A Hybrid Approach to Evaluate Business Efficiency, Profitability, and Market Valuation: An Empirical Validation

This study proposes and empirically validates a hybrid analytical framework that links internal business capabilities to firm performance through a two-stage process combining Data Envelopment Analysis (DEA) and Backpropagation Neural Networks (BPNN). Drawing on the Resource-Based View (RBV), Dynamic Capability Theory (DCT), and Signaling Theory (ST), we evaluate how marketing, operations, R&D, and human resource (HR) capabilities influence both profitability-based and market value-based outcomes. In the first stage, DEA assesses the relative efficiency of each capability by comparing the input-output transformation ratios across firms. In the second stage, BPNN models the nonlinear interactions among these capabilities and their predictive power for both internal (profitability, growth, and stability) and external (market value, Tobin’s Q, stock return) performance outcomes. Comparative results reveal that firms in different industries tend to emphasis different functional capabilities, with R&D and HR capabilities most critical in high tech, operation capability dominant in manufacturing, and marketing and R&D capabilities most influential due to their sector-specific competitive priorities. This research contributes methodologically by introducing a scalable hybrid model for strategic capability evaluation and theoretically by clarifying how functional efficiencies translate into both financial returns and market value cognition. It also offers practical insights for managers aiming to benchmark performance and prioritize capability investments based on firm type and industry context.