Social labs as temporary intermediary learning organizations to help implement complex normative policies. The case of Responsible Research and Innovation in European science governance

1. Introduction

The concept of the “learning organization” (Senge, 1990) has recently gained new currency. Örtenblad’s (2019) edited volume has brought together a wide range of stories that convey the topicality of Senge’s conceptualizations in view of understanding and describing organizations’ adaptability to rapid sociotechnical change. Hansen, Jensen, and Nguyen (2020) have drawn attention to the relevance of Senge’s book The Fifth Discipline in the context of recent debates on responsible innovation (RI), arguing that “[some of the habits practiced by a learning organization facilitate responsible innovation; some are responsible innovation” (Hansen et al., 2020). Interestingly, with the notable exception of Dixon (2019) in the Örtenblad edited volume, the attention for governing organizations as (potential) learning organizations in this recent work is limited. Hansen et al. (2020) focus on for-profit companies in their description of how these may be tempted to become learning organizations to strengthen their competitive position by engaging in RI. They only mention governance when they point out that the notion of RI “enjoys … government backing,” referring to the related discourse (Owen, Pansera, Macnaghten, & Randles, 2021a, Owen, von Schomberg, & Macnaghten, 2021b) on Responsible Research and Innovation (RRI).

Indeed, RRI was embraced notably by the European Commission (EC) as a “grand” narrative from 2014 on to bring via research and innovation (R&I) funding policies in line with the values, needs and expectations of society. To the EC, RRI as a policy concept offered an opportunity to juxtapose ambitions in view of Europe’s competitive position with normative considerations about connection between science and citizens (cp Zwart, Landeweerd, & van Rooij, 2014) [1]. These considerations had been put center stage in the Science and Society Action Plan in 2001 and tallied with ideas that were put forward in academic circles from the early 2000s on responsibility in innovation. What emerged from a cross-fertilization between these debates was an understanding of RRI as a dedicated intention to bridge gaps between science, research and innovation communities on the one hand, and society at large on the other.

RRI was defined by the EC as a policy concept in 2013, denoting a:

transparent, interactive process by which societal actors and innovators become mutually responsive to each other with a view on the (ethical) acceptability, sustainability and societal desirability of the innovation process and its marketable products (in order to allow a proper embedding of scientific and technological advances in our society) (Von Schomberg, 2013, p. 2).

Implementation plans were articulated at about that time too: In 2014, the “Rome Declaration on Responsible Research and Innovation in Europe” called for action to promote RRI within European Institutions, Member States, regional authorities and research and innovation funding organizations (EC, 2014). The idea was to build capacity for RRI, review and adapt narratives for research and innovation and implement institutional changes that foster RRI and a more democratic research and innovation ecosystem in the European Research Arena (Braun, 2019, pp. 243–266; Owen et al., 2021a, 2021b).

However, in spite of these efforts, the “backing” that R(R)I enjoyed at EC level in practice appeared to a large extent merely a fictitious reality, as implementation in actual science and innovation governance proved problematic and limited (Griessler, Braun, Wicher, & Yorulmaz, 2022; Novitzky et al., 2020). Thus, RRI and RI seem to devolve into a Catch-22 situation: for governmental arrangements like the EC to provide a stimulus to for-profit organizations to cultivate RI in the way Hansen et al. (2020) suggest, they need themselves to endorse and implement the concept. Only if the diverse organizations that together make up the science governance system become learning organizations, in other words, can they actively support companies and research institutes to actively determine what “responsibility” is and how to translate ideas on the subject and associated normative considerations into research and innovation practice (Egeland, Forsberg, & Maximova-Mentzoni, 2019; Loeber & Cohen, 2021).

