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This study aims to investigate a declaration of the principles of the Société de Philosophie des Sciences de Gestion/Society for the Philosophy of Organisation sciences (SPSG). Organisation sciences still need to be questioned and rendered more complex, even mistreated, and the concepts they apply have yet to be clarified, mastered and organised to go beyond management ideologies that obscure the project to develop a genuine science, with pseudo-rationalisation replacing real thought.

This task could be accorded to a philosophy of organisation sciences that should be understood as a “philosophy of science of organisation sciences”.

The aim of such a philosophical programme is twofold: to expose the presuppositions and predispositions of organisation scholars and to analyse and clarify their scientific theories and concepts.

The ambition of the SPSG is to contribute to the development of that philosophical programme.

In this chapter, we present a critical assessment of contemporary organization theory variously described as either multiperspectival or fragmented. We argue that analytic philosophy as one of the major tools used for theorizing about organizations has had a major influence on the development of organization theory and largely explains the current state of affairs. At its core, we argue, is a fundamental methodological fissure in analytic philosophy itself: the distinction between descriptive and revisionary methods. The principal focus of descriptive analysis in organization theory is how agents use everyday language in organizational contexts, often by invoking language games. In contrast, revisionary approaches, concerned about the privileging of theories embedded in everyday language, as well as the complexity and ambiguity of ordinary-language use, aim for explicit theory evaluation and greater clarity by recasting ordinary language in formal systems, such as scientific, especially empiricist, theories, characteristic of the mainstream of theorizing about organizations from the 1940s onward. For a number of theoretical and epistemological reasons logical empiricism or positivism is no longer a widely held view either in the philosophy of science or in the organization theory. We examine some critical issues regarding logical empiricist epistemological foundations and propose a methodological naturalistic framework that supports the ongoing growth of knowledge in organization theory, naturalistic coherentism. In developing this new conception of science we thus opt for a revisionary methodology, but one that is beholden to neither the traditional logical empiricist/positivist conception of (organization) science nor the relativism and conservatism of postmodernist theory, widely considered to be the successor of positivist organization theory.

As a discipline, health organisation and management is focused on health-specific, collective behaviours and activities, whose empirical and theoretical scholarship remains under-utilised in the field of implementation science. This under-engagement between fields potentially constrains the understanding of mechanisms influencing the implementation of evidence-based innovations in health care. The aim of this viewpoint article is to examine how a selection of theories, models and frameworks (theoretical approaches) have been applied to better understand phenomena at the micro, meso and macro systems levels for the implementation of health care innovations. The purpose of which is to illustrate the potential applicability and complementarity of embedding health organisation and management scholarship within the study of implementation science.

The authors begin by introducing the two fields, before exploring how exemplary theories, models and frameworks have been applied to study the implementation of innovations in the health organisation and management literature. In this viewpoint article, the authors briefly reviewed a targeted collection of articles published in the Journal of Health Organization and Management (as a proxy for the broader literature) and identified the theories, models and frameworks they applied in implementation studies. The authors then present a more detailed exploration of three interdisciplinary theories and how they were applied across three different levels of health systems: normalization process theory (NPT) at the micro individual and interpersonal level; institutional logics at the meso organisational level; and complexity theory at the macro policy level. These examples are used to illustrate practical considerations when implementing change in health care organisations that can and have been used across various levels of the health system beyond these presented examples.

Within the Journal of Health Organization and Management, the authors identified 31 implementation articles, utilising 34 theories, models or frameworks published in the last five years. As an example of how theories, models and frameworks can be applied at the micro individual and interpersonal levels, behavioural theories originating from psychology and sociology (e.g. NPT) were used to guide the selection of appropriate implementation strategies or explain implementation outcomes based on identified barriers and enablers to implementing innovations of interest. Projects aiming to implement change at the meso organisational level can learn from the application of theories such as institutional logics, which help elucidate how relationships at the macro and micro-level have a powerful influence on successful or unsuccessful organisational action. At the macro policy level, complexity theory represented a promising direction for implementation science by considering health care organisations as complex adaptive systems.

This paper illustrates the utility of a range of theories, models and frameworks for implementation science, from a health organisation and management standpoint. The authors’ viewpoint article suggests that increased crossovers could contribute to strengthening both disciplines and our understanding of how to support the implementation of evidence-based innovations in health care.

The purpose of this study is to contribute to the understanding of the failure of science parks to become a central actor in the knowledge economy and, with the help of new organizational theory, to propose new solutions.

The paper reviews a number of recent studies of science parks and their effect on innovation and economic growth, measured by revenue or survival rate of new firms, but demonstrating no positive result of the parks. The paper then introduces modern organization theory, specializing in analyzing the processes of creating, managing, organizing, and transferring knowledge distributed through a number of networks and other volatile organizations in order to investigate the set‐up of science parks in the knowledge economy.

