Operationalizing foresight: designing and validating a contextual model for forecasting emerging technologies in iran’s banking industry

Objective: This study aims to introduce and empirically validate the Technology Prediction Processor (FPRO), a strategic foresight framework designed to enhance technology anticipation and decision-making within the banking and fintech sectors of developing economies. Originality/Value: FPRO advances the literature by integrating foresight capacity, information infrastructure, predictive accuracy with systematic validation, and strategic agility into a unified operational model. Unlike traditional forecasting tools, the framework is specifically tailored to environments facing accelerated digital transformation and technological uncertainty, demonstrating high transferability across emerging markets. Method: A mixed-methods design was applied. First, qualitative insights were collected through semi-structured interviews with sixteen senior banking and fintech experts. A three-round fuzzy Delphi technique refined the thematic structure. Quantitative validation was conducted using survey data from 286 industry professionals, analyzed through Structural Equation Modeling (SEM). Results: The FPRO model presented excellent statistical robustness (RMSEA = 0.022; CFI = 0.97; χ²/df = 1.14). The findings confirm that the four strategic drivers—foresight capacity, information infrastructure, predictive accuracy, and strategic agility—jointly enhance institutional decision-making and digital transformation performance in the financial sector. Conclusion: The Technology Prediction Processor is a reliable foresight tool capable of supporting policymakers, executives, and financial organizations in navigating technological disruptions. While validated in Iran, the model shows strong applicability to other developing markets undergoing digital transformation.