In the case of EU science and innovation governance, it is however difficult to pinpoint which organizations are required to learn and can qualify as learning organizations. The governance of science and innovation in the European polity is scattered among a network of organizations and intermediary institutions (Hartmann & Kjaer, 2015; van der Meulen & Rip, 1998). Intermediaries such as standard-setting organizations, ethics boards at national or supranational level, research councils, advisory bodies and university organizations work in a relationship with their environment “as a back-and-forth of constructing demands, of localized and idiosyncratic interpretations of policy discourses, and of material and structural constraints in practice” (Åm, 2013, p. 467). As a consequence, learning within the European science governance system is dispersed and scattered among a range of intermediary institutions, and if it takes place, it is hard to pinpoint (Schuijer, van der Meij, Broerse, & Kupper, 2022).

This paper seeks to address this challenge by exploring how learning on RRI in the EU science and innovation governance system can be provoked and how Senge’s insights can clarify the possibilities. To that end, we hypothesize that settings created for individuals from within the EU science governance system to engage in RRI-related learning can serve as temporary, intermediary learning organizations (and may thus help implement RRI in relevant governance arrangements) if these spaces fit the requirements that Senge (1990; see too Senge, Scharmer, Jaworski, & Flowers, 2005) identifies as essential for a learning organization. We assume the possibility of a relation between intermediary organizations and RRI drawing on Åm (2013) who showed how the discourse of RI (“responsible development” at the time in the field of nanotechnology) triggered new demands for a multitude of heterogeneous actors involved in science and innovation as well as in the governance of research and development, which were articulated and passed on by intermediary organizations. To explore this line of reasoning empirically, we will draw on the experiences with organizing so-called “Social Labs” as a space for reflection and learning on the subject of RRI. The Labs were set up in the context of the NewHoRRIzon [2] project, funded by the EC within its 8th Framework Program for funding research and innovation “Horizon2020” (H2020), which aimed at promoting the uptake of RRI in that very funding framework. NewHoRRIzon was among the more encompassing of projects to promote RRI, addressing all of H2020’s component parts in 19 Social Labs in total. Of these, for the current purpose, we will select five cases (based on a maximum variation principle) to explore in-depth two interlinked research questions to inquire how Social Labs can play a role in embedding RRI in European science governance. Our first question: How can Social Labs function as temporary learning organizations? reflects on the purpose of Social Labs, while the second question: Under which conditions can Social Labs inform learning about responsibility in research and innovation in European science governance? concerns their context and framing within a specific governance setting.

Below, after elaborating our research methods, we will draw on the work on learning organizations (Senge, 1990; Senge et al., 2005; Örtenblad, 2019) to conceptualize how a temporary setting can play a role as intermediary learning organization, in view of the first question. First, we describe how we operationalized the Social Lab method in the context of five different cases related to European science governance and funding. We will then discuss the selected Social Labs through the lens of Senge’s five disciplines to assert if and how the Labs functioned as a special case of temporary learning organizations. We will then look at our data to discuss the conditions under which Social Labs inform science governance institutions’ learning about and, potentially, implementing RRI in their organizational processes.

2. Research methods: engaged research

Social labs (Hassan, 2014) are experimental venues focused on creating solutions to complex societal challenges. Furthermore, they present opportunities for participatory forms of action research (Timmermans, Blok, Braun, Wesselink, & Nielsen, 2020). The latter is defined as a “participatory process concerned with developing practical knowing in the pursuit of worthwhile human purposes. It seeks to bring together action and reflection, theory and practice, in participation with others, in the pursuit of practical solutions to issues of pressing concern to people” (Reason & Bradbury, 2008, p. 4).

It was with this purpose in mind that we set up, together with a consortium of researchers from all over Europe, a range of Social Labs on RRI. The main aim of the Labs was to:

• create spaces for discussion and reflection on the state of RRI within the specific domain of the European research arena (learning about RRI);

• develop a social challenge that participants felt pressing within their research ambit or context (learning about the social embeddedness of research);

• address the identified challenges by co-designing and implementing experiments called “pilot actions” (experiential learning);

• critically reflect upon both the experiments and critical moments encountered in the pilot action process in a range of dedicated workshops with Lab-participants, notably on particular choices made (reflective learning); and

• discuss and develop evidence-based policy action (policy-oriented learning) to assist RRI uptake in a specific domain.