Using Nonaka's concept of ba as a metaphor for the new tradition in organizational theory, the paper finds very few – if any – signs of these new ways of organizing in traditional science parks. The paper argues that principles from modern knowledge organizations has to be installed in science parks if the parks are to regain the initiative and become an important actor in the new regime of knowledge production. Otherwise, science parks must be viewed as an outdated institution, left over from the industrial society.

The paper proposes a system of certification and quality assessment that might speed up the change in science parks from organizations formed by the industrial society to organizations serving the needs of the knowledge society.

The paper is an original contribution to the theory of science parks and innovation policy. The use of new organizational theory on knowledge management, illustrated by Nonaka's concept of ba, presents a new solution to overcome the traditional thinking on how to organize science parks.

This paper looks at science communication through an organisational lens with the aim of assessing the relevance of different organisational forms for science communication.

The paper explores science communication in different organisational forms. Based on conceptual considerations and by reviewing existing empirical literature, the paper selects and compares three organisational forms of science communication: the editorial office of a daily newspaper, the press office of a university and the Science Media Centre.

The paper shows the relevance of organisation for science communication by comparing three organisational forms. The first two, the science news desk and the press office, have the character of a sub-system of an organisation, while the third, the Science Media Centre, forms its own organisation. The paper shows how the respective set-up shapes science-media contacts with a focus on the occurrence and resolution of conflicts.

The paper proposes a conceptual framework for studying science communication through an organisational lens but leaves comparative empirical studies of all types to future research. Yet, it outlines and compares implications of the formal organisation of science communication from a conceptual point of view.

The findings provide information on the structural impact of different organisational forms on science communication and point to where conflicting expectations, and thus potential conflicts, are most likely to occur in each case. A reflection of structurally conflicting expectations and how they can be overcome in specific situations is of high practical value for all science communication activities.

Organisational theorists have long argued that organisations are the key to understanding society. Despite their undoubted relevance, however, organisations and their influence on science communication have so far been much less analysed – both conceptually and empirically – than its contents, its practices and its impacts on public understanding, public policy, and on science and scientists. The paper contributes to the emerging field with conceptual considerations towards an organisational sociology of science communication.

This study aims to discuss science governance in Europe and the network of associated nonprofit institutions. The authors posit that this network, which comprises both (partial) learning organizations and non-learning organizations, has been observed to postpone taking up “responsibility” as an issue in science governance and funding decisions.

This paper discusses the challenge of learning and policy implementation within the European science governance system. By exploring how learning on responsible innovation (RI) in this governance system can be provoked, it addresses the question how Senge’s insights in organizational learning can clarify discourses on and practices of RI and responsibility in research. This study explores the potential of a new organizational form, that of Social Labs, to support learning on Responsible Research and Innovation (RRI) in standing governance organizations.

This study concludes that Social Labs are a suitable format for enacting the five disciplines as identified by Senge, and a Social Lab may turn into a learning organization, be it a temporary one. Responsibility in research and innovation is conducive for learning in the setting of a Social Lab, and Social Labs act as intermediary organizations, which not merely pass on information among actors but also actively give substantive shape to what they convey from a practice-informed, normative orientation.

This empirical work on RRI-oriented Social Labs therefore suggests that Social Lab–oriented temporary, intermediary learning organizations present a promising form for implementing complex normative policies in a networked, nonhierarchical governance setting.

Based on this research funding and governance organizations in research, policy-makers in other domains may take up and create such intermediary organizations to aid learning in (science) governance.

This research suggests that RRI-oriented Social Labs present a promising form for implementing complex normative policies, thus integrate learning on and by responsible practices in various governance settings.

European science governance is characterized by a network of partial Learning Organization (LOs) and Non-Learning Organization (nLOs) who postpone decision-making on topics around “responsibility” and “solving societal challenges” or delegate authority to reviewers and individual actors, filtering possibilities for collaborative transformation toward RRI. social lab (SLs) are spaces that can address social problems or social challenges in an open, action-oriented and creative manner. As such, they may function as temporary, intermediary LOs bringing together diverse actors from a specific context to work on and learn about issues of science and society where standing organizations avoid doing so. Taken together, SLs may offer temporary organizational structures and spaces to move beyond top-down exercise of power or lack of real change to more open, deliberative and creative forms of sociopolitical coordination between multiple actors cutting across realms of state, practitioners of research and innovation and civil society. By taking the role of temporary LOs, they may support existing research and innovation organizations and research governance to become more flexible and adaptive.

