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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

Yıl 2012, Cilt: 8 Sayı: 17, 1 - 25, 01.01.2012
https://doi.org/10.11122/ijmeb.2012.8.17.343

Öz

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

Kaynakça

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  • Benardos, P. G., & Vosniakos, G. C. (2007). Optimizing feed-forward artificial neural network architecture. Engineering Applications of Artificial Intelligence, 20, 365–382.
  • Benders, J., & Manders, F. (1993). Expert systems and organizational decision-making. Information and Management, 25, 207-213.
  • Blomme, R. J., Rheede, A., & Tromp, D. M. (2010). The use of the psychological contract to explain turnover intentions in the hospitality industry: a research study on the impact of gender in the turnover intentions of highly educated employees. International Journal of Human Resource Management, 21,144- 162.
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  • 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.
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  • 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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  • Collins, J. M., & Clark, M. R. (1993). An application of the theory of neural computation to the prediction of workplace behavior: an illustration and assessment of network analysis. Personnel Psychology, 46(3), 503-522.
  • Costigan, R. D., Insinga, R. C., Berman, J., Kranas, G., & Kureshov, V. (2011). Revisiting the relationship of supervisor trust and CEO trust to turnover intentions: A three-country comparative study. Journal of World Business, 46, 74–83.
  • Culpepper, R. A. (2011). Three-component commitment and turnover: An examination of temporal aspects. Journal of Vocational Behavior, 79, 517–527.
  • 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.
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YÖNETİM VE ORGANİZASYON ALANINDA ÖRGÜTSEL DEĞİŞKENLER ARASI İLİŞKİLERDE REGRESYON VE YAPAY SİNİR AĞLARI YÖNTEMLERİNİN KARŞILAŞTIRMASI

Yıl 2012, Cilt: 8 Sayı: 17, 1 - 25, 01.01.2012
https://doi.org/10.11122/ijmeb.2012.8.17.343

Öz

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

Kaynakça

  • Ahn, B. S., Cho, S. S., & Kim, C. Y. (2000). The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications, 18, 65–74.
  • Allen, N. J., & Meyer, J. P. (1996). Affective, continuance and normative commitment to the organization: An examination of construct validity. Journal of Vocational Behavior, 49, 252-276.
  • Allen, T. D., Herst, D. E., Bruck, C. S., & Sutton, M. (2000). Consequences associated with work-to family conflict: A review and agenda for future research. Journal of Occupational Health Psychology, 5, 278-308.
  • Anderson, S. E., Coffey, B. S., & Byerly, R. T. (2002). Formal organizational initiatives and informal workplace practices: Links to work-family conflict and job- related outcomes. Journal of Management, 28(6), 787-810.
  • 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.
  • Asiltürk, İ., & Cunkaş, M. (2011). Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Systems with Applications, 38, 5826–5832.
  • Audrain, A. F. (2002). The attribute-satisfaction link over time: A study on panel data. Proceedings of the 31st EMAC Conference, 28-31 May 2002, University of Minho European Marketing Academy (EMAC), Braga, Portugal.
  • Bansal, A., Kauffman, R. J., & Weitz, R. R. (1993). Comparing the modeling performance of regression and neural networks as data quality varies: a business value approach. Journal of Management Information Systems, 10(1), 11-32.
  • Bode, J. (1998). Decision support with neural networks in the management of research and development: Concepts and applications to cost estimation. Information & Management, 34 (1), 33–40.
  • Benardos, P. G., & Vosniakos, G. C. (2007). Optimizing feed-forward artificial neural network architecture. Engineering Applications of Artificial Intelligence, 20, 365–382.
  • Benders, J., & Manders, F. (1993). Expert systems and organizational decision-making. Information and Management, 25, 207-213.
  • Blomme, R. J., Rheede, A., & Tromp, D. M. (2010). The use of the psychological contract to explain turnover intentions in the hospitality industry: a research study on the impact of gender in the turnover intentions of highly educated employees. International Journal of Human Resource Management, 21,144- 162.
  • 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.
  • Brey, T., Teichmann, A., & Borlich, O., (1996). Artificial neural network versus multiple linear regression: predicting P/B ratios from empirical data. Marine Ecology Progress Series, 140, 251–256.
  • Byron, D. (2005). A meta-analytic review of work-family conflict and its antecedents. Journal of Vocational Behavior, 67, 169-198.
  • 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.
  • Co, H. C., & Boosarawongse R. (2007). Forecasting Thailand’s rice export: Statistical techniques vs. artificial neural networks. Computers and Industrial Engineering, 53, 610-627.
  • Collins, J. M., & Clark, M. R. (1993). An application of the theory of neural computation to the prediction of workplace behavior: an illustration and assessment of network analysis. Personnel Psychology, 46(3), 503-522.
  • Costigan, R. D., Insinga, R. C., Berman, J., Kranas, G., & Kureshov, V. (2011). Revisiting the relationship of supervisor trust and CEO trust to turnover intentions: A three-country comparative study. Journal of World Business, 46, 74–83.
  • Culpepper, R. A. (2011). Three-component commitment and turnover: An examination of temporal aspects. Journal of Vocational Behavior, 79, 517–527.
  • 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.
  • Haykin, S. (2001). Kalman filtering and neural networks. Toronto: John Wıley & Sons, Inc.
  • Heiat, A. (2002). Comparison of artificial neural network and regression models for estimating software development effort. Information and Software Technology. 44, 911–922.
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Toplam 104 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Research Article
Yazarlar

Kürşad Zorlu Bu kişi benim

Yayımlanma Tarihi 1 Ocak 2012
Yayımlandığı Sayı Yıl 2012 Cilt: 8 Sayı: 17

Kaynak Göster

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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