Among the many “pilot actions” (experiments) to promote the uptake of RRI in research and innovation across the 19 Social labs in total, not all were fully followed through [3]. In real-life settings contextual, resource or human factors hindered experimentation; therefore, researchers made the decision that experiments should not be addressed in binary terms of failure and success. Rather, they were to be seen as part of the process of reflection, learning, planning, re-planning, re-reflecting: sharing experiences and expertise from related but diverging fields, vantage points and positions within the research and innovation environment, starting, stopping and re-starting again, as experiential learning that is key to the process occurs (Kolb, 1984). Experiments that were stopped, merged or radically altered offered additional insights to the context, organizational setup or framework of the specific research domain in which Social Lab participants were engaged.

As concerns our position as researchers, because we were involved in designing and running the Social Labs, but not in developing and implementing the pilot actions, our role in these can be characterized as “engaged researchers”: researchers actively being involved in a social field in the pursuit of solving important problems, while at the same time combining this with a scientific knowledge generation process (Levin & Ravn, 2007). Engaged research involves both researcher(s) and researchee(s) working together on problem definition and problem-solving and through this process develop new knowledge. Yet, like in action research (Susman & Evered, 1978), our work resulted in the joint production of actionable knowledge by acting on the world (Chatterton et al., 2007, p. 218) in which “scholarly theorizing and practitioners’ narratives” are combined (Cicmil, Williams, Thomas, & Hodgson, 2006, p. 677; Loeber & Vermeulen, 2016). Such action and the resulting knowledge is inherently value laden and morally committed (Macdonald, 2012). To guard the validity of our insights and conclusions, we opened up our findings and inferences to probing by Lab organizers and participants (Montuschi, 2014, p. 129). To that end, during the Social Lab workshops (in each Lab, three workshops were organized over the time span of approximately two years), each of the researchers collected written material (flipovers and post-its), and notes were taken of interactions (online) between participants in-between workshops. Reflections and analyses created by the researchers were discussed in the Workshops and at cross-sectional conferences. Key findings in the form of narratives about the process as well as experiments were reflected upon by participants, and consensus was sought to arrive at shared understandings of achievements within the Social Labs. Resulting data was collated into narratives that were used as a basis for reflective interviews with Social Lab managers by the evaluators of the project (Loeber & Cohen, 2018). Findings were fed back to Lab participants during the third workshop by way of member check and were jointly reflected upon and validated during or after the event [4].

For the purposes of this paper, the above materials were subjected to qualitative content analysis (Mayring, 2021) by the authors of this paper to analyze how the experiences in the Social Labs satisfied the “five disciplines” identified by Senge (1990) to characterize a learning organization. Below, we limit the presentation of our empirical material to a selection of five Labs, reflecting H2020 sections that are uniquely governed by a varying set of institutions. These were selected for the purpose of this paper to include maximum variety in terms of different organizational forms that operate in the fuzzy environment of science governance in the EU.

3. Learning and non-learning organizations in science governance

To consider how our Social Labs could function as temporary learning organizations, let us consider Senge’s work (Senge, 1990; Senge et al., 2005) on the subject and relate that to the domain at issue here, that of the science governance network in the domain of the EU. A generic definition of the learning organization is provided by Giesecke and McNeil (2004, p. 55) as:

an organization skilled at creating, acquiring, and transferring knowledge and at modifying its behavior to reflect new knowledge and insights. […] Learning organizations translate new knowledge into new ways of behaving. In a learning organization, managers and staff encourage work-related learning, the exchange of information between employees to create new ideas and knowledge, and continuous improvement.

Senge famously detailed five prerequisites required for organizations to act in a learning manner, that is, anticipating and successfully adapting to a changing environment through “adaptive” and “generative learning” (Chiva, Grandío, & Alegre, 2010).

