Management of Knowledge-Intensive Organizations

ELLIE OKADA

Management of Knowledge-Intensive Organizations Governance Models for Transformative Discovery

Management of Knowledge-Intensive Organizations

Ellie Okada

Management of Knowledge-Intensive Organizations Governance Models for Transformative Discovery

Ellie Okada Boston Cancer Policy Institute Somerville, MA, USA

ISBN 978-3-319-97372-2 ISBN 978-3-319-97373-9  (eBook) https://doi.org/10.1007/978-3-319-97373-9 Library of Congress Control Number: 2018950410 © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover image: © mammuth/E+/Getty Cover design by Tom Howey This Palgrave Macmillan imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

To my parents in heaven

Preface

This book is to investigate governance models that address innovative and ethical academic scientists and knowledge-intensive organizations (KIOs). The theoretical basis is a management and governance framework of KIOs that has developed through two research streams: knowledgeintensive service firms and professional service firms. This study modifies them to address academic-specific issues at the boundaries of knowledge, autonomy, regulation, and identity. The original interest of this study is a process model of academic scientists to explore an autologous procedure of biologics. The initial focus is on the autologous immune cell injection (the one that Dr. Rosenberg at the National Cancer Institute invented) and stem cell- bone marrow transplant (hospital mode). The scholarly interest is to examine processes that facilitate translating discoveries to a subsequent stage. On the other hand, there emerges another pathway that involves industry organizations. In this regard, this study focuses on the process and designs of academic KIOs that facilitate discoveries and translation to first human studies within the basic science stage. Bodies of investigations are the translational science of cancer-induced bone pain, epilepsy, stem cell technologies, and vaccine science for multidrug resistant tuberculosis. This research narrows the scope of inquiry to the translational science process of discovery and translation within a basic science stage. This study does not include clinical stages. This study selects cases by basing on the fitness to theories of management and governance of KIOs, not on the vii

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scientific superiority. Although there are descriptions of science processes, they are to facilitate understanding of the organizational processes. This study starts from the self-regulation of science and trusteeship governance of KIOs. Then, it seeks overlaps with other governance models, such as agency-perspective governance, fairness-based stakeholder governance, and a liberal account of common good-based stakeholder governance. The basis of extending self-regulation is the concept of organizational boundaries (Santos & Eisenhardt, 2010). The underlying assumption is that this study defines governance as management of boundaries and relations (Choi, Hilton, & Millar, 2004; Sherer & Leblebici, 2015). This study extends previous literature to explore boundary conceptualizations specific to academic KIOs. Recent years witness academic KIOs experience several discourses. External forces that are to guide them in the wrong direction may unexpectedly work. In this regard, there are concerns about the bias of research agendas in academic sciences. There are undoubtedly such invisible but external forces. At the same time, there are also other causes. It often happens that some barriers obstacle to obtain institutional validation to proceed translational research. As Belmont Report states, highly motivated and talented academic scientists tend to focus on their goals and may unintentionally overlook other implications of their conduct. Even gifted and ethical academics can also fail if they are committed to a high quality of science and follow the best procedures of science at that moment. Therefore, cautious validation is necessary. On the other hand, it often happens that causes are a lack of specification that invites emotional and political polarization. From the management and governance perspectives, what matters is governance. Causes of corruptions should be carefully differentiated. As previous management literature tells, an organizational design defines members’ behaviors. Thus, this book investigates (i) specific factors that make some academic science fields understudied, and (ii) governance models to modify such bias from management theories of KIOs. This study examines barriers to obtain institutional validation of preconditions to proceed translational research, value-based factors that stem from ambiguous and less-specified areas, intellectual property policies, and alliance patterns that introduce invisible forces of capture. This research project owes to several suggestion and encouragements. Professor Joseph L. Bower introduced me to a Science-Based Business

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Initiative seminar, a faculty seminar at Harvard Business School. The arguments there deepen my understanding of competing factors that affect the management of science. This seminar is a joint seminar with Economics of Science and Engineering Workshop by Professor Richard B. Freeman at Harvard. Based on the discussion, I came to understand how regulatory strategies affect scientists’ behaviors. Dr. John Trumpbour at Labor and Worklife Program at Harvard Law School tells unintended consequences that surround science practices. Although biomedical sciences encounter several ethical dilemmas, my critical weakness is law and ethics. In this regard, Health Law Workshop at the PetrieFlom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School and Bioethics Consortium at Center for Bioethics at Harvard Medical School help me to have some sense of ethics. Also, this study owes to Professor Susan Pharr and Dr. Shinju Fujihira at Harvard Weatherhead Center for International Affairs, and Professor Hugh Patrick at Columbia Business School. Without my stay as a visiting scholar, I would not have an opportunity to study the self-regulation of academic science. I hope that, despite several limits of this study, scientific discoveries will be better transformed in the coming years to liberate those who are afflicted. Somerville, USA

Ellie Okada

References Choi, C. J., Hilton B., & Millar, C. (2004). Emerging business systems. Hampshire and New York: Palgrave Macmillan. Santos, F. M., & Eisenhardt, K. M. (2005). Organizational boundaries and theories of organization. Organization Science, 16(5), 491–508. Sherer, P. D., & Leblebici, H. (2015). Governance in professional service firms. In B. Hinings, D. Muzio, J. Bronschak, & L. Empson, (Eds.), (pp. 189–212).

Contents

1 Introduction 1 1 Management of Knowledge-Intensive Organizations 1 1.1 Theoretical Framework 1 2 Academic Knowledge Production and K ­ nowledgeIntensive Organizations 4 2.1 Two Research Streams 4 2.2 Knowledge Production Systems in Academic Sciences 6 2.3 Concerns Over Academic Knowledge Production 8 2.4 Governance of Academic Knowledge Production 9 3 Governance Models of Knowledge-Intensive Organizations 10 3.1 Definition of Governance 10 3.2 Relations with Neighboring Fields 11 3.3 Governance Models in Management Theories of KIOs 11 4 Organization of Chapters 13 References 17 Part I 2 Translational Science and Boundary Conceptualization 27 1 Translational Science and Framework of KnowledgeIntensive Organizations 27 1.1 Components of Translational Science 27 1.2 Consistencies with KIO Framework 28 xi

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2 Boundaries and Governance 2.1 Knowledge Integration and Boundaries 2.2 Applications to Translational Science 2.3 The Need of the Academic-Specific Boundary Conceptualization 3 Framework of Knowledge-Intensive Organizations, Boundaries, and Buffer 3.1 Threshold Elements of Boundary Spanning 3.2 Systems Model of Buffering 3.3 Modalities of Governance 4 Conclusion References

29 29 32 37 38 38 39 40 46 47

3 Trusteeship Governance and Challenges to Scientific Knowledge-Intensive Organizations 55 1 Introduction 55 2 Captures, Goal Replacement, Nonacademic Legitimacies 56 2.1 Captures 56 2.2 Academic Captures 58 2.3 Goal Replacement 59 2.4 Measures to Identify Captures 61 3 Governance Models 62 3.1 Governance Models of Academic KIOs 62 3.2 Mitigating Inconsistencies in Governance in Translational Science 65 3.3 Directions for Enhancing Models 66 4 Conclusion 67 References 69 Part II 4 Institutional Barriers and Governance 75 1 Introduction 75 2 Barriers to Institutional Validation 77 2.1 Informed Consent as a Buffer 77 2.2 Fair Transaction Model of Consent 80 2.3 Measurement of Preconditions 85 3 Fairness-Based Stakeholder Theory in Translational Science 87 3.1 Fairness-Based Stakeholder Theory and Informed Consent 87

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3.2 Obligations of KIOs in Fairness-Based Stakeholder Theory 3.3 Implementation Mechanism 4 Conclusion References

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90 91 92 94

5 Research Policy and Knowledge-Intensive Organization 99 1 Introduction 99 2 Research Policies and Bioethics 102 2.1 Competing Factors in Research Policies 102 2.2 Transnational Efforts vs. State-Centric Efforts 103 2.3 Institutional Approach 104 2.4 Intellectual Property Policies and Humans’ Biological Cells 107 2.5 Moral Stakeholders and Civic Epistemology 109 3 Discovery Pathways 112 3.1 Basic Research in the US and German KIOs 112 3.2 Translational Pathways in the US and German KIOs 115 4 Conclusions 120 References 122 6 New Governance Models for Discoveries of Vaccine Science 127 1 Introduction 127 2 Research Alliances of Multilateral Knowledge­­ Intensive Organizations 129 2.1 WHO as a KIO 129 2.2 Private–Public Partnerships in Management Theories 131 2.3 Buffers for Autonomy of KIOs 135 2.4 Governance in Management Theories 135 3 Application to Vaccine Research 136 3.1 Possible Bias in the Selection of Research Agendas 136 3.2 Background: MDR-TB Initiative 138 3.3 Problems in Governance 142 4 Governance Model for Vaccine Discoveries 144 4.1 Vaccine Science and Alliance Governance of KIOs 146 5 Conclusion 147 References 149

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Part III 7 Science and Insights from Humanistic Disciplines 159 1 Introduction 159 2 Intellectual Property Policies of Biomedical Consortia 160 2.1 Proprietary Knowledge in Scientific Commons 160 2.2 Intellectual Property Policies as Governance Mechanisms 163 2.3 Governance Theories and Property Rights 174 3 Self-Regulation of Science and Moral Identity 174 3.1 Integration Towards Innovation Pathways 174 3.2 Bridging Mechanisms for Governance 176 3.3 Reaching to Common Ground with the Agency-Based Governance 176 4 Conclusion 177 References 178 8 Conclusion 183 1 Toward Governance Models for Academic KnowledgeIntensive Organizations 183 2 Self-Regulation and Trusteeship Governance 184 2.1 Directions of Extension: Agency-Perspective Governance 185 2.2 Directions of Extension: Stakeholder-Perspective Governance 188 3 Intellectual Property Policies as Governance Mechanisms 192 3.1 Imposing Ownership Requirement 192 3.2 Unbundling of Rights 192 4 Conclusion 193 4.1 Theoretical and Practical Implications 193 4.2 Limits and Future Studies 195 References 195 Index 199

Abbreviations

ALLEA (ALL European Academies) The European Federation of Academies of Sciences and Humanities CBER Center for Biologics Evaluation and Research CDC Center for Drug Evaluation and Research CDRH Center for Devices and Radiological Health CFR Codes of Federal Regulation DOE Department of Energy EC The European Commission EU The European Union FDA Food and Drug Administration HHS U.S. Department of Health and Human Services MCS Minimal conscious state MDG The Millennium Development Goals MDR-TB Multi-drug resistant tuberculosis NCI National Cancer Institute NHGRI National Human Genome Research institute NIH National Institute of Health NPR New Public Reforms OECD The Organization for Economic Cooperation and Development OGCP Office of Good Clinical Practice POC Proof of concept xv

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Abbreviations

POCM Proof of Clinical Mechanisms PPPs Private-Public partnerships SGC Oxford Academic Health Science Network and Structural Genomics Consortium TB Tuberculosis TRIPS Trade-Related Aspects of Intellectual Property Rights UK The United Kingdom UN The United Nations US The United States of America USPTO The United State Patent and Trademark Office WHO The World Health Organization WTO The World Trade Organization

List of Tables

Chapter 2 Table 1 Boundary conceptualizations Table 2 Relations with frameworks of academic KIOs

30 41

Chapter 3 Table 1 Regulatory and academic captures 61

Chapter 5 Table 1 Comparison on cognitive framing of stem cell science and translation paths 119

Chapter 6 Table 1 Motivations to build partnerships 133 Table 2 The stages of knowledge exchange in developing a standardized regimen 141

Chapter 7 Table 1 Case selection and the justification 164 Table 2 Intellectual property policies of life sciences/ biomedical consortia 170

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CHAPTER 1

Introduction

1   Management of Knowledge-Intensive Organizations 1.1   Theoretical Framework Scholarly management has not thoroughly investigated governance models that address innovative and ethical academic institutions and scientists. The objective of this book is to explore governance frameworks of knowledge-intensive organizations (KIOs) that are to transform ­scientific discoveries to address human diseases. This study bases on management and governance theories of KIOs that originate in two research streams: studies on knowledge-intensive service firms (Bettencourt, Ostrom, Brown, & Roundtree, 2002; Choi, Hilson, & Millar, 2004; Choi & Millar, 2005; Den Hertog, 2000) and those on professional service firms (PSFs) (Hinings, Muzio, Broschak, & Empson, 2015a; von Nordenflycht, 2010). Here, European Commission (EC) comprehensively defines KIO as an entity in which knowledge is the primary production factor and goods offered (EC, 2012, p. 6). This broad definition encompasses academic institutions and multilateral organizations such as World Health Organization in the scope. The present study bases on this definition in investigating science governance of academic KIOs. Meanwhile, von Nordenflycht (2010) characterizes the organizational structure of KIO as not being controlled by outside and commercially motivated owners. In this context, norms and codes ensure trusteeship behaviors of constituents (von Nordenflycht, 2010). This basic structure has been altered in academic KIOs as © The Author(s) 2019 E. Okada, Management of Knowledge-Intensive Organizations, https://doi.org/10.1007/978-3-319-97373-9_1

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below mentioned. Here, academic KIOs constitute not only of uni­ versities but also of independent research institutes that conduct basic scholarly research (see, Radder, 2010b), such as Max Planck Institute and Scripps. Among several governance models, this book focuses on trusteeship governance (Sherer & Leblebici, 2015; von Nordenflycht, 2010) that encompasses self-regulation of academic sciences (Resnik, 2008; Ziman, 2000). This book investigates (i) specific factors that make some academic science fields understudied, and (ii) governance models to modify such bias from management theories of KIOs. Companies recognize science and technologies as a source of organizational capability change (Arora, Belenzon, & Patacconi, 2015; Pisano, 2006) and corporate renewal (Zahra, Neubaum, & Hayton, 2016). While biotechs explore applications for academic discoveries, multinational ­pharmaceuticals seek opportunities to involve in their endeavors. Channels include corporate venture investment,1 in-licensing, late-stage clinical trials, marketing, and manufacturing. By strengthening relations with academic KIOs and biotechs, established pharmaceuticals have largely changed their pipeline portfolios to strengthen biologics.2 In general, technology-leader companies recognize academic sciences as a source of spillovers (Alcacer & Chung, 2007; Teece, Pisano, & Shuen, 1997). Here, academic biomedical sciences are more than spillovers for therapeutics development industries. By funding or directly participating in ­academic research, pharmaceuticals have outsourced basic research to academic KIOs (Arora et al., 2015; Contopoulos-Ioannidis, Ntzani, & Ioannidis, 2003; Etzkowitz & Leydoesdorff, 2000; Etzkowitz & Webster, 1995). Meanwhile, there emerged understudied science fields despite their scientific significance and potential impacts on humans (Kunneman, 2010; Radder, 2010b). Radder (2010b), among others, ascribe this phenomenon to the involvement of commercially motivated organizations and other nonacademics. The study of this book identifies that, though there are undoubtedly external forces that undermine academic sciences, several institutional and organizational constraints are also contributing to biases that make certain fields understudied. Examples include epilepsy science and neurobiological understanding of translational pain science (Chapter 4). In encountering several conflicting factors, this study aims to understand and moderate issues from perspectives of management and governance of KIOs. By starting from self-regulation of academic KIOs, the study seeks overlaps with other governance models, including

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agency-based governance (Riordan & Williamson, 1985; Williamson, 1979, 1981), fairness-based stakeholder theory (Miller & Wertheimer, 2011; Phillips, 2003. See also, Evan & Freeman, 1988; Freeman, 1984: Rawls, 1958/1999; Wicks, Gilbert, & Freeman, 1994), and liberal accounts of common good-based stakeholder theory (Argandona, 1998). Agency-based governance is to determine a threshold of i­ntroducing external governance into internal governance of KIOs (Riordan & Williamson, 1985). It connects internal governance to contractual relations that consist of a bundle of property rights. As regulatory policies adopt agency-based concepts in optimizing incentives and protections of those in weak positions in contractual relations (Armour, Hansmann, & Kraaman, 2017) (Chapter 8), agency theory helps to understand a structure of external factors that lead KIOs to optimal behaviors. On the other hand, stakeholder theory concerns over moral obligations toward those who have legitimate interests to organizations. Problems arise on the stakeholder legitimacy and their reasoning. In this regard, this study focuses on fairness-based stakeholder theory and liberal accounts of common good-based stakeholder theory. The former defines a moral obligation of stakeholder fairness over that due regarding civil society obligations (Phillips, 2003). It has consistency with biomedical society’s “fair transaction model” (Miller & Wertheimer, 2011) that reasonably limits investigators’ responsibilities in the institutional process. Regarding the latter, the concept of common good relates to duties and obligations toward stakeholders in achieving the common good while positively positioning private incentives (Argandona, 1998, p. 1100). In this book, common goods are vaccine discoveries and translation capacities of KIOs. The basis of extending self-regulation is the concept of organizational boundaries (Santos & Eisenhardt, 2005). The underlying assumption is that this study defines governance as management of boundaries and relations (Choi et al., 2004; Sherer & Leblebici, 2015). This study extends previous literature to explore boundary conceptualizations specific to academic KIOs. Here, research questions are two folds: (i) What are specific factors that make particular academic biomedical sciences understudied despite their scientific significance and potential impacts on humans? (ii) What kinds of governance models and mechanisms modify the biases from the perspective of management theories of KIOs? A methodology is an inductive approach. This study sets propositions and tries to extend or reject them by examining selected cases

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of translational science. Case selection criteria are fitness to the management and governance theories of KIOs, and not of the scientific superiority. This study has a potential to contribute to management and governance theories of KIOs by seeking overlapping parts of heterogeneous governance models. This study also brings practical implications for ­academic KIOs to bridge transformative discoveries to insights that come from humanistic disciplines.

2  Academic Knowledge Production and ­ Knowledge-Intensive Organizations 2.1   Two Research Streams There are two research streams that examine KIOs. The former is a study of knowledge-intensive service firms (Bettencourt et al., 2002; Choi & Millar, 2005; Choi et al., 2004; Den Hertog, 2000). The latter is a study of professional service firms (PSFs) (Anand, Gardner, & Morris, 2007; Greenwood & Empson, 2003; Hinings et al., 2015a; von Nordenflycht, 2010). The former characterizes knowledge as being inalienable. Because of this nature, the market-based exchange is not necessarily a dominant type in the knowledge exchange. Reciprocity and gift (Choi & Millar, 2005) also have a presence. Such heterogeneous forms of knowledge exchange coexist in translational science. Translational science is a process to apply discoveries of laboratory studies to human studies. This line of research, the molecular mutations research, in particular, has a tradition to share data in a closed science community in a reciprocal manner. Also, researchers heavily rely research object, such as human body cells and tissues, on donation. In this space, gift transactions occur. Therefore, a governance model for those differentiated from market-based transactions is in need. Among knowledge transaction types, this study focuses on reciprocal knowledge transaction. The research stream of PSFs presents another aspect. This stream has examined organizational aspects of PSFs, such as organizational structure (von Nordenflycht, 2010), partnership modes (Greenwood & Empson, 2003), professional ethics (Gunz, Gunz, & Dinovitzer, 2015), and governance (Greenwood, 2007; Sherer & Leblebici, 2015), among others. Hinings, Muzio, Broschak, and Empson (2015b) develop a definition of PSFs that should satisfy all of four characteristics: a primary activity, knowledge, governance, and identity. In PSFs, a primary activity is

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the application of specialist knowledge to the creation of customized solutions of clients’ problems. Core assets consist of specialist technical knowledge of professionals and their in-depth knowledge of clients. Extensive individual autonomy and contingent managerial authority characterize governance in which core producers own or control core assets. Regarding identity, core asset producers recognize each other as professionals and are recognized as such by clients and competitors (Hinings et al., 2015b). These components do not fit academic KIOs. The primary activity of academic KIOs are academic knowledge production and education3 in which academics focus on pursuing the true description of underlying structures (Kunneman, 2010; Radder, 2010b). Core assets are accumulations of disciplinary knowledge, the institutional feature of objectivity (see, Reiss & Sprenger, 2017), the know-how of education, and contributions to academic communities and society. It is their academic trainings and research activities that shape academic identity (regarding identity, see Weick, 1995). Despite differences, the characterization of governance of PSFs is useful and can be extended to apply to academic KIOs. Also, PSFs are specialized organizations of KIOs (Millar, Peters, & Millar, 2018). Therefore, this book occasionally refers to theories of PSFs regarding governance and ethics. Then, a problem is how to position self-regulation of science in management theories. It is the philosophy of science that studies the governance of science. In this field, self-regulation of science is an established model for centuries. The underlying assumption is that science is a social institution that has autonomy regarding activities of science (Resnik, 2008; Ziman, 2000). Because of a specialized nature of academic knowledge, the society endows self-regulation to science through a social contract, though it is in the ongoing, renegotiation process with the society (Gibbons, 1999; Nowotny, Scott, & Gibbons, 2001). On the other hand, management studies examine self-regulation as one of the psychological attributes of organization members (Goleman, 2004, p. 88; Jarvenpaa & Majchrzak, 2016; Klein, 1989). Some studies governance aspects but as an ineffective, voluntary regulation of industry association without legal ties (see, King & Lenox, 2017). It is an emerging thought that recognizes an attribute of self-regulation as a constituent of a governance model of PSFs (such as law firms) (Greenwood, 2007; Sherer & Leblebici, 2015. Also see, von Nordenflycht, 2010). Sherer and Leblebici (2015) positions self-regulation as a constituent of the trusteeship governance of PSFs.

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This book focuses on self-regulation as a starting point of the governance of academic sciences, as academics have a distinct style of knowledge transaction from firms. As self-regulation is an attribute of trusteeship governance of PSFs, this book regards the trusteeship governance as an organizational umbrella of self-regulation. As stated earlier, there are outstanding tensions that challenge the self-regulation of biomedical sciences (Fang, Stern, & Casadevall, 2012; Radder, 2010b; Steen, 2011). Millar et al. (2018) raise awareness as follows: —unless (different) value systems are commensurable, it is impossible to be sure that what is developed as knowledge and transmitted as knowledge is complete, objective, reproducible, and a sound basis for the right actions. Reconciling the value systems is a central challenge for the management and governance of KIOs.

In this regard, this book seeks directions to extend the boundaries of trusteeship governance by investigating understudied or politicized fields. Here, cases of understudied areas include neurobiological aspects of translational science for cancer-induced bone pain and epilepsy, and vaccine science for multidrug resistant tuberculosis. The politicized field is stem cell research. This study selects understudied fields by referring to Tamas (2013), publications by National Academy of Medicine, and PubMed database. 2.2   Knowledge Production Systems in Academic Sciences Self-Correcting Nature of Science Science functions as a map to recouple and recombine knowledge in the nonlinear processes of technological innovation (Fleming & Sorenson, 2004). In the case of biomedical science, this relation is relatively direct (Arora et al., 2015). It is likely that this relative directness is a source that invites several practical interests. Despite the outside interests, self-regulation remains to govern the knowledge production that is underpinned by self-correcting nature of sciences. The self-correcting nature of sciences originates in academics’ intrinsic motivation to pursue truth (Radder, 2010b; Truog, 2017). A system of peer-review underpins this nature. In this space, the sharing of research

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results is a central component of academic norms (Anderson, Ronning, De Vries, & Martinson, 2010; Merton, 1942). It is Department of Energy and National Institute of Health (DOE & NIH) (1992) that institutionalizes this norm (Okada, 2018). A subsequent agreement, so-called Bermuda’s legacy, confirms this policy (Contreras, 2011). For example, NIH’s Undiagnosed Disease Program (NIH-UDP) developed a digital infrastructure that is to materialize distributed cognition hypothesis. The hypothesis assumes that a cognitive process is delimited by the functional relationships among participants, rather than by a spatial colocation of them. The sharable electronic laboratory notebook (ELN) is one of the integrated components of the collaboration mechanism (Links et al., 2016). They reduce transaction costs of distant knowledge exchange while clarifying their functional responsibilities. The practice of transparent reciprocity not only facilitates the process. It also helps to assure the validity of produced knowledge with the underpinning of a field-specific process and methodology (Okada, 2018). Recombination with Heterogeneous Knowledge In fact, limiting the knowledge exchange in a closed community runs a risk of screening out what might otherwise contribute to yielding breakthrough knowledge recombination. Also, staying in a closed community bears a risk of not being aware of hidden scientific relations (see also, Mukunda, 2012; Trispas, 2009). On the other hand, building reciprocity with heterogeneous individuals also bears a risk of bringing outside interests and freeriding. Nevertheless, the recombination with other disciplines and heterogeneity has a potential to bring breakthroughs. For example, the so-called omics-based4 biomarkers that enable precision originate in the serial analysis gene expression (SAGE) in the 1990s (see, Adams et al., 1991; Nielsen, 2008; Velculescu, Zhang, Vogelstein, & Kinzler, 1995). The basis of SAGE is that the genes expressed within an organism determine the characteristics of the organism (Adams et al., 1991). A methodology, SAGE, allows the quantitative and simultaneous analysis of a large number of transcripts that organisms present (Velculescu et al., 1995). The SAGE is the recombination of computer science and biology. Through this integration, the omics-based biomarkers are expected to distinguish health-related characteristics of humans (Disis, Tarczy-Hornoch, & Ramsey, 2013; Institute of Medicine: IOM, 2012). A problem in breakthrough fields is that it often happens that methodologies have not developed while a scientific field has developed

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(Kuhn, 1962). In such case, scientists can overcome issues by cocreating new methodologies and validation processes.5 On the other hand, there is a different force that is pushing academic sciences opposite to the traditional academic norms. It is a renegotiated social contract. It limits the autonomy of science and self-regulation of science by requesting the participation of the industry and nonexpert public in the validation of contents of academic sciences (Gibbons, 1999; Nowotny et al., 2001). Industry involvement itself is not necessarily new. In fact, Contopoulos-Ioannidis et al. (2003) provide evidence that, among promising findings to clinical applications published between 1979 and 1983 in major journals, the most reliable predictor of the move to the randomized trial is the industry involvement in the initial publication (Contopoulos-Ioannidis et al., 2003; Ioannidis, 2004). On the other hand, the industry involvement raises a concern about data withholding and a bias to science (Ioannidis, 2004; Krimsky, 2007; Radder, 2010b). As a counter-veiling force (Cf. Pfeffer & Salancik, 1978), civic epistemology (Jasanoff, 2005) is supposed to neutralize the influence. On the other hand, the public also can be a moral stakeholder that does not represent a universal morality (Attas, 2004). Though outsiders’ insights often enhance morality and utility of science, they also bring nonacademic and nonscientific legitimacies that make scientists less likely to focus on truth (see, Radder, 2010b). Therefore, governance defined as the management of relations and boundaries has a significant function in this domain. 2.3   Concerns Over Academic Knowledge Production Related to the above, Radder and colleagues expressed concerns on the commodification of academic research. The commodification encompasses the condition that people state the academic knowledge in line with the market exchange and practical motivations (Radder, 2010b). This concept is different from the “application.” Many commentators ascribe the phenomena of commodification to Byhe Dole Act (1981). However, as some authors also state (Radder, 2010a), patenting of university inventions began before the Byhe Dole Act. Eminent examples are the Massachusetts Institute of Technology and Stanford University. In these universities, academic scientists explore research questions in the applied science while deepening basic sciences.6

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A series of empirical research suggests that the Byhe Dole did not weaken the basic research: academic scientists who actively involved in the application also broadened their scope of the basic research (Schut, von Paassen, Leeuwis, & Klerlx, 2014; Thursby & Thursby, 2011). Nevertheless, empirical research (Czamitzki, Glänzel, & Hussinger, 2008; Mowery & Ziedonis, 2002) support their concerns over the quality of scientific research. Czamitzki et al. (2008) argue that, in the German academic KIOs, academics enhance the research quality when they collaborate with nonprofit entities (such as independent research institutes and international/multilateral organizations), whereas the quality declines when they collaborate with industry organizations. As far as reviewing literature, in overall, the patenting of university inventions enhanced the capacity of academics while retaining basic sciences. However, empirical research has not confirmed this relation in the biomedical science. In addition, Christensen (2011) reveals that, while university reforms expanded “official” autonomy of universities, they restricted the “actual” autonomy of universities. The global trend of the university reforms originated in public management reforms in Australia and New Zealand (Christensen, 2011) that introduced market-based principles to ­public management (Ghobadian, Gallear, Viney, & O’Regan, 2004). Also, as stated in Christensen (2011), universities accepted the reforms with a wrong expectation that the reform would increase the autonomy of university by gaining control over funds. From the above, it is worthwhile to investigate whether the official intentions of university reforms manifested in the quality of academic knowledge, or generated corruption and understudied fields. 2.4   Governance of Academic Knowledge Production The commodification of academic sciences generates academic knowledge corruption that manifests in two areas: process aspects of corruption and content aspects of corruption (Radder, 2010b). The former occurs in outcome qualities of research such as bias, errors, and irreproducibility. Contributing factors are outside and nonacademic legitimacies, such as the involvement of commercially motivated organizations and a public attracted by a seductive technology (Kunneman, 2010; Radder, 2010b).

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On the other hand, there are methodological difficulties in investigating the contents aspects that embodied in a biased selection of research agenda. A possible source is a capture. However, it is tough to discern whether capture really happens (Carpenter & Moss, 2014b. Also see, Gunz et al., 2015). In this regard, this book adapts the methodology by Carpenter (2014) (Chapter 3). Regarding the stages of research, this study focuses on the translation process within the sphere of basic science. It entails procedures that occur before the application for first human studies. Please note that this study does not include clinical studies and human studies themselves. Regarding the translational “medicine” that focus on subsequent phases, see, Cheng and Dilts (2013). Also, this book does not intend to facilitate commercialization, since qualities of discoveries and translation also determine good applications.

3  Governance Models of Knowledge-Intensive Organizations 3.1   Definition of Governance This book defines governance as the management of relations and boundaries that lie between two or multiple value systems of knowledge (See, Choi et al., 2004; Jones, Hesterly, & Borgatti, 1997; Sherer & Lebrebici, 2015). Under this definition, this book explores academic-specific organizational boundaries mainly by referring to Santos and Eisenhardt (2005). They identify four dimensions of organizational boundaries (Santos & Eisenhardt, 2005), i.e., boundaries of (i) efficiencies (Williamson, 1981, among others), (ii) competence (Penrose, 1959, among others), (iii) power (Pfeffer & Salancik, among others), and (iv) identity (Weick, 1995, among others). In this conceptualization, academic KIOs are outside of the scope. On the other hand, academic KIOs have a unique style of knowledge exchange. For example, “power” does not fit academic norms in conducting research. Therefore, this book modifies the boundary conceptualizations by considering the nature of knowledge exchange, the norms (Anderson et al., 2010; Merton, 1942), and institutional features (Whitley, 2000) of academic KIOs. The academic-specific organizational boundaries are developed as boundaries of (i) regulation, (ii) knowledge, (iii) autonomy, and (iv) identity (Chapter 2).

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3.2   Relations with Neighboring Fields The neighboring fields define governance differently. In public management, governance concerns over societies such as steering societal development (de Vries, 2013). Scholars of public policy and political science define global health “governance” as diffusion of authority from statebased institutions to new arrangements that encompass both state and non-state actors and international and multilateral organizations (Buse & Walt, 2002; Reich, 2002; Rosenau & Czempiel, 1992; see, Kenworthy, MacKenzie, & Lee, 2016). In this book, the unit of analysis often overlaps with those of the public policy and political science. For example, the topic of Chapter 6 is partnerships that include World Health Organization (WHO). However, while the scholarly focus of public policy and political science is on power and institutional arrangement from societal perspectives (see, Kenworthy et al., 2016; Sachs, 2001), the focus of management studies is on sources of power, efficient and mutually beneficial boundaries, and management of organizational dependence (see, Pfeffer & Salancik, 1978; Rangan, Samii, & van Wassenhove, 2006). In this regard, this book analyzes WHO as a KIO. Also, it examines partnerships regarding contingency relations between alliance designs and governance mechanisms. 3.3   Governance Models in Management Theories of KIOs Sherer and Leblebici (2015) classifies governance models of PSFs into four types: agency-based governance, stakeholder theory, partnership ethos, and trusteeship governance. Here, the underlying assumption of trusteeship governance is that complexity of the modern society necessitates professionals who have specific expertise on which average citizens can rely for their decisions (Barker, 1992; Gunz et al., 2015; Koehn, 1994). This condition generates information asymmetry with clients. However, contrary to the assumptions of the agency-based theory, the professionals regulate themselves based on internal codes and codes of professional societies (Greenwood, 2007, pp. 187, 191; also see, Gunz et al., 2015; von Nordenflycht, 2010, p. 169). As an organizational level, the trusteeship governance regards boards not as those who control a CEO and managers but rather as guardians of the institutions (Sherer & Leblebici, 2015, p. 193; also see, Gunz et al., 2015).

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Here is a tension with agency-based governance that starts from the separation of ownership and management (Jensen, 1983; also see, Contreras, 2011; Sherer & Leblebici, 2015). This separation generates concerns over how to address agency costs that derive from inconsistencies between owners (a principal) and agents (Jensen & Meckling, 1976; Williamson, 1979). Then, a subject matter of governance encompasses incentive models to induce agents’ positive behaviors while monitoring them. In this regard, societal and economic uncertainties request academics to be innovative, creative, accountable, collaborative, and attractive to attract funding. However, based on creation theory, opportunities of creation can only be understood ex-post, whereas discoveries already exist there (Alvarez, Barney, & Anderson, 2013; Burgers & Vrande, 2016). Thus, ex-ante incentive (funding) is not always commensurable with creativity. Collaboration does not always yield high-quality scientific papers (Bikard, Murray, & Gans, 2013). Also, relations between creativity and collaboration depend on several factors (Bikard et al., 2013). Then, it is challenging to address innovative academic scientists. If boards monitor creativity and discovery with an assumption of opportunistic behaviors, it is contradictory against the academics’ intrinsic motivation and self-correcting nature of science. Here, the trusteeship behaviors of academics do not necessarily encompass creativity. However, empirical research shows that the trusteeship and tolerance for early failures, rewarding long-term success, and freedom to experiment are more likely to stimulate innovative academic scientists (Azoulay, Zivin, & Manso, 2011). Therefore, placing the self-regulation at the center is supported by evidence. Meanwhile, as already mentioned, for-profit companies have facilitated their organizational flexibility by outsourcing the basic science while they represent a source of funding (Gottweis, Salter, & Waldby, 2009; Etzkowitz & Webster, 1995). The extension of patentability of scientifically engineered living organisms also facilitates the industry involvement (Gottweis et al., 2009, p. 9) (Chapter 5). In the case of pharmaceutical companies, such relations are their business model (see also, Arora et al., 2015). In sum, the economic and social demands of outside involvement alter the organization structure of KIOs that has characteristic of no-outside ownership. This structure is to preserve trusteeship behaviors of academic scientists. Here is a complexity to address governance of transformative discoveries.

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Next to investigate is its internal integrity in stakeholder relations. Stakeholder theory, in general, concerns over the interests and wellbeing of those who affect the achievement of the organization’s objectives. Thus, it straddles between organizational management and ethics (Phillips, p. 15). A central rationale for the theory is the Kantian principle in which all human beings should be treated as ends, not as means to ends (Evan & Freeman, 1988; Fort, 2001). The stakeholder approach is appropriate to extend the governance model of academic KIOs. At the same time, stakeholders’ diverse values, such as ownership of bodily tissues, require additional efforts to preserve moral integrity of academic KIOs, particularly in biomedical sciences. This book examines fairness-based stakeholder theory and liberal accounts of common good-based stakeholder theory in the context of translational neurobiological science and vaccine science.

4  Organization of Chapters This study organizes the contents of this book as follows. The part I focuses on investigating the governance model of academic KIOs and identifying potential threats. In Chapter 2, based on the definition of governance (Choi et al., 2004; Sherer & Leblebici, 2015), this study examines relations between organizational boundaries, boundary spanning, and a buffer (Aldrich & Herker, 1977; Child & McGrath, 2001, p. 1137; Lynn, 2005; Santos & Eisenhardt, 2005, among others). In applying the framework to the translational science of academic KIOs, it investigates positive and negative roles of a buffer in a system of knowledge production. For this purpose, this study modifies the conceptualization of organizational boundaries (Jones et al., 1997; Santos & Eisenhardt, 2005) to those specific to academic KIOs. In defining academic-specific boundaries, it examines how each boundary conception relates to translational science and alliances of academic KIOs. In investigating threshold elements of enhancing boundaries and activating a buffer, the chapter concludes by summarizing problems each boundary raises in the spectrum of the translation of science. Chapter 3 investigates the governance of academic KIOs in relations to contents aspects of knowledge corruption. It argues how mismanaged translational science has a potential to introduce “capture” (Carpenter &

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Moss, 2014b; Gunz et al., 2015) to academic KIOs. A particular focus is on the academic capture that embodies in a biased selection of research agenda. As public management studies provide a volume of evidence of captures (see, Carpenter & Moss, 2014a), this chapter adapts their concepts and methodologies to studies in the management and g ­ overnance of academic KIOs. This chapter provides a foundation with later chapters to analyze in what directions the trusteeship governance of academic KIOs should be extended to produce unbiased contents of academic sciences. Chapter 4 applies the boundary conceptualization of autonomy for examining institutional barriers in two understudied fields. They are neurobiological pain science and epilepsy research. Despite scientific significance, these areas have the common methodological problems in obtaining the institutional validity of subjects’ consent. Based on regulatory requirements, subjects participate in the cooperative scheme of scientific research by informed consent. Despite the scientific significance and potential impacts on humans, this barrier places difficulties in the practice of science. This chapter discusses barriers to organizational governance and institutional validation regarding the precondition of scientific research. It investigates directions to extend trusteeship governance by focusing on (i) the precondition of the informed consent, and (ii) the role of methodology supported by knowledge-intensive research tools. Here, the research tools have a potential to affect the measurement of the precondition of neurobiological science research. Part II examines science, morality, and academic identity through the cases of the eminent academic and multilateral KIOs. In this part, chapters investigate how moral stakeholders affect a legitimate scope of research and how academic KIOs respond this issue. Here, this book follows the definition of Attas (2004) that defines a moral stakeholder as those who do not necessarily have contractual relations with an organization but represent a group of people on morally relevant grounds, and their claims are not necessarily held universally against everyone. In the fairness-based stakeholder approach, they are derivative stakeholders. In this framing, the moral stakeholder is differentiated from civic epistemology since their claims do not always hold universally to society. This book also differentiates moral stakeholders from a socially disobedient activist. It is beyond the scope of management studies to understand whether a socially disobedient activist is a moral agent.

