The social impact of Big Science-industry networks: the ITER case
1Liuc - Università Cattaneo, Italy; 2ENEA - Italian National Agency for New Technologies, Energy and Sustainable Economic Development
Big Science provides opportunities for developing leading technologies. Large amount of public money is invested in Big Science and it is clear the need to account for this expenditure. Studies proven that immediate benefits exist. Part of them derives from collaborations between scientific partners and industry.
The social impact is the creation of social benefit. Unfortunately, measuring such benefit as consequence of Big Science projects is a challenging task (Bozeman and Youtie, 2017). Following a relevant stream of contributions, the social capital can be considered as the “root” of the social impact as it provides the foundation for systemically conceptualizing the social impact (Bornmann, 2013). Indeed, the core concept of social capital is that when participating to a network, the value of parts summed together is higher than the sum of each single piece (Nahapiet, J., & Ghoshal, 1998). Thus, as the social impact is a matter of multiple actors and factors, and science outcomes are better understood in terms of “collective knowledge” and not as single one (Bozeman, 2003), social capital can provide an interesting lens to analyze and evaluate it.
Although adopting a social capital perspective is useful to better understand the social impact, theoretical and empirical contributions are still scant. In particular, firms are mainly analysed as single actors and not as organizations belonging to a network that generates benefits toward society (Castelnovo et al., 2018).
(1) How do Big Science-industry networks impact on the social capital creation in order to generate a social impact?
(2) What are the dimensions of social impact affected by Big Science-industry collaborations?
The paper adopts a qualitative methodology. Following the Public Value Mapping theory (Bozeman, 2003) and the type of research questions, we use a longitudinal and explorative case study (Yin 2013), which comprises the network of actors (among others ENEA, the public Italian research centre that studies the nuclear fusion energy) interacting with the firm Walter Tosto (WT) in order to produce an important spare for the ITER project. This is the most important project which aims to perform the nuclear fusion and addresses the Big Challenge of finding alternative sources to carbon fuels.
Two types of sources are used: primary and secondary data. Primary data refer to interviews. The head of the fusion department, the head of the human resources and a sample of employees were interviewed. They also report the experience of other involved actors, i.e. subcontractors, research and educational institutions which participate to the project. Then, they were recalled in order to verify the information collected. This allows to have a view of the impact of the Big Science-industry collaboration on the network and on the social community.
The protocol of interview comprises:
(1) the evolution of the social capital dimensions (structural, relational and cognitive) to discover how the social capital has been created (Inkpen and Tsang, 2005);
(2) the areas of social impact, following the schema provided by the public network theory: individual, organization and community levels (Provan and Milward, 2001)
Secondary data refer to contracts, internal procedures, WT and its subcontractors financial data and investments. They allow to triangulate data and enrich the information collected with primary data, in order to answer the research questions. Data collected have been analysed with a deductive approach to depict how the evolution of the social capital has led to generate social benefits.
The established network comprises WT, other contractors, research centres, subcontractors and educational institutes. The network is stable and highly sustainable. Step by step there has been an increasing of knowledge between actors.
Consequently to change of social capital, social impacts occur at three levels. At an individual/employee level, employees show a “nuclear culture”, they feel job stability and high level of safety&security, they have higher economic benefits and they interact with several actors. ITER allows women to enter labs and production plants (not widely common). At a network level, there has been an exchange of knowledge and resources. The knowledge acquired is applied to other context and common markets. The network allows some firms to not run out of business. At a community level, people benefit in terms of employment (60 people have been hired). The collaboration with education institutes allows students to follow specialization courses for free and new directives have been set: courses proposed by WT have been accepted by the Education Minister. Service companies and infrastructures (airports and hotels) have significant economic benefits. The area around WT has gained an enormous market and it has been promoted at a national level by mass media.
Contribution to Scholarship
The present paper provides an advancement in studying the social impact generated by Big Science researches, using a network perspective and relying on the concept of social capital. From a methodological point of view, it provides a new way to assess the social impact, considering the fact that not all the benefits can be monetized and the fact that the value is produced by the network of actors involved that collaborate together. From a theoretical point of view, it shows how the Big Science-industry collaboration generates a social impact and it provides a framework of cause-effect relationships, where the evolution of social capital allows to trace a change in terms of social benefits.
Contribution to Practice
The practical contribution mainly concerns policy makers. The paper shows how since the construction of Big Science’ projects there is a generation of social benefit. It may be a point that justify the expenditure in basic research, also in comparison with applied science. It shows how the benefits are at different levels and that they occur when a network is created. For that, policy maker should set directions on pushing the collaboration between Big Science, industry and the community.
The conference focuses on bridging research, industry and society. The paper proposes a new way to evaluate social impacts that basic research projects generate toward society, thanks to research-industry networks. It focuses on ITER, one of the most relevant projects to address the Grand Challenges of finding new energy sources.
Bornmann, L. (2013). What is societal impact of research and how can it be assessed? A literature survey. Journal of the American Society for Information Science and Technology, 64(2), 217-233.
