مدل هوشمند پیش‌بینی بلوغ سرمایه فکری در شرکت‌های دانش‌بنیان با استفاده از یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران.

2 دانشیار، گروه مدیریت، دانشگاه صنعتی مالک اشتر، تهران، ایران.

3 دانشیار، گروه مهندسی صنایع، واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران.

4 دانشیار، گروه مهندسی صنایع، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران.

10.22091/jemsc.2025.12548.1266

چکیده

هدف این پژوهش، طراحی مدلی هوشمند برای پیش‌بینی بلوغ سرمایه فکری در شرکت‌های دانش‌بنیان مستقر در شهرک‌های صنعتی با استفاده از الگوریتم‌های یادگیری ماشین است. این تحقیق به لحاظ هدف، کاربردی-توسعه‌ای و از لحاظ روش، توصیفی-مدل‌سازی و در زمینه جمع‌آوری داده‌ها به‌صورت آمیخته می‌باشد. داده‌ها از طریق بررسی ادبیات موضوع، مصاحبه با خبرگان و دو پرسشنامه جمع‌آوری شده است. برای تحلیل داده‌ها، روش‌های مختلفی از جمله روش دلفی، تحلیل عاملی تأییدی و الگوریتم یادگیری ماشین مانند جنگل تصادفی، K نزدیک‌ترین همسایه، درخت تصمیم، بیز ساده و شبکه عصبی پرسپترون چندلایه با استفاده از نرم افزارهای Spss ، PLS و کتابخانه های مختلف Python به کار رفته است. نتایج نشان داد که تمامی مدل‌ها توانایی پیش‌بینی سطح بلوغ سرمایه فکری را دارند، اما مدل شبکه عصبی پرسپترون چندلایه MLP عملکرد بهتری نسبت به سایر مدل‌ها بر اساس معیارهای مختلف از جمله دقت، صحت، حساسیت و امتیاز F1 از خود نشان داد و بهترین نتایج را با مقادیر 88/37٪، 89/75 ٪، 88/37٪، 86/51 ٪ و 0/918 در مساحت زیر منحنی ROC ارائه کرد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Intelligent Model for Predicting Intellectual Capital Maturity in Knowledge-Based Companies Using Machine Learning

نویسندگان [English]

  • Hossein Azizinejad 1
  • Gholamreza Tavakoli 2
  • Mohammad Ehsanifar 3
  • Amir Najafi 4
1 PhD Student, Department of Industrial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 Associate Professor, Department of Management, Malek Ashtar University of Technology, Tehran, Iran.
3 Associate Professor, Department of Industrial Engineering, Arak Branch, Islamic Azad University, Arak, Iran.
4 Associate Professor, Department of Industrial Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran.
چکیده [English]

The aim of this research is to design an intelligent model for predicting the maturity of intellectual capital in knowledge-based companies located in industrial parks using machine learning algorithms. This study is applied-developmental in purpose and descriptive-modeling in methodology, utilizing a mixed approach for data collection. The data were gathered through a review of the literature, interviews with experts, and two questionnaires. For data analysis, various methods were employed, including the Delphi method, confirmatory factor analysis, and machine learning algorithms such as random forests, K-nearest neighbors, decision trees, naive Bayes, and multi-layer perceptron neural networks, using SPSS, PLS software, and various Python libraries. The results indicated that all models were capable of predicting the level of intellectual capital maturity; however, the multi-layer perceptron (MLP) model outperformed the others based on several criteria, including accuracy, precision, sensitivity, and F1 score, yielding the best results with values of 88.37%, 89.75%, 88.37%, 86.51%, and 0.918 in the area under the ROC curve.

کلیدواژه‌ها [English]

  • Intellectual capital
  • Intellectual capital maturity
  • Knowledge-based companies
  • Machine learning

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