Document Type : Research Article
Author
Department of Management, Yong In University, Yongin, Republic of Korea
10.30473/coam.2025.76551.1362
Abstract
This study develops a nonlinear dynamic modeling framework to analyze and predict performance behavior in industrial environments using competitive-intelligence-related variables. Four organizational resource components are formulated as elements of a discrete-time state vector, and their influence on system output is modeled through a nonlinear state-transition function. Empirical observations collected from a steel manufacturing company were used to identify the unknown dynamics through a feed-forward artificial neural network trained via a gradient-based optimization procedure. Reliability of the measurement instrument was verified using Cronbach’s alpha coefficients of 0.92 and 0.86 for the independent and dependent constructs, respectively. The identified model demonstrates stable convergence, with the minimum prediction error achieved near iteration 1500, and outperforms a linear baseline in mean-squared error and correlation accuracy. The proposed formulation provides a mathematically oriented approach for reconstructing performance-driven system behavior and establishes a foundation for future extensions involving adaptive estimation, robust analysis, and optimal control strategies in industrial systems.
Highlights
- Proposes a nonlinear dynamic framework that links four organizational resource components of competitive intelligence (CI) to sustainable competitive advantage (SCA) in the steel industry, using a discrete-time state-vector representation and a nonlinear state-transition function learned by a feed-forward artificial neural network (ANN).
- Empirical data from 175 employees support positive, significant contributions of CI dimensions to SCA: major organizational resources (r = 0.439), environmental resources (r = 0.375), competitive resources (r = 0.509), and strategic resources (r = 0.488), indicating that CI components collectively reinforce long-term competitiveness.
- The ANN-based dynamic model achieves high predictive fidelity, with a minimum training error observed around iteration 1,500 and an R² of 0.78 for explaining SCA variance, demonstrating strong nonlinear capture of CI–SCA relationships beyond linear baselines.
- Model convergence and reliability are validated: Cronbach’s alpha for measurement constructs indicates good internal consistency (0.92 for independent constructs, 0.86 for dependent constructs); convergence occurs near the same iteration count cited for optimal performance, supporting model stability.
- The approach provides a mathematically grounded framework for reconstructing performance-driven system behavior in resource-intensive manufacturing settings and serves as a foundation for future extensions in adaptive estimation, robust analysis, and optimal control strategies.
- Demonstrates the practical value of CI-driven dynamic modeling as a decision-support tool: it outperforms traditional linear approaches and offers a structured pathway for resource integration and strategic planning in globalized markets.
- The study’s findings support the integration of CI dimensions into resource management and strategic decision-making, with implications for designing adaptive, data-informed governance and control mechanisms in steel production and similar high-stakes industrial environments.
Keywords
Main Subjects
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