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In this space, Chapter 5 examines (i) what factors determine transnational vs. state-centered zones of stem cell research policies (ii) how these factors affect organizations and translation pathways of basic research at the US and German academic KIOs. A methodology is an inductive– deductive analysis. Factors investigated are policy legacies, intellectual property regulations that reflect property theories of human body parts, and the competition style of moral stakeholders. The results show that, (a) given similar policy legacies between the USA and Germany, the ambiguity in defining thresholds of cells and organisms with a self-directing capacity invites politics. Competition styles of moral stakeholders affect zones of science. (b) Societal justification affects the cognitive processing and translational pathways of KIOs. (c) Global research alliances complement missing factors for each other. The particular focus is on stem cell research policies. Chapter 6 aims to analyze factors that make specific areas of translational vaccine sciences understudied. Factors investigated are (i) a capture derived from dependence, and (ii) governance mechanisms for research alliance. In assuming World Health Organization as a KIO, Chapter 6 examines (i) mechanisms of capture in research partnerships, and (ii) seeks directions to extend trusteeship governance. Bodies of investigation are translational research of vaccine science for multidrug resistant tuberculosis (MDR-TB). In spite of the scientific progress of the ability to sequence the entire genomes of bacteria (Rappuoli, Blank, & Lambert, 2011), an approved tuberculosis vaccine is only one since the early twentieth century (Prabowo et al. 2013; Cayabyab, Macovei, & Campos-Nelo, 2012). A change of the whole system of vaccine science is likely to undermine KIOs’ unique responsibilities to control pathogen. Threats identified are dependence in the co-specialization alliance. By regarding vaccine discoveries as a common good, this chapter extends the trusteeship governance to encompass common good-based stakeholder theory that positively positions the private incentives while retaining unique responsibilities of KIOs. Chapter 7 investigates how academic KIOs can manage the boundary of regulation in bridging science to insights from humanistic disciplines. It examines (i) governance mechanisms of intellectual property policies in research consortia in which the private incentive, public responsibilities, and academic science norms coexist; (ii) factors that determine the scope of moral responsibilities that academic KIOs perceive.

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The internalization is a promising alliance design. The problem is to control claims of rights over inventions. The agency-based governance helps extend the trusteeship governance by unbundling property rights and ownership. By detaching and reconfiguring rights toward benefits, this direction is also consistent with the common good-based stakeholder theory. At the same time, the combination of moral identity and ownership (Hannah, Avolio, & May, 2011) determine the humanistic issues that KIOs perceive commitment. Chapter 8 critically reflects previous chapters. Threats that make specific fields understudied are identified as follows. i. difficulties in assuring the institutional validity of informed consent for particular subgroups, ii. The lack of specificity of thresholds in morally sensitive areas, and iii.  The lack of alliance design that optimizes incentives, ex-ante/ ex-post, and manages the unbalanced dependence. Regarding (i), KIOs can mitigate issues by integrating fairness-based stakeholder theory and responding to regulatory strategies that provide additional protection for disadvantaged parties. Regarding (ii), regulators need additional efforts to provide specificity that grounds on reasons. Regarding (iii), in addition to managing dependence, KIOs should design alliance and intellectual property policies that optimize ex-ante/ ex-post incentives. It is also consistent to common good-based stakeholder theory. Thus, despite structural conflicts, it is possible for KIOs to integrate external mechanisms with a catalyst.

Notes 1.  Corporate venture investment is a source for established companies to enhance their innovation capabilities. See, Basu, Wadhwa, and Kotha (2016, p. 210). 2. For example, among Pfizer’s phase 3 pipeline, the ratio of specialty was 17% and that of primary care, 75% in 2009. Pfizer announced that, with the M&A of Wyeth, the ratio of specialty would increase to 30% whereas that of primary care would decrease to 55% by 2012. Source Pfizer’s Annual Report for the fiscal year 2009.

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3. Though education is an important component of activities of academic KIOs, this book focuses on the research conduct of translational science of academic KIOs. 4. Omics is a field of research and technologies that enable to measure an enormous number of biomolecules. Examples include genomics that investigates DNA sequences, proteomics that investigates large number of protein, and transcriptome that investigates RNA molecules. See, Institute of Medicine (IOM) (2012). 5.  As for the validation process of omics-marker development, see, IOM (2012). 6. Presentations by professors of biology, MIT. The seminars at a US university, held in 2016.

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Sachs, J. (2001). Thinking boldly. Bulletin of the World Health Organization, 79(8), 772. Santos, F. M., & Eisenhardt, K. M. (2005). Organizational boundaries and theories of organization. Organization Science, 16(5), 491–508. Schut, M., von Paassen, A., Leeuwis, C., & Klerlx, L. (2014). Towards dynamic research configurations: A framework for reflection on the contribution of research to policy and innovation processes. Science and Public Policy, 41(2), 207–218. Sherer, P. D., & Leblebici, H. (2015). Governance in professional service firms. In B. Hinings, D. Muzio, J. Broschak, & L. Empson (Eds.), The Oxford handbook of professional service firms (pp. 189–212). Oxford: Oxford University Press. Steen, R. G. (2011). Retractions in the medical literature: How many patients are put at risk by flawed research? Journal of Medical Ethics, 37(11), 688–692. Tamas, B. (2013). The future of drug discovery: Who decides which diseases to treat? London: Academic Press. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. Thursby, J. G., & Thursby, M. C. (2011). Has the Bayh-Dole act compromised basic research? Research Policy, 40(8), 1077–1083. Trispas, M. (2009). Technology, identity, and inertia through the lens of ‘the digital photography company’. Organization Science, 20(2), 441–460. Truog, R. D. (2017). The United Kingdom sets limits on experimental treatments. JAMA, 318(11). https://doi.org/10.1001/jama.2017.10410. Velculescu, V. E., Zhang, L., Vogelstein, B., & Kinzler, K. W. (1995). Serial analysis of gene expression. Science, 270(5235), 484–487. de Vries, M. S. (2013). The challenge of good governance. The Innovation Journal The Public Sector Innovation Journal, 18(1), 1–9. Weick, K. E. (1995). Sensemaking in organization. Thousand Oaks and London: Sage. Whitley, R. (2000). The intellectual and social organization of science (2nd ed.). Oxford: Oxford University Press. Wicks, A. C., Gilbert, D. R., & Freeman, R. E. (1994). A feminist reinterpretation of the stakeholder concept. Business Ethics Quarterly, 4, 475–497. Williamson, O. E. (1979). Transaction-cost economics: The governance of contractual relations. The Journal of Law and Economics, 22(2), 233–261. Williamson, O. E. (1981). The economics of organization: The transaction cost approach. American Journal of Sociology, 87(3), 548–577. Zahra, S. A., Neubaum, D. O., & Hayton, J. C. (2016). Handbook of research on corporate entrepreneurship. Cheltenham and Northampton: Edward Elgar Publishing. Ziman, J. (2000). Real science. Cambridge: Cambridge University Press.

Part I

CHAPTER 2

Translational Science and Boundary Conceptualization

1  Translational Science and Framework of KnowledgeIntensive Organizations The study of this book defines “governance” as management of boundaries and relations (Choi, Hilson, & Millar, 2004; Sherer & Leblebici, 2015). Then a problem arises regarding relations between organizational boundaries (Santos & Eisenhardt, 2005), boundary spanning, and a buffer (Aldrich & Herker, 1977; Child & McGrath, 2001, p. 1137; Lynn, 2005, among others) in the framework of knowledge-intensive organizations (KIOs). This chapter investigates when to span boundaries and when to activate a buffer in the academic knowledge production. For this purpose, it modifies the conceptualization of organizational boundaries (Santos & Eisenhardt, 2005) and explores academic-specific boundaries by analyzing components of translational science regarding its knowledge production process. Then this study examines (i) how each boundary conceptualization relates to knowledge production systems of academic KIOs, and (ii) what problems will arise at each boundary in the spectrum of the translation in the sphere of basic science. 1.1   Components of Translational Science Translational science is an integrative process in which, by starting from basic science, all phases are expected to inform other ones with the involvement of patient-derived biological materials or patients. The spectrums © The Author(s) 2019 E. Okada, Management of Knowledge-Intensive Organizations, https://doi.org/10.1007/978-3-319-97373-9_2

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range from the biological basis of health and disease, interventions to individuals, and to the public (National Institute of Health: NIH).1 Among these spectrums, this book focuses on the governance issues that occur in the basic biomedical research and the process of translation (application of the discoveries). Thus, the clinical study is outside of the scope. Here, by following Bush (1945), this study defines basic science research as research performed without thought of practical ends. It results in general knowledge and an understanding of nature and its laws (Bush, 1945, p. 13; Rubio et al., 2010).2 This book mainly focuses on the study of de-identifiable biological materials. The governance reflects the distinction of “de-identifiable” and “identifiable” (Chapter 7). Translational science itself is a process model (Rubio et al., 2010; Trochim, Kane, Graham, & Pincus, 2011). It is not necessarily new (see, Contopoulos-Ioannidis, Ntzani, & Ioannidis, 2003). What is distinct for the contemporary translational science is the integration of mechanism bases of biological understanding (see, Adams et al., 1991; Boon et al., 2002). The intention is to accelerate knowledge coproduction of basic scientists, clinical scientists, and regulatory scientists. The ultimate goal is to make a patients’ care more “patient-centered” (Hamburg & Collins, 2010; Zenhouni, 2005). In the study of this book, “patient-centered” denotes that the interventions have a ground on mechanisms drawn from patients’ derived cells, tissues, and other specimens. 1.2   Consistencies with KIO Framework The image of translational science is consistent with the final stage of the knowledge integration envisioned by Hertog (2000, p. 522). Hertog (2000) envisions a knowledge circulation in which a system aligns and integrates private, public and academic knowledge production with the help of a catalyst organization. In this vision, crucial elements of organizational relations are process-oriented, intangible knowledge flows, and transformation (Hertog, 2000). Therefore, this book investigates cases of translational science in studying management and governance of academic KIOs. The study of this book organized this chapter as follows: The next section develops academic-specific organizational boundaries to examine their relations with governance. The Sect. 3 applies the academic-specific

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framework to translational science settings and reframes functions of a buffer. Then it concludes with implications.

2  Boundaries and Governance 2.1   Knowledge Integration and Boundaries In the system of knowledge production, the integration of spectrums necessitates boundary spanning (Kaplan, Cowan, & Milde, 2017; Michelson, 2016; Tushman & Scanlan, 1981). The previous literature defines the boundary spanning as a mechanism by which organizational units or subunits are linked to external sources of knowledge by a boundary spanner (a boundary spanning individual) (Tushman, 1977; Tushman & Scanlan, 1981). It is also defined as a mechanism to translate concepts and transfer knowledge across different groups and network units (Grinsven, Heusinkveld, & Benders, 2016; Hansen, Nohria, & Tiemey, 1999; Marrone, Tesluk, & Carson, 2007). From the above, this study defines boundary spanning as an integrative mechanism by which organizational units are linked to external systems of knowledge production and translate and transfer knowledge in the process of transformation. In the literature of boundary spanning, organizational boundaries are the demarcation of information/resources/knowledge/identity between one system and others. Boundaries protect members within an organization from external influences by regulating flows of knowledge, material, and people that go into or out of the system. Each system has a specific logic of identity (Dutton, Dukerich, & Harquail, 1994; Helfat, 1997; Leifer & Delbecq, 1978; Santos & Eisenhardt, 2005, 2009). Here, organizational unit includes divisions (Cooper & Smith, 1992; Miller, Fern, & Cardinal, 2007), teams (Friedman & Podolny, 1992; Spender & Kessler, 1995), interorganizational relations (Gulati, Nohria, & Zaheer, 2000), and networked relations (Ettlie & Reza, 1992). This study identifies four elements of organizational boundaries in the previous literature of boundary spanning. They are: (a) a boundary of information/resources/knowledge, (b) a boundary with a function of protection, (c) a boundary with a capacity of regulation, and (d) a boundary of identity (see, Table 1). These components are consistent to the boundary conceptualization by Santos and Eisenhardt (2005) that

Tushman (1977), Tushman and Scanlan (1981) Pfeffer and Salancik (1978) Riordan and Williamson (1985), Williamson (1975) The boundary of identity Trispas and Gavetti (2000), Trispas (2009)

The boundary of resources/information/ knowledge The boundary of protection The boundary of regulation

Pfeffer and Salancik (1978), Porter (1980) Williamson (1975)

The boundary of power

The boundary of identity Weick (1995), Sull et al. (1997)

The boundary of efficiency

Penrose (1959), Teece et al. (1997)

Relevant theories

The boundary of competence

Components

Components

Relevant theories

Boundary conceptualization by Santos & Eisenhardt (2005)

Components of boundary spanning

Table 1  Boundary conceptualizations

The boundary of identity

The boundary of autonomy The boundary of regulation

The boundary of knowledge

Components

Reframed boundary conceptualization for translational science

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encompasses boundaries of (i) competence, (ii) power, (iii) efficiency, and (iv) identity. In the conceptualization of Santos and Eisenhardt, (i) the boundary of competence concerns over integrating organizational resources. The intention is that the assembled clusters will generate firm-specific capabilities (Penrose, 1959; Rumelt, 1984; Teece, 1984; Wernerfeld, 1984, among others) and yield rent through isolating mechanisms (Teece, Pisano, & Shuen, 1997). The assumption is that competence resides in routines and processes (Nelson & Winter, 1982) that are shaped by distinctive activities of individuals (Teece et al., 1997). At the same time, resources determine not only abilities but also disabilities of the organization (Christensen & Overdorf, 2000) since established core capabilities have an aspect of core rigidities (Leonard-Barton, 1992). Therefore, competences internal and external to organizations are to be combined with organization-specific resources to address changing environments (Teece et al., 1997). As a resource-based view frames information, capabilities, competence, and knowledge as “resources,” the boundary conceptualization of competence is equivalent to (a) the boundary of resource/information/knowledge. (ii) The boundary of power (Santos & Eisenhardt, 2005) concerns over reducing dependence on environmental constraints and other organizations that control resources (Pfeffer & Salancik, 1978; Porter, 1980, pp. 24–33, 258). Organizations engage in transactions of resources and social legitimacy with others. In this course, they organize behaviors of participants into structures (Pfeffer & Salancik, 1978; Meyer & Rowan, 1977). Pfeffer and Salancik define organizational boundaries in this context as the organization’s control over the actions of participants relative to the power of other social actors over the same activities (Pfeffer & Salancik, 1978, pp. 258–259). Dependence in this context allows the external constraint and control (Pfeffer & Salancik, 1978, p. 258) while firms can change the directions of influence by strategy (Porter, 1980). This power conceptualization also encompasses the activity of the interorganizational coordination that mutually enables access to each other’s resources. The coordinated behaviors avert environmental control and stabilize outcomes (Pfeffer & Salancik, 1978, pp. 43, 144) but also bring social costs derived from the loss of independence to the organization (Choi et al., 2004).

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Although practices of control in the real world vary, this conceptualization is consistent with (b) the boundary of protection that protects the organization from external influences. (iii) The boundary of efficiency (Santos & Eisenhardt, 2005) concerns over reducing transaction costs in the exchange of resources. When an object of knowledge production is an (a) organization-specific one, (b) the one that is difficult to measure, or (c) the one created through different knowledge, the external governance is costly. Under these situations, (A) internalization and shared commitment under an incomplete contract (Caves, 1996; Hart & Moore, 1988; Williamson, 1979), (B) authoritative hierarchy and coordination, or (C) standardization will reduce transaction costs of negotiation and governance (North, 1990; Williamson, 1983). Governance in this context relates to transaction cost-based management (Williamson, 1975). This conceptualization corresponds to (c) the boundary of regulation that governs the exchange of information/ knowledge. (iv) The boundary of identity (Santos & Eisenhardt, 2005) concerns over social contexts for sense-making (Weick, 1995). Here, s­ ense-making is a process that is grounded in identity construction, a retrospective of experiences, enactive of sensible environments, social and ongoing focuses on and by extracted cues, and driven by plausibility rather than accuracy (Weick 1995, p. 17). It generates a commitment to formulating and implementing a strategy (Sull, Tedlow, & Rosenbloom, 1997; Trispas & Gavetti, 2000). Sense-making also serves as a filter to interpret external cues (Trispas, 2009). This conceptualization matches (d) boundaries of identity. The first to fourth columns of Table 1 summarizes these corresponding relations. The next to investigate is the application to translational science to seek the necessity of academic-KIO-specific conceptualization. 2.2   Applications to Translational Science Boundary of /Resources/Information/Knowledge/Competence In the translational science, the motivation of the boundary spanning comes from enhancing (i) competence. Several statements of regulatory agencies articulate this motivation. Here, I summarize the intentions of US agencies as follows (regarding the early-day EU context, see Lehman, Lacombe, Therasse, & Eggermont, 2003).

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—, the NIH community of biomedical research has discovered how interface RNA (RNAi) turns off specific genes that are responsible for the proliferation of tumor cells. Scientists can transform such insights into new research tools and potential new treatment strategies (Zenhouni, 2005). Also, in response to the complex, new public health challenges, genome-related technologies and massive research data (so-called “big data”) enabled multi-dimensional studies of complex but common diseases that originate in thousands of molecules (Hamburg & Collins, 2010; IOM, 2012; Zenhouni, 2005). Moving from concept to clinical use requires basic, translational, and regulatory science. Regarding the insufficient evidence of a downstream market to entice the private sector, NIH and FDA will develop more integrated pathways that connect all the steps that involve academic researchers, regulators, government-supported centers, and public–private partnership (Hamburg & Collins, 2010). Thus, they intend to accelerate knowledge coproduction of basic scientists, clinical scientists, and regulatory scientists in the context of insufficient evidence of a downstream market. The ultimate goal is to make a patients’ care more “patient-centered” (Hamburg & Collins, 2010; Zenhouni, 2005). In the study of this book, again, “patient-centered” denotes that the interventions have a ground on mechanisms drawn from patients’ derived cells, tissues, and other specimens. In this manner, the translational science approach involves the collaboration between different disciplines that consist of different systems of knowledge production, methodologies, and norms. In the early days, identified barriers are the gaps between existing disciplinary norms (Meslin, Blassime, & Cambon-Thomsen, 2013), different definitions for the technical terms (IOM, 2012), the lack of standardization for data sharing and reporting results (IOM, 2012), inconsistencies in methodologies, organizational coordination, and governance (Kunneman, 2010; Okada, 2018), and the lack of proper responsibility allocation (IOM, 2012). Here, the boundary spanning not always brings positive outcomes but also different criteria of controlling outcome qualities (Kunneman, 2010). The scientists-initiated process and Guidelines (Okada, 2018), and related governance modality (see, IOM, 2012) mitigate these barriers. At the same time, barriers also exist in relations with an external research infrastructure. For example, in early days, scientists suffered from a lack of well-characterized biological materials in

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bio-banks (Knopper & Chadwick, 2005; Meslin et al., 2013). When biomedical scientists acquired materials directly from clinical scientists (donated biological specimen), their quality was not necessarily controlled enough for the time-sensitive biomedical research (see, IOM, 2012). This study associates such external and internal constraints with the boundaries of multiple systems of knowledge (including methodologies, resources, and norms of sciences). Conversely, the translational science approach has continuously enhanced the boundaries of disciplinary knowledge and even transformed them to redefine the field. In this sense, the motivation of the translational science is consistent with the dynamic capabilities, organizational learning, and process rejuvenation and domain redefinition. Here, the concepts of rejuvenation and domain redefinition come from studies of corporate entrepreneurship (Covin & Miles, 1999; Dess et al., 2003; Kuratko & Covin, 2015). In corporate entrepreneurship, rejuvenation encompasses changes of organizations’ internal processes, structures, and capabilities to improve the implementation ability of strategies (Covin & Miles, 1999; Dess et al., 2003). Domain redefinition entails defining and deploying organizations’ new competence in social exchange (Dess et al., 2003). These concepts apply to several layers of translational science in academic settings. The component that encompasses these concepts is “knowledge,” since critical resources for translational science are also knowledge-intensive ones. Therefore, “the boundary of knowledge” will be suitable for the conceptualization for translational science. The Boundary of Protection/Power Boundaries of a system insulate the internal constituents from external systems. Boundaries protect the components from external influences. In firms, Pfeffer and Salancik (1978), among others, associate the ability of protection with power that reduces asymmetric dependence (see, Pfeffer & Salancik, 1978). Related to this, when the concentration of power occurs in an economic or social environment to achieve something, it is more likely that countervailing, strong opposition happens (Pfeffer & Salancik, 1978, pp. 51–53). A problem is whether such relations fit alliance teams or units of translational science. As far as academic research is concerned, the power conceptualization is not appropriate. If a few scientists used material power accrued to

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them to influence other less independent scientists, their behaviors would discourage other scientists and research productivity would decline (see also, Kanter, 1983 regarding innovativeness). At the same time, academic KIOs have struggled to retain academic and scientific autonomy under the situation of industry involvement: Knowledge coproduction is double-edged for them. A problem is whether there is asymmetric resource dependence between academic KIOs and commercially motivated organizations to the extent that net power accrued cause dependence of KIOs. The concept of independence of academic sciences originates in the moral philosophy of science, rather than the power conceptualization derived from dependence on critical resources. Although renegotiated social contract of sciences (Gibbons, 1999; Nowotny, Scott, & Gibbons, 2001. Regarding social contract, see Baker, 1980) may have some relevance to power conceptualization, it occurred as a part of a global movement of the new public reforms that focus on efficiency and accountability in public management (Christensen, 2011). The current study mainly concerns the autonomy of academic science and threshold elements to limit it. The underlying assumption is that academics and academic KIOs also have the autonomy to modify their first-order motivation if they so choose (Dwarkin, 1988; Trevino, 1986). In a normative sense, power should not involve in academic knowledge production. Considering these points, the boundary of autonomy will be more suitable to the framework of self-regulation of KIOs. Therefore, this study adopts the boundary of “autonomy” to define the academic-specific boundary conceptualization (see, Table 1). The Boundary of Regulation/Efficiency The boundary of efficiency comes from agency-based governance. It determines an efficient boundary between what organizations should internally govern and what is external to an organization (Poppo & Zenger, 1995; Riordan & Williamson, 1985). A metrics is a transaction cost (Williamson, 1975). When a new investment is (a) specific to an organization (or a project), internal governance is preferred. When commercially motivated organizations participate in a research team of academics or through funding in the discovery stage, they prefer internal management. A potential threat to academics is that the timing of publication tends to delay and industries restrict data circulation (Krimsky, 2007; Leonelli, 2010).

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At the same time, when potential governance costs of (b) measuring performances are high, internal governance such as Steering Committee is preferred (Haas, 2010; Riordan & Williamson, 1985). Related to such economic relations, efficiency in transaction costs also plays a role in designing a boundary spanning. For example, the boundary of efficiency affects the design of research alliances, new contracts, and partnerships (Gottweis, Salter, & Waldby, 2009; Rangan, Samii, & van Wassenhove, 2006). In this book, Chapters 3 and 6 discuss issues of research alliance and partnerships. As an external demand, the increased societal complexities direct regulatory attention to lowering measurement costs, increasing precision and standardization through formal rules. From transaction cost perspectives, official rules will reduce monitoring and enforcement costs. Contrary to intuitions, such arrangements are expected to modify informal regulations to be more active or replace them with more effective ones (North, pp. 46–47). In this regard, the measurement, precision, and standardization are essential components for validating results of translational science. For example, the IOM 2012 Guidelines require a rigorous reproducibility of discoveries by independent samples or other research groups before the new knowledge are published and applied to humans (IOM, 2012). Such procedure necessitates the precision in specifying research questions (IOM, 2012). Precisions in research questions will take more time and energy spent in a research planning. In this regard, it is possible for the term “efficiency” to invite a misunderstanding. Also, though “setting formal rules to increase efficiency” will spotlight better uses of informal rules (North, 1990, p. 46, also see the next section), the terminology “efficiency” is misleading also in this context. Considering these points, the boundary of “regulation” will better conceptualize the causal relations in translational science. Boundary of Identity Previous literature associate identity with a set of unwritten codes or norms that represent shared beliefs about legitimate behavior for an organization (Trispas, 2009). From law and economics perspectives, organizational or professional identity becomes essential in a relational contract. For example, parties build informal agreements on noncontractible rights, i.e., a decision-making right for unforeseeable events

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(Hart, 2003; Hart & Moore, 1988). Under the condition, parties’ identity makes a meaning (Sherer & Leblebici, 2015). Identity has a close relation with sense-making (Weick, 1995). For example, when a disruptive environmental change (Bower & Christensen, 1995) invokes disturbance, organizations filter the events through their cognition and interpret them consistent with their identity (Trispas, 2009). Thus, identity functions as a filter in the sense-making process and shapes organizational capabilities and cognitions (Trispas & Gavetti, 2000). The boundary of identity has a potential to span in a network in the search for flexibility (Child & McGrath, 2001). At the same time, excessive boundary spanning of identity affects the autonomy and sense-making in academic sciences. It has a potential to yield capture and knowledge corruption (Radder, 2010). Now that social demands encourage academics to partner with industry and other nonacademics, KIOs should carefully manage the influence on identity. In considering these points, this boundary conceptualization has fitness also to translational science and academic KIOs. 2.3   The Need of the Academic-Specific Boundary Conceptualization The application of boundary conceptualization to translational science suggests the necessity of exploring academic-KIO-specific boundary conceptualization. The literature review reveals missing areas of governance in the management studies. They are “autonomy” and “self-regulation” of science. In other words, a gap emerges between science governance and management studies. A possible explanation of this gap is that it appears from varied perspectives on approaching sciences in these fields. In management studies, scholarly interests in science are in (i) a process that encompasses technical or strategic tasks uncertainties (Cf. Van de Ven, Delbecq, & Koening, 1976; Whitley, 2000), and (ii) a potential of future organizational capabilities. The first stream concerns over managing such uncertainties by rules, methodologies, and designs. The second stream consists of resource-based perspective, organizational learning, and dynamic capabilities that identify science as a source of knowledge, abilities, and renewal (see, Ahaja & Katila, 2004; Cohen & Levinthal, 1990; King & Tucci, 2002; Pisano, 2016; Teece et al., 1997). In this domain, a process (of science practices: see, Fleming & Sorenson, 2004) is also a

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resource that defines organizational capability (Easterby-Smith, Lyles, & Peteraf, 2009; Nelson & Winter, 1982; Teece et al., 1997). On the other hand, as stated earlier, “science” itself is a social institution that has autonomy in decisions in the philosophy of science. Their scholarly literature regards self-regulation of science as an established governance model (Resnik, 2008; Ziman, 2000). There is a limit to approach academic KIOs by assuming self-regulation just as a mental competence or a voluntary associations’ rules without a legal tie. Therefore, there is a legitimate reason to develop academic-specific boundary conceptualization that embeds autonomy and self-regulation of academics. Proposition 1  When this study defines governance as the management of boundaries and relations, the boundary conceptualizations have significance in determining contents and mechanisms of governance. The academic-KIO-specific boundary conceptualizations are the boundaries of knowledge, autonomy, regulation, and identity.

3  Framework of Knowledge-Intensive Organizations, Boundaries, and Buffer 3.1   Threshold Elements of Boundary Spanning In translational science, newly defined, confirmed, and validated knowledge in one system is translated and transferred to other systems. Under this condition, while boundaries are a defining characteristic (Aldrich & Herker, 1977) of disciplines and organizational units, a requirement of faster learning, integration, and feedback makes boundaries fuzzy (see, Child & McGrath, 2001, p. 1137). Child and McGrath (2001) characterize a fuzzy boundary as a necessary process of boundary spanning. They investigate the fussy boundary in association with a node of the global knowledge network. On the other hand, too much spanning often disturbs all phases. In the case of science process, there is a need to insulate a specific process to validate results. For example, IOM (2012) Guidelines for translational omics test development draw a “bright line” between (i) the discovery and test validation stage and (ii) the evaluation for clinical utility and use stage. In the discovery phase of the first stage, candidate test developed must be precisely defined and confirmed, preferably, by an independent sample. In

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the validation phase of the first stage, scientists should fully define, validate, and lock down the confirmed candidate tests (IOM, 2012). Before the IOM 2012 Guidelines, several discoveries that were published and later invalidated moved to the clinical stage and clinical scientists applied them to humans (Steen, 2011). The Guidelines significantly improved quality of translational omics-research outcomes by reducing a rate of reproducibility failures whose discoveries were published and subsequently invalidated (Okada, 2018). Thus, setting aside from a well-deliberated fraud (which is a part of criminology), critical failures such as harms to humans can be reduced by a system of the buffer. 3.2   Systems Model of Buffering Here, previous literature defines the buffer as a boundary role that insulates, defends, or regulates (Aldrich & Herker, 1977, p. 218; Lynn, 2005) an organizational system and process. The traditional literature argues a buffer in association with a management system of manufacturing that stabilizes and regulates input–output relations. It is embedded in organizational structures to coordinate interorganizational ties or conform to institutional arrangements that lead organizations to be more likely to thrive technical and institutional complexity (Meyer, Scott, & Deal, 1981/1992; Thompson, 1967). In the transition to a knowledge-based economy, scholarly interests that advocate networked firms/teams tend to undermine buffers by characterizing them as a source of inertia, a loss of competitiveness, and protection of old thoughts. Their way of protection is the exposure to the environmental uncertainty and adapt to change (Child & McGrath, 2001). On the other hand, buffers also help firms to adapt to environmental uncertainty and produce order by protecting them from disruptive changes (Bower & Christensen, 1995; Lynn, 2005; Meyer & Rowan, 1977/1992; Miner, Amburgey, & Stearns, 1990). In facing the different research streams, Lynn (2005) proposes a reconciliation framework by examining a diversity of conceptualizing a buffer in the management studies. In defining two core forms, insulation and regulation,3 Lynn identify two missing dimensions in counterarguments, i.e., requisite variety and uncertainties. Here, the requisite variety denotes a level of uncertainty that determines whether a flexible organizational design functions well in predicting and absorbing environmental uncertainty, or there is a need to introduce a buffer (Lynn, 2005,

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pp. 47–48). Then he explores the framework that consists of (i) a threshold of requisite variety to deal with uncertainty and (ii) continuous vs. discontinuous change that necessitates lowering or strengthening buffers. Among them, the dimension (i) is an extension of Thomson (1967) that integrates the systems theory of requisite variety (Cohen & Levinthal, 1990; Lynn, 2005; Teece et al., 1997; Zahra & George, 2002). In this sense, Lynn’s framework is a part of system’s theory that addresses sustainability. Table 2 organizes the boundary conceptualization of academic KIOs, components of the framework of KIOs, and the relations with the buffer. Proposition 2 While the original motivation is a boundary spanning among disciplines, processes of translational science necessitate several layers of buffers that represent model confirmation, validation, and evaluation. They regulate the operations of translation and transfer of knowledge to other units or systems. Related to buffers, the organizational structure of KIOs may have inherently embedded buffers of insulation by not having outside or commercial ownership. In the real world, this framework fluctuates through nonexpert participation and public–private partnerships. Then, the next step is to investigate governance modalities to buffer the sensitive space of KIOs. Proposition 3 The theoretical framework of the organizational structure of KIOs embeds a buffer of insulation by not involving outside or commercial ownership. As this form of insulation fluctuates through the nonexpert and commercial involvement, KIOs should introduce some complementing elements. 3.3   Modalities of Governance Governance models do not work in an isolated manner. They work in connections with modalities of governance. Lessig (1999) proposes the following four modalities of governance: i. Laws (including contract) ii. codes of conduct iii. (internal and social) norms and iv. choice architecture.

