Modelling user behaviour for AI-driven financial decision systems: A mixed-methods (PLS-SEM, IPMA, and TRIZ) investigation of mutual-fund innovation adoption among academicians of central universities in India
Though the digital financial services ecosystem in India is rapidly adopting AI-enabled delivery, mutual fund adoption among educated, salaried people remains disproportionate, raising two questions: why are mutual funds being adopted, and what should be prioritised when designing advances in robo-advisory? By presenting digital investment platforms as a systematic innovation in the Indian fintech ecosystem (after Unified Payments Interface and the Account Aggregator framework), we propose a user-behaviour model of AI-based financial decision-making and examine the key factors influencing mutual-fund adoption among academic institutions of Indian central universities. The paper follows the diffusion of innovations approach to interpretation and operationalises it with an extended Theory of Planned Behaviour, where attitude, subjective norms, fintech self-efficacy, financial literacy, and religiosity are theorised as predictors of intention to adopt, and fintech self-efficacy mediates the attitude–intention relationship. It integrates three bodies of research (behavioural modelling [PLS-SEM], importance-based prioritisation [IPMA], and invention design [TRIZ]) into one fintech-adoption research pipeline; inculcates an innovation-specialised self-efficacy construct within the Rogers–Ajzen synthesis; and illustrates how an empirical user behaviour model can be transformed into a prioritised, design-ready blueprint of AI robo-advisors in India. A sample of 412 academicians from 24 central universities (September 2024–February 2025) was used to apply the three-stage design. Support for 9 out of 10 hypotheses and the model’s ability to explain 43.8% of the variation in investment intention were achieved. The top adoption antecedents included subjective norms (importance weight = 0.272), attitude (0.256), financial literacy (0.231), fintech self-efficacy (0.201), and religiosity (0.040). The target design-leverage, though, was fintech self-efficacy: it performed worst and was the only antecedent that modulated the attitude–intention relationship significantly (β = 0.114). The four design contradictions the expert panel retained were resolved using five TRIZ inventive principles, yielding a prioritised design blueprint for next-generation robo-advisory platforms.
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