustainable
development; therefore, appropriate technology and knowledge transfers (Battistella, De Toni, andPillon, 2016; Bozeman, 2000; Perkmann et al., 2013) are required. In other words, the creation of
intellectual property is based on reciprocal relationships among different sectors and requires strategicand organizational management.
Although creation of intellectual property is strongly encouraged in publicly funded R&D
programs, making the R&D outcomes fruitful in terms of industrialization and commercialization isstill a challenging problem. Specifically, in the case of state-of-the-art R&D, there is information
13

asymmetry and mismatch in the sense of purpose between knowledge producers (i.e., academia) andknowledge consumers (i.e., practitioners). To elaborate further, a knowledge logistics system(Wijnhoven, 2003; Wijnhoven, 1998) that can autonomously and continuously supply high-quality
knowledge to knowledge consumers must be established.
In the present study, the structure of a large-scale R&D project as a system that includes
various actors from different disciplines and sectors, and their subsequent inter-organizational
collaborations are analyzed. In particular, the focus is biopharmaceutical field, which relies on directinteractions between academic institutions and industry. Then the key elements that contribute to
generating innovative outcomes are explored and the roles and responsibilities of the intermediatries
that catalyze for R&D activities are discussed. The present study also aims to develop acomprehensible theory on the mechanisms that promote or impede creation of intellectual property
and to develop a sustainable strategic and organizational management system for creation of
intellectual property. This study carries out a case study using the mixed-methods approach(Eisenhardt,1989b; Jick, 1979). An exploratory study was conducted to investigate the factors that
promote or impede creation of intellectual property by using a combination of quantitative analysis
based on collaboration networks built from bibliographic data and qualitative analysis of a semistructured interview.

2.2. Literature review
In university–industry collaboration, technology transfer remains an important topic (Bozeman, 2000).
Delivering research outcomes generated in universities to industry (e.g., through the form of patent
licensing) should have the potential to solve societal problems. However, technology transfer is abidirectional process with feedback rather than a unidirectional move from academia to industry(Meyer-Krahmer and Schmoch, 1998). Previous studies of the innovation process suggest that theinnovation process is not linear as represented by “technology push” or “needs pull.” Rather, there areinteractions between the fundamental research in research institutions and technology development inindustry (Rothwell, 1992).
In particular, in science-intensive sectors such as the biopharmaceutical industry,
fundamental research in universities strongly influences industry (Cohen, Nelson, and Walsh, 2002).
In addition, citations between patents and scientific publications tend to be strong in science-intensive
sectors, leading to strong relationships between the outcomes of academic research and technologies
used in industry. Furthermore, users of equipment for scientific experiments (i.e., scientists) contributeto innovation. Indeed, to be precise, the process known as technology transfer should be called“information exchange” to emphasize this bidirectional interaction (Meyer-Krahmer and Schmoch,1998).
Given this background, the importance of efforts to realize open innovation is widely
recognized because it is distinguished from technology transfer in a narrow sense (e.g., the transfer of14

intellectual property such as patents) and means that university-industry relationships for researchcollaboration are built earlier (Perkmann and Walsh, 2007). In the context of open innovation, there
are two types of university-industry relationships: research partnerships (joint research and research
centers) and research services (contract research and academic consulting) (Perkmann and Walsh,2007). Research partnerships are formal R&D arrangements of the collaborations among organizations
in R&D, mainly realized with political effort. Research services provide knowledge or techniques toexternal clients (often in industry) from researchers in universities. The Fraunhofer Institute inGermany is a successful model, where R&D activities are conducted based on industry needs (Beiseand Stahl, 1999).
In research collaboration, academia and policymakers agree that interdisciplinary research
has the potential to influence the established paradigm and to offer a breakthrough for problems unable
to be solved only using the knowledge or techniques from traditional monodisciplinary research.
Hence, many policies to promote interdisciplinary research, including governmental initiatives, havebeen launched. However, the term “interdisciplinary” has multiple meanings and ambiguous criteria,
and there is no broadly accepted consensus about what interdisciplinary is or is not (Huutoniemi et al.,2010; Stember, 1991). Thus, evaluations of interdisciplinarity have been difficult to perform and havemainly relied on conventional peer review. The problems with this process include the high workload
for reviewers and difficulty excluding reviewer bias (Kostoff, 1994). In recent years, bibliometricmethods, namely evaluations using quantitative indicators of interdisciplinarity, have been adopted(Stirling, 2007; Wagner et al., 2011). In practice, however, bibliometric indicators using information
on publications or patents are unsuitable for managing ongoing R&D projects because of the time lag
until they are published (Kodama, Watatani, and Sengoku, 2013). Moreover, Lauto and Sengoku(2015) pointed out issues in terms of human resource development, finding that the reward system forthose engaged in interdisciplinary research is insufficient.
In such transdisciplinary research, since stakeholders with various interests are usually
involved, principal–agent relationship (Eisenhardt, 1989a; Holmstrom, 1979; Jensen and Meckling,1976) among them can result conflicts of interest which can cause undesirable agent behaviors, suchas adverse selection and moral hazard, for the principals because of information asymmetry (Akerlof,
1970; Bergh et al., 2019; Bergmann and Friedl, 2008; Holmstrom, 1979; Ollier and Thomas, 2013;
Sappington, 1991). These problems can inhibit the efficient creation of outcomes and, furthermore,incur additional costs to prevent them. Adverse selection (Akerlof, 1970) is a phenomenon in which
the inherently desirable option for the principal is driven out due to the information asymmetry before
transactions. In general, there are two types of methods to prevent adverse selection: screening andsignaling (Connelly et al., 2011; Delfgaauw and Dur, 2007; Riley, 2001). Screening is that the
principals obtain information about the agent’s type by presenting them with multiple choices andmake them choose according to the agent’s type. Signaling is to solve information asymmetry
disclosing agent’s own type to the principals. On the other hand, moral hazard is a phenomenon in15

which the expected performance is undermined by the agent’s opportunistic behavior because theprincipals cannot observe the agent’s behavior. The means of preventing moral hazard include
monitoring and incentive contracts (Arnott and Stiglitz, 1991; Levin, 2003; Lewis and Bajari, 2014).
Monitoring is a means of inhibiting agent’s opportunistic behavior by the principal’s observation how
agent performs. Incentive contracts are a way that the agent to select the desirable behavior for the
principal by aligning the interests of the principal and the agent.

2.3. Focus of the case study
As the focus of case study, the governmental R&D support initiative that was implemented in Japanover a five-year period from 2009 to2013 was selected: The Funding Program for World-Leading
Innovative R&D on Science and Technology (FIRST Program) 1 and Development of Innovative
Diagnostic and Therapeutic Systems Based on Nanobiotechnology, known as NanoBio First2.

2.3.1 FIRST Program
The FIRST Program is a large-scale governmental R&D program that was implemented in Japan overa five-year period. This program provided large R&D funds for projects organized by 30 Japan’s top
researchers were selected for sustainable growth in international industrial competitiveness bypromoting world-class advanced R&D. This program has five major categories: “life science,”“medical engineering,” “material science,” “mathematics, physics, and information science,” and“machinery and system development.”
The FIRST Program aims to liberate researchers from troublesome tasks unrelated toresearch activity. For example, each subsidized project has an Operational Support Institution thatcoordinates inter-organizational collaborations among participating organizations and assists with thestrategic creation of intellectual property from research outcomes. In addition, enabling researchers tospend research funds in the following year provides them with financial freedom during an R&D
activity. This research fund has the following classification: Expense A (direct expenses), Expense B(expenses for the Operational Support Institution), and Expense C (indirect expenses). It also provides
an additional research fund to accelerate R&D further. Expense Band Expense C must be less than
20% of Expense A

2.3.2 NanoBio First
NanoBio First is one of the projects funded by the FIRST Program and aimed to create a system forrealizing medical care from early diagnosis to curative treatment for intractable diseases, such as

1

https://www.jsps.go.jp/english/e-first/index.html [last visited on 18 October 2020]2

http://park.itc.u-tokyo.ac.jp/nanobiof/en/index.html [last visited on 18 October 2020]16

cancer, by using nanobio devices that integrate the various functions of medical devices and drugs at
the nanoscale. For provisioning such system, a seamless and comprehensive process in which there
are various technologies across multiple disciplines is required. Examples include the detection of thewarning signs and lesions by early diagnosis using imaging techniques, the combination of surgeries
and drug therapies for safe and effective treatment, and the reconstruction of the lesion after treatment.
Because of the interdisciplinarity in the process, this project demands close collaboration
with engineering, medicine, pharmacies, and industry. To promote interdisciplinary or cross-field
R&D, this project had smaller research domain (called subthemes there) to create a (i) nano-diagnosis
system, (ii) nano-drug delivery system, (iii) minimally invasive nano-treatment system, and (iv) nanoreconstruction system. Each of these four subthemes has a leader (called the subtheme leader,including project leader). Subtheme leaders manage the R&D in the domain in which they in charge.
They coordinate R&D activities cooperating with universities, large companies, national research
institutes, startup firms, and the Japan Science and Technology Agency, which is the Operational
Support Institution in this project. In addition, how to implement technology seeds from their results(i.e., research outcomes) to contribute to society was investigated by monitoring global trends innanotechnology R&D because the commercialization of advanced medical technology affects medical
and social systems as a whole.

2.4. Method
Exploratory analyses were conducted according to the following procedure.
1. Data on each project in the FIRST Program for program-level analyses and bibliographic dataon papers and patents created in NanoBio First for project-level analyses were collected.
2. Quantitative analysis: Program-level statistical modeling and project-level network analyses
were conducted using the collected data.
3. Qualitative analysis: Semi-structured interview with project leader were conducted to deepen
the insights obtained from the quantitative analysis.

2.4.1 Data collection for quantitative analyses
Data collection for program-level statistical modeling
The dataset used in the program-level analysis was obtained from the ex post evaluation reportpublished by the Japan Cabinet Office3.

Data collection for constructing co-author network of papers
First, internal documents that was described list of published papers in NanoBio First during project3

Ex post evaluation of Funding Program for World-Leading Innovative R&D on Science and Technology, available

from https://www8.cao.go.jp/cstp/sentan/sentan_jigo.html [accessed on 18 October 2020]17

period (2009-2013) was obtained. Then, based on the list of papers, an adjacency matrix of a co-author
network and a list of information about each node were generated after downloading bibliographic
information from Web of Science (Clarivate Analytics).

Data collection for constructing co-inventor network of patents
As s same manner of the papers, list of applied patents in NanoBio First during project period was
obtained. On the basis of this internal documents, a list of patents created in NanoBio First wascompiled and aggregated patent information by family. PDF files of the patent documents were
downloaded from the Japan Platform for Patent Information (J-PlatPat)4, a database of the National
Center for Industrial Property Information and Training (INPIT) in Japan, and Espacenet5, an online
database of the European Patent Office. Information on the inventors and their affiliation was extracted
from the documents of each patent family and compiled for each family. Finally, 67 patent families
data were used as the dataset for the analysis.

2.4.2 Quantitative program-level statistical modeling and project-level network analysis
Program-level statistical modeling
Using this dataset, descriptive statistics, visualizing the observations and an exploratory factor analysis
were conducted. Then observations were classified into two types of variables: resources for R&D
projects (input variables) and performances resulting from R&D activities (output variables). Researchfunds and the number of researchers were the input variables. By contrast, the number of academic
publications, academic presentations, patent applications, and patent registrations were set as theoutput variables. After the exploratory factor analysis, the structural relationships of the FIRST
Program were schematically drawn using structural equation modeling. Although this method issomewhat unsuitable for this dataset because of the sample size and skew of distributions, it is usefulfor theory building from a case study.

Project-level network analysis based on co-author network of papers
A co-author network is a network in which nodes represent authors and edges represent the coauthorship. In this analysis, the co-author network was constructed using the following procedure: (i)information about the authors and their affiliations was extracted from the paper data and a list of
author information was generated; (ii) an adjacency matrix was generated by setting edges between
authors (i.e., nodes ) for all combinations of co-author in each paper; (iii) after anonymizing for privacy
protection, the adjacency matrix was transformed into a network object to add information about theaffiliation of each node. (iv) To identify the key actors in this co-author network, the node’s degree

4 https://www.j-platpat.inpit.go.jp [last visited on 18 October 2020]5https://worldwide.espacenet.com [last visited on 18 October 2020]18

and betweenness centrality was calculated.

Project-level network analysis based on co-inventor network of patents
A co-inventor network is a network in which nodes represent inventors and edges represent theinterrelationship of co-inventors. In this analysis, the co-inventor network was constructed using thefollowing procedure: (i) information about the inventors and their affiliations was extracted from thepatent family data and a list of inventor information was generated; (ii) an adjacency matrix was
generated by setting edges between inventors (i.e., nodes ) for all combinations of co-inventors in eachpatent; (iii) after anonymizing for privacy protection, the adjacency matrix was transformed into a
network object to add information about the affiliation of each node. (iv) To identify the key actors inthis co-inventor network, the node’s degree and betweenness centrality was calculated.

2.4.3 Qualitative Analysis: Semi-structured interview with project leader
The next step of the analysis is a qualitative study aimed at enriching the results of the quantitative
analysis with additional in-depth and contextualized insights into the actual understanding of NanoBio
First and the reasons that promote patent creation. A 60- minute semi-structured interview with the
project leader was conducted and asked following four questions.
⚫

How could NanoBio First achieve early creation and exploitation of intellectual property, such as
patent registration and licensing?

⚫

What was expected of collaborations between a university and a startup firm in NanoBio First?

⚫

What kind of actor had the high centrality as extracted from the network analysis?

⚫

Why was another startup firm established after NanoBio First when there already existed one
before?

These questions were selected as results of narrowing the scope of the interview through reading of
publicly available documents such as post evaluation reports, field research such as preparatoryinterviews with project leaders and other key actors, and the results of our quantitative network
analysis (see the next section), which showed that actors in startups have a high degree centrality andbetweenness centrality and that collaboration between universities and startups would play animportant role in the creation of intellectual property. This interview was conducted on October 2,2020.

2.5. Results
2.5.1 Results of program-level statistical modeling
Table 2.1 summarizes the descriptive statistics of observations. To capture the overview of theobservations related to the R&D projects subsidized by the FIRST Program, the data on input andoutput variables were presented using a bar chart. Figures 2.1, 2.2, 2.3, and 2.4 show the researchfunds for each R&D project, the number of researchers, the number of published papers (mostly in19peer-reviewed journals), and the number of academic presentations, respectively. Compared withpublished papers, more academic presentations were submitted without peer review. Figure 2.5 shows
the number of patent applications and registrations. There were too few patent registrations despite themany applications in most of these projects. However, NanoBio First exhibited a relatively highnumber of patent registrations, implying a better mechanism for creating intellectual property.

Table 2.1 Descriptive statistics of observations
Observation
Expense A

Mean
2815.7

S.D.
638.04

Max.
4366

Min.
1500

Med.
2752

Expense B

242.7

108.42

617

71

255

Expense C

269.97

93.15

577

144

260.5

Additional expense322.87333.23

1195

0

195

Researcher

106.87

68.58

317

25

88.5

Post doctral fellow

9.83

10.28

43

0

6.5

Total paper

154.67137.56

518

10

118.5

Peer reviewed paper

146.33133.17

506

10

109.5

Academic presentation

627

533.92

2793

25

528

Peer reviewed academic presentation

239.53214.41

1072

0

188

Domestic patent application

42.97

35.56

136

2

32.5

Fireign patent application

24.13

29.36

119

0

16

Domestic patent registration

2

3.01

14

0

1

Foreign patent registration

0.8

2.37

12

0

0

20

Figure 2.1 Research funds for each R&D project.

Figure 2.2 Number of researchers.
22

Figure 2.3 Number of published papers.

23

Figure 2.4 Number of submitted academic presentations.

24

Figure 2.5 Number of patent applications and registrations.

25

As a result of the factor analysis, factor loadings estimated were shown in Table 2.2. Then,the number of factors was assumed to be 3, generalized least square method was selected as theestimation method and the rotation was set at Oblimin (Oblique rotation) in the analytical process. The
structural relationships of the R&D project were modeled using structural equation modeling,
assuming a path from the input variables to the output variables through certain factors. The
transformation 𝑥′ = log(𝑥 + 0.5) was applied to all observations (Yamamura, 2002). Figure 2.6
shows the modeled path diagram. Additionally, Table 2.4 shows the values on measures of fit for themodel shown in Figure 2.6. The results indicate that the factors related to the number of researchers
directly affected academic outcomes and were simultaneously influenced by direct expenses (ExpenseA). Therefore, the amount of research funds has an indirect influence on academic outcomes. Thisfinding suggests that the more funding is used to employ full-time researchers such as post-doctoral
fellows, the greater are the academic outcomes, especially peer-reviewed papers.

Table 2.2 Factor loadings

Total paper

Academicoutcomes
0.89

Patentregistrations
0.065

Patentapplications-0.066Peer reviewed paper

0.897

0.059

-0.066

Academic presentation

0.984-0.0640.037

Peer reviewed academic presentation

0.907-0.0240.063

Domestic patent application

0.01

0.09

0.809

Foreign patent application-0.03-0.0540.831

Domestic patent registration

0.128

0.837

0.096

Foreign patent registration-0.0920.886-0.051

Output variable

Table 2.3 Correlations among the factors
Academic outcomes
Patent registrations
Patent applications

Academic outcomes
1
0.323
0.005

Patent registrations
0.323
1
0.277

Patent applications
0.005
0.277
1

Table 2.4 Measure of fit for structural equation modelingValue

p value
0.359

CFI
0.982

RMSEA
0.045

DOF
50

GFI
0.837

AGFI
0.703

Figure 2.6 Path diagram of the structural equation modeling results.