The involvement of commercially motivated organizations

Interaction with nonexperts and moral stakeholders

No outside ownership Internal codes and norms

Boundary of regulation

Boundary of identity Academic identity

Regulate insulate and spanning

Regulate insulate or spanning; Defense

Regulate insulate and spanning

Requisite variety-adaptation to societal demands vs. academic norms Requisite variety-conflict resolution through external intervention vs. self-regulation through internal ethics codes Requisite variety-societal demands vs. academic identity

Requisite variety-adaptation to uncertainty vs. the lack of validation that leads to knowledge corruption

Expected functions of Threshold of requisite buffers variety to introduce buffers

Collaborative Defense research Outside involvement

Integration of basic, clinical, and regulatory sciences Application of discoveries from biological specimen

Academic knowledge (academic norms of pursuit truth, academic knowledge exchange, methodologies, knowledgeintensive research tools) Autonomy of academic science

Boundary of knowledge

Boundary of autonomy

Forms of boundary spanning in translational science

Boundary conceptu- Components of KIO alization of academic framework KIOs (Radder, 2010; Starbuck, 1992; von Nordenflycht, 2010)

Table 2  Relations with frameworks of academic KIOs

Trusteeship governance; Stakeholder theory

Trusteeship governance; stakeholder theory; Agency-perspective governance

Trusteeship governance

Trusteeship governance; agency-perspective governance; Stakeholder theory

Governance Models of KIOs

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In some cultural situation and a mutual distrust atmosphere, a contract does not necessarily work as is supposed to be to induce desirable behaviors of related parties (Buckley & Casson, 2010; Choi et al., 2004; North, 1990; Sherer & Leblebici, 2015). In such cases, (v) hostage and managerial practices are used as a strategy to regulate each party’s behaviors (Buckley & Casson, 2010; Choi et al., 2004; Sherer & Leblebici, 2015). Therefore, this study adds the fifth mode to the governance modalities. The next is to seek links between governance modalities and management of KIOs. Legal Contract and Management Management theories have examined contract mainly in association with the agency perspective governance. By focusing on principal–agent relations, this research stream addresses a corporate relationship with metrics of transaction costs and agency costs (see, Hart, 2003; Rangan, Samii, & van Wassenhove, 2006). The underlying assumption is a legal fiction that regards a firm as a nexus of contract (Bratton, 1989; Jensen, 1983). Contract works when the uncertainty and the externality are low or moderate (Choi et al., 2004; Rangan et al., 2006). Under the condition, contractual legality provides a foundation of ethical behaviors of parties (Choi & Millar, 2005). When a volume of uncertainty and externality is enormous, parties use a form of partnership (Rangan et al., 2006). From economics perspectives, the contract is a scheme to optimize values and reduce transaction costs of rent-seeking entities. It facilitates transactions that occur with a time lag since it actualizes relations between property rights and economic benefits (Pejovich, 1990). In this context, the contract law reduces transaction costs by securing property rights and determining bundles of rights that can be transferred by contractual agreements (North, 1990; Pejovich, 1990). Academic KIOs use contracts in joint research with other entities, funding, licensing, and material transfers, among others. In legal perspectives, a contract is a regulatory strategy to minimize gaps between the parties’ expectations and assure their cooperative behaviors (Armour, Hansmann, & Kraaman, 2017). Also, a contract in legal practices ensures both of arms-length exchange (a discrete contract

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that relies on court resolutions) and managerial hierarchy (a relational contract that does not rely on enforcement: Williamson, 1993). Such multiplicity yields tensions for legal scholars to accept the conceptualization of nexus of contract in legal theory (Bratton, 1989). Codes of Conducts Codes of conduct are an articulated form of desired behaviors of related parties. Here, a scope of organizations can vary from a research team to an alliance network. As they are a documentation of principles, specifications, and expectations, they can include universal normative accounts. Examples include those released by multilateral organizations. On the other hand, they do not encompass enforceable rights and obligations for related parties. Therefore, conformity to codes necessitates specific arrangements (Marchant, Abbott, & Jack, 2013). The mechanisms include an organizational structure, a tie to legal rights and obligations, support from a judicial authority, choice architecture, among others. In the case of industry firms, codes of conduct are a commonly preferred governance mechanism, either of internal or external ones. It is also a preferred modality in a research alliance, either of parallel or integrative alliance designs (Cf. Haas, 2010). Traditionally, internal or unwritten codes regulated trusteeship behaviors of academic KIOs. As the organizational structures evolve, when and how to introduce external codes are matters of concern. Choice Architecture Choice architecture (Kahneman & Sunstein, 2005; Kahneman & Tversky, 1979; Shafir, 2013; Sunstein, 2016) has developed through the disciplinary recombination of economics, psychology, and behavioral science. Scholars attribute its origin to Simon (1957, pp. 198–199)’s conception of bounded rationality. It further recoupled with the law and public policies, shaped regulatory science, and yielded policy tools of liberal accounts. Policymakers often use this tool in the economic regulation of healthcare in which players (such as hospitals, insurance companies, and subjects) are not necessarily in competitive relations, and the frequency of choice for the constituents (such as subjects) is not many (in some cases, once in their lifespan). The choice architecture does not necessarily work for a heterogeneous individual (Abdukadirov, 2016, p. 143). Even so, it has been adopted as

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a governance modality, independently or in combination with enforceable laws, to suggest desired pathways while respecting individuals’ autonomy. Norms and Responsible Research and Innovation Norms lie underneath the documented laws, codes, and architectures. They affect human behaviors directly, or through documented governance modalities. Academic norms (Anderson et al., 2010; Merton, 1942) directly bind academics’ behaviors. They are sharing of scientific results, organized skepticism, universalism, and organized skepticism. These components realize in academic publications and knowledge transaction and help academics to focus on underlying structures of objects (see, Radder, 2010). Academics inherit norms through educations and knowledge transactions and shape academic identity. On the other hand, the practices of emerging sciences introduced additional threats that require norms beyond academic sciences. Previous literature collectively terms norms in conducting research on emerging sciences as responsible research and innovation (hereafter, RRI). Among the slightly different interests of researchers and policymakers, Stigloe, Owen, and Macnaghten (2013) derived four common components through an inductive approach. They are (i) anticipation (Barben, Fischer, Selin, & Guston, 2008), (ii) inclusion, (iii) reflexivity, and (iv) responsiveness (Burget, Bardone, & Pedaste, 2017; Owen, Macnaghten, & Stigloe, 2012). Here, Stigloe et al. (2013) define RRI as “taking care of the future through collective stewardship of science and innovation in the present.” The practical concerns are how to align them into processes (Burget et al., 2017). For example, interdisciplinary unit of academic sciences has a variation in research methodologies. In the case of deep brain stimulation,4 the research on improved implantable pulse generators5 requires the involvement of biology and computer science. As both disciplines have different systems of knowledge production and controlling outcome qualities (see, Kunneman, 2010), research results can generate inconsistencies. In this regard, one of the best practices is that scientists cocreate outcome criteria through “midstream reflexivity” (Bosso, 2016).6 Thus, scientists are cocreating new processes to balance innovation and preservation of academic norms. Here, RRI is a system of individual norms regarding research conducts, not organizational norms (see, Burget et al., 2017; Stigloe et al.,

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2013). Related to this point, Stigloe et al. (2013) argue for the necessity to integrate RRI to governance. Among several governance mechanisms, codes of conduct will work as a device to incorporate norms that RRI represents into processes of alliance research (Okada, 2017). Proposition 4  Dimensions of RRI that represent own norms should be incorporated into governance mechanisms, particularly into the codes of conduct. Hostage/Managerial Practice/Strategy If a cultural trait of parties generates uncertainties about their behaviors, or a mutual distrust atmosphere presents, (v) “hostage” is used to inducing mutually beneficial behaviors (Buckley & Casson, 1988; Caves, 1996; Choi et al., 2004). Examples of a mutual distrust atmosphere include joint ventures in which partners hold unequal equity of ownership (Buckley & Casson, 1988; Caves, 1996, p. 75). Sherer and Leblebici (2015) also raise awareness to specific managerial practices as a facilitator of coordination and conflict resolutions in the governance of PSFs. This insight on an administrative practice is consistent with relational contract in Gibbons and Henderson (2012). They frame this concept as “collaboration sustained by the shadow of the future as opposed to formal contracts enforced by courts (Gibbons & Henderson, 2012, p. 1350). The concept of relational contract originates in economics that questions under what circumstances collaboration should be “relational,” (Williamson, 1993). Here, subject matters of organization studies are sources of commitment, sustained relations, and issues of credibility and clarity (Gibbons & Henderson, 2012). Their focus comes from the unable to imitate organizational capability of collaborative ties (Gibbons & Henderson, 2012). The managerial practices of this type are not necessarily associated with uncertain cultural traits or a mutual distrust atmosphere. This focus of a setting is even contradictory with “hostage.” Also, hostage targets a more specific behavior of parties. However, if the “unable to imitate organizational capability of collaborative relations” is a transcended form of a collaboration mechanism that targets specific behaviors, both have a common ground. In filing a gap between the two streams, this book renames the fifth governance modality to “strategy” that targets specific desired behaviors.

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Proposition 5 Strategy that aims specific desired behaviors should be added to the modality of governance.

4  Conclusion This chapter examines relations between organizational boundaries, boundary spanning, and a system of buffer (Aldrich & Herker, 1977; Child & McGrath, 2001, p. 1137; Lynn, 2005; Santos & Eisenhardt, 2005, among others) in the framework of academic KIOs. As the present study defines governance as management of boundaries and relations (Choi & Millar, 2005; Sherer & Leblebici, 2015), the boundary conceptualization has significance in determining the contents and mechanisms of governance. In referring to previous literature, this chapter developed the academic-KIO-specific boundary conceptualizations, i.e., the boundaries of knowledge, autonomy, regulation, and identity. Each boundary should incorporate a system that senses a level of uncertainties and discerns whether to lower or strengthen a buffer. In this system, Child and McGrath’s environmental exposure and adaptation (also see, McGrath, 2001) correspond to a mode that addresses a low or moderate uncertainty (Lynn, 2005). On the other hand, when a level of uncertainty is high or a change is disruptive (Bower & Christensen, 1995), a buffer should be activated (Lynn, 2005). In the translational science of academic KIOs, confirmation, and validation of research results construct a barrier embedded in the process. An organizational structure that has no outside or commercial ownership is also a buffer to design trusteeship behaviors of academics. As the organizational structure of KIOs has fluctuated, complementing mechanisms should be introduced in governance models. Governance models work in modalities (Lessig, 1999) that consist of law/contract (Choi & Millar, 2005; Sherer & Leblebici, 2015), codes of conducts, norms, a choice architecture, and strategy. Dimensions of RRI (Stigloe et al., 2013) should be incorporated into governance processes. Here, boundaries of knowledge and regulation have coherence with all of the dimensions. On the other hand, the boundaries of autonomy and identity have coherence with midstream reflexivity (see, Bosso, 2016) which has a potential to yield tensions with other dimensions of RRI. The next chapter is to provide a foundation to extend trusteeship governance. In doing so, relations between boundaries are investigated.

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Notes 1. https://ncats.nih.gov/translation/spectrum. Accessed on October 18, 2017. 2. This definition is made in 1945 by a director of the US Office of Scientific Research and Development. 3. The former enables dynamic adaptation while protecting stability-sensitive areas from threats. The latter extends the traditional input–output stabilization while retaining exposure to the environmental dynamics. See, Lynn (2005). 4. Deep brain stimulation is a surgical procedure used to treat several disabling neurological symptoms—most commonly debilitating motor symptoms of Parkinson’s disease (National Institute of Neurological Disorders and Stroke). 5. h ttps://www.ninds.nih.gov/Disorders/Patient-Caregiver-Education/ Fact-Sheets/Deep-Brain-Stimulation-Parkinsons-Disease-Fact. Accessed on December 22, 2017. 6.  A similar presentation was made at seminar at Center for Bioethics, Harvard Medical School, 2016. According to an ethicist at the University of Hawaii, they at first conduct a survey to understand the variation of research norms.

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CHAPTER 3

Trusteeship Governance and Challenges to Scientific KnowledgeIntensive Organizations

1  Introduction This chapter aims to (i) examine how capture occurs at the boundaries of knowledge-intensive organizations (KIOs) and (ii) explore possible directions to extend the trusteeship governance of academic KIOs. More specifically, it investigates how mismanaged boundaries of translational science bring outside legitimacies that have a potential to introduce “captures” and “goal displacement” (Carpenter & Moss, 2014; Gunz, Gunz, & Dinovitzer, 2015; Warner & Havens, 1968). Academic KIOs even replace goals with means. This study calls this phenomenon as a goal replacement. Capture and goal replacement manifest themselves in a biased selection of research agenda. This “contents aspect of knowledge corruption” is one of the most pressing concerns raised by Radder (2010) and Kunneman (2010). In addressing this aspect of corruption, this chapter provides a foundation with later chapters to examine to what directions should the trusteeship governance of academic KIOs be enhanced to produce unbiased knowledge. According to Radder (2010) and Kunneman (2010), not only commercial interests but all kinds of practical interests outside of science can generate bias. Examples include the public attracted by a socially desirable and seductive technology (Radder, 2010). The attracted public can develop a particular “moral” stake (Attas, 2004) in academic institutions. They can become a part of a special interest group (see, Radder, 2010. Also see, Banchoff, 2011), and urge the academic institutions to create and use the technology in spite of its unproven status. © The Author(s) 2019 E. Okada, Management of Knowledge-Intensive Organizations, https://doi.org/10.1007/978-3-319-97373-9_3

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These invisible forces affect not only the autonomy but also the ­identity of academics that lead to generating a biased selection of research agenda. Thus understudied research areas emerge in spite of their scientific significance and potential impacts on humans (Radder, 2010). Please note that such arguments do not oppose to the application (see, Radder, 2010) or civic epistemology conceptualized by Jasanoff (2005). The problem is that all sorts of practical interests can affect the academic autonomy and identity (Radder, 2010). In this regard, the public management has a rich source to address a loss of autonomy and identity of regulatory policymakers. In the public administration, scholars have investigated a phenomenon in which a regulator is consciously or unconsciously affected by entities that they regulate in the course of exercising their public responsibilities (see, Carpenter & Moss, 2014). Similar problems can occur in academic KIOs: Intensive interactions with outsiders have a potential to invite a subordination of academic integrity. This study organizes the chapter as follows. The next section reviews literature mainly in the public policy fields and adapts their concept to the management framework of KIOs. In addressing governance models of KIOs, it investigates how the models and boundaries have a vulnerability to invite the subordination of academic sciences. Then the chapter argues possible directions to mitigate the bias to facilitate research areas that are understudied but have scientific significance. It also seeks directions to bridge academic sciences to insights that come from humanity disciplines.

2  Captures, Goal Replacement, Nonacademic Legitimacies 2.1  Captures Major Categories of Captures In the public management, the concept, capture, denotes visible and invisible influences that affect autonomy and identity of regulators. Carpenter (2014a) defines capture as a violation of norms of regulators in which they act instead advance the commercial or political interests of special interest groups that they are regulating.

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Management scholars also investigate captures in professional service firms (PSFs). In PSFs, the capture mainly comes from clients (Gunz et al., 2015). For example, lawyers are professionals who should direct their primary loyalty towards law and the legal system. They represent clients in their interactions with the operation of the law (Leicht & Fennel, 2001). However, because of the change of regulatory environment including corporatization of law firms (Greenwood, 2007; Leicht & Fennel, 2001), cost-conscious corporate clients began to “shop around” legal services they “consume” (Leicht & Fennel, 2001) whose behaviors represent a consumer model of PSFs. These environmental forces influence the actions of lawyers that have a potential to subordinate lawyers’ primary loyalty to law. In reviewing these research streams, issues of academic KIOs are closer to those of regulatory institutions regarding the organization structure, competing environmental forces, and responsibilities for generalized or public interests. Therefore, this chapter will mainly refer to the public management literature and complement them with that of PSFs. There are two types of capture: the traditional form of capture and the corrosive form of capture. In the traditional style, capture occurs when organizations with a market power push regulators to develop entry-­limiting regulations, which will block the market-entry of innovative competitors (Stigler, 1971). By placing constraints on new entries, existing firms can prolong their routines without exposing themselves to threats that come from entrepreneurs. This form of capture has become rare. A possible explanation is that, because of global prevalence of neoliberal regimes, established firms began to accelerate a corporate renewal by building alliances with entrepreneur firms (Zahra, Neubaum, & Hayton, 2016, pp. 5–6). Instead, the corrosive form began to prevail. The corrosive capture is to push regulators to loosen regulations not based on a due mechanism of deregu­ lation but through several types of undue influences (Carpenter, 2014b). The corrosive capture provides an intuitive legitimacy by removing a costly regulation that might constrain firms’ innovative endeavors. However, the removal of law does not always stimulate innovativeness. For example, as stated in Chapter 8, regulatory strategies have a function to assure safety to the public and protect disadvantaged parties in any class of contractual relations (Armour, Hansmann, & Kraaman, 2017). In this regard, 45 Codes of Federal Regulation (CFR) 46 protect research subjects who are exposed to unknown risks (see, Chapter 4). Loosening of the regulation might ease the recruitment of research subjects and the

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approval of research protocol. But it has a potential to transfer costs to the public. Court cases suggest that a loosened practice of the 45 CFR 46 results in injury or death of subjects (cf. Lo, 2009). The corrosion occurs by loosening regulatory autonomy, conscience, and the unique norm of the fidelity of regulators to their statutory obligations (Carpenter, 2014b, p. 154). Among several kinds of corrosion, cultural capture may be the most serious. In the cultural capture, the regulator unconsciously begins to think like the regulated and has little awareness of being captured (Carpenter & Moss, 2014, p. 18; also see, Radder, 2010). In a simplified idea, captures occur by transferring costs to beneficiaries or a public. Therefore, to balance interests, regulators often take a measure of public participation in the policy formulation or evaluation processes. Examples include public involvement of the FDA process.1 However, issues are not simple. In some cases, the public is also affected by special interests. For example, Carpenter provides evidence on entry-barrier capture of the US FDA. In general, companies have an opportunity of taking fast-mover advantage from a faster review. However, contrary to the expectation, the pressure to the faster review came from patients’ advocates, not from an industry (Carpenter, 2014b, p. 170). It is not to criticize advocate groups. They are just focusing on their advocacy but may depend on their critical resources to others.2 Here, threshold elements to be considered will be rights of beneficiaries, harm to the public, undermining of the regulatory autonomy, and subordination of unique responsibilities of regulators. Then, fundamental questions to examine captures are (a) whether the resulting outcomes advance private interests of special interest groups at the expense of the general interest, and (b) did the beneficiaries of the resultant consequences intentionally planned to achieve such outcomes ex-ante (Carpenter & Moss, 2014, p. 20). 2.2   Academic Captures In adapting the theory of capture to the framework of academic KIOs, this chapter defines the academic capture as follows: Academic capture is a violation of norms of academic KIOs in which academic KIOs and academics act not to contribute to the disciplinary knowledge instead advance the outside interests of practically motivated organizations and special interest groups that they are interacting. There are two types of captures: a traditional capture, and a corrosive capture including cultural capture.

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The traditional capture occurs when practically motivated organiza­ tions or special interest groups involve in academic sciences through direct research participation, funding, or the validation of the contents of science. Their involvement limits the autonomy of academic science by bringing outside legitimacies that lead to introduce bias to the science process (Radder, 2010). On the other hand, the cultural capture appears more informally. For example, there is a prevalence of unwritten atmosphere that attributes academics’ competence to a tie to business (see, Radder, 2010). Such atmosphere is invisible but often manifest in several kinds of decisions on and over KIOs. The sources of the unseen forces may be an intensive interaction with practically motivated organizations, surrounding communications, and others. Academic scientists may also unconsciously do self-persuasion3 to change their beliefs. They even make efforts to change behaviors, not through the direct pressure of the practically motivated organizations but the regular communications and judgment on them. In considering the above, the traditional and noncultural corrosive capture is more likely to occur through the mismanagement of boundary of autonomy of academic KIOs. On the other hand, the cultural capture is more likely to occur through the maladministration of the boundary of identity. Proposition 1 Traditional and noncultural capture in academic KIOs will occur through the involvement of practically motivated organizations that will affect a boundary of academic autonomy, or through the dependence of critical resources to other groups that will loosen their independence. Proposition 2 Cultural capture in academic KIOs will occur through the intense interaction with outside and nonacademic legitimacies that affect a boundary of identity. 2.3   Goal Replacement Goal replacement occurs when means to fulfill a goal becomes a goal. A typical case arises in a funding transaction. Funding is a means to cover costs for resources to address scientifically significant questions. However, a goal to solve scientific questions often is replaced with a means of getting funding.

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Regulators use funding as an incentive in the regulatory s­trategy (Armour et al., 2017; Carpenter, 2014, p. 8). Funding is a legitimate science policy to affect contents aspects of science (Resnik, 2008). Academic scientists can broaden a capacity with a new research tool by investigating what might not otherwise be observable. On the other hand, it is likely that, at a certain point, an additional monetary incentive no more contributes to stimulating the intended outcomes.4 For several institutional reasons, KIOs perceive to be obliged to maintain or expand the external funding but such perceived obligations have a potential to constrain academics’ autonomy (Resnik, 2008; Ziman, 2000) in science. Here is a room for a goal replacement to occur. An incentive hypothesis is an established tool in the regulatory policy (Armour et al., 2017). On the other hand, as individual human behaviors and choices construct institutions (North, 1990, p. 5; Simon, 1957), the studies of KIOs should be extended to that of scientists’ responses. A goal replacement can be one category of captures. When academic KIOs suffer from resource constraints for a prolonged period and fund sources are not diversified, they may prioritize funders’ intentions in resource allocation. This condition is what Pfeffer and Salancik (1978, p. 113) state as “at that point where the organization’s control over activities diminishes and the control of other organizations begins.” Also, during the intense interaction with funders, academics and KIOs may come to think like funders in selecting research agendas. They may even begin self-persuasion to adapt to funders’ interests. This condition represents cultural capture. Therefore, the subsequent sections and chapters examine the goal replacement by including in captures. Proposition 3  A goal replacement is regarded as one mechanism of captures. In this context, academic KIOs’ control over activities declines and funders’ or collaborators’ intentions begin to subordinate academic integrity rather than support research conduct with a scientific significance. They affect boundaries of autonomy and the identity of academic KIOs. Proposition 4  Mismanagement of boundaries of autonomy and identity has a potential to generate the contents aspects of knowledge corruption in academic KIOs. The mismanagement can introduce a biased selection of research agendas and create understudied fields in academic sciences. Table 1 summarizes the academic captures and affected boundaries by referring to regulatory captures.

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Table 1  Regulatory and academic captures Regulatory captures Definition

Academic captures

Capture is defined as a violation of norms of regulators in which they act instead to advance the commercial or political interests of special interest groups that they are regulating (Carpenter, 2014a)

How corrosive captures occur

Cultural form of captures

Affected boundaries Noncultural form of corrosive capture Cultural form Burdens transferred to

Capture is defined as a violation of trusteeship norms of academic KIOs in which academics act not to contribute to the disciplinary knowledge instead to advance the outside interests of practically motivated organizations and special interest groups that they are interacting Special interest groups affect Special interest groups influthe regulatory process by ence the academic science prosubordinating regulatory cess by weakening of academic autonomy and fidelity of independence and institutional regulators to their statutory objectivity of science obligations Regulators begin to think Academics begin to think like like the regulated and groups that bring outside cannot easily conceive legitimacies and cannot easily another way of approaching conceive another way in proproblems ducing academic knowledge. Academics even begin self-persuasion to adapt to outside interests Boundary of autonomy

Boundary of autonomy

Boundary of identity A public, the original beneficiary

Boundary of identity Academic communities, academics, society

2.4   Measures to Identify Captures One of the difficulties of examining academic capture is that it is tough to figure out the threshold elements of learning from outsiders. Because of this difficulty, the study of this book will adopt the methodology of the public management. Among several means to identify captures, the public administrators mainly take an evidence-based method and a pro­ cedural justice. The former is to persistently accumulate scientific evidence to form a consensus on what is the desired outcome and whether bias or unintended consequence is introduced (Carpenter, 2014a; Cohen, Daniels, & Eyal, 2014).

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The evidence-based method can preserve objectivity in the i­nstitutional environment. Also, it ensures transparency and accountability in a consensus process of decision-making. At the same time, the too much strict practice of accountability can screen out an accumulation of small evidence that has not grown to generate a sizable scale of robust evidence. On the other hand, the method of procedural justice is to identify the special interests involved in space and to examine the mechanism of their influence to obtain desired outcomes (Carpenter, 2014a, pp. 60–61). This book mainly adopts this method in later chapters. The justification of this method is that the core dimension of translational science is the process. The investigation on how the involvement of specific groups affects the process fits the context. The study of this book will (a) seek a defensible model of academic sciences by introducing normative and institutional components of academic sciences (Kuhn, 1962; Merton, 1942; Nelson, 2003; Whitley, 2000), (b) investigate behaviors and motivations of entities that bring nonacademic legitimacies, and (c) state whether or in what manner academic integrity is affected by reflecting their interests (see, Carpenter, 2014a, p. 63; Warner & Havens, 1968). The next section will discuss prior research on governance models of KIOs. Then it tries to introduce scholarly research on norms of science and academics, and then explore directions to extend governance frameworks of KIOs.

3  Governance Models 3.1   Governance Models of Academic KIOs Trusteeship-Perspective Governance Trusteeship-perspective governance is a governance model in which trustees manage resources that belong to the organization for the best interests of third-party beneficiaries. Self-regulation, trusteeship norms, and internal codes that regulate trusteeship behaviors of the constituents

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construct this model (Greenwood, 2007; Sherer & Leblebici, 2015; von Nordenflucht, 2010). Although previous literature explores this governance model for PSFs, it can be extended to apply for the governance model of academic KIOs. In academic KIOs, academic scientists produce disciplinary knowledge in the form of publication and data. Being different from professionals, they provide a more generalized understanding of the truth. Direct beneficiaries are academic science communities. At the same time, produced knowledge transmitted to a public domain will be applied by private and public actors, go through several nonlinear pathways of applications (Nelson & Winter, 1982) and ultimately benefit society. Therefore, beneficiaries of knowledge production are third parties to academic KIOs. Then a problem is whether academic KIOs is structured to assure the best interests of the third parties. In general, organizations are designed to structure the process to increase their organizational capabilities and decrease the transaction costs. Here, the focus is on task uncertainty (Freeman, 1984; Galbraith, 1974; North, 1990; Tushman & Nadler, 1978). Previous literature characterizes the organizational environment of KIOs as a muted competition regarding a transaction of knowledge (von Nordenflycht, 2010; see, Choi & Millar, 2005). The organizational structure of “no outside ownership” (von Nordenflycht, 2010) supports this environment. Although competition is necessary, too much competition is contradictory to a system of peer-review (Whitley, 2000). Particularly, this pattern of ­knowledge ­production corresponds to reciprocal knowledge exchange. Preserving Trusteeship Behaviors of Academic Sciences Norms of academic science consist of the following dimensions (Anderson, Ronning, DeVries, & Martinson, 2010; Merton, 1942). i.  openness (sharing of scientific results, methods, and the consequent), ii. universalism (scientists should evaluate scientific work and findings from preestablished impersonal criteria), iii. disinterestedness (in which works of scientists remain uncorrupted by self-interested motivations), and iv.  Organized skepticism (detached scrutiny of beliefs regarding empirical and logical criteria).

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Among them, openness (sharing) is contradictory to industry science (Nelson, 2003) except in specific arrangements in a precompetitive area. Industry organizations benefit society through different pathways. Commercially motivated organizations protect knowledge produced by their proprietary investment in several forms of intellectual property, such as patents, know-how, data, and material transfer agreements, among others. In securing proprietary knowledge, companies exercise a control right for the circulation of produced knowledge (see, Leonelli, 2010). Intellectual properties facilitate the diffusion of knowledge and the delivery of benefits of discoveries to the public in the form of products (see, Schinkel & Schillberg, 2016 for the example of gene editing). On the other hand, as utility criteria limit the scope of patentability, a large part of the knowledge of early-stage discoveries and data produced in industry organizations remain undisclosed (IOM, 2012). Ruling Conflicts with the Public in Practicing Academic Norms A problem arises when there is a conflict between scientific values and societal values (see, Chapters 4 and 5). A problem in this context is whether science should mitigate the conflict by itself or society should take apart to limit the scientific autonomy (Resnik, 2008. See also, Gibbons, 1999; Nowotny, Scott, & Gibbons, 2001). As seen in Chapter 4, both directions can moderate the tensions. When scientific discoveries are validated, and about to be applied to a small group of humans, Code of Federal Regulations (government regulations), institutional rules, and organization codes limit scientific autonomy to protect subjects (e.g., 45 CFR 46). Society can use government (Resnik, 2008) to protect vulnerable parties in any contractual relations (Armour et al., 2017). Science itself also can moderate the tension by innovations of processes and research tools that will facilitate institutional validity and safety. Though potential harms and violations of others’ rights limit scientific autonomy, it does not mean that other measures (Marchant, Abbott, & Allenby, 2013) replace the self-regulation. Because of the unique characteristics of knowledge production of academics, academic KIOs should position the self-regulation of science in the center of the academic science governance.

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3.2   Mitigating Inconsistencies in Governance in Translational Science In the practice of academic science, one of the significant inconsistencies comes from the mixed form of governance mechanisms (Kunneman, 2010). The industry’s claim on property rights to produced knowledge has a logical consistency with the agency-perspective governance. From the agency-theory perspective, a choice of efficient governance mechanisms is contingent to asset specificity (Williamson, 1979). The optimal configuration of formal and relational governance mechanisms depends on the nature of the assets they produce (Hoetker & Mellewigt, 2009). Property-based assets produced by industries are compatible with formal controls. Here, formal controls consist of governance mechanisms with hard indicators. On the other hand, knowledge-based assets will better suit to relational governance mechanisms because of its difficulty to specify the exact process and outcomes in advance (Hoetker & Mellewigt, 2009). Here, relational governance mechanisms include steering committees and expert committees, among others (Haas, 2010). In the interdisciplinary knowledge production of academics (such as the recombination of computer science and biology. See Velasquez, Zhang, Vogelstein, & Kinzler, 1995), inconsistencies are likely to originate in a disciplinary difference in controlling outcome criteria (Kunneman, 2010). When academic KIOs and commercial entities build an alliance in which industry companies invest in project-specific assets that will follow commercialization pathways, commercial entities will require formal governance mechanisms. Such mechanisms include more hard outcome criteria that encompass intellectual property rights and potential impacts on financial outcomes. Here, different governance mechanisms coexist that may make academic knowledge production susceptible to inconsistencies (see, Kunneman, 2010). Regarding this point, Trochim, Kane, Graham, and Pincus (2011) propose a time-series model that includes timing of publication and intellectual property filing. Chapter 7 discusses intellectual property policies in other collaborative mechanisms. Regarding the biased agenda selection, academic KIOs also can minimize the bias through the leadership of committees. However, when members of committees are affected by academic captures, it would be hard to preserve autonomy and identity of academic KIOs. The surrounding conversations and atmosphere will reinforce academic scientists

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to select research agendas that will favor the source of captures. Scientists have an option to move to other KIOs. However, if their identity is affected, their movement may be constrained. On the other hand, not all of the biased selection of agendas is attributable to academic captures. Here are also other organizational barriers to conducting scientific research that make them understudied. The following section will explore what directions the governance models can be extended to mitigate the captures and barriers. 3.3   Directions for Enhancing Models There are three possible directions that the trusteeship governance should be enhanced. They are the integration of fairness-based stakeholder theory, common good-based stakeholder theory, and agency-perspective governance. Stakeholder Theories While governance concerns over managing relations and boundaries, stakeholder theory concerns over managing relationships with stakeholders. As stated in Chapter 4, stakeholder theory has developed from a Kantian view in which all the people should be treated in ends, ­equitably, not as means. Although this view is a universal principle, it is not necessarily comprehensive in an organizational setting. Defining scope of stakeholders is necessary. In this regard, this book investigates the fairness-based stakeholder theory (Phillips, 2003) and liberal accounts of common good-based stakeholder theory (Argandona, 1998). Here, the fairness-based stakeholder theory divides stakeholders into normative stakeholders and derivative stakeholders. This theory regards that a person enters into a cooperative scheme of an organization by an explicit or implicit consent (Phillips, 2003). In a translational science setting, a subject comes into a cooperative scheme of research by consent. Concerning consent, this theory has relevance for fair transaction model of informed consent (Chapter 4). The other direction is the integration of common good-based stakeholder theory (Chapter 6). Contrary to intuitive expectations, the common good-based stakeholder theory does not exclude the involvement of private organizations. That means it does not reject the private–public

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partnerships (see, Mahoney, McGahan, & Pitelis, 2009; Sachs, 2001). It just prohibits the subordination of unique norms (Buse & Walt, 2002; Reich, 2002; Stevenson, 2016) of KIOs. In this space, the common good is not equivalent to social welfare. Also, it should be distinguished from the communism, utilitarian, or consequentialist conception (Argandona, 1998; Finnis, 1983; Velasquez, 1992). In Chapter 6, the common good examined is vaccine science and discovery. Agency-Perspective Governance The last one is the integration of agency-based governance. It is to manage a boundary of regulation in academic KIOs. This theory widely has been used to analyze many types of organizations. For example, Gabrielsson and Huse (2017)5 examine boards of entrepreneur firms and find their function as a risk-bearing to leverage benefits of specialization. They take a practical approach to control the agency problems (Gabrielsson & Huse, 2017, p. 43). In a PSF setting, Greenwood and Emerson (2003) investigate the partnership6 of law firms from the agency-theory perspective and conclude this form to be efficient for PSFs. On the other hand, the assumption of owner’s control over agents’ opportunistic behaviors has a conflict with the self-regulation of academic science. Then, this book examines the agency-based governance in relations with the boundary of regulation and a bundle of property rights to seek overlaps with the self-regulation of academic science.

4  Conclusion This chapter investigates the management of boundaries of academic KIOs in relations with capture and goal replacement. It pays specific attention to the boundaries of autonomy and identity. Capture in academic KIOs will occur through the involvement of practically motivated organizations and public groups. They will bring nonacademic interests and affect a boundary of autonomy. Cultural capture in academic KIOs will occur through the intense interaction with them, surrounding conversations, and following self-persuasion of academics. Thus, academic KIOs are not aware of their captured condition. They affect a boundary of identity that works as a filter (Trispas, 2009) to construct research activities.

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Goal replacement is a part of academic capture that mainly occurs through the funding incentives. With funding, academic scientists can broaden a capacity with a new research tool by investigating what might otherwise not be observable. On the other hand, at a certain point, it is likely that additional funding no more contributes to pursue research questions. However, KIOs often feel obliged to make maladaptation to funders’ intentions. This phenomenon is relevant to Pfeffer and Salancik (1978, p. 74). “Attentional process creates an organizational environment, shifts the focus from characteristics of the objective environment to those of the decision process by which organizations select and ignore information.” Then, economic and social forces that affect the autonomy and identity of KIOs become the created environment by KIOs themselves. Unwritten atmosphere affects academic identity and in turn, induces attenuation process that leads to bias research agendas. In this regard, this book will introduce (a) normative and institutional components of academic sciences (Kuhn, 1962; Merton, 1942; Whitley, 2000), (b) investigate behaviors and motivations of entities that bring nonacademic legitimacies, and (c) seek mechanisms that outside interests affect the academic integrity. Then it explores directions to extend self-regulation and trusteeship model of academic KIOs.

Notes 1. See, Zuckerman & Gellad (2016, September 9). 2. Regarding reasons why it is tough for public groups to be a power, see, Pfeffer and Salancik (1978). 3. Regarding self-persuasion, see, Faden and Beauchamp (1986). 4. Jourdan and Kivleniece (2014) examine this effect by using a context of public sponsorship for French film industry. Although a specific mechanism is different, there are several evidence of similar curvilinner relationship in sponsorship of academic KIOs. 5.  Gabrielsson and Huse (2017) state that internal and external boards provide human capital. Thus, both of resource-dependence theory and agency-theory are applicable to investigate governance mechanisms of entrepreneur firms. 6. Sherer and Leblebici (2015) include “partnership ethos” as one of governance models of PSFs. As this book focuses on academic KIOs, it does not argue partnership ethos as a governance model.

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service firms (pp. 189–212). Oxford: Oxford University Press. https://doi. org/10.1093/oxfordhb/9780199682393.013.10. Simon, A. (1957). Rationality and administrative decision making. In Models of man: Social and rational (pp. 196–206). New York: Wiley. Stevenson, M. (2016). The entrenchment of the public-private partnership paradigm, In N. Kenworthy, R. MacKenzie, & K. Lee (Eds.), Case studies on corporations & global health governance: Impacts, influence and accountability (pp. 119–129). London: Rowman & Littlefield International. Stigler, G. J. (1971). The theory of economic regulation. The Bell Journal of Economics and Management Science, 2, 3–21. Trispas, M. (2009). Technology, identity, and inertia: Through the lens of “The Digital Photography Company,” Organization Science, 20(2), 441–460. Trochim, W., Kane, C., Graham, M. J., & Pincus, H. A. (2011). Evaluating translational research: A process marker model. Clinical Translational Science, 4, 153–162. Tushman, M. L., & Nadler, D. A. (1978). Information processing as an integrating concept in organizational design. Academy of Management Review, 3(3), 613–624. Velasquez, M. G. (1992). Business ethics: Concepts and cases. Upper Saddle River: Prentice Hall. Velasquez, V. E., Zhang, L., Vogelstein, B., & Kinzler, K. W. (1995). Serial analysis of gene expression. Science, 270(5235), 484–487. Von Nordenflycht, A. (2010). What is a professional service firm? Towards a theory and taxonomy of knowledge intensive firms. Academy of Management Review, 35(1), 155–174. Warner, W. K., & Havens, A. E. (1968). Goal displacement and the intangibility of organizational goals. Administrative Science Quarterly, 12(4), 539–555. Whitley, R. (2000). The intellectual and social organization of science (2nd ed.). Oxford: Oxford University Press. Williamson, O. E. (1979). Transaction-cost economics: The governance of contractual relations. The Journal of Law and Economics, 22(2), 233–261. Zahra, S. A., Neubaum, D. O., & Hayton, J. C. (2016). Handbook of research on corporate entrepreneurship. Cheltenham and Northampton: Edward Elgar. Ziman, J. (2000). Real science. Cambridge: Cambridge University Press. Zuckerman, D., & Gellad, W. (2016, September 9). The ethical involvement of patients in FDA regulatory evaluation of new products. In A. S. Kesselheim (Moderator). A Health Policy and Bioethics Consortium. Boston: Harvard Medical School.

Part II

CHAPTER 4

Institutional Barriers and Governance

1  Introduction This chapter investigates barriers to institutional validation and application of proper methodologies in understudied research fields. Scientific studies of neurobiological pains and epilepsy are bodies of example. Though the translational science is regarded as promising in these fields, they have common organizational barriers to governance regarding the preconditions of research. In translational science, subjects enter into a cooperative scheme of research by informed consent. The prerequisites of research are regulated by Law—in the USA, Code of Federal Regulations Title 45 Public Welfare, Department of Health and Human Services, Part 46 (45 CFR 46). However, it is often tough to assure the validity of the consent. The assessment standards of the consent capacity also raise issues for specific groups. Even if academic knowledge-intensive organizations (KIOs) overcome the barrier of consent, the measurement of scientific traits constructs another barrier. With these structured barriers, this chapter examines the following questions. (i) How does the consent capacity of subjects affect the institutional validation process of the KIOs? (ii) When is the precondition of the informed consent process compromised, in what manner self-regulation of KIOs should be limited? What kind of governance theory underlies the modification? (iii) What kind of roles are knowledgeintensive research tools playing in the neurobiological science research?

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The gradual shift to translating discoveries to its applications appears to have become an essential trend in pain research (Kruger & Light, 2010). The same thing can hold to the epilepsy research. The term “translation” is approximately equivalent to “applied,” and not always from animal studies to humans. In many cases, discoveries of mechanisms in laboratories are applied directly to a small group of humans. It is “hypothesis-driven” research to explore “mechanism-based” therapies (see, Kruger & Light, 2010). Under this condition, this chapter aims to modify the self-regulation of academic and scientific KIOs (hereafter, academic KIOs) by the stakeholder theory. The stakeholder theory has its origin in two research streams: A strategic management (Freeman, 1984; Stanford Research Institute 1963 internal memorandum, as cited in Freeman, 1984; Thompson, 1967), and ethics. The ethics origin started from incorporating a Kantian view (Donaldson & Dunfee, 1994; Evan & Freeman, 1988) that treats all humans fairly and as ends. The stakeholder theory has continuously refined its scope (Clarkson, 1995; Donaldson & Preston, 1995; Evan & Freeman, 1988; Freeman, 1994; Phillips, 2003; Wicks, Gilbert, & Freeman, 1994) by reflecting several criticisms from neighboring fields (Phillips, 2003). For the aim above mentioned, this chapter investigates the consent process of translational science by basing on the fairnessbased theory. Phillips (2003) explores the fairness-based approach as a part of the stakeholder theory in which stakeholders voluntarily choose to enter into a cooperative scheme of an entity by an explicit or implicit consent (Phillips, 2003; Cf., Rawls, 1963/1999, p. 77). This proposition is consistent with translational science in which research subjects enter into a cooperative scheme of research by informed consent. While Phillips developed this theory by referring to the liberal account of Rawls (1963), this organization theory is consistent with the fair transaction model in bioethics (Miller & Wertheimer, 2011). Regarding the measurement, traditional measurement of neurobiological pain sciences used subjects’ reporting (Mao, 2009). The lack of objective measurement (see, Reiss & Sprenger, 2017) impedes feedback loop of the scientific endeavor. In the evolving science fields, the development of proper methodologies (Kuhn, 1962) and new technologies for research tools such as neuroimaging mitigate the problems that stem from measurement. In addition to improving the analysis of experimental variants, the knowledge-intensive research tools have a potential to impact on the assessment of consent capacity of subjects.

