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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, , 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

  • 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.
  • Heuvelmans, G., Muys, B., & Feyen, J. (2006). Regionalization of the parameters of a hydrological model: comparison of linear regression models with artificial neural nets. Journal of Hydrology, 319, 245–265.
  • Hsu, C., Lin, J., & Chao, C. K. (2011). Comparison of multiple linear regression and artificial neural network in developing the objective functions of the orthopedic screws. Computer Methods and Programs in Biomedicine, 104, 341–348.
  • Jain, B. A., & Nag, B. N. (1997). Performance evaluation of neural network decision models. Journal of Management Information Systems, 14 (2), 201–216.
  • Jiaoi, L., & Li, H. (2010). QSPR studies on the aqueous solubility of PCDD/Fs by using artificial neural network combined with stepwise regression. Chemometrics and Intelligent Laboratory Systems, 103, 90–95.
  • Judge, T. J., & Colquitt, J. A. (2004). Organizational justice and stress: The mediating role of work-family conflict. Journal of Applied Psychology, 89, 395- 404.
  • Khashei, M., Hamadani, A .Z., & Bijari, M. (2012). A novel hybrid classification model of artificial neural networks and multiple linear regression models. Expert Systems with Applications, 39, 2606–2620.
  • Karatepe, O. M., & Sokmen, A. (2006). The effects of work role and family role variables on psychological and behavioral outcomes of frontline employees. Tourısm Management, 27(2), 255-268.
  • Karatepe, O., & Uludağ, O. (2008). Affectivity, conflicts in the work–family interface, and hotel employee outcomes. International Journal of Hospitality Management, 27, 30-41.
  • Kim, Y. S., Street, N., Russell, G., & Menczer, F. (2005). Customer targeting: a neural network approach guided by genetic algorithms. Management Science, 51(2), 264-276.
  • Kim, Y. S. (2008). Comparison of the decision tree, artificial neural network, and linear regression methods based on the number and types of independent variables and sample size. Expert Systems with Applications, 34, 1227–1234.
  • Kiranyaz, S., Ince, T., Yildirim, A., & Gabbouj, M. (2009). Evolutionary artificial neural networks by multi-dimensional particle swarm optimization. Neural Networks, 22(10), 1448-1462.
  • Kose, E. (2008). Modeling of colour perception of different age groups using artificial neural Networks. Expert Systems with Applications, 34, 2129-2139.
  • Kuo, R. J., Wu, P., & Wang, C. P. (2002). An intelligent sales forecasting system through integration of artificial neural Networks and fuzzy neural Networks with fuzzy weight elimination. Neural Networks, 15, 909-925.
  • Kossek, E., & Ozeki, C. (1999). Bridging the work-family policy and productivity gap: a literature review. Community, Work & Family, 2(1), 7-30.
  • Kuzmanovski, I., & Aleksovska, S. (2003). Optimization of artificial neural networks for prediction of the unit cell parameters in orthorhombic perovskites: Comparison with multiple linear regression. Chemometrics and Intelligent Laboratory Systems, 67, 167–174.
  • Lane, V. R., & Scott, S. G. (2007). The neural network model of organizational identification. Organizational Behavior and Human Decision Processes, 104(2), 175-192.
  • Lambert, E. G., Hogan, N. L., Jiang, S., Elechi, O., Benjamin, B., Morris, A., Laux, J., & Dupuy, P. (2010). The relationship among distributive and procedural justice and correctional life satisfaction, burnout, and turnover intent: An exploratory study. Journal of Criminal Justice, 38, 7-16.
  • Li, Y. E. (1994). Artificial neural networks and their business applications, Information & Management, 27, 303-313.
  • Lisboa, P. J. G., & Taktak, A. F. G. (2006). The use of artificial neural networks in decision support in cancer: a systematic review. Neural Networks, 19, 408-415.
  • Liu, X., Kang, S. & Li, F. (2009). Simulation of artificial neural network model for trunk sap flow of pyrus pyrifolia and its comparison with multiple-linear regression. Agricultural Water Management, 96, 939–945.
  • Livingstone, D. J., Manallack, D. T., & Tetko, I. V. (1997). Data modeling with neural networks: advantages and limitations, Journal of Computer-Aided Molecular Design, 11, 135-142.
  • Lyness, K. S., & Thompson, D. E. (1997). Above the glass ceiling? A comparison of matched samples of female and male executives. Journal of Applied Psychology, 82, 359-375.
  • Mata, J. (2011). Interpretation of concrete dam behavior with artificial neural network and multiple linear regression models. Engineering Structures, 33, 903–910.
