Abstract
The complexity and importance of environmental, societal, and other challenges require new forms of science and practice collaboration. We first describe the complementarity of method-driven, theory-based, and (to the extent possible) validated scientific knowledge in contrast to real-world, action-based, and contextualized experimental knowledge. We argue that a thorough integration of these two modes of knowing is necessary for developing ground-breaking innovations and transitions for sustainable development. To reorganize types of science–practice collaborations, we extend Stokes’s Pasteur’s quadrant with its dimensions for the relevance of (i) (generalized) fundamental knowledge and (ii) applications when introducing (iii) process ownership, i.e., who controls the science–practice collaboration process. Process ownership is a kind of umbrella variable which comprises leadership (with the inflexion point of equal footing or co-leadership) and mutuality (this is needed for knowledge integration and developing socially robust orientations) which are unique selling points of transdisciplinarity. The extreme positions of process ownership are applied research (science takes control) and consulting (practice takes process ownership). Ideal transdisciplinary processes include authentic co-definition, co-representation, co-design, and co-leadership of science and practice. We discuss and grade fifteen approaches on science–practice collaboration along the process ownership scale and reflect on the challenges to make transdisciplinarity real.
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Introduction
Keeping planet Earth viable in the twenty-first century and beyond without disruptions calls for identifying successful coping strategies to manage current fundamental challenges including climate change, pandemic threats, international security, ensuring food security, biodiversity loss, rebound effects of new technologies, digitalization, migration, sustainable energy systems, and sustainable resource management. The need to better understand the fundamental character of interactions between nature and society and necessary adaptive capacities are central subjects of sustainability science (Clark and Harley 2020; Kates et al. 2001). The complexity, contextualization, and multifaceted nature of such problems require the utilization of knowledge and epistemics (i.e., ways of knowing) of different interest groups (including governmental actors, industry, non-profit organizations, NGOs and other stakeholders) and cultures (including indigenous people). Knowledge also differs between modes of thought (e.g., intuition vs analytics) and depends on the perspectives of interest (Scholz 2011). We argue that to properly utilize and develop science that can cope with these challenges, different forms of science–practice collaboration have been and continue to be developed, and this compels us to define, interrelate, and effectively use the capacities of both science and practice for linking practical experiential wisdom and academic rigor (Renn 2021). We also reflect on how science knowledge may affect the actions of decision makers.
First, to understand what determines the potential of collaboration, we define the essence of each one, scientific and practical knowledge. Simplified, science knowledge serves to construct a reference system of validated common knowledge that is (1) consistent and/or (2) empirically proven or validated. In contrast, practical knowledge serves to master life, to compete in daily life, and to secure and strengthen one’s position in society. From a realist perspective, scientists construct theories and models to realistically describe structures and processes of the actual world. The objectives of practitioners differ. For example, business agents must compete and survive in the market and provide sufficient turnover and profit; politicians in democratic systems strive to be re-elected; and NGOs must find appropriate ways of gaining public acknowledgment for their values and norms.
Second, science and practice differ primarily in the criteria they use for validating what is good or successful. In a traditional conception of science, the objective is to construct theories and models approaching real structures, and the primary driver is curiosity. In contrast, practitioners’ success is based on feedback from their social and natural environment that support or endanger the viability of their systems. For representatives of commercial organizations, for example, market success and economic return are decisive. For politicians and actors of nongovernmental organizations, popularity, social recognition, and the number of votes/memberships are significant. A good strategy is specific, situated (e.g., adapted to the socio-cultural constraints in contrast to generic science knowledge; Gherardi 2008; Hunter 2009), and often intuitive or implicit (Greenhalgh 2002; Raab and Gigerenzer 2015).
Third, from our perspective, collaboration goes beyond cooperation. Cooperation is directed toward the division of labor and knowledge, while collaboration stresses mutuality. It includes an intended joint process of creating prerequisites and constraints and framework conditions, as well as the intertwining of ideas and actions on the course toward a common goal. Thus, science–practice collaboration comprises goal-oriented interaction and knowledge integration to address a challenge or endeavour that is of interest to both practitioners and scientists. For instance, the challenge of complex, socioecological transitions calls for going beyond interdisciplinarity and utilizing the multitude of epistemics, which are related and linked to different interests of subgroups of society and their experiential foundations (wisdom), which have often been acquired by cultural rationales.
The aim of transdisciplinary processes is to provide added value for science and practice through the effective, functional integration of knowledge from both. The integration of knowledge from science and practice provides a special form of epistemics, but also the integration of sciences for a targeted interdisciplinarity is part of (i) epistemic integration. Further, a transdisciplinary project is characterized by integration of (ii) systems, (iii) interest groups/social interest, (iv) modes of thought, and (v) cultures (Scholz 2011; Scholz and Steiner 2015b). All these forms serve capacity building of all stakeholder groups by mutual learning on equal footing/co-leadership of science and practice is our understanding—and thus may serve as a short definition—of transdisciplinary processes.
In this paper, we describe different types of science–practice collaboration and discuss when, how, and why transdisciplinary processes may complement other established approaches such as political consultancy, Triple Helix, Third Mission, open innovation, citizen science, and action research. We do this from a systemic sustainability perspective. Sustainable development is seen (Scholz 2017) as:
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an ongoing search for and inquiry about problems and critical environmental dynamics that
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identify and assess critical risks and vulnerabilities for the maintenance of (sub-)systems considered essential for the viability of humankind or those systems that are judged valuable to maintain
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from a socio-cultural normative perspective such as intra- and intergenerative justice.
The number and extent of contributions from science and practice differ along three stages. The ongoing search for threats (1) may be viewed as a joint process. Thus, practitioners might take the lead on problems that require experiential knowledge, while scientists take it in domains where abstract knowledge is required, e.g., long-term climate change. In the course of assessing vulnerabilities and resilience, science is likely to take the lead, while the formation of normative goals and objectives to address the results of their assessments is usually considered a matter for societal democratic processes (Scholz 2017).
We argue that scientists and practitioners have different reference, reward, and validation systems, yet there is heterogeneity within and between science and practice as well. Scientists may see themselves and their role as serving the public good, which aligns with the fact that, in many countries, it is taxpayers who finance public science institutions. Nevertheless, there is an ongoing discussion of the role of science activists (or lobbyists in “the coat of science”), e.g., when defining goals for climate protection, the risks society should take, or what constitutes sustainable consumption (Scholz 2017; Wittmayer 2016). The presented approach of transdisciplinarity stresses that the normative side of coping with a transdisciplinary problem is a primary subject of practice/stakeholders and not of science (Scholz 2020).
“Science vs practice: who is generating what type of knowledge?” first describes what knowledge is generated by science and what by practice. We delineate how transdisciplinarity differs from societal and business-driven Third Mission research and explain the complementarity of science and practice with respect to rationales and drivers. Then, we outline motives and rationales for why scientists and practitioners collaborate. “Process ownership in science–practice collaborations” introduces the process ownership scale which provides a third dimension to Pasteur’s quadrant (Stokes 1997) by differentiating constellations of leadership by science or practice. The “Discussion”, and “Conclusions and outlook” focus on the motivations and drivers of both science and practice actors and elaborate on the reasons of why transdisciplinary processes are appropriate for sustainable transitions, based, in particular, on equal process ownership and mutual learning for capacity building of science and practice.
Science vs practice: who generates what type of knowledge?
The bifurcation of the Third Mission vs transdisciplinarity
With respect to science–practice collaboration, Erich Jantsch’s (Jantsch 1970) contribution at the 1970 OECD Conference on Interdisciplinarity is: Problems of Teaching and Research in Universities provided a visionary view of the transdisciplinary university: … a transdisciplinary structure for the university … [includes] three types of organizational units – systems design laboratories, function-oriented departments, and discipline-oriented departments – which focus … the education/innovation system, i.e., on method and organization rather than on accumulated knowledge. (Jantsch 1970, p. 403). Science–practice collaboration-based transdisciplinarity emerged from this idea of function-oriented departments.
At the same time, the US National Academy of Science and the Social Science Research Survey Committee addressed “social crises” and demanded that science contribute to an
… increased depth of understanding human behavior and the institutions of society; and second, in better ways to use this understanding in devising social policy and the management of these affairs. (NAS 1969)
In the USA, Mahan Jr. (1970) also suggested the concept of transdisciplinarity. Yet, the notion of transdisciplinarity was, finally, restricted to an integrative, theoretically, and methodologically based (i.e., “true”) interdisciplinarity that provided concepts for better understanding the foundations of authentic social behavior (e.g., for concepts such as “general behavioral principles”), for instance, when providing concepts such as “general behavioral principles” (Mahan Jr. 1970, p. 20). Collaboration with practice was neither discussed nor considered.
