Business models for sustainability (BMfS) are relevant topics on research agendas, given their orientation toward sustainability issues. However, traditional versions of these models are often ill-equipped at solving complex social problems. Cross-sector partnerships for sustainability (CSPfS) have been recognized as a new paradigm that mitigates the failure of traditional models. Impact investing, and social impact bonds (SIBs) in particular, represent an interesting field of research in innovative business models for sustainable finance, even though the literature does not consider SIBs within this broader field. We propose an exploratory study based on qualitative methods aimed at conceptualizing SIBs within the framework of BMfS and understanding how SIB collaboration varies across social sectors and geographical areas. Our study identifies three different models of SIBs characterized by the different degrees of collaboration between actors: (i) SIB as a fully collaborative partnership; (ii) SIB as a low-collaborative partnership; and (iii) SIB as a partially collaborative partnership. Our findings are useful to policy makers and practitioners involved in the SIB design, suggesting that a fully collaborative SIB model may stand a better chance of achieving the expected social impacts.

In the last years, Social Impact Bonds (SIBs) have gained popularity in the impact investing space. A number of scholars and practitioners are debating—in theory and practice—the opportunities, challenges and obstacles of these financial models. Amongst others, social uncertainty evaluation metrics appear as a critical factor for the future development of the SIB market. The present work aims to shed some light on this issue, by realizing a practical application of a model—which is an extension of a framework previously proposed—for social uncertainty evaluation in SIBs. In our exploratory analysis, 34 SIBs were selected for the empirical tests. We combined the Analytic Hierarchical Process (AHP) with the creation of aggregate measure, deriving by suitable indicators at the end of the tree. Our findings open new avenues for future research in the field of uncertainty factors in the SIB landscape. Finally, our results represent a basis for implementing a prediction model for social uncertainty evaluation.