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In this regard, North argues that perfect measurement and enforcement are implicitly assumed in efficient factors (of transactions). At the same time, their existence entails a complex set of institutional arrangements that encourage the rapid and low-cost transmission of information, the invention, and innovation of new technologies, among others (North, 1990, p. 64). This chapter extends this proposition by investigating how new technologies affect the measurement of the precondition of informed consent and other institutional factors that, in turn, stimulate discoveries to be applied. This book uses the word “subject” or “participant” in the translational science setting to avoid therapeutic misconception.1 The study organizes this chapter as follows. The next section investigates the theoretical consistency between the fair transaction model of informed consent and fairness-based stakeholder governance. It assumes informed consent as a buffer, and frames barriers by observing translational pain science and epilepsy research. Then, the section three explores the direction to extend trusteeship governance by incorporating fairness-based stakeholder theory.

2  Barriers to Institutional Validation 2.1   Informed Consent as a Buffer Biomedical scientists act based on law, codes of conduct of professional associations, and internal codes. They are assumed to have an understanding of what are the best interests of the research subjects, and the risk/benefit profile of the procedure they adopt. It is a legal duty for them to obtain informed consent from research subjects before performing the experimental process. The underlying legal basis is the battery or negligence2 theories of liability (Faden & Beauchamp, 1986, p. 26). On the other hand, the ethical principles to be applied are (i) respect the autonomy of subjects, (ii) beneficence (risk/benefit profile), and (iii) justice (The National Commission, 1978, hereafter, Belmont Report, pp. 4–10; Beauchamp & Childress, 2013; Faden & Beauchamp, 1986). In the EU context, respecting autonomy is included as a part of the respect of dignity (European Commission, 2009, p. 29). The informed consent requirements are more strict in research settings than those in medical settings. It is because the primary beneficiary of research is academic/ science communities and future patients, not a subject who undergoes

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the procedure. On the other hand, the primary beneficiary of medical practice is a patient. Therefore, the legal doctrine regulates the contents and process of informed consent in research settings. Translational science necessitates further caution: It is different from both of the pure scientific experiment and medical practices. Its critical component is to apply a mechanism of discovery to humans and simultaneously collect experimental data through a feedback process. There are two pathways for the application. One is to use a mechanism of discovery directly to a small group of humans. The other is to go through preclinical studies (such as animal studies), and then apply to a small group of humans before entering the stages of clinical trials. In translational science, though subjects are supposed to respond to the procedures favorably, the toxicity has not well been manipulated in human bodies (Dresser, 2009; Lo, 2009). Scientists can not know the established risk-benefit profiles and there are unknown risks. The primary objective of the procedure is to obtain generalized knowledge. Therefore, biomedical scientists have ethical and legal obligations and further precautions in avoiding therapeutic misconceptions (Henderson et al., 2007). Also, they must explain available alternatives including the non-intervention, and their risks and benefits. Further, Institutional Review Board (IRB) is required to have more expertise in identifying possible pitfalls and approving or denying a proposed research protocol. They have legal obligations to ensure the consistency and appropriate level of protection embodied in contents and process of informed consent. The reason for such strict requirements is because, in addition to possible cognitive biases of researchers, highly motivated and talented scientists tend to focus on their goals and may unintentionally overlook other implications of their conduct (Belmont Report, 1978). Though the first human studies are, in regular courses, applied to healthy volunteers, biomedical scientists may exceptionally apply unproven investigational procedures to those who afflict a serious illness3 and do not respond to existing treatments or untreatable life-threatening conditions (Chapman, 2011; Dresser, 2009). In this sense, cancers, among others, correspond to exceptional cases. In this sense, informed consent is a buffer between the boundary of autonomy of subjects and KIOs. Here, the autonomy of subjects entails their comprehension and voluntary decision to accept specific known and unknown risks.4

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Proposition 1 Informed consent functions as a buffer between the boundary of autonomy of subjects and KIOs in translational science. Informed Consent and Governance Mechanism of Enforcement As informed consent is a buffer embedded in a legal governance mechanism, competent subjects must provide explicit written consent before participating in the scientific endeavor. The written consent document is an enforceable measure. It often appears as secondary subjects of a lawsuit (Bayers & White, 2004). It is a legal liability of an investigator if he/she fails to obtain adequate informed consent in a proper process. A problem is a consent capacity of the subjects to fulfill the precondition of informed consent. In the Common Law doctrine, the origin of informed consent dates back to the 1767 England Court decision of medical liability. The theoretical background is the contract theory and battery (Faden & Beauchamp, 1986, p. 116). After the 1947 Nuremberg Code and the 1964 World Medical Association’s Declaration of Helsinki (see, Shuster 1997; Vollman & Winau, 1996), the research ethics evolved into law. Courts introduced the component of respecting the autonomy to the US legal cases of biomedical research in the mid-1970s (Beauchamp & Childress, 2013; Presidential Commission, 2016). However, it is not always easy to practice these concepts. It is, in many cases, almost impossible that a subject has a “substantial” understanding of the procedure and acts as a principal. It is not only because of information asymmetry about the methods but also a subjects’ condition of illness and physical, psychological, and cognitive vulnerability (see, Belmont Report, 1978). In this regard, the precondition of informed consent consists of two threshold elements. One is (i) an individual’s autonomous authorization of intervention and participation in research, and the other is (ii) conformity to the social rules of consent (Beauchamp & Childress, 2013). Here, the first element assumes a subject’s comprehension, appreciation, and reasoning (Buchanan, 2004; Casarett, Karlawish, Sankar, & Hirschman, 2002) and the absence of coercion by others (Belmont Report, 1978). Appreciation is the ability to see how the information applies to decision-makers and their situation. The reasoning is the ability to forecast the impact of a decision on the decision-makers’ condition (Casarett et al., 2002). As a buffer, the level of competency should entail

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the error margin when negative consequences of the subjects’ choice can be substantial for their well-being (Buchanan, 2004). It is tough to satisfy the constituents of the first requirement in many cases because of subjects’ physical, psychological, and cognitive vulnerability. Then, in the second sense, informed consent refers to (ii) conformity to the social rules of approval that requires investigators to obtain “legally or institutionally valid” permission from a subject before proceeding research procedures (Beauchamp & Childress, 2013). This requirement conforms to the fair transaction model (Beauchamp & Childress, 2013, p. 122; Wertheimer, 2012). 2.2   Fair Transaction Model of Consent Consent, when valid, is a transaction between two or more people that generates moral transformation. The primary function of consent is to make permissible conduct in a bilateral transaction (Wertheimer, 2012, p. 293). The fair transaction model is differentiated from the autonomous authorization model that assumes subjects’ capacity of reasoning in their preference and choices (Wertheimer, 2012, pp. 301–302). Instead, the fair transaction model gives due consideration to the reasonable limits of an investigator’s responsibilities in ensuring adequate understanding on the part of subjects who consent to research (Miller, 2012; Miller & Wertheimer, 2011; Wertheimer, 2012, p. 302). In this sense, it is an extension of Rawls’ (1958/1999, p. 59) conception of “fairness of justice.” On the other hand, even by basing on the fair transaction model, obtaining informed consent is not always straightforward in some types of research. Notably, the threshold elements of (a) competence and (b) self-determination (voluntariness) of subjects are harsh to evaluate and institutionally validate. Here, the barriers to the evaluation of precondition compromise the institutional validity of the informed consent. Cancer pain science and epilepsy research are good examples. Difficulties in the Threshold Elements Regarding the threshold elements, issues of (i) comprehension (understanding) include information asymmetry and subjects’ capacity of appreciation and reasoning. On the other hand, issues of (ii) selfdetermination consist of the absence of coercion (free and voluntary will).

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In this domain, factors that influence (iii) self-determination encompass subjects’ physical ability, psychological ability (Rasiel, Weinfurt, & Schulman, 2005; Roberts, 2002), and human interactions that affect the psychology. For example, research shows that, when subjects have little alternative or very sick, they are inclined to select and consent to risky options (Rasiel et al., 2005). Although the impacts of psychological conditions are essential, it is beyond this chapter to deliberate psychological factors. This chapter mainly focuses on science and technology that have a potential to impact on the relations between self-regulation of academic KIOs and stakeholder fairness. Therefore, this chapter concentrates mostly on the physical factors that affect preconditions of consent. Such factors include a cognitive impairment that stems from physical, neurobiological disorders. Proposition 2 Difficulties in assessing the threshold elements of informed consent can be a barrier to assuring the institutional validity of consent that has a potential to cause specific science fields understudied. The Case of Translational Pain Science Cancer-induced bone pain (CIBP) has remained a pressing challenge to the biomedical society. It consists of a complex combination of pain states. Pain is a defensive mechanism that allows an organism to survive. The pain pathway in general consists of sensory nerve endings that respond to specific stimuli, transmit the information to the spinal cord, and then to supra-spinal levels. Here, the processing of the sensory information occurs (Bannon, 2012). There are three general categories of pain: (i) Acute pain; (ii) Inflammatory pain, which relates to tissue injury and the subsequent release of inflammatory mediators from multiple cell types; and (iii) Neuropathic pain, which originates in damage to the somatosensory system. Among them, CIBP is a pain state with overlapping but distinct features of (ii) inflammatory and (iii) neuropathic pain. Bone metastasis develops bone frailty and destruction, which creates a local inflammatory environment and the threat of spinal cord compression (Cf. Falk & Dickenson, 2014, among others). Neuropathic part entails not only neurobiological causes but also other factors, such as emotional,

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psychological, and social factors. Roughly 70% of individuals who die of cancer develop bone metastasis and one-third of them suffer from CIBP (Kane et al., 2015). One of the challenges is to separate (i) disease-induced factors, (ii) neurobiological factors, and (iii) nonbiological factors such as psychological factors (Bannon, 2012, pp. 168–169; O’Connor & Dworkin, 2009). Failures in distinguishing among sources have a potential to lead to abusive consequences. For example, it often happens that biomedical scientists misdiagnose pain states due to functional neurological disorders (FNDs). FNDs are neurological symptoms that arise from a psychiatric origin (Rommelfanger et al., 2017). Although many have a sign of low-back pain, they belong to an overlapping category of neurobiology, physiology, psychiatry, clinical psychology, among others (Health Improvement of Scotland, as cited in Stone, 2016). However, as the FNDs are usually an unknown factor, a wrong pharmacological intervention tends to be taken, and over-dosed (J. Stone and C. Mitchell, communication at Neuroethics seminar: Functional neurological disorders, Center for Bioethics, Harvard Medical School, Janurary 26, 2017). In this regard, a series of recent research shows that translational science approach is one of the promising pathways to increase the neurobiological understanding of CIBP (Delaney, Fleetwood-Walker, Colvin, & Fallon, 2008). As stated earlier, translational science has two paths: (i) applying precise mechanisms to a small group of humans, and (ii) going through preclinical studies such as animal studies before applying to a small group of humans (Bannon, 2012, p. 172). The both necessitate informed consent that buffers the boundary of autonomy. The Lack of Appreciation and Reasoning When there is no valid advance directive, and when the preconditions of informed consent are not assured, state laws usually provide a choice architecture to seek a surrogate’s consent (see, Glezer et al., 2011). However, the implementation of the architecture still requires a caution: surrogates do not always represent subjects’ best interests. Also, subjects’ past preference does not necessarily correspond to that of the very moment of the intervention. Thus, it is likely that the best way is to make efforts to include subjects in the informed consent process which might otherwise exclude them because of difficulties to obtain the institutional validation.

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In this effort, a difficulty persists. Casarett et al. (2002) conducted a comparative analysis of factors that potential subjects consider in deciding the enrollment in pain research. The focused group is patients with cancer pains, and the control group is those with chronic non-cancer pains. The methodology is a structured interview. According to them, though both groups share the same determinants regarding risks and potential benefits of the enrollment, determinants of willingness to enroll are different between the two. The desire to the enrolment of non-cancer patients was related to pain severity and the desire for better pain management. However, those with cancer pains did not respond to these determinants. Casarett et al. (2002) interpret the results as follows. “Cancer patients may have suffered from the mild cognitive impairment that reduced their ability to think critically about how participation in research might affect them”; “Presence of pain and other symptoms distracts patients with cancer from thinking critically about the ways that participation in research might affect them,” among others. The ability to anticipate the implications of research participation is relevant to “appreciation” and “reasoning” in decisions. Both are essential threshold elements of informed consent. As a result, patients with cancer pains are less likely to be prepared to participate in a valid informed consent process (Casarett et al., 2002). In this manner, the precondition of informed consent in cancer pain research tends to be more compromised relative to non-cancer pain research. The 45 CFR 46 prohibits to proceed to study when the institutional validity of consent is not assured. If it is tough to obtain institutional validity, investigators on average will not dare to take a risk to research the field. There are several practical solutions that researchers propose. For example, investigators can try asking and communicating with a subject at several time points. By asking at several timings, investigators are going to draw consistent answers from the subjects. FDA also released a Draft of Guideline for IRB, investigators, and sponsors regarding difficult conditions of obtaining informed consent (21 CFR50.31& 50.20; 21 CFR 56.111(b); FDA 20145; see also, Fins, 2005; Fins, Schiff, & Foley, 2007, p. 306). Still, a question remains regarding whether their consent represents their true will. Therefore, the fair transaction model has a meaning by

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acknowledging the reasonable limits of responsibilities of investigators to ensure adequate understanding on the part of subjects. The Case of Epilepsy Epilepsy research entails another difficulty regarding the informed consent. Translational science for epilepsy connects several neuroscience fields regarding the results obtained from studying patient-derived cells and tissues (Gasior & Wiegand, 2012). Schramm (2014) explores the scheme of translational epilepsy science. It consists of the connections between epilepsy surgery, epileptology, neuropsychology, neuroimaging, neuropathology, and basic science. Here, one of the difficulties in epilepsy research is to distinguish true epilepsy from functional epilepsy [psychogenic non-epilepsy seizure: PNES (Gillig, 2013)] and cancer-associated seizure (Maschio, 2012; Singh, Rees, & Sander, 2007). Although their features that occur in uncontrollable movements are the same, they have different neurobiological pathways. Abnormal brain electrical activity causes the seizure of right epilepsy. On the other hand, those of PNES usually reflect a psychological conflict or a psychiatric disorder, not unusual brain electrical activity. The cancer-associated seizure needs additional considerations. However, individuals with PNES are not necessarily aware that their seizures are non-epileptic (Gillig, 2013). Deception for the Reason of Research Design To identify true epilepsy or a precise type of epilepsy, biomedical scientists often induce episode. In this context, a subject must suffer from a severe episode produced. Here, triggering seizure involves deception (Benbadis, Tolchin, & Boyd, 2016, February 4). Deception is an information manipulation that uses intentional strategies. Here, approaches, in general, include lying, withholding of information, a valid assertion that omits a vital qualification, and misleading exaggeration to make persons believe what is false (Faden & Beauchamp, 1986, p. 363). The deception in the consent process of epilepsy research corresponds to the category of information withholding. The nondisclosure in deception violates respecting autonomy in the informed consent process. Traditionally, this type of deception has been permitted based on the utilitarian- perspective bioethics: “The wrong diagnosis is costly to a subject as well as to the society. It can also harm

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a human’s physical body when a non-epilepsy individual participates in research as a subject. On the other hand, a right diagnosis promotes the best interest of the subject. Thus the total benefits far outweigh the disadvantage of deception (Benbadis et al., 2016).” Also in some research fields, full disclosure in informed consent distorts assumptions of research design. In studies that involve in psychological aspects, subjects may act well based on what they think experiments are seeking (Kimmel, 1989). In such case, the full disclosure in informed consent brings bias to science. However, in the liberal accounts of ethics, lying/deception violate justice as fairness even if they come from goodwill (Rawls, 1958/1999). Then, a question arises whether there is a way to prioritize the subject’s autonomy, satisfy justice, and still promote accurate diagnosis and preserve unbiased research design. One measure to accommodate these conflicts is “authorized deception.” Biomedical scientists can obtain consent by disclosing that the informed consent entails a deception (Benbadis et al., 2016) to avoid the distortion of research design. The authorized deception satisfies the requirement of justice as fairness. Then, a problem related to the objective of this book is in what directions this study extends the trusteeship governance that entails self-regulation of scientists in translational science that involves the difficulties in the informed consent process. 2.3   Measurement of Preconditions Assessing Consent Capacity Assuring the precondition of informed consent also confronts with measurement challenges. Legal and bioethics scholars have pointed out a room to improve the current standard to measure consent capacity, MacArthur Competence Assessment. The MacArthur Competence Assessment Tool for Clinical Research (MacCAT-CR) consists of interactive questioning that includes the following components. “Understanding” of disclosed information, “appreciation” of the effects of participation, “reasoning” about participation, and “an ability to communicate choice” (Kim et al., 2007, among others). It has a problem for a certain subgroup (Cf. Lemmens, 2016; Kim & Lemmens, 2016) such as those with fluctuating cognitive impairment, mood swings, speech loss (because of stroke, etc.), autism, depression, and reversible seizure,

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among others. Among these subgroups, it is possible for those who preserve an ability to speak to involve in a consent process by a method of multiple timing of consent (Fins, 2005; Fins, Schiff, & Foley, 2007). However, “the interactive questioning” is still contradictory with the subgroups characterized by speech disorders, difficulties of “interaction,” and “communication with others.” Kim and Lemmens (2016) and Lemmens (2016) reveal a biased “choice” of premature death of certain subgroups in end-of-life (EOL) setting. The problem of biased “choice” of risky alternatives can also occur in a translational science setting. In this regard, the law is not necessarily appropriate for a problem that accompanies a continuum of fluctuated consciousness (Kim & Lemmens, 2016). Here is a room to the practice of responsible research and innovation (RRI). At the same time, the revision of the consent capacity measurement may also be necessary for the future to include more subjects in the informed consent process and provide them with an opportunity to express their own choice. Here is a need of knowledge-intensive research tools. Roles of Knowledge-Intensive Research Tools—Neuroimaging A study shows that, by using the functional magnetic resonance imaging (hereafter, fMRI), measurement of decision-making capacity has a potential to allow behaviorally non-responding subjects to participate in their own care decisions (Weijer et al., 2014). Further, the study provides evidence that a subject with the minimally conscious state (hereafter, MCS) and no physical ability can communicate with a biomedical scientist with the aid of fMRI (Weijer et al., 2014). The design of the above study of MCS is to ask yes or no questions to subjects and request them to imagine playing tennis when the answer is yes and walking from room to room when the answer is no. In the study, subjects responded to several questions by imagining different behavioral visions which were designed to be reflected in the subjects’ hemodynamic change in the brain (Weijer et al., 2014). Also, one of the subjects provided a correct answer on the date of the research even after the passage of several years after the accident (Weijer, 2016, October 25). Although further evidence is in need, a series of scientific evidence confirms that the research results of fMRI are reasonably reproducible (Choe et al., 2015; Gholipour, Moeller, Pittau, Dubeau, & Gotman, 2011). The accumulation of scientific evidence has a potential to revise

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methodologies of measuring consent capacity to include them in their own decisions. Thus, the knowledge-intensive research tool not only helps scientists to measure pathological condition, individual variation, and multidimensional features of pain (Mao, 2009; Matthews et al., 2006; Wager et al., 2013) but also have a potential to refine institutional validity of subjects’ consent. Proposition 3 Knowledge-intensive-research tools have a potential to develop new methodologies to modify threshold elements of institutional validity to include more subjects in the informed consent process. In this regard, science and technology can mitigate institutional barriers to conducting sciences that are understudied and combine science with an insight that comes from humanistic disciplines.

3  Fairness-Based Stakeholder Theory in Translational Science 3.1   Fairness-Based Stakeholder Theory and Informed Consent The fair transaction model of informed consent is consistent with self-regulation of science and trusteeship governance of KIOs. It reasonably allows a specific scope of information asymmetry that investigators possess. At the same time, it is also consistent with fairness-based stakeholder theory in management studies. The fairness-based stakeholder theory has developed through the integrated perspectives of governance and ethics. By extending the Kantian’s ethics, organization scholars explored a liberal account of stakeholder theory and a contractual account of stakeholder theory. The former claims that all the stakeholders have a right to be treated as ends and with equity (Donaldson & Dunfee, 1994; Evan & Freeman, 1988). It concerns over anticipating stakes of stakeholders and proactively incorporating them into a strategic process and implementation (Freeman, 1984). The latter has a basis on the property theory (Honore, 1961, among others) that concerns over the allocation of rights and obligations associated with a property. As property rights are inseparable from human rights, owners of rights should not use the rights in a way that violates fundamental rights of others. Under a condition that

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there are diversified views on distributive justice, managers have a responsibility also for nonowner stakeholders who have a moral interest (or a stake) to a corporation (Donaldson & Preston, 1995, pp. 83–84). In any research streams, stakeholders are those who have legitimate interests (Freeman, 1984) in procedural and substantive aspects of organizations’ activities. Organizations may not violate the legitimate rights of others to determine their future; each group of stakeholders merits consideration for its own sake (Donaldson & Preston, 1995, p. 67; Evan & Freeman, 1988, p. 100). Here, problems arise regarding the scope of stakeholder legitimacy and their reasoning. Organizations have a moral obligation toward stakeholders; they have responsibilities for the effects of their actions on stakeholders (Evan & Freeman, 1988; Phillips, 2003). Organizations should pay simultaneous attention to the legitimate interests of all proper stakeholders (Donaldson & Preston, 1995; Evan & Freeman, 1988). However, it does not mean that all stakeholders should be equally involved in organizational processes and decisions (Donaldson & Preston, 1995, pp. 85–87; Freeman, 1984, p. 45; Phillips, 2003). Then, a problem is what is a fair treatment of stakeholders regarding organizational processes and decisions. By referring to liberal accounts of Rawls (1971, 1958/1999), Phillips (2003) conceptualizes fairness regarding reciprocity. Here, Phillips (2003) distinguishes types of stakeholders by paying attention to (i) legitimacy and (ii) moral obligations that an organization should owe. Then he classified stakeholders to normative stakeholders and derivative stakeholders. Normative stakeholders are those to whom the organization has a moral responsibility of stakeholder fairness over that due to other social actors regarding the civil society obligations. On the other hand, derivative stakeholders are those whose actions and claims must be accounted for by managers due to their potential effects on the normative stakeholders. On the other side, non-stakeholders are those who are likely to have little impact on an organization or normative stakeholders and to whom the organization has no moral obligation in addition to what they are due to humans in general (Phillips, 2003). In this framework, the relationship between stakeholders and the organization characterize equity of stakeholders in a way that each receives consideration based on their contribution to the organization (Phillips, 2003, pp. 124–131). This claim has a reason in fairness as reciprocity (Rawls 1971/1999, p. 192).

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The term “contribution” is misleading in clinical settings where resources should be distributed based on needs. However, as translational science is a scientific process, this framing has a reason to apply. Based on Phillips (2003, pp. 124–126, 135), stakeholders with normative legitimacy are those from whom the organization has voluntarily accepted benefits and to whom there arises a moral obligation. This ethical and normative definition of stakeholders has consistency with the strategic management aspects of stakeholder theory regarding “voluntariness” (see, Freeman, 1984). This concept has a basis on voluntarism that managers anticipate stakes of stakeholders and incorporate them into a strategy formulation process (Freeman, 1984). It also has consistency with reciprocal knowledge transaction that entails (legal and contractual) obligations (Choi & Millar, 2005). What is different is that the scope of responsibility is extended to include moral obligation. Based on Phillips’s definition, subjects of translational science are normative stakeholders to whom KIOs have a legal and moral obligation of stakeholder fairness. Their legitimacy of stakeholders’ status comes from their participation in the research scheme of the KIOs. Then, by basing on fairness-based stakeholder theory, it is possible to reframe informed consent as a person’s voluntary acceptance of entering a mutually beneficial scheme of cooperation. According to Phillips (2003, p. 92), the participation in a cooperative project requires a sacrifice or contribution on the parts of the participants. This insight also corresponds to translational science, as subjects accept unknown risks in participating in the scheme. From the viewpoint of the fairness-based stakeholder theory, the informed consent is a subject’s voluntary decision and approval of the participation in a cooperative scheme of translational science. Though there is a risk of free riding, the participants create the obligation of fairness among them in the collaborative project in proportion to the benefits accepted (see, Phillips, 2003). A protocol must go through the investigation of IRB and authorities to assure a proper balance between risk and benefit and procedural justice, The Difference from the Tacit Consent These functions of informed consent are different from a “tacit consent” of an integrative social contract by Donaldson and Dunfee (1994). A subject matter of the integrative social contract is a relationship between humans and a specific economic, social, and political regime. Here,

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humans are interpreted to have implicitly consented to enter a particular system. This macro-space consent binds the micro-space consent in which people join several kinds of projects and communities through consent (see, Phillips, 2003). In other words, the tacit consent regards that a subject’s decision conforms to that of larger groups to which the subject belongs. On the other hand, such binding of tacit consent is equivalent to an implicit consent in medical settings. The legal foundation of informed consent originates in the battery. However, through bioethics of physician–patient relations, physicians are supposed to touch patients’ bodies for their medicinal purposes. Presumed consent in emergency research is similar to the tacit consent but different. Because of the lack of consent capacity of subjects, many of standard of care in emergency settings have not gone through well-controlled trials. FDA expects that permitting specific emergency research will be mutually beneficial (U.S. Department of Health and Human Studies [HHS], 2013/2011). In the emergency setting, a subject who might otherwise die is presumed to have consented to enter a research scheme of a potentially life-saving investigational procedure. The assumption is that self-regulated investigators and KIOs should know the best interests of subjects. What is different from the tacit consent is the investigators’ obligation to make efforts to seek surrogate decision-makers through a choice architecture and an IRB approval. In addition to considering competing factors and additional responsibilities for protecting subjects, an IRB-approved informed consent document must be prepared (21 CFR 50.24; 21 CFR 50.25; also see, 45 CFR 46). 3.2   Obligations of KIOs in Fairness-Based Stakeholder Theory In based on the fairness-based theory (Phillips, 2003, pp. 98–100), biomedical scientists and KIOs have several moral obligations to the subjects in addition to those derived from the protection of subjects’ human rights. Such obligations include the disclosure of potential risk/benefit profile of the procedure and the existence of unknown risks. Although the risk/benefit disclosure is one component of informed consent and is a legal duty, the contents and methods of the disclosure are within the moral obligation of an investigator. Conversely, if a potential subject insists on entering the cooperative scheme with an expectation of a therapeutic effect while the procedure

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does not meet a reasonable threshold of scientific justification, the investigator has an ethical obligation to refuse to provide the procedure (Truog, 2017. Also see, codes of American Medical Association). Thus, in the translational science, a possible direction to modify trusteeship governance is to integrate the fairness-based theory of governance. It is to manage the boundary of autonomy of KIOs and subjects. Proposition 4 The trusteeship governance that encompasses self-regulation of academic KIOs is to be enhanced to integrate fairness-based stakeholder theory of governance. It is to manage the relations with subjects and boundary of autonomy of KIOs. The fairness-based stakeholder theory is consistent with the fair transaction model of informed consent and moral and ethical obligations of biomedical society. 3.3   Implementation Mechanism Mediating Institution and a Scalable Team Science Here, a small group team works as a mediating institution proposed by Fort (2001). Although what Fort (2001) addresses is business firms, the analogy of normative models about relations between corporations and stakeholders is useful for the team science of academic KIOs. According to Fort, mediating institutions are those standing between the individual and the large institutions. The mediating institution becomes the incubator for the solidarity that extends to those outside of one’s in-group. In this regard, a research team is a mediating institution. In this context, what small groups do that more massive structures do not (Fort, 2001 p. 24) include a function to provide a more immediate feedback mechanism on how actions affect others. Because of this feedback mechanism, it is possible to reinforce or modify desired anticipations, actions, committed attitudes, and beyond. The informed consent process also adopts the team approach. When cancer-associated seizers compromise the consent capacity of a subject, an investigator can arrange a multidisciplinary team that consists of cancer scientists, neurobiologist, psychiatrist, and clinical psychologist to diagnose and predict the subjects’ behavioral traits and obtains a valid consent from the subject. Governance modalities complement such mediated institution.

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This function is relevant to the multiple layers of loops of validation in the translational science process. The infrastructure of National Institute of Health Undiagnosed Disease Program (NIH-UDP) (Chapter 1) suggests scalability by integrating modular units of global niche experts (Links et al., 2016) into a structured system.

4  Conclusion Regarding the barriers that stand in understudied scientific fields, this chapter investigated the institutional validation in pain science research and epilepsy research. Despite scientific significance to understand neurobiological mechanisms, both fields are under-researched in academic institutions. Both share the similar difficulty in governance. Among several institutional validation issues, this chapter examined threshold elements of informed consent and the objectivity in measuring consent capacity. Because of the physical conditions of subjects in these fields, it is tough to satisfy the preconditions of the valid consent. In this regard, the fair transaction model of bioethics focuses on safeguarding for valid consent. In this model, investigators and their subjects are treated fairly by giving due consideration to the reasonable limits of an investigator’s responsibilities to ensure adequate understanding on the part of subjects who consent to research. In the management theory, the fairness-based stakeholder theory is consistent with this model. Under this theory, stakeholders are those groups from whom the organization has voluntarily accepted benefits, and to whom there arises a moral obligation (Phillips, 2003, p. 135). In the translational science setting, the academic KIOs take benefits of the scientific knowledge from subjects and owe moral, ethical, and legal obligations to protect subjects’ rights and best interests. This reciprocal transaction necessitates additional elements to self-regulation of science. In the fairness-based stakeholder theory, subjects are consenting to participate in a “cooperative scheme.” Thus, informed consent functions as a buffer to manage boundaries of knowledge and autonomy between a KIO and subjects. Therefore, the trusteeship governance that encompasses self-regulation of academic KIOs is to be enhanced to integrate fairness-based stakeholder theory of governance. In the translational neuroscience, the advancement of a knowledgeintensive research tool for neurobiology not only increases the precision

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in measuring scientific variants but has a potential to restructure the assessment of consent capacity in the future. Empirical research shows that even MCS subjects can respond to several questions through imaging tools and results are reasonably reproducible. As the measurement is an integral component of governance mechanisms, they can mitigate the governance barriers regarding institutional validity. Thus, the knowledge-intensive research tools help to break translational barriers that manifest themselves in methodology, measurement, and even a standard of institutional validity. Therefore, in the academic KIOs, knowledge-intensive research tools construct a particular knowledge base (see, Starbuck, 1992). This proposition is what exactly North (1990) said that: Technological innovation invokes institutional change.

Then, a next question is what factors shape translational pathways of emerging technologies. In this regard, the next chapter investigates elements that affect research policies, governance, and resultant scientific processes.

Notes 1. Therapeutic misconception exists when individuals do not understand that the defining purpose of clinical research is to produce generalizable knowledge, regardless of whether the subjects enrolled in the trial may potentially benefit from the intervention under study or from other aspects of the clinical trial. See, Henderson et al. (2007). 2. A battery is an intentional and legally unpermitted physical contact with another person, whereas a negligence is the tort of unintended harmful action or omission (Faden & Beauchamp, 1986, pp. 28–29). 3. Serious disease or condition is defined as a disease or condition associated with morbidity that has substantial impact on day-to-day functioning [(21 CFR312.300 (b)]. 4. This does not mean that autonomy is the precondition of responsibility. See, Kühler, & Jelinek. (2013). Also see, Chapter 7. 5. FDA (2014), Informed Consent Information Sheet: Guidance for IRBs, Clinical Investigators, and Sponsors, https://www.fda.gov/ RegulatoryInformation/Guidances/ucm404975.html#requirementsdiscussion.Last, accessed on May 7, 2018. See, Section V. Step 3: Impaired Consent Capacity.

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Truog, R. D. (2017). The United Kingdom sets limits on experimental treatments. JAMA, 318(11), 1001–1002. U.S. Department of Health and Human Studies. (2013/2011). Guidance for institutional review boards, clinical investigators, and sponsors: Exception from informed consent requirements for emergency research. Washington, D.C. Vollman, J., & Winau, R. (1996). Informed consent in human experimentation before the Nuremberg Code. BMJ, 313(7070), 1445–1449. Wager, T. D., Atlas, l. Y., Lindquist, M. A., Roy, M., Woo, C-W., & Kross, E. (2013). An fMRI-based neurological signature of physical pain. The New England of Journal of Medicine, 368, 1388–1397. Weijer, C. (2016, October 25). Consciousness unbound. In T. Cochrane (Moderator). A Neuroethics Seminar. Center for Bioethics, Harvard Medical School, Boston. Weijer, C., Peterson, A., Webster, F., Graham, M., Cruse, D., Fernandez-Espejo, D., … Owen, A. M. (2014). Ethics of neuroimaging after serious brain injury. BMC Medical Ethics, 15, 41. Wertheimer, A. (2012). The fair transaction model of informed consent: An alternative to autonomous authorization, In F. G. Miller (Ed.), The ethical challenges of human research: Selected essays (pp. 291–306). New York: Oxford University Press. Wicks, A. C., Gilbert, D. B., & Freeman, R. E. (1994). A feminist reinterpretation of the stakeholder concept. Business Ethics Quarterly, 4(4), 475–497.

CHAPTER 5

Research Policy and Knowledge-Intensive Organization

1  Introduction Part II examines (i) in what pathways moral stakeholders can affect a legitimate research scope of academic knowledge-intensive organizations (KIOs) and (ii) patterns that academic KIOs address this issue. This book follows Attas (2004)’s definition for moral stakeholders that define them as those who do not necessarily have contractual relations with an organization but represent a group of people who are on morally relevant grounds, and their claims are not necessarily held universally against everyone. Stakeholders are not necessarily familiar with precise distinctions in science, whereas there are always unknown risks in the application of sciences. In the fairness-based stakeholder approach, moral stakeholders are a part of derivative stakeholders because of their ability to influence normative stakeholders. In this framing, the moral stakeholder is different from civic epistemology argued in Jasanoff (2005) since their claims do not always hold universally to society. Moral stakeholders are also differentiated from a socially disobedient activist since it is unclear whether a socially disobedient activist is a moral agent.1 The chapter investigates (i) how research policies reflect different moral values and affect basic research and strategies of translation in academic KIOs; what are the roles of professional regulators (see, Hinings, Muzio, Broschak, & Empson, 2015) in this space, and (ii) in what directions academic KIOs should extend the trusteeship governance.