Around 1970, traditional criteria and rationales for disciplinary science came under pressure. Interdisciplinary fields and applied research acknowledged the complexity of societal structures and problems as well as the demand by practice to benefit from universities and public knowledge institutions (such as the Max Planck Institutes). Some scientists have argued that traditional forms of validation are no longer possible in complex systems, and singular casualties have become unimportant (see, e.g., post-normal science; Funtowicz and Ravetz 2003). The traditional science standards were further undermined by commercial interests in applied projects and fraudulent scientific papers driven by academic career pressures (e.g., “publish or perish”). Thus, it became increasingly difficult to define what good science is and what can be considered as a validated or even proven reference from the practitioner side, e.g., governments, courts, business and industry, and the public at large.
The increasing challenge of distinguishing between research conducted by science and that undertaken by practice can be illustrated by the Triple Helix Model of university–industry–government relations (Etzkowitz and Leydesdorff 1995) and the Third Mission (Asplund and Nordman 1999) approach. The term Third Mission has been utilized in many contexts and has mostly denoted the utilization of universities beyond scientific research and academic education. In 1994, Director of the Cornell Program on Dairy Markets and Policy Andrew M. Novakovic defined the goal of the Third Mission:
The third mission is to assist and advise members of industry and policy makers as they seek to understand or develop dairy policies or new marketing institutions, mechanisms, and practices. (Novakovic 1994)
Overcoming the bottleneck of funding shortages with yields from the marketification and commodification of research and higher education (Laredo 2007; Zomer and Benneworth 2011) was the key driver for the formation of the Third Mission. The public expected the university to contribute revenue to their investment by providing contract-based services to industrial and governmental interests, and this demand has nourished the heterogeneity of universities and flattened the boundaries between the university and private consultancy companies that also apply scientific methods.
Etzkowitz and Leydesdorff (1995) extended the Third Mission concept to the Triple Helix concept, which is a slight revision of Clark’s “triangle of coordination” (1983) between the “academic oligopoly” (i.e., the universities), the market (i.e., the economy, commercial users), and the state authority (i.e., politics and/or the state).
Boundaries between public and private, science and technology, university and industry are in flux. … Companies increasingly look to universities, …, as a potential source of useful knowledge and technology …, often encouraged by government – at both regional and national levels …. (Etzkowitz and Leydesdorff 1998, pp. 203–204)
The concept of the Third Mission was further extended because of governments’ needs to implement political programs. For instance, the social and planning sciences “were facing a shift of funding toward policy programs” (Zomer and Benneworth 2011). Urbanization and the development of sustainable cities have been key topics in this context, and societal needs and the demands for urgency, usefulness, and societal relevance have gained significance. As a consequence, new departments, curricula, institutions, and universities of applied sciences have been established to meet these needs.
Undoubtedly, business and industry interests have been the primary engines driving the Third Mission. A structural analysis has also been provided by the ground-breaking book The New Production of Knowledge and the concept of Mode 2 research, meaning “Mode 2 knowledge is carried out in the context of application” (Gibbons et al. 1994). The idea of contract-based research with professors as (low-cost) knowledge workers (Scott 2007) highlights this. The conception of the entrepreneurial university emphasizes the North and South American view (Thorn and Soo 2006) of the Third Mission. Much earlier, Clark (1983) had become aware of national (and cultural) differences. For instance, in Swedish universities, not only “economic life” but also the total “surrounding society” were seen as subjects of the Third Mission.
Another critical fact is that, for more than a century, a significant share of research has been conducted by large private companies in open or covert cooperation (Bernal 1954/1965; Scholz 2020). All these maintain high-profile research departments that promote marketable products as well as important scientific findings as a secondary product (which can also be published in high-ranking journals as long as a company’s competitive advantage is not endangered).
What are the characteristics of knowledge produced by science and practice?
The conception of the considered type of transdisciplinarity relies on the complementarity of science and practice. This complementarity is often not shared or understood, primarily by scientists. To overcome this, we describe the drivers, rationales (epistemics), reward systems, and the societal role of science and practice. This allows to understand who, e.g., a senior biochemist who is doing research for a pharmaceutical company follows different goals from a colleague employed at a public university (and why their work falls under the purview of different ministries).
The science–practice complementarity was, first, inspired by the distinction between Mode 1 and Mode 2 science (Gibbons et al. 1994). Mode 1 deals with a “discipline-based” setting and by problem-solving of and for academic communities. Mode 2 science draws “on sources beyond any set of disciplines … because not all participants in knowledge production come from universities. Some might come from government laboratories, some from industry, and others from social action groups and concerned citizens perhaps with no particular scientific training at all. In Mode 2 everyone has something to contribute to the formation of a research agenda.” (Gibbons 2013) Second, one may consider “science–practice” complementarity as a generalization of the discussions in psychology, nursing science, and related fields about this distinction (Hoshmand and Polkinghorne 1992; Sheppard 1995). Third, Scholz (2011) distinguished between the drivers and rationales of different human systems, e.g., between scientists (working at public or non-profit institutions to produce knowledge as a public good; being paid by the taxpayers) and practitioners. For instance, the driver of scientists who are operating in Mode 1 is to contribute to better understand reality (i.e., approaching truth), which is the search for fundamental knowledge, consistent reasoning, academic acknowledgement, etc. These goals have been described by Merton (1973) or Bunge (2012). Naturally, there are also Mode 2 scientists working on societally relevant problems who operate in a public good frame. On the other hand, practitioners’ primary drivers are supposed to be successful when being capable of solving practical problems. The commercial driver of market success or the politicians’ drivers to become elected may be taken as examples.
Given the complexity of coupled human–environment systems, knowledge from all domains of science, in principle, is relevant from a sustainable-transitioning perspective (Matson et al. 2016; Scholz 2011). In this context, we acknowledge that, from an epistemological perspective, practitioners’ experiential knowledge is necessary for a holistic framing that allows for an adequate conceptual structuring of complexity and for a proper understanding of the role of contextualization in the practical significance/relevance of ill-defined or wicked societal problems. The key role of science is to provide a consistent and theory-based description and model depicting as many of the properties, structures, causalities, and dynamics of a system as possible. Scientific statements are empirically validated as far as possible. This holds true not only for controlled laboratory settings, but also for settings coping with real-world problems (see Table 1).
Here, the first obstacle is to decide which scientific knowledge and theories are helpful to better represent a complex real-world problem. In this context, one specific challenge is to identify the state of the art in science, which consistently labors under the specter of unavoidable incompleteness; this is, nevertheless, an intrinsic element of the scientific process. Thus, scientific theories never completely and finally describe “reality.” Given a specific problem, some theories are more adequate than others and some fail to meet the contemporary criteria for scientific quality. As characterized by Sarewitz (2000), “science is sufficiently rich, diverse, and Balkanized to provide comport and support for a range of subjective, political positions on complex issues.” This asks for processes which avoid immature or biased scientific studies taken as truth.
An even greater challenge in science–practice collaboration for sustainable system transitioning is coping with normative and cultural dimensions. The normative dimension is intrinsic not only on the practice side: scientific theories depend on the worldview (i.e., cosmology) and the Zeitgeist. This is the case for the social sciences and the humanities, but we can also find extreme cases in the natural sciences. For instance, during World War II, the Nazi regime abandoned nuclear physics promoted by the German Nobel laureates in physics, Stark (1937) and Lenard (1936), in favor of developing Aryan physics (excluding probabilistic fuzziness). Within the social sciences, psychology might serve as a prominent example. Behavioral psychology may be taken as a contemporary example, as it almost exclusively traces the relation of behavioral responses to environmental stimuli. In contrast, humanistic psychology takes a much broader view, integrating “knowledge of the individual’s mind, body, and behavior within an awareness of social and cultural forces.” This means that scientific approaches themselves are value laden and include normative assumptions. We argue that science–practice dialogs have to communicate the normative assumption on both sides, science and practice; for example, approaches that lack rigor or are characterized by extreme normative assumptions should be excluded. The message is that normative values are included in science to some extent (Scholz 2017), but we should ensure that they remain subordinate and that scientists reveal the basic components of their worldview in an open manner.
Ensuring the utilization of scientific state-of-the-art knowledge based on consistent theories and—if the subject allows—sufficient empirical validation represents a more specific challenge. As matters addressed in science–practice collaborations are often complex, a specific challenge is relating the pivotal elements of a real-world system analysis to scientific insights and conclusions.
