BibTex RIS Kaynak Göster

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Yıl 2013, Sayı: 10, - , 20.04.2015

Öz

In this study, comparison between Artificial Neural Networks and Multi Linear Regression analyze methods by using the relation between variables is aimed. Research sample is based on eight different companies, which has at least 50 employees, 171 employers that in business in Kırşehir. In this study; Organizational Justice, Organizational Trust and sub factors related to them are used as variable groups. According to the factor analyze and Conbach Alpha parameters, it’s seen that survey is valid and trust worthy as well. For comparing these methods performances, impact parameters relative evolution, Coefficient (R2) and Root Mean Square Error (RMSE) criteria have been noted. Under the light of the results, the Artificial Neural Network method can be used as an alternative method for defining the relation between variables and considering to the Multi Linear Regression method, relatively the Artificial Neural Network method can provide more trust worthy results as well

Kaynakça

  • ARUPJYOTI, Saikia ve IRAGAVARAPU, Suryanarayana (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, (1), 515-522.
  • BENARDOS, P. G. ve VOSNIAKOS, G. C. (2007), “Optimizing feed-forward artificial neural network architecture”, Engineering Applications of Artificial Intelligence, 20 (3), 365–382.
  • BRASHEAR, G. Thomas., MANOLIS, Chris ve BROOKS, M, Charles (2005), “The effects of control, trust, and justice on salesperson turnover” Journal of Business Research, 58 (3), 241-249.
  • BREY, T., TEICHMANN, A. ve BORLICH, O., (1996), “Artificial neural network versus multiple linear regression: predicting P/B ratios from empirical da-ta”, Marine Ecology Progress Series, 140, 251–256.
  • CHELGANI,S.Chehreh, HOWER,C. James ve HART,B.(2011), “Estimation of free-swelling index based on coal analysis using multivariable regression and artificial neural network”, Fuel Processing Technology, 92 (3), 349–355.
  • CHRISTENSEN, Clayton M. (1997), The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail, Harvard Business School Press., Boston,
  • COLQUITT, A. Jason, SCOTT, A. Brent, JUDGE, A. Timothy ve SHAW, C. John (2006), “Justice and personality: Using integrative theories to derive moderators of justice effects”, Organizational Behavior and Human Decision Processes, 100 (1), 110–127.
  • DeCONINCK, B. James ve STILWEL, C. Dean (2004), “Incorporating organizational justice, role states, pay satisfaction and supervisor satisfaction in a model of turnover intentions”, Journal of Business Research, 57(3) 225-231.
  • DIRKS, T. Kurt, KIM, H. Peter, Ferrin, L. Donald ve Cooper, D. Cecilly (2011), “Understanding the effects of substantive responses on trust following a transgression”, Organizational Behavior and Human Decision Processes, 114 (2), 87-103.
  • EDMONDSON, Amy (1999), "Psychological Safety and Learning Behavior in Work Teams" Administrative Science Quarterly, 44, 350-383.
  • ELMAS, Çetin (2003), Yapay Sinir Ağları (Theory, Architecturue, Education, Application), Seckin Yayıncılık, Ankara.
  • ENKE, David ve THAWORNWONG, Suraphan (2005), “The use of data mining and neural Networks for forecasting stock market returns”, Expert Systems with Applications, 29(4), 927-940.
  • FANG, Yu-Hui. ve CIU, Chao-min. (2010), “In justice we trust: Exploring knowledge-sahring contiuance intentions in virtual communitues of practice” Computers in Human Behavior, 26(2), 235-246.
  • FOLGER, Robert ve CROPANZANO, Russell (1998), Organizational Justice And Human Resource Management, California: Thousand Oaks-Sage Publications
  • GELMAN, Andrew. ve HILL, Jennifer (2007), Data Analysis Using Regression and Multilevel/Hierarchical Models, NY: Cambridge University Press.
