Social impact bonds (SIBs) have emerged as one of the most innovative financial instruments designed to support the social service sector in the delivery of innovative social programs. Despite the growing interest of academics and practitioners in SIBs, the debate appears polarized around a series of recurrent aspects, and only a limited number of studies have sought to understand the risks and motivations related to similar initiatives. Using an exploratory approach based on a mixed-method grounded theory methodology, this study analyzed the results of 12 questionnaires that asked experts about their experiences and perceptions in SIB project development and implementation. The study identified and assessed three main groups of motivations, critical success factors, and risk factors by focusing on the private-sector SIB actors with the aim of understanding their motivations and their perceived main success drivers and risk factors. This work contributes to the knowledge on the conditions for attracting private sector actors and supporting policymakers in the development of new SIB models. The findings could facilitate the development of risk management practices for the purpose of stimulating the participation of private actors in SIB initiatives.

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.