ÇALIŞANLARIN ÖRGÜTSEL BAĞLILIKLARININ CART KARAR AĞACI ALGORİTMASI İLE BELİRLENMESİ

Yıl 2020, Cilt: 6 Sayı: 2, 66 - 87, 22.12.2020

Hakan Koçak

https://doi.org/10.29131/uiibd.831032

Cited By: 1

Öz

Örgütsel bağlılık örgütler için önemli bir kavramdır, zira yapılan araştırmalar bağlılık sahibi çalışanların örgütün amaç ve hedeflerini benimseyerek örgütün bu amaç ve hedeflere ulaşması için daha fazla ve isteyerek çaba sarf ettiklerini ve örgütlerinde kalmak için daha istekli olduklarını ortaya koymaktadır. Bu çalışmada veri madenciliği teknikleri kullanılarak örgütsel bağlılığın tahmin edilmesi amaçlanmaktadır. Bu amaçla 2019 yılında pskilojik sözleşme ihlal algısı ve örgütsel güvenin örgütsel bağlılık üzerindeki etkisini belirlemeye yönelik olarak yapılan bir araştırmanın anket yöntemi ile elde edilen verileri üzerinde CART karar ağacı algoritması uygulanarak örgütsel bağlılık verileri üzerinde sınıflandırma yapılmıştır. Karar ağacı modelinin oluşturulması ve diğer veri analizleri için Python programlama dili ve Scikit-Learn makine öğrenmesi kütüphanesi kullanılmıştır. Araştırma sonucunda karar ağaçlarının örgütsel bağlılığın tahmin edilmesinde yüksek oranda başarı sağladığı görülmüştür. Ayrıca araştırma sonuçları örgütsel bağlılığın tahmin edilmesinde anketteki hangi ifadelerin daha önemli olduğunu ortaya koymuştur.

Anahtar Kelimeler

veri madenciliği, karar ağaçları, örgütsel bağlılık

Kaynakça

• Argyris (1960). Understanding organizational behavior. London: Tavistock Publications.
• Arnold, J. (2005). Work psychology: Understanding human behaviour in the workplace (4th ed.). London: Prentice Hall Financial Times.
• Breiman L., Friedman J.H., Olshen R.A. and Stone C.J. (1984). Classification and regression trees. Monterey, CA: Wadsworth.
• Brownell, E.O. (2000). How to Create Organizational Trust. Manage, 52(2).
• Chen, H-F. and Chen, Y. C. (2008). The impact of work redesign and psychological empowerment on organizational commitment in a changing environment: An example from Taiwan's state-owned enterprises. Public Personnel Management, 37(3): 279-302
• Chien, C. F., Chen, L. F. (2008). Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert systems with applications, 34: 280- 290.
• Cziko, G.A. (1989). Unpredictability and indeterminism in human behavior: Arguments and implications for educational research. Educational researcher, 18(3): 17-25.
• Dougherty, G., (2013). Pattern recognition and classification. New York: Springer.
• Eisenberger, R., Huntington, R. Hutchison, S. and Sowa, D. (1986). Perceived organizational support. Journal of Applied Psychology, 71: 500-507.
• Huff, L., Kelley L. (2003). Levels of organizational trust in individualist versus collectivist societies: A seven-nation study. Organization Science, 14(1): 81-90.
• Kiefer, T. ve Briner, R.B. (2006). Emotion at work. P. Jackson and M. Shams (Der.) Developments in work and organizational psychology: Implications for international business. Oxford, UK: Elsevier Ltd.
• Koçak, H. (2019). Örgütsel güven ve psikolojik sözleşme ihlal algısının örgütsel bağlılık üzerindeki aracılık rolü. Uluslararası İktisadi ve İdari Bilimler Dergisi, 5(2): 126-143.
• Lashinger, K. S. H. and Finegan, J. (2005). Using empowerment to build trust and respect in the workplace: A Strategy for addressing the nursing shortage. Nursing Economics, 23(1).
• Levinson, H., Price, C. R., Munden, K. J., Mandl, H. J. and Solley, C. M. (1962). Men, management and mental health. Cambridge: Harvard University Press.
• Lewicki, R. J. and Bunker, B. B. (1996). Developing and maintaining trust in work relationships. R. M.
• Kramer and T. R. Tyler (Der.) Trust in organizations: Frontiers of theory and research: İçinde 114-139. California: Sage Publications,
• Loh, W.Y., Shih, Y.S. (1997). Split selection methods for classification trees. Statistica Sinica, 7(4): 815-840.
• Maimon, O., Rokach, L. (2010). Data mining and knowledge discovery handbook. New York, A.B.D: Springer.
• McAllister, D. J. (1995). Affect and cognition-based trust as foundations for interpersonal cooperation in organizations. Academy of Management Journal, 38(1): 24-59.
• Meyer, J. P. and Herscovitch, L. (2001). Commitment in the workplace: Toward a general model. Human Resource Management Review, 11: 299-326.
• Miller, K. (2003). Values, attitudes and job satisfaction. S. P. Robbins, A. Odendaal & G. Rood (Der.) Organizational behavior: Global and southern African perspectives. Cape Town: Pearson Education South Africa
• Mingers J. (1989). An empirical comparison of pruning methods for decision tree induction. Machine Learning, 4: 227–243.
• Morrow, P. (1983). Concept redundancy in organizational research: The case of work commitment. The academy of management review, 8(3): 486 - 500.
• O’Reilly C. and Chatman J., (1986). Organizational commitment and psychological attachment: The Effects of compliance, identification and internalization on prosocial behavior. Journal of Applied Psychology, 71(3): 492-499.
• O’Reilly, C. A. (1989). Corporations, culture, and commitment: Motivation and social control in organizations. California Management Review, 31(4): 9-25.
• Porter, L. W., Steers, R. M., Mowday, R. T. and Boulian, P. V. (1974). Organizational commitment, job satisfaction and turnover among psychiatric technicians. Journal of Applied Psychology, 59(5): 603-609.
• Quinlan J.R, (1993). C4.5: Programs for machine learning. San Mateo, CA: Morgan Kaufmann.
• Steers, R. M. (1977). Antecedents and outcomes of organizational commitment. Administrative Science Quarterly, 22: 45-46.
• Wiener, Y., (1982). Commitment in organizations: A normative view. Academy of Management Review, 7(3): 418-428.
• Schein, E. H. (1980). Organizational psychology (3rd edition). Englewood Cliffs, NJ: Prentice-Hall.
• Sels, L., Janssens, M. and Brande, I. V. D. (2004). Assessing the nature of psychological contracts: A validation of six dimensions. Journal of Organizational Behavior, 25(4): 461-488.
• Tsang, S., Kao, B., Yip, K.Y., Ho, W.S., Lee, S.D. (2011). Decision trees for uncertain data. IEEE Transaction on Knowledge and Data Engineering, 23(1): 67-78.
• Rokach, L., Maimon, O. (2014). Data mining with decision trees theory and applications. Singapore: World Scientific Publishing Co. Pte. Ltd.
• Tangirala, S. (2020). Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm. International Journal of Advanced Computer Science and Applications, 11 (2).
• Yong, Z. (2005). Decision tree’s pruning algorithm aased on deficient data sets. In Proceedings of the Sixth International Conference on Parallel and Distributed Computing, Applications and Technologies, 5-8 Dec. 2005, Dalian, China.