According to Senge et al. (2005), first, leaders in learning organizations learn to apply tools to create a shared vision: forge common meaning/focus and mutually agree what the learning targets, improvement strategies and challenge-goals should be to get there. System thinking unravels often hidden subtleties, influences, leverage points and unintended consequences of plans and programs to help map and analyze situations, events, problems and possible causes/courses of action to find better (and often not obvious) change options/solutions. Personal mastery is a process of life-long commitment involving passion, inquisitive thinking and resilience while engaging individuals with a heightened ability to produce results they want to achieve in life through generative learning. They also surface deep-seated mental models: beliefs, values, mind-sets and assumptions that determine the way people think and act and to challenge assumptions that may help build shared understanding. Team learning aims at “thinking together” – sharing experience, insights, knowledge and skills with each other about how to do things better and develop critical reflection skills and conduct more robust, skillful discussions with teams and each other. In our empirical investigation we reflect on how these disciplines may be traced in Social Lab settings when learning revolves around RRI.

In science governance, networks of intermediary institutions outsource (most of) the decision-making to safeguard assumed autonomy. Thus, non-learning provides organizational benefits (Brunsson, 1998). Far from being “unintelligent” or “inflexible,” non-learning organizations have the following:

• high level of tolerance to contradictions;

• offer organizational and idealistic discretion; and

• are emancipated organizations that do not have to worry about “getting things straight” as compared to a normative ideal or well-defined set of rules or standards.

Procedural characteristics of non-learning organizations foster a “particular distribution of responsibility [that] encourages organizations to pass problems onto somebody else, or to let them remain with somebody else” or “shift problems somewhere else, where it can be argued that they belong” (Brunsson, 1998, p. 429). This may be particularly true of structures in which governance is executed through an informal network of intermediary institutions that create a complex ecosystem (Cosens et al., 2021).

For example, when it comes to taking care of “responsibility” and “resolving problems,” decision-making in science governance is delegated to reviewers, to ethics committees, to advisers, to bibliometric scientific impact assessment data and other indirect measures. Applying organizational non-learning, instead of learning specific new ways or modes of operation, enables research and innovation intermediary actors to accept contradictions (of methods, of disciplines, of worldviews, of epistemologies and so forth) while accomplishing their specific governance tasks (providing funding, resolving an ethical dilemma, organizing a policy conference) (Brunsson, 1998, p. 430). This tolerance of contradictions within the ecosystem of research and innovation offers science governance institutions a certain freedom: objectives (e.g.: “make the world better by science” or “be accountable to the public”) may be disconnected from actual operational behavior (e.g.: “provide funding” or “make a decision on research integrity”).

The “no-change [by learning] option” of non-learning organizations helps these organizations “postpone change” and accomplish slow and imprecise learning that is deemed important for organizations to conduct themselves in hectic, contradictory and power-laden environments (Brunsson, 1998, p. 431) such as research and innovation. However, non-learning to preserve autonomy may be detrimental for research and innovation that aims at “proper embedding of scientific and technological advances in our society” (Von Schomberg, 2013, p. 2) especially in a world that is characterized by increased sociotechnical change. Among these changes was the gradually shifting emphasis from science communication, with scientists informing the scientifically illiterate public, to public engagement, implying the active involvement (Conceição et al., 2020) of non-scientists. This eventually amounted to the idea that “societal actors work together during the whole research and innovation process in order to better align both the process and its outcomes, with the values, needs and expectations of European society” (EC, 2012). It was this line of reasoning that the EC’s H2020 funding program was captured with the label RRI. While RRI was made a cross-cutting issue in the program, which ran from 2014 to 2020, and 0.6% of the total R&D budget was earmarked to further RRI, that did not imply its smooth uptake in the aforementioned network of intermediary institutions and (supra-)national funding organizations that regulate science (Novitzky et al., 2020). It is in view of this understanding of possible benefits that non-learning – i.e. non-adoption of RRI – might bring, that we hypothesize that Social Labs may function as temporary, intermediary (Kjaer, 2015) learning organizations in European science governance by serving as venues for reflective, experimental and real-world informed learning.

4. Social Labs as temporary learning organizations

In this section, we explore how the Social Labs we created functioned as intermediary learning organizations by looking at the specific characteristics Senge identified, as described above: systems thinking, personal mastery, mental models, shared vision building and team learning. Before we do so, we present an overview, first, of how we operationalized the Social Lab method in the context of European science governance and, second, of the selected Labs and their outset for working with RRI, which was investigated in a “diagnosis phase.”