2.5.2 Result of network analysis based on co-author network
In the co-author network of papers in Figure 2.7, each node represents an author and the color of eachnode indicates the actor’s affiliations: pink represents university, blue represents industry, and yellow
represents public research institution.
The co-author network frorm the bibliographic information of papers created in from
NanoBio First had 728 nodes, 5596 edges, and 5 connected components. A large cluster formed aroundthe project leader as close relationship with the project leader has been observed. As can be seen in
the co-author network, the edges are concentrated in a few actors, that is, only certain actors have highcentrality. In other words, there are both dense and sparse parts in the whole network. Most of theactors with high centrality were researchers at the university, in particular principal investigator (PI).
However, there are some actors belonging to public research institutions and firms who havehigh centrality. In particular, actors of research institutes for applied R&D, especially the National
Cancer Center, tend to have a higher betweenness centrality than degree centrality (data not reported).
Hence, clinicians who belonged to the national institute play the role of a bridge between the small
clusters. In addition, the industrial actors with relatively high centrality in the co-author network of
the paper are confirmed to belong to startup companies (NanoCarrier).

Figure 2.7 Co-author network of papers created in NanoBio First. The color of a node represents its
attributes: purple, university; light blue, industry; yellow, public institute; blue, no data.

29

2.5.3 Result of network analysis based on co-inventor network
The composition of the patent family created in NanoBio First used in the analysis is summarized in
the Table 2.4. The “Number of affiliations” column represents the number of patent applicants orpatentee per sector. The “Number of created intellectual property” column shows the subtotal numberof created patent families for each sector, where the counts overlap in the case of joint applications.
The number of patent families filed in a single sector is shown in parentheses. The “Subtotal numberof inventors” column represents the subtotal number of inventors listed in the patent document foreach sector. The row of “Others” represents inventors whose affiliation could not be identified fromthe patent documents.
Table 2.8 The composition of the patent family created in NanoBio First used in the analysis
Researcher’s
Number of affiliations Number of created Subtotal number ofaffiliation
intellectual property
inventors
University
6
41 (15)83
Industry10
46 (19)26
Public512 (3)
17
Other
7
The co-inventor network constructed with patent data from NanoBio First had 133 nodes,
543 edges, and 10 connected components. This co-inventor network is illustrated in Figure 2.8. Oneof the structural features of the co-inventor network was that each cluster always included actors
belonging to the industrial sector. Although some patents were applied by only a single institute(university or public research institutes), cluster formation in the co-inventor network clearly show
collaborative relationships among inventors at NanoBio First associated with the product (i.e.,
patents); this indicates synthesis of knowledge between researchers in academia and practitioners inindustry. This suggests that actors who belong to industrial area deliver knowledge to develop
enterprises through R&D outcomes to actors who belong to academia, thereby serving as acomplementary knowledge source.
Tables 2.5 and 2.6 list the top 25 actors with high degree centrality and betweenness
centrality, respectively. The actors with high degree centrality were researchers from a university andwere actively involved in creation of the patents in this project. Furthermore, apart from university
researchers, public research actors also had relatively high betweenness centrality. These actors weremainly principal investigators (PIs) at public research institutes and they had higher betweenness
centrality than degree centrality because they provide brokerage between members inside and outsideof their organizations to collaborate with other organizations. Furthermore, as a common feature, only
a few actors had high degree and betweenness centralities. This high centrality concentration of
specific actors suggests that this co-inventor network has a core/periphery structure (Borgatti andEverett, 1999; Fontiand Maoret, 2016).
Notably, inventor 33, who belongs to the industrial sector, had high degree and betweenness
30

centralities compared to other inventors in the industrial sector. Inventor 33 served as a chief science
officer (CSO) at that time for the university-originated startup firm founded by the project leader.
Considering the intellectual property strategy in R&D projects based on university–industry–
government collaboration, since this actor had a high centrality, in other words, was included in thecore, the importance of the collaborative relationship between the university and startup firm wasindicated. In fact, the startup firm to which this actor belongs filed many patent applications jointlywith a university and contributed to creation of intellectual property.

Figure 2.8 Network of co-inventors of patents created in NanoBio First. Each node is color-coded
according to the sector to which the inventor belongs: purple, university; light blue, industry; yellow,
public institute; gray, no data.

31

Table 2.5 Top 25 actors with high degree centrality in the co-inventor network of patents
Inventor
SectorDegree centrality
Inventor 93
University
80
Inventor 110
University
59
Inventor 55
University
37
Inventor 98
University
27
Inventor 33
Industry
25
Inventor 71
University
20
Inventor 49
University
19
Inventor 10
University
17
Inventor 11
University
16
Inventor 65
University
16
Inventor 84
University
16
Inventor 46
University
14
Inventor 1
University
12
Inventor 118
University
12
Inventor 122
University
12
Inventor 126
University
12
Inventor 19
University
11
Inventor 50
Public11
Inventor 72
Public11
Inventor 82
University
11
Inventor 83
University
11
Inventor 85
University
11
Inventor 91
University
11
Inventor 100
University
11
Inventor 101
University
11

32

Table 2.6 Top 25 actors with high betweenness centrality in the co-inventor network of patents
Inventor
Sector
Betweenness centrality
Inventor 93
University
2025.55
Inventor 110
University
629.10Inventor 55
University
140.94Inventor 98
University
139.83Inventor 69
University
135.97
Inventor 52
Public107.70Inventor 33
Industry
88.94
Inventor 10
University
57.25
Inventor 118
University
47.00
Inventor 26
Public41.50
Inventor 50
Public41.50
Inventor 72
Public41.50
Inventor 130
Public41.50
Inventor 49
University
39.00
Inventor 65
University
31.33
Inventor 11
University
20.92
Inventor 71
University
19.41
Inventor 1
University
13.67
Inventor 84
University
10.21
Inventor 43
Industry
10.20
Inventor 126
University
9.73
Inventor 46
University
7.50
Inventor 14
University
6.20
Inventor 123
Industry
4.95
Inventor 62
University
4.00

33

2.5.2 Results of qualitative semi-structured interview
Table 2.7 is a summary of semi-structured interview’s questions and responses to project leader. First,
the project leader had an unwavering mindset that outcomes of R&D can be made available for societal
benefit through patenting. By sharing this mindset, NanoBio First was able to achieve agile intellectual
property management, such as publishing academic papers and applying for patents concurrently.
Therefore, it is thought to have led to the early commercialization of the intellectual property that
researcher at the NanoBio First closely consulted with a patent attorney hired by using funds from the
newly approved budget for R&D support to practice the creating a “strong patent” capable of industrial
application even in the middle stages of the R&D.
NanoBio First also adopted a strategy that emphasized collaboration with startups rather
than existing large companies, such as licensing the intellectual property created to startups on apriority basis. The collaboration between universities and startups was expected to provide
opportunities for researchers in academia to learn experientially the process of social implementationof their research outcomes and to function as a platform for the aforementioned mindset sharing.
Although startups have limited resources compared to existing large companies, it seems that thestartups were expected as collaborative partners for educational objectives such as the acquisition oftacit knowledge rather than benefits in terms of resource.
Inventor 33 was a CSO at NanoCarrier, which is a university-originated startup firm foundedby the project leader before the commencement of NanoBio First project. Inventor 33 had previously
worked at a large biotech company where he carried out research related to drug delivery (an expertiseof the project leader), and was the director of the corporate laboratory there. Inventor 33 is a specialist
with a deep understanding of intellectual property practices and their importance, and was one of the
key decision makers on intellectual property serving as a CSO of NanoCarrier. This fact supports thesuggestion from the results of the quantitative network analysis that a person who has dual skills inscience and business administration plays an important role in the creation of intellectual property.
After the FIRST Program, the project leader established a new startup, AccuRna Inc.
(Kawasaki, Japan), to focus resources on an R&D organization specializing in nucleic acid drugs,
while an existing startup company (NanoCarrier) was already established. AccuRna entered into a
capital and business alliance with NanoCarrier and set up a system to secure and utilize management
resources in the early stages and aquired exclusive rights to the intellectual property created in
NanoBio First, which gave it a competitive advantage. In addition, the establishment of AccuRna wasa great opportunity to attract people with expertise in nucleic acids or investors who are only interested
in new technologies. It was expected to involve new stakeholders that NanoCarrier had not been able
to approach in the past. In other words, talent was pooled into a new organization specializing in a
specific domain, and the accompanying storage of knowledge tied to the person became feasible. The
stored knowledge could be transferred from one organization to another through organizational
34

consolidation, such as a corporate acquisition. AcuRna was finally acquired by NanoCarrier in
September 2020, which integrated the human resources of both human resources and the knowledge,
such as know-how tied to them and knowledge transfer between organizations was achieved.

35

Table 2.7. Summary of semi-structured interview’s questions and responses to project leader
Question
Observed fact
Implication
Q 1:
Conventional patenting scheme is problematic because university 
Shared mindset that was recognizedHow could NanoBio First
researchers seek assistance with patenting from technology licensing
reciprocity between R&D and social
achieve early creation andorganizations (TLOs) only during the final stages of R&D
implementation contributed to I agile
exploitationofintellectual Researcher at the NanoBio First closely consulted with a patentcreation of intellectual property
property,suchaspatentattorney hired by using funds from the newly approved budget forregistration and licensing?
R&D support to practice the creating a “strong patent” capable ofindustrial application even in the middle stages of the R&D
Not only research leaders but also practitioners should identify thesimilarities and differences between publishing and patenting theirown R&D outcomes, which are not contradictoryQ 2:
Through the collaboration with the startups, it was expected that the 
The Acquisition of tacit knowledge thatWhatwasexpectedofresearchers in academia experientially learn the seriousness of
can only be obtain through on-the-jobcollaborations
betweenabusiness and the process of implementing their own research results
training in real business environment
university and a startup in
into society
was emphasized, rather than benefits in
NanoBio First?
Interacting with existing large companies hardly to provide theterms of R&D resources
opportunity of such a real case study
Q 3:
Inventor 33 was the chief scientific officer of NanoCarrier's at the time A person who has dual skills in
What kind of actor had the highand made important decisions regarding intellectual property, who
technologicaldevelopment
andcentrality (i.e., Inventor 33 in
had been the head of a research laboratory at a major biotechnology
business administration played an
Figure 2.8) as extracted from thecompany prior to joining NanoCarrier
important role in the creation andnetwork analysis?
dissemination of intellectual property.
Q 4:
AccuRna was established as an organization to conduct R&D 
A new startup specializing in a new
Why was another startup (i.e.,
specializing in new modalities (i.e., nucleic acid drugs), which
modality/application formed a specific
AccuRna) established after
intended to concentrate the management resources
intellectual
resource
pool
that
NanoBio First when there already 
NanoCarrier achieved advancement of an anticancer drug (small
accelerated the product development
existed one before?
molecule drug) under development to the clinical trial stage at thatprocesstime, but R&D in a new field was undesirable due to thediversification of management resources and a risk of backlash frominvestors

2.6. Discussion
2.6.1 Management at the program level
The results of the program-level analysis suggest different mechanisms in R&D activities between the
publication of papers for theoretical advancement and creation of intellectual property such as patentapplications and patent registrations. Furthermore, the important finding that patent applications andregistrations are affected by different factors implies different mechanisms behind these two processes
too. When the FIRST Program is considered to be a system, these implications suggest the existenceof three subsystems: (i) a subsystem to promote the creation of academic outcomes, (ii) a subsystem
to promote patent applications, and (iii) a subsystem to promote patent registrations. Moreover, acommon structure should provide interfaces to coordinate these subsystems. Put simply, an R&D
program such as the FIRST Program should provide a holistic system to create synergy between the
publications from academic research institutions and intellectual property expected as part ofindustrialization and commercialization activities.

2.6.2 Core/periphery structure in a co-author network of papers and co-inventor network ofpatents
In the co-author network and co-inventor network obtained from the analysisof NanoBio First, thestrength of the link between nodes, or the relationships between actors, depends on the destination. In
other words, each actor has different relationships as collaborators such as a “strong tie” or a “weaktie” (Granovetter, 1973). To better understand the structural feature of NanoBio First, the focus should
be strength of the tie (i.e., the closeness of the relationship between actors).
Observing the centrality of each actor in the co-author and co-inventor networks, ties tend
to be concentrated on some actors such as the project leader and subtheme leaders. These actors withhigh centrality are members of NanoBio First and most built close relationships with each other before
the start of the project. In each network, some actors aggregated and formed a cluster using these close
relationships, while others are loosely connected in clusters. Therefore, a core/periphery structure(Borgatti and Everett, 1999) can be observed in these networks, which indicates that some clusters arecomposed of actors connected by strong ties, whereas others have weak ties. In the present study, theset of members of an R&D project who share close relationships are defined as the core, and the
periphery otherwise.

2.6.3 Building a knowledge logistics system and the contribution of a startup firm
The results of the network analysis suggest that collaboration between actors from both academia andindustry can promote creation of intellectual property by synthesizing the complementary knowledge
inherent in each sector. Hence, embedding such actors with complementary knowledge into the corein NanoBio First would accelerate knowledge sharing, and forming a transaction-free zone (Baldwin,2008) would facilitate efficient knowledge exchange across the sectors. Reduced communication costs

within a core would accelerate R&D cycle and efficiently create R&D outcomes (Milojevic, 2014;
Zhang et al., 2007).
On the other hand, the results of the interview indicated that researchers were encouraged toacquire practical knowledge (mindset, business diligence, skills, and know-how) on creation of
intellectual property as well as on its social implementation through an on-the-job-training (Mincer,
1962) with a startup firm. In other words, NanoBio First practiced cultivation of human resources
skilled in both science and business administration (Nakajima, Miyashita, and Sengoku, 2018). To
cultivate such talent, NanoBio First particularly highlighted the transformation of the mindset ofresearchers in academia. In this study, the terms “sequential mindset” and “reciprocal mindset” were
defined to distinguish between mindsets that should be broken out and that should be cultivated insuch a transformation. A sequential mindset is a mode of thought assuming that technology andknowledge transfer proceeds from basic research to applied research and development in stages, as ina linear model of innovation (Godin, 2006), and that social implementation is not considered until the
final stage of research and development. Meanwhile, the reciprocal mindset is a mode of thought that
actively seeks to acquire and store tacit knowledge in domains closer to social implementation toachieve concurrent creation of academic and practical outcomes with considerations of the interaction
between R&D and social implementation. Cultivating such reciprocal mindset contributed to earlyacquisition of “strong patents” deserving industrial applications.
To summarize the above discussion, NanoBio First incorporated a process of learning(Wijnhoven, 2003) to acquire explicit and tacit knowledge across different domains or sectors. Rapid
knowledge production cycle in the transaction-free zone inside the core enabled development of aknowledge logistics system for agile generation of “strong patents” and early creation of intellectual
property. In particular, the involvement of the startup firm NanoCarrier was crucial in theimplementation of the above-mentioned system.

2.6.4 Information asymmetry in intellectual property management
The present study focuses on the principal–agent relationship (Eisenhardt,1989a; Holmstrom, 1979;
Jensen and Meckling, 1976) caused by information asymmetry between knowledge producers (projectbody) and knowledge consumers (industry or program body as a mediator) in knowledge logistics,
and considers the possibility that conflicts of interest impede efficient creation of intellectual property.
In this assumption, the knowledge consumers correspond to the principal and the knowledge producer
to the agent and the agent has either sequential or reciprocal mindset, as private information. Figure
2.9 shows a schematic representation of the payoff relationship between knowledge producers andknowledge consumers based on the framework of the principal–agent model. When information
asymmetry exists in the process of intellectual property creation, the principal–agent relationship cancause problems, such as adverse selection and moral hazard.

38

Figure 2.9. Schematic diagram of the payoff relationship in the principal–agent relationship between
knowledge producers and knowledge consumers. A circle in the symbol indicates that it is a beneficial
choice for the relevant principal or agent's stance, a triangle indicates that it is difficult to determine
either, and a cross indicates that it is an unbeneficial one.

Utilization of a startup firm for preventing adverse selection
When principals evaluate whether an agent will appropriately create intellectual property underinformation asymmetry, they tend to adopt the number of patent applications as evaluation criteria
because patent registrations require a certain amount of time, right from patent application toregistration, and involve additional costs and uncertainty. Based on this evaluation criteria, agents witha sequential mindset tend to be judged as “superior” to agents with a reciprocal mindset, which would
be desirable for creation of intellectual property rather than the because of the greater number of patentapplications. Therefore, there exists a problem of adverse selection. In the worst scenario, agents witha sequential mindset drive out agents with a reciprocal mindset, resulting in a flood of low-quality
patents that are unsuitable for industrial use.
In general, there are two measures to prevent adverse selection: screening and signaling;(Connelly et al., 2011; Delfgaauw and Dur, 2007; Riley, 2001); in a case like the FIRST Program, theestablishment of a startup firm before calling for projects can contribute to both. Specifically, screening
corresponds to designing R&D programs with two types of grant plans: a plan in which only projects
that have been undertaken through industry-academia collaboration with established startup firms
obtain generous budgets and support; and another plan in which projects are requested few forcommercialization but limited budgets and support. This would prevent the adoption of projects thathave little interest in social implementation of R&D outcomes. Simultaneously, signaling agents’“seriousness” about innovation through establishment of a startup firm offers a useful way to manifest
the value of investment to principals.