  • GOUL, Michael, SHANE, Barry ve TONGE, M. Fred (1986), “Using a knowledge based decision support system in strategic planning decisions: an empirical study”. Journal of Management Information Systems, 2(4), 70-84.
  • HAMZAÇEBI, Coşkun (2011), Yapay Sinir Ağı (Artificial Neural Network), Bursa: Ekin Yayınları.
  • HAYKIN, Simon (1999), Neural Networks, Second Edition, N.J: Prentice Hall,
  • HEIAT, Abbas (2002), “Comparison of artificial neural network and regression models for estimating software development effort” Information and Software Technology. 44(15) 911–922.
  • HSU, Ching-Chi, LIN, Jinn, ve CHAO, Ching-Kong, (2011), “Comparison of multiple linear regression and artificial neural network in developing the objective functions of the orthopaedic screws”, Computer methods and programs in biomedicine,104(3), 341–348.
  • JAHANDIDEH, Sepideh, JAHANDIDEH, Samad, BARZEGARI Asadabadi, E., ASKARIAN, Mehrdad, MOVAHEDI, M. Muhammad., HOSSEINI, Somayyeh, ve JAHANDIDEH Mina (2009), “The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation” Waste Management, 21(11), 2874-2879.
  • KALLEBERG, Arne L. (1990), The Comparative Study of Business Organizations and their Employees: Conceptual and Methodological Issues. Comparative Social Research, 12, 153-175.
  • KAPTAN, Saim, (1998) Bilimsel Araştırma ve İstatistik Teknikleri. Ankara: Bilim Kitap Kırtasiye Ldt. Şti.
  • KAYNAR, Oğuz. ve TAŞTAN, Serkan (2009), "Zaman Serisi Analizinde MLP Yapay Sinir Ağları ve ARIMA modelinin Karşılaştırılması", Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 33(162), 161‐172.
  • KALLEBERG, Arne L., (1990), “The Comparative Study of Business Organizations and their Employees: Conceptual and Methodological Issues” Comparative Social Research, (12), 153-175.
  • KAPTAN, Saim, (1998), Bilimsel Araştırma ve İstatistik Teknikleri, 11. Baskı, Ankara: Bilim Yayıncılık.
  • KAYNAR, Oğuz ve TAŞTAN, Serkan, (2009), "Zaman Serisi Analizinde MLP Yapay Sinir Ağları ve ARIMA Modelinin Karşılaştırılması", Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, (33), 161‐172.
  • KHASHEİ, Mehdi, HAMADANİ, Ali Z. ve BİJARİ, Mehdi, (2012), “A Novel Hybrid Classification Model of Artificial Neural Networks and Multiplelinear Regression Models”, Expert Systems with Applications, 39(3), 2606-2620.
  • KİM, Yong S., STREET, Nick W., RUSSELL, Gary ve MENCZER, Filippo (2005), “Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms” Management Science, 51(2), 264-276.
  • KUZMANOVSKİ, Igor ve ALEKSOVSKA, Slobotka. (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.
  • LAMBERT, Eric (2003), “The impact of Organizational Justice on Correctional Staff”, Journal of Criminal Justice, 31(2), 155– 168.
  • LAMBERT, Eric G., HOGAN, Nancy L., JIANG, Sheanhe, ELECHI, Oko, BENJAMIN, Barbaranne, MORRİS, Angela,LAUX, John M. ve DUPUY, Paula (2010), “The Relationship Among Distributive and Procedural Justice and Correctional Life Satisfaction, Burnout, and Turnover Intent: an Exploratory Study”, Journal of Criminal Justice, 38(1), 7-16.
  • LARSON, Carl E. ve LAFASTO, Frank M. J. (1989), TeamWork: What Must Go Right/What Can Go Wrong, 1. Baskı, California: SAGE Series in Interpersonal Communication,
  • LANE, Vicki R. ve SCOTT, Susanne G.(2007), “The Neural Network Model Of Organizational Identification”. Organizational Behavior and Human Decision Processes, 104(2), 175-192
  • LIU, Xiaozhi, KANG, Shaozhong ve LI, Fusheng (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(6), 939–945.
  • MINBASHIAN, Amirali, BRIGHT, Jim ve BIRD, Kevin, (2009), A Comparison of Artificial Neural Networks and Multiple Regression in The Context of Research on Personality and Work Performance”, Organizational Research Methods, 13(3), 540-561.