  • McComb, S. A., Green, S. G., & Dale Compton, W. (2007). Team flexibility’s relationship to staffing and performance in complex projects: An empirical analysis. Journal of Engineering and Technology Management, 24(4), 293– 313.
  • Meyer, J. P., Srinivas, E. S., Lal, J. B., & Topolnytsky, L. (2007). Employee commitment and support for an organizational change: Test of the three component model in two cultures. Journal of Occupational and Organizational Psychology, 80 185-211.
  • Montagno, R., Sexton, R. S., & Smith, B. N. (2002). Using neural Networks for identifying organizational improvement strategies. European Journal of Operational Research, 142(2), 382–395.
  • Nabiyev, V. V. (2010). Yapay zeka (Artificial intelligence). Ankara: Seçkin Yayıncılık.
  • Namasivayam, K., & Zhao, X. (2007). An investigation of the moderating effects o organizational commitment on the relationships between work–family conflict and job satisfaction among hospitality employees in India. Tourism Management, 28, 1212-1223.
  • Netemeyer, R., Boles, J. & McMurrian, R. (1996). Development and validation of work family conflict and family-work conflict scales, Journal of Applied Psychology, 81(4), 400-410.
  • Northcraft, G. B. & Neale, M. A. (1990). Organizational behavior, Chicago: The Dryden Press.
  • Öztemel, E. (2003), Artificial neural network, İstanbul: Papatya Yayıncılık.
  • Paruelo, J. M. & Tomasel, F. (1997). Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and regression models. Ecological Modeling, 98, 173-186.
  • Pao, H. T. (2008). A comparison of neural network and multiple regression analysis in modeling capital structure. Expert Systems with Applications, 35, 720–727.
  • Pasewark, W. R. & Viator, R. E. (2006). Sources of work-family conflict in the accounting profession. Behavioral Research in Accounting, 18, 147-65.
  • Proctor, R. A. (1991). An expert system to aid in staff selection: a neural network approach. International Journal of Manpower, 12(8), 18-21.
  • Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after all: a cross discipline view of trust. Academy of Management Review, 23, 393- 404.
  • Scotter, J. V. (2000). Relationships of task performance and contextual performance with turnover, job satisfaction, and affective commitment. Human Resource Management Review, 10(1), 79-95.
  • Shaffer, M. A., Harrison, D. A., Gilley, K. M., & Luk, D. M. (2001). Struggling for balance amid turbulence on international assignments: work-family conflict, support and commitment. Journal of Management, 27, 99-121.
  • Simpson, P. K. (1990). Artificial neural systems: Foundations, paradigms, applications, and implementations. New York: Pergamon Press.
  • Somers, M. (1995). Organizational commitment, turnover, and absenteeism: An examination of direct and indirect effects. Journal of Organizational Behavior, 16, 49-58.
  • Sousa, S., Martins, F., Alvim-Ferraz, M. & Pereira, M., (2007). Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environmental Modeling and Software, 22(1), 97-103.
  • Streich, M., Casper, W. J., & Salvaggio, A. N. (2008). Examining couples' agreement about work-family conflict. Journal of Managerial Psychology, 23, 252-272.
  • Subramanian, N., Yajnik, A., & Murthy, R. S. R. (2004). Artificial neural network as
  • an alternative to multiple regression analysis in optimizing formulation parameters
  • (http://www.aapspharmscitech.org). liposomes. of cytarabine AAPS PharmSciTech, 5(1)
  • Trachtenberg, J. V., Anderson, S. A., & Sabatelli, R. M. (2009). Work-home conflict and domestic violence: A test of a conceptual model. Journal of Family Violence, 24, 471-483.
  • Tu, J. V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes, Journal of Clinical Epidemiology, 49, 1225-1231.
  • Tung, K., Huang, I., Chen, S. L., & Shih, C. (2005). Mining the generation xers’ job attitudes by artificial neural network and decision tree-empirical evidence in Taiwan. Expert Systems with Applications, 29, 783–794.
  • Uysal, M. & Roubi, S. E. (1999). Artificial neural networks versus multiple regression in tourism demand analysis. Journal of Travel Research, 38, 111-118.
  • Wang, Y. & Elhag, T. (2007). A comparison of neural network, evidential reasoning and multiple regression analysis in modeling bridge risks. Expert Systems with Applications, 32, 336–348.
  • Wasserman, P. (1993). Advanced methods in neural networks. New York, NY: VanNostrand Reinhold.
