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The purpose of this paper is to examine how the practices, processes and expertise embedded within Social Impact Bonds (SIBs) distinctively mediate the tensions between outcome payers’ competing and contradictory programmatic discourses.

We use qualitative research methods and employ concepts drawn from the governmentality literature to analyse interviews with SIB outcome payers.

SIBs are shown to challenge the degree of negative influence of biopolitics, neoliberalism and financialization by highlighting a broader and more holistic set of influences. SIB operations pre-empt and counteract perceived risks and are refined through a “learning by doing” effect. In contrast to other approaches to funding social interventions, the SIB structure attributes and independently validates outcomes. Payments to investors are based on the achievement of outcomes and are funded by the outcome payers. SIBs’ operational processes allow the responsibilities of the various parties to be explicitly assigned and contracted. The interests are aligned, yet the cultural differences harnessed.

This paper is one of the first to apply governmentality concepts to SIBs. By focusing on outcome payers, the paper provides new perspectives on the practices, processes and expertise of governing and the programmatic discourses of governing, as well as their relationship. The insights offered are supported by one of the largest and most diverse empirical SIB samples including 34 interviews where 43 individuals reflect on their experiences across 32 unique outcome payer organisations.

The rapid development and widespread application of artificial intelligence tools have raised concerns about how designers are embracing these technologies. This study investigates the factors influencing designers' behavioral intention to use and disclose the use of generative artificial intelligence.

A quantitative research approach was employed, designing a structured questionnaire based on Self-Determination Theory to assess the impact of various psychological and social dimensions. The questionnaire included dimensions such as autonomy, competence, relatedness, social influence, value fit and social innovativeness. A Partial Least Squares Structural Equation Modeling analysis was conducted on 309 valid responses from diverse design fields.

Competence and relatedness are significant factors influencing designers' continuance intention to use generative artificial intelligence. Although autonomy does not significantly affect continuance intention, it plays a crucial role in the decision to disclose artificial intelligence participation. Social influence and value fit significantly shape autonomy, competence and relatedness, while the impact of social innovativeness is relatively limited.

This study clarifies the factors influencing designers' continuance intention and disclosure of generative artificial intelligence tools from both individual and social dimensions, enhancing the understanding of the relationship between designers and generative artificial intelligence tools. It provides valuable insights for the development of artificial intelligence technology and the future trends in the design industry, offering significant theoretical and practical value.