A Data Mining Application for Classification of Performance Values Under the Conditions of Digitalization of HR Processes

Yıl 2023, Cilt: 2 Sayı: 2, 44 – 53, 31.12.2023

Gözde Katırcıoğlu , Emel Kızılkaya Aydogan , Yılmaz Delice

Öz

İnsan kaynakları yönetimi, modern şirketlerde kaliteli personeli korumanın ve geliştirmenin anahtarıdır. Kurumların insan kaynakları verilerinin analiz edilmesi, sorunların tespit edilmesi ve stratejinin belirlenmesi avantaj sağlamaktadır. İşletmelerde insan kaynakları yönetiminin (İKY) dijitalleşmesi konusu her geçen gün artmaktadır. Endüstri 4.0 olarak da bilinen dördüncü sanayi devrimiyle birlikte dijitalleşme, kurumsal iş ve işlemlerin temeli olarak görülen insan kaynakları yönetimini de etkilemektedir. Bu alanda giderek daha fazla işletme, yapay zeka ve veri madenciliği yöntemleriyle verimliliği ve tasarrufu artıran insan kaynakları yönetimine yatırım yapmaktadır. Veri madenciliği uygulamaları ise, verileri anlamlandırıp bilgiye dönüştürmek ve kuruluşlara karar verme süreçlerinde yardımcı olmak amacıyla kullanılmaktadır. Bu çalışmanın amacı dijitalleşmenin insan kaynakları yönetimi uygulamaları üzerindeki etkisini incelemek ve bu konudaki eğilimleri tespit etmektir. Bu kapsamda veri madenciliği yöntemlerinden karar ağacı ve kural çıkarma algoritmaları kullanılmıştır. Gerçek veriler üzerinde yapılan deneyler ve karşılaştırma çalışmaları, mevcut verileri kategorize etme konusunda oldukça iyi olduğunu ortaya koymuştur. Elde edilen kurallar, şirketin potansiyel çalışan adaylarının davranışları hakkında fikir sahibi olmasını sağlamıştır. Bu çalışmanın önemi, iş dünyasında dijitalleşmenin ve yapay zekanın insan kaynakları yönetimine olumlu etkilerini geliştirilen teknikle ortaya koymaktır.

Anahtar Kelimeler

İnsan Kaynakları Yönetimi (İKY), Veri Madenciliği, Dijitalleşme, Karar Ağacı

Proje Numarası

Birinci Uluslararası İnsan Kaynakları Yönetimi Kongresi (1st International Human Resources Management Congress )