  • MISHRA, Aneil K, (1996), “Organizational Responses to Crisis: The Centrality of Trust”, Roderick .M. Kramer, Tom R. Tyler(der.), Trust in Organizations Frontiers of Theory and Research içinde, California: Sage Pub.
  • MONTAGNO, Ray, SEXTON, Randall S. ve SMITH, Brien N. (2002), “Using Neural Networks for Identifying Organizational Improvement Strategies”, European Journal of Operational Research, 142(2), 382–395.
  • NABİYEV, Vasif V. (2010), Yapay Zekâ: İnsan-Bilgisayar Etkileşimi, 3. Baskı, Ankara: Seçkin Yayıncılık.
  • NADİRİ, Halil ve TANOVA, Cem, ( 2010), “An Investigation of The Role of Justice in Turnover Intentions, Job Satisfaction, and Organizational Citizenship Behavior in Hospitality Industry”, International Journal of Hospitality Management, 29(1), 33-41
  • NIEHOFF Brian ve MOORMAN H. Robert.(1993), “Justice as a Mediator of the Relationship Between Methods of Monitoring and Organizational Citizenship Behavior”, Academy of Management Journal, 36(3), 527-556.
  • OKKAN, Umut ve MOLLAMAHMUTOĞLU, Ayşe (2010), “Yiğitler Çayı, Günlük Akımlarının Yapay Sinir Ağları ve Regresyon Analizi İle Modellenmesi”, Dumlupınar Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23, 33-48.
  • PALOCSAY, Susan W. ve WHITE, Marion M. (2004), “Neural Network Modeling in Crosscultural Research: a Comparison with Multiple Regression”, Organizational Resarch Methods, 7(4), 339-399.
  • PAO, Hisiao T. (2008), “A Comparison of Neural Network and Multiple Regression Analysis in Modeling Capital Structure”, Expert Systems with Applications, 35, 720–727.
  • PARUELO, Jose M. ve TOMASEL, Fernando (1997), “Prediction of Functional Characteristics of Ecosystems: a Comparison of Artificial Neural Net-works and Regression Models”, Ecological Modelling, 98(2-3), 173-186.
  • ROUSSEAU, Denise M.,SITKIN, Sim M.,BURT, Ronald S.,CAMERER, Colin (1998), “Not So Different After All: A Crossdiscipline View of Trust”, Academy of Management, 23(3), 393-404.
  • SCARBOROUGH, David ve SOMERS, Mark (2006), Neural Networks in Organizational Research, 1. Baskı, Washington: American Psychological Association.
  • SHI, Guangren, ZHOU, Xingxi, ZHANG, Guangya, SHI, Xiaofeng ve LI, Honghui (2004), “The use of artificial neural network analysis and multiple regression for trap quality evaluation: a case study of the Northern Kuqa Depression of Tarim Basin in western China”, Marine and Petroleum Geology 21(3), 411–420.
  • SHOCKLEY-ZALABAK, Pamela. ve MORLEY, Donald D. (1989), "Adhering to Organizational Culture: What Does It Mean, Why Does It Matter?" Group & Organization Management, 14(4) , 483-500.
  • SHOCKLEY-ZALABAK, Pamela, Ellis, Kathleen, and Cesaria, Ruqqero (2000), IABC Research Foundation unveils new study on trust. Communication World, 17(6), 7-9.
  • SMITH, Kenwyn K. ve BERG, David. N. X. (1997), Paradoxes of Group Life: Understanding Conflict Paralysis, and Movement in Group Dynamics, 1. Baskı, San Francisco: Wiley, John & Sons, Incorporated
  • SUBRAMANIAN, Narayanaswamy, YAJNIK, Archit. ve MURTHY, Rayasa S.R.(2004), “Artificial Neural Network as an Alternative to Multiple Regression Analysis in Optimizing Formulation Parameters of Cytarabine Liposomes”, AAPS PharmSciTech, 5(1), 11-19
  • TOLON, Metehan (2007), Tüketici Tatmininin Yapay Sinir Ağları Yöntemiyle Ölçülmesi ve Ankara’daki Perakendeci Mağazaların Müşterileri Üzerinde Bir Uygulama”, Gazi Üniversitesi Sosyal Bilimler Enstitüsü, Yayımlanmamış Doktora Tezi, Ankara
  • TU, V. Jack (1996). “Advantages and Disadvantages of Using Artificial Neural Networks Versus Logistic Regression for Predicting Medical Outcomes”, J. Clin Epidemiol, 49(11), 1225-1231.
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ÖRGÜTSEL ADALET VE GÜVEN ARASINDAKİ İLIŞKİLER KULLANI-LARAK YAPAY SİNİR AĞLARI VE ÇOKLU DOĞRUSAL REGRESYON YÖNTEMLERİNİN KARŞILAŞTIRILMASI