  • Vandenberghe, C., Bentein, K., & Stinglhamber, F. (2004). Affective commitment to the organization, supervisor, and work group: Antecedents and outcomes. Journal of Vocational Behavior, 64, 47–71.
  • Vellido, A., Lisboa, P. J. G. & Vaughan, J. (1999). Neural networks in business: a survey of applications (1992–1998). Expert Systems with Applications, 17, 51– 70.
  • Wasti, A. S. (2002). Affective and continuance commitment to the organization: test of an integrated model in the Turkish context. International Journal of Intercultural Relations, 26, 525–550.
  • Wong, B. K., Bodnovich, T. A., Selvi, Y. (1997). Neural network applications in business: A review and analysis of the literature (1988-95). Decision Support Systems, 19, 301-320.
  • Wong, B. K., Lai, V. S., & Lam, J. (2000). A bibliography of neural network business applications research: (1994-1998). Computers & Operations Research, 27, 1045-1076.
  • Wong, T. C., Wong, S. Y., & Chin, K. S. (2011). A neural network-based approach of quantifying relative importance among various determinants toward organizational innovation. Expert Systems with Applications, 38, 13064-13072.
  • Yegnanarayana, B. (2006). Artificial neural networks. New Delhi: Prentice-Hall of India.
  • Zurada, J. M. (1992). Introduction to artificial neural networks. West Publishing Company.
  • Zurada, J., Karwowski, W., & Marras, W. S. (1997). A neural network-based system for classification of industrial jobs with respect to risk of low back disorders due to workplace design. Applied Ergonomics, 28(1), 49-58.
  • Zorlu, K. (2011). A research on organizational cultural factors determining the level of development of the innovative studies in universities and on the instructors in Ahi Evran University. Uluslararası Yüksek Öğretim Kongresi, YÖK, 27-29 Mayıs, İstanbul.
  • Zorlu, K. (2011). Effect of strategic learning system and organization structure on e- government performance: A survey in public sector by means of articial neural network. 8th International Conference on Intellectual Capital, Knowledge Management & Organizational Learning, Taiwan.
  • Zorlu, K. (2012). The perception of self-esteem and self-efficacy as transforming factors in the sources of role stress and job satisfaction relationship of employees: A trial of a staged model based on the artificial neural network method. African Journal of Business Management, 6(8), 3014-302.

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

Yıl 2012, , 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.
  • Heuvelmans, G., Muys, B., & Feyen, J. (2006). Regionalization of the parameters of a hydrological model: comparison of linear regression models with artificial neural nets. Journal of Hydrology, 319, 245–265.
  • Hsu, C., Lin, J., & Chao, C. K. (2011). Comparison of multiple linear regression and artificial neural network in developing the objective functions of the orthopedic screws. Computer Methods and Programs in Biomedicine, 104, 341–348.
  • Jain, B. A., & Nag, B. N. (1997). Performance evaluation of neural network decision models. Journal of Management Information Systems, 14 (2), 201–216.
  • Jiaoi, L., & Li, H. (2010). QSPR studies on the aqueous solubility of PCDD/Fs by using artificial neural network combined with stepwise regression. Chemometrics and Intelligent Laboratory Systems, 103, 90–95.
  • Judge, T. J., & Colquitt, J. A. (2004). Organizational justice and stress: The mediating role of work-family conflict. Journal of Applied Psychology, 89, 395- 404.
  • Khashei, M., Hamadani, A .Z., & Bijari, M. (2012). A novel hybrid classification model of artificial neural networks and multiple linear regression models. Expert Systems with Applications, 39, 2606–2620.
  • Karatepe, O. M., & Sokmen, A. (2006). The effects of work role and family role variables on psychological and behavioral outcomes of frontline employees. Tourısm Management, 27(2), 255-268.
  • Karatepe, O., & Uludağ, O. (2008). Affectivity, conflicts in the work–family interface, and hotel employee outcomes. International Journal of Hospitality Management, 27, 30-41.
  • Kim, Y. S., Street, N., Russell, G., & Menczer, F. (2005). Customer targeting: a neural network approach guided by genetic algorithms. Management Science, 51(2), 264-276.
  • Kim, Y. S. (2008). Comparison of the decision tree, artificial neural network, and linear regression methods based on the number and types of independent variables and sample size. Expert Systems with Applications, 34, 1227–1234.
  • Kiranyaz, S., Ince, T., Yildirim, A., & Gabbouj, M. (2009). Evolutionary artificial neural networks by multi-dimensional particle swarm optimization. Neural Networks, 22(10), 1448-1462.