Kaynakça

• Abdulsalam, S. O., Babatunde, A. N., Hambali, M. A., & Babatunde, R. S. (2015). Comparative analysis of decision tree algorithms for predicting undergraduate students’ performance in computer programming. Journal of Advances in Scientific Research & Its Application (JASRA), 2, 79-92.
• Akduman, G. (2019). Digital recruitment: Evaluation of the impact of the digital world on the human resources recruitment function with conceptual and application examples. International Journal of Arts and Social Studies, 2(3), 24-44.
• Amershi, S., & Conati, C. (2006). Automatic recognition of learner groups in exploratory learning environments. In: Ikeda, M., Ashley, K.D., Chan, TW. (Eds.) Intelligent Tutoring Systems. Lecture Notes in Computer Science, 4053. Springer
• Argüden, Y., & Erşahin, B. (2008). Veri madenciliği: Veriden bilgiye, masraftan değere. Alkim Kağıt Sanayi ve Ticaret A. Ş.
• Bongiorno, G., Rizzo, D., & Vaia, G. (2018). CIOs and the digital transformation: A new leadership role (pp. 1-9). Springer International Publishing.
• Breiman L., Friedman J. H., Olshen R. A. & Stone C. J. (1984). Classification and regression trees. Wadsworth.
• Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37-46. https://doi.org/10.1177/001316446002000104.
• Domingos, P., & Hulten, G. (2000). Mining high-speed data streams. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 71-80. https://doi.org/10.1145/347090.347107
• Duggal, P., & Shukla, S. (2020). Prediction of thyroid disorders using advanced machine learning techniques. In 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 670-675). IEEE. https://doi.org/0.1109/Confluence47617.2020.9058102
• Erdem, S., & Özdağoğlu, G. (2008). Analyzing of emergency data of a training and research hospital in aegean region using data mining. Anadolu University Journal of Science and Technology, 9(2), 261-270.
• Fan, W., Wang, H., Yu, P. S., & Ma, S. (2003). Is random model better? on its accuracy and efficiency. In Third IEEE International Conference on Data Mining (pp. 51-58). IEEE. https://doi.org/10.1109/ICDM.2003.1250902
• Filizöz, B., & Orhan, U. (2018). Industry 4.0 in the context of human resources management: A literature study. Cumhuriyet University Journal of Economics and Administrative Sciences, 19(2), 110-117.
• Ghosh, A., Guha, T., & Bhar, R. B. (2015). Identification of handloom and powerloom fabrics using proximal support vector machines. Indian Journal of Fibre & Textile Research, 40(1), 87-93. https:// doi.org/10.56042/ijftr.v40i1.3809
• Han J., & Kamber M. (2006). Data mining: Concepts and techniques. Morgan Kaufmann, Elsevier Science.
• Kalıkov, A. (2006). Veri madenciliği ve bir e-ticaret uygulaması [Yüksek Lisans Tezi]. Gazi Üniversitesi.
• Karaboğa, T., & Karaboğa, H. A. (2022). Digitalization in human resources management: A bibliometric review. Turkish Studies-Economics, Finance, Politics, 17(2), 343-364. https:// doi.org/10.7827/TurkishStudies.58236
• Landwehr, N., Hall, M., & Frank, E. (2005). Logistic model trees. Machine learning, 59, 161-205.
• Maimon, O. Z., & Rokach, L. (2005). Data mining and knowledge discovery handbook, Springer.
• Mingers, J. (1989). An empirical comparison of pruning methods for decision tree induction. Machine Learning, 4, 227-243.
• Mulyana, R., Rusu, L., & Perjons, E. (2021). It governance mechanisms influence on digital transformation: A systematic literature review. In Twenty-Seventh Americas' Conference on Information Systems (AMCIS), Digital Innovation and Entrepreneurship, Virtual Conference, August 9-13, 2021 (pp. 1-10). Association for Information Systems (AIS).
• Quinlan, J. R. (1993). Programs for machine learning, 4(5).
• Ruël, H.J.M., Bondarouk, T.V., & Van der Velde, M. (2007). The contribution of e‐HRM to HRM effectiveness. Employee Relations, 29(3), 280-291. https://doi.org/10.1108/01425450710741757
• Ruël, H.J.M., & Bondarouk, T. V. (2009). Electronic human resource management: Challenges in the digital era. The International Journal of Human Resource Management, 20(3), 505-514. https://doi.org/10.1080/09585190802707235
• Shen, Y. (2007). A formal ontology for data mining: principles, design, and evolution [Doctoral dissertation], Université du Québec à Trois-Rivières.
• Shen, Y., Xing, L., & Peng, Y. (2007). Study and application of Web-based data mining in e-business. In eighth ACIS international conference on software engineering, artificial intelligence, networking, and parallel/distributed computing, 1, 812-816. IEEE. https://doi.org/10.1109/SNPD.2007.117
• Synnestvedt, M. B., Chen, C., & Holmes, J. H. (2005). CiteSpace II: Visualization and knowledge discovery in bibliographic databases. In AMIA annual symposium proceedings (2005, p. 724). American Medical Informatics Association.
• Tan, K. C., Yu, Q., & Ang, J. H. (2006). A dual-objective evolutionary algorithm for rules extraction in data mining. Computational optimization and applications, 34, 273-294. https://doi.org/10.1007/s10589-005-3907-9
• Uğurlu, H. Ü. A., & Doğan, A. (2023). Digital transformation and digitalized recruitment function in human resources management. Kocaeli University Journal of Social Sciences, 1(45), 1-16. https://doi.org/10.35343/kosbed.1247587
• Ulrich, D., & Dulebohn, J. H. (2015). Are we there yet? What's next for HR?. Human Resource Management Review, 25(2), 188-204. https://doi.org/10.1016/j.hrmr.2015.01.004
• Van Grembergen, W., & De Haes, S. (2009). Enterprise governance of information technology: Achieving strategic alignment and value. Springer Publishing Company, Incorporated.
• Vial, G. (2021). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118-144. https://doi.org/10.1016/j.jsis.2019.01.003
• Witten, I. H., & Frank, E. (2002). Data mining: Practical machine learning tools and techniques with Java implementations. Acm Sigmod Record, 31(1), 76-77. https://doi.org/10.1145/507338.507355
• Yıldırım, P., Birant, D., & Alpyildiz, T. (2018). Data mining and machine learning in textile industry. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(1), e1228. https://doi.org/10.1002/widm.1228
• Yılmaz, C., & Yılmaz, T. (2023). The effect of industry 4.0 on human resources management: HRM 4.0. Hak İş International Journal of Labor and Society, 12(32), 11-28. https://doi.org/10.31199/hakisderg.1214130
• Zhao, Y., & Zhang, Y. (2008). Comparison of decision tree methods for finding active objects. Advances in Space Research, 41(12), 1955-1959. https://doi.org/10.1016/j.asr.2007.07.020

