نوع مقاله : مقاله پژوهشی

نویسندگان

• محمد علی سیاه سرانی کجوری
• محمودرضا چراغعلی

استادیار، گروه مدیریت و اقتصاد، دانشگاه گلستان، گرگان، ایران

چکیده

در عصر اطلاعات، دانش مهم‌ترین و باارزش‌ترین سرمایه هر سازمانی به شمار می‌رود. این پژوهش، بر اساس هدف از نوع پژوهش‏‌های کاربردی است که با استفاده از روش پیمایشی و رویکرد تحلیلی انجام شده است. جامعه آماری مدیران، اعضای هیئت‌علمی و یاوران علمی دانشگاه گلستان در سال 1403 بودند که با توجه به محدود بودن جامعه پژوهش از جدول مورگان استفاده شد که حجم نمونه موردنیاز برای پژوهش حاضر با توجه به جامعه آماری 365 نفری، 187 به دست آمد که بر همین مبنا تعداد 190 پرسش‌نامه و با استفاده از روش نمونه‌گیری طبقه‌ای نسبی گردآوری شد. به‌منظور سنجش پایایی ابزار پژوهش از آلفای کرونباخ و به‌منظور بررسی روایی پرسش‌نامه نیز از تحلیل عاملی تأییدی مرتبه اول و دوم استفاده شد. یافته‌ها نشان داد در موانع نگهداشت و توسعه دانش منابع انسانی، سه عامل فرهنگ سازمانی، ساختار سازمانی و مدیریت وضعیت پایین‌تر از حد متوسط و دو عامل فناوری اطلاعات و منابع انسانی با اختلاف ناچیزی بالاتر از حد متوسط قرار دارند، همچنین به‌کارگیری شبکه‌های عصبی مصنوعی به‌منظور پیش‌بینی میزان نگهداشت و توسعه دانش منابع انسانی نشان داد مدل پژوهش حاضر با تعداد سه نورون در لایه پنهان، به بالاترین میزان دقت پیش‌بینی یعنی 893/0 خواهد رسید و ابعاد پاداش، پشتیبانی و تسهیم دانش به ترتیب بالاترین نقش را در میزان قدرت پیش‌بینی‌کنندگی مدل دارند. نتایج نشان داد شاخص‌های زیرساختی مدیریت دانش در دانشگاه گلستان به‌ویژه در سه عامل فرهنگ سازمانی، ساختار سازمانی و مدیریت نیازمند توجه و اقدام فوری است.

کلیدواژه‌ها

• مدیریت دانش
• دانشگاه گلستان
• زیرساخت پیاده‌سازی
• شبکه عصبی مصنوعی
• توسعه دانش
• منابع انسانی

موضوعات

• مدیریت دانش

عنوان مقاله [English]

The Use of Artificial Neural Networks in Predicting the Success Rate of Maintaining and Developing Human Resources Knowledge

نویسندگان [English]

• Mohammad Ali Siahsarani Kojuri
• Mahmood Reza Cheraghali

Assistant Professor, Department of Management and Economics, Golestan University, Gorgan, Iran

چکیده [English]

1.Introduction
Universities need to manage their knowledge assets and work creatively to maximize the enablers and minimize the barriers associated with knowledge management processes. Globally, universities are considered key drivers of the economy and have significant potential to act as engines of economic growth and development. However, universities face a wide range of challenges, including the emergence of the knowledge society, the globalization and internationalization of universities, reduced funding and government support, increasing undergraduate enrollment, and expanding access (Ramjeawon & Rowley, 2020). Effective knowledge management practices help universities improve their efficiency and effectiveness, become more competitive, and contribute to the wealth of their countries (Fussy, 2018).
The purpose of the present research is to examine the status of the infrastructural indicators of the successful implementation of knowledge management at Golestan University at three levels: managers, faculty members, and scientific assistants, and design a predictive model for it. For this purpose, the status of various infrastructure dimensions such as management, information technology, human resources, organizational culture, and organizational structure for the successful implementation of knowledge management in the university was examined, and then based on the knowledge management infrastructure indicators and using artificial neural networks, an optimal predictive model was presented.