The basic properties and functions of science knowledge are described in Fig. 1.
In its ideal form, scientific knowledge is general and fundamental in the sense that it explains a large domain of reality. Natural science is universal, as all aspects of the universe are subject to the same natural laws. Scientific theories are consistent, and empirical validation takes place based on scientific methods. We distinguish between codified and written knowledge and the living aspects of knowledge that exist within scientists and their communities. According to Piaget’s genetic epistemology (1968), an individual’s higher-ordered (abstracted) knowledge cannot be acquired through everyday knowledge. Higher-ordered knowledge is based on the acquisition of key elements of knowing and methods practiced in higher-educational and research institutions that convey abstracted and scientific knowledge, which has developed over the course of human phylogenesis. The development of cognitive knowledge is moving along the path (stages) of phylogenetically acquired and codified levels (e.g., concrete operations on matters we may visually imagine/experience are preceding abstracted formal operations). This calls for competent educational institutions at all levels that maintain, condense, and develop (abstracted and) scientific knowledge and standards (see Fig. 1, upper part).
Practice knowledge serves to master life and to cope with complex real-world problems. It is seen as a foundation of skills and competences underlying behavior (Risopoulos-Pichler et al. 2020). Practical knowledge is based on (economic) heuristics and functional (simple) heuristics (Gigerenzer 2021), thus following the satisficing principle. Also practitioners’ experiential knowledge includes abstraction and reflective observations on real-world settings (Kolb 1984). Practical knowledge is shaped by and embedded in attitudinal, motivational, emotional and personal, and contextual factors. Simplified, it serves to meet the needs and—sometimes—idiosyncratic objectives of individuals and other human systems.
Reflections on and motives for science–practice collaborations
When reflecting on the reasons practitioners choose to collaborate with scientists, based on the proposed complementarity of knowledge, we identify the following primary motivations. Science helps to structure (Mingers and Rosenhead 2004) complex problems in natural, social, and technological systems. It also describes major causal impacts and their interactions in a qualitative and quantitative data-based way. This may result in a principal risk assessment where different sources of knowledge are needed (Jasanoff 1993; Renn and Klinke 2004).
Given a complex problem, the challenge is to identify the key factors, subsystems, and entities and their (rules of) interaction for providing a robust representation of the system’s basic dynamics. Scientific knowledge may be related and “drawn upon” the situated knowledge, both in a reflective manner and to provide a “conceptual language” to understand and reflect on “experienced complexities” (West et al. 2019, p. 534).
Actually, practice has a long list of demands to which science may contribute. These include solving complex real-world problems, improving human well-being, governmental counseling, consulting non-political clients, resolving conflicts and disputes, capacity building and empowerment, identifying options for changing the world to improve living situations, creating pathways to the future (e.g., understanding barriers to and mechanisms of societal change and digital transformation, developmental aid), managing resilience, and generally, explaining how the world and the universe function. Most of these examples call for problem-driven applied research.
Scientists’ motives for collaborating with practice are equally wide ranging. The wish to escape the ivory tower can be motivated (a) by a desire to contribute to societal problem-solving (doing something good for society or earning money for the university or for oneself). Often, (b) collaboration with practice by means of participatory research is needed to gain access to certain data. Particularly in applied research, not only knowledge from different disciplines, but also (c) practitioners’ concrete contextual and historical system expertise is needed for developing an appropriate system model. This was demonstrated by Wynne’s (1996) case studies on the nuclear-waste assessment of sheep pastures. Scientists were unable to differentiate between historical nuclear fallouts (e.g., nuclear testing around 1950, the Windscale fire in Sellafield in 1957, and Chernobyl in 1986). It was farmers’ practical knowledge about their sheep’s grazing behaviors that made it possible for scientists to differentiate between the different sources of nuclear contamination. Thus, farmers were contextual experts for the concrete, real-world system. They represented and owned an intuitive, holistic, experience-based method of knowing or epistemics (Dreyfus and Dreyfus 2005), which will always be the case. In a more generalized sense, there are many questions for which science requires practical knowledge. Another example is the case of presumed mineral phosphorus (P) scarcity (essential for global food security). Here, geo-economical experts reject the environmental scientists’ erroneous modeling of peak phosphorus in 25 years (Scholz and Wellmer 2021).
Process ownership in science–practice collaborations
The selection, definition, and differentiation among the different approaches took a multi-year process. The process for developing the process ownership scale is described in Box 1. Simplified, the process of generating the ownership scale is divided into the sub-concepts, co-leadership and mutual learning. It is a kind of G-factor in the multivariate analysis of the unique selling points of the 15 approaches. Process ownership meets a critical concern of science–practice collaboration, i.e., the question of who takes control of the collaboration process. This includes, particularly, who is the principal in defining the research question, who takes data ownership, and who decides in what way(s) the results are used and published. Scholz (2020) distinguished between independent applied research and contract-based research. Forms of science–practice collaboration differ widely depending on who takes process ownership. We argue that process ownership became an important characteristic of conducting applied and use-inspired basic research in the frame of Pasteur’s quadrant, which was introduced by Stokes (1997). Stokes distinguished between relevance for applications and relevance for fundamental knowledge (the x-axis and y-axis of Fig. 2A). Classical pure basic research was represented by Niels Bohr (and his research on the foundations of nuclear physics). Stokes’s message was that we may distinguish two types of applied research. One, often associated with Thomas Edison’s work, is oriented toward problem-solving and is close to what practical engineers are doing. The other has been called use-inspired basic research. Here, Louis Pasteur’s biochemical approach to vaccination serves as the model. Overall, since the development of Mode 2 science (Gibbons et al. 1994), the interaction and collaboration of science and practice have shaped many domains of science (and not only technical universities).
The poles of the process ownership scale
There are two poles on the process ownership scale. Seen as one extreme is the classical university-hosted applied science, which only partially includes practitioners [1]. All numbers in italicized square brackets represent ranks of process ownership presented in Fig. 2B along the z-axis (CSS 2022). The highest degree of independence in applied research (practiced in democratic countries) is provided by university or science-foundation funding (public or private) that is not related to specific thematic research programs and seeks only high-level (peer-reviewed) research. Specifically, we may say that this kind of scientific research is typically conducted in a university’s highly protected (though competitive) comfort zone. At the opposite end of the pole is contract-based research [17], where a principal takes control of the research process. Here, we may think about pharmaceutical-approval studies or political research on opinion formation regarding political programs. The research question formulated and the design selected, whether a study is published, and other important constraints are, ultimately, controlled by the principal. This type of research can be conducted by both public and private laboratories and institutes.
Variants of transdisciplinarity
Transdisciplinary processes in which active collaboration between science and practitioners takes place (Renn 2021; Scholz and Steiner 2015a) represent the midpoint of the process ownership scale, i.e., a kind of inflection point [10] between science and practice leadership dominance. In an ideal transdisciplinary process, scientists and practitioners meet on an equal footing to better understand, conceptualize, and describe a complex real-world problem aimed at both improving practitioners’ decisions and actions and providing scientists with a better understanding and structuring of a problem that is scientifically challenging and not yet sufficiently understood. Scientists and practitioners take joint responsibility for the process and its results, based on an authentic process of mutual learning. This can best be achieved by taking co-leadership when accepting the otherness of the other’s roles and interests (Polk 2014; Scholz and Steiner 2015a); thus, practice leaders must know that scientists develop theories by writing papers. To do so, scientists need certain data that are not of immediate interest for solving practical problems. For their part, scientists must delve into real-world problems and empower practitioners to share their experience for finding solutions that secure the viability and resilience of relevant practical subsystems and processes (of their interest). This requires an equitable appraisal of high-quality knowledge from practice and from science. Although, ultimately, the practitioner takes responsibility for the practical decisions and the scientist for the theories and publications, there is some joint accountability and responsibility for the process and for socially robust orientations, for instance to system transitions, seen as the major outcome of a transdisciplinary process. The socially robust orientations emerging from transdisciplinary science–practice collaboration may serve as orientations, signposts, and guardrails for sustainable development.
From an operational perspective, co-leadership means that both sides, science and practice, actively participate with equal rights and terms in all essential issues of (1) the preparation phase (including the negotiation of the goals and the guiding question), (2) the process planning (which includes stakeholder analysis and selection of those who participate and the process/schedule and structure/organizational chart of a project) including the joint problem representation, (3) the core phase (including products which are produced, reviewed, and evaluated), and (4) the post-processing phase in which the results are used. Please note that co-leadership should be formally or informally ratified and communicated internally and externally. In transdisciplinary processes, co-leadership and balanced participation should take place at all levels (e.g., subprojects). All critical issues (e.g., what results are communicated how) ask for an explicit joint agreement. This may ask for ratified codes of conduct.