A Data Mining Application for Classification of Performance Values Under the Conditions of Digitalization of HR Processes

Yıl 2023, Cilt: 2 Sayı: 2, 44 – 53, 31.12.2023

Gözde Katırcıoğlu , Emel Kızılkaya Aydogan , Yılmaz Delice

Öz

Human resources management is the key to maintaining and developing quality personnel in modern companies. Analyzing the human resources data of institutions, identifying problems, and determining the strategy provide a significant competitive advantage. The topic of digitalization of human resources management in businesses is increasing day by day. With the fourth industrial revolution, also known as Industry 4.0, digitalization also affects human resources management (HRM), which is seen as the basis of corporate business and transactions. In this field, more and more businesses are investing in human resources management, which increases efficiency and savings with artificial intelligence and data mining methods. Data mining applications are used to make sense of data and transform it into information and to assist organizations in decision-making processes. The aim of the present study is to examine the impact of digitalization on human resources management practices and to identify trends in this regard. In this context, decision tree and rule extraction algorithms, which are data mining methods, were used. Experiments and comparison studies conducted on real data have revealed that it is quite good at categorizing the available data. The rules obtained enabled the company to have insight into the behavior of potential employee candidates. The importance of this study is to reveal the positive effects of digitalization and artificial intelligence in the business world on human resources management with the developed technique.

Anahtar Kelimeler

Human Resources Management (HRM), Data Mining, Digitalization, Decision Tree

Proje Numarası

Birinci Uluslararası İnsan Kaynakları Yönetimi Kongresi (1st International Human Resources Management Congress )