5. Discussion

In science governance, intermediary organizations serve a variety of goals, by gathering and conveying information, by engaging, convening and supporting constituencies, developing quality standards, leveraging resources, preparing policy-oriented documents and so on (Anthony & Austin, 2008). While doing so, some intermediary organizations function merely as a channel for conveying knowledge without altering the content of what is transferred, while others function as change agents, with a clear normative orientation, acting as “champions” for some cause (Martiskainen & Kivimaa, 2018; Kivimaa, Boon, Hyysalo, & Klerkx, 2019). In our case, the Social Labs were intended to take that latter role in view of RRI, to which end they should enable learning on the subject. Let us consider how both aspired roles for the Social Labs – learning and intermediating – worked out in practice.

Our data show that the context in which Social Labs were set up was of crucial importance to the role they played in terms of learning and mediation. Learning occurred in the context of institutions that were open to change, either by top-down organizational push as in the case of the JRC or bottom-up individual pull as was the case with MSCA. Representatives of EURATOM saw the opportunity to discuss change and use the intermediary nature of the Social Lab to further their agenda: pushing awareness of the need for including nuclear power in the energy mix of the future. In general, we found that Social Labs became venues for exchange and mediation between representatives of different institutions of science, and a wider network of social actors and institutions of policy, either in a context in which institutions were prone to change (such as in the case of JRC) or where stakeholders felt empowered to articulate their own role as change agents (in the case of the MSCA). In Social Labs operating in a context that was more closed toward change, as the ERC or EIT, the intermediary functions were mostly directed toward creating some momentum or, by convening and engaging critical constituencies (as in the case of the ERC), creating framework conditions for potential change. However, without clear links or institutional openings to mediate between the permanent organization and diverse groups of stakeholders, the possibilities for inciting learning – on RRI and on engaging with societal challenges – fell short. This was the case with the ERC which declined participation in the Social Lab [8], and EURATOM where only the supervisory representative from the EC participated. This confirms what was observed in Section 3, that some organizations in the science governance system in Europe deliberately present themselves as non-learning organizations, while blocking access too to those temporary intermediary settings that could challenge their collective mental models toward learning and change.

In addition to context, also the temporary status of the Social Lab had an impact, notably on the Labs’ potential as learning organization. The Social Labs were designed as temporary settings, as they were part of a project with a limited timeframe. While the limited timeframe had been a baseline condition, because of the voluntary basis of participation, it was often seen as an impediment: participants in some Labs for instance felt unsure about how their pilot action outcomes were to be continued or embedded in permanent institutions, if at all. Another implication of the temporary status was that those participants who were delegated by permanent formal organizations participated through-out the Social Lab process, as they had the resources to do so. Researchers or other actors who were engaged without pay and on top of other duties saw participation and pilot experimentation as challenging to manage, even if they were interested or willing to commit. This clearly hindered living up to the learning ambitions, especially that of team learning which required committed engagement throughout the process.

Another effect of the temporary dimension was that in some cases, a narrative on RRI emerged in haste that thereupon became dominant, pushing the Social Lab in a particular direction while foreclosing alternative narratives built on different visions. That in turn hindered team learning too because of the speed in which pilot actions were to be designed and executed. In other cases, a Social Lab did not come up with an energizing shared vision, a prerequisite for determining targets, improvement strategies and goals for learning. This was especially evident in the case of EIT and EURATOM, in the Labs on which members felt that a shared vision was not possible to achieve. In the latter case, as a result, the CSO participant who was critical of the nuclear-enthusiastic approach of those organizationally attached to EURATOM or working within nuclear research, opted out of the Social Lab process. This in turn limited the participants from actually learning toward change, as mental models were moored firmly within the status-quo frame.