Naturally, the operational lead in certain activities may be allocated to science (e.g., in scientific modeling) or to practice (e.g., in practice networking) (Krütli et al. 2010; Stauffacher et al. 2008). Yet, both sides may ask for full transparency and negotiate whether and how outcomes are used. It is important to reflect on the implicit power relations which may cause asymmetries, e.g., if science frames the process by certain methods. Rosendahl et al. (2015) refer to feminist standpoint theory and stress that transdisciplinary processes require “the explicit and transparent positioning of oneself: this also holds true for scientists.” (Rosendahl et al. 2015, p. 26).
Transdisciplinarity often serves as a method for strategic sustainability planning and management (Matson et al. 2016). This means, for example, that participatory formative scenario construction and evaluation are conceived as a joint venture of practitioners and scientists. For certain stages, e.g., goal and system definition, both contribute equally, while—depending on the problem to be solved—the guiding hand may be on the science or practice side (the phases of different leadership are illustrated by Krütli et al. 2010). Thus, for consistency and resilience assessment in scenario construction, scientists may take the lead, and for other subprocesses, e.g., scenario interpretation and the development of implementation strategies, practitioners lead.
Of note, similar definitions of transdisciplinarity exist that do not stress co-leadership (Lang et al. 2012), but instead target participatory research when including practitioners. The version we presented emerges from a realist, normal science conception, while some transdisciplinary researchers refer to a post-normal conception as proposed by Funtowicz and Ravetz (2003). A key claim of this view is that classical scientific theories lose their value in complex, contextualized settings (Klein 2004). Yet, the post-normal approach to transdisciplinarity often remains in the frame of theoretical reflection when it is considered without processes of co-leadership and authentic collaboration with practitioners.
Transdisciplinarity also plays an important role in overcoming the widespread hostility toward the traditional prospects of research on indigenous peoples, which were also labeled the Europeanization or colonization of research (Smith 2013). An early contribution to transdisciplinarity is Article 10 of the “Manifesto de transdisciplinaridade” which declares “No single culture is privileged over any other” (Morin et al. 1994). In other words, indigenous knowledge, as a form of situated knowledge, is different, but of equal value to abstracted Western science knowledge. The latter is included in the “accepting the otherness of the other” principle of transdisciplinarity presented above, which may be seen as a prerequisite of equal process ownership in intercultural studies.
Other forms of science–practice collaboration
Several types of science–practice collaboration show some similarity to transdisciplinary processes. We consider variants of community-based participatory (action) research (Israel et al. 2017; Wallerstein and Duran 2017) to be very close to transdisciplinary processes and slightly in the direction of dominant science process ownership [9]. There are three main differences from the by us proposed conception of transdisciplinary processes. The first is full co-leadership (thus, related to the potential threat of losing control of the process). The second is the aspiration of transdisciplinarity that transdisciplinary processes may result in certain types of directed, use-inspired research and, thereby, affect, enrich, and transform scientific disciplines (i.e., the impact of transdisciplinarity on science). Third, transdisciplinarity, as we conceive it, considers practice knowledge and science knowledge as essentially different (see Fig. 2), but equally important. Precedence in the course of a transdisciplinary process depends primarily on the type of problem under consideration. There are numerous projects in community health and community design which follow the conception of community-based participatory (action) research in the United States not using the term transdisciplinarity (Kessel et al. 2003).
There are other forms of action research [8]. In this type of science–society collaboration, scientists’ societal concerns and interests become important. Lewin’s (1946) seminal experiential action research was motivated by his interest in how minorities (Jews and Black Americans) fail to adapt. Lewin utilized analysis of variance (ANOVA) to measure the variation among and between groups regarding the effects of contextual factors and interventions. Today, we find a wide range of variants of action research, one of which, for example, is “shallow” activistic action research. Here, attaining scientists’ normative goals in practice is the major aim and key validation criterion. Please note that the ANOVA models have also been used in transdisciplinary processes for tangible matters such as smallholder farmers’ maize yields, where it helps to measure the effect of farmers’ participation in a transdisciplinary mutual learning process (Njoroge et al. 2015) or to analyze different stakeholder groups judgments on future scenarios (Scholz and Stauffacher 2007).
In action research, scientists often become science activists or normative advocates who take broad control over goals, processes, and outcomes. About 27% of sustainability science researchers running processes including stakeholders judged that the scientist is “a stakeholder himself, bargaining for his interest” (Mielke et al. 2017, p. 10651). In the well-elaborated Dutch approach for transition management ([7]; see e.g., Loorbach 2014) a co-evolution of science and practice toward sustainability is targeted, in particular when ignoring mainstream incrementalism, and aspiring a pluralism in “partisan mutual adjustment” (Lindblom 1979, p. 522). Scientists may even function as niche partisans (e.g., when collaborating with grassroot movements; see e.g., Loorbach et al. 2020). In fact, this might become necessary, given certain autocratic structures, for example, if other types of research are not allowed or not funded.
Initially, citizen science [6] emerged from environmental sciences. In ecology, citizen scientists serve as “sensors” (Goodchild 2007, p. 211) to increase “the scale of ecological field studies with continent-wide, centralized monitoring efforts and, more rarely, tapping of volunteers to conduct large, coordinated, field experiments” (Dickinson et al. 2010, p. 149). Thus, citizens frequently serve as unpaid, part-time research assistants. Yet, most “citizen science projects also strive to help participants learn about the organisms they are observing” (Bonney et al. 2009, p. 977) and thus contribute to developing bioliteracy (Hooykaas et al. 2019). Current citizen science develops strategies for stakeholder selection and collaboration. From a societal perspective, the involvement of volunteers in research has increased the scale of ecological field studies with continent-wide, centralized monitoring efforts and, more rarely, the use of volunteers to conduct large, coordinated, field experiments (Dickinson et al. 2012). Yet, the validity and reliability of data collected by citizen scientists require critical reflection and evaluation (Clare et al. 2019).
As expressed by the phrase “bench to bedside,” part of the foundation of the Journal of Translational Research in 1915, translational research [5] built the bridge between (biological) laboratory, medical technology developments and patients’ needs. Although, in principle, this includes a number of commercial and clinically driven interests, many approaches, such as Stokols’s team science (Stokols et al. 2008), are typically dominated by interdisciplinary science teams. Two features of this type of research are related to our conception of transdisciplinarity. First, team science utilizes targeted (use-inspired), interdisciplinary research for tangible (not only medical) real-world problems. Second, the teams usually include scientists and practitioners applying their complementary knowledge.
Often, the term participatory research [4] is mixed with transdisciplinarity. We consider participatory research a form of applied science, which (discontinuously) includes practitioners to better understand context and complexity or for an integrated assessment, for example, of climate impacts in a certain region (Salter et al. 2010). Sometimes in participatory research (such as in team science), who is participating in whose venture is unclear. In general, scientists maintain control regarding the form and intensity of science–practice collaboration. Mobjork (2010) differentiated “participatory transdisciplinarity” as presented above from “consulting transdisciplinarity” in which actors have the role of responding and reacting to the research conducted and researchers bear their thoughts and perspectives in mind during the research; the societal actors are not actively incorporated into the knowledge production process. Actually, this is what we defined as action research. In social studies, participatory research may include processes of “sequential reflection and action, carried out with and by local people rather than on them” (Cornwall and Jewkes 1995, p. 1667). Studies of organizational management may benefit by including CEOs’ ideas, principles, and visions for identifying novel patterns of organizational processes. We should mention that any transdisciplinary process includes participatory research as a basic pillar. According to a survey by Mielke et al. (2017), sustainability scientists include mostly politicians and members of civil society via workshops and interviews. From the practitioners’ perspective, transdisciplinary processes contribute to the formation of a reflective practitioner (Schön 2017) who is able reflect in and on actions, e.g., by utilizing his/her experiences made in science discourses.
The science-dominated side of this process ownership scale continues with [3] a science-shop-like knowledge transfer (Leydesdorff and Van Den Besselaar 1987). Here, scientists usually decide whose questions are answered and whose are not. Finally, ethnographic research [2] (as used in geography, anthropology, and human ecology; Whyte 1943) includes strong, intimate mutual learning leading to interaction with, for example, indigenous people, yet it may be conceived as a valuable method for disciplinary applied science [1].