Monitoring and incentive mechanisms for avoiding moral hazard
Owing to information asymmetry originating from highly specialized knowledge, another problem,

namely moral hazard, can arise between principals and agents in cutting-edge R&D projects. Even if
an agent displayed opportunistic behavior, for instance, submitting an application without requesting
patent prosecution, the principal would not know the reasons in detail without understanding thecontent of individual patents, which require highly specialized knowledge. Hence, agents would havea strong incentive to select such opportunistic behavior because achieving patent registrations requires
costs and effort, not only at the time of patent application but also at the time of patent prosecution
request.
Enhanced monitoring and incentive contracts would be effective in preventing moral hazard(Arnott and Stiglitz, 1991; Levin, 2003; Lewis and Bajari, 2014). In the FIRST Program, milestoneevaluations such as periodic monitoring were conducted, which contributed to prevention of moral
hazard to a certain degree. However, program designers should be aware of the limitations ofmonitoring enhancement offered by the increasing size and complexity of an R&D project. On theother hand, a simple incentive contract ensures that only projects that actively create intellectual
property would receive additional R&D funding.
To exercise these measures properly, a mechanism design must satisfy the incentive
compatibility for agents. For young researchers, for example, contracts that lead to stable employment,such as offering permanent posts and priority hiring, will be able to provide stronger incentives thanfinancial induction because there is a certain amount of risk involved in engaging in activities forintellectual property creation instead of their academic research (Lauto and Sengoku, 2015).

2.6.5 Limitations and future perspectives
The present study has several limitations. First, the generalizability cannot be guaranteed because thepresent study performed a theory building from a single case study. Further theoretical development
is expected by expanding the applicable object to other R&D programs or projects in the future. Thisrequires that availability of data for comparative analysis among different cases should be ensured. In
other words, policy-makers should be aware of institutional design that enables to conduct analysis
for so-called evidence-based policy making after the completion of an R&D program or project.
Second, there is the problem of the statistical validity of the used dataset (e.g., sample size)in the analysis at program level. The number of cases used in this study is relatively high (30), but it
is not sufficient to guarantee statistical robustness and most data did not satisfy normality. The present
study compromised pursuing statistical validity to execute a practical analysis because it was difficult
to avoid these problems completely and apply each analytical method strictly.
Third, the analysis in this study was static: the dynamics within the R&D project could not
be considered because the information used was a summary at the end of the project. Managing an
ongoing R&D project may require a deeper understanding of its dynamic processes.
Fourth, since the present study intended to reveal a mechanism that promotes or impedes
creation of intellectual property, the proposed model that explains adverse selection and moral hazard
40

in the process of creating intellectual property is not mathematically rigorous. A mechanism designfor mission-oriented R&D program or project will require a mathematically rigorous approach. The
development of mathematical models representing incentive mechanisms can also contribute toempirical analysis (e.g., statistical analysis) for the process of intellectual property creation.
Fifth, the R&D outcomes, such as trade secrets or know-how, that are strategically hidden
to prevent imitation by competitors are not considered because the present study mainly analyzed
patent information. To consider more strategic decision-making, the proposed model should modify
the behavior or types of players. An improved model will facilitate analysis of decision-making in
more complex situations. In addition, to deal with the realistic decision-making process, or to avoid
the bounded assumptions of players in principal–agent theory (or game theory), introducing a
psychological or behavioral economic approach to the analysis would be desirable.

2.7 Conclusion
Present study investigated the structural relationships among the elements of complex and large-scale
R&D projects in which various actors participate and interact to promote innovation. In Addition, toreveal the mechanisms that promote or impede creation of intellectual property in a large-scale
publicly funded R&D project, a case study using a mixed-methods approach, combining quantitativeand qualitative analyses was conducted. Focusing on the FIRST Program, which is a national policy
for promoting innovative and world-leading R&D, how the NanoBio First promoted intellectual
property creation was examined. The quantitative analysis using the co-author and co-inventor
networks had a feature of core/periphery structure. The qualitative analysis of the management policy
on intellectual property confirmed that the project leader emphasized cultivating university researchers’
mindset towards social implementation through collaboration with a startup firm and to establish
systems for flexible resource management according to the R&D stage.
Based on these results, using the framework of principal–agent theory, the decision-making
process with information asymmetry between knowledge producers and knowledge consumers wasmodeled for systematic explanation of the mechanism that promotes or inhibits the creation of
intellectual property in large-scale R&D projects involving diverse actors. To handle adverse selection
and moral hazard in the process of intellectual property creation, the effectiveness of solutions suchthat agents should plan the R&D strategy, including collaboration with a startup firm at the time of
project adoption, was discussed. Moreover, principals should stipulate incentive contracts that provide
additional R&D funding depending on the degree of intellectual property creation.
Finally, the theoretical and practical implications of the present study are described. The
theoretical implication is that bridging the gap between two scholarly streams, policy science research
and management science research, developed a methodological basis for dealing with program andproject management measures in an integrated manner based on a mixed-methods approach. In
addition, from the perspective of principal–agent theory, the mechanisms that promote and impede the41

creation of intellectual property in large-scale R&D projects were explained. Meanwhile, the practical
implication is the contribution to the institutional design of future R&D programs and projects. The
utilization of startups and the incentive design for sustainable intellectual property creation werehighlighted, especially in transdisciplinary research oriented program and projects.

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CHAPTER 3. Complexity of collaboration and knowledge structure in atransdisciplinary R&D project3.1 Introduction
In recent years, scientific research has been required not only in the pursuit of purely academic results,
but also to contribute to the development of the technology oriented toward industrialization andinnovation with the creation of economic value (Etzkowitz 1998; Etzkowitz and Leydesdorff 2000;
Leydesdorff 2003). In response to the demand that science should contribute to solving societal
problems, public research programs are facing the problem of increasing size and complexity.
Scientific research in publicly funded research programs has shifted from the activities of small groups
to large-scale research and development projects. Interdisciplinary and transdisciplinary research,which are regarded as sources of innovation, have been promoted simultaneously (Anzai et al. 2012).
These changes have strengthened the importance of the “science of team science,” which is
the knowledge about the strategic and organizational operation of scientific research (Börner et al.
2010; Stokols et al. 2008a, b). Scientometrics is expected to contribute as a means of providing
knowledge for such a “science of team science.” However, when applying traditional approaches toactual decision-making, the problem that scientometric indices tend to lose value as the materials forappropriate decision-making has been highlighted. This is due to the separation between these andpractice in scientific research activities caused by focusing on only the technical aspects and being
applied with an excessively reductionist approach (Barre 2019; Ràfols 2019). In other words, themethodology of management of basic and academic scientific research in universities and publicresearch institutions, or “management of science6” is underdeveloped as compared to the establishedtechnology and innovation management (Anzai et al. 2012; Kodama et al. 2013).
The motivation for the present research is to design an instrument for the management ofinterdisciplinary research projects that reflect structural characteristics and its complexity. In particular,
the present study is interested in the measurement of the structural complexity of ongoing research
projects using scientometric methods. Appropriate measurement and control of the project’s
complexity are useful means of achieving the project goal (Kapsali 2011, 2013). To contribute todecision-making in scientific research, the objects of measurement and methods on complexity shouldmeet the following requirements: (i) real-time measurement of activities is objectively andquantitatively possible (i.e., without time lags such as backward citations)7; and, (ii) the method canbe universally applicable to decision-making in scientific research programs or projects of different
disciplines.
6 The term “management of science” is used as a contrast to technology management, an established discipline that

deals with strategic and organizational management theories for research and development in industry.
7

For example, citation analysis is a method for the impact assessment based on networks representing the citation

structure of academic articles. However, such analysis is not suitable for real-time evaluation because there is a time
lag until the time a paper is cited.

46

The present study aims to propose an analytical framework with indices to measure thecomplexity of collaboration and knowledge structures to support strategic and organizational
management of scientific research in interdisciplinary research projects. Collaboration and knowledge
structures are represented as networks that can be constructed from bibliometric data of publishedarticles. The proposed framework provides instruments that assist decision-making using quantitativeand objective complexity indicators derived from the structure of these networks. To design an
instrument that meets these needs, the study focused on the collaboration structure between actors andthe knowledge structure between concepts in interdisciplinary research projects. Then, the present
study set the research question: How can the complexity of the collaboration and knowledge structure
in interdisciplinary research projects be evaluated? In this study, collaboration structure represents therelationship between diverse actors engaged in scientific activities, while knowledge structure
represents the relationship between the concepts described in the scientific publications.
Scientific activities can be represented as a complex network of ideas, scholars, andpublications (Fortunato et al. 2018; Wang and Schneider 2020). Hence, in scientometrics, networks
constructed from bibliographic data are employed for quantitative evaluations of the impact andinterdisciplinarity (Ávila-Robinson and Sengoku 2017b; Wagner et al. 2011). Such network-based
quantitative evaluation often uses measures that are computed for individual nodes in the network (e.g.,
degree centrality). Although they reflect the properties of individual components, indicators defined
per node are unsuitable for observing the properties of an entire project. In particular, optimizing thewhole system to achieve the project goals will be required in mission-oriented projects such as those
supported by government programs. In addition, a project management perspective, which is within
the scope of this study, requires that the status of such projects be comparable over time.
Based on the above argument, a summary of the methodology for quantifying the complexityof collaboration and knowledge structures is presented below. In the present study, graph entropy(Mackenzie 1966; Sen et al. 2019, 2020; Gomez-Pilar et al. 2018) is introduced as an indicator of thecomplexity of the network structure. The present study uses the Shannon entropy (Shannon 1948),
which is calculated from a probability distribution of the normalized edge weights of a weighted
network. Entropy is often used as measure of the diversity of systems, especially interdisciplinarity inthe context of scientometrics (Silva et al. 2013; Wang and Schneider 2020; Wagner et al. 2011). An
increase in the complexity of the collaboration and knowledge structures in a project can be viewed
as diversification of the collaborator and the knowledge to be utilized. This allows us to measure thecomplexities of the collaboration and knowledge structure by building a co-author network (a networkthat represents the collaboration structure) and a co-word network (a network that represents theknowledge structure). Furthermore, the current study adopts cross-sectional and longitudinal analyses.
A cross-sectional analysis examines the changes in complexity caused by structural division of the
project structure into its components, while a longitudinal analysis examines the changes ofcomplexity due to the evolution of the network over time. In other words, our analysis deals with two
47

types of networks; static and dynamic networks. The combination of these analytical methods andnetworks is derived from four types of network analysis. The procedures of analysis are as follows:
1. Co-author and co-word networks are constructed using an aggregation of the bibliographic dataof publications created during a research project.
2. Qualitative observations on the structural characteristics and dynamic evolution are made byvisualizing the networks.
3. Cross-sectional and longitudinal analyses are conducted through the calculations of thecomplexity measures of the aforementioned networks.

3.2 Case study and hypotheses
The Center of Open Innovation Network for Smart Health (COINS)8 was selected as a case study.
COINS is a research project funded by the Center of Innovation Science and Technology-based
Radical Innovation and Entrepreneurship Program (COI STREAM)9. The COI STREAM is a largescale research program funded by Japan’s Ministry of Education, Culture, Sports, Science, andTechnology (MEXT). The program intensively supports high-risk and challenging interdisciplinary
and cross-disciplinary research and development to establish an innovation platform for sustainable
innovations that would be difficult for universities to achieve by themselves.
COINS’ vision is to create a “smart life care society” in which patients autonomously
become healthy in their daily lives without the psychological, physical, and economic burdens of
medical care. COINS aims to achieve this vision by creating an “In-body hospital” through the use ofsmart nanomachines, which are the size as a virus. Such an application of nano-biotechnology will
circulate 24 hours a day within the human body to diagnose and treat diseases. The management
regime comprises the core organization of this project that is independent of the university, theKawasaki Institute of Industrial Promotion, a foundation operated by the City of Kawasaki (i.e., local
government). Researchers hired by the Institute are working to realize open innovations in the
Innovation Center of NanoMedicine (iCONM).
The project, which was subsidized by the COI Program and runs for nine years (FY 2013–2021), is divided into three phases: Phase 1 (FY 2013–2015); Phase 2 (FY2016–2018); and Phase 3(FY 2019–2021). This study focuses on the outcomes up to Phase 2 of the COI STREAM. To achieve
the vision described above, COINS has six research domains, referred to as “subthemes,” summarizedin Table 1.

8 https://coins.kawasaki-net.ne.jp/en/index.html [last visited on 12 Aug 2020]
9 https://www.jst.go.jp/tt/EN/platform/coi.html [last visited on 12 Aug 2020]

48

Subtheme
Subtheme 1
Subtheme 2
Subtheme 3
Subtheme 4
Subtheme 5
Subtheme 6

Table 1 Subthemes of the COINS
Research topics
Nanomachines that can target and eliminate intractable cancer
Innovative technology for the treatment of cranial nerve diseases
Nanomachines that carry messenger RNA (mRNA) for tissue reconstruction andnano-sized vaccine
The system for in-home cancer diagnosis, which requires no blood sampling
Theranostic system, which combines nanomachines and medical equipment ultraminimally invasive treatment
Social system for implementation of the results into society

The present study analyzes the relationship between collaboration and knowledge structure
in an interdisciplinary research project. Interdisciplinary research projects are expected to create
complex insight by integrating diverse knowledge from individual scientists or small groups.
According to previous studies in the field of technology management, a structure of a product, which
is an outcome of the activities for innovations, interacts with the structure of an organization thatdevelops that product (Henderson and Clark 1990). Such findings enable us to derive a basic
hypothesis that changes in collaboration structure can influence changes in knowledge structure ininterdisciplinary research projects:
Hypothesis 1: In an interdisciplinary research project, changes in collaboration structure are correlatedwith changes in knowledge structure.
At a glance, the qualitative description of this hypothesis seems to be trivial. However, thequantitative statement is not trivial because it cannot be revealed the process of changing collaborationor knowledge structures without any instruments of the quantitative measurement for that process. In
other words, quantifying how much a structure changes according to another and how structuralchange proceeds, would provide empirical evidence for management of interdisciplinary research
project. In the research on the management of interdisciplinary research project, which is concernedwith strategy planning or organizational design for scientific research, observations for hypothesisbuilding (or data measurement) and analyses for hypothesis testing are inseparable. In this sense, ourhypothesis is not a priori, but a data-driven hypothesis (Van Helden 2013; Kell and Oliver 2004) froman observational case study.
As a concept that plays a complementary role to the data-driven approach, the present study
proposes hypothesis-driven data instrumentation, in which a series of methods for data measurement
is implemented to test hypotheses that have not yet been quantitatively represented. The present study
introduces the complexity based on network structure as a quantity examining changes of thecollaboration and knowledge structures and measures the structural changes observed when someinterventions are applied to these networks.
49

To conduct the multi-level analysis for the structure of scientific research, the present study
adopts a micro-meso-macro architecture (Ávila-Robinson and Sengoku 2017a; Börner et al. 2010) andsets three levels: micro-structure (individual actors), meso-structure (subthemes), and macro-structure(project as a whole) 10 . A cross-sectional analysis investigates the changes in complexity bydecomposing the structure into three levels. Based on Hypothesis 1, the following hypothesis isderived, regarding the change of the complexity of collaboration and knowledge structure with the decomposition from macro into meso or meso into micro:
Hypothesis 2: When collaboration and knowledge structures are decomposed into their substructuresby the same criterion, their reduced complexity is covariant.
As the project progresses over time, although the collaboration among actors and knowledge
related to the research topic become more complex, project management to prevent too complex astructure is required at the end of the project. In other words, since research projects essentially tend
to be more complex, the external interventions to suppress excessive increase in complexity can affectthe success of the project. This argument leads to the following hypothesis regarding the changes incomplexity over time:
Hypothesis 3: If there is no external intervention, the complexity of collaboration and knowledge
structures in research projects will increase over time.

3.3 Method
3.3.1 The method to compute graph entropy
The present study uses the graph entropy (Mackenzie 1966; Sen et al. 2019, 2020; Gomez-Pilaret al.
2018) calculated using the weights of the edges of the weighted network as a measure, to quantify the
structural complexity of the co-author network—which represents the collaboration structure—andthe co-word network—which represents the knowledge structure. Given a weighted network 𝐺 =(𝑉, 𝐸), the graph entropy 𝐻(𝐺) of the network 𝐺 is defined by the following equation
𝐻(𝐺) = − ∑ 𝑞𝑖,𝑗 log2 𝑞𝑖,𝑗𝑞𝑖,𝑗 ≠0where 𝑞𝑖,𝑗 is the normalized quantity so that the sum of the weights of the edges is 1. If a weight
between node 𝑖 and 𝑗 in a graph 𝐺 = (𝑉, 𝐸) denotes 𝑒𝑖𝑗 ∈ 𝐸,

10 Note that micro, meso, or macro levels are a relative concept. In the present study, the structure of the project as awhole is defined as a macro-structure. However, the research programs funding the project, as well as the science andtechnology innovation policies, correspond more to a macro-level structure.