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Research policies have a function to determine the pathways of scientific innovation (Faulkner, 2009). On the other hand, strategic responses of KIOs to the regulatory constraints have not thoroughly examined. Here, the approaches that shape research policy regimes vary, i.e., transnational approach (Faulkner, 2009), state-centric approach (JustoHanani & Dyan, 2014), value-based institutional approach (Banchoff, 2005), and a cultural exchange approach (Gottweis, Salter, & Waldby, 2009), among others. In this plural context, a problem is what components affect academic KIOs and how. For the above purposes, this chapter conducts the inductive–deductive case analysis (Eisenhardt, 1989) by introducing regulatory constraints to the management and governance framework of KIOs. Bodies of investigation are the US and German cases related to stem cell research. The investigation shows that (a) although moral stakeholders have a global network, the reflection of their values in regulatory policies varies depending on policy legacies (Banchoff, 2005, 2011) and political-salient natures of a subject matter (Justo-Hanani & Dayan, 2014). (b)  Such difference affects a zone that represents jurisdiction of emerging technologies (Barry, 2006; Faulkner, 2009) and resource allocation to critical actors (Banchoff, 2005). (c) It is likely that differences of zones and resource allocation affect the cognitive processing of KIOs. Nonetheless, the US and German academic KIOs thrive the conditions by different research strategies and translation pathways. Academic and biomedical KIOs perceive stem cell research programs see as being of great importance. It is because of the significance of the scientific question, as well as its potential impacts on the future medicine. Nations also have come to have interests in the stem cell research regarding the aging population (Gottweis et al., 2009) since its applications include therapeutics for age-related neurological disorders. Potential of stem cell interventions derives not only from healing damages but also from reconfiguring functions (Webster, Haddad, & Waldby, 2011). This function creates a contrast to the existing therapeutic and surgical interventions that work by masking a cellular process and direct interventions (Lewis, 2013). Therefore, some suggest that it will develop a third arm that complements conventional drugs

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and biologics (Lewis, 2013; Webster, 2013) and the fourth arm that provides technological services for the traditional development of the drug (Kiskinis & Eggan, 2011; Lewis, 2013). It even has a potential to provide alternatives in such fields as diabetes and neurological disorders for an aging population (Deech & Smajdor, 2007; Gottweis et al., 2009) and transplantable organs (Thomson et al., 1998; Wiedemann, Simon, Schicktanz, & Tannert, 2004). Though the stem cell science has developed initially into the applications for bone marrow transplant and reproductive technologies for infertility, the potential use has grown, especially since the Wisconsin invention.2 On the other hand, stem cell interventions have involved many technical and regulatory uncertainties, setting aside from issues of general acceptance. Technical difficulties of scale-up have persisted since early days. Stem cell therapies traditionally developed as autologous medical practices in which small firms manufacture and supply manipulated cells to inject at hospitals (Hospital Model). On the other hand, since the 1998 Wisconsin invention, small science-based firms began to develop biologic drugs regulated by authorities to sell in the commercial market (Pharmaceutical Model) (see, Kiskinis & Eggans, 2011;  Martin, Coveney, Kraft, Brown, & Bath, 2006). The progress of new manufacturing sciences moderates the technical difficulties of scale-up in this domain. Around 2016, transnational pharmaceuticals also began to invest in stem cell research conducted in science-based firms that have a tie to academic hospitals. However, the stem cell research has additional uncertainties that other basic sciences do not have. A research procedure involves the extraction of stem cells from an embryo that is a human organism with a self-directing capacity (George & Tollefsen, 2008). Such research process yielded diversified moral concerns on embryos’ life and ethical reasoning in the regulatory process. Scientists have invented several alternative procedures that do not involve the embryo destruction, such as induced pluripotent stem cells (iPS). Scientific and moral-philosophical investigations have suggested more precise, embryo-nondestructive procedures, such as the altered form of somatic nuclear cell transfer (hereafter, SCNT) and researching amniotic and placental stem cells (Bartlett, 2014; George & Tollefsen, 2008; Kiskinis & Eggan, 2011; Wilmut, Bai, & Taylor, 2015). However, newly emerging issues evoke the early days’ tension between the solidarity for the sick and the moral status of embryos repeatedly in different ways (Holge, 2014). Under such condition, it is hypothetical whether public policy tools by regulatory professionals including bioethicists provide relatively more

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room of self-regulation to academic KIOs or not; Whether KIOs in restrictive policy regimes produce different basic research streams. Also, management studies have not thoroughly examined in what manner academic KIOs explore discovery and translational research strategies in a given condition. The following sections discuss these questions through inductive–deductive approach by investigating literature and public documents (mainly during the 1960s–2000s). The next section investigates how research policies are constructed, in a transnational level and a country level, and what roles the bioethics perform in the research policy formulation. Section three examines the US and German cases by focusing on a Harvard related research institute and Max Planck Institute. Then the chapter discusses the direction of the possible extension of the trusteeship governance and the self-regulation of academic KIOs.

2  Research Policies and Bioethics 2.1   Competing Factors in Research Policies The translational stem cell science initially started in the 1960s as joint research of biomedical scientists in the USA and the UK. The objective is to address infertility. In the 1960s and early 1970s, the German and French academic KIOs also joined the competition over the reproduction-related discoveries (Banchoff, 2011). After this period, two breakthroughs invoked diversified pathways. One is the invention of the Dolly (UK) in 1977. The breeding industry such as the fishery and veterinary fields began to apply mammalian cloning technologies (SCNT) (Lagutina, Fulka, Lazzari, & Galli, 2013). It also paved ways to introduce iPS cells as well as genetic changes in later years. The latter realized as one of so-called gene editing technologies, i.e., clustered, regularly interspaced, short palindromic repeat (CRISPR) technology (Wilmut et al., 2015, p. 7). The second is the invention of Wisconsin University (1998). After this invention, scientists have sought potential of therapeutic applications (therapeutic cloning) which have expanded to apply to several disease fields (Banchoff, 2005; Gottweis, et al., 2009; Kiskinis & Eggan, 2011). Intended applications in the early 2010s include cell replacement therapies for immunodeficiency, visual dis-functions, neurological dis-functions, spinal cord injury, autologous transplantation of cells, tissues, and

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organs, genetic diseases such as autism spectrum disorders and type 1 and type 2 diabetes, heart failures, among others (Kiskinis & Eggan, 2011). On the other hand, human embryonic stem cell research raised diversified moral concerns regarding (i) cloning technologies and (ii) a research process that involves the destruction of embryos. The ultimate concern over the former is the cloning of humans. However, there are a series of variations that are technically tough to judge the morality: Examples include cloning of embryos/human embryonic stem cells (hESC) for research purposes and cloning of embryos/hESC for therapeutic purposes (Resnik, 2002, p. 150). Although the UK regulation permits research cloning, what they allow is the experiment of cells that cannot be a complete human organism (see, Gottweis et al., 2009). Meanwhile, biomedical scientists began to adopt an ethically supported therapeutic cloning (SCNT) (Hurlbut, 2005; George & Tollefsen, 2008, p. 214) for therapeutic purposes (Bartlett, 2014). Regarding the latter, moral objections came from arguments on the moral status and human rights of embryos that originated in human right concerns (Banchoff, 2005, 2011; Gottweis et al., 2009; Jasanoff, 2005) and religious concerns (Banchoff, 2005, 2011). Among them, except for Italy that silently neglected the global debates (Metzler, 2007), the diversified arguments had better be framed as the institutional and cultural components, not as religious concerns (Gottweis & Prainsack, 2006).3 Traditionally, to analyze competing factors that shape policy regimes, political scientists have used the stakeholder-interest perspectives to frame the politics. However, stakeholders’ interests cannot explain issues of morality such as embryos’ “right” (Banchoff, 2005, 2011; Holge, 2014). Not only material “interest-driven” factors but also moral, philosophical values have competed for each other (Banchoff, 2005, 2011) to affect the legal and legitimate scope of life science experiments (Holge, 2014). Based on the above, the following proposition can be set. Proposition 1 Regulatory policies that intersect material- vs. morality-based dimensions shape the range of research activities of KIOs. 2.2   Transnational Efforts vs. State-Centric Efforts There are in general two types of endeavors in addressing regulatory policies of emerging technologies: transnational efforts (Faulkner, 2009) and state-centric efforts (Justo-Hanani & Dayan, 2014). For example,

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transnational, supranational efforts appear in the EU Parliament and EC directives. In this space, the EU is a site of the construction and negotiation of zones (Faulkner, 2009). Here, the “zone” is a term conceptualized as jurisdictional fields of technologies and knowledge that regulators attempt to define (Barry, 2006; Faulkner, 2009). The assumption is that jurisdiction of new technology is not autonomously determined (Faulkner, 2009). For example, if a hazardous particle is spreading through the air, the nature of the risk is transnational. Such transnational nature of the technology requires transnational coordination efforts for the policymaking (Marchant, Abbott, & Allenby, 2013). Such regulatory efforts shape a scope of legitimate research activities within a jurisdiction. On the other hand, the USA and EU also perform state-centric efforts in regulating politically salient issues on transnational risk (Justo-Hanani & Dayan, 2014). By investigating nanotechnology, Justo-Hanani and Dayan (2014) conclude that transnational efforts are limited to technical and politically moderate issues such as testing methods and risk assessment criteria, whereas controls over highly political problems remain in each state authority. Here, political issues include the threshold that triggers a regulatory control and reporting criteria. In this space, the North American continent and the EU continent systematically differ in the use of formal authorities and the private-actor involvement (Justo-Hanani & Dayan, 2014, pp. 172, 174–176; Montpetit & Rouillard, 2008). Based on the above, this study set the following proposition. Proposition 2 When activities of moral stakeholders become politically salient, regulators prioritize state-centered efforts over transnational efforts in defining and controlling zones. There are systematic differences between the USA and the EU in determining the balance of governance modalities. In stem cell research, nations have their interests to manage their future population health. Therefore, this chapter defines zones as nationstates while observing transnational efforts of the EU. 2.3   Institutional Approach Value-Based Institutional Approach In this context, Banchoff proposed a value-based institutional approach that reflects moral values of nations.

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Banchoff’s model consists of (a) a constitutional framework and (b) a policy framework. The former (a) includes norms and practices that define the structure and competencies of states, and the latter (b) encompasses specific laws, regulations, and rulings that limit state activity. In the (a) constitutional framework, the (i) constitutional legacy determines actor constellation in which a centralized (or decentralized) authority executes power (or develop consensus) to (ii) designate individuals who play roles in building research policy regimes. For example, the UK norms of parliamentary sovereignty, legal rules, and electoral system concentrate political authority in the House of Commons and Prime Minister (Banchoff, 2005). In the (b) policy framework, (iii) policy legacy determines (iv) balance of interests by binding up with (i) constitutional legacy. This layer connects state institutions to the balance of interests around a given issue, which generate uneven flows of material benefits (resources), information and network opportunities over time (Banchoff, 2005). Examples include the UK 1990 Human Fertilization and Embryology Act that provided a significant legacy by allowing the research cloning. Contrary to the UK, the USA and Germany have similar policy legacies that restrict the instrumental use of embryos (cf. Banchoff, 2005; George & Tollefsen, 2008, p. 11; Gottweis et al., 2009): • The German Embryo Protection Act of 13 December 1990 (hereafter, EPA: Bundesgesetzblatt, 1990 part I, pp. 2746–2748) institutes several prison terms for efforts to fertilize an egg cell. • The 1871 German Criminal Code4 Section 218 declares intentional abortion illegal. • The US Dickey Amendment bans federal funding for research that involves the creation and destruction of embryos, any organism not protected under 45 CFR 46 (the Dickey-Wicker Amendment of 1996 to the appropriations bills for the Department of Health and Human Services, Labor, and Education, SEC 509). Although Amendment of Article 1 of EPA (Bundesgesetzblatt, 2011, Part I, p. 2228) partially accepted preimplantation genetic diagnosis in specific cases, the original intentions of the laws and the Amendment remain the same. Then, a question arises what factors construct different research policies between the USA and Germany, particularly after the Wisconsin invention.

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In the Banchoff’s model, policy legacies also bind up with interest groups’ competitiveness. At the same time, (vi) legislative debates and judicial interpretation also are working in legitimating the allocation. Here, if this study extends “interest groups” to include “moral stakeholders,” the characteristics of moral stakeholders will affect resource allocation by connecting value-based issues to material matters. As the court is in principle neutral in the judgment, moral stakeholders will influence legislative debates that legitimate the resource allocation. Therefore, this study investigates the behaviors of moral stakeholders regarding the impact on the scientific autonomy. Proposition 1′  The justifications of science activities and resource allocation to critical actors (including academic KIOs) are shaped by policy legacies, legislative debates by policymakers and society, and judicial interpretations. Moral stakeholders will affect legislative debates in this space. Cultural Exchange Approach Related to the determinants of the US and German research governance, subject matters are the following factors: (i) the use of official authority and the private-actor involvement; (ii) the nature and reasoning of moral stakeholders. Regarding the EU system, the EU transnational efforts and the state-centered efforts interact with each other. Under this condition, Gottweis et al. (2009) focus on what they call “cultural exchange.” The EU has two-tier zones regarding the stem cell research: the EU zone (Faulkner, 2009) and the state zones. The EU divided the research process into technological components that reflect state-specific cultural values.5 Then, the EU parliament negotiated with states regarding the EU-based policies and the country-specific policies (Gottweis et al., 2009). What is distinct for the EU based regulations is the Article 6(1) of Directive 98/44/EC of the European Parliament and of the Council of 6 July 1998 on the legal protection of biotechnological inventions. It prohibits patenting of inventions where their commercial exploitation would be contrary to odre public (public-interests test)6 or morality. The Article 6(2) itemizes examples of such inventions that include uses of human embryos for industrial and commercial purposes (Art. 6(2)(c)). The EU Sixth and Seventh Framework Programme do not fund stem cell research that aims to alter human genetic stock and research cloning.

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Although the US laws do not articulate the basis of odre public, the USA has adopted the principle-based bioethics in the regulatory policymaking. The components that the US Federal Funding Programs prohibit and the EU Framework Programmes prohibit are almost the same. On the other hand, the scope of patentability notably differs (McGee & Banger, 2002; Golden, 2010; Gottweis et al., 2009). This difference may stem from the private-sector involvement as a legitimate funding source for science. Therefore, this study investigates intellectual property policies and underlying theories. 2.4   Intellectual Property Policies and Humans’ Biological Cells In general, robust regimes of intellectual property policies stimulate science and innovation by expanding downstream markets (see, Vakili & McGahan, 2016; Court of Justice of the EU, 16 June 2005, Case No. 45603; ALL European Academies, 2011). As a legal property represents relations (Hohfeld, 1913, p. 30; Munzer, 1990), intellectual property policies work as a governance mechanism that defines rights, obligations, and other relationships (Munzer, 1990). On the other hand, from the property law viewpoints, humans’ biological cells/tissues/organs should not follow the market-based transactions (Magnus, Caplan, & McGee, 2002; Munzer, 1990). Property law has provided a foundation of bioethics regarding humans’ biological cells/tissues/organs. In this domain, stem cell research raises unique morality concerns over intellectual property regimes. In general, the property law has provided greater protection with individuals by granting them with a limited scope of property rights in their body parts (Andrews & Nelkin, 2002, pp. 209–211, 215; Munzer, 1990, pp. 44–45). After a series of legal trials, by 1995, the US court acknowledged that tissue outside of a person’s body could be considered as property, which provides greater control over how that tissue is used (Andrews & Nelkin, 2002, p. 215). A problem in applying property theory to the body material comes from the use of the analogy of the market exchange on humans. Among stem cell research, hESC has significance in determining the human identity. The hESCs have a potential to grow into humans (George & Tollefsen, 2008). In this regard, cells derived from tumors do not have the same level of personal and legal significance on the human identity as hESCs, since tumors do not grow

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into humans (Ossorio, 2002, 241). Therefore, providing property rights to inventions produced from hESC research raises problems of commodification of life (McGee & Banger, 2002), since it treats humans as a property (Hanson, 2002). Here, the term commodification comes from an economic concept of a commodity whose value is determined by its potential for market exchange (Hanson, 2002). In this regard, the property theory has two streams: a labor theory and an alternative view (Hanson, 2002; Munzer, 1990; Ossorio, 2002, pp. 230–237). In the labor theory, a researcher who removed cells/tissues and transformed them into something useful for scientific or biomedical uses can have a greater claim to ownership of the material. On the other hand, the alternative view concerns over bundles of rights that persons have in their human body parts and whether these rights should be regarded as property rights such as powers to exclude and transfer (Hanson, 2002; Munzer, 1990, p. 25; Ossorio, 2002, pp. 225, 230– 237). Giving people a property right in their body tissue would allow them to have greater control over how scientists use that tissue. In applying these research streams to the legal practice, the threshold elements (Munzer, 1990, p. 45) are whether the body parts are integral parts of a human person and species’ identity (Hanson, 2002; Ossorio, 2002). The alternative view allows patenting of human body parts that are remote from the personhood identity, such as inventions that stem from tumor cells. Thus, the debates on the moral status of embryos and commodification of life are the two sides of the same coin: The former occurred in the legislative debates and the latter, related to the intellectual property regimes. In this regard, the patentability of stem cell research in the USA and the EU are closing each other by going through different reasoning: The USA approaches issues through technical interpretation of obviousness (Golden, 2010; Seige & Stephens, 2002; USPTO, 2014). In the USA, USPTO issued patent-subject-matter eligibility criteria for nature-based products (December 16, 2014). The subject matter of investigation is whether a claim of a nature-made product (a cell) has markedly different characteristics from any naturally occurring counterparts in their natural state (USPTO, 2014). By using examples of claims on an isolated human-made pacemaker stem cell (that expresses a specific marker: pp. 13–16), it explains how the specification of phenotypically different cells makes a difference in the eligibility. On the other hand, in the EU, problems have been the interpretation of odre public and human embryonic stem “cells.” Academic life science and humanity societies began

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to claim the refinement of the requirement of Directive Art. 6(2)c, an itemized list based on odre public. The reason is that “the organism that has “in itself” the inherent capacity of developing into a human being” and “the mere fact the organism commences a process of development” should be differentiated (ALL European Academies, 2017, p. 3). A question remains whether the legal and ethical variations in justification affect behaviors of academic KIOs and subsequent translational pathways. Proposition 4 Though the property right theories concern over universal morality regarding intellectual property policies of living cells/ tissues/organs, the patentability varies among regions and countries depending on their institutional designs of governing relations. The above represents the condition where, despite Natural Law, positive laws vary among nations. As the Natural Law is the precept of collaboration (see, Finnis, 1980), the theoretical background of diversified governing relations should be sought. 2.5   Moral Stakeholders and Civic Epistemology In parallel to the regulatory policies, civic epistemology (Jasanoff, 2005) manifests itself in several forms: public debates, social movements and coalitions, and emotional discourses. As far as reviewing literature, a country where civic epistemology had the most substantial impact on the stem cell research is Germany. For example, during the absence of formal regulations in the 1980s, Bundesaerztekrammer (National Chamber of Doctors) issued guidelines to regulate research with embryos defined as organisms from fertilization to implantation and made provisions for research cloning under certain special conditions (Iliadou, as cited in Gottweis et al., 2009). It invoked a severe civic objection. In this context, two governance models emerged: self-regulation of biomedical scientists, and a formal law (Gottweis et al., 2009). Here, the law-based governance assumes that citizens can use government as their representatives to regulate science (see, Resnik, 2008). To support the formal law model, a radical civil movement, feminist groups, and conservative groups made a coalition to confront with the biomedical society and urged the government to protect embryos (Gottweis et al., 2009). In this manner, the diversified German groups

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evolved into a “civic epistemology” (Jasanoff, 2005) and confronted with the self-regulation of the academic and scientific KIOs. The ethical reasoning of this movement has originated in the secular reasoning of the respect of “human right” (Gottweis & Prainsack, 2006). Under this condition, an emotional discourse also occurred (Banchoff, 2011). In this regard, Aristotle’s philosophy claims that an emotion (ethos) has an equivalent position as a reason (logos) (Gottweis & Prainsack, 2006). This account of emotion appears to be illogical from managerial economics viewpoints. However, according to Gottweis et al. (2009), a deprivation from marginalized women occurred in science clusters in the liberal stem cell research regimes. Considering that the marginalized population does not necessarily have a communication channel with policymakers, a device to advocate themselves may be an emotion. In this sense, the German moral stakeholders shifted their status from moral stakeholders (derivative stakeholders) to a legitimate normative stakeholder that represents societal values. Proposition 5 Moral stakeholders have a potential to shift their status from a derivative stakeholder to a normative stakeholder by developing and representing societal values. Then, a question arises why the commodification arguments did not gain the same status in the US society while the US legal scholars have long investigated the commodification of life? According to S. Jasanoff (2017, November 2),7 there are two forms of governance: right-based governance and risk-based one. In the USA, there has been a steady movement of reproductive right (see, Deech & Smajdor, 2007; Gottweis et al., 2009; Jasanoff, 2011). The attention to the reproductive right emerged both in scholarly bioethics (Merrick & Blank, 2003) and public debates (Jasanoff, 2011). The claim of the reproductive right stemmed from the infertility population, phsically challenged population, and historically marginalized population (Deech & Smajdor, 2007; Jasanoff, 2011). Although reproductive cloning, therapeutic cloning, and research cloning are different, a civic framing may not separate them. On the other hand, though German scientists were competing in the stem-cell-based reproductive technology in the 1960s (Banchoff, 2011), the reproductive right gained little attention in Germany (Gottweis et al., 2009; see also, Helfferich, 2013). In extending “interest groups” of Banchoff’s framework to include moral stakeholders, the US interest groups compete for each other

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whereas they are more coordinated in Germany. George and Tollefsen (2008) describe the US policymakers’ decision in 2008 that liberalized the research policy of hESC. They balanced the competing values of reproductive right and harms to embryos. At that time, “cells” and an organism that grows into humans were not necessarily differentiated. Factors That Affect Regulatory Professionals’ Decisions The above tells that the nature of moral stakeholders shaped different pathways of regulating emerging stem cell research. The health problems associated with aging and degenerative conditions, such as Parkinson’s disease, Alzheimer’s disease, and heart failures, are partly derived from the issues of poorly regenerating tissue. Thus, states and global society perceive stem cell research as having a potential to address such illness (Deech & Smajdor, 2007; King, Mulligan, & Stansfield, 2014/2013). Therefore, the nations’ interests in stem cell research are similar. The USA and Germany had the similar policy legacies and started from the same restrictive research regime. Though the USA slightly liberalized the restriction in the early 1980s, the US Federal funding agencies and the EU Framework Programme had similar funding policies. They permit funding hESC research when researchers use surplus invitro-fertilization (IVF) embryos that were initially intended for reproductive uses. Here, the couple’s consent is a binding condition. The underlying assumption is that (surplus IVF) embryos which are destined to die might better live their lives by sacrificing themselves to promote scientific understanding and contribute to the future patients (French bioethicist and scientist at French Bioethics Council. See, Banchoff, 2011). On the other hand, since the Wisconsin invention, the US stem cell research policies began to evolve by distinguishing funding mechanisms: The federal funding that follows the Dickey Amendment, and nonfederal funding including states’ and private funding that liberalizes stem cell policies (Banchoff, 2011). The states of California and Massachusetts prepared their funding policies legitimated by the US federalism. The federalism is a governmental and legislative structure that allows states to experiment in line with the US Federal policies under certain conditions (Lijphart, 2012). The nonfederal funding, in general, allows therapeutic cloning under a particular restriction. Under this condition, the US

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academic KIOs and R&D units of major TNCs built their codes of conducts regarding stem cell research.8 The condition suggests a possibility that the nature of interest groups (moral stakeholders vs. civic epistemology) shape different pathways of research in academic KIOs.

3  Discovery Pathways This section investigates discovery and translational pathways of the stem cell research by controlling related factors that may affect the channels. The observation countries are the USA and Germany where societal debates on stem cell research were most intense (Wiedemann et al., 2004). Observations of academic KIOs include a Harvard affiliated research institute in the USA and Max Planck Institute (Max-PlanckGesellschaft) in Germany. The reasons for the case selection is that (i) Harvard was one of a few institutions globally that could conduct SCNT for humans in the 2000s. (ii) In Max Plank, there was a distinct scholar who persistently argued for the significance of the stem cell science in spite of the societal confrontation (see, Banchoff, 2011). Also, the concept of SCNT initially appeared in Germany in the early twentieth century. Observations of disease fields include diabetes, coronary heart disease, neurological disorders, and organ regeneration/transplantation (see, Wiedemann et al., 2004). The reason for selecting these areas is that, contrary to the autologous stem cell plantation, these fields became promising and realistic applications after the Wisconsin invention. This section conducts a structured case analysis to set propositions by using published documents. 3.1   Basic Research in the US and German KIOs Then, a question is how research policies that reflect moral values affect basic and translational research of academic KIOs.9 In this sphere, the categorization tells a cognitive process of framing (see, Frumkina & Mikhejev, 1993). In following Frumkina and Mikhejev (1993, p. 4), categorization is (a) the process of decision making aimed to subsume words and objects under some group according to their relatedness; (b) the result of such an assignment.10 In this procedure, the meaning itself is a natural outcome of the categorization processes (Frumkina & Mikhejev, 1993).

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Then, a question is whether academic KIOs exposed in different environmental constraints will categorize research areas differently. Also, a system of organizational decision process also affects the behaviors of members by shaping their filters (Pfeffer & Salancik, 1978, p. 75; Trispas, 2009). Then, it is possible that the system of framing, in turn, affect the behaviors of individual scientists. Then, by applying Proposition 1′, it is hypothetical whether academic KIOs will categorize dimensions of basic and translational research related to the legitimacies that society attached to the investigation. Empirical Observation In this regard, the comparison between Max Planck and Harvard reveals a different cognitive process in organizing basic research. Researchers in Max Planck conduct the stem cell research in departments that are categorized as follows: 1. Department of tissue morphogenesis 2. Department of the cell and developmental biology 3. Department of vascular cell biology There is also a research project conducted by a group, i.e., Stem cell and regeneration group. • Lessons in regeneration-learning from the flatworm In Harvard, stem cell research is conducted in various laboratories dispersed in several schools and departments. They are unified under the umbrella of Harvard Department of Stem Cell and Regenerative Biology (HSCRB). The labs in the HSCRB have both of education as well as research programs. An institution specialized in research is the Harvard Stem Cell Institute (HSCI). The research areas of HSCI are categorized in terms of diseases. The labeling of categories in HSCRB and HSCI is described as follows: The research areas conducted in the laboratories of HSCRB: 1. Aging and a stem cell 2. Cancer 3. Cardiovascular development and metabolic disease 4. Directed differentiation 5. Epigenetics 6. Epithelial development and disease

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7. Hematopoietic development and disease 8. Immune tolerance 9. Kidney development and repair 10. Nervous system development and disease 11. Nervous system development and regeneration 12. Noncoding RNA 13. Nuclear reprogramming 14. Pancreas development and diabetes 15. Reprogramming 16. Skin and hair follicle development and regeneration 17. Tissue homeostasis The disease programs conducted in HSCI: 1. Blood program 2. Cancer program 3. Cardiovascular program 4. Diabetes program 5. Kidney program 6. Nervous system disease program 7. Skin program A rough comparison of the categorization shows a clear distinction between the two. (i) Max plank frames research areas through biological mechanisms by using words of “cells” and “tissues.” (ii) Another distinct feature is the stick to the regeneration in Max Planck. According to the Regeneration Group, a human has a limit in studying regeneration. Contrary to humans, flatworms have a limitless ability to regenerate itself. Thus, they built a particular research group to research flatworm regeneration. Please note that regeneration is the crucial function that nation-states have interests in stem cell research (see, ALL European Academies, 2011; Deech & Smajdor, 2007; King et al., 2014/2013). Regarding the individual scientists’ behaviors, they describe their research activities and topics by emphasizing iPS and its manufacturing at least in 2017. Please note that there were two barriers to the application of stem cell research: One is the ethics barrier discussed above and the other is a technical barrier to scaling up in manufacturing. In May 2018, both concerns seem to have been mitigated even regarding hESC research. While the categorization of the department emphasizes the basic research, scientists are also addressing translation of discoveries to animal studies.

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On the other hand, HSCI categorizes stem cell research through disease applications. The disease categorization also dominates in HSCRB. After the disease categorization, research questions on biological mechanisms follow in web-based public disclosure. Examples of matter include the one such as “What are the specific molecular regulators of blood stem cell self-renewal?”11 Such questions are a part of public education. Such categorization reflects the regulatory and societal justification of research funding in the USA. On the other hand, the influence over individual scientists’ behaviors is not necessarily, and further analysis is in need. The categorization represents meanings that organizations attach to their activities. Although further evidence is required, the above logic of cognitive framing extends the Proposition 1′ as follows: Proposition 1″  The regulatory and societal justifications of scientific research affect the cognitive process of framing science in academic KIOs. Policy legacies and societal debates shape these justifications with a catalyst of normative and derivative stakeholders. The next problem is in what manner subsequent translational pathways reflect such difference. 3.2   Translational Pathways in the US and German KIOs In Max Planck, scientists conduct translational research in alliance with separate entities, such as Lead Discovery Center (LDC). It is a spin-off organization from Max Planck Innovation (a technology transfer organization of Max Planck). Scientists of Max Planck also participate in external translational science organizations, such as Brandenburg Center for Regenerative Medicine, Berlin, and the Center for Advanced Regenerative Engineering (CARE) (Federal Ministry of Education and Research, 2010; Max Planck Gesellschaft, 2016). Such structure of research conduct implies that Max Planck concentrates on basic research while developing loosely coupled ties with translational science-specific KIOs. The research agenda of translational science consists of the following: The applications of iPS such as • Replacement of tissue and damaged organ, • metabolic, • brain (regeneration of brain cells as a result of heart failures and Parkinson’s disease), and • heart disease;

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Prediction of side effects and toxicity of drug candidates (service model). The further observation reveals that the application studies focus on the following fundamental barriers of the stem cell research. • Addressing ethics barriers: Scientists can manufacture stem cells from somatic cells and do not rely on the harvesting of embryonic stem cells. • Addressing technical barriers to the scale-up: The iPS procedure allows for the manufacture of large quantities of stem cells (Max Planck, 2016). In this manner, scientists at Max Planck have been addressing the moral-based problem by science. The above applications also correspond to the applications that are argued globally. Here, () represents research in Max Planck: • Diabetes (metabolic), • Coronary heart disease (heart disease), • Neurological disorders (regeneration of brain cells and Parkinson’s disease), and • Organ regeneration (replacement of tissue and damaged organ). On the other hand, a direct alliance with external private entities seems to be rare. One of few exceptional cases is a strategic alliance between Max Planck Institute of Biochemistry, one of the largest institutes within Max Planck, and Vichem Chemie Research, a Hungarian-German biotechnology firm. When they built an alliance in 1992, the predecessor of Vichem Chemie Research, Biosignal, was a university spin-off specialized in developing the application of a novel signaling transduction inhibitory compound (Vichem Chemie Research, 2010; Miliaras, 2014. Regarding the compound, see, Tejeda et al., 2008). Therefore, this case is an extension of the academic–academic alliance. In the case of HSCRB and HSCI, scientists translate discoveries to organizations inside and outside of the university. The university’s catalyst office or the office of technology transfer arranges them. As Harvard has several sorts of affiliated hospitals, the agenda of translational science includes the traditional autologous model for the hospital use (hospital model). Thus, the general list comprises: • Bone marrow transplant • Hematopoietic stem cell

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Agendas in 2017 include: • organs for transplantation, organ repair • congenital disability • heart failure • diabetes • neurological disorders The relations with the anticipated applications in a global context are as follows. Here, () represents research in HSCI in 2017 and 2018: • Pancreas development and diabetes [Diabetes (2017 and 2018)], • Cardiovascular development and metabolic disease [heart failure (2017); heart diseases (2018)], • Nervous system development and disease; Nervous system development and regeneration [Neurological disorders (2017); ALS, Alzheimer disease, Parkinson’s disease, multiple sclerosis (2018)], and • Kidney development and repair [Organ transplant/organ repair (2017); Kidney diseases (2018)]. Regarding alliances with private entities: HSCI is active in engaging partnerships with private entities both in Boston/Cambridge area and foreign sites. The coalition became particularly active since 2012. The alliance with German companies occupies a significant portion in the early days. In 2012, Jansen Pharmaceuticals (a biologics unit of J & J) and Evotec AG (Germany) built a strategic alliance on diabetes by basing on HSCI discoveries. This alliance includes licensing a portfolio of small molecules and biologics from Harvard, Howard Hugh Medical Institute, and HSCI to Evotec and Jansen. In 2013, HSCI and Evotec, AG built a strategic alliance in 2013 to identify compounds that prevent or slow down the loss of motor neurons. The loss of motor neurons is a characteristic of the ALS (the human disease amyotrophic lateral sclerosis). It is to leverage human motor neuron assays (that HSCI and HSCBR developed from ALS patient-derived iPS cells) by using Evotec’s high-throughput screening infrastructure and expertise. The alliance agrees to expand the scope of the partnership model to other HSCI scientists and Harvard scientists to drive the development of drug candidates.12

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In 2016, Boehringer Ingelheim (Germany) established a research collaboration with HSCI’s Harvard Fibrosis Network to discover new ways of treating fibrotic diseases, chronic kidney disease and nonalcoholic steatohepatitis. To leverage this alliance, HSCI is working to unite and coordinate university’s schools and affiliated hospitals. The sponsor of this project is Boehringer Ingelheim under the governance of joint steering committee. In 2017, GSK and HSCI entered into a five-year alliance agreement in stem cell science to hasten the therapeutic development of multiple diseases. In this domain, GSK invests in stem cell research at Harvard and affiliated hospitals. The focused disease fields encompass: • neurological, • cardiac, • cancer, • diabetes, • musculoskeletal, and • obesity. It also funds seed grants at HSCI. The collaboration includes human resource exchange programs both for researchers and stuff. The alliance is governed by a joint steering committee that consists of HSCI and GSK scientists and managers. Different or Coherent Pathways This study identifies the different pathways as follows. In Max Planck, it is likely that scientists spent energy to overcome ethics and technical concerns through science. The research alliance is mainly an extension of academic–academic partnerships. They conduct translational science in separate entities specialized in translational research. As scientists almost completed in overcoming ethical dilemmas and technical uncertainty, it is future research to investigate the following paths. In HSCI, early days alliances were mainly with German biotechs. It has an effect to complement each other’s resources and competence and thus span the boundary of knowledge. The pattern of alliance contracts is quite professional beyond the academics. The HSCI has leveraged the agreed relations and expanded the partnership model to other HSCI scientists

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and Harvard scientists. As stated in the next chapter, such type of agreement is critical in making co-specialization successful (see, Doz & Hamel, 1998). From the observation, the Proposition 1″ should be extended to add the following: Proposition 6  The justifications of science by normative and derivative stakeholders affect the cognitive framing of scientific research in academic KIOs through regulatory policies and direct interactions. Even so, the institutional complementarity is working in a global research alliance. Table 1 summarizes the comparison between HSCI and Max Planck Institute in 2017. Table 1  Comparison on cognitive framing of stem cell science and translation paths

Cognitive processing of framing basic research Relations with institutional framework Policy legacy Legislative debates

HSCI in 2017

Max Planck Institute in 2017

Framing from diseases

Framing from biological mechanisms

Including diseases that relate to reproductive justice Emphasizing disease applications Emphasizing patient-derived iPS (patient-centeredness)

Addressing ethical and technical barriers Stick to iPS

Possible responses to moral stakeholders (or civic epistemology) Relations with anticipated applications, globally Diabetes Diabetes Coronary heart diseases Heart failure Neurological disorders Neurological disorders Organ regeneration Devices for translation

Researching cells and tissues (emphasizing biological mechanisms)

Metabolic Heart disease Regeneration of brain cells and Parkinson’s disease Organs for transplantation, Replacement of tissues and organ repair damaged organ Hospital model; Collaboration with indeStrategic alliance with private pendent (academic, public, entities including German bio- or scientific) KIOs specialtech and European-based TNCs ized for translation

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4  Conclusions This chapter investigates (i) how research policies reflect different moral values and affect basic research and strategies of translational research in academic KIOs. What are the roles of professional regulators (see, Hinings et al., 2015) in this space, and (ii) in what directions academic KIOs should extend the trusteeship governance. The body of investigation is stem cell research. The analysis shows that (i) (a) although moral stakeholders have a global network, the reflection of their values in regulatory policies varies depending on policy legacies (Banchoff, 2005, 2011) and the political salience of a subject matter (Justo-Hanani & Dayan, 2014). In addition to them, the nature of moral stakeholders affects the self-regulation of academic KIOs. In the USA, several moral stakeholders compete for each other, some made a coalition with academic KIOs, and paved the way for the Federal government to experimentally permit some states to loosen the restriction under the justification of Federalism. In Germany, they developed into a civic epistemology and pushed the government to limit the self-regulation of science formally. (b) Such variation affects a zone that represents a jurisdiction of emerging technologies (Barry, 2006; Faulkner, 2009) and resource allocation to critical actors (Banchoff, 2005). The methodologies of regulators also differ. In the USA, they use principled bioethics to mitigate conflicts. They moderate tensions through technical interpretations of a subject matter. In the EU, while respecting self-regulation of academic KIOs, they actively use civic epistemology to anticipate potential pitfalls. One of the recent trends is a coordinated action of academics to refine the interpretation of odre public. They request the EU parliament to specify the threshold element of “cells” and “an organism that has a potential to grow into humans.” (ii) It is likely that zones and resource allocations affect the cognitive processing of KIOs. One of the US eminent academic KIO structures their organization units of stem cell research by categorizing them through disease fields. On the other hand, one of German eminent academic KIOs structures its unit through biological mechanisms. Such difference reflects the societal justification of stem cell research. As the categorization demonstrates the cognitive process, it is likely that the societal rationale of academic sciences partly affects the organizational design of KIOs.

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In research conduct, academic KIOs should anticipate stakes of moral stakeholders as derivative stakeholders. They have a potential to affect normative stakeholders such as regulatory policymakers and societal debates in defining zones and legitimate scope of emerging technologies. When activities of moral stakeholders become politically salient, countries tend to prioritize state-centered efforts over transnational efforts. Under this condition, regulatory policies of emerging technologies vary by reflecting policy legacies, legislative debates, and the nations’ willingness to use bioethics principles. Among them, moral stakeholders and civic epistemology affect the legislative debates. Contrary to the variation of regulatory policies, the property right theories concern over ownership and universal morality regarding intellectual property policies of biological cells/tissues/organs. The US and EU intellectual property regimes are closing each other from different pathways. The USPTO refines the technical interpretation of obviousness in nature-made products (including stem cell inventions); In the EU, ALL EU Academies are requesting the refinement of the definition of odre public in distinguishing “cells” and “organism that grow into humans.” The regulatory and societal justifications of scientific research affect the cognitive process of framing science in academic KIOs. In this space, it is possible that policy legacies and normative and derivative stakeholders create the rationale. Translation pathways also vary depending on the societal justifications and organizational designs. The comparative analysis of the eminent US and German academic KIOs supports the propositions. This direction is inverse to the attentional process of organizations proposed in Pfeffer and Salancik (1978). In the social control of organizations, the attentional process of organizations creates an environment by filtering environmental cues (Pfeffer & Salancik, 1978; Trispas, 2009). In this regard, future research should investigate the interaction between the cognitive processing and the attentional process. Here, resultant applications are almost same as the global interests. Restrictive research policies are not necessarily working negatively to German scientists. Case observations of the translational pathways of the eminent US and German academic KIOs show that institutional complementarity is likely to be working at the global level. The further research should add evidence regarding this point.

