Volume 14, Issue 4 (2025)
ORMR 2025, 14(4): 105-126 |
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Moghadamnia H, shahbandarzadeh H, Mortazavi M, Farhangi A. Identifying the Drivers of Team Effectiveness through Artificial Neural Network (Case Study: Khuzestan Steel Company). ORMR 2025; 14 (4) :105-126
URL: http://ormr.modares.ac.ir/article-28-72451-en.html
URL: http://ormr.modares.ac.ir/article-28-72451-en.html
1- PhD student, Department of Public Administration, Faculty of Management and Economic, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2- Associate Professor, Department of Industrial Management, Faculty of Business and Economics, Persian Gulf University, Bushehr, Iran. ,shabandarzadeh@pgu.ac.ir
3- Associate Professor, Department of Public Administration, Faculty of Management and Economic, Tarbiat Modares University, Tehran, Iran.
4- Professor, Department of Public Administration, Faculty of Management and Economic, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2- Associate Professor, Department of Industrial Management, Faculty of Business and Economics, Persian Gulf University, Bushehr, Iran. ,
3- Associate Professor, Department of Public Administration, Faculty of Management and Economic, Tarbiat Modares University, Tehran, Iran.
4- Professor, Department of Public Administration, Faculty of Management and Economic, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Abstract: (165 Views)
The purpose of this research is to identify the drivers of team effectiveness in Khuzestan Steel Company through artificial neural network. This research is based on the purpose of applied-development research, based on the data collection method, it is a descriptive-survey research, and in terms of data type, it is a quantitative research approach. The statistical population of research is 1020 employees and managers of Khuzestan Steel Company, of which 360 employees were selected as a sample using a simple and purposeful random sampling method. The results showed that the artificial neural network, with a high percentage of accuracy (in identifying indicators that increase effectiveness: that is, 97.3 in the training section and 95.8 in the testing section, also in identifying indicators that do not increase effectiveness: that is, 96.5% in the training section and 92.9% in the testing section), has been able to work correctly and predict that this indicates the very high efficiency and sensitivity of this system. Also, out of the 125 indicators presented, 65 active independent indicators were successful in predicting team effectiveness, which this method was able to identify by defining zero and one variables. Artificial neural network in the data analysis led us to the conclusion that team learning, group orientation and agile mindset indicators are the most important drivers in team effectiveness, respectively, and there is a need for a deep study on these dimensions.
Article Type: Descriptive & Survey |
Subject:
Organizational Behavior and Human Resource Management
Received: 2023/11/15 | Accepted: 2024/11/23 | Published: 2025/03/30
Received: 2023/11/15 | Accepted: 2024/11/23 | Published: 2025/03/30
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