Research on science communication in organizational contexts is scarce – even though many cases can be found where organizations from science and beyond communicate about science-related issues, or where organizational contexts have an impact on the communication of individual scientists and scientific organizations. Therefore, it is time for an “organizational turn” in science communication research, and for more scholarly emphasis on the specific cases that science-related communication in, from and about organizations presents. Such an approximation would benefit both science communication research and analyses of strategic and organizational communication.

This special issue of the “Journal of Communication Management” on “Communicating Science in Organizational Contexts” is a step in this direction: It compiles commentaries from leading scholars in the respective fields as well as research articles coming from various disciplines and conceptual as well as methodological paradigms. In the editorial, we assess overlaps between scholarship on science communication and strategic communication, respectively, based on a meta-analysis of journals in the field(s), develop a guiding heuristic for analyzing science communication in organizational settings, and introduce the contributions to the special issue.

The meta-analysis shows that overlaps between science communication research and scholarship on strategic communication are scarce. While organizations and their communication appear occasionally, and increasingly often, in science communication research, scholars of strategic communication only rarely analyze science communication.

The meta-analysis is limited to the publications of five scholarly journals over ten years. It still demonstrates the lack of research in the intersection of scholarship on science communication and strategic communication.

Scientific organizations are rapidly extending and professionalizing their strategic communication, and an increasing number of organizations beyond science communicate on science or science-related issues. Understanding science communication in organizational settings, therefore, is crucial for practitioners in both areas.

Analyzing science communication in organizational settings is of increasing importance – yet few studies exist that have done it, and the respective research fields devote not much attention to one another. The special issue is a first foray into this new, intersectional field.

The purpose of this paper is to investigate social and technical drivers of data science practices and develop a standard model for assisting organizations in their digital transformation by providing data science capability/maturity level assessment, deriving a gap analysis, and creating a comprehensive roadmap for improvement in a standardized way.

This paper systematically reviews and synthesizes the existing literature-related to data science and 183 practitioners' considerations by employing a survey-based research method. By blending the findings of this research with a well-established process capability maturity model standard, International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 330xx, and following a methodological maturity development framework, a theoretically grounded model, entitled as the data science capability maturity model (DSCMM) was developed.

It was found that organizations seek a capability/maturity model standard to evaluate and improve their current data science capabilities. To close this research gap, the DSCMM is developed. It consists of six capability maturity levels and twenty-seven processes categorized under five process areas: organization, strategy management, data analytics, data governance and technology management.

This paper validates the need for a process capability maturity model for the data science domain and develops the DSCMM by integrating literature findings and practitioners' considerations into a well-accepted process capability maturity model standard to continuously assess and improve the maturity of data science capabilities of organizations.

Many recent studies have voiced the growing concern that the body of knowledge that springs from organization science is hardly taken notice of in management practice. This has given rise to urgent calls for making organization research more relevant to practitioners and an intensive debate on how to realize this aim has set in (e.g., Hodgkinson, Herriot, & Anderson, 2001; Rynes, Bartunek, & Daft, 2001; MacLean & MacIntosh, 2002; Baldridge, Floyd, & Markoczy, 2004; Van de Ven & Johnson, 2006). In most of the existing literature one can identify three main reasons for the observable lack of connection between organization research and practice: research is not sufficiently focused on the “real” problems of practitioners (e.g., Rynes, McNatt, & Breetz, 1999), research results are not properly disseminated to practitioners (e.g., Spencer, 2001), and the language of science is not properly translated into the language practitioners' use (e.g., Starkey & Madan, 2001; Van de Ven & Johnson, 2006). The underlying assumption is that if scientists redressed these shortcomings, their findings would be utilized by practitioners and thus the gap between theory and practice would be bridged.

We discuss the impact of complexity science on the design and management of health care organizations over the past decade. We provide an overview of complexity science issues and their impact on thinking about health care systems, particularly with the rising importance of information systems. We also present a complexity science perspective on current issues in today’s health care organizations and suggest ways that this perspective might help in approaching these issues.

We review selected research, focusing on work in which we participated, to identify specific examples of applications of complexity science. We then take a look at information systems in health care organizations from a complexity viewpoint.

Complexity science is a fundamentally different way of understanding nature and has influenced the thinking of scholars and practitioners as they have attempted to understand health care organizations. Many scholars study health care organizations as complex adaptive systems and through this perspective develop new management strategies. Most important, perhaps, is the understanding that attention to relationships and interdependencies is critical for developing effective management strategies.

Increased understanding of complexity science can enhance the ability of researchers and practitioners to develop new ways of understanding and improving health care organizations.

This analysis opens new vistas for scholars and practitioners attempting to understand health care organizations as complex adaptive systems. The analysis holds value for those already familiar with this approach as well as those who may not be as familiar.