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Notes







1. Phillips (2003) argues that, if social activist groups indeed represent claims of community moral concerns, the organization should engage in discourse with that groups due to the stakeholder obligations to the local community. The moral stakeholders in this chapter are still differentiated from such groups, since community norms may also be divided. 2. In the publication, Thomson, A. in Wisconsin University claimed a potentially limitless source of cells for drug discovery and transplantation therapies. See, Thomson et al. (1998). 3. At first, German arguments appeared as religious concerns. However, if they were religious concerns, it is contradictory that the German regulation permits the experiment with imported embryos. 4. http://www.gesetze-im-internet.de. Criminal Code Chapter  1 First Title Section 218 in the version promulgated on 13 November 1998, Budesgesetzblatt Nr. 75, p. 3322. https://www.bgbl.de/eaver/bgbl. 5.  Gotweis et al. (2009) categorize cultural components into (i) embryo source [such as in vitro-fertilization (IVF) supernumerary], (ii) embryo creation date, (iii) hESC line origin (such as imported), (iv) hESC line creation date, (v) hESC line research purpose. 6. Also see, the Court of Justice of the EU Decision on 18 October 2011 (Case 34/10). 7.  According to S. Jasanoff, the risk-based governance encompasses the considerations on “harm to others,” one of the threshold elements of self-regulation of science. See, Resnik (2008). 8. Disclosure at websites of major pharmaceutical TNCs. 9.  The observation is collected at the following websites. http://www. mpi-muenster.mpg.de/96828/departments. https://hscrb.harvard. edu/Research. https://hsci.harvard.edu/disease-programs. Accessed on September 7, 2017. 10. According to Frumkina and Mikhejev (1993), classification is the experimental procedures and categorization is used to speak about cognitive process as par excellence. 11.  https://hsci.harvard.edu/disease-programs. 12.  https://www.evotec.com/archive/en/Press-releases/2013/Evotec-andHarvard-Stem-Cell-Institute-form-CureMN-collaboration-to-advanceALS-research/2435/1.

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CHAPTER 6

New Governance Models for Discoveries of Vaccine Science

1  Introduction This chapter examines governance mechanisms of research alliances that take forms of private–public partnerships (PPPs) and public–public partnerships. It applies the trusteeship governance to the partnerships of knowledgeintensive organizations (KIOs) and seeks directions to modify the model. In this regard, this chapter examines Argandona (1998)’s stakeholder theory since it positively positions the involvement of private incentives in creating a common good. Here, the common good is a concept that exists in the free market and liberal regime. This chapter focuses explicitly on vaccine science, discoveries, and capacity of translation. This chapter identifies unbalanced dependence as a threat to undermine different norms and responsibilities in KIO alliances. There are a series of scholarly management articles that address dependence on partnerships, mainly that occurs in the co-specialization alliance (Doz & Hamel, 1998). However, previous literature examines mostly partnerships between private firms and has not thoroughly addressed those that involve academic or multilateral KIOs. It is often tough to adopt firms’ decision patterns in KIOs. As far as reviewing literature (Ghobadian, Gallear, Viney, & O’Regan, 2004, among others), there are inconsistencies between public KIOs and private firms regarding the internal process of decision-making. Therefore, this chapter seeks potentially successful patterns and governance of the research alliances of KIOs.

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A body of investigation is the new translational approach to vaccine science conducted by WHO. This study categorizes multilateral organizations including WHO into KIOs whose main production factors and goods offer are specialized knowledge. The organizational structure also corresponds to the management framework of KIOs in which no outside ownership (von Nordenflycht, 2010) involves. This study identifies the tuberculosis (TB) vaccine (Cayabyab, Macovei, & Campos-Nelo, 2012), particularly those for multi-drug resistant tuberculosis (MDR-TB) as an understudied field. In spite of the scientific progress of the ability to sequence the entire genomes of bacteria (Rappuoli, Blank, & Lambert, 2011), and in spite of unmet needs, an approved TB vaccine is only one since the early twentieth century (Cayabyab et al., 2012; Prabowo et al., 2013). Based on this fact, this chapter examines whether the private-sector involvement undermined the WHO’s unique responsibilities in controlling pathogen. The research questions consist of the following: (i) Why KIOs take forms of partnerships in research alliances? What are potential threats that make specific vaccine science understudied in spite of the ­advancement of sequencing the entire genomes of bacteria? (ii) To what directions the study should modify trusteeship governance model for global health KIOs? In addressing these questions, this chapter examines the case of the WHO Multi-Drug Resistant Tuberculosis (MDR-TB) initiative. The study organizes this chapter as follows: First, it examines the underlying logic of forming partnerships, the involvement of academic ­ KIOs, and governance models that correspond to the alliances. A given condition is the WHO’s core responsibilities for establishing global norms and standards (Buse & Walt, 2002; Reich, 2002b). The next section applies the management and governance framework in the previous section to the new vaccine development of KIOs. It identifies constraints in advancing sciences for the new TB vaccines and analyzes causes of restrictions from the management theory viewpoints. The last section investigates directions of enhancing the trusteeship governance by integrating common good-based stakeholder theory. Management studies tend to associate the common good with global public goods that have characteristics of being non-excludable, non-rival, and significantly positive externalities (Chen, Evans, & Cash, 1999; Stiglitz, 1999). However, according to Argandona, public goods are just a partial good. By referring to common good-based stakeholder theory, this chapter defines global common good as those beyond global public goods: Finally, this chapter suggests a partial detachment of rights (see,

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Palfrey, 2017) that derived from the intellectual property while retaining certain rights and ownership. Such directions require controlling a bundle of property rights (Roberts, Breitenstein, & Roberts, 2002) that are associated with agency-based governance while maintaining the ownership. The next chapter examines the detail regarding the agency-based management.

2  Research Alliances of Multilateral ­­ Knowledge-Intensive Organizations 2.1   WHO as a KIO WHO is expected to produce specialized knowledge about global health. In analyzing contingencies between governance and global health alliances, this section first reviews core functions and responsibilities of WHO. The core functions and responsibilities of WHO are summarized as follows (Buse & Walt, 2002; Reich, 2002b; Stevenson, 2016; WHO, 2011): i. Acting as the world’s health conscience (such as human rights and equity) and providing a moral framework and agenda for health; ii. Establishing global norms and standards; iii. Promoting and protecting the global commons (that encompass the creation of transnational public goods such as R&D capacity (see, WHO, 2011), information dissemination, and control of cross-border externalities. Here, transnational externalities include environmental risks, a spread of pathogens, and trade of illegal substances); and iv.  Providing strategic and supportive cooperation to KIOs at the country level (for example, see WHO, 2016). Among them, the responsibility that directly relates to the formation of PPPs is the promotion and protection of the global commons including creating transnational public goods. It requires a research investment and coordinated actions with state governments (Smith, 2009) to fulfill this responsibility (Buse & Walt, 2002; Marchant & Wallach, 2013, p. 137). Such requirements necessitate WHO to build a common expectation with state governments, and mobilize state actions and resources that go through internal and external processes of consensus (Buse & Walt, 2002; Pfeffer & Salancik, 1978, p. 147). In these processes, the private

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actors’ involvement enhances the access to additional knowledge and resources (see, Klein, Mahoney, McGahan, & Petlis, 2010) under the nations’ budget scarcity (Mahoney, Pablos-Mendez, & Ramachandran, 2004; Sachs, 2001). However, it also bears a risk of subordination of norms and standards of global health to private incentives (Buse & Walt, 2002; Stevenson, 2016; Williams & Rushton, 2011). These motivations and concerns over the private involvement are the same as the industry collaboration at academic KIOs. Therefore, it is relevant to analyze the threshold elements of subordination and possible alternatives in the governance framework of KIOs. Organizational Background Next is to examine the context that WHO engaged in the PPPs. Literature of public management categorizes WHO as those that have the following structure: (a) public entities with no (outside) ownership rights, (b) those with managed competition, or those without competition (Ghobadian et al., 2004, p. 294; also see, Rangan, Samii, & van Wassenhove, 2006). These characterizations correspond to the organizational structure of KIOs (see, von Nordenflycht, 2010). WHO is a public organization established as a specialized agency of the United Nations (see, Chorev, 2012). It is a public good towards which no one claims ownership. As mentioned earlier, WHO has a unique responsibility to act as the world’s health conscience. This norm is equivalent to academic autonomy. WHO has the autonomy to e­stablish norms and standards with professional ethics, even if it requires the coordinated action with state governments. This unique responsibility also constructs the identity of WHO. Therefore, this organization has boundaries of autonomy and identity similar to academic KIOs. Relations with the New Public Management Reforms WHO has experienced discourses similar to academic KIOs. They are mainly due to the new public management (NPM) reforms. They are social demands for inserting new market-based principles and competition to the public administration. The reforms also motivated public KIOs to use a strong financial position of the private sector (Buse & Walt, 2002; Christensen, 2011; Ghobadian et al., 2004). Under the condition, the external environment that can critically affect boundaries of WHO is the entire system of interconnected

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individuals and organizations that relate to one another and WHO through the several types of transactions (see, Pfeffer & Salancik, 1978, p. 63). Strategic Partnerships Such environmental change motivated multilateral KIOs to adopt PPPs in addressing global health issues. The UN institutionalized Millennium Development Goals (MDG) that provided legitimacy to build PPPs. It also offered the equal levels of responsibilities to private entities in developing and managing global health strategies (Stevenson, 2016). Though the aim is to reduce global health disparities (Palfrey, 2017; Reich, 2002b; Stevenson, 2016, pp. 122, 126), the concerns over the private involvement have persisted (Chorev, 2012). The concerns include those over the biased selection of research agenda, similar to academic KIOs. The empirical evidence is mixed. Without a donation program, it would have been impossible to disseminate children’s essential vaccines, globally (Reich, 2002a). On the other hand, empirical investigations also support the concerns over the private involvement (Buse & Walt, 2002; Stevenson, 2016). For example, Buse and Walt (2000) investigate governance of global PPPs by referring to the good-governance criteria of World Bank (1994). It consists of (a) representative legitimacy, (b) accountability, (c) competency and appropriateness, and (d) respect for due process. According to them, all of the dimensions are affected. From a public sector viewpoint, a problem is when to develop a strategic partnership in the policy delivery area (Ghobadian et al., 2004, p. 294). This problem is consistent with Radder’s concerns over the mixture of public responsibilities and private incentives in academic KIOs (see, Radder, 2010). The problem may be more severe in the multilateral KIOs since, although the PPP is just one of the alliance designs in management theories, it is a governance paradigm (Buse & Harmer, 2007) from the public management perspective. 2.2   Private–Public Partnerships in Management Theories In management studies, previous literature mainly investigates PPPs from a private-sector viewpoint. In this sphere, the objective of PPPs is the joint value creation under the contemporary political, economic, and social forces (Austin, 2000; Barret et al., 2002; Kanter, 1997). Given this objective, subject matters of investigations are as follows.

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a.  the alignment of incentives through organizational designs (Kivleniece & Quelin, 2012; Mahoney et al., 2009; Rangan et al., 2006); b. fair allocation of created values (Kivleniece & Quelin, 2012); and c.  the contingencies with governance mechanisms (Haas, 2010; Kivleniece & Quelin, 2012; Rangan et al., 2006). Traditionally, determinants of alliance designs are opportunity costs of resources and those of governance mechanisms (Rangan et al., 2006; Williamson, 1991). Rangan et al. (2006) extend this account by adding components of “private benefits” and “public benefits from the private transaction.” Kivlenieche and Quelin (2012) further add an element of externalities. In aligning incentives, private actors are motivated to build PPPs when governance costs of other transactional arrangements are high and perceived uncertainty in net benefits is high. Otherwise, they prefer contract in managing relations (Rangan et al., 2006. See also, Hart, 2003; North, 1990). Among partnership designs, they are more likely to build integrative PPPs when (i) the higher the market or technology uncertainty, (ii) the higher degree of positive market externalities, or (iii) the higher the values drawn from public-sector specific knowledge. Otherwise, they prefer parallel designs (Kivleniece & Quelin, 2012). Table 1 summarizes the prior research (Buse & Walt, 2002; Rangan, et al., 2006; Reich, 2002b; Sachs, 2001; Stevenson, 2016) regarding factors that can motivate WHO and private actors to build PPPs. Incentive Alignment in Each Stage of PPPs Austin (2000) divides a continuum of cross-sector partnerships into stages that consist of philanthropic, transactional, and integrative stages. The conceptualization of the continuum has relevance to understanding the process of translational science. In translational science, the first stage is a scientific stage that has a characteristic of scientific knowledge exchange. When scientists agree on research design, they assign functions to relevant scientists. This action is considered to be moving into the next stage in which parties shift their transaction type to a more specific in their roles and more reciprocal in knowledge exchange. It is possible to name this stage as the ­transactional stage. After scientists confirm and validate their discovery, the ­ clinical  units will apply the mechanism to a small group of humans via animal studies

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Table 1  Motivations to build partnerships For-profit organizations

WHO

Resources owned for transactions

Factors that motivate Resources owned for to build PPPs transactions

Factors that motivate to build PPPs

Market efficiency

Perceived market uncertainty

Market efficiency

Authority in the global standard setting and enforcement Technological Perceived technolog- Public responsibiliknow-how ical uncertainty ties in global norms and standards — Perceived market Legitimacies in externality the global standard setting and enforcement Market-based knowl- Public sector-specific Public sector-specific edge and the comknowledge knowledge petence of resource mobilization Funds — —

Technological know-how —

Market-based knowledge and resource mobilization Private sectors’ funds

or directly use it to a small group of humans. In this stage, diversified scientific units will integrate the newly created knowledge into a protocol. Therefore, this stage can be the integrative stage. The later stages will be more integrative, though they are outside of this study. KIOs may integrate more diversified scope of knowledge and, in turn, incorporate the new insight into their conventional methods, or into their unique core responsibilities. Therefore, except the philanthropic stage in which the surplus wealth of a few will become the property of many (Carnegie, as cited in Lucas, 2002),1 Austin’s conceptualization of the continuum has relevance to understanding translational science. In the continuum of the alliance, types of incentive alignment vary (Austin, 2000). In the philanthropic stage, the resources exchanged by parties are not necessarily equal in shared values. On the other hand, in the transactional stage, parties begin to trade core competencies reciprocally in the joint value creation. In the final scene, parties can integrate their core competencies more into the joint value creation (Austin, 2000; Barrett, Austin, & McCarthy, 2002). In the transactional and integration

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stages, parties are more likely to emphasize the expectation over a fair distribution in the results and accountability of performance (Austin, 2000; Doz & Hamel, 1998; Kivleniece & Quelin, 2012). The misaligned incentives will result in unbalanced expectations. In translational science, the most critical stage is likely to be the period in which parties move their knowledge exchange type from the scientific exchange to the transactional stage. Even if the higher-order motivation is pursuing truth, the positioning of their core knowledge and functions, the period of tasks, and the fair distribution of the ownership and rights of intellectual property (including publications) have a potential to affect their second-order motivation (see, Vakili & McGahan, 2016). In this period, parties will more seriously seek to align incentives. Proposition 1 The continuum of research alliance of translational science can be divided into stages that consist of the scientific stage, the transactional stage, and the integrative stage. The knowledge transaction type will shift from the scientific transaction, the reciprocal transaction, to the integrated transaction along with the evolution of continuum. Here, the incentive alignment will be more critical in the transactional stage where parties should more seriously seek pre-optimization (reciprocity in the transaction) and post-optimization (fair distribution) of incentives. Among the motivations of partnerships (Table 1), what WHO offers are (i) reduction of market uncertainty and (ii) reduction of market externalities through (iii) the authority and legitimacies in the global standard setting and the enforcement. On the other hand, what WHO expects for private sectors are (i) efficiencies in market solutions, (ii) technological know-how, (iii) market-based resources and their mobilization, and (iv) funds (see, Table 1). The aligned incentives in PPPs will help to increase the potential to achieve defined public health goals. On the other hand, when private funds and resources are more critical in the transaction, there emerges a room of capture that may undermine technological elements to fulfill public-specific responsibilities. Related to this point, Doz and Hamel (1998) examine the sound patterns of general R&D cooperation. One of the robust models is internalization. This pattern occurs to develop new technologies with the support of public funds. It combines knowledge to reduce uncertainties and accelerate learning (Doz & Hamel, 1998, Fig. 4.2).

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As the motivation of global health PPPs in part comes from the private sectors’ funds, PPPs will require additional mechanisms to assure the trusteeship behaviors of KIOs. 2.3   Buffers for Autonomy of KIOs Previous literature examined alliance designs from agency and transaction cost perspectives. A problem remains to what extent private actors respect the autonomy of global-health KIOs in executing a public responsibility. The case of Merck-WHO partnership suggests that maintaining the each other’s heterogeneity is one of the contributing factors (Frost, Reich, & Fujisaki, 2002, p. 108). In other words, parties should sharply distinguish public responsibilities and private incentives while recognizing the common objective in the alliance. In this regard, alliance designs need to introduce buffers in the process of knowledge exchange. 2.4   Governance in Management Theories Governance inconsistencies derived from the internal process Ghobadian et al. (2004, p. 297) help to understand several differences between private actors and public actors in general. Among them, the following will be particularly relevant to governing PPPs: i. An opportunity for aligning the interest of principal and agent. It is high in private organizations whereas low or medium in public organizations; ii. Targets are determined at the discretion of managers in a private sector whereas objectives are predominately established externally (through political/governmental organizations or regulatory policies) in a public sector (Ghobadian et al., 2004, p. 297). The item (ii) needs additional explanations. WHO is a specialized agency of the UN that consists of member states’ governments. From the principal–agency perspective, the ultimate principals are member states. Here, the interests in the public health are diverse among nations (see, Chorev, 2012). Under such condition, WHO should act for the conscience of global public health with professional autonomy while balancing interests among resource-rich and resource-disadvantaged nations (see, Chorev, 2012).

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Therefore, aligning the interests with principals (member states) is relatively low compared to private entities. Regarding the target, targets of global health do not vary so frequently compared to the private-sector entities in which managers have to respond to environmental variations quickly. Then, the variation in the internal decision processes will yield inconsistencies that parties should manage through governance. Proposition 2 In the research alliance, multilateral KIOs and private partners should govern the boundary of autonomy regarding their core functions, responsibilities, and the internal process of decisions. Governance Mechanisms and Responsibility Reasoning Given such differences, the next to examine is the preferred governance mechanisms in general. From private actors’ viewpoints, the preferred governance varies depending on the alliance designs, such as parallel or integrative (Haas, 2010). However, codes of conduct are a preferred mechanism in any designs (Cf. Haas, 2010). This account is relevant for the global health alliances. Here is an opportunity in which governance mechanisms integrate dimensions of responsible research and innovation (RRI. See, Barben, Fischer, Selin, & Guston, 2008; Burget, Bardone, & Pedaste, 2016; Forsberg, Gianluda, Karapiperis, Woensel, & Arnaldi, 2015; Owen, Macnaghten, & Stigloe, 2012; von Schomberg, 2013; Stigloe, Owen, & Macnaghten, 2013) to alliance designs. In other words, governance mechanisms should have responsibility reasoning (see, Finnis, 1980; Maclagan, 1998) and an ethical foundation. Then, the next section investigates the current and potential directions to extend the governance model by observing the case of vaccines for MDR-TB.

3  Application to Vaccine Research 3.1   Possible Bias in the Selection of Research Agendas The biased selection of the academic research agenda (Radder, 2010. Also see, Kenworthy, MacKenzie, & Lee, 2016; Reich, 2002b) is likely to have occurred in vaccine science. In 2016, roughly 1.7 million died from TB. MDR-TB remains health security threat (WHO, 2018).2 On the other hand, according to a preliminary PubMed search, the number of peer-reviewed publications with PubMed keywords of “drug-resistant

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tuberculosis” and “vaccine” is 5 in 2016, and 11 in 2011. This result is similar to rare cancers (fewer than 15 cases out of a population of 100,000 per year. Rare Diseases Act of 2002 Public law 107–280).3 Please note that cancers afflict both of resource-rich and resource-disadvantaged countries, whereas MDR-TB currently afflicts resource-disadvantaged nations and the low-income population in wealthy countries (see, Persely, 2016; WHO, 2018). When examining by expanding the range to “tuberculosis and vaccine,” the number of publication increases to 123 in 2016 and 226 in 2011. However, regarding a product approved, only one vaccine for TB is available which passed the official investigation in the early twentieth century (Cayabyab et al., 2012; Prabowo et al., 2013). This condition does not match with a large number of needs. Also, an effective vaccine for adults has not been developed (WHO, 2014). On the other hand, a closer look at peer-reviewed scientific publications provides contradictory messages. An unprecedented increase in new vaccine development has occurred over the past three decades. (Rappuoli et al., 2011)

On the other hand, in the case of TB vaccine, A series of vaccine candidates failed to deliver clinical utility in spite of significant progress in developing candidates. (Cayabyab et al., 2012) A mechanism basis that a pathogen develops resistance also has not been fully understood in the field. (Anderson & Woodworth, 2014)

The above three tell that the significant progress in vaccine science is the ability to sequence entire genomes of bacteria that enable reverse vaccinology and the next generation technologies (Rappuoli et al., 2011). However, (a) vaccine science of TB fails in translation, and (b) vaccine science for MDR-TB does not conform to the overall trend. Scientists have not fully understood mechanisms of resistance that can produce promising candidates. Here, a problem exists in the discovery stage and translational stage. Therefore, it is worth investigating to what extent the scarcity of TB vaccine science is due to capture, inconsistencies in research alliances, or the change of the more extensive system.

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Traditional Vaccine Development Traditionally, national laboratories and government agencies have researched vaccine sciences in general. In the case of flu vaccines, there are networks of national laboratories, government agencies, and multilateral organizations. For example, WHO collaborates with five centers for reference/research on influenza in the world. The centers receive influenza viruses from National Influenza Centers (NIC)4 globally. They analyze them and share the results by submitting them to the NIC and WHO.5 In the USA, the Biomedical Advanced Research and Development Authority (BARDA) coordinates a broad inter-agency partnership to support the development of new, improved influenza vaccines.6 The system of national laboratories is useful in governing potential threats of dual uses. Promising Arrangements and Limits There are also positive results in the private involvement in the vaccine discovery. One of the examples is the vaccine discovery site in Siena, Italy. Currently, scientists are developing new vaccines that use reverse vaccinology (a whole-genome sequencing approach to developing a vaccine). Academic and public KIOs from 6 EU countries and an R&D unit of a transnational pharmaceutical company built PPPs to conduct discoveries. SCLAVO, a not-for-profit organization, is launching, performing, and coordinating projects that consist of R&D, standardization of immunological assays, and the contribution to a policymaking.7 At the same time, a closer look reveals that, at least in 2017, vaccines developed through the advanced science are those for diseases that prevail both in the developed and developing countries. It is unclear to what extent they allocate resources to vaccine science for diseases that afflict those in resource-disadvantaged regions and nations. Then, by focusing on MDR-TB, the next section investigates institutional arrangements for vaccine science on diseases that afflict those in resource-disadvantaged regions and countries. 3.2   Background: MDR-TB Initiative Early Initiatives In 2017, the death caused by TB was ranked the global top according to WHO. In the USA, states of California, Texas, and New York are the top

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three in the reported cases. All have more than 4% incidence of the state population (Stewart, Tsang, Pratt, Price, & Langer, 2018). Here, the TB incidence includes MDR-TB. MDR-TB is an infectious disease caused by an isolate of Mycobacterium TB, which is resistant to at least two of the essential firstline anti-TB drugs. The extensively DR-TB (XDR-TB) is a TB that is resistant to all of the first-line essential medicines. MDR-TB can be contracted directly or can develop with an inadequate therapy for a drug-sensitive TB (Rosenberg & Rhatigan, 2011). Non-adherence to a treatment regimen also results in worse resistance. Since the mid-twentieth century, WHO has established an effective Directly Observed Treatment, Short-course (DOTS: 6 month-course) to address general TB. However, DOTS cannot cure MDR-TB (Farmer & Kim, 1998). It necessitates second-line agents (Farmer & Kim, 1998; Rosenberg & Rhatigan, 2011). Physicians can cure MDR-TB if they address it appropriately (Reichman & Tanne, 2002). However, a critical difficulty lies in diagnostics tests. Conventional methods require an extended period to detect Mycobacterium and determine drug sensitivity. The traditional techniques take 8–12 weeks, even in 2008 (Migliori, Matteelli, Girillo, & Pai, 2008; Reichman & Tanne, 2002). Amplification from MDR-TB to XDR-TB occurs during this extended period of diagnostics since physicians cannot establish adequate treatment strategies. A distinctive feature of recent decades is co-infection with HIV (Isaakidis et al., 2011; Reichman & Tanne, 2002; see, WHO, 2013), which makes diagnosis further difficult. Such conditions have required a breakthrough innovation of accurate point-of-care diagnostics, in addition to an expansion of laboratory service and quality-assured second-line drugs (Walter et al., 2012; WHO, 2014). Notably, a discovery that enables accurate point-of-care diagnostics has been critical over decades. The MDR-TB Initiative has its origins in an individual’s compassionate enthusiasm and leadership in Peru. It resulted in establishing a public health model of South America (Wahl & Kogut, 2007). In combination with this dedicated effort, a US-based biomedical scientist in an academic KIO initiated an individualized approach (Farmer & Kim, 1998). The individualized approach achieved a success rate of 85% in a defined population (Harvard Medical School, hereafter HMS, 2011). Meanwhile, the medical society and WHO reached a consensus to introduce DOT-Plus (a complementary DOTS-based strategy with provisions

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for treating MDR-TB) in countries where DOTS had already established or being established (Farmer & Kim, 1998). The individualized approach became a conventional regimen of DOT-Plus in the 1990s (WHO, 2014). Here, a problem from public health viewpoints is that the individualized approach has a limit in addressing a large population. The success depends on the skills of healthcare providers (HMS, 2011). Therefore, in parallel to the individualized approach, WHO launched a project to develop a standardized regimen with the involvement of a transnational pharmaceutical company (Wahl & Kogut, 2007). Here, the development of an accurate and fast point-of-care diagnostics is critical in establishing a standardized regimen. After a decade of the absence of innovation, PPPs that involve small biotechs began to yield breakthroughs in drug sensitivity tests (hereafter, DST: Migliori et al., 2008). Among them, two were scientifically robust to introduce into a standardized regimen (WHO, 2008). They have a basis on two molecular biomarkers derived from sequencing technology. WHO released a policy statement to biomedical communities to research how to integrate these two into the conventional diagnostics (WHO, 2008). In this context, the next to understand is the governance process that legitimated the biotech involvement and led to success in the WHO programs. Legitimacies of the Involvement of Biotech Firms There is little publication about the discovery related to molecular DSTs for MDR-TB. Therefore, the process of the discovery and development of the DSTs is not clear. According to a scientific paper, it is a Belgium-based biotech that developed and commercialized the one, and it is a Netherland-based biotech that produced the other. Both used the arrangement of PPPs. There is one publication on the biological mechanism of the discovery written by scientists of the Belgium-based firm. In considering the above observation, a possible explanation is that for-profit biotechs and public funding agencies built PPPs and discovered and developed the assays (a source of DST). The most probable paradigm for the partnership is the market failure paradigm.8 Just before the WHO’s endorsement for the two new assays, academic scientists in potentially recipient countries actively began to publish the research results of the feasibility studies on the assays in their countries.

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Though the DSTs were already in routine use in the EU countries, the risk-benefit profiles in the EU do not necessarily assure the feasibility in recipient countries. It suggests that WHO adopts an evidence-based method in the internal decision process. The above observation suggests that WHO was making efforts to prevent captures by adopting the evidence-based method. Then, it is worthwhile to investigate governance mechanisms of TB research alliances by focusing on published documents. Governance Mechanisms for the Innovation Process This study divides the timeline of the TB partnership into three stages of knowledge exchange after the introduction of DOT-Plus (Table 2). Here, Proposition 2 can be modified as follows. Proposition 2′  The knowledge transaction type of research projects evolves from a stage of scientific investigation, a transactional stage, and an integrative stage which integrates outcomes into different norms and responsibilities of KIOs. The adoption of the evidence-based method helps prevent captures. Table 2  The stages of knowledge exchange in developing a standardized regimen Stages

Events

Governance mechanisms

1. Stage of scientific investigation (scientific exchange) 2. Stage of transaction

The launch of DOT-Plus The individualized regimen established The endorsement of two new assays for DST Roadmap and standardization of DST development

3. Stage of integration (of outcomes to unique responsibilities of WHO)

WHO TB Strategy (2014) Comprehensive action roadmap

Relational governance (Working group created in side of WHO) Codes of conduct, choice architecture, specific methodologies to control externalities that will affect the quality of DST (transportation, storage containers, etc.) Codes of conduct, choice architecture, standardization. Imposing ownership rights of public goods, and others (Besley & Ghatak, 2001; Greco, 2015; Schmitz, 2014) 

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The next to investigate is whether preferred or proper governance mechanisms shift along with the stages. For this purpose, this study focuses on the development of standardized regimen by reviewing published documents of WHO. The right-hand side of Table 2 shows the results. The investigation reveals that the first stage adopts the relational governance. In the stages 2 and 3, WHO actively releases codes of conduct. They include not only a choice architecture (for example, see WHO, 2013, 2017) but also precise methods to control externalities (such as temperature) to preserve the effectiveness of the DST. Such methodologies include details of transportation and storage containers to control temperature.9 In moving to the stage 3, WHO explored a standardized scheme and a roadmap regarding assay development (see, Figures in Lienhardt et al., 2016; WHO, 2015). It maps pipelines into three dimensions which consist of (i) complexity that assay should address, (ii) development/evaluation stages, and (iii) biological functions and clinical utilities of assays (see, Figures in WHO, 2015; Lienhardt et al., 2016). In this space, WHO specifies the nature and quality of DSTs and provides governance through standardization. Here, the specification and clear presentation of the standardized scheme are considered to help science communities to choose their preferred risk status and the degree of uncertainty. This governance mechanism can also have an effect of shielding between the mixture of public responsibility of WHO and private incentives of discovery entities (Okada, 2017). In considering the stages of research projects and corresponding governance mechanisms, this study extends the Proposition 2 as follows. Proposition 2″  Proper governance mechanisms vary depending on the alliance designs and the stages of research projects. The increase of specification is likely to enable the shift of governance mechanisms. Any governance mechanisms should convey responsibility reasoning and an ethical foundation of research conduct. 3.3   Problems in Governance According to the roadmap of WHO (2015), the next to address is a therapeutic vaccine, vaccines for adults,10 and for latent TBs. These fields correspond to the WHO’s core function to promote the global

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commons. Regarding the therapeutic vaccine, an immunological approach is promising in avoiding the development of drug resistance (Anderson & Woodworth, 2014; Prabowo et al., 2013). Problems are (i) organizational arrangement and (ii) corresponding governance mechanisms in the translational science approach. The problem is that many of the new vaccine science remain in the stage of discovery and before the translation. In this stage, the governance by standardization is not appropriate. More relational governance would be preferred. However, staying in the relational governance also has a limit, particularly in sharing the research results with dispersed niche experts. Regarding the alliance design, concerns over possibilities of undermining the critical functions of UN agencies come mainly from the private sector involvement (Buse & Walt, 2002, p. 182; Reich, 2002b, pp. 13–14).11 The participation of academic KIOs also has a potential to bring the concerns though to a lesser degree. For example, before proposing the individualized regimen of MDR-TB to WHO, the academic KIO repeatedly deliberated not to undermine the core responsibilities of WHO (HMS, 2011). Such prudence stems from the foundation of academic and professional ethics. Given the academic and professional ethics, positive and negative consequences depend on governance from management theory perspectives. Governance cannot resolve scientific questions. In the case of TB vaccine, scientists have not thoroughly analyzed the mechanism-bases that the pathogen develops resistance. The lack of specification makes translation more difficult (Prabowo et al., 2013). However, appropriate governance can improve the quality of science. One way to facilitate basic research is the internalization and smooth exchange of scientific results (Cook-Deagan, 2007; Merton, 1942). Regarding the internalization, the WHO and the US Center for Disease Control and Prevention (CDC) are now building an alliance to create a centrally managed database for a lab network in the TB vaccine development. Here, academic and scientific KIOs are encouraged to deposit data into the database (Cf., Leonelli, 2010). Science-based firms also can participate in the community by storing research results that they exchange throughout the scientific community. From a process governance perspective, such semi-contractual relations correspond to internalization that allows an exchange of transaction-specific and in-alienated knowledge (such as sharing of e-research notes). It facilitates the transfer of knowledge by taking transaction-cost advantage

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of ex-ante contracting and ex-post monitoring of other science KIOs (Williamson, 1979; see also, Caves, 2007, p. 7). The transparency within the internalized network helps to clarify the scope of responsibility (see, Hofmann & Frese, 2011). The reciprocal and transparent process governance reduces a data withholding that may stem from the private involvement (Choi & Millar, 2005; Okada, 2018). Such arrangement has a potential to promote scientific understanding on the mechanism through public–public partnerships. Such arrangement by public–public partnership can be an extension of self-regulation of science. It also reduces transaction costs from the agency perspective. In the transactional stage, it can connect a validated scientific mechanism to the database of the subsequent human studies. Such a link has a potential to fasten the integration of underlying mechanisms and clinical utilities (Anderson & Woodworth, 2014). The next problem is the ethical foundation that underpins the governance.

4  Governance Model for Vaccine Discoveries By basing on the market failure paradigm, Stevenson (2016) analyzes the factors that legitimated the formation of global PPP as follows: The cost associated with meeting newly harmonized quality assurance standards for pharmaceuticals was for the majority of national governments not affordable. Therefore, public sector entities passed on their responsibility for developing preventive agents, diagnostic and therapeutics to industry (Mahoney et al., 2004; Stevenson, 2016). Meanwhile, established pharmaceuticals were also experiencing a discourse because biotech firms were about to change the market and technology. The extensive merger and acquisition (M&A) of transnational pharmaceuticals further intensified the competition (Cf. Stevenson, 2016). Under such condition, some health-related agencies began to seek directions towards collaboration between the public and the private (Buse & Walt, 2002, p. 174). Considering this trajectory, the arrangement of PPPs paved a way of cooperation and knowledge coproduction but also introduced a risk of regulatory capture and a biased selection of research agendas. Considering that discoveries of vaccine science are a common good, a problem is to seek a direction to integrate common good-based stakeholder theory. In this regard, Finnis (1980) defines a common good as a set of conditions which enables the member of the community to

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attain for themselves the values, objectives, or to realize goals reasonably for the sake of which they have reason to collaborate with each other (Finnis, 1980, p. 155). Argandona (1998)’s common good-based stakeholder theory is consistent with this definition. Among their characterization of the common good, relevant features for vaccine science partnerships are as follows: • The common good belongs equally to all individuals. Therefore, it is not confined to the provision of public goods (Argandona, 1998). • The common good is not equivalent to social welfare, and scholars should distinguish it from the utilitarian conception (Argandona, 1998; Finnis, 1980, p. 154; Velasquez, 1983). • If achieving a good necessitates coordinated actions, seeking the common good (or of society) is for him/her to secure his/her good (Argandona, 1998; Finnis, 1980). • Thus, developing the common good does not exclude the private incentives. His/her objective to achieve the common good comes from his/her objective to seek his/her good (Argandona, 1998; Finnis, 1980, pp. 139–140). • The common good only excludes the subordination of common good in pursuit of private ends (Argandona, 1998; see, Stevenson, 2016). • The difficulties come from distributive justice (Doz & Hamel, 1998; Finnis, 1980, pp. 164–177; Kivleniece & Quelin, 2012). The distributive justice is beyond the scope of this book. This study focuses on a problem of the governance over the subordination the common good. In the governance, the concept of common good is associated with duties and obligations towards stakeholders in achieving the common good (Argandona, 1998, p. 1100). Then, the problem here is how to integrate the common good-based stakeholder theory to the alliance governance of KIOs. More specifically, the problem is what to manage at what boundaries to achieve the obligation (to translate a discovery) in the vaccine science alliance of KIOs. Proposition 3  An objective of KIO to achieve common good and fulfill duties and obligations to normative and derivative stakeholders comes

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from its goal to seek its good. In a research alliance, governance should address a possible subordination of the pursuit of the common good. 4.1   Vaccine Science and Alliance Governance of KIOs From the perspective of KIOs, a problem is how to extend the trusteeship governance to address the common good (vaccine discovery and the capacity of the translation). In reaching an individual good to the common good, what parties should manage is dependence. In the case of for-profit firms, there are promising patterns of the alliance in which self-interested parties with proprietary assets jointly create values (Bartlett & Ghoshal, 1989; Caves, 2007, p. 7; Doz & Hamel, 1998). Related to them, Doz and Hamel (1998) provide a useful analysis on how dependence emerges and how to manage it in alliances. They categorize alliance patterns into three types: co-option, co-specialization, and internalization.12 Among them, the “co-specialization” that mobilizes separate and indispensable resources and knowledge is promising but more likely than others to yield a problem of dependence. In this pattern, a conflict does not occur as far as each partner’s indispensable contributions to the alliance are balanced and they perceive the benefits they receive as fair (Doz & Hamel, 1998; Kivleniece & Quelin, 2012; Also see, Pfeffer & Salancik, 1978, p. 259). They are contributing with their own and collect benefits, accordingly (Argandona, 1998). Once an unbalanced condition emerges, mutual tolerance is not always a precondition for a balance. In the case of firms, a successful scenario to regain a balance is that two equally self-interested partners pull strong enough for their advantage and keep the alliance moving while continually monitoring the dependence (Doz & Hamel, 1998, p. 202). On the other hand, a scenario of negative consequences is that when one of partners’ independence is not realistic, it makes efforts to maintain its indispensableness by undermining the other, or each tries to make its partner less indispensable (Doz & Hamel, 1998). Here, it is not autonomous in PPPs that “equally self-interested partners pull strongly for their advantage.” As stated earlier, the scope of discretion of managers and internal process of the decision are different between the public and private management. With exceptional cases (see, Chapter 5), academic and multilateral KIOs are not accustomed to pulling strongly enough to leverage the joint results (a project-­specific outcome) to use in other segments of the KIO. Also, when private

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partners perceive the collected benefits unfair but the termination of the relations is unrealistic, they can be motivated to influence research agendas that lead to undermining the unique responsibility of KIOs. Dependence also emerges in the internalization though to a lesser degree. The determinants of success are, in general, the following. i. receptivity of participating organizations and ii. relative transferability of the contributions (Doz & Hamel, 1998; Kilveniece & Quelin, 2012, p. 287; Kogut & Zander, 1992), in addition to iii. the learning intents of each other (Doz & Hamel, 1998). On the other hand, a tension emerges between the proprietary knowledge and scientific commons. The support infrastructure is in need to specify each other’s responsibility and extend trusteeship governance to include the common good-based governance. Chapter 7 discusses the concrete forms.

