Administrative Sciences

New Zealand’s social housing sector faces the dual challenge of addressing affordable housing shortages while coping with an energy-inefficient housing stock. In this context, blended finance is increasingly seen as a potential solution to mobilise commercial capital when public resources fall short. However, the primary challenge lies not in attracting this finance but in effectively governing it. This study investigates the governance arrangements necessary to ensure that blended finance is credible, legitimate, and scalable for energy-efficient social housing in New Zealand. Using a qualitative case study, the research draws on semi-structured interviews with 16 decision-makers from community housing providers, Kāinga Ora, central government, advisory practices and institutional investors. Interview data were analysed using framework analysis alongside two other triangulated data sources. The study identifies four interconnected governance challenges: fragmented and unevenly distributed institutional capacity; position-dependent and asymmetric trust; structural tension between financial and social logics; and the predominance of instrumental rather than democratic legitimacy. Based on these findings, the study introduces the Energy-Efficient Social Housing Blended Finance Governance Framework (EESHBFG), which conceptualises governance as the critical infrastructure underpinning the credibility, legitimacy, and scalability of blended finance initiatives to decarbonise social housing and presents a staged governance roadmap that is explicitly propositional. This study contributes an empirically grounded account of blended finance governance within a small, regulated housing market and provides a framework for assessing governance readiness, while recognising that claims regarding tenant and Iwi/Māori legitimacy require direct validation from those stakeholders.

The increasing adoption of artificial intelligence (AI) in financial institutions has transformed workplace practices, making it essential to understand how responsible AI influences employee outcomes. Drawing on Social Exchange Theory, this study examines the impact of responsible AI signals—specifically autonomy, beneficence, and explainability—on employee behavior through the mediating roles of AI-driven engagement and AI-driven satisfaction, while accounting for the moderating effect of techno-overload. Data were collected from 310 employees working in banking and financial institutions in Pakistan using a structured questionnaire. The conceptual model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that responsible AI signals significantly enhance AI-driven engagement, AI-driven satisfaction, and employee behavior. AI-driven engagement positively influences AI-driven satisfaction, while AI-driven satisfaction significantly improves employee behavior. Although the direct relationship between AI-driven engagement and employee behavior was not supported, dual-mediation analysis demonstrates that engagement and satisfaction mediate the relationship between responsible AI signals and employee behavior. Furthermore, techno-overload weakens the positive relationship between responsible AI signals and AI-driven engagement; however, it does not significantly moderate the relationship with AI-driven satisfaction. The study contributes to the literature on responsible AI and organizational behavior by demonstrating how ethical and transparent AI practices foster positive employee outcomes. From a practical perspective, financial institutions should prioritize responsible AI implementation and effective workload management to maximize employee engagement, satisfaction, and innovative workplace behavior.

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