طراحی مدل تأثیر غیرخطی پویایی محیطی بر عملکرد سازمانی با استفاده از شبکه‌های عصبی مصنوعی: مطالعه موردی شرکت‌های خودروسازی

حسین   جلیلی رنجبری; سجاد نقدی; وحید   احمدیان; صمد رحیمی اقدم

Authors

Hussain Jalili Ranjbari PhD Student, Department of Management - Strategic Management, Aras Campus, University of Tabriz, Tabriz, Iran Author https://orcid.org/0009-0004-0756-7314
Sajad Naghdi Associated Professor, Department of Management, Faculty of Economics and Management, University of Tabriz, Tabriz, Iran https://orcid.org/0000-0003-1899-0957
Vahid Ahmadian Associated Professor, Department of Management, Faculty of Economics and Management, University of Tabriz, Tabriz, Iran Author https://orcid.org/0000-0001-6982-7514
Samad Rahimi Aghdam Associated Professor, Department of Management, Faculty of Economics and Management, University of Tabriz, Tabriz, Iran Author https://orcid.org/0009-0008-9404-7518

Keywords:

Environmental dynamics, organizational performance, artificial neural network, genetic algorithm, automotive industry

Abstract

This study aimed to develop and evaluate an intelligent model for explaining and predicting the nonlinear relationships between environmental dynamism and organizational performance in Iranian automotive companies. This applied, quantitative, descriptive-correlational, and ex post facto study used historical data from automotive companies listed on the Tehran Stock Exchange during 2013–2024. From an initial population of 100 companies, 20 were purposively selected based on continuity of operations, data availability, and completeness, with the company-year serving as the unit of analysis. Environmental dynamism was operationalized across five dimensions: economic, technological, legal/regulatory, social, and environmental, while organizational performance was assessed using financial and operational indicators. Following data cleaning and normalization, an artificial neural network comprising three hidden layers with 128, 64, and 32 neurons, ReLU activation functions, and the Adam optimizer was developed. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithms were compared for model optimization, and sensitivity analysis was conducted to determine the relative predictive importance of environmental dimensions. The baseline ANN achieved MSE=0.0185, RMSE=0.1360, MAE=0.105, and R²=0.887 on the test set, demonstrating substantial predictive capability for nonlinear relationships. GA optimization produced the strongest performance compared with PSO and ABC, reducing MSE by 23.8% to 0.0141 and increasing R² to 0.915. Sensitivity analysis identified the economic dimension as the most influential predictor (0.31), followed by technological (0.24), legal/regulatory (0.19), social (0.14), and environmental (0.12) dimensions. Environmental dynamism exhibits a complex nonlinear relationship with organizational performance in the automotive industry. Integrating ANN with GA substantially improves predictive accuracy and explanatory performance. The predominance of economic and technological dimensions highlights the strategic importance of continuous environmental monitoring, technological capability development, and intelligent decision-support systems for strengthening organizational adaptability and performance under dynamic conditions.

Downloads

Download data is not yet available.

References

Abou-Moghli, A. (2025). The Interplay between Knowledge Management and Organizational Performance Measurement through the Mediating Effect of Innovation Capability. Knowledge and Performance Management, 9(1), 45-61. https://doi.org/10.21511/kpm.09(1).2025.04

Aftab, J., Veneziani, M., Sarwar, H., & Ishaq, M. I. (2024). Entrepreneurial Orientation and Firm Performance in SMEs: The Mediating Role of Entrepreneurial Competencies and Moderating Role of Environmental Dynamism. International Journal of Emerging Markets, 19(10), 3329-3352. https://doi.org/10.1108/IJOEM-07-2021-1151

Agazu, B. G., Kero, C. A., & Debela, K. L. (2025). Transformational Leadership and Firm Performance: A Systematic Literature Review. Journal of Innovation and Entrepreneurship, 14(1), 29. https://doi.org/10.1186/s13731-025-00476-x

Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2024). Artificial Intelligence-Driven Innovation for Enhancing Supply Chain Resilience and Performance under the Effect of Supply Chain Dynamism: An Empirical Investigation. Annals of Operations Research, 333(2), 627-652. https://doi.org/10.1007/s10479-021-03956-x

Cui, J. (2025). The Explore of Knowledge Management Dynamic Capabilities, AI-Driven Knowledge Sharing, Knowledge-Based Organizational Support, and Organizational Learning on Job Performance: Evidence from Chinese Technological Companies. https://doi.org/10.2139/ssrn.5083169

Ghadami, B., Fotros, M. H., & Haji, G. (2025). Modeling the Dynamic Relationship among Institutions, Environmental Pollution, Economic Development, and Public Health Using Bayesian Averaging Approaches. Stable Economy, 6(3), 133-169.