Bozeman, B. (2003). Public value mapping of science outcomes: theory and method. Knowledge flows and knowledge collectives: Understanding the role of science and technology policies in development, 2, 3-48.
Bozeman, B., & Youtie, J. (2017). Socio-economic impacts and public value of government-funded research: lessons from four US National Science Foundation initiatives. Research Policy, 46(8), 1387-1398.
Castelnovo, P., Florio, M., Forte, S., Rossi, L., & Sirtori, E. (2018). The economic impact of technological procurement for large-scale research infrastructures: Evidence from the Large Hadron Collider at CERN. Research Policy, 47(9), 1853-1867.
Inkpen, A. C., & Tsang, E. W. (2005). Social capital, networks, and knowledge transfer. Academy of management review, 30(1), 146-165.
Nahapiet, J., & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational advantage. Academy of management review, 23(2), 242-266.
Provan, K. G., & Milward, H. B. (2001). Do networks really work? A framework for evaluating public‐sector organizational networks. Public administration review, 61(4), 414-423.
Yin, R. K. (2017). Case study research and applications: Design and methods. Sage publications.
Social system relations in the diffusion of social innovation into low resource markets
Cranfield School of Management, United Kingdom
The complex nature of the “grand challenges” often requires complex solutions and multiple actors. Exogenously developed social innovations (EDSIs) have been identified as a powerful means of addressing these challenges, thus, we need to understand key factors that enable diffusion in order to facilitate the large-scale adoption of such innovations.
Previous research indicates that there are particular barriers to the diffusion of EDSIs in low resource markets, including; culture, religion, literacy, competing needs, geographical proximity (Prahalad, 2006), lack of information of local exigencies, the socio-economic context (Halme et al., 2012; Viswanathan & Sridharan, 2012; Chao et al., 2014).
The theoretical model of diffusion hinges on communication units or, as Rogers refers to them, communication channels (comprised of individuals and organizations) which influences the rate of adoption (Rogers, 2010). Diffusion can either be facilitated and frustrated by the communication both between and within the units. The quality of relations, between diffusers in the context of innovation into low resource markets is under-researched. This failing to realize the important effects and influence of the wider innovation ecology (Daugherty, 2011) on the diffusion process could result in the unsuccessful adoption of social innovations.
Despite social systems being highlighted by Rogers and their apparent relevance for the market, our understanding of the factors that influence the adoption of EDSIs into LRMs is not well understood. There is a lack of studies that rigorously looked at the diffusion process where multiple actors considered as diffusers.
What are the key factors and relational dynamics of innovation ecology of multiple diffusers that leads to the successful diffusion of exogenously developed social innovations in low resource markets?
The systematic review (Denyer &Tranfield, 2009) adopted a framework analysis as method of synthesis (Ritchie and Spence 2009; Dixon-Wood, 2011) combined with the approach of grounded theory to complete our framework (Strauss, 1987; Strauss and Corbin, 1994).
We reviewed 74 articles, drawn from the last two decades of literature (since the increased attention on social innovation as a proposed solution to grand challenges) to develop our framework for the successful diffusion of EDSIs into LRMs. Of these 74, 27 empirical cases specifically examined the role of social system relations in the process of diffusion.
The initial framework of key characteristics of EDSIs that were successfully adopted into LRMs consisted of four dimensions Aims, Processes, Climate and Structure each made up of specific factors.
We tested the ability of this framework to also capture characteristics of social system relations by examining 27 empirical cases. This highlighted the need to incorporate new features into the framework. Specifically, a new dimension ‘capacity’ was required to capture adaptive and self-organizing capacity of innovation ecologies as salient characteristics of system relations that could not be incorporated into the previous framework. Furthermore, new factors were added to the four dimensions already described by the framework; indigenous knowledge and emergent new information pathways were added to ‘processes’; and new form of trust was added to ‘climate’.
Current innovation diffusion processes fail to show respect for the creativity and intelligence of indigenous people, they tend to come packaged with exogenous participatory processes, encourage scaling-up, and ignore innovation that is already occurring. Successful diffusion would be likely to occur between interactions between multiple actors within the social system of diffusers.
Contribution to Scholarship
The review concludes that examining diffusion through the lens of innovation ecologies provides a contribution to innovation and management theory. Specifically, we argue for a need to shift perspective from product to system focus, from short term to long term collaboration and integrating indigenous knowledge into new emergent information pathways.
There is a call to reconfigure Roger’s diffusion of innovation theory on its communication behaviours and relational dynamics of social systems in this process. Though, there have been attempts to construct circular or spiral models of diffusion (Murray et al., 2011), more evidence and empirical data is required to develop a dynamic, circular model for the diffusion process with specific focus on the diffusion of exogenously developed social innovations into low resource markets.
Post-modern international development provides a useful setting to observe multi actor interactions in the process of diffusion that involve addressing challenges related to diffusers and adopters asymmetry.