50

𝑒𝑖𝑗

(𝑖 ≠ 𝑗)𝑞𝑖,𝑗 = {∑(𝑒𝑖𝑗 ∈ 𝐸)
0(𝑖 = 𝑗)As is clear from above equation, the graph entropy 𝐻(𝐺) of the network 𝐺 is the Shannon entropy
when the distribution of edge weights normalized so that their sum is 1 is considered as a probability
distribution.
The graph entropy is a quantity about the complexity of the whole network. If the network
is large, the interest for analysis is often only a part of the whole network. Furthermore, it is useful tobe able to define an index that measures the local complexity of the network, that is, a graph entropy
for a sub-network. The sub-graph entropy of a sub-network 𝐺′, which is sub-network of network 𝐺, isdefined as follows:
𝐻(𝐺′) = − ∑ 𝑞′𝑘,𝑙 log2 𝑞′𝑘,𝑙
𝑞′𝑘,𝑙 ≠0where 𝑞′𝑖,𝑗 is a re-normalized quantity of the edge weights of the sub-network 𝐺′. Since the edges ofthe sub-network 𝐺′ consist only of parts of the edges of the original network 𝐺 and the sum of these is
not equal to 1, the edge weights need to be re-normalized when calculating the sub-graph entropy. If
a weight between node 𝑘 and 𝑙 in a sub-graph 𝐺′ = (𝑉′, 𝐸′) denotes 𝑒′𝑘𝑙 ∈ 𝐸′,𝑒′𝑘𝑙(𝑘 ≠ 𝑙)𝑞′𝑘,𝑙 = {∑(𝑒′𝑘𝑙 ∈ 𝐸′)
0(𝑘 = 𝑙)The graph entropy 𝐻(𝐺) has a nature that its value depends on the number of edges in the
network 𝐺 . It can intuitively understand that the complexity of a network with a large number of edges
is likely to be greater than in a network with a small number of edges. Therefore, when comparing thegraph entropy between networks with different numbers of edges, conversion of the graph entropy
into a quantity that is independent of the number of edges is required. Fortunately, the graph entropy
can be normalized by the theoretically calculatable maximum value to limit within the range from 0
to 1. When the weights of the edges in a weighted network 𝐺 are uniformly distributed and the alledges have equal weights (i.e., equivalent to an unweighted network), the value of graph entropy ismaximized. Then, the maximum value of the graph entropy 𝐻𝑚𝑎𝑥 can be calculated using the numberof edges 𝑁𝑒𝑑𝑔𝑒 in the network as follows:
𝐻𝑚𝑎𝑥 = −1
𝑁𝑒𝑑𝑔𝑒∑ log2

1
𝑁𝑒𝑑𝑔𝑒= log2 𝑁𝑒𝑑𝑔𝑒

Hence, a network 𝐺 ’s the normalized graph entropy 𝐻𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 (𝐺) can becalculated throughdividing the graph entropy 𝐻(𝐺) by its maximum value as follows:
𝐻𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 (𝐺) =

𝐻(𝐺)
𝐻(𝐺)
=
𝐻𝑚𝑎𝑥 log2 𝑁𝑒𝑑𝑔𝑒

These equations lead to an important nature of graph entropy: the graph entropy 𝐻(𝐺) of a network 𝐺
51

is expressed in the form of the product of the logarithm of the number of edges 𝑁𝑒𝑑𝑔𝑒 in the networkand the normalized graph entropy 𝐻𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 (𝐺).
𝐻(𝐺) = log2 𝑁𝑒𝑑𝑔𝑒 × 𝐻𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 (𝐺)This fact means that the graph entropy can be decomposed into two parts: one part is determined by
the number of edges in the network, which is independent of the edge weights; and the another one isdetermined by the shape of the distribution of the edge weights. In other words, when a change ingraph entropy is observed, such decomposition into components enable us to investigate the reason
for the change.

3.3.2 Data collection
Based on internal documents from COINS, academic outcomes published in the period up to Phase 2(2013–2019) were listed and bibliographic data on publications were collected from the Web of
Science, a scholarly literature database provided by Clarivate Analytics. The bibliographic information
for “Article,” “Review,” and “Proceedings Paper” were used for the analysis. Furthermore, only
publications from subthemes 1 to 5 dealing with research topics related to nano-biotechnology wereset for the analysis; that is, publications from subtheme 6, which dealt with the social sciences, were
excluded. Finally, the bibliographic information of 281 publications were used in the analysis.

3.3.3 Software
To build the co-author and co-word networks, the present study used Python for data processing andcalculations of the graph entropy and Gephi to visualize these networks. For the layout of nodes in the
network, the Fruchterman-Reingold algorithm (Fruchterman and Reingold 1991) embedded in Gephi
was used. For more details, see the Appendix A.

3.3.4 Analytical proceduresThis study used the following procedures:
1. To build both static and dynamic networks, the following datasets were constructed from thecollected bibliographic information: an aggregated dataset of bibliographic information, a corpus
of titles and abstracts by publication, panel data of summarized lists of institutions andpublications by author, panel data of adjacency matrices of co-author networks, and panel data ofadjacency matrices of co-word networks.
2. The static co-author and co-word networks of COINS as a whole were constructed by converting
aggregated panel data of the adjacency matrices for all intervals (2013–2019) into network objects.
3. Qualitative observations on the structural features and time evolution of these networks were madefrom the static network diagram and the dynamic network animations of the networks.
4. Cross-sectional analysis: For static co-author and co-word networks representing the macrostructure (whole project), the meso-structure (per subtheme), and the micro-structure (per52individual actors), the graph entropy was computed to investigate the changes of the structuralcomplexity caused by structural decomposition in COINS. The co-author/word meso/micronetworks was defined as a sub-network structure of the original co-author/word macro-network.
A subset of nodes in a particular meso/micro-network was defined as all the listed authors or
identified words in the group of publications that belongs to a particular subtheme or an individual,
respectively. Then, each co-author/word meso/micro-network was generated based on each ofthese subsets. For example, in the case of constructing an author's micro (individual) level network,
all the publications by this author were identified, and subsequently, a co-author network wasmade of all the nodes in these publications. Similarly, a co-word network of an author was madebased on the words extracted from titles and abstracts in the author’s publications.
5. Longitudinal analysis: For the dynamic co-author and co-word networks, graph entropy andnormalized graph entropy by year was computed and the changes in the structural complexity ofthese networks over time were investigated by depicting time-course plots, whose horizontal axis
is year, vertical axis is the graph entropy or the normalized graph entropy of the co-author or coword networks, for co-author and co-word networks, respectively, and a trajectory, whose
horizontal axis is the graph entropy or normalized graph entropy of the co-author network, verticalaxis is the graph entropy or normalized graph entropy of the co-word network.
6. A semi-structured interview was conducted with the research leader to interpret the results of theanalysis and validate the data-driven hypotheses derived from it.
For details of these processes, see the Appendix A.

3.4 Results
3.4.1 Overview of the co-author and co-word networks
Three types of co-author and co-word networks were constructed, representing the structure of the
project as whole (macro-structure), per the subtheme (meso-structure), and per actor (microstructure). The network constructed for the project as a whole network was referred as a macronetwork, per subtheme as the meso-network, and per actor as a micro-network.
Figure 3.1 shows the co-author macro-network and Figure 3.2 shows the co-word macronetwork. The constructed co-author macro-network had 1025 nodes and 8386 edges, and the coword macro-network had 7432 nodes and 22,013 edges. The nodes in the co-author macro-network
shown in Fig. 3.1 represent authors, while the colors of the nodes represent their affiliations bysector: purple for universities, red for the core organization (iCONM), light blue for industry, yellow
for public institutions, and blue for no data. The co-author macro-network comprises several clusters
that include university researchers, as well as actors from public institutions and industry. Thisconfirms the characteristics of transdisciplinary research projects where various stakeholders
collaborate to address common issues. The static network diagram of the co-author and co-word
macro-networks showed the following structural features: the former was globally connected except
53

one isolated sub-network whereas the latter had one large network of connected components as wellas numerous isolated sub-networks. Nodes of the co-author macro-network tended to form multiple
modules, whereas those of the co-word macro-network spread outward from the core in cluster-likebranches.
The co-author and co-word meso-network were constructed by extracting sub-networks
from the original network (i.e., macro-network) based on subtheme attributes assigned to each node.
Figures 3.3-3.7 and 3.8-3.12 show the co-author and co-word meso-networks, respectively. The
structural features of networks were as follows: those for subthemes 1, 2, 3, and 5 had both commonand specific clusters for each subtheme. The network for subtheme 4 had a distributed network withmany small clusters and without a core cluster, as observed in the other subthemes.
Figures 3.13 and 3.14 show the time evolution of the co-author and co-word macronetworks, respectively. An observation of the time evolution of dynamic co-author and co-word
networks revealed different characteristics: the co-author network was gradually connected throughthe emergence of brokerages between clusters, after nodes initially formed clusters andagglomerated. In the co-word network, new concepts evolved out of the core cluster and small
clusters emerge simultaneously. Furthermore, the emergence of new concepts that connectedbranches and existing small clusters propagated the knowledge in other fields.

54

Figure 3.1 Network structure of co-authorship of the publications from the whole COINS project. The
color of a node represents its attributes: purple, university; red, core organization (iCONM); light blue,
industry; yellow, public institute; blue, no data.

55

Figure 3.2 Network structure of co-word relations of the publications from the whole COINS project.

56

Figure 3.3 Co-author meso-network of the publications from subthemes 1 of COINS project. The colorof a node represents its attributes: purple, university; red, core organization (iCONM); light blue,
industry; yellow, public institute; blue, no data.

57

Figure 3.4 Co-author meso-network of the publications from subthemes 2 of COINS project. The colorof a node represents its attributes: purple, university; red, core organization (iCONM); light blue,
industry; yellow, public institute; blue, no data.

58

Figure 3.5 Co-author meso-network of the publications from subthemes 3 of COINS project. The colorof a node represents its attributes: purple, university; red, core organization (iCONM); light blue,
industry; yellow, public institute; blue, no data.

59

Figure3.6 Co-author meso-network of the publications from subthemes 4 of COINS project. The
color of a node represents its attributes: purple, university; red, core organization (iCONM); light
blue, industry; yellow, public institute; blue, no data.

60

Figure 3.7 Co-author meso-network of the publications from subthemes 5 of COINS project. The colorof a node represents its attributes: purple, university; red, core organization (iCONM); light blue,
industry; yellow, public institute; blue, no data.

61

Figure 3.8 Co-word meso-network of the publications from subthemes 1 of COINS project.

62

Figure 3.9 Co-word meso-network of the publications from subthemes 2 of COINS project.

63

Figure 3.10 Co-word meso-network of the publications from subthemes 3 of COINS project.

64

Figure 3.11 Co-word meso-network of the publications from subthemes 4 of COINS project.

65

Figure 3.12 Co-word meso-network of the publications from subthemes 5 of COINS project.

66

Figure 3.13 Time evolution of the co-author macro-network. The upper row represents the period of Phase 1 (FY 2013-2015) and the lower row represents theperiod of Phase 2 (FY 2016-2018) in the COI Program. The nodes and edges in this network cumulatively increase over time.

Figure 3.14 Time evolution of the co-word macro-network. The upper row represents the period of Phase 1 (FY 2013-2015) and the lower row represents theperiod of Phase 2 (FY 2016-2018) in the COI Program. The nodes and edges in this network cumulatively increase over time.
683.4.2 Cross-sectional analysis of the complexity of collaboration and knowledge structures
Graph entropy of the co-author and co-word network were calculated for each static macro-, meso-,
and micro-network. Since the calculated graph entropy is a quantity defined for the static network
itself, changes in the complexity of these network structures caused by the structural decomposition
should be measured by their differences. To measure such changes, the difference in the graph entropy
between the macro- and the meso-network, and between the macro- and the micro-network was used.
Figure 3.15 plots the difference in the graph entropy between the macro- and the mesonetwork. The horizontal axis represents the difference in the co-author network and the vertical axis
represents the difference in the co-word network. Each point plotted in Figure 3.15 represents eachsubtheme, and the value of graph entropy was calculated from the co-author and co-word mesonetworks shown in Figure 3.3-3.7 and 3.8-3.12.
Figure 3.16 plots the difference between the graph entropy of the macro- and the micronetwork. The horizontal axis represents the difference in the co-author network and the vertical axis
represents the difference in the co-word network. Each point plotted in Figure 3.16 represents an
individual, and the values of graph entropy are calculated from the co-author and co-word micronetworks, which are defined for each individual, as well as the meso-network defined for eachsubtheme shown in Figure 3.3-3.7 and 3.8-3.12. According to Figure 3.16, a tendency for the points
representing each actor to align linearly was observed.

Figure 3.15 Two-dimensional plot of subthemes according to two types of graphical entropy values.
The horizontal axis represents the difference of graph entropy between the macro and meso-networkof co-authorship whereas the vertical one represents the difference of those of co-word network.

70

Figure 3.16 Two-dimensional plot of individuals according to two types of graphical entropy values.
The horizontal axis represents the difference of graph entropy between the macro and micro-networkof co-authorship whereas the vertical one represents the difference of those of co-word network.

71

3.4.3 Longitudinal analysis of the complexity of collaboration and knowledge structures
For the dynamic co-author and co-word networks, graph entropy and normalized graph entropy by
year were computed from the panel data of the adjacency matrices. Figure 3.17-3.25 shows the timecourse plot and trajectories of graph entropy and normalized graph entropy over time for the co-author
and co-word macro-networks and meso-network. Figure 3.21, 3.23, 3.25 were scaled for easier to see.
The results reveal the following trend: although the graph entropy of the co-author network increased
from the beginning of the project, the increment gradually decreased as the project progressed.
Moreover, a decreasing trend in the latter half of the project was observed. Meanwhile, the graphentropy of the co-word network also increased after the project started and its increment became
smaller. However, it continued to increase over time during the observed period. In addition, for boththe co-author and the co-word networks, the variations in the graph entropy of the meso-networks
were large at the beginning of the project and became smaller as the project progressed. Finally, thetime variations in the graph entropy of the meso-networks became equal to those of the macro-network.
Looking at the trajectory of graph entropy, the fact that the graph entropy of the co-author
and the co-word networks increases monotonically with time was shown. On the other hand, trajectoryof the normalized graph entropy showed a different tendency from the (not normalized) graph entropy,
which confirms the differences among the subthemes. First, for subthemes 1, 3 and 5, the normalized
entropy of the co-author network decreases over time, but the normalized entropy of the co-word
network decrease once and then increases. Subtheme 2 shows that the normalized entropy of the coword network continues to rise while the normalized entropy of the co-author network decreases. In
subtheme 4, the normalized entropy of the co-word network tends to rise, but the normalized entropyof the co-author network decreases after rising once. (These tendency in the trajectory of normalizedgraph entropy looks like similar to the classification derived by the qualitative analysis in Chapter 4,suggesting some relationship.)72

Figure 3.17 Time-course plot of the whole project and 5 subthemes. The horizontal axis represents theperiod of observation in the present study. The vertical axis represents graph entropy over time for theco-author macro-network and meso-network.

73

Figure 3.18 Time-course plot of the whole project and 5 subthemes. The horizontal axis represents theperiod of observation in the present study. The vertical axis represents graph entropy over time for the
co-word macro-network and meso-network.

74

Figure 3.19 Trajectory of the graph entropy of co-author and co-word networks. The horizontal axis
represents the graph entropy for the co-author macro-network and meso-network. The vertical axis
represents the graph entropy for the co-word macro-network and meso-network.

75

Figure 3.20 Time-course plot of the whole project and 5 subthemes. The horizontal axis represents theperiod of observation in the present study. The vertical axis represents normalized graph entropy overtime for the co-author macro-network and meso-network.

76

Figure 3.21 Time-course plot of the whole project and 5 subthemes (scale adjusted for easier to see).
The horizontal axis represents the period of observation in the present study. The vertical axis
represents normalized graph entropy over time for the co-author macro-network and meso-network.

77

Figure 3.22 Time-course plot of the whole project and 5 subthemes. The horizontal axis represents
the period of observation in the present study. The vertical axis represents normalized graph entropy
over time for the co-word macro-network and meso-network.

78

Figure 3.23 Time-course plot of the whole project and 5 subthemes (scale adjusted for easier to see).
The horizontal axis represents the period of observation in the present study. The vertical axis
represents normalized graph entropy over time for the co-word macro-network and meso-network.

79

Figure 3.24 Trajectory of the normalized graph entropy of co-author and co-word networks. The
horizontal axis represents the normalized graph entropy for the co-author macro-network and mesonetwork. The vertical axis represents the normalized graph entropy for the co-word macro-networkand meso-network.

80Figure 3.25 Trajectory of the normalized graph entropy of co-author and co-word networks (scale
adjusted for easier to see). The horizontal axis represents the normalized graph entropy for the coauthor macro-network and meso-network. The vertical axis represents the normalized graph entropy
for the co-word macro-network and meso-network.

813.5 Discussion
3.5.1 Interpretation of the structure of co-author and co-word networks
Observed structural features can be interpreted more clearly by adding qualitative
information about the target of case study11. The co-author meso-network (Fig. 3.3-3.7) seems toreflect the collaboration structure that can be compatible with both efficiency and diversity intransdisciplinary research activity. This network has the core focusing on basic research in nanobiotechnology and each specific cluster of subthemes 1, 2, 3, and 5 for applied and clinical research.
The knowledge structure represented by the co-word meso-network (Fig. 3.8-3.12) also reveals asimilar tendency. The core cluster at the center of the network represents the fundamental knowledgeof nano-biotechnology, whereas the peripheral part is interpreted as applied knowledge.
Meanwhile, subtheme 4 focused not on nano-biotechnology itself, but on its application,that is, the development of diagnostic devices. The co-author and co-word meso-network of thissubtheme reflect the characteristics of this research domain. A small number of people can conduct
this research in an isolated environment due to relatively less labor-intensive processes (e.g., animal
experiments, which are often used in basic nano-biotechnology research); the new concepts originatedin device development are far from the established body of knowledge in basic science.
The phenomena observed in time evolution of dynamic networks can be interpreted as areflection of the management policy. In the early phase of the project, each subtheme was assigned
individual research topics and generated research outcomes from basic science. However, such a
research strategy was inadequate to realize the vision of the project as a whole. After the projectprogressed, as the outcomes obtained by that time were expected to integrate and reconstruct fromdiverse interdisciplinary or transdisciplinary fields to an established monodisciplinary one, thecollaboration structure consisting of many modules was gradually connected. Furthermore, theknowledge structure evolved to a higher level through the deepening of the knowledge basis of nanobiotechnology and expanding the applicable limits. The research leader concurred with the above
interpretation, confirming that the results of our analysis accurately reflected the status of ongoing
research projects. It was suggested that the visualization of the collaboration and knowledge structure
can be used to plan future research strategies.