International Organization of Motor Vehicle, M. (2025). Vehicle Sales Statistics. https://www.oica.net/category/sales-statistics/

Javaid, W., & Ullah, S. (2025). Data Driven Simulation-Based Optimization Model for Job-Shop Production Planning and Scheduling: An Application in a Digital Twin Shop Floor. Journal of Simulation, 1-15. https://doi.org/10.1080/17477778.2025.2469687

Kafetzopoulos, D., Samara, E. T., & Theodorakioglou, Y. (2024). Multiple Organizational Capabilities for Effective Business Model Innovation and Increased Performance. European Journal of Innovation Management, 27(8), 2925-2943. https://doi.org/10.1108/EJIM-01-2023-0065

Keathley-Herring, H., Van Aken, E., & Letens, G. (2024). Implementing Organizational Performance Measurement Systems: Measures and Success Strategies. International Journal of Productivity and Performance Management, 73(9), 2972-3007. https://doi.org/10.1108/IJPPM-05-2023-0266

Kgakatsi, M., Galeboe, O. P., Molelekwa, K. K., & Thango, B. A. (2024). The Impact of Big Data on SME Performance: A Systematic Review. Businesses, 4(4), 632-695. https://doi.org/10.3390/businesses4040038

Li, N., Zhou, H., & Xu, M. (2024). From Text to Insight: Leveraging Large Language Models for Performance Evaluation in Management. https://arxiv.org/abs/2408.05328

Lüdeke-Freund, F., Froese, T., Dembek, K., Rosati, F., & Massa, L. (2024). What Makes a Business Model Sustainable? Activities, Design Themes, and Value Functions. Organization & Environment, 37(2), 194-220. https://doi.org/10.1177/10860266241235212

Luo, D., Guan, Z., Ding, L., Fang, W., & Zhu, H. (2025). A Data-Driven Methodology for Hierarchical Production Planning with LSTM-Q Network-Based Demand Forecast. Symmetry, 17(5), 655. https://doi.org/10.3390/sym17050655

Ministry of Industry, M., & Trade. (2025). Performance Reports. www.mimt.gov.ir/fa/general_content/478327-سالانه.html

Muliati, M., Totanan, C., Pattawe, A., Iqbal, M., Mile, Y., & Mayapada, A. G. (2024). Enhancing SME Green Performance: The Role of Environmental and Social Responsibility Programs and Environmental Dynamism. International Journal of Sustainable Development & Planning, 19(2). https://doi.org/10.18280/ijsdp.190238

Nosratpanah, R., Barani, S., Ashrafzadeh, A., & Atashi, G. (2024). The Impact of Dynamic Service Innovation Capabilities on Firm Performance: The Moderating Role of Perceived Environmental Dynamism and the Mediating Roles of Service Innovation and Competitive Advantage. Business Management, 16(1), 137-166.

Okech, R. (2025). Strategic Planning and Sustainable Organizational Performance. International Journal of Management Studies, 12(1), 15-30.

Onngam, W., & Charoensukmongkol, P. (2024). Effect of Social Media Agility on Performance of Small and Medium Enterprises: Moderating Roles of Firm Size and Environmental Dynamism. Journal of Entrepreneurship in Emerging Economies, 16(6), 1611-1633. https://doi.org/10.1108/JEEE-11-2022-0331

Peng, M. Y. P., Zhang, L., Lee, M. H., Hsu, F. Y., Xu, Y., & He, Y. (2024). The Relationship between Strategic Human Resource Management, Green Innovation and Environmental Performance: A Moderated-Mediation Model. Humanities and Social Sciences Communications, 11(1), 1-13. https://doi.org/10.1057/s41599-024-02754-7

Riazi Nejad, M., & Riazi Nejad, M. (2025). Simulation of a Green Management Development Model Using Systems Thinking with a Business Development Focus: A Case Study of Bank Mellat Construction Company. Urban Environmental Management, 3(3), 1-16.

Sadeghi Moghadam, M., Nejatian, S., Parvin, H., Bagherifard, K., & Yaghoubian, S. H. (2025). Solving Dynamic Optimization Problems with an Improved Imperialist Competitive Algorithm. Signal and Data Processing, 22(2), 3-30.

Salimi Zavieh, S. G., Kazazi, A., Raeisi Vanani, I., & Ghazi Nouri, S. S. (2026). Data-Driven Biopharmaceutical Production Planning with LSTM-Based Demand Forecasting and Multiobjective Optimization Using a Genetic Algorithm and the Epsilon-Constraint Method. Systems Engineering and Productivity, 6(2), 113-159.

Shiming, L., Weizhong, Y., & Xinyu, D. (2024). Neural Network Modeling of Nonlinear Fluid Dynamics: A Comprehensive Survey. Computers & Fluids, 268, 106960. https://doi.org/10.1016/j.compfluid.2024.106960

Su, X., Zeng, L., Shao, B., & Lin, B. (2025). Data-Driven Optimization for Production Planning with Multiple Demand Features. Kybernetes, 54(1), 110-133. https://doi.org/10.1108/K-04-2023-0690

Sullivan, Y., & Fosso Wamba, S. (2024). Artificial Intelligence and Adaptive Response to Market Changes: A Strategy to Enhance Firm Performance and Innovation. Journal of Business Research, 174, 114500. https://doi.org/10.1016/j.jbusres.2024.114500

Tekala, K., Baradarani, S., Alzubi, A., & Berberoğlu, A. (2024). Green Entrepreneurship for Business Sustainability: Do Environmental Dynamism and Green Structural Capital Matter? Sustainability, 16(13), 5291. https://doi.org/10.3390/su16135291

Wiroonrath, S., Phanniphong, K., Inmor, S., & Na-Nan, K. (2026). Green Leadership in the Food Industry Influences Performance through Resilience and Environmental Dynamics. Discover Sustainability, 7, 18. https://doi.org/10.1007/s43621-025-02329-9

Wiseman, K. (2024). Environmental Assessment and Planning in South Africa: The SEA Connection. In (pp. 155-166). CRC Press. https://doi.org/10.1201/9781003578932-15