Yıl 2013, Sayı: 10, - , 20.04.2015

Öz

Bu araştırmada değişkenlerarası ilişkiler kullanılarak Yapay Sinir Ağları ve Çoklu Doğrusal Regresyon yöntemlerinin karşılaştırılması amaçlanmıştır. Araştırmanın örneklemi Kırşehir’de faaliyet gösteren ve çalışan sayısı 50’nin üzerinde olan işletmelerin 171 personelinden oluşmaktadır. Araştırmada değişken grubu olarak Örgütsel Adalet ve Örgütsel Güven ile bunlara bağlı alt faktörler kullanılmıştır. Yapılan Factor Analizi ile Cronbach Alpha katsayılarına göre anketin geçerli ve güvenilir olduğu belirlenmiştir. Söz konusu analiz yöntemlerinin performanslarını karşılaştırmak için etki katsayılarının göreli değerlen-dirmesi, açıklama katsayısı (R2) ve ortalama karesel hata karakökü (RMSE) kriter olarak kullanılmıştır. Araştırmadan elde edilen bulgulara göre Yapay Sinir Ağları yönteminin değişkenler arası ilişkilerin belirlenmesinde alternatif bir yöntem olarak kullanılabileceğini ve göreli olarak Çoklu Doğrusal Regresyon yöntemi karşısında belirli üstünlüklere sahip olduğunu ifade etmek mümkündür.