The Triple Helix approach [11], which may be considered a kind of elitist triad between industry, politics, and science (Scholz 2020), has been discussed extensively above. The term Third Mission is frequently used synonymously. We distinguish two lines of Third Mission-oriented science activities. One is the (typically industry-interest-driven) commercialization of science, which was highlighted by Etzkowitz:
The ‘capitalization of knowledge’ is at the heart of the entrepreneurial academic mission, linking universities to users of knowledge more tightly and establishing the university as an economic actor in its own right. (Etzkowitz 2017, p. 128)
Etzkowitz’s definition includes the peril that only research with market value will be funded. In the second line, research on and implementation of (governmental) policy programs is conducted under the Third Mission label. Usually, contract-based research is the form practiced in Third Mission. In fact, there is a broad range of contract-based research extending from framing the research topic to a meticulous and detailed description of outcomes and results [16] according to the sociopolitical interests of, for example, governmental funders. In both lines, we can identify the perils related to contract-based research that conflict with the principle of freedom of science.
Public participation [12] can be viewed as a governmental inclusion of stakeholder groups and scientists. It has been applied often in urban and regional development and for the identification of risk-management strategies for hazardous technologies (e.g., nuclear power plants). In general, setting agendas and deciding to end public participation are under the government’s control. Several similarities exist between public participation and open innovation [13]; open innovation can be considered the business world’s variant of participation. On the one hand, open innovation includes stakeholders and science knowledge at different levels of development; on the other, it aims at new forms of added commercial value. There are also solution-oriented institutes partially funded by the public. Such contract-based research [14] relies on delivery-note-like work packages. In general, industry and government consultancies [15] follow similar business principles, but often do not allow for independent research.
We argue that none of the presented methods is better than the others; all are means of science–practice collaboration. They differ with respect to process ownership, the key concept of structuring them in Fig. 2.
In the age of Anthropocene, human behavior has become a geological factor. Thus, natural scientists have become aware of the ozone hole, climate change, loss of biodiversity, and air and water pollution and eutrophication and have identified critical over-pumping of groundwater and overfishing on a large scale (see Cash et al. 2003). These previously unknown phenomena may affect the Earth’s systems in ways that critically endanger the resilience of human life. Science should serve the public good, and scientists have the major dual responsibility to inform society about these challenges to empower governmental actors to reach effective and meaningful coping strategies (Jensen-Ryan and German 2019; Suni et al. 2016).
Discussion
Strengths and boundaries of transdisciplinary processes for socially robust orientations
The Zurich 2000 conference (Klein et al. 2001; Scholz et al. 2000a, b) can be seen as the start of the formation of transdisciplinary practice as a third mode of doing and utilizing science by doing science with society that complements disciplinarity and interdisciplinarity. Two issues were seen as key principles: first, mutuality, in particular mutual learning between science focus knowledge integration (Scholz 2000); second, there was the idea of authentic co-responsibility and co-leadership to allow for balance and efficiency when coping with wicked (or ill-defined), complex, value-laden, societally relevant problems that cannot be adequately managed using other approaches. Transdisciplinarity may be viewed as a means of strategic sustainable management. This paper elaborates that equal process ownership of science and practice (see Fig. 2) is a unique feature of transdisciplinary processes and may well serve as an umbrella concept. We used process ownership as a feature or dimension to differentiate different forms of science collaboration.
Equal process ownership also promotes the generation of socially robust orientations (Scholz 2011). These emerge from (1) integrating epistemics from science and practice to overcome the fragmentation of knowledge, (2) tapping into scientific state-of-the-art knowledge when (3) producing knowledge that can be understood principally by all representatives of stakeholder groups (and thus includes the potential for consensus formation, at least when defining a problem) and (4) which acknowledges not only uncertainties, but also the incompleteness of knowledge included (i.e., ignorance involved in any human knowledge) and (5) communicates the specific constraints (e.g., time, amount, source of funding, etc.) of knowledge production. Thus, transdisciplinarity is in contrast to many anti-differentiationists’ view which “… deny the division between nature and culture, science and society, science and technology, and between research and enterprise” (Shinn 2005, p. 744).
Transdisciplinary processes of the presented type may even become a kind of tool for democracy if the stakeholders involved provide a balanced view of the interests of a spectrum of society. This is only possible if scientists and practitioners follow the basic rules of conduct of transdisciplinary processes, including accepting the otherness of the other and strictly confining to pre-competitive issues and excluding day-to-day politics. Thus, usually politicians are excluded (as they usually follow a shortsighted agenda) if they do not represent a public position. Usually, representatives of public agencies or positions take a balanced commons perspective.
The participation of representatives of key stakeholder groups and scientists (and the subsequent discussion) with equal process ownership serves to build capacity for sustainable decisions. Concrete action in business, day-to-day policy, applied research, etc., follows later. It is important to note that equal process ownership best, if not only, functions with real-world cases (settings; Vilsmaier et al. 2015). In all rules, the discussions on topics and themes are dominated by scientific disciplinary jargon and neither allows for a shared understanding of a problem nor the partnership with stakeholders. Case-based mutual learning guarantees a joint reference, which can be taken as a starting point and as a means for developing a shared problem understanding and problem representation when using and relating verbal, pictorial, numerical or formal mathematical representations (Jahn et al. 2012 talk about the formation of a common reserach object). Typically, mobility or energy transitions are studies in certain cities or areas, on plastic or phosphorus pollution in certain lakes or seas; insight into questions related to indigenous people are also studied on a case-based level. The description of reality of a real world provides an unambiguous reference which allows the identification of differences in risk perception, perspectives and values among the participants. Based on this, social solutions may be discussed and constructed.
Knowledge systems for actions of science–practice collaboration approaches
The presented, widely applied version (Scholz and Steiner 2015c) of transdisciplinary processes [10] is a capacity for sustainable decisions oriented one. But you may also find (direct solutionist, or) action-oriented [8] approaches of transdisciplinarity stating: “transdisciplinary research delivers high-quality solutions for practice actors facing societal problems” (Bergmann et al. 2012), which expresses a mission of action research. We think that this is rather a matter of consultancy [15].
Sometimes, the knowledge-based differentiationist’s complementarity of science and practice view (see Fig. 1; Shinn 2005) is given up (such as in “shallow action research” [8] or “transition management” [7]). Scientists themselves seem to become decision makers and actors. This poses new and challenging questions from a process ownership perspective.
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What is the moral and/or democratic legitimacy when scientists take process ownership [e.g., via the epistemic authority of the IPCC (Gustafsson and Lidskog 2018) or the IPBES (Shinn 2005)]?
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Science must inform, but is political action part of science?
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When does advocacy science, as a driver of science process ownership, endanger the integrity of science?
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How does worldview or political opinion affect the scientific knowledge produced? Can the mission of an honest knowledge broker (Pielke Jr. 2002) be fulfilled when taking an advocacy perspective?
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Is it necessary, under certain constraints, for scientists to take a solutionist, action-oriented approach [8] instead of a capacity-oriented one?
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When must the knowledge- and epistemics-centered approach be overcome and action-orientation-based work come to the foreground?
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Under what constraints is science process ownership necessary from a sustainable development perspective?
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What is the relationship of legitimized decision makers (e.g., working as coleaders) to elected policymakers or property owners (of land, resources, etc.)?
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Must the role of process ownership be assessed differently in the democratically developed Western world, the developing world, and various autocratic countries such as China?
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In what contexts do we face limited social degrees of freedom where scientists’ processing of leadership may play an important role in breaking societal lock-in positions? What other methods (e.g., policy consultancy [15]) may be chosen, e.g., as an intermediate project to prepare the case for a transdisciplinary venture?
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How can we promote the dynamic change of science and practice headship which dynamically changes (depending on the task and topic; Stauffacher et al. 2008) to attain an overall balanced leadership?
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Is it possible to measure the degree of involvement of scientists and practitioners? What aspects (e.g., controlling methods, fundings) endanger equal process ownership?
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Conclusions and outlook
First, which form of science–practice collaboration is adequate depends on (i) the nature of the problem being addressed (e.g., what scientific or practical issue is the focus) and (ii) which actors are involved in what roles and with what powers, i.e., who takes process ownership. The latter includes which actors have control over the outcomes generated and who may utilize—and who may benefit from—data and other products of the process. This is also linked to (iii) the purpose of the collaboration. These three aspects allow us to better distinguish between participatory research (or citizen science) and transdisciplinary processes.
