Customer-Oriented Knowledge Management Modeling Using the MLP Method
Keywords:
Knowledge Management, Customer-Oriented, Neural NetworkAbstract
Customer-oriented knowledge management is a comprehensive approach aimed at developing a broad and integrated organizational vision, with its primary focus on achieving innovation and organizational effectiveness. This study examined customer-oriented knowledge management in technology-based companies located in Tehran using an artificial neural network approach. The research method was quantitative, survey-based, and applied in nature. Data were collected through a questionnaire administered to 386 managers and experts. To predict and evaluate patterns, a Multilayer Perceptron (MLP) neural network was utilized. The results indicated that input components such as customer-oriented knowledge management processes and behavioral data had strong correlations with output variables including customer satisfaction, innovation, and customer loyalty. The model demonstrated high predictive accuracy based on evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and the coefficient of determination (R²). The R² value of 0.83 reflected the model’s desirable performance. In the learning curve analysis, both training and testing errors decreased rapidly and stabilized, indicating optimal learning of the model and prevention of overfitting. The findings suggest that neural networks can serve as an effective tool for implementing customer-oriented knowledge management in technology-based companies, contributing to improved strategic decision-making processes and enhanced customer satisfaction.
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Copyright (c) 2025 Yasaman Rezayazdi (Author); Nader Sheikholeslami Kandelousi (Corresponding author); Maryam Khademi, Nazanin Pilevari (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