3.5.2 Changes caused by structural decomposition in the complexity of collaboration andknowledge structures
As is often observed in large-scale research projects, groupings to allot individual topics to small
research groups are considered to reduce complexity by decomposing the collaboration and knowledge
structures into their substructures. The cross-sectional analysis confirmed that the reduction incomplexity of collaboration structure was correlated with that of the knowledge structure when these
11 This information was obtained from internal meetings, symposia, and personal communication with researchersand staff working at the core organization (iCONM) through the author’s participation as a member of the COINS.

82

were decomposed into their substructures. This suggests that the collaboration and knowledge
structures in research projects are interdependent, and a change in one structure can induce a change
in the other. If this implication is correct, the nature of such collaboration and knowledge structures
can be beneficial for managing interdisciplinary research projects with scientific research, because
organizing appropriate research groups enable us to facilitate the creation of the desiredinterdisciplinary knowledge.
The nature of the graph entropy, based on the distribution of edge weights, is such that its
value depends not only on the distribution of edge weights but also on the network size. In other words,
the more edges there are in the network or the more papers there are, the larger the value of graphentropy is likely to be12. Therefore, the difference in the graph entropy for each meso-network can beinterpreted as representing the magnitude of interdisciplinarity as a quantification of the variety andbalance of the relationships between the components of the collaboration and knowledge structures in
the research activities for each subtheme13 (Stirling 2007; Wagner et al. 2011).
Based on the aforementioned insights, the present study built a data-driven hypothesis that,the creation of interdisciplinary knowledge was intended to achieve change in the knowledge structure
through deliberately manipulating the collaboration structure. However, this project emphasized
emergence of interdisciplinary knowledge based on internal inductions to a shared vision, rather than
external interventions such as organizational restructuring of management to achieve project goals14.
The maintenance of a complex collaboration structure is considered a necessary condition for thecreation of transdisciplinary knowledge. On the other hand, it is not considered to be a sufficientcondition for inducing changes in knowledge structures.

3.5.3 Changes caused by time evolution in the complexity of collaboration and knowledge
structures
Considering the objectives of interdisciplinary research projects, the aforementioned structures will
evolve such that their complexity increases over time. In addition, as explained in the previous section,
the graph entropy tends to become larger as the number of edges in the network increases with theincrease in the number of papers. Therefore, the complexity of these structures in interdisciplinary
research projects is expected to increase over time. Although the graph entropy of the co-author andco-word network tends to increase over time, the fact that these increments falter implies there is a

12 If more detailed system complexity should be measured, we should consider not only the objective complexity that

depends on the system size, such as the graph entropy introduced in present study, but also the subjective complexitythat reflects the disparity (or distance) from the reference model (Efatmaneshnik and Ryan 2016).
13 The present is intended to measure the structural complexity which reflects the strength of ties between the network
components (i.e., authors or words). For example, the complexity can be different magnitude even if the number ofedges is the same, because the strength of ties between researchers who have been collaborating with many coauthorships for a long time is different from the strength of ties between students and their supervisors who appear
only in a single co-authored paper.
14 For creation of interdisciplinary project outcomes, the research leader referred necessity of the mindset that
individual scientists voluntarily set research agenda to realize the vision of the project.

83

factor that suppresses the increase in the complexity of the collaboration and knowledge structures,
which can be interpreted as the result of project management preventing these structures frombecoming complex. From the project management perspective, excessively complicated collaborationand knowledge structures are undesirable; hence, the design and implementation of externalinterventions are needed to suppress the increase in complexity. Put simply, interdisciplinary research
projects require flexible management to control their complexity according to the project progresses.
The historical pattern of the complexity of these structures implies the research strategy andthe management style taken in this case. As shown in Figure 3.13 and 3.14, the saturation of thecomplexity over time was caused by a gentle increase in the number of edges. In the early years, it canbe interpreted that the adoption of strategies to promote interdisciplinary research offered an increasein complexity, but that the complexity of these structure was controlled and reduced as a result of theinterventions to converge on accountable project outcomes for national investments. According to theresearch leader, because of the operations by an entity independent of established organizations, theproject provides an environment in which scientists can voluntarily engage in open and agile interorganizational collaboration, thereby promoting active changes in the collaboration structure. The factthat the complexity of the collaboration structure began to decrease at a certain time while that of theknowledge structure continued to increase over time can be interpreted the reflection of such project
operations.
Moreover, the saturation of graph entropy over time might be caused by the saturation of thenumber of edges, based on a proportional relationship between the time variation of the graph entropy
and of the logarithm of the number of edges (data not shown). This tendency was interpreted as areflection of the observed management style where subthemes and subtheme leaders (core individuals)
were fixed and had been unchanged. In other words, If the subthemes have been drasticallyreorganized and/or these core individuals have been replaced in a different management style, by thedefinition of the graph entropy, the increase in the number of edges would have not been saturated andthe distributions of edge weights would have been changed over time, resulting in changes in thepattern of graph entropy values. However, because of a limitation to test this speculation it in this study
that deals with a single case, it should be examined separately in future studies based on a comparisonof multiple cases.

3.5.4 Contributions and limitations
The theoretical contribution of the present study is the proposal of a framework for the strategic andorganizational management of scientific research, which reveals insights through the extension ofscientometric methodology to real-time activity measurement. The proposed framework can provide
evidence for the management of public scientific research, and may contribute to evidence-based
management (Rousseau 2006) as well as the systematization of the theory for management of84

interdisciplinary research project15.
The practical implications support the practice of evidence-based management, especiallyin publicly funded scientific research projects. Incorporating the process of the hypothesis-driven data
instrumentation can accelerate the hypothesis-testing cycle for data-driven hypotheses in managementof interdisciplinary research project. Practitioners of management of interdisciplinary research project,scientific research project managers, and research administrators16 should build and test their own
hypotheses to form the basis of the managerial decision-making. Introducing the methodology fordata-driven decision-making will be fruitful for the ongoing development of management ofinterdisciplinary research project (Figure 3.26).
Finally, the present study has several the limitations. First, the graph entropy of a co-word
network depends on the selection of nodes. Some factors, such as the criteria of the word eliminationin the preprocessing, how to extract key phrases from the text, and calculations of edge weights in the
network edges which may cause some issues in the data. Second, the generalizability of the findings
and discussions cannot be guaranteed as single-case analysis was employed. More in-depth evidence
is desirable through comparative studies across projects with controlled conditions (e.g., multiple
projects in a single funded program), systematic reviews, or meta-analyses. Third, the argument on thetime evolution of dynamic networks is only qualitative descriptions. The present study avoided the
deeper topic of quantitative aspects of network time evolution itself as this is outside the scope of thisresearch. Future research can utilize quantitative analyses for understanding the dynamics of network
evolution. Fourth, the graph entropy introduced in the present study depends on the size of the system(i.e., the number of edges) and does not consider a distance from a reference model, which is typicallya network where the edge weights follow a uniform distribution (Efatmaneshnik and Ryan 2016).
Incorporating the notion of this distance may provide a better measure of complexity.

15 In the practice of management, the fact that only evidence-based knowledge is not a unique solution for decision-

making is highlighted (Lengnick-Hall and Griffith 2011). Desirable decision-making requires balanced andappropriate use of accessible knowledge at the time.
16 Not all of them are always familiar with individual research topics of the state-of-the-art science. Of course, due todesirability they also understand such research topics, scientists themselves often play the role of project managers
for scientific research. Meanwhile, recent research and development projects increasingly emphasize transdisciplinary
research. Managing scientific research involving various stakeholders by scientists alone has already been the limit.

85

Figure 3.26 A proposed framework for the management of interdisciplinary project based on the present study. Each box represents an analytical outcome inaccordance to the level of visualization (horizontal) and analytical approaches (vertical). The shaded areas are based on the empirical evidence of the present
study whereas white ones are described hypothetically.

3.6 Conclusions
This study focused on the relationship between the collaboration and knowledge structures in aninterdisciplinary research project and the static and dynamic characteristics of the co-author and coword networks obtained from bibliographic information. Furthermore, the structural complexity ofthese networks was quantified by the graph entropy, which was used to perform some evaluations at
the micro (individual), meso (subtheme) and macro (project as a whole) levels. The insights derived
from measuring the structural complexity for the management of interdisciplinary research projects
were verified through an interview with the research leader. The proposed framework for strategic andorganizational management in scientific research can offer real-time measurement of the activities andbroad coverage over many fields.
The observational characteristics of the aforementioned structures revealed theinterdependence between these structures, reflection of the domain-specific properties at the meso
level, and the different dynamics in time evolution. The cross-sectional analysis demonstrated acorrelation between the changes in the complexity of the structures caused by structural decomposition.
This result suggested that the complexity of such structures in an interdisciplinary research project
was managed by organizing appropriate research groups. Moreover, changing the collaboration
structure can induce the change in the knowledge structure. The longitudinal analysis confirmed thatthe variability of the complexity decreased over time in both the collaboration and knowledge
structures. This reflects the strategy that this project pursued interdisciplinarity (or innovative researchoutcomes) in the early stages and the integration of obtained outcomes for realizing the vision in thelater stages.

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CHAPTER 4. Configurational approach to generate prescriptive knowledge
based on qualitative description of contexts, interventions, mechanisms, andoutcomes
4.1 Introduction
Science and technology innovation is a driver for economic growth and industrial development; it isalso expected to create the means to solve social issues. In recent years, large-scale and complex R&D
programs and projects have been adopted with the aim of addressing global societal issues such as the
SDGs. As these projects are usually funded by governmental initiatives (i.e., public funds),
responsibility for implementation, accountability to society, and transparency of the decision-making
process have to be ensured. This means that R&D programs and projects subsidized by public funds
are expected to practice evidence-based management by utilizing the knowledge of management
science (Rousseau, 2006).
Promoting evidence-based management necessitates the accumulation of empirical evidence
on managerial phenomena in the target domain and recognition of the differences between
management science and other disciplines (especially, natural sciences). Management science is adiscipline that pursues solution-oriented knowledge to construct new systems and interventions or to
improve the performance of existing entities, unlike other disciplines whose mission is to describe
natural and social phenomena, such as physics and sociology (Van Aken, 2004). The purpose ofmanagement science is to acquire prescriptive knowledge that enable us to design interventions for
effective and pragmatic management through improvement of systems or their performances, not only
providing knowledge to analyze the managerial phenomena but also clarifying the relationships among
their elements and the hidden mechanisms.
To provide empirical evidence for acquiring such prescriptive knowledge, hypothesis-driven
data instrumentation that enables observation of organizational and managerial phenomena in largescale interdisciplinary R&D projects is proposed in the previous chapter and instrumentation forchanges in the complexity of collaboration and knowledge structures represented by co-author and coword networks is realized by introducing graph entropy. Instrumentation is a methodology for thequantification and measurement of concepts that are difficult to observe directly by conventional
means, because the measurement object does not physically exist, for example, collaboration or
knowledge. In other words, the object of instrumentation is a concept that is difficult to measure
directly at least at the time. It can only be measured by achieving instrumentation, however, themeasurable quantity that represents the concept should reflect the actual situation. Regarding theobjects that are difficult to measure, executing the contradictory requirement to confirm the validity
by other means is requested. Paradoxically, considering that instrumentation is a concept to encompass
even its application to automatic control, the evidence obtained needs to realize instrumentation of
objects that have not been measured hitherto yet, but can be verified by the implementation of the90

measurement objects. To build a well-grounded theory for management of science, handling theparadox between instrumentation and implementation is necessary.
In this chapter, in the context of a large-scale interdisciplinary R&D project, identificationof the mechanisms that generate interventions to achieve research collaboration that result in outcomes,
such as the knowledge to achieve the mission, is conducted. The framework that arranges theconfiguration among contexts, interventions, mechanisms, and outcomes of a case is applied to R&D
activities in large-scale projects and the mechanisms for generating interventions to achieve
collaboration were conceptualized. In particular, the discussions are how the project as a whole andeach subtheme leader implemented mechanisms to generate interventions to achieve researchcollaboration for creating outcomes in their respective contexts, how the entities for R&D activities ineach subtheme (meso-level) are designed and implemented to achieve the entire project objectives(macro-level), and how the characteristics of individual researchers (micro-level) are blended toachieve these objectives (meso-level).

4.2 Framework to generate prescriptive knowledge through configurational approach
4.2.1 Problems related to the research synthesis of case studies on the management of a largescale R&D program and project
To practice evidence-based management, it is not only necessary to accumulate empirical evidence
related to the organizational and managerial phenomena of interest, but also to prescribe solutions toproblems based on generalizable insights derived from the multiple evidence accumulated. Owing tothis prescriptive aspect, implementation of the prescriptive solutions for managerial and organizational
problems requires increased generalization through the integration of empirical evidence from existing
research. This approach is called research synthesis (Barnett-Page and Thomas, 2009; Van Aken andRomme, 2009), and there are several established methods, such as meta-analyses (Gurevitchet al.,2018) and systematic reviews (Tranfield, Denyer, and Smart, 2003). However, the application ofexisting research synthesis methods to the management of R&D programs and projects is problematicfor several reasons, including the uniqueness of the case-specific characteristics or the inherent natureof the research synthesis approach. For appropriate research synthesis, the research results and findings
should be articulated so that case-specific information is not lost, that is, explicit presentations in eachcase study request a description of the case study, in what context, what interventions, by whatmechanisms are generated, and what outcomes are produced.
In the research synthesis of case studies of the management of large-scale R&D programs
and projects, four problems may be considered. First, the difficulty of case collection owing to thesmall number of available large budget cases or there are no cases with similar managerial andorganizational phenomena. Second, the context-specificity of interpretations, which means the loss ofsuperiority of each case through the process of research synthesis because the interpretation of thephenomenon in the original research depends on the context of the case. Third, the heterogeneity of91

outcomes across different disciplines due to unsuitability of the same criteria (e.g., using article orpatents as in the previous chapter) to evaluate the research outcomes as their contribution of theobjectives of the program on project depends on their discipline. Fourth, the compatibility ofgenerative mechanisms for intervention, which means that the mechanisms that resulted in research
synthesis as a prescription for the actual managerial problems to be solved need to be compatible withthe mechanisms that generate interventions to achieve the intended outcomes in the original case.
These problems can be partially resolved by using a bibliometric approach in individual casestudies, as discoursed in the previous chapters. Representing the structural features of collaborationand knowledge in a quantitatively analyzable form like networks enable us to observe managerial andorganizational phenomena in individual research activities, to discover related existing theories inmanagement and organization sciences, and to interpret potential contexts based on observational facts
that can be discovered by quantitative approaches. Constructing evaluation criteria that reflect thecharacteristics of the field by adopting multiple measures and expanding the number of documents tobe measured can mitigate some of the above problems. However, to reveal how interventions were
designed and implemented to achieve the objectives of the program/project in individual cases, andespecially how the mechanisms functioned for generating interventions to achieve transdisciplinary
collaboration, bibliometric methods are insufficient, and qualitative information based on fieldresearch, such as participant observation, is necessary.

4.2.2 Arrangement framework for configuration of contexts, interventions, mechanisms, andoutcomes
The present study explores how research collaborations to generate the knowledge for the
project’s mission were designed and implemented using a framework to arrange the configurations ofcontext, interventions, mechanisms, and outcomes in focused cases. This arrangement framework is
inspired by CMO configuration, a framework for program evaluation used in evidence-based policy
making (Pawson, 2002), and CIMO-logic, a framework for synthesis of existing research results andinsights to derive design propositions for the prescription of management problems (Denyer, Tranfield,
and Van Aken, 2008). This framework has two major parts: one part describing the interventions andtheir generative mechanisms for meso-level research collaboration to obtain macro-level outcomes in
the context of overall project management and the other part describing the interventions and theirgenerative mechanisms for micro-level research collaboration to obtain meso-level outcomes in thecontext of each subtheme management. The former focuses on the entire project management, whichis the process of translating operations from macro-level objectives to meso-level activities, while thelatter focuses on the characteristics of each subtheme leader, who translates meso-level activities into
individual micro-level actions and discusses how these translations were integrated and implemented
as an entire system.

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4.3 Descriptions of contexts, interventions, mechanisms, and outcomes for the entire
project and subtheme leaders based on qualitative observations
4.3.1 Context, interventions, mechanisms, and outcomes of the entire projectContext of the entire project
COINS is an R&D project funded by the COI Program, which aims to provide intensive support for
high-risk but challenging interdisciplinary and cross-field integration based on a future vision ofsociety and life realized by innovation that transcends existing disciplinary and organizationalboundaries. This implies that this project sets research agenda and seeks their solutions based onsocietal demands, that is, it is a mission-oriented project.
Unlike existing projects that aim to improve industrial and medical value by developing
existing technologies and treatment, this project ultimately aims to implement a new concept of a“smart life care society,” in which people can autonomously gain better health in their daily lives. For
the same, this project aims to achieve an “in-body hospital” by 2045 using smart nanomachines basedon nanobiotechnology. This vision of an “in-body hospital” depicts that everyone will be liberated
from the psychological, physical, and economic burdens of exiting medical care while spending daily
lives (e.g., preventing the loss of quality of live (QOL) with hospitalization), using virus-size smart
nanomachines that patrol in the human body 24 hours a day to diagnose and treat diseases.
A unique feature of this project is that the core organization is the Innovation Center of
NanoMedicine (iCONM), which is operated by the Kawasaki Institute of Industrial Promotion, anaffiliated organization of the local government of Kawasaki City. This organization has managed the
project as an entity independent from existing organizations like universities, to mitigate theirinfluence. In addition, the junior researchers here are employed by the Kawasaki Institute of Industrial
Promotion, which provides R&D environments and ensures their positions to a certain extent.
Another feature of this project is its pseudo-continuity. The key members of this project also
participated in NanoBio First, which is supported by the FIRST Program, and this project virtuallyfunctions as a successor to NanoBio First. This allows to shorten the processes needed to organize
appropriate research teams because the key members have already created trust among them.

