IJLTEMAS International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) IJLTEMAS 2278-2540 IJLTEMAS 153 10.51583/IJLTEMAS.2026.150700148 Architecture Operationalizing and Validating Contextual Financial Judgment: Scale Development and Structural Modeling for AI-Driven Personal Finance Meenakshi Wagh Dr. Department of Business Economics and Banking, St. Mira’s College for Girls, Pune, India India 15 7 1908 1930 10 08 2026 15 08 2026 25 08 2026 Financial Literacy Contextual Financial Judgment (CFJ) Personal Finance Scale Development Structural Equation Modeling (SEM) Choice Architecture Abstract

With Artificial Intelligence (AI) and automated choice architectures remodelling consumer finance, traditional measures of financial literacy are ineffective in measuring how individuals network with, assess and supersede algorithmic stimuli. For bridging this theoretical and empirical gap, this paper operationalises a new construct - Contextual Financial Judgment (CFJ) – defined as a unique cognitive-attitudinal competence to appraise programmed financial recommendations vis-a-vis non-quantifiable, qualitative life realities. Building upon Dual-Process Theory, Human Agency Theory and the Industry 5.0 human-centric AI paradigm, this study introduces a comprehensive psychometric scale architecture and Structural Equation Modeling (SEM) framework to measure human agency in personal finance. Firstly, the study establishes a theoretically refined 12-item scale consisting of three reflective first-order dimensions: Contextual Financial Awareness (CFA), In-Depth Systemic Knowledge (ISK) and Evaluative and Overriding Attitude (EOA). Secondly, Contextual Financial Judgment (CFJ) is integrated as a second-order latent construct into a structural model that links exogenous antecedents like Algorithmic Exposure and Perceived Financial Importance, to vital downstream outcomes like Decision Autonomy, Mitigation of Impulse Financial Behaviour and Long-Term Value Alignment, while Explainable AI (XAI) is modelled as a moderating mechanism strengthening the translation of systemic knowledge into overriding attitude. This is further supported by six testable propositions (P1 to P6) to guide future empirical testing. Finally, the study creates a conceptual structural architecture of AI-mediated financial governance and presents a comprehensive, five-step methodological roadmap for psychometric validation (EFA, CFA and PLS-SEM/CB-SEM), thereby offering a rigorous foundation for evaluating consumer agency in AI-driven financial ecosystems.

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