5  Conclusion This chapter investigated governance of research alliance in multilateral KIOs. A body of investigation is WHO’s new approach to vaccine science for MDR-TB, one of understudied science fields. In regarding vaccine discoveries and translational capacity as common goods, this chapter examined factors that motivate KIOs to build R&D partnerships, potential threats that make this field understudied in spite of the advancement of sequencing the entire genomes of bacteria, and directions to enhance trusteeship governance for health-related multilateral KIOs. Threats identified are (i) unbalanced dependence on partnerships and (ii) the change of the entire system of knowledge production. Both led to the subordination of unique responsibilities of KIOs embodied in a biased research agenda. In this regard, this chapter examined a liberal account of common good-based stakeholder theory that starts from a private incentive to achieve a common good. More specifically, it identified a potential threat as an unbalanced dependence in a co-specialization alliance to fulfill the obligation to stakeholders to translate a vaccine discovery. In the case of firms’ co-specialization, they can regain a balance by pulling strong enough to their advantage and keep the alliance moving while continually monitoring the dependence (Doz & Hamel, 1998).

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On the other hand, because of inconsistencies of internal decision process between a for-profit firm and a public KIO, this success pattern does not always occur except a few cases. When more should be done in a discovery stage (scientific transaction stage), the internalization is a stable pattern with the support of public sponsors. Here, the critical bottlenecks for the new TB vaccines are the lack of understanding of the biological mechanisms and the capacity for translation. In this regard, WHO and CDC developed a partnership to build an infrastructure to facilitate scientific knowledge on mechanisms of resistance. It also connects discoveries in academic KIOs to the database of subsequent human studies to fasten the translation to the clinical settings. Such arrangement is consistent with the self-regulation of science. It also enhances the trusteeship governance to address obligations to stakeholders to achieve common good (discoveries and translation of vaccine science). A potential threat to the internalization is the tension between the proprietary knowledge and science commons. The next chapter will apply this notion by investigating the intellectual property policies of partnerships.

Notes



1.  Here, philanthropy is distinguished from charity, in which the surplus wealth is distributed in small sums to the people themselves. See, Carnegie, as cited in Lucas (2002). 2. Regarding the nature of communicable diseases, see, Smith (2009). 3. For example, the number of publication on acrospinoma is 11 in 2011 and 21 in 2016. 4.  NICs are national institutions designated by ministries of health and recognized by WHO to participate in the WHO Global Influenza Surveillance and Response System. See, Regional and global influenza laboratory networks. Available at http://www.euro.who. int/en/health-topics/communicable-diseases/influenza/sur veillance-and-lab-network/regional-and-global-influenza-laboratory-networks. Accessed on September 18, 2017. 5. Regional and global influenza laboratory networks. Ibid. 6. The US Center for Disease Control and Prevention. Available at https:// www.cdc.gov/flu/about/qa/advances.htm. Accessed on September 18, 2017.

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7.  The 6 countries are Italy, Netherland, Sweden, UK, Denmark, and Switzerland. See, www.sclavo.org/en/associate-members. Last accessed in May 11, 2018. 8. According to Mowery (2001), there are three competing paradigms for R&D policy models. They are the market failure paradigm, the mission paradigm, and the cooperative technology paradigm. See, Mowery (2001), pp. 51–54. Also see, Gottweis et al. (2009). 9. See, WHO (2008). 10. According to scientific literature, there is an effective immunization vaccine for children, but not for adults. 11. The second criticism comes from a claim that technological interventions are being prioritized as solutions to problems that have deeper relevance to political and social determinants (Stevenson 2016, p. 125). This problem is not limited to the partnerships. For example, some kinds of type I/ type II diabetes are derived from individuals’ molecular-level mutations, but are, in many cases, closely related to obesity that is largely derived from life style and dietaries. WHO and National Academy of Medicine (NAM) are addressing such social determinants of health by introducing best practices of any communities. NAM Annual Meeting 2016-Scientific Program. 2016, October 17, Washington, D.C.  12. Among them, co-option is an alliance that turns potential competitors into allies and providers of the complementary goods and services that allow new businesses to develop. Co-specialization is the synergistic value creation that results from combining separate resources, positions, and knowledge sources. See, Doz and Hamel (1998).

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Forsberg, E.-M., Gianluca, Q., O’Kane, H., Karapiperis, T., Van Woensel, L., & Arnaldi, S. (2015). Assessment of science and technologies: Advising for and with responsibility. Technology in Society, 42, 21–27. Frost, L., Reich, M. R., & Fujisaki, T. (2002). A partnership for ivermectin: Social worlds and boundary objects. In M. R. Reich (Ed.), Public-private partnerships for public health (pp. 87–113). Cambridge, MA: Harvard Centre for Population and Development Studies. Ghobadian, A., Gallear, D., Viney, H., & O’Regan, N. (2004). Future of the public-private partnership. In A. Ghobadian, D. Gallear, N. O’Regan, & H. Viney (Eds.), Public-private partnerships: Policy and experience (pp. 271–302). Hampshire and New York: Palgrave Macmillan. Gottweis, H., Salter, B., & Waldby, C. (2009). The global politics of human embryonic stem cell science: Regenerative medicine in transition. New Hampshire and New York: Palgrave Macmillan. Greco, L. (2015). Imperfect bundling in public-private partnerships. Journal of Public Economic Theory, 17(1), 136–146. Haas, M. (2010). The double-edged sword of autonomy and external knowledge: Analyzing team effectiveness in a multinational organization. Academy of Management Journal, 53(5), 989–1008. Hart, O. (2003). Incomplete contracts and public ownership: Remarks, and application to public-private partnerships. The Economic Journal, 13(March), C69–C76. Harvard Medical School (Rosenberg, J., & Rhatigan, J.). (2011). Multidrugresistant tuberculosis treatment in Peru, Cases in Global Health Delivery, Harvard University. Hofmann, D. A., & Frese, M. (Eds.). (2011). Errors in organizations. New York: Routledge. Isaakidis, P., Cox, H., Varghese, B., Montaldo, C., Mansoor, H., Ladomirska, J., … Reid, T. (2011). Ambulatory multi-drug resistant tuberculosis treatment outcomes in a cohort of HIV-infected patients in a slum setting in Mumbai, India. PLoS One, 6(12), e28066, 2–9. Kanter, R. M. (1997). From spare change to real change: The social sector as beta site for business innovation (pp. 153–177). Cambridge: Harvard Business School on Innovation, Harvard Business School Press. Kenworthy, N., MacKenzie, R., & Lee, K. (Eds.). (2016). Case studies on corporations & global health governance: Impacts, influence and accountability. London: Rowman & Littlefield International. Kivleniece, I., & Quelin, B. V. (2012). Creating and capturing value in public-private ties: A private actor’s perspective. Academy of Management Review, 37(2), 272–299. Klein, P. G., Mahoney, J. T., McGahan, A. M., & Pitelis, C. N. (2010). Toward a theory of public entrepreneurship. European Management Review, 7, 1–15.

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Kogut, B., & Zander, U. (1992). Knowledge of the firm. Combinative Capabilities, and the Replication of Technology, Organization Science, 3(3), 383–397. Leonelli, S. (2010). The commodification of knowledge exchange: Governing the circulation of biological data. In H. Radder (Ed.), The commodification of academic research: Science and the modern university (pp. 132–157). Pittsburgh: Pittsburgh University Press. Lienhardt, C., Lonnroth, K., Menzies, D., Balasegaram, M., Chakaya, J., Cobelens, F., … Raviglione, M. (2016). Translational research for tuberculosis elimination: Priorities, challenges, and actions. PLoS Medicine, 13(3), e1001965. doi:10.1371. Lucas, A. O. (2002). Public-private Partnerships: Illustrative examples. In M. R. Reich (Ed.), Public-private partnerships for public health (pp. 19–39). Cambridge, MA: Harvard Centre for Population and Development Studies. Maclagan, P. (1998). Management and morality: A developmental perspective. Thousand Oaks: Sage. Mahoney, J. T., McGahan, A. M., & Pitelis, C. N. (2009). The interdependence of private and public interests. Organization Science, 20(6), 1034–1052. Mahoney, R., Pablos-Mendez, A., & Ramachandran, S. (2004). The introduction of new vaccines into developing countries: III. The role of intellectual property. Vaccine, 22(5), 786–792. https://doi.org/10.1016/j. vaccine.2003.04.001. Marchant, G. E., & Wallach, W. (2013). Governing the governance of emerging technologies. In G. E. Marchant, K. E. Abbott, & B. Allenby (Eds.), Innovative governance models for emerging technologies (pp. 136–152). Cheltenham: Edward Elgar. Merton, R. K. (1942). A note on science and democracy. Journal of Legal and Political Sociology, 1, 115–126. Migliori, G. B., Matteelli, A., Cirillo, D., & Pai, M. (2008). Diagnosis of multidrug-resistant tuberculosis and extensively drug-resistant tuberculosis: Current standards and challenges. Canadian Journal of Infectious Diseases and Medical Microbiology, 19(2), 169–172. Mowery, D. C. (2001). The US national innovation system after the Cold War. In P. Laredo, & P. Mustar (Eds.), Research and innovation policies in the new global economy: An international comparative analysis (pp. 15–46). Cheltenham and Northamption: Edward Elgar Publishing. North, D. (1990). Institutions, institutional change and economic performance. Cambridge: Cambridge University Press. Okada, E. (2017, June). Responsible organization, partnership design, and governance in addressing global common goods. Presented at International Conference of Responsible Organization in Global Context, organized by Georgetown University and Universite de Versailles, Washington, DC.

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Okada, E. (2018). Knowledge corruption and governance in academic knowledge-intensive organizations: The case of molecular mutations research. Journal of Public Affairs, 18e1698. https://doi.org/10.1002/pa.1698. Owen, R., Macnaghten, P., & Stilgoe, J. (2012). Responsible research and innovation: From science in society to science for society, with society. Science and Public Policy, 39, 751–760. Palfrey, Q. A. (2017). Expanding access to medicine and promoting innovation: A practical approach. Georgetown Journal on Poverty Law and Policy, 24(2), 162–197. Pfeffer, J., & Salancik, G. R. (1978). The external control of organizations: A resource dependence perspective. New York: Harper & Row. Prabowo, S. A., Groschel, M. I., Schmidt, E. D. L., Skrahina, A., Mihaescu, T., Hastuk, S., … van der Werf, T. S. (2013). Targeting multidrug-resistant tuberculosis by therapeutic vaccines. Medical Microbiology and Immunology, 302, 95–104. Radder, H. (2010). The commodification of academic research. In H. Radder (Ed.), The commodification of academic research: Science and the modern university (pp. 1–23). Pittsburg: University of Pittsburg Press. Rangan, S., Samii, R., & van Wassenhove, L. K. (2006). Constructive partnerships: When alliances between private firms and public actors can enable creative strategies. Academy of Management Review, 31(3), 738–751. Rappuoli, R., Blank, S., & Lambert, P. H. (2011). Vaccine discovery and translation of new vaccine technology. Lancet, 378, 360–368. Reich, M. R. (Ed.). (2002a). Public-private partnerships for public health. Cambridge (US): Harvard Center for Population and Development Studies. Reich, M. R. (2002b). Introduction: Public-private partnerships for public health. In M. R. Reich (Ed.), Public-private partnerships for public health (pp. 1–18). Cambridge, USA: Harvard Center for Population and Development Studies. Reichman, L. B., & Tanne, J. H. (2002). Timebomb—The global epidemic of multidrug resistant tuberculosis. New York: McGraw Hill. Roberts, M. J., Breitenstein, A. G., & Roberts, C. S. (2002). The ethics of public-private partnerships. In M. R. Reich (Ed.), Public-private partnerships for public health (pp. 67–85). Cambridge: Harvard Centre for Population and Development Studies. Rosenberg, J., & Rhatigan, J. (2011). Multidrug-resistant tuberculosis treatment in Peru, Cases in Global Health Delivery, Harvard. Sachs, J. (2001). Thinking boldly. Bulletin of the World Health Organization, 79(8), 772. Schmitz, P. W. (2014). Optimal ownership of public goods reconsidered. Economics Letters, 125, 21–24. Smith, R. (2009). Global health governance and global public goods. In K. Buse, W. Heine, & N. Drager (Eds.), Making sense of global health governance: A policy perspective (pp. 122–136). New York: Palgrave Macmillan.

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Part III

CHAPTER 7

Science and Insights from Humanistic Disciplines

1  Introduction The purpose of this chapter is to investigate the boundary of regulation that knowledge-intensive organizations (KIOs) should manage in bridging “science” to “insights that come from humanistic disciplines (Cf. Kunneman, 2010).” It seeks (i) governance mechanisms of intellectual property policies in research alliances and consortia in which the private incentive, public responsibilities, and academic/science norms coexist; (ii) the scope of human right issues for which academic KIOs perceive moral responsibility. In this context, this chapter considers science governance that connects individual morality, organizational ethics, and laws. It is a public policy tool to impose ownership of public goods to improve policy outcomes. Evidence supports this economists’ proposition. Contrary to this incentive hypothesis, a possible tension emerges between the intellectual property rights derived from ownership and science commons. In this regard, this chapter argues that the trusteeship governance can harmonize with the agency-based governance by unbundling ownership, property rights, and obligations, and by partially detaching functions that property rights have. It is also consistent with the common good-based stakeholder theory in which private incentives, public responsibilities (Buse & Walt, 2002), and academic norms (Merton, 1942) coexist. At the same time, human right issues for which academic KIOs perceive as responsible (Stigloe, Owen, & Macnaghten, 2013) depend on the moral identity that regulates themselves and moral © The Author(s) 2019 E. Okada, Management of Knowledge-Intensive Organizations, https://doi.org/10.1007/978-3-319-97373-9_7

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ownership (Hannah, Avolio, & May, 2011) that defines their sense of responsibility. Previous chapters examined factors that make specific science fields understudied. They necessitate the management of the boundaries of knowledge, autonomy, and identity of KIOs in a reciprocal knowledge transaction. On the other hand, property rights that this chapter investigates themselves represent a bundle of “relations (Munzer, 1990).” Conflicts derived from an owner of property rights is a structured one and has a potential to affect other boundaries. For example, to facilitate discoveries of biological mechanisms, the internalization is one of the successful patterns of research alliance. A problem is a potential tension between intellectual property rights and scientific commons. In addressing this problem, this chapter regards intellectual property policies of consortia as a governance mechanism and examines their ex-ante/ex-post conflict resolution patterns. It investigates possibilities of a partial convergence of trusteeship governance with agency-based governance by unbundling and reconfiguring rights of intellectual property. The study organizes this chapter as follows. The next section analyzes patterns of governance mechanisms of intellectual property policies in consortia. Here, a consortium is an extended form of a research alliance. As a part of this effort, this section reviews the literature on public policies that impose ownership of public goods to improve public policy outcomes. The third section examines relations between moral identity and ownership, the scope of human rights that academic sciences address, and science governance to bridge individual morality to ethics. The last section concludes and provides implications.

2  Intellectual Property Policies of Biomedical Consortia 2.1   Proprietary Knowledge in Scientific Commons Benefits of Imposing Ownership Right What is new for the WHO’s new TB Strategy is its stress on the significance of the research investment and energy spent on the side of local entities: Academic institutions and scientists in countries with the biggest burden of diseases should be leaders in driving more energy and investment in research. (WHO, 2014)

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This stance is consistent with Besley and Ghatak (2001), Greco (2015), and Schitz (2014). In their articles, the ownership of a public good is regarded to lie in parties that value the benefits relatively more. Imposing ownership requirements is a way of public policy to improve the welfare of them. In this sense, WHO’s new Strategy (2014) and Action Framework (WHO, 2015) are recommending ownership of member states that value the benefits relatively more. Its implementation mechanism is consistent with WTO TRIPS Agreement (World Trade Organization: WTO, 1994). Relevance to TRIPS Agreement TRIPS partly provides an implementation mechanism of WHO Strategy. The objectives of the TRIPS Agreement are as follows: i. To reduce distortions and impediments to international trade, and taking into account the need to promote the adequate protection of intellectual property rights, and ii.  To ensure that measures and procedures to enforce intellectual property rights do not themselves become barriers to legitimate trade (WTO, 1994, p. 4). For this purpose, any advantage, favor, privilege or immunity granted by a member to the nationals of any other country shall be accorded immediately and unconditionally to the citizens of all other members (Art. 4). The coverage of protection includes maintenance and enforcement of intellectual property rights and matters affecting the use of intellectual property rights (Note 1, p. 6). Impacts on Global Science Network Imposing ownership right necessitates strengthening legal and moral obligations derived from the property right and beyond. As a part of it, the integration of local scientists into a global science community affects to explore their professional and academic norms. Such an arrangement is consistent with the concept of global sustainability (see, Argandona, 1998; Mahoney, McGahan, & Pitelis, 2009). Also, the newly developed sense of moral identity and the appreciation also affect to enhance their relative autonomy vis-à-vis their local (science) community (see, Fort, 2001, p. 24). If the local science community

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embeds wrong incentives to local scientists that may have them disadvantaged in joining the global science network, the participation in a small but global system may provide them a relative autonomy to resist to social barriers. In fact, related to the feasibility studies of two new assays of MDR-TB, academics in potential recipient countries actively published their research results in international journals. The Impacts of Research on Neglected Diseases in Basic Science The above stance raised several hypotheses regarding the effect on scientific knowledge production. Among them, Vakili and McGahan (2016) investigate questions on (i) whether the intellectual property right protected under the TRIPS stimulated research on neglected diseases in basic science, globally and locally, and (ii) whether, or why, the policy delivered a different result of neglected illnesses than for non-neglected diseases. The first question derives from the incentive hypothesis: The intellectual property right protection under TRIPS will incentivize basic science of neglected diseases (and applied research on non-neglected diseases) by expanding the downstream market (Vakili & McGahan, 2016. Regarding competing hypotheses, see, Engelberg & Kesselheim, 2016; Sampat et al., 2014, pp. 208–209). Vakili and McGahan investigate the incentive hypothesis in the newly TRIPS-compliant country regarding the increase of citation-weighted numbers of publications authored by scientists in the newly TRIPScompliant nation. Their empirical research supports the theory. Potential Limits and Moving Forward Vakili and McGahan (2016) provide significant evidence regarding the incentive hypothesis derived from the institutional protection. In this regard, WHO’s new TB Strategy is promising in expanding local research capacity in combination with the TRIPS Agreement. A potential limitation is an economic condition, setting aside from the problem of translational capacities. A policy tool to impose ownership rights work when willingness and ability to pay meet the social value of the innovation (Palfrey, 2017). Based on Palfrey (2017), a patent system works well in developing goods for diseases that afflict relatively wealthy populations (even in less developed countries), in which willingness and ability to pay are proxies for the social value of the innovation. Thus,

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intellectual property cannot be an incentive in developing goods for the global poor (Palfrey, 2017). In this regard, the additional arrangements are necessary. 2.2   Intellectual Property Policies as Governance Mechanisms Governance Strategy of Semiconductor Consortia When uncertainty, externality, as well as tangible costs of development are high, related parties often find a way by building consortia. A consortium is an extension of partnerships and a system of research collaboration in the field of market failure. The basic research of semiconductors and network development technologies often adopt the method of the consortium. It is because, in these fields, the standardization and network externalities play as a significant component for the subsequent product development. Then, questions are (i) who owns property rights of inventions and responsibilities derived from them, (ii) how they manage ownership among collaborators and users outside of the consortium. In this context, intellectual property policies of consortia are a governance mechanism that regulates the boundary of regulation.1 For the above questions, this section compares intellectual property policies of consortia in the semiconductor, network development, and life science/biomedical fields. The case selection criteria are the involvement of private incentives and the development of common goods. Cases and the justification of the case selection are as follows (Table 1). As a variation of policies is more significant in biomedical consortia, this study starts with simplifying traditional patterns of semiconductor/ web standards. Classic Cases of Semiconductor and Network Development Consortia The early stage SEMATECH is a classic case of a successful government-industry consortium. It officially belongs to Program of the US National Innovation System. The core component relies on expanded funding by the Defense Department for private technology development in dual-use technologies. The justification is that the economic benefits of spin-offs from military to civilian technology application had declined (Mowery, 2001). This arrangement significantly improved the

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Table 1  Case selection and the justification Cases

Justification

Int. SEMATECH (1996–2015)

A nonprofit, membership organization whose membership consists of industry organizations. Include academic KIOs in projects. Activities include building international de-juri standards, technology and roadmap development that will overcome physical limits A nonprofit research institute initiated by academic scientists with a support of the state government. Programs include diversified actors. Activities include building international de-juri standards, technology development that will overcome physical limits, and developing chip-related biotechnology A nonprofit consortium in which member organizations (industry and academics), full-time staff, and the public work together to develop web standards A private–public partnership (PPP) that involves academic KIOs. Funded by governments, foundations, and transnational companies (TNCs). Aims to identify three-dimensional structure of proteins and taking compounds through to proof of clinical mechanisms (POCM), and placing the data and reagents generated into a public domain (source presentations by SGC; OECD, 2011) A private foundation (nonprofit) that manages a registry of standard biological parts and makes them available through a standard form open source license (OECD, 2011) A pilot project by NCI and NHGRI. Inherit Bermuda principle of Human Genome Project with an exception of individually-identifiable data A public–private consortium. It aims for building stem cell banks, open protocols, and standardized systems in stem cell technology (OECD, 2011)

IMEC

W3C

Structured Genomic Consortium (SGC)

BioBricks Foundation (BBF)

The Cancer Genome Atlas (TCGA)

Stem Cells for Safer Medicine (SC4SM)

(continued)

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Table 1  (continued) Cases

Justification

United Nations Development Programme (UNDP)/World Bank/WHO Special Program for Research and Training in Tropical Diseases (TDR)

A partnership of multilateral KIOs built by WHO. Aims for (i) R&D for targeted diseases, and (ii) strengthening the R&D capacity of disease-endemic countries. The process consists of comparative biochemistry (conducted mainly in academic KIOs), synthesis of chemical compounds, biological screening and clinical evaluation (Lucas, 2002). A project involves academic KIOs and industry organizations

performance of US semiconductor firms (Macher, Mowery, & Hodges, 1998). The Federal funding for the SEMATECH ended in 1996 (Mowery 2001). SEMATECH changed its aims to establishing global standards and roadmaps. It began to include foreign companies in members and academic KIOs in their projects. This stage of SEMATECH (1994–2015) is called as International SEMATECH (Int. SEMATECH). The industry-supported semiconductor consortia widely adopted its basic intellectual property policies. They correspond to a concept of a bundle of property rights. In general, the bundle of property rights includes the following. • Right to possess, use, manage, and receive income; • Powers to transfer, waive, exclude, and abandon; • The liberties to consume or destroy; • Immunity from expropriation without compensation; • The duty not to use harmfully; • Liability for execution to satisfy a court judgment; and • Limited property rights such as easements, bailments, franchises, and some licenses (Munzer, 1990, pp. 22–23). The policy of the unbundling of ownership, rights, and obligations is expected to mitigate conflicts that come from ownership of rights. The following policies represent a part of a bundle of property rights and the unbundling of them.

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i. Assign ownership of intellectual property to an inventor’s organization (including SEMATECH). ii. The assignee provides a free license to the collaborating organizations in developing the intellectual property. iii. A contract binds the participating organizations not to use the intellectual property created at the consortium (foreground IP) without the approval of collaborators. The reason for the concentration of ownership is to remove barriers to the enforcement. If the members divide the ownership into several organizations, such division adds transaction costs to obtain the agreement of the enforcement from all of the collaborating organizations. Trouble often occurs when a company that violates intellectual property right is a customer of one of the collaborating companies. In this manner, the intellectual property policy functions as a governance strategy to avoid a conflict of ownership rights ex-ante. At the same time, collaborating companies have rights to collect economic benefits by using the power in their product development. Therefore, the consortia distribute commercial interests fairly among the collaborating companies, ex-post. Thus, the intellectual property policy is managing the boundary of regulation as well as the rights and obligations derived from it. IMEC, a Belgium-based consortium, has a slightly different policy. Its policies in the Industrial Affiliation Program are: i. to avoid intellectual property blocking for partners, ii. securing intellectual property rights for partners, and iii. enabling publications.2 For these purposes, IMEC shares rights with partners through license and co-ownership (Van den Broeck & Ryckaert, 2006; Ryckaert & Van den Broeck, 2008). The following policies are for programs in which diversified actors including academic KIOs participate. i.  R&D contracts without further research blocking clauses (co-ownership) ii. Enabling IP protection and publication iii. Restrictive IP policy for predetermined technology (IMEC ownership, IP exclusions in funded projects)

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iv. IP bundling v. Fair financial compensation (Van den Broeck & Ryckaert, 2006). The additional distinguishing feature is that IMEC allows the partner organizations to use the intellectual property freely after the completion of the project. This difference from the SEMATECH policy arises from the difference of patent rules in the USA and the EU. In the USA, under 35 USC 262, joint owners have a right to fully exploit the patented invention including transferring, subdividing, or licensing a patent without the consent of the co-owners. Therefore, a contract that binds partners’ behaviors is necessary. On the other hand, in the most of EU countries, co-owners do not have a right to license or assign interests of the intellectual property without the consent of other co-owners. They just have a right to use the intellectual property for their benefit without accounting to the other owners (Banks, Datlow, Felder, & Wolfram, 2011; Bedlebos, Cassiman, Faems, Leten, & Looy, 2012; Merges & Locke, 1990). In this regard, the consequence of applying the policies are the same between IMEC and SEMATECH. Also, one of IMEC’s objectives is to avoid blocking against members’ product development. As inventors own the foreground intellectual property jointly with IMEC, the further contract is not necessary to prevent blocking activities. IMEC’s policies emphasize the protection of members from environmental forces in addition to managing members’ relations. Thus, the strategy to unbundle ownership and property rights depend on the regulatory rules and objectives of consortia. Proposition 1 Intellectual property policies of consortia work as an ex-ante/ex-post governance mechanism. It manages the boundary of regulation and the rights and obligations derived from the property. They unbundle the ownership and rights and fairly distribute benefits among collaborating members. Contents of policies vary depending on the regulatory rules and objectives of consortia. In general, project-based intellectual property policies regulate the relations with non-members. Examples are as follows. i.  Participants declare non-assertion. Companies outside of the consortium receive licensing under the Reasonable and NonDiscriminatory (RAND) condition. When an invention becomes a

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global standard, third parties receive the licensing under the Fair, Reasonable, and Non-Discriminatory (FRAND) condition. ii. In-licensing from universities began to gain significance. Fair compensation to academic KIOs is a part of IP policy (see, Broeck & Ryckaert, 2006). Governance Strategy of Network Development Technologies World Wide Web Consortium (W3C) is a good example that developed Reasonable or Free (RF) licensing policies. The web standards they developed define an Open Web infrastructure for application developers.3 In 2004, W3C formulated RF licensing requirement as a part of their IP Policies. It binds members regarding any Essential Claims4 related to their work in a particular Working Group (Art. 3.1). Although the interpretation of “Reasonable or Free” needs discretion of Working Groups, this obligation binds participants throughout the life of the patents, regardless of the changes of the participation status of members (Art 3.1). In this consortium, members are mainly academic KIOs and large and small for-profit entities that develop and ensure interoperability of web products. As developed standards assure network externality, benefits are distributed not only to members but the relevant industries, end-users, and the global society. Also, the scope and organization types of participants and the beneficiaries are broader. Thus the coverage of a common good is more comprehensive, because of the nature of the technology and their business models. Despite these differences, the Proposition 1 is applicable also to the governance strategy of W3C. Biomedical Discoveries and Intellectual Property Policies A problem is whether the above governance model works for biomedical discoveries. In this field, an exclusivity of an essential intellectual property right has played a significant component for drug development in the pharmaceutical industry. Although a collaborative model with small biotech firms has established, it is unlikely that the core components of revenue sources have rapidly changed. The advanced high-throughput technology and know-how of pharmaceutical TNCs continue to enable them to discover new chemical structures. Also, new combinations of

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small molecules and the repurpose allow them to build alternative ways to convey improved efficacies through a drug. On the other hand, it takes a long time for scientists to translate biomedical discoveries. It can be a good reason to build consortia to shorten the prolonged period by reducing transaction costs. Observations reveal that their ownership policy can be classified as follows: i. Open source/open access/clearinghouse (hereafter, Open source), ii. Centrally owned, and iii. Strengthening confidentiality. Table 2 summarizes the intellectual property policies of selected life science and biomedical consortia. The most significant features come from the privacy and confidentiality. In TCGA, there are two-tiered data access polity: open access data tier for non-identifiable specimens; and controlled access data tier for identifiable specimens defined at 45CFR46. 102(f ) (National Cancer Institute [NCI] & National Human Genome Research Institute [NHGRI], 2014, pp. 5–6, 12–13; Also see, Contreras, 2011). Consider that humans possess ownership and (limited) property rights such as excludability over their bodily parts. Specimen donors maintain a right of privacy over their personal information (see, Munzer, 1990, pp. 92–93; the Health Insurance Portability and Accountability Act: HIPPA) that can be identifiable through research tasks (such as data integration) and coding systems (NCI & NHGRI, 2014). Related to the technological development of high-throughput whole genome-wide sequence and the increased risk of re-identification, the rules in 2005 for informed consent of a donor of tissues and cells were revised in 2014. A donor should consent regarding the broad sharing of the specimen and clinical data that go through the internet, the possibility of future research use, and the use of an electrical database with partial public access and the risk of loss of privacy (NCI & NHGRI, 2014, pp. 7–8; TCGA website). In this context, investigators have a responsibility to protect subjects. Contractual Obligations for Knowledge Exchange A consortium is a system of reciprocal knowledge exchange that entails legal and ethical obligations. As mentioned earlier, the components of knowledge exchange consist of (a) disclosure, (b) integrity, and (c)

Focus on basic synthetic biology

Focus on discovery and translational science

Relevance with academic sciences

Distinct IP policies

BBF Private Foundation (nonprofit) Place data to a pub- A standard format of lic domain. open source license Delay patent filing Those contribute to until positive POCM standard biologresults are obtained. ical parts declare Assure free access to non-assertion global academia Users can use, design, and improve standard biological parts without obligations to return back their innovation to BBF

SGC PPP

Consortia Organization forms

Open source/open access/clearinghouse TCGA A joint project of two federal agencies (i) Non-identifiable dataset: Open access (ii) Individuallyidentifiable data: Controlled access. Require users’ acknowledgement of Data Access Certification that includes restrictions on research use, security, transferability, among others The absence in the Certification of the protected period for data generators Generate genomic sequence and related data. Also track phenotype data

Table 2  Intellectual property policies of life sciences/biomedical consortia Strengthening confidentiality

(continued)

The process includes comparative biochemistry mainly conducted in academic KIOs

TDR Alliance by three multilateral KIOs Nondiscriminatory Industry organizalicenses to members tions do not have and third parties to disclose the In-licensing intelchemical structure lectual property of of a compound in members and makes the course of the it available to users project of SC4SM registry Industry organizations can use TDR’s screening facility

SC4SM PPP

Centrally owned

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OECD (2011), Website of BBF, https://biobricks. org: last access in Jan. 2018

Sources

Oxford and SGC (2015), OECD (2011), Website of SGC, http:// www.thesgc.org: last access in Jan. 2018

Reciprocity

Knowledge Reciprocity exchange mechanism

Open source/open access/clearinghouse

Table 2  (continued)

NCI & NCHRI (2014); Contreras (2011), Website of TCGA, https:// cancergenome.nih. gov: last access in Jan. 2018

Reciprocity

OECD (2011), Website of SC4SM, http://gtr. rcuk.ac.uk/projects?ref=100607: last access in Jan 2018

Reciprocity

Centrally owned

Reciprocity Industry organizations provide services to TDR regarding (i) special reagents required by research scientists, (ii) Good Manufacturing Practice for biological reagents that will be tested in humans, and (iii) joint efforts in clinical evaluation Lucas (2002), Website of TDR, http://www.who. int/tdr/en: last access in Jan. 2018

Strengthening confidentiality

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contractual legality (Choi & Millar, 2005). Intellectual property policies of consortia fulfill these components. (a)  Disclosure is a prerequisite of joint research. Its requirement encompasses the background intellectual properties (ex-ante monitoring) and all of the foreground intellectual property generated in the course of collaboration (intellectual property policies of several consortia; EC, 2002). By disclosing background intellectual properties, collaborators can use the communicated knowledge without infringing the intellectual property right and keep confidentiality (EC, 2002). (b) The process of technology development will assure integrity. In the consortia, the produced knowledge is shared and integrated into the technology development that the participating organizations will subsequently use in their product development. Though the competitiveness of end products depends on other internal and external factors of firms, the newly developed essential technology can positively affect their overall competitiveness. If some organization violates integrity, the bias will spread and, in turn, negatively impact the violating organization. Therefore, strong pragmatic motives work to validate the quality of knowledge exchanged (see, Choi & Millar 2005, p. 76, As empirical evidence, see, Okada, 2018). Also, under laws such as the US 42 CFR, relevant agencies have the authority to review, monitor, and investigate allegations of research misconduct. In the case of misconduct, legal power charges several layers of criminal sanctions. (c) Contractual legality provides a framework of ethical behaviors of participants (see, Choi & Millar, 2005). Here, a potential problem arises from national laws of participating organizations. The federal laws vary regarding intellectual property (EC, 2002), informed consent processes, and governance systems (Choi, Hilton, & Millar, 2004), etc. While harmonizing efforts are taking place, investigators have a responsibility to assure that contents of contracts and research procedures such as informed consent processes meet all of the participating KIOs’ national laws. Future research should more fully address the variation of governance.

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Based on the above: Proposition 2  The trusteeship governance should be enhanced to manage a bundle of rights and related obligations at the boundary of regulation in the reciprocal knowledge exchange. This direction is also consistent with the common good-based stakeholder theory in which KIOs’ commitments to benefit science communities originates in their obligations to seek benefits for themselves. Extensions to Schemes of Multilateral KIOs In the case of global health PPPs, there are several endeavors of WHO to manage the boundary of regulation. In some cases, WHO assured the protection of confidentiality of TNCs in PPPs. However, in addition to strengthening confidentiality, there are also other alternatives. Some cases require non-assertion of intellectual property rights, such as in donation programs. There is also a scheme of voluntary licensing in which international organizations fund for-profit organizations (Palfrey, 2017). In this scheme, although for-profit firms retain their ownership and exercise licensing, the payment is made by international organizations. In these cases, private incentives that come from ownership components are controlled and detached from industry organizations. Such detachment is complemented and compensated by other arrangements. At the same time, there are situations in which parties renegotiate commercial contracts in unanticipated circumstances. Also, courts can declare contracts unenforceable on the grounds of public policy (Farnsworth, 1998; Roberts, Breitenstein, & Roberts, 2002). It is a compulsory licensing that is allowed to national authorities as the flexibility of TRIPS Agreement (Correa, 2014, pp. 429–430). Considering several schemes of health sector development, the ownership of property right itself does not hinder the common good. Instead, the contents of property rights themselves are an essential part of building a common good. What is problematic is an application of the rights in a manner that harms or disrespects rights of normative and derivative stakeholders. Therefore, this study sets the Proposition 3 as follows. Proposition 3 Intellectual property policies of consortia work as a governance mechanism that manages and controls the boundary

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of the regulation regarding the rights, obligations, protection, and non-assertion. The strategy includes unbundling of ownership and property rights that vary depending on the nature of technologies and regulatory rules. 2.3   Governance Theories and Property Rights In the management theories of governance, the ownership accompanies with a control in the framework of the agency theory. Principals own control rights over the behaviors of agents by ex-ante/ex-post monitoring and the enforcement. In consortia, non-owner collaborators do not necessarily abandon their rights. They have rights to use and collect benefits while having obligations to monitor infringements and cooperate in the enforcement. What is distinct for the consortium is that participants enter the cooperative scheme to create common goods with their free and voluntary will. The contractual obligations emerge regarding disclosure, RF or FRAND licensing, non-assertion or transfer of ownership in some cases, and others. They are also subject to public law, multilateral agreements, and a national authority regarding the enforceability of contracts and licensing. Instead, they have a right to use and benefit from produced common goods that might otherwise be not achievable by themselves. Here, consortia are also scientific KIOs and participating organizations are under the trusteeship governance of KIOs.