Kaynakça

  • ARUPJYOTI, Saikia ve IRAGAVARAPU, Suryanarayana (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, (1), 515-522.
  • BENARDOS, P. G. ve VOSNIAKOS, G. C. (2007), “Optimizing feed-forward artificial neural network architecture”, Engineering Applications of Artificial Intelligence, 20 (3), 365–382.
  • BRASHEAR, G. Thomas., MANOLIS, Chris ve BROOKS, M, Charles (2005), “The effects of control, trust, and justice on salesperson turnover” Journal of Business Research, 58 (3), 241-249.
  • BREY, T., TEICHMANN, A. ve BORLICH, O., (1996), “Artificial neural network versus multiple linear regression: predicting P/B ratios from empirical da-ta”, Marine Ecology Progress Series, 140, 251–256.
  • CHELGANI,S.Chehreh, HOWER,C. James ve HART,B.(2011), “Estimation of free-swelling index based on coal analysis using multivariable regression and artificial neural network”, Fuel Processing Technology, 92 (3), 349–355.
  • CHRISTENSEN, Clayton M. (1997), The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail, Harvard Business School Press., Boston,
  • COLQUITT, A. Jason, SCOTT, A. Brent, JUDGE, A. Timothy ve SHAW, C. John (2006), “Justice and personality: Using integrative theories to derive moderators of justice effects”, Organizational Behavior and Human Decision Processes, 100 (1), 110–127.
  • DeCONINCK, B. James ve STILWEL, C. Dean (2004), “Incorporating organizational justice, role states, pay satisfaction and supervisor satisfaction in a model of turnover intentions”, Journal of Business Research, 57(3) 225-231.
  • DIRKS, T. Kurt, KIM, H. Peter, Ferrin, L. Donald ve Cooper, D. Cecilly (2011), “Understanding the effects of substantive responses on trust following a transgression”, Organizational Behavior and Human Decision Processes, 114 (2), 87-103.
  • EDMONDSON, Amy (1999), "Psychological Safety and Learning Behavior in Work Teams" Administrative Science Quarterly, 44, 350-383.
  • ELMAS, Çetin (2003), Yapay Sinir Ağları (Theory, Architecturue, Education, Application), Seckin Yayıncılık, Ankara.
  • ENKE, David ve THAWORNWONG, Suraphan (2005), “The use of data mining and neural Networks for forecasting stock market returns”, Expert Systems with Applications, 29(4), 927-940.
  • FANG, Yu-Hui. ve CIU, Chao-min. (2010), “In justice we trust: Exploring knowledge-sahring contiuance intentions in virtual communitues of practice” Computers in Human Behavior, 26(2), 235-246.
  • FOLGER, Robert ve CROPANZANO, Russell (1998), Organizational Justice And Human Resource Management, California: Thousand Oaks-Sage Publications
  • GELMAN, Andrew. ve HILL, Jennifer (2007), Data Analysis Using Regression and Multilevel/Hierarchical Models, NY: Cambridge University Press.
  • GOUL, Michael, SHANE, Barry ve TONGE, M. Fred (1986), “Using a knowledge based decision support system in strategic planning decisions: an empirical study”. Journal of Management Information Systems, 2(4), 70-84.
  • HAMZAÇEBI, Coşkun (2011), Yapay Sinir Ağı (Artificial Neural Network), Bursa: Ekin Yayınları.
  • HAYKIN, Simon (1999), Neural Networks, Second Edition, N.J: Prentice Hall,
  • HEIAT, Abbas (2002), “Comparison of artificial neural network and regression models for estimating software development effort” Information and Software Technology. 44(15) 911–922.
  • HSU, Ching-Chi, LIN, Jinn, ve CHAO, Ching-Kong, (2011), “Comparison of multiple linear regression and artificial neural network in developing the objective functions of the orthopaedic screws”, Computer methods and programs in biomedicine,104(3), 341–348.
  • JAHANDIDEH, Sepideh, JAHANDIDEH, Samad, BARZEGARI Asadabadi, E., ASKARIAN, Mehrdad, MOVAHEDI, M. Muhammad., HOSSEINI, Somayyeh, ve JAHANDIDEH Mina (2009), “The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation” Waste Management, 21(11), 2874-2879.
  • KALLEBERG, Arne L. (1990), The Comparative Study of Business Organizations and their Employees: Conceptual and Methodological Issues. Comparative Social Research, 12, 153-175.
  • KAPTAN, Saim, (1998) Bilimsel Araştırma ve İstatistik Teknikleri. Ankara: Bilim Kitap Kırtasiye Ldt. Şti.