The substantive focus on RRI proved a trigger for reflection on mental models and personal mastery: the idea of doing research from a responsibility perspective called for self-awareness and reflections on research relationships. Discussions on what RRI might entail invited participants to learn more about the topic as an EC policy construct, but also about what it could entail in their respective domains of research; designing pilots necessitated to understand RRI on the praxis level, translating abstract principles into action in real life. Pilots, such as the “Quadrologue,” “Nuclear dating” or the “RRI manifesto” stemmed from learning and personal mastery and resulted in tools that other, non-participating researchers could use to reflect their research practice and learn about the potential of RRI in their own professional context [9]. In other words, the Social Lab process of planning, designing, reflecting, re-planning, re-designing, re-reflecting and so forth was conducive to enhancing personal mastery of the skills involved in “doing” RRI.

In most cases, a sense of “success” among Lab participants and Social Lab teams implied that the disciplines were performed tacitly. Notably in those moments when Labs were found to be challenged – with participants dropping out, pilot actions being abandoned etc. – facilitators assisted participants to do “the work” involved in learning, e.g. of building shared visions and enabling team learning. Solutions usually in such cases were made the topic of co-design efforts together with participants.

The self-reflection required in the articulation and implementation of practice-based RRI challenged participants to scrutinize their mental models in terms of RRI. Discussions in the Social Labs often upended traditional research and innovation beliefs, mind-sets and assumptions. This was for instance evident in the case of the JRC: a revision of mental models led to a complete overhaul of an already planned project, toward including citizens, accommodating alternative narratives and ways of addressing the problem of autonomous driving. Reflections on science and innovation practice in terms of social engagement, science education and gender led to the joint preparation of an unexpected proposal for a research project in EURATOM, which focused on nuclear education and training in an RRI framework. The planning of and reflection on the pilots challenged the way participants thought about ambitions of research and innovation as well as their own motivations for working in research or innovation. Some of the experiments proved to be transformational: physics PhD students who engaged in “Nuclear dating” were overwhelmed by the interdisciplinary discussions (de Villafranca et al., 2022); JRC researchers were surprised to see how ordinary citizens in interviews brought up completely different points of views related to autonomous driving than researchers had assumed. In other words, discussing research agendas and projects in terms of RRI challenged deeply embedded assumptions regarding thinking about and doing science.

The joint reflections in most cases resulted in building shared understandings and a joint vision on responsibility in science and innovation in almost all Social Labs, with the notable (partial) exceptions of the EURATOM and EIT Labs. The experiences with EURATOM and EIT underscored Senge’s point that building a shared vision is a prerequisite for group formation and group learning. In the Labs where building a shared vision succeeded, the Social Lab can be argued to have turned into a temporary organization: the action orientation, the joint learning and the experimentation/reflection process transformed a group of heterogenous people into an organization, albeit temporary, working toward a shared goal or purpose in view of mainstreaming RRI in the European Research Arena. They have members with specific qualifications that are assigned with tasks; they are not closed systems, but interact with their environment; have shared, informal and formal, goals and purpose; have their own rationality and structure (Abels, 2018; Mandl & Mandl, 2021).

System thinking in the Social Labs was enhanced because of the heterogeneity in participant selection: participants from “all over” a science governance domain formed a group to discuss mutually shared challenges and perspectives beyond individual experiences and preferences. More importantly, experimentation with RRI in practice and reflection on mental models, explicated how “non-RRI-informed” characteristics of the science (governance and funding) system influenced research and innovation practices. Individual self-awareness as a consequence amounted to system awareness. The Labs thus provided a setting in which participants came to realize that “the system resides in everyone,” as Senge explains in Reese (2020, p. 13): “People think the learning organization is a system and the system lives outside of me,” a common view which he posits can change under particular circumstances that produce “an ‘ah ha’ moment.” Experiences in the Socials Labs discussed here underscore this claim. For instance, such an “ah ha-moment” was witnessed in MSCA, where Lab participants on day one of the first workshop sighed that it was “the system” that kept them from engaging in RRI-informed research practice, while on day two, after dedicated facilitation, a participant enthusiastically blurted out: “Oh, but it is really us [researchers] then, who are at the wheel, we make up the system” (pers. communication MSCA WS1).