  • Kose, E. (2008). Modeling of colour perception of different age groups using artificial neural Networks. Expert Systems with Applications, 34, 2129-2139.
  • Kuo, R. J., Wu, P., & Wang, C. P. (2002). An intelligent sales forecasting system through integration of artificial neural Networks and fuzzy neural Networks with fuzzy weight elimination. Neural Networks, 15, 909-925.
  • Kossek, E., & Ozeki, C. (1999). Bridging the work-family policy and productivity gap: a literature review. Community, Work & Family, 2(1), 7-30.
  • Kuzmanovski, I., & Aleksovska, S. (2003). Optimization of artificial neural networks for prediction of the unit cell parameters in orthorhombic perovskites: Comparison with multiple linear regression. Chemometrics and Intelligent Laboratory Systems, 67, 167–174.
  • Lane, V. R., & Scott, S. G. (2007). The neural network model of organizational identification. Organizational Behavior and Human Decision Processes, 104(2), 175-192.
  • Lambert, E. G., Hogan, N. L., Jiang, S., Elechi, O., Benjamin, B., Morris, A., Laux, J., & Dupuy, P. (2010). The relationship among distributive and procedural justice and correctional life satisfaction, burnout, and turnover intent: An exploratory study. Journal of Criminal Justice, 38, 7-16.
  • Li, Y. E. (1994). Artificial neural networks and their business applications, Information & Management, 27, 303-313.
  • Lisboa, P. J. G., & Taktak, A. F. G. (2006). The use of artificial neural networks in decision support in cancer: a systematic review. Neural Networks, 19, 408-415.
  • Liu, X., Kang, S. & Li, F. (2009). Simulation of artificial neural network model for trunk sap flow of pyrus pyrifolia and its comparison with multiple-linear regression. Agricultural Water Management, 96, 939–945.
  • Livingstone, D. J., Manallack, D. T., & Tetko, I. V. (1997). Data modeling with neural networks: advantages and limitations, Journal of Computer-Aided Molecular Design, 11, 135-142.
  • Lyness, K. S., & Thompson, D. E. (1997). Above the glass ceiling? A comparison of matched samples of female and male executives. Journal of Applied Psychology, 82, 359-375.
  • Mata, J. (2011). Interpretation of concrete dam behavior with artificial neural network and multiple linear regression models. Engineering Structures, 33, 903–910.
  • McComb, S. A., Green, S. G., & Dale Compton, W. (2007). Team flexibility’s relationship to staffing and performance in complex projects: An empirical analysis. Journal of Engineering and Technology Management, 24(4), 293– 313.
  • Meyer, J. P., Srinivas, E. S., Lal, J. B., & Topolnytsky, L. (2007). Employee commitment and support for an organizational change: Test of the three component model in two cultures. Journal of Occupational and Organizational Psychology, 80 185-211.
  • Montagno, R., Sexton, R. S., & Smith, B. N. (2002). Using neural Networks for identifying organizational improvement strategies. European Journal of Operational Research, 142(2), 382–395.
  • Nabiyev, V. V. (2010). Yapay zeka (Artificial intelligence). Ankara: Seçkin Yayıncılık.
  • Namasivayam, K., & Zhao, X. (2007). An investigation of the moderating effects o organizational commitment on the relationships between work–family conflict and job satisfaction among hospitality employees in India. Tourism Management, 28, 1212-1223.
  • Netemeyer, R., Boles, J. & McMurrian, R. (1996). Development and validation of work family conflict and family-work conflict scales, Journal of Applied Psychology, 81(4), 400-410.
  • Northcraft, G. B. & Neale, M. A. (1990). Organizational behavior, Chicago: The Dryden Press.
  • Öztemel, E. (2003), Artificial neural network, İstanbul: Papatya Yayıncılık.
  • Paruelo, J. M. & Tomasel, F. (1997). Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and regression models. Ecological Modeling, 98, 173-186.
  • Pao, H. T. (2008). A comparison of neural network and multiple regression analysis in modeling capital structure. Expert Systems with Applications, 35, 720–727.
  • Pasewark, W. R. & Viator, R. E. (2006). Sources of work-family conflict in the accounting profession. Behavioral Research in Accounting, 18, 147-65.
  • Proctor, R. A. (1991). An expert system to aid in staff selection: a neural network approach. International Journal of Manpower, 12(8), 18-21.
  • Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after all: a cross discipline view of trust. Academy of Management Review, 23, 393- 404.
  • Scotter, J. V. (2000). Relationships of task performance and contextual performance with turnover, job satisfaction, and affective commitment. Human Resource Management Review, 10(1), 79-95.