3  Self-Regulation of Science and Moral Identity 3.1   Integration Towards Innovation Pathways Moral Identity and Moral Ownership Additional efforts are in need to manage boundaries of autonomy and identity to integrate insights from humanistic disciplines. In the case of stem cell science, competing moral values yielded diversified moral judgment, motivations, and behaviors (see, Banchoff, 2011; Gottweis, Salter, & Walby, 2009). Such diversity stems from moral identity and moral ownership of individuals (Hannah et al., 2011).

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Here, moral identity denotes morality that influences identity, whereas moral ownership denotes a construct that will create different causations and contingencies in the psychological approbation process (Hannah et al., 2011). In this domain, evidence reveals that self-identity strongly affects individuals’ self-regulation of thoughts and behaviors (Carver & Scheier, 1998; Hannah et al., 2011; Lord & Brown, 2004; Markus & Wulf, 1987). On the other hand, moral ownership affects the extent to which members perceive moral responsibility for the ethics of their actions, others, and their organizations (Bandura, 1991, 1999, as cited in Hannah et al., 2011). Thus, individuals’ psychological and behavioral responses to ethical dilemmas in a given situation vary (see, Trevino, 1982). When extending the above to KIOs’ behaviors, the study sets the following proposition. Proposition 4 The moral identity and moral ownership of academic KIOs define their scope of self-regulation and a sense of moral responsibility and behaviors regarding ethical issues of emerging sciences. Human Right and Science Governance Here, the concept of human right is not necessarily grounded in national laws. Regarding this point, this study identifies two sources for the human right concept: i. a liberal, common good conception of justice and ii.  The property theory that is extended to include a social contract (Donaldson & Dunfee, 1994; Donaldson & Preston, 1995, p. 81; Finnis, 1980, pp. 198–230; Freeman & Evan, 1990; Rawls 1993/1999, pp. 536–537, 546–547, 550; UN, 1948). Then, the nature of self-regulation and moral responsibility that a KIO perceive for human right issues must vary. From KIOs’ perspectives, this study extends the Proposition 4 as follows. Proposition 4′  The moral identity and moral ownership of academic KIOs define the scope of self-regulation and a sense of moral responsibility that KIOs perceive for human right issues. They have a potential

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to affect the research agendas that KIOs select related to integrating insights from humanistic disciplines into sciences. 3.2   Bridging Mechanisms for Governance Variations in the Trusteeship Governance The Proposition 4′ will create changes in trusteeship governance models. Think about the German case of the stem cell research (Chapter 5). When conflicts emerged between scientific values and the public, the public built a broader coalition and used the government to limit the autonomy of science by laws. After two decades, academics are forming an alliance that ranges from sciences to moral philosophy to request regulatory authority to specify the threshold element of odre public. On the other hand, in the USA, moral stakeholders competed for each other by advocating thoughts of harm to human rights and a right of reproductive justice. They developed loosely coupled coalitions with academic KIOs. Under this condition, professional regulators have used the principled-based bioethics to generate policy tools. IRB is working as a catalyst among regulators, KIOs, and the public. Also, written contracts have additional technical advantages (Craswell & Schwarts, 1994; Hart, 2003; Palfrey, 2017). It not only internalizes subsequent costs after the completion of a common good (Hart, 2003) but the contract process triggers organizational learning in both parties. This process deepens trust and trusteeship norms (Argyris, Bercovitz, & Mayer, 2007; Lumineau, Frechet, & Puthod, 2011). Some scholarship takes the contract as the undermining of trust. A possible explanation for this thought is that contract, and its associated rights and obligations tend to be understood solely regarding peculiar rights (Finnis, 1980). However, the evidence of Argyris et al. (2007) suggests that parties with different moral identity and ownership can transcend their heterogeneity in a continuous contract process. 3.3   Reaching to Common Ground with the Agency-Based Governance Agency theory sees an organization as a bundle of contracts (Demsetz, 1983; Fama & Jensen, 1983; Cf. Munzer, 1990, pp. 320–324; Sherer & Leblebici, 2015). On the other hand, standing on the stakeholder

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theory, various groups own moral interests that are referred to as a stake even though they have no contractual relations with the organization (Donaldson & Preston, 1995). Although scholars tend to see stakeholder theory as a counterargument against agency-based governance, agency theory does not confine the rights to shareholders. Nor it does not limit contract solely to monitor performance and opportunistic behaviors of a counterpart. Instead, the contract can deepen trust and trusteeship behaviors. Contract plays as an essential component in the reciprocal (scientific) knowledge exchange (Choi & Millar, 2005; Hart, 2003; Rangan, Samii, & van Wassenhove, 2006). In this sense, it is possible to harmonize stakeholder theory with the agency-based governance with a catalyst of contract and property right in reciprocal scientific knowledge exchange. Proposition 5 In the reciprocal scientific knowledge exchange, trusteeship governance of KIOs can be extended to integrate agency-based governance and stakeholder theory with a catalyst of contract and the unbundling of property right.

4  Conclusion This chapter examined science governance in integrating insights that come from humanistic disciplines into science. Bodies of investigations are intellectual property policies in research consortia in which several motivations coexist. It also considered moral identity and ownership that determine self-regulation and perceived moral responsibilities of academic KIOs. Economic theories suggest that imposing ownership rights of public goods improve policy outcomes. On the other hand, a potential tension emerges related to proprietary knowledge and science commons (CookDeegan, 2007) in joint research. In this regard, in a research consortium setting, intellectual property policies work as a governance mechanism that manages the boundary of regulation, a bundle of rights and obligations derived from property. Strategies include unbundling of rights, limiting powers (such as non-assertion), a detachment of private incentives, and complementing arrangements to reward inventors and collaborators ex-post. In this space, the trusteeship governance is enhanced to manage the boundary of regulation in the reciprocal knowledge exchange.

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This direction also has an overlap with the common good-based stakeholder theory in which KIOs’ obligations to benefit third party beneficiaries come from their duties to seek benefits for their own. In this sense, the trusteeship governance can harmonize with the agency-based management with a catalyst of contract and property right. This direction is also consistent with the common good-based stakeholder theory. On the other hand, the moral identity and moral ownership of academic KIOs define the scope of self-regulation and a sense of moral responsibility that KIOs perceive for ethical issues on human right. Thus, a diversified combination of moral identity and moral ownership is considered to affect the research agendas in integrating insights from humanistic disciplines into sciences. Such combination may further change the zones to regulate emerging sciences. Even so, global alliances and consortia can create a global network to practice science.

Notes 1. Regarding the legal and economic perspective, see, Granstrand (2006). This perspective should be differentiated from the governance defined as the management of boundaries. 2. This policy has a significant meaning, because the grace period is restrictive in the European patent laws. See, OECD (2011); The EPO’s Economic and Scientific Advisory Board (ESAB) (2015). 3.  The website of W3C. https://www.w3.org/Consortium/. Accessed on October 4, 2017. 4. Regarding Essential IPR, see, Bekker and Updegrove (2013).

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CHAPTER 8

Conclusion

1  Toward Governance Models for Academic Knowledge-Intensive Organizations This book investigates (a) factors that make specific science field understudied, particularly those that bring the threat of captures embodied in the content aspects of knowledge corruption and (b) the potential directions to extend trusteeship governance of academic knowledge-intensive organizations (KIOs). This study defines governance as the management of boundaries and relations (Choi & Millar, 2005; Sherer & Lebliebici, 2015). Then, this book modifies the boundaries of general organizations and explores academic KIO specific ones, that consist of:

i. The boundary of knowledge, ii. The boundary of autonomy, iii. The boundary of regulation, and iv. The boundary of identity.

This study extends trusteeship governance and self-regulation of academic KIOs that encompass these boundaries. Factors examined are (i) barriers to the institutional process of validation, (ii) impacts of moral stakeholders on the zone definition and legislative debates, (iii) dependence effect derived from alliance designs, and (iv) intellectual property policies that manage relations in consortia. Methodologies are conceptual framing and case analysis. The focus is on © The Author(s) 2019 E. Okada, Management of Knowledge-Intensive Organizations, https://doi.org/10.1007/978-3-319-97373-9_8

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the governance over contents aspects of knowledge corruption of academic KIOs that may have specific scientific fields understudied or politicized. Fields of sciences researched in this book are the translational science of cancer-induced bone pain and epilepsy (Chapter 4), stem cell research (Chapter 5), and vaccine science for multidrug-resistant tuberculosis (MDR-TB) (Chapter 6). In these fields, institutional, technological, and morality-based uncertainties involve. Even so, it is likely that the capture (Carpenter, 2014; Carpenter & Moss, 2014) has occurred in vaccine sciences for MDR-TB. From the perspectives of management and governance of KIOs, the object of control in this field is unbalanced dependence in research partnerships. In general, co-specialization is a promising alliance design. It obtained the institutional legitimacy from the United Nations (UN) (Stevenson, 2016. Also see, Mahoney & Maynard, 1999) by mobilizing different and indispensable resources and knowledge (for example, see, WHO, 2008), globally. However, a general pattern in aligning incentives and restoring balances does not necessarily work in the PPPs partly because of inconsistencies of the internal decision process. In alliances that involve academic and multilateral KIOs, partnerships entail a potential threat at boundaries of autonomy and identity by subordinating KIOs’ unique responsibilities and conscience (Stevenson, 2016) in the discovery of mechanisms and the translation. In confronting issues, this study enhances trusteeship governance and self-regulation of academic KIOs to seek overlaps with other governance models. It contributes to the governance and management frameworks of KIOs by adding threshold elements that make certain academic sciences understudied. In doing so, this study suggests directions to extend the governance model specific to academic KIOs. As a practical implication, this study suggests a potential path to integrate insights from humanistic disciplines into academic sciences.

2  Self-Regulation and Trusteeship Governance Self-regulation of science has been an established governance scheme for over centuries, even if it is under the ongoing renegotiation process. It has been studied mainly in the philosophy of science. A self-correcting nature of science and a system of peer-review support the self-governance. On the other hand, management studies recognized self-regulation as one of the psychological attributes, not a governance model. It is in recent years that management studies began to identify the self-regulation as an attribute of the trusteeship-perspective governance in professional

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service firms (PSFs) (Greenwood, 2007; Sherer & Leblebici, 2015; von Nordenflycht, 2010). The trusteeship governance is a governance model that has developed from research streams of PSFs. In this model, trustees manage resources of the organization for the benefit of third parties. It consists of selfregulation of constituents, trusteeship norms, and internal codes that regulate trusteeship behaviors of professionals (Sherer & Leblebici, 2015; von Nordenflucht, 2010). As stated in Chapter 1, the definition of PSFs by Hinings, Muzio, Broschak, and Empson (2015) does not correspond to components of academic KIOs. Academic KIOs have their unique primary activities, the nature of knowledge to be produced, and identity. However, they share the common constructs regarding the governance model. At the same time, since academic KIOs have distinct organizational boundaries, the trusteeship model should be extended in several directions. This book sought a path to reach the trusteeship governance and self-regulation as follows. 2.1   Directions of Extension: Agency-Perspective Governance One potential route is to create overlaps with agency-perspective governance. The underlying assumption of agency-based governance is the inconsistent objectives and information asymmetry between the principal (an owner) and agents (nonowner managers) and its resultant opportunistic behaviors of agents (Williamson, 1979). In this space, governance is expected to optimize the agency costs, ex-ante and ex-post. Agency-perspective governance has a theoretical conflict with the organization structure of KIOs that has a characteristic of no-outside ownership. Also, the assumption of the opportunistic behaviors of agents (academics) is contradictory to the motivation of academics to pursue truth. Nevertheless, the extension is possible by adopting a legal framework that provides ethics foundation (Choi & Millar, 2005) and legal assumptions that a bundle of rights constructs property. Regulatory and Governance Strategies The agency perspective is an essential component of the economic structure of corporate law. The expected function is to optimize agency costs in governance. A contract is a typical device of governance that consists of voluntary and unanimous agreement among affected parties (Easterbrook & Fischel, 1991. Also see, Choi & Millar, 2005; Rangan, Samii, & van Wassenhove, 2006).

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Here, the ex-ante optimization does not necessarily assure the ex-post optimization of the agency costs (Easterbrook & Fischel, 1991) since an incentive that motivates agents does not always ensure a fair distribution of results. Therefore, a scheme of incentives is necessary to produce desired and optimized behaviors of agents. Also, contractual arrangements do not always protect individuals in a disadvantaged position in contractual relations. In this domain, regulatory policies defend disadvantaged parties in transactions. For example, under the definition of the human subject at 45CFR46 102(f), obtaining identifiable private information or identifiable specimens for research purposes constitutes human subjects research (HHS, 2008 Guidance) that Code of Federal Regulations regulates. Armour, Hansmann, and Kraaman (2017) characterize regulatory strategies to be extended to protect disadvantaged parties in any class of contractual relationships, whereas they characterize governance strategies as hierarchical elements of the principal–agent relations. Regarding the legal scheme, Armour et al. (2017) map the regulatory strategies into a two-dimension dyad that consists of (i) regulatory strategies for ex-ante/ex-post schemes and (ii) governance strategies for ex-ante/ex-post schemes. Here, rules correspond to an ex-ante scheme to mitigate agency constraints whereas standards correspond to an ex-post scheme to reduce agency constraints. For example, Chapter 4 examined rules of informed consent contained in 45CFR46. They are ex-ante scheme to protect research subjects. A legal obligation to disclose conflicts of interests that may affect research subjects also conforms to the ex-ante scheme. Chapter 4 framed informed consent as a buffer mainly to manage the boundaries of knowledge and autonomy. However, these ex-ante schemes of regulatory strategy are also controlling the boundary of regulation of academic KIOs by introducing external rules that limit their self-regulation. At the same time, Armour et al. (2017) map the governance strategies into a dyad that consists of the ex-ante/ex-post schemes and three dimensions of (d) appointment rights, (e) decision rights, and (f) agency incentives. Among them, the aspect that is particularly relevant to this book is (f) “agency incentives.” This dimension consists of the ex-ante scheme of trusteeship associated with agency incentives and ex-post scheme of reward that is relevant to agency incentives.

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Alignment of Agency Incentives In the law, the incentive alignment corresponds to trusteeship strategy that spans both regulatory and governance strategy. It is designed, ex-post, to adequately reward agents for their performance in advancing the interests of their principals (Armour et al., 2017). In this regard, ownership, contract, and incentives construct agency-perspective governance in management studies (Choi & Millar, 2005; Sherer & Leblebici, 2015; von Nordenflycht, 2010; Williamson, 1979, 1981). KIOs should design incentives ex-ante to preserve trusteeship behaviors of constituents and align incentives to reward them ex-post adequately. They need to manage relations between ownership, rights, and obligations of inventors, collaborators, other components, and beneficiaries in building alliances. In the vaccine science, a change of an entire system of environmental forces (Pfeffer & Salancik, 1978) necessitates multilateral KIOs to mobilize industry resources and processes to achieve goals of vaccine programs, which legitimately introduced industry values to set global vaccine strategies. Here, values denote a standard of prioritization of decisions (Christensen & Overdorf, 2000/2001). On the other hand, despite the overall progress of the entire genome sequencing of bacteria followed by reverse vaccinology and the next generation technologies (Rappuoli, Blank, & Lambert, 2011), new vaccines for MDR-TB are understudied (Chapter 6). In general, internalization is appropriate to facilitate scientific knowledge exchange while reducing transaction costs (see, Riordan & Williamson, 1985). A potential tension emerges from proprietary knowledge. In this regard, legal practices frame a property as relations of parties that are constructed by a bundle of rights (Munzer, 1990). This insight is helpful particularly for ex-post optimization by unbundling rights. The intellectual property policy of research alliances and consortia functions as a governance mechanism by managing internal and external relations that consist of bundles of rights. In this sphere, agency-perspective governance is commensurate with trusteeship perspectives catalyzed by the control of property rights. Regarding the delivery phase optimization, Palfrey (2017) provides real-world evidence of incentive alignment and downstream rewarding in partnerships for diseases that are prevalent only in developing countries.

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2.2   Directions of Extension: Stakeholder-Perspective Governance Stakeholder theory incorporates Kantian ethics into organization theories that have a potential to affect strategic priorities and resource allocation (Freeman, 1984). Management studies tend to regard stakeholder theories as a counteracting thought to agency-based governance: it focuses on principal–agent relations to design to introduce desired behaviors from principal perspectives. However, as below mentioned, the stakeholder theory has overlaps with both of trusteeship governance and agency-based governance. This book adopts Phillips (2003)’s fairness-based stakeholder theory that is consistent with fair transaction model of biomedical society, and Argandona (1998)’s liberal accounts of common good-based stakeholder theory. Fairness-Based Stakeholder Theory Difficulties in obtaining the institutional validity make specific academic research understudied. Translational sciences for cancer-induced bone pain (CIBP) and epilepsy fall into this category. As a translational science is research in its nature, a probability of harm derived from unknown uncertainty is much higher than medical practices. Also, the direct beneficiary is not a subject. Therefore, a stronger buffer should be introduced to insulate relations between subjects and KIOs. In the translational science, laws regulate the informed consent process that manages the boundaries of knowledge and autonomy between an academic KIO and subjects. A subject enters a cooperative scheme (Phillips, 2003) of the research scheme with valid consent. The 45CFR46 prohibits KIOs from proceeding the process without an institutionally validated consent. Informed consent document is a legal document that bears enforceability Here, primary investigators owe not only legal but moral responsibilities in research (Stigloe, Owen, & Macnaghten, 2013) to protect the best interests of subjects. Therefore, the trusteeship governance is relevant in this space. The preconditions of valid informed consent are subjects’ understanding and self-determination capacity that includes “appreciation” and “reasoning.” The physical and psychological conditions of subjects often compromise the prerequisite of informed consent in neurobiological studies. The issues of institutional validity are one of the factors that the area of these studies does not proceed.

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In the bioethics, the fair transaction model has relevance for s­afeguarding valid consent under challenging situations. In this model, investigators and their subjects are treated fairly by giving due consideration to the reasonable limits of an investigator’s responsibilities to ensure adequate understanding on the part of subjects who consent to research (Miller & Wertheimer, 2011). Such relations with subjects have a consistency with the fairness-based stakeholder theory. Under this theory, stakeholders are those from whom the organization has voluntarily accepted benefits, and to whom there arises a moral obligation. Also, an individual enters a cooperative scheme with an organization by consenting (Phillips, 2003). In the translational science, the academic KIOs accept benefits of the scientific understanding from subjects and owe moral obligations to protect subjects’ best interests. Subjects enter a cooperative scheme with legally and ­institutionally validated consent. Thus, the moral obligations of investigators straddle between the trusteeship governance and stakeholder theory, whereas regulatory strategy imposes legal commitments to investigators. In this manner, translational science enhances the boundary of knowledge on biological understanding while limiting the boundary of autonomy of investigators. Such governance helps to balance autonomy of science and the protection (see, Armour et al., 2017; Resnik, 2008) of subjects. Measurement and Knowledge-Intensive Research Tools The translational neuroscience requires another consideration of governance. It is the methodology to measure scientific variants and consent capacity of subjects. In this context, this study focuses on measurement of consent capacity. The current methodology of assessing consent capacity of subjects does not necessarily fit subjects who have physiological and psychological difficulties in the communicative interaction. The exclusion of specific subgroups not only violates Kantian ethics of justice but has a potential to bias a scientific understanding. The biased science derived from the exclusion has a potential to bring inconsistencies in the subsequent application phases (see, Cohen & Graver, 2017). Although states have a choice architecture to determine a surrogate decision-maker, research shows that the surrogate’s decision does not necessarily represent best interests or actual consent of the subject. Therefore, efforts should be made to include

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subjects in a consent process which might otherwise exclude them through the institutional validation process. The advancement of a knowledge-intensive research tool will contribute to restructuring the methodology to measure the consent capacity in the future. Based on empirical evidence, minimally conscious state (MCS) subjects retain an ability to communicate with the aid of imaging technologies. In this sense, new methodologies underpinned by new technological development can invoke institutional change (North, 1990). As North 1990 states, new methods and technologies can mitigate the governance barriers. In this manner, fairness-based stakeholder theory and new methodologies bear a capacity to overcome the current institutional obstacles at the boundaries of knowledge and autonomy. Moral Stakeholders and Translation Pathways When the public develops moral stakes to academic sciences, issues come to more complicated. Moral stakeholders (Attas, 2004) often limit the autonomy of academic sciences by affecting regulatory policies and the zone definition. Zones construct a permissible and nonpermissible scope of science and innovation pathways (Barry, 2006; Faulkner, 2009), transnationally (Faulkner, 2009) or in a state-centric manner (Justo-Hanani & Dayan, 2014). When technological components reflect cultural values, policy legacies affect the resource allocation to critical actors (Banchoff, 2005). On the other hand, the US and German stem cell research policies show that, by starting from the similar policy legacies, research policies can diverge from a certain point. Chapter 5 shows that the behavioral difference in stakeholders stip­ulates the gap in research policies. In the USA, moral stakeholders who advocate opposing rights competed for each other that led to a federalism resolution, whereas German stakeholders took a coordinated action to develop to civic epistemology. The different societal justifications (Gottweis, Salter, & Waldby, 2009) on the emerging science are likely to manifest themselves in the difference in the cognitive process in framing sciences. In spite of regulatory and ethical constraints (Eisenburg, 2002; Hanson, 2002; Ossorio, 2002; Resnik, 2002), the US and German academic KIOs are overcoming resource constraints by building international alliances within the scope of laws and bioethics. The ultimate research outcomes and applications are relatively similar because of the institutional complementarity in a global level. After a decade, the EU academics that range from sciences to humanistic disciplines began to make a coalition to

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request the refinement of threshold elements of odre public, i.e., “cells” and “organisms that have a potential to grow into humans.” Then, the next problem is how to govern global research alliances of academic KIOs. Common Good-Based Stakeholder Governance While agency-based management focuses on aligning incentives in contractual relations, there is a limit from a private actors’ perspective when expected uncertainties and externalities are high in a scientific research alliance (see, Rangan et al., 2006). In such an area, a partnership with public actors is a preferred mechanism. Private–public partnerships (PPPs) have a potential to yield breakthroughs by configuring separate capabilities (see, Doz & Hamel, 1998) but also have a potential to generate captures (Carpenter & Moss, 2014; Radder, 2010; Stevenson, 2016) by mismanaging boundaries of autonomy and identity of KIOs. In this context, the private involvement does not conflict with the creation of common good as far as individuals participate with their own and collect values accordingly (Argandona, 1998). The common good is not necessarily an equivalent of social welfare or a public good (Argandona, 1998). This common good conception is closer to an enhanced mechanism of multiple strategic alliance. In this regard, Chapter 6 identifies dependence in partnerships as a source of captures. Among alliance designs, co-specialization is more likely to introduce dependence when parties’ unique contributions become unbalanced, and the distribution of reward is competitive (Doz & Hamel, 1998). In the case of firms, two equally self-interested partners would regain balance by pulling to their advantage and keep the alliance moving while continually monitoring the dependence (Doz & Hamel, 1998, p. 202). However, academic and public KIOs are not accustomed to “pulling strongly enough.” Also, a difference in the internal decision process has a potential to become an obstacle to restoring a balance. In the global health alliances, when a depended party perceives the ex-post incentives unfair, and it is tough to terminate relations, the depended party can be motivated to influence the agendas to collect benefits. One of possible, traditional patterns is the internalization in the scientific exchange stage, preferably funded by public sponsors (Cf. Coase, 1937; Doz & Hamel, 1998). This pattern facilitates knowledge exchange by reducing transaction costs (Riordan & Williamson, 1985) while, with

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the aid of infrastructure, clarifying each one’s functional responsibilities. On the other hand, recent phenomena are an establishment of biomedical consortia funded through PPPs. As seen in Structured Genomic Consortium (SGC), being funded through PPPs, participants facilitate proceed discoveries to proof of clinical mechanisms (POCM) and place the data and reagents generated into a public domain (OECD, 2011; SGC).

3  Intellectual Property Policies as Governance Mechanisms Then, a theoretical and practical question in governance is how to manage ownership in research alliances of KIOs. In this domain, two extremes, i.e., imposing ownership (Besley & Ghatak, 2001; Greco, 2015; Schitz, 2014) and the basis of the common good (Argandona, 1998) coexist. 3.1   Imposing Ownership Requirement First, imposing ownership requirement of public goods is a public policy tool to improve the welfare of recipients. This policy has relevance for the incentive hypothesis originated in the agency-theory-based regulatory and governance strategy (Armour et al., 2017). TRIPS Agreement (World Trade Organization [WTO], 1994) adopts this polity. Vakili and McGahan (2016) provide evidence that the protection of intellectual property right under TRIPS incentivized basic science research on neglected diseases, globally and locally, by expanding the downstream market. 3.2   Unbundling of Rights At the same time, what is essential is that imposing ownership components in public goods works under the condition where willingness and ability to pay meet the social value of the innovation (Palfrey, 2017). Intellectual property cannot be an incentive in developing goods for a population of the so-called global poor (Palfrey, 2017). Therefore, additional institutional arrangements are necessary for producing products for a population in resource disadvantaged countries and the low-income area in resource-rich countries. In this context, intellectual property policies of consortia, an extended form of partnerships, function as a governance mechanism. They unbundle and reconfigure rights and ownership, control power of property

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rights (such as non-assertion), and obligations derived from them. They design mechanisms to optimize incentives, ex-ante and ex-post. Here, a consortium is a system of knowledge exchange that entails legal and ethical obligations. The knowledge transaction types vary from internalization (scientific exchange), reciprocity, and a mixed form. The active consortia incorporate three dimensions of knowledge exchange proposed by Choi and Millar (2005), i.e., transparency, integrity, and contractual legality. For example, because of the disclosure of background intellectual property, collaborators can use partners’ intellectual property rights without infringement and keep confidentiality (EC, 2002). In succeeding consortia such as SGC, collaborating members share know-how in proceeding joint efforts (OECD, 2011). They are the basis of creating a common good starting from private incentive to seek their good. In this manner, trusteeship governance can be extended to have overlaps with agency-perspective governance and stakeholder theories. Both of fairness-based and common good-based theories have a connection with agency-theory-based regulatory strategies. Also, it is possible to partially merge the common good-based governance and agency-based governance with a catalyst of unbundling of property rights. Then, in practice, it is possible to proceed science research that is consistent with insights from humanistic disciplines.

4  Conclusion 4.1   Theoretical and Practical Implications This book identified threats that make specific scientific fields understudied. They are i. Difficulties in assuring the institutional validity of informed consent for particular subgroups in translational science, ii. The lack of specificity of threshold elements in morally sensitive areas, and iii.  The lack of alliance design that optimizes incentives, ex-ante/ ex-post, and manages the unbalanced dependence, particularly in co-­ specialization. The mismanagement will lead to the undermining of unique norms and responsibilities (Radder, 2010; Stevenson, 2016) of academic and multilateral KIOs in biomedical projects.

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Regarding the institutional validity of informed consent, the self-regulation of science is to be enhanced to connect to fairness-based stakeholder theory with a catalyst of fair transaction model. At the same time, regulatory strategies originated in agency-based approach provides additional protection for subjects who are disadvantaged in contractual relations. Also, scientists are mitigating issues through the development of measurement methodologies with the support of knowledge-intensive research tools. Regarding the morally sensitive areas, ambiguity leads to moral and emotional discourses that invoke political polarization. In the stem cell science, threshold elements were not evident between “cells” and “organisms that have a self-directing capacity to grow into humans.” Whether a subject matter is a technical interpretation or the interpretation of odre public, a specificity that ground on reasons is necessary. Regarding the lack of alliance design, the design should incorporate governance mechanisms to continually monitor unbalanced dependence that will affect unique responsibilities and conscience of KIOs. Also, the ex-ante/ex-post optimization of incentives depends on a design of intellectual property policies. In this sense, a design defines behaviors of organizational members and participants (see, Pfeffer & Salancik, 1978). This study has a potential to contribute to governance theories of academic KIOs by seeking directions to extend trusteeship governance and self-regulation of academic sciences. As a theoretical implication, it is possible to harmonize trusteeship-governance and agency-­perspective governance with catalysts of unbundling of property rights on the one hand, and regulatory and governance strategy of incentive on the other. This direction is also consistent with common good-based stakeholder theory that does not exclude the involvement of private incentives. Also, the trusteeship governance is to be extended to overlap with fairness-based stakeholder theory in the translational science with a catalyst of fair transaction model of informed consent. This direction is consistent with the regulatory strategy that defines additional protection to vulnerable parties in contractual relations. As a practical implication, the book provides evidence of the convergence of academic sciences and insights from humanistic disciplines suggested by Kunneman (2010). This convergence is possible through the designs of research alliances and corresponding governance models. The critical point is to design governance suitable to knowledge transaction types, control or detach property rights and private incentives, and unbundle and reconfigure property rights.

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4.2   Limits and Future Studies At the same time, there are several limits in this study. First, this book mainly focuses on developing propositions by reframing theories and the observation of newly emerging phenomena. This study investigates factors that make specific academic science fields understudied by basing on management and governance studies of KIOs. On the other hand, it is possible for different theories and neighboring disciplines to analyze issues differently. In this regard, this book does not discuss other possible interpretations. Therefore, empirical evidence should be added to support, extend or reject propositions. Also, the observations of the samples are likely to be biased to scientists’ populations who are engaged in the breakthrough fields, accustomed to the transparent and reciprocal knowledge exchange, have proper organizational support, and a foundation of bioethics. On the other hand, there are many academic KIOs and academic scientists who make a significant contribution to disciplinary knowledge under several constraints. Thus, future research should enhance the methodologies and the scope of sample. Second, systematic differences among zones, such as between the USA and EU, should be more thoroughly investigated. Related to this point, future research should address impacts of different consortia designs. Here, consortia themselves embody the organizational structure of KIOs. Here, impacts include quality of scientific publications, proceeding to POCM, etc. Third, regulatory and societal justifications of scientific research are likely to affect the cognitive process of framing science in academic KIOs. The comparative analysis of the eminent US and German academic KIOs introduces this proposition. On the other hand, the causal relation is opposite to the attentional process of organizations by Pfeffer and Salancik (1978). A possible explanation is that the interaction between the cognitive processing and the attentional process shapes innovation pathways. Therefore, future research should investigate this process. Despite these limits, it is the hope that scientific discoveries will be better transformed in the coming years to liberate those who are afflicted.

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Index

A Academic capture. See Capture Academic knowledge-intensive organization, 1–3, 5, 9, 10, 13, 15, 17, 27, 28, 32, 35, 37, 38, 40–43, 46, 55, 58–61, 63–65, 67, 68, 76, 81, 91, 92, 99, 100, 102, 106, 109, 112, 113, 115, 119–121, 128, 130, 131, 139, 148, 159, 164, 165, 166, 168, 170, 175–178, 183–186, 190, 194, 195 Agency-based governance incentive, 3, 159 Alignment of agency incentives, 187 of incentive, 132–134, 187 of interests, 187 Alliance design, 11, 16, 43, 131, 135, 136, 142, 183, 193 Autonomy boundary of. See Boundary B Bias

in selecting research agendas, 10, 14, 55, 60, 65, 131, 136, 144 Boundary of autonomy, 14, 35, 46, 59, 60, 67, 78, 82, 91, 130, 174, 184, 189, 191 of identity, 29, 32, 37, 59, 67, 183 of knowledge, 34, 38, 46, 92, 118, 160, 183, 186, 188–190 of organization, 3, 10, 13, 27, 29 of regulation, 15, 32, 67, 159, 163, 166, 173, 177, 183, 186 spanning, 13, 27, 29, 32, 33, 36–38, 40, 46 Buffer, 13, 27, 29, 39, 40, 46, 77–79, 92, 135, 188 C Capture academic, 14, 58, 60, 61, 65, 68 corrosive, 57–59 cultural, 58–60, 67 Codes of conduct, 40, 43, 45, 46, 111, 142

© The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2019 E. Okada, Management of Knowledge-Intensive Organizations, https://doi.org/10.1007/978-3-319-97373-9

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200  Index Commodification of academic sciences, 9 of life, 108, 110 Common-good-based stakeholder theory, 3, 13, 15, 16, 66, 128, 144, 145, 147, 159, 173, 178, 188, 194 Consortium, 15, 159, 160, 163–170, 172–174, 177, 178, 187, 192, 195 Contract bundle of, 3, 176 Corrosive capture. See Capture Cultural exchange approach, 100, 106 D Dependence, 11, 15, 16, 31, 34, 35, 59, 68, 127, 146, 147, 183, 184, 191, 193, 194 F Fairness-based stakeholder theory. See Stakeholder theory G Governance, 183 agency-based. See Agency-based governance common good-based. See Commongood-based stakeholder theory fairness-based. See Fairness-based stakeholder theory modality of, 33, 44–46 science, 37, 64, 159, 160, 175, 177 stakeholder-theory-based. See Stakeholder theory strategy, 163, 166, 168, 187, 192, 194 transaction-cost-based. See Transaction cost

trusteeship-based. See Trusteeship governance I Identity boundary of. See Boundary moral. See Moral identity Incentive hypothesis, 60, 159, 162, 192 Insights that come from humanistic disciplines, 4, 87, 159, 177 Institutional approach value-based, 100, 104 Institutional validity, 14, 16, 64, 80, 81, 83, 87, 93, 188, 193, 194 Intellectual property policy, 166, 187 Internal codes, 11, 41, 62, 77, 185 K Knowledge exchange reciprocal, 63, 132, 169, 173, 177, 195 Knowledge-intensive organizations (KIOs) academic. See Academic knowledge-intensive organization research tool, 14, 41, 75, 76, 86, 87, 92, 93, 190, 194 M Methodology, 3, 7, 10, 14, 15, 33, 34, 37, 41, 44, 61, 75, 83, 87, 93, 120, 142, 183, 189, 190, 194, 195 Moral stakeholder. See Stakeholder Moral identity, 159–161, 174–178 Moral ownership, 174, 175, 178 Multilateral organization, 1, 9, 11, 43, 128, 138

Index

N Norms. See Trusteeship norms O odre public, 106–109, 120, 121, 176, 191, 194 Organizational boundaries. See Boundary Organizational structure of KIOs, 1, 40, 46, 130, 195 Ownership imposing, 161, 177, 192 moral. See Moral ownership of public goods, 159–161, 192 P Partnership private-public (PPP), 66, 127, 131, 164, 191 public-public, 127, 144 Property right bundle of, 3, 67, 129, 165 unbundle, 16, 167, 177, 193, 194 R Reciprocal knowledge exchange. See Knowledge exchange Regulatory policy state-centric efforts, 103, 104, 106, 121 transnational efforts, 103, 104, 106, 121 Renegotiated social contract. See Social contract Responsibility public, 15, 56, 131, 135, 142, 159

  201

S Science governance. See Governance Self-correcting nature of science, 6, 12, 184 Self-regulation of academic science, 2, 67, 194 Social contract, 5, 8, 35, 89, 175 Stakeholder derivative, 14, 66, 88, 99, 110, 115, 119, 121, 145, 173 moral, 8, 14, 15, 41, 99, 100, 104, 106, 109–111, 119–122, 176, 183, 190 normative, 66, 88, 89, 99, 110, 115, 119, 121, 145, 173 theory. See Stakeholder theory Stakeholder theory common good-based, 3, 16, 66, 145, 159, 173, 178, 188, 191 fairness-based, 3, 13, 14, 16, 66, 77, 87, 89–92, 188–190, 194 property-based, 65 T Transaction gift, 4 market-based, 4, 107 reciprocal, 4, 89, 92, 134, 160. See also Reciprocal knowledge exchange Transaction cost, 32, 35, 36, 42, 63, 135, 144, 166, 169, 187, 191 Translational science, 4, 6, 13, 17, 27–30, 32–34, 36–38, 40, 41, 46, 55, 62, 66, 75–79, 82, 84–86, 89, 91, 92, 115, 116, 118, 132–134, 143, 170, 184, 188, 189, 193, 194

202  Index Translation. See Translational science Trusteeship governance, 2, 5, 6, 11, 14–16, 41, 46, 55, 66, 77, 85, 87, 91, 92, 99, 102, 120, 127, 128, 146–148, 159, 160, 173, 174, 176–178, 183–185, 188, 189, 193, 194 Trusteeship norms, 61, 62, 176, 185

U Unbundling. See Property right V Value-based institutional approach. See Institutional approach zone, 15, 100, 104, 106, 120, 178, 183, 190, 195