Leveraging Artificial Intelligence for Impact Investing: A Framework Linking Information Transparency and Investor Motivation

Abstract

Artificial intelligence (AI) is increasingly transforming financial decision-making through predictive analytics, automated information processing, personalised recommendations, and real-time data analysis. Simultaneously, impact investing has emerged as an important approach to capital allocation that seeks to generate measurable social and environmental outcomes alongside financial returns. Despite the growing convergence of AI-enabled finance and sustainable investment, limited research has examined how AI may influence investors’ motivation to participate in impact investing. This paper addresses this gap by developing a conceptual framework that positions AI as an enabler of investor motivation in impact investing. Drawing on Behavioural Finance, the Technology Acceptance Model (TAM), and Self-Determination Theory (SDT), the paper proposes that AI influences investor motivation primarily through enhanced information transparency, improved impact measurement, reduced information asymmetry, and lower perceived investment risk. AI-enabled analytical capabilities can support investors in processing complex financial and non-financial information, evaluating impact-related outcomes, and making more informed investment decisions. These capabilities may enhance investor confidence by strengthening perceptions of transparency and reducing uncertainty surrounding impact investments. However, the framework recognises that AI does not automatically generate investor trust or motivation; perceived usefulness, ease of use, transparency, and confidence in AI-generated information influence the extent to which investors rely on AI-enabled decision support. The proposed framework conceptualises AI as a complementary technology that augments rather than replaces human investment judgement. Four propositions are developed to explain the relationships between AI-enabled decision support, perceived transparency and impact-measurement reliability, perceived investment risk, investor motivation, and intention to engage in impact investing. The framework contributes to the emerging literature on AI-enabled sustainable finance by integrating technological, behavioural, and motivational perspectives and positioning investor motivation as the central behavioural mechanism linking AI capabilities with impact-investment intention. The study provides a theoretically grounded foundation for future empirical research and offers implications for impact-investment managers, fintech platforms, and policymakers seeking to use AI to strengthen transparency, investor confidence, and sustainable capital allocation.