  • Shaffer, M. A., Harrison, D. A., Gilley, K. M., & Luk, D. M. (2001). Struggling for balance amid turbulence on international assignments: work-family conflict, support and commitment. Journal of Management, 27, 99-121.
  • Simpson, P. K. (1990). Artificial neural systems: Foundations, paradigms, applications, and implementations. New York: Pergamon Press.
  • Somers, M. (1995). Organizational commitment, turnover, and absenteeism: An examination of direct and indirect effects. Journal of Organizational Behavior, 16, 49-58.
  • Sousa, S., Martins, F., Alvim-Ferraz, M. & Pereira, M., (2007). Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environmental Modeling and Software, 22(1), 97-103.
  • Streich, M., Casper, W. J., & Salvaggio, A. N. (2008). Examining couples' agreement about work-family conflict. Journal of Managerial Psychology, 23, 252-272.
  • Subramanian, N., Yajnik, A., & Murthy, R. S. R. (2004). Artificial neural network as
  • an alternative to multiple regression analysis in optimizing formulation parameters
  • (http://www.aapspharmscitech.org). liposomes. of cytarabine AAPS PharmSciTech, 5(1)
  • Trachtenberg, J. V., Anderson, S. A., & Sabatelli, R. M. (2009). Work-home conflict and domestic violence: A test of a conceptual model. Journal of Family Violence, 24, 471-483.
  • Tu, J. V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes, Journal of Clinical Epidemiology, 49, 1225-1231.
  • Tung, K., Huang, I., Chen, S. L., & Shih, C. (2005). Mining the generation xers’ job attitudes by artificial neural network and decision tree-empirical evidence in Taiwan. Expert Systems with Applications, 29, 783–794.
  • Uysal, M. & Roubi, S. E. (1999). Artificial neural networks versus multiple regression in tourism demand analysis. Journal of Travel Research, 38, 111-118.
  • Wang, Y. & Elhag, T. (2007). A comparison of neural network, evidential reasoning and multiple regression analysis in modeling bridge risks. Expert Systems with Applications, 32, 336–348.
  • Wasserman, P. (1993). Advanced methods in neural networks. New York, NY: VanNostrand Reinhold.
  • Vandenberghe, C., Bentein, K., & Stinglhamber, F. (2004). Affective commitment to the organization, supervisor, and work group: Antecedents and outcomes. Journal of Vocational Behavior, 64, 47–71.
  • Vellido, A., Lisboa, P. J. G. & Vaughan, J. (1999). Neural networks in business: a survey of applications (1992–1998). Expert Systems with Applications, 17, 51– 70.
  • Wasti, A. S. (2002). Affective and continuance commitment to the organization: test of an integrated model in the Turkish context. International Journal of Intercultural Relations, 26, 525–550.
  • Wong, B. K., Bodnovich, T. A., Selvi, Y. (1997). Neural network applications in business: A review and analysis of the literature (1988-95). Decision Support Systems, 19, 301-320.
  • Wong, B. K., Lai, V. S., & Lam, J. (2000). A bibliography of neural network business applications research: (1994-1998). Computers & Operations Research, 27, 1045-1076.
  • Wong, T. C., Wong, S. Y., & Chin, K. S. (2011). A neural network-based approach of quantifying relative importance among various determinants toward organizational innovation. Expert Systems with Applications, 38, 13064-13072.
  • Yegnanarayana, B. (2006). Artificial neural networks. New Delhi: Prentice-Hall of India.
  • Zurada, J. M. (1992). Introduction to artificial neural networks. West Publishing Company.
  • Zurada, J., Karwowski, W., & Marras, W. S. (1997). A neural network-based system for classification of industrial jobs with respect to risk of low back disorders due to workplace design. Applied Ergonomics, 28(1), 49-58.
  • Zorlu, K. (2011). A research on organizational cultural factors determining the level of development of the innovative studies in universities and on the instructors in Ahi Evran University. Uluslararası Yüksek Öğretim Kongresi, YÖK, 27-29 Mayıs, İstanbul.
  • Zorlu, K. (2011). Effect of strategic learning system and organization structure on e- government performance: A survey in public sector by means of articial neural network. 8th International Conference on Intellectual Capital, Knowledge Management & Organizational Learning, Taiwan.
  • Zorlu, K. (2012). The perception of self-esteem and self-efficacy as transforming factors in the sources of role stress and job satisfaction relationship of employees: A trial of a staged model based on the artificial neural network method. African Journal of Business Management, 6(8), 3014-302.
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

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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