Second, equal process ownership of science and practice make transdisciplinary processes different from other forms of science–practice collaboration. Ideally, this results in authentic co-leadership allowing for collaboration and, thereby, co-creation, co-definition, co-design, co-representation, and co-responsibility. The product is socially robust orientations on sustainable transformation based on mutual learning between and within groups of scientists and representatives of key stakeholder groups. Such orientations produce more effective coping strategies for challenging wicked, complex, societally relevant problems. For these types of problems, each party (science and practice) is, on its own, overburdened for a variety of reasons related to the complexity and diversity of phenomena and impacts on different sociotechnological contexts, cultures, or scales. Practice and, in particular, key stakeholder groups that demonstrate a commitment to sustainability may learn which kinds of actions should be promoted and which should not. Moreover, science may become aware of the limits of disciplinary knowledge and what institutions are already available and capable of successfully providing targeted interdisciplinary processes as part of transdisciplinary processes for sustainable transitions.
Data availability
Authors can confirm that all relevant data are included in the article and/or its supplementary information files.
References
Asplund P, Nordman N (1999) Attitudes toward the third mission: a selection of interviews from seven universities in Sweden. Working Paper No. 15. CERUM: Centre for Regional Science, Umeå
Bennett LM, Gadlin H (2012) Collaboration and team science: from theory to practice. SAGE Publication, Los Angeles, CA
Bergmann M, Jahn T, Knobloch T, Krohn W, Pohl C, Schramm E (2012) Methods for transdisciplinary research: a primer for practice. Campus Verlag, Frankfurt
Bernal JD (1954/1965) Science in history, vol. 1–4. Penguin, Hammondsworth
Bonney R, Cooper CB, Dickinson J, Kelling S, Phillips T, Rosenberg KV, Shirk J (2009) Citizen science: a developing tool for expanding science knowledge and scientific literacy. Bioscience 59(11):977–984
Bunge M (1966) Technology as applied science. Technol Cult 7(3):329–347
Bunge M (2012) Scientific research II: the search for truth. Springer Science & Business Media, Berlin
Cash DW, Clark WC, Alcock F, Dickson NM, Eckley N, Guston DH et al (2003) Knowledge systems for sustainable development. Proc Natl Acad Sci 100:8086–8091
Chesbrough HW (2003) The era of open innovation. Mit Sloan Manag Rev 44(3):35–41. Retrieved from <Go to ISI>://WOS:000182263900011
Christopher S, Watts V, McCormick A, Young S (2008) Building and maintaining trust in a community-based participatory research partnership. Am J Public Health 98(8):1398–1406
Clare JDJ, Townsend PA, Anhalt-Depies C, Locke C, Stenglein JL, Frett S et al (2019) Making inference with messy (citizen science) data: when are data accurate enough and how can they be improved? Ecol Appl 29(2):e01849
Clark BR (1983) The higher education system: academic organisations in cross-national perspective. University of California Press, Berkeley
Clark WC, Harley AG (2020) Sustainability science: toward a synthesis. Annu Rev Environ Resour 45:331–386
Cohn JP (2008) Citizen science: can volunteers do real research? Bioscience 58(3):192–197. https://doi.org/10.1641/b580303
Cooper CB, Dickinson J, Phillips T, Bonney R (2007) Citizen science as a tool for conservation in residential ecosystems. Ecol Soc 12(2):11. Retrieved from <Go to ISI>://WOS:000252310900037
Cornwall A, Jewkes R (1995) What is participatory research? Soc Sci Med 41(12):1667–1676. Retrieved from <Go to ISI>://A1995TK46600012
CSS (2022) Biofiels factsheet (Pub. No-CSS08-09). Center of Sustainable Systems, University of Michigan. Ann Arbor
Devecchi L (2012) Von politischen Grabenkämpfen zur kooperativen Planung. Der Paradigmenwechsel in der Stadtentwicklungspolitik hin zu einem urbanen Regime in Zürich. disP Plan Rev 48(4):45–55
Dickinson JL, Zuckerberg B, Bonter DN (2010) Citizen science as an ecological research tool: challenges and benefits. In: Futuyma HJ, Shafer HB, Simberloff D (eds) Annual review of ecology, evolution, and systematics, vol 41, pp 149–172
Dickinson JL, Shirk J, Bonter D, Bonney R, Crain RL, Martin J et al (2012) The current state of citizen science as a tool for ecological research and public engagement. Front Ecol Environ 10(6):291–297
Dienel P (1970/1991) Die Planungszelle. Westdeutscher Verlag, Opladen
Dienel CP, Renn O (1995) Planning cells: a gate to “fractal” mediation. In: Renn O, Webler T, Wiedemann P (eds) Fairness and competence in citizen participation. Springer, Berlin, pp 117–140
Dreyfus HL, Dreyfus SE (2005) Peripheral vision: expertise in real world contexts. Organ Stud 26(5):779–792. https://doi.org/10.1177/0170840605053102
Drolet BC, Lorenzi NM (2011) Translational research: understanding the continuum from bench to bedside. Transl Res 157(1):1–5
Du J, Leten B, Vanhaverbeke W (2014) Managing open innovation projects with science-based and market-based partners. Res Policy 43(5):828–840. https://doi.org/10.1016/j.respol.2013.12.008
Etzkowitz H (2017) Innovation Lodestar: the entrepreneurial university in a stellar knowledge firmament. Technol Forecast Soc Change 123:122–129
Etzkowitz H, Leydesdorff L (1995) The triple helix—university-industry-government relations: a laboratory for knowledge based economic development. EASST Rev 14(1):14–19
Etzkowitz H, Leydesdorff L (1998) The endless transition: a “triple helix” of university-industry-government relations. Minerva 36(3):203–208. Retrieved from <Go to ISI>://WOS:000076641700001
Etzkowitz H, Webster A, Healey P (1998) Capitalizing knowledge: new intersections of industry and academia. Suny Press, Albany
Fischer C, Leydesdorff L, Schophaus M (2004) Science shops in Europe: the public as stakeholder. Sci Public Policy 31(3):199–211
Freire P (1993) Pedagogy of the oppressed. 1970, vol 125. Continuum, New York
Funtowicz SO, Ravetz JR (2003) Post-normal science. In: International Society for Ecological Economics, Online encyclopedia of ecological economics. https://www.isecoeco.org/pdf/pstnormsc.pdf
Gassmann O, Enkel E, Chesbrough HW (2010) The future of open innovation. R&D Manag 40(3):213–221. Retrieved from <Go to ISI>://WOS:000277419600001
Gherardi S (2008) Situated knowledge and situated action: what do practice-based studies promise. In: Hansen H, Barry D (eds) The SAGE handbook of new approaches in management and organization studies. Sage, Singapore, pp 516–527
Gibbons M (2013) Mode 1, mode 2, and innovation. In: Carayannis EG (ed) Encyclopedia of creativity, invention, innovation and entrepreneurship. Springer, New York, pp 1285–1292
Gibbons M, Limoges C, Nowotny H, Schwartzmann S, Scott P, Trow M (1994) The new production of knowledge. Sage, London
Gigerenzer G (2021) Axiomatic rationality and ecological rationality. Synthese 198:3547–3564
Goodchild MF (2007) Citizens as voluntary sensors: spatial data infrastructure in the world of Web 2.0. Int J Spat Data Infrastruct Res 2(2):24–32
Graydon O (2012) Fraunhofer research model comes to the UK. Nat Photonics 6(12):796–797
Greenhalgh T (2002) Intuition and evidence—uneasy bedfellows? Br J Gen Pract 52(478):395–400
Gustafsson KM, Lidskog R (2018) Organizing international experts: IPBES’s efforts to gain epistemic authority. Environ Sociol 4(4):445–456
Häberli R, Grossenbacher-Mansuy W (1998) Transdisziplinarität zwischen Förderung und Überforderung. Erkenntnisse aus dem SPP Umwelt. Gaia 7:196–213
Hooykaas MJD, Schilthuizen M, Aten C, Hemelaar EM, Albers CJ, Smeets I (2019) Identification skills in biodiversity professionals and laypeople: a gap in species literacy. Biol Conserv 238:108202
Hoppe R (1999) Policy analysis, science and politics: from ‘speaking truth to power’ to ‘making sense together.’ Sci Public Policy 26(3):201–210
Hoshmand LT, Polkinghorne DE (1992) Redefining the science–practice relationship and professional training. Am Psychol 47(1):55
Hunter L (2009) Situated knowledge. In: Riley SJ, Hunter L (eds) Mapping landscapes for performance as research. Palgrave, London, pp 151–153
Israel BA, Schulz AJ, Parker EA, Becker AB, Allen AJ, Guzman JR, Lichtenstein R (2017) Critical issues in developing and following CBPR principles. In: Wallerstein N, Duran B, Oetzel JG, Minkler M (eds) Community-based participatory research for health: advancing social and health equity, vol 3. Wiley, New York, pp 31–46
Jahn T, Bergmann M, Keil F (2012) Transdisciplinarity: between mainstreaming and marginalization. Ecol Econ 79:1–10. https://doi.org/10.1016/j.ecolecon.2012.04.017
Jantsch E (1970) Inter- and transdisciplinary university: a systems approach to education and innovation. Policy Sci 1:403–428
Jasanoff S (1993) Bridging the two cultures of risk analysis. Risk Anal 13(2):123–129. Retrieved from <Go to ISI>://WOS:A1993KY73700001
Jensen-Ryan DK, German LA (2019) Environmental science and policy: a meta-synthesis of case studies on boundary organizations and spanning processes. Sci Public Policy 46(1):13–27
Kapoor D, Jordan S (2009) Education, participatory action research, and social change. Palgrave, New York
Kates RW, Clark WC, Corell R, Hall JM, Jaeger CC, Lowe I et al (2001) Sustainability science. Science 5517:641–642
