Article’s

Digital twin (DT) technology is increasingly applied to financial markets to create dynamic, simulation-based representations of trading environments, yet no validated instrument exists to measure how DT maturity shapes trader behavior. Objective: This study develops and validates a multidimensional Digital Twin Maturity (DTM) scale and examines its influence, alongside AI trust and simulation quality, on trading decision-making within a financial market simulation. Methodology: A quantitative, cross-sectional survey-experimental design was employed. Sample: A structured questionnaire measured on a five-point Likert scale was administered to 150 active retail traders who participated in a simulated trading environment. Statistical techniques: Reliability analysis, descriptive statistics, Pearson correlation, multiple regression, independent samples t-test, and one-way ANOVA were computed using Microsoft Excel. Key findings: All constructs demonstrated strong internal consistency (Cronbach’s alpha 0.84-0.93). Digital twin maturity, AI trust, and simulation quality significantly and positively predicted trading decision-making (R2 = 0.612, p < .001), while risk perception exerted a significant negative influence. No significant gender difference emerged, whereas trading experience produced significant group differences in decision quality. Practical implication: The validated scale offers financial institutions, FinTech developers, and brokerage platforms a diagnostic tool to benchmark digital twin maturity and design simulation-based trading environments that strengthen trader confidence and decision accuracy.