Reflecting on and discussing RRI in relation to their respective research areas led participants to address issues of system embeddedness. Some pilot actions that were co-created in the Social Labs under scrutiny, like the “RRI show” and “RRI training for NCPs” explicitly aimed to address system level change and suggest participants’ engagement with systems thinking. Such pilot action experimentation contributed to the realization that, while working as individuals or on discrete projects, they are part of the European Research Arena (ERA) and became aware of the limitations that this embeddedness presented, for instance, because of ERA’s bureaucracy or its bibliometric framing of the reference for “research excellence.”

The Social Labs’ intermediating role critically hinges on a combination of the conditions created within the Labs to foster learning and the conditions under which Social Labs may inform learning about responsibility in research and innovation in European science governance. Because the Labs were set up as temporary organizations, they were not seen as posing a bureaucratic challenge (with the possible exception of the ERC) to permanent science governance organizations. This arguably created favorable conditions for learning transfer: because the Social Labs would eventually dissolve, permanent organizations were offered the opportunity to take up what they were open to and ignore what they experienced as threats to “getting their job done.” Considering that most science governance organizations are non-learning organizations (by choice), the temporary Social Labs presented a suitable format to inform the governance system about RRI, more so than if permanent governance organization were coerced into learning on the topic. This might strongly benefit the uptake of what they perceived as required for RRI suiting their context and drop what did not fit these criteria.

The temporary nature of the Labs proved beneficial for the Labs to perform their intermediary function. This worked well notably in the Labs which were explicitly endorsed by the respective H2020 lead organization. But even if that was not the case, participants themselves were able to establish exchanges between their Lab and relevant organizations in their respective domain, such as in the case of MSCA. If the environment was less supportive, as in ERC and EIT, managers and facilitators made efforts to turn their Labs into intermediating organizations. This was possible in the case of the ERC, while less so in the case of the EIT. As observed above, this proved to be detrimental for the Social Lab: no shared vision and team learning emerged, with little potential impact for the uptake of RRI in the EIT ecosystem.

The intermediating role differed among the Social Labs. In some Labs, participants saw RRI uptake limited by system-wide barriers, such as the publish-or-perish logic dominating the entire science system. In other Social Labs, the lack of institutional feedback-loops was considered limiting. Regardless of such differences, all Labs presented deliberative and creative forums for sociopolitical coordination between multiple actors, cutting across realms of state, research and innovation and civil society.

6. Conclusion

Even though it was to varying degrees that the five selected Social Labs succeeded in enacting all five disciplines as identified by Senge, our research and experiments suggest that Social Labs as organizational form can function as learning organizations, because in principle, they present a setting in which these disciplines can be enacted. Furthermore, we found that the substantive focus of the learning involved, on responsibility in research and innovation, proved conducive to system thinking. The space which Labs offered individuals brought to the fore, with variations, how the science (governance and funding) system influenced their thinking and acting as researchers. Because participants addressed aspects of the system which they considered problematic or ill-informed from an RRI-perspective and based on their own normative position or the diagnoses produced in the NewHoRRIzon project in preparation of the Social Lab process, they addressed system level issues in a learning-by-doing manner on the level of practice, questioning the conditions that govern (their part of the) science governance system.

The fact that Social Labs by design were temporary organizations offered advantages and disadvantages for learning on RRI. It brought about a sense of urgency among participants to address the issue of responsibility. Yet the temporary nature of the Labs also detracted from the learning potential. Challenging across the board was the establishment of a shared vision among participants, while instigating individual reflection on mental models and personal mastery proved particularly feasible. Haste hampered shared vision building even further.