  • KAYNAR, Oğuz. ve TAŞTAN, Serkan (2009), "Zaman Serisi Analizinde MLP Yapay Sinir Ağları ve ARIMA modelinin Karşılaştırılması", Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 33(162), 161‐172.
  • KALLEBERG, Arne L., (1990), “The Comparative Study of Business Organizations and their Employees: Conceptual and Methodological Issues” Comparative Social Research, (12), 153-175.
  • KAPTAN, Saim, (1998), Bilimsel Araştırma ve İstatistik Teknikleri, 11. Baskı, Ankara: Bilim Yayıncılık.
  • KAYNAR, Oğuz ve TAŞTAN, Serkan, (2009), "Zaman Serisi Analizinde MLP Yapay Sinir Ağları ve ARIMA Modelinin Karşılaştırılması", Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, (33), 161‐172.
  • KHASHEİ, Mehdi, HAMADANİ, Ali Z. ve BİJARİ, Mehdi, (2012), “A Novel Hybrid Classification Model of Artificial Neural Networks and Multiplelinear Regression Models”, Expert Systems with Applications, 39(3), 2606-2620.
  • KİM, Yong S., STREET, Nick W., RUSSELL, Gary ve MENCZER, Filippo (2005), “Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms” Management Science, 51(2), 264-276.
  • KUZMANOVSKİ, Igor ve ALEKSOVSKA, Slobotka. (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.
  • LAMBERT, Eric (2003), “The impact of Organizational Justice on Correctional Staff”, Journal of Criminal Justice, 31(2), 155– 168.
  • LAMBERT, Eric G., HOGAN, Nancy L., JIANG, Sheanhe, ELECHI, Oko, BENJAMIN, Barbaranne, MORRİS, Angela,LAUX, John M. ve DUPUY, Paula (2010), “The Relationship Among Distributive and Procedural Justice and Correctional Life Satisfaction, Burnout, and Turnover Intent: an Exploratory Study”, Journal of Criminal Justice, 38(1), 7-16.
  • LARSON, Carl E. ve LAFASTO, Frank M. J. (1989), TeamWork: What Must Go Right/What Can Go Wrong, 1. Baskı, California: SAGE Series in Interpersonal Communication,
  • LANE, Vicki R. ve SCOTT, Susanne G.(2007), “The Neural Network Model Of Organizational Identification”. Organizational Behavior and Human Decision Processes, 104(2), 175-192
  • LIU, Xiaozhi, KANG, Shaozhong ve LI, Fusheng (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(6), 939–945.
  • MINBASHIAN, Amirali, BRIGHT, Jim ve BIRD, Kevin, (2009), A Comparison of Artificial Neural Networks and Multiple Regression in The Context of Research on Personality and Work Performance”, Organizational Research Methods, 13(3), 540-561.
  • MISHRA, Aneil K, (1996), “Organizational Responses to Crisis: The Centrality of Trust”, Roderick .M. Kramer, Tom R. Tyler(der.), Trust in Organizations Frontiers of Theory and Research içinde, California: Sage Pub.
  • MONTAGNO, Ray, SEXTON, Randall S. ve SMITH, Brien N. (2002), “Using Neural Networks for Identifying Organizational Improvement Strategies”, European Journal of Operational Research, 142(2), 382–395.
  • NABİYEV, Vasif V. (2010), Yapay Zekâ: İnsan-Bilgisayar Etkileşimi, 3. Baskı, Ankara: Seçkin Yayıncılık.
  • NADİRİ, Halil ve TANOVA, Cem, ( 2010), “An Investigation of The Role of Justice in Turnover Intentions, Job Satisfaction, and Organizational Citizenship Behavior in Hospitality Industry”, International Journal of Hospitality Management, 29(1), 33-41
  • NIEHOFF Brian ve MOORMAN H. Robert.(1993), “Justice as a Mediator of the Relationship Between Methods of Monitoring and Organizational Citizenship Behavior”, Academy of Management Journal, 36(3), 527-556.
  • OKKAN, Umut ve MOLLAMAHMUTOĞLU, Ayşe (2010), “Yiğitler Çayı, Günlük Akımlarının Yapay Sinir Ağları ve Regresyon Analizi İle Modellenmesi”, Dumlupınar Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23, 33-48.
  • PALOCSAY, Susan W. ve WHITE, Marion M. (2004), “Neural Network Modeling in Crosscultural Research: a Comparison with Multiple Regression”, Organizational Resarch Methods, 7(4), 339-399.
  • PAO, Hisiao T. (2008), “A Comparison of Neural Network and Multiple Regression Analysis in Modeling Capital Structure”, Expert Systems with Applications, 35, 720–727.
  • PARUELO, Jose M. ve TOMASEL, Fernando (1997), “Prediction of Functional Characteristics of Ecosystems: a Comparison of Artificial Neural Net-works and Regression Models”, Ecological Modelling, 98(2-3), 173-186.