2.Literature Review

Previous studies have identified several enablers (factors that enhance knowledge management) and barriers (factors that have an adverse effect on knowledge management). Many of these factors can have a positive or negative impact on knowledge management processes such as knowledge creation, knowledge sharing, and knowledge transfer. The most frequently identified factors include culture, rewards and incentives, technology, leadership, organizational structures, and university-industry linkages. Few studies have also identified the importance of strategies and policies, human resources, and resources and budget (Ramjeawon & Rowley, 2020). In past research, approaches to barriers to knowledge management implementation have varied. Wolf et al. (2024) identified three root challenges that frequently appear in the path of knowledge management implementation: 1- Organizational prerequisites for implementing a knowledge sharing culture 2- Use of ICT for knowledge transfer 3- Knowledge transformation. Remus (2012) distinguishes challenges to implementing knowledge management based on the stage of the implementation process in which they appear (such as knowledge creation, sharing, transfer, or retention).

3.Methodology

This research is based on the purpose of applied research and was conducted using a survey method and an analytical approach. The statistical population of the research was managers, faculty members, and scientific assistants. Due to the limited research population, the Morgan table was used. The sample size required for the present study was 187 based on the statistical population of 365 people, and on this basis, 190 questionnaires were collected. Data collection was carried out through a questionnaire using a relative stratified sampling method. In the first part of the questionnaire, the demographic information of the respondents, including gender, education, etc., was collected, and in the second part, the research variables were measured. In the first step, the status of Golestan University in terms of infrastructure indicators for maintaining and developing human resources knowledge was examined at three levels of managers, faculty members, and scientific assistants, and the results of each class were compared. In the second step, artificial neural networks were used to predict the maintenance and development of human resource knowledge based on infrastructure indicators.

4.Results

An examination of the status of human resource knowledge maintenance and development factors at the overall level of Golestan University shows that, in a general view, the level of management variables, organizational structure, and organizational culture is not in a desirable state and is below the average level, and the human resource and information technology variables are above the average level. In order to predict the success rate of human resource knowledge maintenance and development at Golestan University, artificial neural networks were used. The results showed that the present research model with three neurons in the hidden layer will reach the highest level of prediction accuracy, which is 0.893. The results of sensitivity analysis using artificial neural networks showed that all the infrastructural dimensions of human resource knowledge maintenance and development studied in the present study are important in the success of knowledge management and their existence is mandatory in the university complex, but the dimensions of reward, support, and knowledge sharing have been assigned the first, second, and third ranks, respectively, which indicates the greater importance of these dimensions than other dimensions based on the impact on the predictive power of the model.

5.Discussion

The results showed that in terms of the status of the human resources knowledge maintenance and development indicators at Golestan University at all three levels of managers, faculty members, and scientific assistants, the status of the two factors of human resources and information technology is above the average level, and the status of the three factors of organizational culture, organizational structure, and management is below the average level. In other words, there is consensus at each level studied at Golestan University regarding the identification of the most important obstacles to the implementation of knowledge management. On the other hand, although in terms of the status of the infrastructural indicators of knowledge management at Golestan University at all three levels of managers, faculty members, and scientific assistants, the two factors of human resources and information technology have obtained numbers above the average level, the numbers obtained are not so impressive and desirable that these two factors can be considered as strengths in the implementation of knowledge management and it can be inferred that the university is in a good position in these two factors and does not need any activity. In fact, the results of this step show that the infrastructural indicators at Golestan University, especially in the three factors of organizational culture, organizational structure, and management, require immediate attention and action.

6.Conclusion

The findings showed that in the barriers to maintaining and developing human resources knowledge, three factors of organizational culture, organizational structure, and management are below average, and two factors of information technology and human resources are slightly above average. Also, the use of artificial neural networks to predict the level of maintaining and developing human resources knowledge showed that the current research model with three neurons in the hidden layer will reach the highest level of prediction accuracy, which is 0.893, and the dimensions of reward, support, and knowledge sharing, respectively, have the highest role in the predictive power of the model. The results showed that the infrastructural indicators of knowledge management at Golestan University, especially in the three factors of organizational culture, organizational structure, and management, require immediate attention and action.
Acknowledgments
We would like to express our deepest gratitude to Golestan University for participating in this research and covering the costs in the form of a customized research project.

کلیدواژه‌ها [English]

• Knowledge Management
• Golestan University
• Implementation Infrastructure
• Artificial Neural Network
• Knowledge Development
• Human Resources