Interventions of the entire project
The core institution iCONM assumes the role of a hub for the creation of interdisciplinary knowledge
by promoting the emergence of interaction among researchers who share facilities and collaboration
among different organizations, taking advantage of the building integrating the facilities with different
functions such as microfabrication, materials evaluation, organic synthesis, cell and gene experiments,
and animal experiments. This interdisciplinary research institution, which functions to mediate therelationships among different kinds of knowledge and the people who deal with them, is a means todevelop platforms for the continuous innovations that are difficult to realize by universities andcompanies alone, which is one of the objectives of the COI Program. COINS has promoted the93integration of research outcomes from different fields by utilizing such institution with distinctive
characteristics.
Initially, this project was intended to achieve the vision of “in-body hospital” by setting up
subthemes with different targets. However, as the R&D stage progressed, the integration of subthemes
progressed, which is expected to create advanced knowledge by incorporating the research outcomes
obtained from each subtheme. From the co-word network representing the knowledge structure, thetendency of the knowledge integration is also confirmed except for subtheme 4, which is a somewhat
different research topic from nanobiotechnology.
Even when the R&D stage progressed, the same management scheme was continued without
any organizational intervention such as changing the subtheme leader. This could be because COINS
emphasizes the “formation of a place” to cultivate the mindset of individual researchers and to promoteinteraction among researchers and practitioners. In fact, general meetings to report the research
progress of each subtheme and research camps to deepen interaction among participants are regularly
held with the intent to facilitate the formation of such places.

Mechanisms of the entire projectIn this project and its predecessor, NanoBio First, features that the network representing thecollaboration structure has a core-periphery structure were observed. This structure allows theactualization of an efficient R&D cycle in the core and ensures diversity by conducting complementaryresearch in the periphery. In fact, the importance of the research topics corresponding to the periphery
was also recognized and incorporated as a component of the project while the core R&D activities
were intensively conducted among actors who share expertise although the subthemes were separately
set during the project’s initiation.
Similar implications can be derived from the establishment of AccuRna and its subsequent
merger into NanoCarrier. Initially, AccuRna was established as an entity for intensive R&D focusedon nucleic acid drugs. Later, as its R&D stage progressed to clinical trials, this firm was merged toleverage the knowledge of NanoCarrier, which already had experience in clinical trials, byconsolidating knowledge, especially tacit knowledge like know-how, into one organization so that
more efficient social implementation of research outcomes would be achieved. Hence, mechanisms
enabling desirable knowledge storage to divide and to merge organizations with core technologies andhuman resources in accordance with the R&D progress were implemented, by building organizations
that specialize in specific domain as separate units apart from the existing one.

Outcomes of the entire projectIn this project, three Ps are considered as important outcomes: paper, patent, and platform. The highquality papers in the field of nanomedicine are obviously important and they endorse the scientific andtechnical validity. As patents are significantly important in this field than others, the research leader
94

said that high quality papers and strong patents are not contradictory, and that one of the project
outcomes is to foster researchers who can create both to promote social implementation. Furthermore,as a part of platform, the continuous innovations that would be difficult for universities and companies
alone would be upheld by startup firms such as Braizon Therapeutics and iXstream, whose basic
technology corresponds with the research outcomes of this project.

4.3.2 Context, interventions, mechanisms, and outcomes of subtheme leader 1
Subtheme leader 1 conducted R&D to improve the performance of nanomicelles, which is a coretechnology of the COINS project. Subtheme leader 1 succeeded to the R&D conducted by Kazunori
Kataoka, who is the research leader of COINS, at the University of Tokyo, and developed
nanomachines to control in vivo and intracellular dynamics of nucleic acid drugs. In Phase 1, subtheme
leader 1 conducted material engineering research to improve the physical properties of nanomicelles,
specifically, downsizing nanomicelle particles and developing siRNA/mRNA-loaded nanomachines,
along with collaborator Yu Matsumoto (Nomoto, et al, 2014; Kim, et al, 2014). Meanwhile, in Phase2, the focus of the R&D of subtheme leader 1 shifted to the drug development such as nanodelivery
systems for anticancer drugs. Although many co-authors of Phase 1 left the project, Horacio Cabral,
who is an associate professor at the University of Tokyo, joined as a new collaborator. Based on thenanomicelles developed in Phase 1, the focus of the R&D was on the implementation of functions
such as cancer cell directionality, intracellular visualization, and response to external stimuli (Cabral,
et al, 2018; Yi, et al, 2019; Watanabe, et al, 2019).

Context of subtheme leader 1
Subtheme leader 1 is from the research leader’s laboratory, who is currently an associate
professor and has the laboratory at the Department of Materials Engineering, Graduate School ofEngineering, the University of Tokyo, and also works on this project as a visiting scientist at iCONM.
Subtheme leader 1’s specialty is biomaterials, and engages in the development of nanomachines toimprove the effectiveness of treatments for intractable diseases using polymeric materials that
encapsulate nucleic acid drugs. The leader mainly targeted cancer and conducted R&D aimed at
improving drug delivery system, which realizes the high accumulation rate in cancerous tissues, bydeveloping nanomachines with higher performance than the polymeric nanomicelle encapsulating
anticancer drugs that are already under clinical trials.

Interventions of subtheme leader 1
As cancer is a focus domain in NanoBio First, and the research collaboration with key
members was already established. Subtheme leader 1 developed the knowledge base for drug delivery
systems using biomaterials, mainly using existing relationships, that is, collaboration between researchleaders or between existing companies such as NanoCarrier. In addition, efforts for the fusion of95

subthemes seems to be relatively emphasized because the knowledge base of drug delivery systems
created in the research of subtheme 1 has high potential for application in other subthemes.

Mechanisms of subtheme leader 1
The reason why subtheme leader 1 selected the collaborators who has already establishedrelationships to address the improvement of the basic technology for the drug delivery system may be
due to the mechanism that promotes the acceleration of knowledge reproduction through collaboration
with actors who have shared expertise, such as the research leader. To enhance the performance of thesystem as a whole, core knowledge generated through research activities that require a high level ofexpertise would be iteratively produced because of the necessity of improving not only the elemental
technologies but also the system architecture in an integrated manner. Hence, it seems that subtheme
leader 1 has reduced communication costs such as those caused by differences of expertise andaccelerated the reproduction of core knowledge by forming a transaction-free zone (Baldwin, 2008)
with actors who had shared expertise, such as the research leader. In addition, the fact that the research
topics covered in subtheme 1 are relatively mature and the increase in actors with shared expertise
could promote the effective functioning of such a mechanism.

Outcomes of subtheme leader 1
Subtheme leader 1 developed nanomachines to treat cancers that have been considereddifficult to treat with conventional nanomedicine such as metastatic cancer. The leader created atechnology to improve the performance of the drug delivery system itself, in other words, the coreknowledge as an outcome, which is the basis of drug delivery systems. For example, enhancing tissue
permeability by downsizing the nanomachine because existing nanomedicines for these diseases are
too large to pass through tissues in vivo.

96

Figure 4.1 Co-author micro-network of the publications from subtheme leader 1 of COINS project.
The color of a node represents its attributes: purple, university; red, core organization (iCONM); light
blue, industry; yellow, public institute; blue, no data.

97

Figure 4.2 Co-word micro-network of the publications from subtheme leader 1 of COINS project.

98

Figure 4.3 A summary of the research strategy for Phase 1 and Phase 2 of subtheme leader 1. Representative key papers were selected based on the judgement of
the biology experts as remarkable research outcomes. Representative co-authors were tallied by dividing co-authors with two or more occurrences into three
categories: only in Phase 1, only in Phase 2, and in both phases. The co-authors illustrated in each phase were the researchers judged to have played an importantrole in that phase. Representative co-words were tallied by dividing co-words with three or more occurrences into three categories: only in Phase 1, only in Phase2, and in both phases. The co-words illustrated in each phase were words that were judged to be related to important outcomes in that phase.

4.3.3 Context, interventions, mechanisms, and outcomes of subtheme leader 2
Subtheme leader 2 is responsible for the R&D to improve the performance of nanomicelles, a coretechnology of the COINS project, in a different research unit from subtheme leader 1. In Phase 1,
subtheme leader 2 succeeded in the research when his supervisor Kazunori Kataoka was at the
University of Tokyo, and carried out fundamental R&D to improve the physical properties ofnanomicelles (Chen, et al, 2014; Chuanoi, et al, 2014). Specifically, nanomicelles were developed andthey performed some functions by transforming into polyion complex vesicles for photoinduced
intracellular delivery with enhanced salt and temperature resistance. In Phase 2, subtheme leader 2focused on R&D to break through the blood-brain barrier (Xie, et al, 2019), which is the biggest
challenge for drug delivery to the brain, but there were no major changes in the organization, and aconsistent team was established under Akihiro Kishimura and Kensuke Osada (e.g., Li, et al, 2017).

Context of subtheme leader 2
Being from the research leader’s laboratory, project associate professor at the Departmentof Bioengineering, Graduate School of Engineering, the University of Tokyo, and visiting scientist at
iCONM, subtheme leader 2 has a relatively similar background to subtheme leader 1. Subtheme leader
2, who was originally engaged in research for cancer too, mainly engaged in research activities related
to treatment technologies for neurological diseases, such as the Alzheimer’s disease.

Interventions of subtheme leader 2
Subtheme leader 2 was involved in the industrialization and commercialization as ascientific advisor to Braizon Therapeutics, a startup firm that was established using the researchoutcome. During the project initiation, a policy was formed wherein the outcomes of the subtheme 2
would be utilized to establish a startup for social implementation. This policy seemed to be decided
due to the social appeal of the project, that is, the concept of drug delivery to the brain by establishinga firm specializing in the treatment of neurological diseases using nanobiotechnology, rather thanlicensing the patents of the basic technology to existing companies. After Braizon Therapeutics wasestablished, collaboration with other subthemes has progressed.

Mechanisms of subtheme leader 2
Subtheme leader 2 is the youngest researcher among the subtheme leaders. The reason why
subtheme leader 2 targeted another intractable disease, neurological disorder, rather than cancer(which is the mainstream of conventional drug delivery systems using nanomedicine), could be
because subtheme leader 2 intended to establish a career path as a researcher by adopting a researchstrategy to avoid the risk of competition with existing research domain. The existence of a mechanism
to generate such strategic behavior suggested that there were both a risk of competition and an
opportunity to explore new domain, resulting in pioneering a drug delivery system for neurologicaldiseases and the creation of a startup that promoted the social implementation of the basic technology
for the system.

Outcomes of subtheme leader 2
The major research outcome of subtheme leader 2 is a technology for polymericnanomicelles which could pass through the blood-brain barrier. The human brain is protected by theblood-brain barrier that prevents the passage of anything except nutrients, like glucose. This barrieralso makes it difficult for the drugs to reach the brain. Subtheme leader 2 succeeded in improving theamount of drug accumulation in the brain by binding glucose to the surface of polymeric nanomicelles.
Subtheme leader 2 obtained this technology’s patent, which is the basis for the launch of Braizon
Therapeutics. This outcome was realized by the precise design technology for introducing glucose
molecules to the surface of polymeric micelles in an appropriate form, which is an application of thecore knowledge of the drug delivery system for cancer treatments. Therefore, the core technology,
which was originally developed in the R&D of drug delivery systems for cancer diseases, wasconverted into peripheral knowledge for the treatment of neurological diseases, resulting in thecreation of a high-impact outcome.

101

Figure 4.4 Co-author micro-network of the publications from subtheme leader 2 of COINS project.
The color of a node represents its attributes: purple, university; red, core organization (iCONM); light
blue, industry; yellow, public institute; blue, no data.

102

Figure 4.5 Co-word micro-network of the publications from subtheme leader 2 of COINS project.

103

Figure 4.6 A summary of the research strategy for Phase 1 and Phase 2 of subtheme leader 2. Representative key papers were selected based on the judgement of
the biology experts as remarkable research outcomes. Representative co-authors were tallied by dividing co-authors with two or more occurrences into three
categories: only in Phase 1, only in Phase 2, and in both phases. The co-authors illustrated in each phase were the researchers judged to have played an importantrole in that phase. Representative co-words were tallied by dividing co-words with three or more occurrences into three categories: only in Phase 1, only in Phase2, and in both phases. The co-words illustrated in each phase were words that were judged to be related to important outcomes in that phase.

4.3.4 Context, interventions, mechanisms, and outcomes of subtheme leader 3
In Phase 1, subtheme leader 3 narrowed down the research focus to drug delivery system development
to focus on gene and nucleic acid delivery and motor-sensory organ therapy by collaborating with
Hiroaki Kinoh, Takehiko Ishii, Hirokuni Uchida, and others. The focus was on the selection ofpolyplex micelles suitable for therapeutic purposes, the implementation of mRNA into nanomicelles,
and the investigation of targeted gene delivery targeting pancreatic cancer (Ge, et al, 2014; Matsui, etal, 2015; Uchida, et al, 2016). In Phase 2, the collaborative R&D in Phase 1 was dissolved, but a teamof core members led by Kazunori Kataoka, Satoshi Uchida, and Kayoko Yanagihara, was established
to expand the scope of R&D to translational research, such as the design of double-stranded messenger
RNA for effective vaccination and the development of treatment methods for bone defects bytransplantation of spheroids (Uchida, et al, 2018; Yanagihara, et al, 2018).

Context of subtheme leader 3
Subtheme leader 3 differs from subtheme leader 1 and subtheme leader 2 in terms of theaffiliation and position at iCONM. Subtheme leader 3 is a professor at the Department of Biofunction
Research, Institute for Biomaterials and Bioengineering, Tokyo Medical and Dental University, and aprincipal research scientist at iCONM. Subtheme leader 3, who is originally a clinician of orthopedic
surgery, conducted R&D in regenerative medicine in the field of orthopedics and drug discovery basedon mRNA, focusing on substances to be delivered such as mRNA, rather than polymeric nanomicelles.
The technology originated in drug delivery systems is used to deliver mRNA, which is an unstable
substance, to its destination, the cell.

Interventions of subtheme leader 3
Subtheme leader 3 also works on the R&D of drug delivery systems for nucleic acid drugs
with AccuRna, serving as the scientific advisor. In combination with other subthemes, the experience
as an orthopedic surgeon and the characteristics of the affiliation are utilized in the researchcollaboration and R&D for clinical applications, such as regenerative medicine or mRNA drug
discovery, from the viewpoint of a user of drug delivery systems.

Mechanisms of subtheme leader 3
The reason why the R&D was conducted from the viewpoint of a user of drug delivery
systems could be because subtheme leader 3 originally treated patients as a clinician. Medical science(as well as management science) is oriented toward prescribing solutions to the problems currently
faced and emphasizes to achieve the best outcomes using the means available at that time. Althoughdistinguishing whether it was consciously or unconsciously done was slightly difficult, the researchcollaboration seemed to be conducted based on such a pattern of thinking.

Outcomes of subtheme leader 3
Subtheme leader 3 created the outcomes related to drug delivery systems using mRNA totreat intractable diseases of the cranial nerves and motor and sensory disorders. Owing to their design,
mRNA drugs are also used in the field of preventive medicine such as vaccines for infectious diseases
and they are expected to be used in the domain of drug delivery system in the future. Concomitantly,
this new modality raises a new challenge to conventional therapeutic approaches. In other words, theseoutcomes were interpreted to actualize medical care beyond the existing framework, by combining
new therapeutic approach using mRNA, which is the peripheral knowledge, and the drug delivery
system, which is the core knowledge.

106Figure 4.7 Co-author micro-network of the publications from subtheme leader 3 of COINS project.
The color of a node represents its attributes: purple, university; red, core organization (iCONM); light
blue, industry; yellow, public institute; blue, no data.

107

Figure 4.8 Co-word micro-network of the publications from subtheme leader 3 of COINS project.

108

Figure 4.9 A summary of the research strategy for Phase 1 and Phase 2 of subtheme leader 3. Representative key papers were selected based on the judgement of
the biology experts as remarkable research outcomes. Representative co-authors were tallied by dividing co-authors with two or more occurrences into three
categories: only in Phase 1, only in Phase 2, and in both phases. The co-authors illustrated in each phase were the researchers judged to have played an importantrole in that phase. Representative co-words were tallied by dividing co-words with three or more occurrences into three categories: only in Phase 1, only in Phase2, and in both phases. The co-words illustrated in each phase were words that were judged to be related to important outcomes in that phase.

4.3.5 Context, interventions, mechanisms, and outcomes of subtheme leader 4
In COINS, subtheme leader 4 is responsible for the peripheral area of diagnostic system development
in a coherent manner. Specifically, based on microfabrication technology and heterogeneous materialintegrated device engineering, the R&D of highly functional biodevices was conducted with Hiroaki
Takehara, who was his laboratory staff member. In Phase 1, on-chip immunoelectrophoresis ofextracellular vesicles and micro-optical fluidic device “lab-on-a-brain” were developed with Takahiro
Ochiya of National Cancer Center Japan (Akagi, et al, 2015; Takehara, et al, 2014). In Phase 2, theR&D from Phase 1 was continued under a new structure with renewal of the unit and collaborative
members: for example, a microfluidic model for optical detection of nanoparticles in blood (Takehara,
Kanda, and Ichiki, 2018).