Kemp R, Loorbach D, Rotmans J (2007) Transition management as a model for managing processes of co-evolution towards sustainable development. Int J Sustain Dev World Ecol 14(1):78–91. Retrieved from <Go to ISI>://000245723500008
Kessel F, Rosenfield P, Anderson N (2003) Expanding the boundaries of health and social science: case studies in interdisciplinary innovation. Oxford University Press, Oxford
Klein JT (2004) Prospects for transdisciplinarity. Futures 36:515–526
Klein JT, Grossenbacher-Mansuy W, Häberli R, Bill A, Scholz RW, Welti M (eds) (2001) Transdisciplinarity: joint problem solving among science, technology, and society. An effective way for managing complexity. Birkhäuser, Basel
Kolb DA (1984) Experiential learning. Experience as the source of learning and development. Prentice Hall, Upper Saddle River
Krütli P, Stauffacher M, Flueler T, Scholz RW (2010) Functional-dynamic public participation in technological decision-making: site selection processes of nuclear waste repositories. J Risk Res 13(7):861–875. https://doi.org/10.1080/13669871003703252
Lang DJ, Wiek A, Bergmann M, Stauffacher M, Martens P, Moll P et al (2012) Transdisciplinary research in sustainability science: practice, principles, and challenges. Sustain Sci 7:25–43. https://doi.org/10.1007/s11625-011-0149-x
Laredo P (2007) Revisiting the third mission of universities: toward a renewed categorization of university activities? High Educ Pol 20(4):441–456
Lenard P (1936) Deutsche Physik, vol 1. Lehmann, München
Lewin K (1946) Action research and minority problems. J Soc Issues 2(4):34–46
Leydesdorff L (1980) The Dutch science shops. Trends Biochem Sci 5(5):R1–R2. https://doi.org/10.1016/0968-0004(80)90041-9
Leydesdorff L, Etzkowitz H (1996) Emergence of a triple helix of university-industry-government relations. Sci Public Policy 23(5):279–286
Leydesdorff L, Van Den Besselaar P (1987) What we have learned from the Amsterdam Science Shop. In: Blume S, Bunders J, Leydesdorfd L, Whitley R (eds) The social direction of the public sciences. Springer, Berlin, pp 135–160
Lindblom CE (1979) Still muddling, not yet through. Public Adm Rev 39(6):517–526
Loorbach D (2014) To transition! Governance panarchy in the new transformation. Inaugural address. Erasmus University Rotterdam, Faculty of Social Science, Rotterdam
Loorbach D, Wittmayer JM, Avelino F, von Wirth T, Frantzeskaki N (2020) Transformative innovation and translocal diffusion. Environ Innov Soc Trans 35:251–260
Mahan Jr JL (1970) Toward transdisciplinary inquiry in the humane sciences. (PhD 70-20,145). United States International University, San Diego, CA
Matson P, Clark WC, Andersson K (2016) Pursuing sustainability: a guide to the science and practice. Princeton University Press, New Jersey
Melese T, Lin SM, Chang JL, Cohen NH (2009) Open innovation networks between academia and industry: an imperative for breakthrough therapies. Nat Med 15(5):502–507. https://doi.org/10.1038/nm0509-500
Mention A-L (2011) Co-operation and co-opetition as open innovation practices in the service sector: Which influence on innovation novelty? Technovation 31(1):44–53. https://doi.org/10.1016/j.technovation.2010.08.002
Merton RK (1973) The sociology of science: theoretical and empirical investigations. University of Chicago Press, Chicago
Mielke J, Vermaßen H, Ellenbeck S (2017) Ideals, practices, and future prospects of stakeholder involvement in sustainability science. Proc Natl Acad Sci 114(50):E10648–E10657
Mingers J, Rosenhead J (2004) Problem structuring methods in action. Eur J Oper Res 152(3):530–554
Mobjork M (2010) Consulting versus participatory transdisciplinarity: a refined classification of transdisciplinary research. Futures 42(8):866–873. https://doi.org/10.1016/j.futures.2010.03.003
Morin E, de Freitas L, Nicolescu B (1994) O Manifesto da transdisciplinaridade, English translation. Triom, São Paulo. https://inters.org/Freitas-Morin-Nicolescu-Transdisciplinarity
NAS (1969) The behavioral and social sciences: outlook and needs. The Behavioral and Social Sciences Survey Committee under the auspices of the Committee on Science and Public Policy, National Academy of Sciences, and the Committee on Problems and Policy, Social Science Research Council. Prentice-Hall, Englewood Cliffs
Newig J, Fritsch O (2009) Environmental governance: participatory, multi-level—and effective? Environ Policy Gov 19(3):197–214. https://doi.org/10.1002/eet.509
Newig J, Pahl-Wostl C, Sigel K (2005) The role of public participation in managing uncertainty in the implementation of the Water Framework Directive. Eur Environ 15(6):333–343. https://doi.org/10.1002/eet.398
Njoroge R, Birech R, Arusey C, Korir M, Mutisya M, Scholz RW (2015) Transdisciplinary processes of developing, applying, and evaluating a method for improving smallholder farmers’ access to (phosphorus) fertilizers: the SMAP method. Sustain Sci 10(4):601–619. https://doi.org/10.1007/s11625-015-0333-5
Novakovic AM (1994) The Cornell program on dairy markets and policy. Retrieved from Ithaca, NY. http://publications.dyson.cornell.edu/outreach/extensionpdf/1994/Cornell_AEM_eb9404.pdf
Nowotny H, Scott P, Gibbons M (2001) Rethinking science—knowledge and the public on an age of uncertainty. Polity, London
Piaget J (1968) Genetic epistemology. Columbia University Press, New York
Pielke RA Jr (2002) Science policy: policy, politics and perspective—the scientific community must distinguish analysis from advocacy. Nature 416(6879):367–368. https://doi.org/10.1038/416367a
Polk M (2014) Achieving the promise of transdisciplinarity: a critical exploration of the relationship between transdisciplinary research and societal problem solving. Sustain Sci 9(4):439–451
Raab M, Gigerenzer G (2015) The power of simplicity: a fast-and-frugal heuristics approach to performance science. Front Psychol. https://doi.org/10.3389/fpsyg.2015.01672
Rahman MA (1991) Action and knowledge breaking the monopoly with participatory action-research. Intermediate Technology, London
Renn O (2021) Transdisciplinarity: synthesis towards a modular approach. Futures 130:102744
Renn O, Klinke A (2004) Systemic risks: a new challenge for risk management: as risk analysis and risk management get increasingly caught up in political debates, a new way of looking at and defining the risks of modern technologies becomes necessary. EMBO Rep 5(1S):S41–S46
Risopoulos-Pichler F, Daghofer F, Steiner G (2020) Competences for solving complex problems: a cross-sectional survey on higher education for sustainability learning and transdisciplinarity. Sustainability 12(15):6016
Rosendahl J, Zanella MA, Rist S, Weigelt J (2015) Scientists’ situated knowledge: strong objectivity in transdisciplinarity. Futures 65:17–27
Rotmans J, Loorbach D (2008) Transition management: reflexive governance of societal complexity through searching, learning and experimenting. In: van den Bergh JCJM, Bruinsma FR (eds) Managing the transition to renewable energy: theory and practice from local, regional and macro perspectives. Edward Elgar, Cheltenham, pp 15–46
Rotmans J, Kemp R, Van Asselt MBA (2001) More evolution than revolution: transition management in public policy. Foresight 3(1):15–31
Salter J, Robinson J, Wiek A (2010) Participatory methods of integrated assessment—a review. Wiley Interdiscip Rev Clim Change 1(5):697–717
Sarewitz D (2000) Science and environmental policy: an excess of objectivity. In: Frodeman RF, Baker VR (eds) Earth matters: the earth sciences, philosophy, and the claims of community. Prentice-Hall, Hoboken, pp 79–98
Scholz RW (2000) Mutual learning as a basic principle of transdisciplinarity. In: Scholz RW, Häberli R, Bill A, Welti W (eds) Transdisciplinarity: joint problem-solving among science, technology and society. Workbook II: mutual learning sessions. Haffmans Sachbuch, Zürich, pp 13–17
Scholz RW (2011) Environmental literacy in science and society: from knowledge to decisions. Cambridge University Press, Cambridge
Scholz RW (2017) The normative dimension in transdisciplinarity, transition management, and transformation sciences: new roles of science and universities in sustainable transitioning. Sustainability. https://doi.org/10.3390/su9060991
Scholz RW (2020) Transdisciplinarity: science for and with society in light of the university’s roles and functions. Sustain Sci 15:1033–1049. https://doi.org/10.1007/s11625-020-00794-x
Scholz RW, Stauffacher M (2007) Managing transition in clusters: area development negotiations as a tool for sustaining traditional industries in a Swiss prealpine region. Environ Plan A 39(10):2518–2539
Scholz RW, Steiner G (2015a) The real type and ideal type of transdisciplinary processes: part I—theoretical foundations. Sustain Sci 10(4):527–544
Scholz RW, Steiner G (2015b) The real type and the ideal type of transdisciplinary processes. Part II—what constraints and obstacles do we meet in practice? Sustain Sci 10(4):653–671. https://doi.org/10.1007/s11625-015-0327-3
Scholz RW, Steiner G (2015c) The real type and the ideal type of transdisciplinary processes. Part II—what constraints and obstacles do we meet in practice? Supplementary Information https://static-content.springer.com/esm/art%3A10.1007%2Fs11625-015-0327-3/MediaObjects/11625_2015_327_MOESM1_ESM.pdf. Sustain Sci 10(4):653–671. https://doi.org/10.1007/s11625-015-0327-3.
