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A COMPARATIVE STUDY OF USING THE METHODS OF MULTIPLE LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORKS IN ORGANIZATIONAL CORRELATIONS FOR THE FIELDS OF MANAGEMENT AND ORGANIZATION

Year 2012, Volume: 8 Issue: 17, 1 - 25, 01.01.2012
https://doi.org/10.11122/ijmeb.2012.8.17.343

Abstract

The purpose of this study is to compare the performances of the Multiple Linear Regression MLR and Artificial Neural Networks ANN used in correlations for the fields of management and organization, and to demonstrate that the ANN method can be used in organizational studies. Therefore, first, comprehensive information is provided about the ANN method and its use in literature and comparative studies. Work–family conflict, affective commitment, and turnover intention have been used as the variable group to compare the performance of the ANN with that of the MLR. Validity and reliability tests have been carried out, and the correlation coefficient R correlation coefficient and RMSE Root Mean Square Error have been considered as the criteria for performance. Within the scope of the findings, the ANN method is found to demonstrate higher R and lower RMSE values, when compared with the MLR method, although the effect coefficients obtained through both methods regarding correlations are noted to be similar

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YÖNETİM VE ORGANİZASYON ALANINDA ÖRGÜTSEL DEĞİŞKENLER ARASI İLİŞKİLERDE REGRESYON VE YAPAY SİNİR AĞLARI YÖNTEMLERİNİN KARŞILAŞTIRMASI

Year 2012, Volume: 8 Issue: 17, 1 - 25, 01.01.2012
https://doi.org/10.11122/ijmeb.2012.8.17.343

Abstract

Bu araştırmanın amacı, Yönetim ve Organizasyon alanında değişkenler arası ilişkilerde kullanılan Çoklu Doğrusal Regresyon analizi MLR ile Yapay Sinir Ağları ANN yönteminin performanslarını karşılaştırarak ANN yönteminin örgütsel araştırmalarda uygulanabilirliğini ortaya koyabilmektir. Araştırmanın örneklemi kamu ve özel sektör kuruluşlarından tesadüfi örnekleme yoluyla seçilen 392 çalışandan oluşmaktadır. Araştırmada geçerlilik ve güvenilirlik testleri yapılmış, performans ölçütü olarak R2 correlation coefficient ve RMSE root mean square error dikkate alınmıştır. Elde edilen bulgular çerçevesinde değişkenler arası ilişkilere yönelik etki katsayıları birbirine benzemekle birlikte, ANN yönteminin MLR yöntemine göre daha yüksek R2 ve daha düşük RMSE değerleri ortaya koyduğu tespit edilmiştir

References

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  • Arupjyoti, S., & Iragavarapu, S. (1998). New electrotopological descriptor for prediction of boiling points of alkanes and aliphatic alcohols through artificial neural network and multiple linear regression analysis. Computers & Chemistry, 22(6), 515-522.
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  • Boone, D. S., & Roehm, M. L. (2002). Retail segmentation using artificial neural networks. International Journal of Research in Marketing, 19 (3), 287-301.
  • Brashear, T., Manolis, C., & Brooks, C. M. (2005). The effects of control, trust, and justice on salesperson turnover. Journal of Business Research, 58, 241-249.
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  • Casper, J. W., Harris, C., Taylor-Bianco, A., & Wayne, J. (2011). Work–family conflict, perceived supervisor support and organizational commitment among Brazilian professionals, Journal of Vocational Behavior, 1, 13.
  • Chelgani, C. S., Hower, J. C., & Hart, B. (2011). Estimation of free-swelling index based on coal analysis using multivariable regression and artificial neural network, Fuel Processing Technology, 92, 349–355.
  • Chiocchio, F., & Frigon, J. Y. (2006). Tenure, satisfaction and work environment flexibility of people with mental retardation: Journal of Vocational Behavior, 68, 175-187.
  • Chokmani, K., Ouarda, T., Hamilton, S., & Ghedira, M. H. (2008). Comparison of ice-affected stream flow estimates computed using artificial neural networks and multiple regression techniques. Journal of Hydrology, 349, 383– 396.
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  • Dvir, D., David, A. B., Sadeh, A., & Shenhar, A.J. (2006). Critical managerial factors affecting defense projects success: A comparison between neural network and regression analysis. Engineering Applications of Artificial Intelligence, 19 535–543.
  • Eby, L., Casper, W., Lockwood, A., Bordeaux, C., & Brinley, A. (2005). Work and family research in IO/ OB: content analysis and review of the literature. Journal of Vocational Behavior, 66, 124-197.
  • Elmas, Ç. (2003). Yapay sinir ağları (artificial neural networks; theory, architecture, education, application). Ankara: Seçkin Publication.
  • Ernst, C. (1994). A relational expert system for nursing management control. Human Systems Management, 4, 286-293.
  • Ennett, C. M., Frize, M., & Walker, C. R. (2001). Influence of missing values on artificial neural network performance. Medinfo, 10, 449-453.
  • Fausett, L. (1994). Fundamentals of neural networks: Architectures, algorithms and applications. New Jersey: Prentice-Hall, Inc.
  • Frone, M. R., Russell, M., & Cooper, M. L. (1997). Relation of work-family conflict to health outcomes: A four-year longitudinal study of employed parents. Journal of Occupational and Organizational Psychology, 70, 325-335.
  • Gevrey, M., Dimopoulos, I., & Lek, S., (2003). Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modeling, 160, 249-264.
  • Good, L. K., Sisler, G. F., & Gentry, J. W. (1988). Antecedents of turnover intentions among retail management. Journal of Retailing, 64(3), 295-314.
  • Goul, M., Shane, B., & Tonge, F. M., (1986). Using a knowledge based decision support system in strategic planning decisions: an empirical study. Journal of Management Information Systems, 2(4), 70-84.
  • Greenhaus, J., & Powell, G. (2003). When work and family collide: deciding between competing role demands. Organizational Behavior and the Human Decision Processes, 90(2), 291-303.
  • Günaydın, K. (2008). The estimation of monthly mean significant wave heights by using artificial neural network and regression methods. Ocean Engineering 35, 1406–1415.
  • Haar, J. M. (2004). Work-family conflict and turnover intention: exploring the moderation effects of perceived work-family support. New Zealand Journal of Psychology, 33(1), 35-39.
  • Hamzaçebi, C. (2011). Yapay sinir ağı (Artificial neural network). Bursa: Ekin Kitabevi.
  • Haykin, S. (1999). Neural networks, Second Edition. New Jersey: Prentice Hall.
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Details

Primary Language English
Journal Section Research Article
Authors

Kürşad Zorlu This is me

Publication Date January 1, 2012
Published in Issue Year 2012 Volume: 8 Issue: 17

Cite

APA Zorlu, K. (2012). A COMPARATIVE STUDY OF USING THE METHODS OF MULTIPLE LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORKS IN ORGANIZATIONAL CORRELATIONS FOR THE FIELDS OF MANAGEMENT AND ORGANIZATION. Uluslararası Yönetim İktisat Ve İşletme Dergisi, 8(17), 1-25. https://doi.org/10.11122/ijmeb.2012.8.17.343

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