Context of subtheme leader 4
Subtheme leader 4 is a professor at the Department of Materials Engineering, Graduate
School of Engineering, University of Tokyo, as well as a principal research scientist at iCONM.
Subtheme leader 4 specializes in materials science, especially in microfabrication technology using
semiconductor manufacturing processes and bioelectronics. Therefore, subtheme leader 4 has adifferent background from the other subtheme leaders; that is, the background is not drug delivery
system using nanobiotechnology, but worked to develop diagnostic devices for early detection ofcancer by measuring miRNA and exosomes in blood.

Interventions of subtheme leader 4
Unlike the other subtheme leaders, subtheme leader 4 is not from the research leader’slaboratory, but has hitherto conducted collaborative research with the research leader. However, they
seem to have complementary roles rather than fusion of their expertise as far as the co-author and coword networks are observed. In addition, subtheme leader 4 work together on R&D with Nikon for along time, but eventually new startup firm, iXstream was established as spinout. At that time, acollaborator who had conducted R&D with subtheme leader 4 was appointed as the CEO.

Mechanisms of subtheme leader 4
Subtheme leader 4 adopted a different research strategy from the other subtheme leaders. It
aimed to realize “smart life care society” and “in-body hospital” vision through diagnosis for early
detection rather than treatment of diseases with drug delivery systems. Subtheme leader 4 selected
research strategy that creates complementary knowledge, did not adopt the direction to develop coreknowledge by merging the expertise with drug delivery systems, that is, a research strategy similar tothat of subtheme leader 3. The reason why subtheme leader 4 selected this research strategy could be
because there are mechanisms to adjust interest, that is, which entities would address social
implementation of the created outcomes. The company (Nikon) with which subtheme leader 4 initiallycollaborated had the strength of peripheral knowledge rather than drug delivery systems. Had thecompany proceeded with the industrialization and commercialization of the research outcomes,
focusing on the creation of peripheral knowledge would have been a rational choice for this company.
In fact, the research topic of subtheme leader 4 got closer to core knowledge after the company dropped
the industrialization and commercialization of the research outcomes and established iXstream, a newentity for social implementation.

Outcomes of subtheme leader 4
Subtheme leader 4 created outcomes of automation on the processes for miRNA analysis,
such as the isolation, purification, and detection of miRNA in exosomes contained in bodily fluids likeblood, by developing integrated biodevice for both early detection and minimally invasive diagnosisof cancer using microfluidic device technology. These outcomes did not come under the coreknowledge of nanobiotechnology related to drug delivery systems, and to achieve the peripheral
knowledge to realize the “smart life care society” and “in-body hospital” vision, not only treatment(main focus of other subthemes) but also diagnosis would be required. Subtheme leader 4 establisheda startup firm called iXstream using the outcomes. This startup is also oriented to medical applicationsof exosome evaluation technology, which is equivalent to peripheral knowledge, rather than drugdelivery systems.

111

Figure 4.10 Co-author micro-network of the publications from subtheme leader 4 of COINS project.
The color of a node represents its attributes: purple, university; red, core organization (iCONM); light
blue, industry; yellow, public institute; blue, no data.

112

Figure 4.11 Co-word micro-network of the publications from subtheme leader 4 of COINS project.

113

Figure 4.12 A summary of the research strategy for Phase 1 and Phase 2 of subtheme leader 4. Representative key papers were selected based on the judgementof the biology experts as remarkable research outcomes. Representative co-authors were tallied by dividing co-authors with two or more occurrences into three
categories: only in Phase 1, only in Phase 2, and in both phases. The co-authors illustrated in each phase were the researchers judged to have played an importantrole in that phase. Representative co-words were tallied by dividing co-words with three or more occurrences into three categories: only in Phase 1, only in Phase2, and in both phases. The co-words illustrated in each phase were words that were judged to be related to important outcomes in that phase.

4.3.6 Context, interventions, mechanisms, and outcomes of subtheme leader 5
Subtheme leader 5 led the largest research unit, consisting of core members such as Kazunori Kataoka(research leader), Horacio Cabral (University of Tokyo), and Takahiro Nomoto (Assistant Professorof his own laboratory), to develop pinpoint diagnosis and treatment technology by integrating thenanomachines that deliver drugs to lesions, a core technology of the COINS project, with medical
devices such as MRI and array transducers. In Phase 1, subtheme leader 5 developed a new technology
for the delivery of siRNA to target solid tumors and lymph node metastasis using poly-ion complex
micelles and a technology for the activation of drug-loaded polymeric micelles by light response andpH change with Kanjiro Miyata (subtheme leaders 1), Yu Matsumoto, and Yutaka Miura in addition
to the core members mentioned above (Oe, et al, 2014; Yen, et al, 2014; Cabral, et al, 2015;
Tangsangasaksri, et al, 2016). However, in Phase 2, many of the collaborative R&D activities in Phase
1 were ended or reduced, subtheme leader 5 and the core members expanded the scope of R&D totranslational research. Specifically, they conducted multifaceted R&D toward clinical applications,such as signal amplification for non-invasive imaging of tumor malignancy, vascular burst technology
to increase the permeability of tumor blood vessels, boron neutron capture therapy for solid tumors,and the development of in vivo rendezvous methods to target cancer cells (Mi, et al, 2016; Matsumoto,et al, 2016; Mi, et al, 2017; Watanabe, et al2019). Moreover, a new collaborative R&D group was
initiated under Yoshihiro Muragaki at Tokyo Women’s Medical University to develop sonodynamic
therapy using nanomicelle technology and high-intensity focused ultrasound in cancer treatment(Horise, et al, 2019).

Context of subtheme leader 5
Subtheme leader 5 had been conducting research on polymeric nanomicelles under the
research leader, and currently has a laboratory as a professor at the Laboratory for Chemical and Life
Science, Institute of Innovative Research, Tokyo Institute of Technology. Like subtheme leader 3 orsubtheme leader 4, subtheme leader 5 is also a principal research scientist at iCONM. Subtheme leader
5 aims to realize minimally invasive treatment systems that enable one-day treatment with less burden
on patients, and imaging systems that can visualize the behavior of nanomachines in the body, by
combining drug delivery systems using polymeric nanomicelles with medical devices. Although thisresearch topic is partially overlapped with subtheme leader 1 in the context of the R&D on drugdelivery systems using polymeric nanomicelles encapsulated anticancer drugs, subtheme leader 5conducted the R&D aiming to develop the knowledge base in a different direction—the application as
an alternative to the existing contrast agents for imaging.

Interventions of subtheme leader 5
As the research topics of subtheme leader 5 are related to all nanomachine research in othersubthemes, the collaboration with other subthemes was mainly focused on, as well as the case ofsubtheme leader 1. A unique feature of subtheme leader 5 is the collaboration with the National
Institute of Radiological Sciences and the National Institutes for Quantum and Radiological Scienceand Technology, which are engaged in R&D on imaging. In addition, research collaboration for clinicalapplication is also conducted.

Mechanisms of subtheme leader 5
The research topics handled by subtheme leader 5 are closely related to the other subthemesof this project. Therefore, subtheme leader 5 plays a significant collaborative role in accelerating theprocess of producing and improving the core knowledge that may become the fundamental technology
for drug delivery systems, by creating a transaction-free zone (Baldwin, 2008) with actors who shareexpertise, such as research leader or subtheme leader 1. The selection of such a form of collaborationsuggested that there are hidden mechanisms that influence the form of collaboration by the type of
produced knowledge. In this case, as the knowledge reproduced by using the produced core knowledge
also becomes core knowledge, a transaction-free zone is formed which accelerates the knowledge
production cycle and continues the incremental improvement of the knowledge base of the drugdelivery system.

Outcomes of subtheme leader 5
Subtheme leader 5 created outcomes that enhance the safety and effectiveness of treatment
by combining the drug delivery system with medical equipment such as MRI to realize one-day
treatment of cancer, which is a key component of the vision of an “in-body hospital.” In particular, theoutcomes related to imaging not only contribute to the realization of precision medicine, but also to
improvement of the drug delivery system itself, by clarifying nanomachines’ behavior in the body todeepen the understanding of the dynamics of nanomachines. In other words, subtheme leader 5 createdcore knowledge as outcomes to reproduce novel core knowledge.

116

Figure 4.13 Co-author micro-network of the publications from subtheme leader 5 of COINS project.
The color of a node represents its attributes: purple, university; red, core organization (iCONM); light
blue, industry; yellow, public institute; blue, no data.

117

Figure 4.14 Co-word micro-network of the publications from subtheme leader 5 of COINS project.

118

Figure 4.15 A summary of the research strategy for Phase 1 and Phase 2 of subtheme leader 5. Representative key papers were selected based on the judgementof the biology experts as remarkable research outcomes. Representative co-authors were tallied by dividing co-authors with two or more occurrences into three
categories: only in Phase 1, only in Phase 2, and in both phases. The co-authors illustrated in each phase were the researchers judged to have played an importantrole in that phase. Representative co-words were tallied by dividing co-words with three or more occurrences into three categories: only in Phase 1, only in Phase2, and in both phases. The co-words illustrated in each phase were words that were judged to be related to important outcomes in that phase.

4.4 Validation based on the R&D Strategy
4.4.1 Classification based on type of knowledge of knowledge producers and knowledge
consumers
The results suggest that the COINS subtheme leaders are likely to adopt different research strategies
while conducting R&D to achieve the common vision “in-body hospital.” Different research strategies
may have been chosen owing to the different relationship between the production and consumption ofknowledge of each subtheme leader. That is, they are considered to be classified based on whether it
is core knowledge or peripheral knowledge that is produced or consumed.

4.4.2 Validation based on real-world R&D strategies: Quadrant of core knowledge producers andcore knowledge consumers
The fact that both produced and consumed knowledge are core knowledge implies that information
asymmetry caused by expertise between knowledge producers and knowledge consumers hardly exist,
and smooth communication on advanced research topics allows rapid rotation of the R&D cycle, thatis, increased efficiency of R&D. In other words, in this quadrant, utilizations of collaboration amongactors with shared expertise, especially the alumni network, as a mechanism to accelerate thereproduction of core knowledge was implied to establish the knowledge base necessary to realize thevision of “in-body hospital” using nanomachines. This quadrant includes subtheme leaders 1 and 5,
who were from research leader’s laboratory, and the specific research activities conducted by thesesubtheme leaders are described below.
The subthemes of the subtheme leaders included in this quadrant are characterized by thefact that the normalized graph entropy of the co-author network continues to decrease, but thenormalized graph entropy of the co-word network increases after decreasing once. As the subtheme
leaders in this quadrant have implemented core knowledge enhancement such as the development of
nanomicelles in Phase 1 and then conducted R&D for clinical applications in Phase 2, the complexityof the knowledge structure has decreased as a result of focusing on specific research topics in Phase
1; however, it is considered that the relative complexity of the knowledge structure increased due tothe expansion of research topics in Phase 2. As for the collaboration structure, the decrease in relativecomplexity slowed down due to the entry of some new actors, but the overall trend is considered tohave continued to decrease.

4.4.3 Validation based on real-world R&D strategies: Quadrant of core knowledge producers andperipheral knowledge consumers
This quadrant has a feature that core knowledge is produced while peripheral knowledge is consumed.
To realize the COINS project’s vision of “in-body hospital,” not only the existing core knowledge ofnanomachines, especially nanomicelles for the treatment of cancer, but also the core knowledge basethat will extend the function of treatment for other diseases, such as Alzheimer’s disease, should beproduced. Therefore, this quadrant included subtheme leader 2, who conducted R&D specific tonanomicelle delivery systems for drug delivery to the brain, a knowledge base for the treatment of
non-cancer diseases that is consumed as peripheral knowledge.
The subtheme of the subtheme leader included in this quadrant is as follows: the normalizedgraph entropy of the co-author network keeps decreasing and the normalized graph entropy of the coword network keeps increasing. If the summary of the research strategy of this subtheme leader waschecked, he continues with the challenging research topic of drug delivery to the brain by breaking
through the blood-brain barrier. On the contrary, there has been little change in the collaboration
structure and the ties with specific members have become stronger. Therefore, it is thought that therelative clutter of the collaboration structure continues to decrease while the clutter of the knowledge
structure continues to increase.

4.4.4 Validation based on real-world R&D strategies: Quadrant of peripheral knowledge
producers and core knowledge consumers
In this quadrant, contrary to the case of previous subsections, peripheral knowledge is produced while
core knowledge is consumed. That is, it corresponds to addressing the question of how to utilize coreknowledge to produce peripheral knowledge to achieve social implementation of the medical concept“in-body hospital.” This quadrant includes subtheme leader 3, who conducted R&D ofnanoreconstruction technology for the maintenance and regeneration of biological functions in the
COINS and has a different background from the other subtheme leaders as an orthopedic surgeon.
The subtheme of the subtheme leader included in this quadrant is characterized by the factthat the normalized graph entropy of the co-author network continues to decrease but the normalizedgraph entropy of the co-word network increases after decreasing once, as in the case of the quadrantof core knowledge producers and core knowledge consumers. Hence, the complexity of the relativeknowledge structure decreased in Phase 1 but increased in Phase 2 as a result of the implementationof the reinforcement of core knowledge in Phase 1 and then the R&D for clinical application in Phase
2. The interpretation of the collaboration structure is similar, and it is considered that the complexityof the relative collaboration structure continued to decrease in Phase 2 because there was no entry of
new actors in Phase 2.

4.4.5 Validation based on real-world R&D strategies: Quadrant of peripheral knowledge
producers and peripheral knowledge consumers
The fact that there are cases where none of the knowledge produced and consumed is core knowledge,that is, both are peripheral knowledge may seem strange at first glance. However, to achieve social
implementation of systems with diverse functions, considering only the production and consumptionof core knowledge can be insufficient. In this context, COINS’ vision is “in-body hospital,” but thefunctions of a hospital are not limited to the treatment of diseases. Furthermore, to realize the vision121

of not only an “in-body hospital” but also a “smart life care society,” proposed in COINS,
incorporating knowledge bases of the existing core technologies of nanomachines or drug delivery
systems, that are not related (at present), is necessary. In fact, subtheme leader 4, who is in this quadrant,
focused on the development of complementary knowledge bases that will be needed to achieve the“in-body hospital”—early diagnosis of cancer using exosomes and miRNAs, rather than the therapies
that focus on other subthemes.
The trajectory of the normalized graph entropy of the subtheme leaders in this quadrantshows that the normalized graph entropy of the co-word tends to increase, while but the normalizedgraph entropy of the co-author network decreases after increasing. This change in the complexity of
the knowledge structure and collaboration structure can be explained by the fact that the research topic
itself is consistent between Phase 1 and Phase 2, but the collaboration style has changed significantly.
This may reflect the fact that the research and development system has changed significantly.

122

Figure 4.16. Classification based on type of knowledge of knowledge producers and knowledge consumers on the core-periphery profile.

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CHAPTER 5. Conclusions
5.1 Summary of key findings
The study centers on the developments of methodology to realize the instrumentation of structuralfeatures on research activities in basic science research. In particular, two system structures in largescale interdisciplinary R&D programs and projects that include basic science research are focused on:
collaboration structure, which represents the cooperative relationship among actors involved inscientific research, and the knowledge structure, which represents the co-occurrence relationshipamong ideas used in scientific research. Realizing the instrumentation of organizational andmanagerial phenomena in scientific research activities enable us to measure data that can be used forstrategic and organizational management of scientific research, by constructing ex ante activity
indicators for scientific research rather than ex post performance indicators. Therefore, the main focusof this study is to support the prescription of solutions to organizational and managerial problems inthe scientific research activity through the development of methods for the characterization andquantification of organizational and managerial phenomena, which would allow the data measurement
for the management of scientific research.
In Chapter 2, program-level analysis for the FIRST Program and project-level analysis for
NanoBio First to investigate inter-organizational collaboration in large-scale R&D projects are
discussed in detail. In the program-level analysis, statistical modeling using data from 30 sites in the
FIRST Program yield two major findings: First, an increase in research funding contributes to anincrease in academic outcomes on a quantitative basis, owing to the adequacy of financial resources
to hire researchers such as post-doctoral fellows who are actually engaged in activities to produce
academic outcomes, but the contribution to the creation of intellectual property is unclear. Second,financial support alone will not lead to the creation of intellectual property, as different mechanisms
might work in patent application and registration. This iterates the need for more explicit measures toreward creation of intellectual property, such as the design of incentive mechanisms, considering theresults of the project-level analysis. For the project-level analysis, by adopting mixed-methods
approach for the case study, that is, combining quantitative analysis based on the centrality indicescalculated from co-author network of papers and co-inventor network of patents created in NanoBio
First and a semi-structured interview with project leader, the factors to promote or impede the creationof R&D outcomes were investigated. The results of the quantitative analysis suggest that the co-author
network of papers and co-inventor network of patents had a core/periphery structure, forming atransaction-free zone inside the core, achieving both efficiency and diversity in R&D. In addition, thesocial implementation of intellectual property suggests that it was promoted through joint applications
with a startup firm that has a complementary role to universities. On the contrary, the results of thequalitative analysis suggest that the creation of intellectual property was promoted by building aknowledge logistics system that enables the acquisition of tacit knowledge (reciprocal mindset)through experiential learning by collaborating with a startup firm. This would help cultivate a dualskilled researcher with a reciprocal mindset who can bridge the gap between academia and industry.
In Chapter 3, the co-author and co-word networks are constructed based on the bibliographic
information of papers generated until phase 2 in the COINS as a network that can approximate thecollaboration and knowledge structure in a large-scale interdisciplinary R&D project. Graph entropy,
which is a measure of structural complexity of the network, is introduced as a method to realize theinstrumentation of static and dynamic features of the collaboration or knowledge structure. Qualitative
observations of these network structures and analyses on the changes in the structural complexity ofthese networks, are conducted in terms of the changes in complexity due to the decomposition of thestructure into project, team, and individual levels as well as the changes in complexity due to the time
evolution of the network over time. The results of qualitative observations on the structure of the coauthor and co-word networks confirm that domain-specific characteristics at the subtheme level of
COINS are reflected in these networks. The cross-sectional analysis demonstrates correlations
between the changes in complexity are induced by structural decomposition. The results suggest thatsuch structural complexity in interdisciplinary research projects can be managed by organizing
appropriate sub research groups. Furthermore, this correlation suggests that knowledge structures canbe induced through collaboration structures, as shown by the qualitative observation between coauthor and co-word networks. The longitudinal analysis confirms that the change in complexity
decreased over time for both collaboration and knowledge structures. This fact may reflect the
project’s strategy of pursuing high interdisciplinarity (or innovative research outcomes) in the earlystages and the integration of the outcomes obtained to realize the vision in the later stages. These
insights into the management of interdisciplinary research projects derived from these measures ofstructural complexity are validated by an interview with research leader.
In Chapter 4, based on the insights obtained from the previous chapters, investigations are
made into how the research strategy was implemented to achieve the project’s vision. In the context
of a large-scale interdisciplinary R&D project, the generative mechanisms for interventions of researchcollaboration to create knowledge that contributes to the achievement of the COINS project’s vision
are discussed. This is done by using the information accumulated through the participant observationof the target cases and arranging the context, interventions, mechanisms, and outcomes for the COINS
as a whole and for each subtheme leader. After summarizing the research strategies of each subtheme
leader, the possibility that they can be categorized from two perspectives is discovered: production
and consumption of knowledge and whether it is core knowledge or peripheral knowledge. This
classification suggested that COINS subtheme leaders were adopting different research strategies
while conducting R&D to achieve the common vision of an “in-body hospital.”

