Absorptive Capacity as Observable Structure: From Latent Variable Inference to Direct Spectral Measurement

We present an operator algebra framework for measuring absorptive capacity (AC) in supply chains that eliminates the need for survey-based inference. Traditional approaches using Structural Equation Modeling with Partial Least Squares (SEM-PLS) treat AC as a latent construct inferred from proxy variables, requiring large sample sizes ($n \geq 100$) and facing fundamental construct validity limitations. Our framework employs four spectral operators---TRANSPORT+, ENTROPY+, CLUSTER+, and FISHER+---that directly read organizational structure corresponding to the four AC dimensions defined by Zahra \& George (2002): acquisition, assimilation, transformation, and exploitation. Validation against synthetic firm data demonstrates that operator algebra achieves Mean Absolute Error (MAE) of 0.101 compared to 0.162 for SEM-PLS with $n=100$ respondents---a 37.7\% improvement in accuracy with zero survey data required. The framework provides density-adaptive weighting analogous to breast tissue imaging, automatically adjusting operator emphasis based on firm maturity. We prove that profinite continuity guarantees prevent deviant measurement rules, ensuring the structure we read corresponds to the structure that exists. This work transforms absorptive capacity from an epistemological problem (inferring latent variables) to an ontological solution (reading observable structure), with immediate applications for real-time organizational diagnostics and causal identification in supply chain research.