Contribution to Practice
The provision of this framework, which outlines the factors that facilitate the successful diffusion of exogenously developed social innovations, could be adopted by a range of key stakeholders (NGOs, governmental organizations and external actors) to support these types of innovations (at different stages of the diffusion process) and so facilitate the achievement of key sustainable development goals.
To solve grand challenges, we expect multi-actor innovation ecologies to co-create new information pathways to support diffusion. There is a lack of evidence to highlight the activities and communication behaviours that are happening through diffusion. Yet these relations are important factors for diffusion that ultimately are associated with its success.
1. Alvord, S. H., L. D. Brown, C. W. Letts. (2004). Social entrepreneurship and societal transformation: An exploratory study. Journal of Applied Behavioral Science 40(3) pp.260–282.
2. Denyer, D. & Tranfield, D. (2009). Producing a systematic review. In: Buchanan, D. & Bryman, A. (eds)
3. Dixon-Woods, M. (2011). Using framework-based synthesis for conducting reviews of qualitative studies. BMC Medical Research Methodology, 9, 39.
4. Dougherty, D. and Dunne, D.D., 2011. Organizing ecologies of complex innovation. Organization Science, 22(5), pp.1214-1223.
5. Dougherty, D., 2017. Organizing for innovation in complex innovation systems. Innovation, 19(1), pp.11-15.
6. Halme, M., Kourula, A., Lindeman, S., Kallio, G., Lima-Toivanen, M. & Korsunova, A. (2016). Sustainability Innovation at the Base of the Pyramid through Multi-Cited Rapid Ethnography. Corporate Social Responsibility and Environmental Management. 23, 113–128
7. Prahalad, C.K. (2004). The Fortune at the Bottom of the Pyramid: Eradicating Poverty through Profits. Upper Saddle River, NJ: Wharton School Publishing.
8. Rogers, E. M. (2010). Diffusion of innovations. (5th edition) Simon and Schuster
9. Viswanathan, M. & Sridharan, S. (2012). Product Development for the BoP: Insights on Concept and Prototype Development from University-Based Student Projects in India. Journal of Product Innovation Management, 29, 52-69.
Tackling societal grand challenges on a sustainable basis: a matter of cultural dynamic capabilities
Paris School of Business, France
George et al. (2016) suggest that "management theories can be applied to address Grand Challenges" (GCs), which are defined as "specific critical barrier(s) that, if removed, would help solve an important societal problem(...)". Further, they identify the 17 Sustainable Development Goals as "the most universal and widely adopted GCs".
In contrast with the dominant approach to sustainable development (SD), Periac et al. (2018) propose that SD should be conceptualized as the achievement and sustainment of a dynamic equilibrium between opposing forces, rather than the achievement of predefined static goals like the SDGs. Furthermore, they suggest that paradox management framework (Smith & Lewis, 2011) should be applied to pursue SD.
Based on this view, effectively tackling Grand Challenges mainly rests on fostering paradox management at society level.
An interesting parallel can be made with the dynamic capabilities literature (Zollo & Winter, 2002) which states that some "organizations are characterized by learned and stable patterns of collective activity through which they systematically generate and modify their operating routines in pursuit of improved effectiveness."
We argue that at society level, dynamic capabilities are crucial for sustainable development, and that they are cultural by nature.
While Periac et al. (2018) point to the importance of addressing Sustaianble Development issues through a paradox management approach at society level, they say very little about how such evolution can occur.
What societal characteristics can be considered relevant drivers in tackling Grand Challenges? And how such characteristics can be developped?
Our article is mainly conceptual. We draw on a transdisciplinary literature review to design a conceptual framework which is meant to provide operational guidance in tackling Grand Challenges.
We also use archive documents about three cases of societal Grand Challenges to illustrate our points and highlight our framework's relevance.
Three cultural dynamic capabilities are identified as relevant and complementary drivers for sustained paradox management within a society, and further, for effective tackling of Grand Challenges :
- understanding and embracement of paradox
- institutional plasticity
- entrepreneurial orientation
Contribution to Scholarship
Conceptualization of sustainable developement and Grand Challenges remain limited so far. Our artice aims at contributing to this literature gap
Contribution to Practice
Tackling societal Grand Challenges appear like a crucial stake of our time, but remains difficult to address. Our model aims at helping decision makers and citizens towards this direction.
Direct link with Tackling Grand Challenges
Teece Pisano & Shuen, 1997 - Dynamic Capabilities and strategic Management - Strategic Management Journal, Vol. 18, Issue 7, pp. 509-533
Smith & Lewis, 2011 - Toward a Theory of Paradox: A Dynamic equilibrium Model of Organizing - Academy of Management Review, Vol. 36, No. 2
Periac David & Roberson, 2018 - Clarifying the Interplay between Social Innovation and Sustainable Development: A Conceptual Framework Rooted in Paradox Management - European Management Review, Vol. 15, Issue 1, pp. 19-35