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5.2 Implications
5.2.1 Theoretical implications
The theoretical implication of this study is that introducing the concept of instrumentation oforganizational and managerial phenomena in scientific research activities is an important step toward
systematizing management of science. Extending the scientometric methodology to strategic andorganizational management of scientific research can provide quantitative basis for the interpretationof the organizational and managerial phenomena observed in scientific research, thus contributing to
the systematization of the theory of management of science.

5.2.2 Practical implications
The practical implication of this study is that it provides an instrument for evidence-based management
to practitioners of management of scientific research. The methodology for instrumentation that enable
the data acquisition which reflects the organizational and managerial phenomena observed in scientificresearch can contribute to data-driven decision-making by project managers and researchadministrators.

5.3 Limitations and future perspectives
The major limitation of this study is that generalizability cannot be guaranteed because it is essentiallyan analysis of a single case study. This study mainly focuses on two projects, NanoBio First and
COINS; however, COINS is essentially a case study based on a single case because it is a successor
to NanoBio First. To increase generalizability, comparative analysis with other cases is required.
However, as mentioned in the previous chapter, due to problems such as difficulties in case collection,
context-specificity of interpretation, heterogeneity of outcomes among different fields, andcompatibility of the generative mechanism for interventions, further theoretical development toincrease generalizability requires normative condition setting such as what are desirable observationthat allow comparison under the same conditions.
In terms of instrumentation of organizational and managerial phenomena, another aspect oflimitation is what theory should be used to measure the object that will be wanted to measure, namely
the problem of the theory-ladenness of observations. For example, the graph entropy introduced inthis thesis depends on the system size, the number of nodes and edges. Since the words or phrases are
extracted from the text as nodes of the co-word network, the value of the graph entropy of the co-word
network also necessarily depends on some factors, such as the pre-processing of the text, the methodof key phrase extraction, and how to give the weights of the edges in the network. Therefore, the theoryof determining what kind of measurement is desirable for the appropriate implementation of
instrumentation for organizational and managerial phenomena is also an important topic for future
developmental studies.

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Acknowledgement
I am grateful to Prof Dr Shintaro Sengoku, my thesis supervisor, who gave me essential guidance andhelpful feedback. I am equally grateful to my co-supervisor, Prof Dr Yuya Kajikawa, for helpful
discussion. I appreciate the feedback offered by Dr Kazutoshi Sasahara, Prof Dr Masahiro Hashimoto,Prof Dr Nobuhiro Nishiyama, and Prof Dr Shuzo Fujimura. Thanks must also go to Prof Dr Kazunori
Kataoka, Prof Dr Hiromichi Kimura, Dr Tomohiro Anzai and other COINS members who gave me
essential information and suggestion, and a scholarly literature database Web of Science provided by
Clarivate Analytics for data provision. I would like to acknowledge the contribution by the membersof Sengoku laboratory. Discussions with members were stimulating, and helpful. Thank you also to
my family for your endless support.

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Appendix A
There are some processes to convert raw bibliographic data into suitable form for analyses after
downloading from the Web of Science. Here, the detailed procedures to construct the following
datasets for analyses are described:


An aggregated dataset of bibliographic information

A text corpus of titles and abstracts by publicationPanel data of the summarized lists of institutions and publications by authorPanel data of the adjacency matrices of co-author networks



Panel data of the adjacency matrices of co-word networks

In the process of constructing these datasets, the present study mainly used pandas, which is a library
to conduct data manipulation and analysis for Python. In addition, the present study describes thegeneration of network objects from the created adjacency matrix and the parameters used for drawing
the network diagram by Gephi.

A.1 Preprocessing of collected data: Converting BibTex files into JSON format
To analyze the complexity of collaboration and knowledge structure, the present study used BibTex
files downloaded from the Web of Science database. Since raw BibTex files are not suitable for dealing
with Python, these files were converted into JSON format. The bibliographic information was
extracted from raw BibTex files using the BibtexParser, which is a library to parse such files for Python.
Then, files that could not be opened using BibtexParser were excluded from the dataset. Finally,
bibliographic data extracted from 281 publications were aggregated to a dataset including information
by publication as follows: document type, list of authors, published year, title, text of the abstract, andso on.

A.2 How to build a text corpus
Building a co-word network requires a corpus of key phrases extracted from the titles and abstracts inpublications. In the present study, the corpus contained lists of key phrases per sentence extracted fromthe title and abstract was built using Natural Language Tool Kit (NLTK), which is a library for natural
language processing in Python. The procedure to build the text corpus is as follows:
1. Text data of the title and abstract per publications were extracted from the bibliographic dataset.
2. To apply the key phrase extraction smoothly, text data were divided by sentences and preprocessed.
3. Sentences were tokenized and symbolic string was removed from some obtained tokens.
4. Part-of-speech (POS) tagging was performed on the obtained tokens in tokenized sentences.
5. Key phrases extraction using rule-based methods were applied to the tokenized sentences and thewords that consisted of the extracted key phrases were lemmatized.
6. Common words were removed from the extracted key phrases as stop words.
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To ensure the reproducibility of the analysis, that is, to eliminate the subjective judgments as much as
possible, regular expressions were employed in the above processes. Applying the above processes tothe dataset of bibliographic information resulted in a corpus containing the key phrases.
First, the text data of the title and abstract were divided by sentence, using “sent_tokenize”
function of NLTK, because of the co-occurrence of the words and phrases that are defined by thesentence. To eliminate the influence of words and phrases that were not relevant to the content of theresearch topics, the following descriptions were removed from each sentence after dividing bysentence:Descriptions about copyright or publisher, such as “(C) 2015 Elsevier,” “Published by Elsevier,”and so on.

Descriptions about statistical analysis, such as “(p<0.05)” and “(N = 30).”Abbreviations, such as “(PEG)” in “poly(ethylene glycol) (PEG).”Next, the sentences containing unnecessary descriptions were tokenized, using the“word_tokenize” function of NLTK as the tokenizer. Some tokens contained symbolic strings such as“/” or “-,” which would create noise in the analysis. Thus, the following processing was applied tosuppress noise in analysis:The tokens joined by symbolic string “/” were split.



The tokens joined by symbolic string “-” were split.

Symbolic string “-” at the head of the tokens were removed.



Symbolic string “/” at the tail of the tokens were removed.
After tokenization, tokens (or words) were executed via POS tagging using Stanford POS

Tagger (Toutanova and Manning 2000), which can be performed using the NLTK wrapper. A pilot
study about Stanford POS Tagger performed almost correctly, however, several problems were also
confirmed: some symbolic strings were recognized as nouns, and some nouns were recognized as
foreign words. To avoid these problems, the following process was applied after POS tagging.


POS tags of the symbolic string such as “:” and “%” were set into another POS tag (SYMBOL).



The strings of the brackets such as “(”, “)” were removed.



POS tags for words recognized as foreign words were converted into nouns.
A rule-based key phrase extraction method was adopted and was accomplished by chunking

using the pattern of occurrence of POS in a sentence based on regular expression, provided by the“RegexpParser” function of NLTK. The rules for key phrase extraction were as follows:
1. Gerund + noun + noun +...
2. Past participle + adjective + adjective +...+ noun +noun +...
3. Adjective + gerund + noun
4. Noun + gerund + noun
5. Noun + past participle + noun
6. Adjective + adjective +... + Noun + Noun +...
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7. Noun + noun +...
8. Nouns
Although the above rules imitated the previous study (Wang et al. 2014; Wang and Wang 2018), they
were not strictly the same. Words composed of the extracted key phrases according to these rules werelemmatized using the WordNetLemmatizer, a lemmatizer available from NLTK. When a word waslemmatized, it was converted into lowercase to eliminate case sensitivity.
Finally, common words, such as “study” and “cell,” were set to stop words and removed
from the extracted key phrases. Words or phrases were set to stop words if they were in WordNet, a
dictionary that can be easily called from NLTK, and removed from the corpus of key phrases.

A.3 How to build a panel data of the information about authors
Building a co-author network requires information about the node attributes (i.e., information about
the authors themselves) as well as the edges (i.e., connections between authors). Panel data containing
the following information for each author were built from the aggregated dataset of bibliographic
information:
Author’s nameCount of author’s affiliation



The main affiliation of an authorA list of publications published the author

Number of publications published the authorAuthor’s ORCID

Author’s Researcher-ID

An affiliation with the highest count of affiliations was selected as the main affiliation.
After building the panel data of the author information, authors’ names were collated andauthor information was updated because some names were duplicated. In the case of an author having
an identifier such as ORCID, the name was collated based on the identifier; otherwise, the names werecollated manually.

A.4 How to build a panel data of adjacency matrices of co-author networks
The procedure to build the panel data for the adjacency matrices of the co-author network was asfollows:
1. All authors, which corresponded to the nodes in the co-author network, were listed from thedataset of aggregated bibliographic information.
2. Collections of bibliographic data of publications by year were extracted to build the panel data ofadjacency matrices from the dataset.
3. A list of authors per publication in that collection was obtained and the edge weights for all
combinations in the list were computed.
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4. The edge weights in the co-author network as a whole were calculated as the sums of each edge
weight per publication for all publications.
5. The edge weights in the co-author network were normalized by dividing by the number ofpublications so that the sum of the edge weights is 1.
The unit of analysis for the co-author network was set to one publication because co-authorship isdefined per publication. The edge weights in the co-author network were calculated as the sum of theinverse of the number of edges in the unit of analysis (each unit of analysis forms a complete graphand if such a graph has N vertices, then the number of edges is N(N−1)/2).

A.5 How to build the panel data of adjacency matrices of co-word networks
The procedure to build the panel data for the adjacency matrices of the co-word network was asfollows:
1. All key phrases, which corresponded to the nodes of the co-word network, were listed from thetext corpus.
2. Collections of corpora consisting of key phrases per publications by year were extracted forbuilding the panel data of adjacency matrices from the corpus.
3. A list of key phrases per sentence in that collection was obtained and the edge weights for all
combinations in the list were computed.
4. The edge weights in the co-word network as a whole were calculated as the sums of each edge
weight per sentence for all sentences.
5. The edge weights in the co-word network were normalized by dividing by the number of sentences
so that the sum of the edge weights is 1.
The unit of analysis for the co-word network was set to one sentence because the co-occurrence of thewords or phrases is defined per sentence. Similar to the co-author network, the edge weights in the coword network were calculated as the sum of the inverse of the number of edges in the unit of analysis.

A.6 Generation of network objects
In present study, to deal with the co-author and co-word networks, the present study uses NetworkX,
which is a library for network analysis in Python. Adjacency matrices of the co-author and co-word
networks were converted into network objects, using the “from_pandas_adjacency” function of
NetworkX. Network objects have the advantage that it allows to set attributes of the nodes, such as
information about the sector of the author’s affiliation in a co-author network.
In the cross-sectional analysis, the coauthor and co-word meso-network and micro-network
are extracted from these macro-networks by using the “subgraph” function of NetworkX. This function
generates a network object of the sub-network by passing a list of nodes, which is a subset of the nodes
in an original network, as an argument. The subset of nodes (i.e., authors and words) used to extract
the co-author/word meso/networks is defined by listing the nodes that represent authors or words in134

the title and abstract that appear in publications published by a group of individuals belonging to asubtheme or by an individual, respectively.
To build a list of meso-level nodes, an article was considered to belong to a subtheme when
we could confirm that an author belongs to that subtheme from the internal documents of the COINS17.
The list of authors and words in an article belonging to asubtheme becomes a subset of nodes for
extracting the co-author/word meso/macro-network. For example, if an author belonging to subtheme
1 and an author belonging tosubtheme 2 publish a co-authored paper, this paper was considered tobelong to both subtheme 1 and 2 thus these authors appear in the co-author meso-network for bothsubthemes, and the words in the title and abstract of this paper also appear in the co-word network ofboth subthemes.
Similarly, in the case of the micro level, a list of authors and words in publications publishedby an author becomes a subset of nodes for extracting the co-author/word micro-network from themacro-network. For example, co-authors in a series of publications published by an individual appear
in the co-author micro-network of the individual and words in the title and abstract of them appear in
the co-word micro-network of the individual.

A.7 Parameters for drawing network diagram by Gephi
The parameters of the Fruchterman-Reingold algorithm for drawing the co-author network and coword network are shown in the table A.1. The parameter of “Speed” was change manually.
Table A.1 Parameters of Fruchterman-Reingold algorithm
Parameter
Co-author network
Area
200000
Gravity
0.25
Speed (initial)
100
Speed (after spread)10

Co-word network
1000000.5
100
1017 The present study assigns a subtheme to each publication to determine the meso-level attributes of all authors

because this document does not cover all authors. In addition, some members varied their subthemes belonged todepending on the year. Then, these members were considered to be included in that subtheme if they belonged to acertain subtheme in any year.

135Appendix B

Figure B.1 Time-course plot of the whole project and 5 subthemes. The horizontal axis represents theperiod of observation in the present study. The vertical axis represents the number of nodes over time
for the co-author macro-network and meso-network.

136

Figure B.2 Time-course plot of the whole project and 5 subthemes. The horizontal axis represents theperiod of observation in the present study. The vertical axis represents the number of nodes over time
for the co-word macro-network and meso-network.

137

Figure B.3 Trajectory of the number of nodes in co-author and co-word networks. The horizontal axis
represents the number of nodes for the co-author macro-network and meso-network. The vertical axis
represents the number of nodes for the co-word macro-network and meso-network.

138

Figure B.4 Time-course plot of the whole project and 5 subthemes. The horizontal axis represents
the period of observation in the present study. The vertical axis represents the number of edges overtime for the co-author macro-network and meso-network.

139

Figure B.5 Time-course plot of the whole project and 5 subthemes. The horizontal axis represents theperiod of observation in the present study. The vertical axis represents the number of edges over time
for the co-word macro-network and meso-network.

140

Figure B.6 Trajectory of the number of edges in co-author and co-word networks. The horizontal axis
represents the number of edges for the co-author macro-network and meso-network. The vertical axis
represents the number of edges for the co-word macro-network and meso-network.

141

Figure B.7 Time-course plot of the whole project and 5 subthemes. The horizontal axis represents
the period of observation in the present study. The vertical axis represents the density over time forthe co-author macro-network and meso-network.

142

Figure B.8 Time-course plot of the whole project and 5 subthemes. The horizontal axis represents theperiod of observation in the present study. The vertical axis represents the density over time for the
co-word macro-network and meso-network.

143

Figure B.9 Trajectory of the density in co-author and co-word networks. The horizontal axis represents
the density for the co-author macro-network and meso-network. The vertical axis represents theaverage clustering coefficient for the co-word macro-network and meso-network.

144

Figure B.10 Time-course plot of the whole project and 5 subthemes. The horizontal axis represents
the period of observation in the present study. The vertical axis represents the average clustering
coefficient over time for the co-author macro-network and meso-network.

145

Figure B.11 Time-course plot of the whole project and 5 subthemes. The horizontal axis represents theperiod of observation in the present study. The vertical axis represents the average clustering
coefficient over time for the co-word macro-network and meso-network.

146

Figure B.12 Trajectory of the average clustering coefficient in co-author and co-word networks. The
horizontal axis represents the average clustering coefficient for the co-author macro-network andmeso-network. The vertical axis represents the average clustering coefficient for the co-word macronetwork and meso-network.

147