We conclude, in view of our first research question that, overall, the Social Labs functioned as temporary organizations. The specific characteristics of a Social Lab: such as their orientation toward RRI, experimentation and reflection on the topic produced the conditions that in principle foster learning. The format of the Social Lab, notably in view of the focus on responsibility in research or innovation, is well-suited to enacting the five disciplines as identified by Senge, in particular system thinking, shared vision building and team learning. If these are in place, Social Labs turn into learning organizations – a group of people collaborating and being managed for a specific purpose. If this is not the case, because of Lab-internal and contextual dynamics, learning takes place among individuals within the organizational setting, as Social Labs explicitly aim for personal mastery and challenging existing mental models. The Social Lab format induces participants to explicate often tacitly held images and assumptions, to open these up to the scrutiny of themselves and others and, because of the heterogeneity of the participant group, to revise these to develop a joint understanding of a desirable future that fosters genuine commitment and engagement.

This brings us to our second point of attention, the conditions under which Social Labs can inform learning about responsibility in research and innovation in European science governance. Our findings show that the Social Labs presented intermediary spaces that enabled exchanges on the perspectives, ambitions and visions of a wide range of stakeholders from across the science (governance and funding) system. Because of their temporary nature Social Labs created the potentiality of learning on RRI between Labs and their context. Because they did not pose a bureaucratic challenge to permanent organizations within the science governance system, they opened the possibility for those organizations to engage in learning on, or at least be informed about, RRI in a way that suited their interests and concerns (also to those which choose to act as non-learning organizations).

To which extent the Social Labs actually assisted in transferring insights about responsibility in research and innovation to permanent organizations and system level is beyond the scope of our present research and requires further investigation. Having said this we conclude from our cases that, given the limitations due to context and the temporary nature of the settings, Social Labs can act as intermediary organizations, which not merely pass on information among actors (in our case about RRI as a policy construct), but also actively give substantive shape to what they convey. In our case, this refers to what RRI might entail in practice from a normative orientation (cp. Åm, 2013; Kivimaa et al., 2019). In other words, the studied Social Labs served, because they were so designed and enacted, as temporary, intermediary learning organizations on RRI. As such, they themselves contributed to creating the conditions under which they could inform learning about responsibility in research and innovation in European science governance system.

By serving as forums for deliberation, they offered opportunities for exchanging information and sociopolitical coordination between multiple actors cutting across realms of state, research and innovation and civil society. The extent to which they were actually able to perform these functions was strongly contingent on contextual dynamics. Furthermore, because of their embeddedness in a networked system they are by definition limited in their ability to foster transformative change even if they function as learning organizations. The transformative change that is involved in moving toward a more democratic research and innovation system (Braun & Griessler, 2018) requires complex modes of learning that go beyond the scope of the pilot actions experimented with on a Social Lab level.

Still, within the confinements of their design and setting, our data show that Social Labs are well suited to address emerging, complex, contestable challenges and normative ideas that require processes of cooperative and contextual discussion and mutual learning. More importantly, they present a useful form for learning in settings “where people continually expand their capacity to create results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free, and where people are continually learning to see the whole together” (Senge, 1990, p. 3). Our results support Hansen et al. (2020) in their claim that RI and RRI on the one hand and habits practiced by a learning organization – in our case, a Social Lab – on the other hand, reinforce one another. What our findings add to their insights is that this relation holds true also for non-for-profit organizations, and notably one in a transient, intermediating role, in a networked governance setting.

It is in view of the governance of science and innovation, that our experiences with Social Labs seem to hold a promise for the future in view of supporting the endorsement of notions around responsibility in innovation and science. Conceived as vehicles to address “wicked” and “non-solutionist” governance challenges and designed to offer input and create knowledge to better govern science, Social Labs are especially suitable to present the collaborative spaces and intermediary organizational arrangements that are called for in view of RRI for reflection, anticipation and engagement. Consequently, Social Labs may well provide the foundations for an experimental governance frame that may be referred to as participatory action governance, that may better fit the implementation of RRI and other critically and democratically inclined areas of operation into the policy fabric of the ERA. A range of Social Labs as intermediary institutions could be knit into a patchy framework of non-hierarchical, experimental settings serving as a coordination structure as opposed to the more hierarchical frame of multi-level governance based on centralized reporting and adaptation. Applying such an approach in respect to RRI may offer a coherent and convincing policy approach for appealing to both policy-makers and science practitioners.