Kaynakça

• Abdulsalam, S. O., Babatunde, A. N., Hambali, M. A., & Babatunde, R. S. (2015). Comparative analysis of decision tree algorithms for predicting undergraduate students’ performance in computer programming. Journal of Advances in Scientific Research & Its Application (JASRA), 2, 79-92.
• Akduman, G. (2019). Digital recruitment: Evaluation of the impact of the digital world on the human resources recruitment function with conceptual and application examples. International Journal of Arts and Social Studies, 2(3), 24-44.
• Amershi, S., & Conati, C. (2006). Automatic recognition of learner groups in exploratory learning environments. In: Ikeda, M., Ashley, K.D., Chan, TW. (Eds.) Intelligent Tutoring Systems. Lecture Notes in Computer Science, 4053. Springer
• Argüden, Y., & Erşahin, B. (2008). Veri madenciliği: Veriden bilgiye, masraftan değere. Alkim Kağıt Sanayi ve Ticaret A. Ş.
• Bongiorno, G., Rizzo, D., & Vaia, G. (2018). CIOs and the digital transformation: A new leadership role (pp. 1-9). Springer International Publishing.
• Breiman L., Friedman J. H., Olshen R. A. & Stone C. J. (1984). Classification and regression trees. Wadsworth.
• Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37-46. https://doi.org/10.1177/001316446002000104.
• Domingos, P., & Hulten, G. (2000). Mining high-speed data streams. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 71-80. https://doi.org/10.1145/347090.347107
• Duggal, P., & Shukla, S. (2020). Prediction of thyroid disorders using advanced machine learning techniques. In 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 670-675). IEEE. https://doi.org/0.1109/Confluence47617.2020.9058102
• Erdem, S., & Özdağoğlu, G. (2008). Analyzing of emergency data of a training and research hospital in aegean region using data mining. Anadolu University Journal of Science and Technology, 9(2), 261-270.
• Fan, W., Wang, H., Yu, P. S., & Ma, S. (2003). Is random model better? on its accuracy and efficiency. In Third IEEE International Conference on Data Mining (pp. 51-58). IEEE. https://doi.org/10.1109/ICDM.2003.1250902
• Filizöz, B., & Orhan, U. (2018). Industry 4.0 in the context of human resources management: A literature study. Cumhuriyet University Journal of Economics and Administrative Sciences, 19(2), 110-117.
• Ghosh, A., Guha, T., & Bhar, R. B. (2015). Identification of handloom and powerloom fabrics using proximal support vector machines. Indian Journal of Fibre & Textile Research, 40(1), 87-93. https:// doi.org/10.56042/ijftr.v40i1.3809
• Han J., & Kamber M. (2006). Data mining: Concepts and techniques. Morgan Kaufmann, Elsevier Science.
• Kalıkov, A. (2006). Veri madenciliği ve bir e-ticaret uygulaması [Yüksek Lisans Tezi]. Gazi Üniversitesi.
• Karaboğa, T., & Karaboğa, H. A. (2022). Digitalization in human resources management: A bibliometric review. Turkish Studies-Economics, Finance, Politics, 17(2), 343-364. https:// doi.org/10.7827/TurkishStudies.58236
• Landwehr, N., Hall, M., & Frank, E. (2005). Logistic model trees. Machine learning, 59, 161-205.
• Maimon, O. Z., & Rokach, L. (2005). Data mining and knowledge discovery handbook, Springer.
• Mingers, J. (1989). An empirical comparison of pruning methods for decision tree induction. Machine Learning, 4, 227-243.
• Mulyana, R., Rusu, L., & Perjons, E. (2021). It governance mechanisms influence on digital transformation: A systematic literature review. In Twenty-Seventh Americas' Conference on Information Systems (AMCIS), Digital Innovation and Entrepreneurship, Virtual Conference, August 9-13, 2021 (pp. 1-10). Association for Information Systems (AIS).
• Quinlan, J. R. (1993). Programs for machine learning, 4(5).
• Ruël, H.J.M., Bondarouk, T.V., & Van der Velde, M. (2007). The contribution of e‐HRM to HRM effectiveness. Employee Relations, 29(3), 280-291. https://doi.org/10.1108/01425450710741757
• Ruël, H.J.M., & Bondarouk, T. V. (2009). Electronic human resource management: Challenges in the digital era. The International Journal of Human Resource Management, 20(3), 505-514. https://doi.org/10.1080/09585190802707235
• Shen, Y. (2007). A formal ontology for data mining: principles, design, and evolution [Doctoral dissertation], Université du Québec à Trois-Rivières.
• Shen, Y., Xing, L., & Peng, Y. (2007). Study and application of Web-based data mining in e-business. In eighth ACIS international conference on software engineering, artificial intelligence, networking, and parallel/distributed computing, 1, 812-816. IEEE. https://doi.org/10.1109/SNPD.2007.117
• Synnestvedt, M. B., Chen, C., & Holmes, J. H. (2005). CiteSpace II: Visualization and knowledge discovery in bibliographic databases. In AMIA annual symposium proceedings (2005, p. 724). American Medical Informatics Association.
• Tan, K. C., Yu, Q., & Ang, J. H. (2006). A dual-objective evolutionary algorithm for rules extraction in data mining. Computational optimization and applications, 34, 273-294. https://doi.org/10.1007/s10589-005-3907-9
• Uğurlu, H. Ü. A., & Doğan, A. (2023). Digital transformation and digitalized recruitment function in human resources management. Kocaeli University Journal of Social Sciences, 1(45), 1-16. https://doi.org/10.35343/kosbed.1247587
• Ulrich, D., & Dulebohn, J. H. (2015). Are we there yet? What's next for HR?. Human Resource Management Review, 25(2), 188-204. https://doi.org/10.1016/j.hrmr.2015.01.004
• Van Grembergen, W., & De Haes, S. (2009). Enterprise governance of information technology: Achieving strategic alignment and value. Springer Publishing Company, Incorporated.
• Vial, G. (2021). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118-144. https://doi.org/10.1016/j.jsis.2019.01.003
• Witten, I. H., & Frank, E. (2002). Data mining: Practical machine learning tools and techniques with Java implementations. Acm Sigmod Record, 31(1), 76-77. https://doi.org/10.1145/507338.507355
• Yıldırım, P., Birant, D., & Alpyildiz, T. (2018). Data mining and machine learning in textile industry. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(1), e1228. https://doi.org/10.1002/widm.1228
• Yılmaz, C., & Yılmaz, T. (2023). The effect of industry 4.0 on human resources management: HRM 4.0. Hak İş International Journal of Labor and Society, 12(32), 11-28. https://doi.org/10.31199/hakisderg.1214130
• Zhao, Y., & Zhang, Y. (2008). Comparison of decision tree methods for finding active objects. Advances in Space Research, 41(12), 1955-1959. https://doi.org/10.1016/j.asr.2007.07.020

Toplam 35 adet kaynakça vardır.

Ayrıntılar

Kaynak Göster

Download or read online: Click here