  • ROUSSEAU, Denise M.,SITKIN, Sim M.,BURT, Ronald S.,CAMERER, Colin (1998), “Not So Different After All: A Crossdiscipline View of Trust”, Academy of Management, 23(3), 393-404.
  • SCARBOROUGH, David ve SOMERS, Mark (2006), Neural Networks in Organizational Research, 1. Baskı, Washington: American Psychological Association.
  • SHI, Guangren, ZHOU, Xingxi, ZHANG, Guangya, SHI, Xiaofeng ve LI, Honghui (2004), “The use of artificial neural network analysis and multiple regression for trap quality evaluation: a case study of the Northern Kuqa Depression of Tarim Basin in western China”, Marine and Petroleum Geology 21(3), 411–420.
  • SHOCKLEY-ZALABAK, Pamela. ve MORLEY, Donald D. (1989), "Adhering to Organizational Culture: What Does It Mean, Why Does It Matter?" Group & Organization Management, 14(4) , 483-500.
  • SHOCKLEY-ZALABAK, Pamela, Ellis, Kathleen, and Cesaria, Ruqqero (2000), IABC Research Foundation unveils new study on trust. Communication World, 17(6), 7-9.
  • SMITH, Kenwyn K. ve BERG, David. N. X. (1997), Paradoxes of Group Life: Understanding Conflict Paralysis, and Movement in Group Dynamics, 1. Baskı, San Francisco: Wiley, John & Sons, Incorporated
  • SUBRAMANIAN, Narayanaswamy, YAJNIK, Archit. ve MURTHY, Rayasa S.R.(2004), “Artificial Neural Network as an Alternative to Multiple Regression Analysis in Optimizing Formulation Parameters of Cytarabine Liposomes”, AAPS PharmSciTech, 5(1), 11-19
  • TOLON, Metehan (2007), Tüketici Tatmininin Yapay Sinir Ağları Yöntemiyle Ölçülmesi ve Ankara’daki Perakendeci Mağazaların Müşterileri Üzerinde Bir Uygulama”, Gazi Üniversitesi Sosyal Bilimler Enstitüsü, Yayımlanmamış Doktora Tezi, Ankara
  • TU, V. Jack (1996). “Advantages and Disadvantages of Using Artificial Neural Networks Versus Logistic Regression for Predicting Medical Outcomes”, J. Clin Epidemiol, 49(11), 1225-1231.
  • TUNG, Kuah-Yeh, HUANG, Ing-Chung, CHEN, Shu-Ling, ve SHIH, Chih Ting (2005), “Mining the Generation Xers’ job attitudes by artificial neural network and decision tree-empirical evidence in Taiwan”, Expert Systems with Applications, 29(4) 783–794.
  • WONG, Bo, K., BODNOVICH, Thomas A., SELVI, Yakup (1997), “Neural network applications in business: A review and analysis of the literature (1988-95)”. Decision Support Systems, 19(4) 301-320.
  • WONG, Bo, K., LAI, Vincent S. ve LAM, Jolie (2000), “A bibliography of neural network business applications research: (1994-1998)”. Computers & Operations Research, 27(11-12), 1045-1076.
  • WONG, Yui-Tim, NGO, Hang-Yue ve WONG, Chi-Sum (2006), “Perceived organizational justice, trust, and OCB: A study of Chinese workers in joint ventures and state-owned enterprises”, Journal of World Business, 41 (4) 344–355
  • WONG, T.C., WONG, S.Y. ve CHIN, K.S. (2011), “A neural network-based approach of quantifying relative importance among various determinants toward organizational innovation”, Expert Systems with Applications, 38(10), 13064-13072.
  • ZAPATA-PHELAN, P., Cindy, COLQUITT, Jason A., Scott, Brent ve Livingston, Beth, (2009), “Procedural justice, interactional justice, and task performance: The mediating role of intrinsic motivation”, Organizational Behavior and Human Decision Processes, 108(1), 93-96.
  • ZORLU, Kürşad (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 & Organisational Learning, Taiwan.
  • ZORLU, Kürşad (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), 3014-3025
  • ZORLU, Kürşad (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 Yöne-tim İktisat ve İşletme Dergisi, 8 (17), 1-25.
Toplam 63 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm MAKALELER
Yazarlar

Kürşad Zorlu Bu kişi benim

Yayımlanma Tarihi 20 Nisan 2015
Yayımlandığı Sayı Yıl 2013 Sayı: 10

Kaynak Göster

APA Zorlu, K. (2015). ÖRGÜTSEL ADALET VE GÜVEN ARASINDAKİ İLIŞKİLER KULLANI-LARAK YAPAY SİNİR AĞLARI VE ÇOKLU DOĞRUSAL REGRESYON YÖNTEMLERİNİN KARŞILAŞTIRILMASI. Uluslararası İktisadi Ve İdari İncelemeler Dergisi(10). https://doi.org/10.18092/ijeas.46781


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