Abstract (EN)

The systemic and multi-level dimension of social innovation (SI) makes it difficult to assess its impacts. However, measuring the effects of SI is an important issue. The ex-ante evaluation of possible impacts allows the engagement of stakeholders and the mobilization of resources. Ex-post evaluation helps to legitimize projects, to improve them and to inform the decision on their continuation.Measuring social impact has been approached in different ways. Earlier work on the evaluation of projects has attempted to integrate the question of their social impact in an ad hoc manner (Kaul, 1999). Socio-economic approaches, such as Social Return On Investment, have applied cost-benefit analyses, consisting of comparing the benefits and investments of a social enterprise (Millar & Hall, 2013), with all the limits linked to transposition (Nicholls, 2009). The Social Impact Assessment developed since the 1990s has provided a global method which has the merit of formalizing good practices (IOCGP, 1994; Mathur, 2011) but which gives a superficial account of the uneven distribution of impacts over a territory and between stakeholders (Esteves, Franks, & Vanclay, 2012). More generally, it is possible to show the limits of methods based on the use of metrics, as the dynamics at work behind the process of SI is complex (Westley & Antadze, 2010).Measuring the impact of SI therefore requires additional methodological developments which take into account the systemic nature of the change dynamic driven by SI which the methods hitherto developed are struggling to transcribe. This raises the question of the design of tools to trace the complex causal chains between various impacts of SI (a social project or the activity of a social enterprise). We propose in this research an analysis of the ways of mobilizing the techniques of causal and cognitive mapping to highlight complex and dense networks of SI projects effects. We crash test these techniques on projects sponsored by a changemaking organisation. This allows us to demonstrate the interest and the limits of using causal mapping to identify the networks of SI impacts.