Managerial Judgment Under Ambiguity: Subjective Value, Confidence Calibration, and a Behaviorally Calibrated Fuzzy Model

Abstract

This study examines how ambiguity shapes managerial subjective value and decision commitment and evaluates a Behaviorally Calibrated Fuzzy Subjective-Value (BCFSV) representation of ambiguity responses. In a repeated-measures 2 × 2 experiment crossing risk versus ambiguity with gain versus loss framing, 525 working adults completed 12 managerial decision scenarios, yielding 6300 trial-level observations. An independent pilot (N = 42; 504 trials) was used to calibrate the fuzzy hesitation/ambiguity-response term (π) against normalized perceived ambiguity using ambiguity exposure, ambiguity width, intolerance of uncertainty, and their interaction. In the main sample, ambiguity increased perceived ambiguity and reduced the pre-choice subjective value of the uncertain option; the statistical indirect effect through perceived ambiguity was −0.405 (95% bootstrap CI [−0.439, −0.373]). Intolerance of uncertainty strengthened the ambiguity–perceived ambiguity relationship, and framing moderated the ambiguity–valuation relationship. The highest pre-choice subjective value across the two options predicted decision commitment and carried a significant indirect association between perceived ambiguity and commitment (−1.584, 95% bootstrap CI [−1.784, −1.391]). Decision confidence also predicted commitment, with a stronger confidence–commitment association among participants showing poorer Raw Brier calibration (b = 0.874, 95% CI [0.630, 1.119]). The pilot-calibrated π mapping generalized to the main sample (r = 0.620), but adding the BCFSV transformation to a same-information benchmark produced no detectable incremental predictive gain for choice or commitment. The findings indicate that ambiguity appraisal, individual differences, and framing shape valuation, while the confidence–commitment association was stronger among participants with poorer confidence calibration. BCFSV thus offers an interpretable, empirically calibrated representation of uncertainty rather than a predictively superior model.

The authors' abstract, as published at the source. Behavioral Sciences, 2026 · DOI ↗