Scholz RW, Steiner G (2016) Approaches of science–practice interaction: how to relate multi-stakeholder knowledge and science knowledge? Paper presented at the Designing higher education learning environments to develop key competencies for sustainability, October 19–21, 2016, Wageningen
Scholz RW, Wellmer F-W (2021) Endangering the integrity of science by misusing unvalidated models and untested assumptions as facts: general considerations and the mineral and phosphorus scarcity. Sustain Sci. https://doi.org/10.1007/s11625-021-01006-w
Scholz RW, Häberli R, Bill A, Welti M (eds) (2000a) Transdisciplinarity: joint problem-solving among science, technology and society. Workbook II: mutual learning sessions, vol 2. Haffmans Sachbuch Verlag, Zürich
Scholz RW, Mieg HA, Oswald J (2000b) Transdisciplinarity in groundwater management: towards mutual learning of science and society. Water Air Soil Pollut 123(1–4):477–487
Scholz RW, Lang DJ, Wiek A, Walter AI, Stauffacher M (2006) Transdisciplinary case studies as a means of sustainability learning: historical framework and theory. Int J Sustain Higher Educ 7(3):226–251. Retrieved from pdf-file not available
Schön DA (2017) The reflective practitioner: how professionals think in action. Routledge, London
Scott P (2007) From professor to ‘knowledge worker’: profiles of the academic profession. Minerva 45(2):205–215
Sheppard M (1995) Social work, social science and practice wisdom. Br J Soc Work 25(3):265–293
Shinn T (2005) New sources of radical innovation: research-technologies, transversality and distributed learning in an post-industrial order. Soc Sci Inf 44(4):731–764
Silvertown J (2009) A new dawn for citizen science. Trends Ecol Evol 24(9):467–471. https://doi.org/10.1016/j.tree.2009.03.017
Smith LT (2013) Decolonizing methodologies: research and indigenous peoples. Zed Books, London
Spradley J (2016) Participant observation. Waveland Press, Long Grove
Stark J (1937) Weisse Juden in der Wissenschaft. Das Schwarze Korps, p 6. https://uni-tuebingen.de/fileadmin/Uni_Tuebingen/Fakultaeten/MathePhysik/Institute/IAP/Forschung/MOettel/Geburt_QM/stark_weisse_juden_1937.html
Stauffacher M, Flueeler T, Krueli P, Scholz RW (2008) Analytic and dynamic approach to collaboration: a transdisciplinary case study on sustainable landscape development in a Swiss prealpine region. Syst Pract Action Res 21(6):409–422. https://doi.org/10.1007/s11213-008-9107-7
Stokes DE (1997) Pasteur’s quadrant. Basic science and technological innovation. Brookings Institution Press, Washington, DC
Stokols D, Hall KL, Taylor BK, Moser RP (2008) The science of team science—overview of the field and introduction to the supplement. Am J Prev Med 35(2):S77–S89. https://doi.org/10.1016/j.amepre.2008.05.002
Sturdy A (1997) The consultancy process—an insecure business? J Manag Stud 34(3):389–413
Suni T, Juhola S, Korhonen-Kurki K, Käyhkö J, Soini K, Kulmala M (2016) National Future Earth platforms as boundary organizations contributing to solutions-oriented global change research. Curr Opin Environ Sustain 23:63–68
Thorn K, Soo M (2006) Latin American universities and the third mission: trends, challenges, and policy options. The World Bank, Washington D.C.
Vilsmaier U, Engbers M, Luthardt P, Maas-Deipenbrock RM, Wunderlich S, Scholz RW (2015) Case-based mutual learning sessions: knowledge integration and transfer in transdisciplinary processes. Sustain Sci 10(4):563–580
Wallerstein N, Duran B (2010) Community-based Participatory Research Contributions to intervention research: the intersection of science and practice to improve health equity. Am J Public Health 100:S40–S46. https://doi.org/10.2105/ajph.2009.184036
Wallerstein N, Duran B (2017) The theoretical, historical and practice roots of CBPR. In: Minkler M, Wallerstein N (eds) Community-based participatory research for health: advancing social and health equity, pp 17–29
West SM, van Kerkhoff L, Wagenaar H (2019) Beyond “linking knowledge and action”: towards a practice-based approach to transdisciplinary sustainability interventions. Policy Stud 40(5):534–555
Whitehead TL (2005) Basic classical ethnographic research methods. Cult Ecol Health Change 1:1–29
Whyte WF (1943) Street corner society: the social structure of an Italian slum. University of Chicago Press, Chicago
Whyte WF, Greenwood DJ, Lazes P (1991) Participatory action research: through practice to science in social research. Particip Action Res 19–55
Wilson E (2018) Community-based participatory action research. In: Liamputtong P (ed) Handbook of research methods in health social sciences. Springer, Singapore, pp 1–15
Wittmayer JM (2016) Transition management, action research and actor roles: understanding local sustainability transitions. (PhD). Erasmus University Rotterdam, Rotterdam. Retrieved from http://hdl.handle.net/1765/94385. Accessed 20 Dec 2020
Wynne B (1996) May the sheep safely graze? A reflexive view of the expert-lay knowledge divide. In: Lasch S, Szerszynski B, Wynne B (eds) Risk, environment & modernity: towards a new ecology. Sage, London, pp 44–83
Zomer A, Benneworth P (2011) The rise of the university’s third mission. In: Enders J, de Boer HF, Westerheijden DF (eds) Reform of higher education in Europe. SensePublishers, Rotterdam. https://doi.org/10.1007/978-94-6091-555-0_6
Acknowledgements
We thank Ortwin Renn, Ilja Steffelbauer, Eva Schernhammer, workshop participants at various venues and the three reviewers for their feedback on previous versions, Günther Schreder for assisting in the statistical analysis, and Elaine Ambrose for her English editing.
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Appendix
Appendix
A description of the 15 approaches is provided in the text. Table 1 sets the approaches of Fig. 2B into context. This is done by illustrating the background of their development (column 2), by listing the key characteristics (column 3) and key references (column 4), which would help the reader to develop a deeper understanding of the approaches.
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Scholz, R.W., Steiner, G. Process ownership in science–practice collaborations: the special role of transdisciplinary processes in sustainable transitioning. Sustain Sci 18, 1501–1518 (2023). https://doi.org/10.1007/s11625-023-01291-7
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DOI: https://doi.org/10.1007/s11625-023-01291-7