You are here

Home » Content

GERİYE YAYILMA ALGORİTMASI KULLANILARAK FİRMA PERFORMANSININ TAHMİN EDİLMESİ

THE USE OF BACK-PROPAGATION ALGORITHM IN THE ESTIMATION OF FIRM PERFORMANCE

Journal Name:

  • İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi

Publication Year:

  • 2006

Key Words:

Keywords (Original Language):

Author NameFaculty of Author
Abstract (2. Language): 
Recently, many researches have been made to find the impact of human resources on firm performance. Many of these studies are conducted on the basis ofstatistical approaches and find the correlation between the human resource management (HRM) measures and firm performance. In this paper, the main aim is to estimate the firm performance through the use of nonlinear model. One method which is used for this nonlinear approach is Artificial Neural Networks (ANN). Artificial Neural Networks are computing systems made up of a number of simple highly interconnected signal or information processing units that are called as artificial neurons. In this work, we used one of the ANN approaches which is called as back-propagation algorithm. In order to collect data, a questionnaire is structured that contains questions related with human resource management and firm performance measures. The data are collected mainly from the manufacturing companies operating in Turkey. Using the data collected, the model is checked whether it is capable of forming therelationship between the HRM input variables and firm performance output variables or not. The experimental results show that through the use of this algorithm, the relationship between the input and output variables can be constructed and moreover, the model can be used as an estimator of firm performance for the companies that are not used in the training of the model.
Abstract (Original Language): 
Son zamanlarda, insan kaynaklarının firma performansı üzerindeki etkisini incelemek için birçok araştırma yapılmıştır. İstatistiksel yöntemler temel alınarak yapılan bu çalışmalar sonucunda, insan kaynakları yönetimi göstergeleri ile firma performansı arasında ilişki olduğu tespit edilmiştir. Bu çalışmanın esas amacı, doğrusal olmayan ve geriye yayılma algoritması olarak da bilinen model kullanılarak firma performansını tahmin etmektir. Bu amaçla kullanılan, doğrusal olmayan yöntemlerden bir tanesi de yapay sinir ağlarıdır. Yapay sinir ağları, yapay nöron olarak adlandırılan birimlerin birbirine bağlanarak sinyal ve bilgi işleme amacıyla kullanılan hesaplama sistemleridir. Bu çalışmada, geriye yayılma algoritması olarak adlandırılanyapay sinir ağları yaklaşımlarından birisi kullanılmıştır. Veri toplama amacıyla, insan kaynakları yönetimiperformans göstergeleri ve firma performans göstergeleri ile ilgili sorulardan oluşan bir anket tasarlanmıştır. Veriler Türkiye’de üretim sektöründe faaliyet gösteren firmalardan toplanmıştır. Toplanan veriler kullanılarak insan kaynakları yönetimi performans göstergeleri ile firmaperformansı göstergeleri arasında bu model yardımı ile bir ilişki kurulup kurulamayacağı test edilmiştir. Deneysel sonuçlar, bu algoritma kullanılarak girdi ve çıktı değişkenleri arasında ilişki kurulabileceğini göstermiştir. Buna ek olarak, modelin eğitilmesinde kullanılmayan firmalar için de bu algoritmanın firma performasının tahmini için kullanılabileceği sonucu elde edilmiştir
FULL TEXT (PDF): 
PDF icon arastirmax-geriye-yayilma-algoritmasi-kullanilarak-firma-performansinin-tahmin-edilmesi.pdf

REFERENCES

References: 

Ağdelen, Z. (2003), “Human Resource Contribution Model and Analysis of the Link
Between Human Resource Management and Firm Performance”, Unpublished Ph.D.
Thesis, İstanbul Technical University, Institute of Science& Technology, İstanbul.
Akaya, G. and Gökçen, T., (2006), “Yapay Sinir Ağları ile Atölye Çizelgeleme
Tasarımı”, YA/EM 2006, Yöneylem Araştırması / Endüstri Mühendisliği- XXVI.
Ulusal Kongresi, 3-5 Temmuz, Kocaeli
Becker, B., E. and Huselid, M., A., (1998), “High Performance Work Systems and
Firm Performance: A Synthesis of Research and Managerial Implications”. Research
in Personnel and Human Resources Journal, 16, 1, 53-101.
Chandler, G., N. and Mcevoy, G., M., (2000), “HumanResource Management,
TQM and Firm Performance in Small and Medium-Size Enterprises”,
Entrepreneurship: Theory and Practice, Fall, 25, 43
Chin-Teng, L. and George Lee, C.S., (1996), “Neural Fuzzy Systems”, Prentice
Hall.
Christopher, J. C. and Ken, G. S., (2006), “Knowledge Exchange and Combination:
The Role of Human Resource Practices in the Performance of High-Technology
Firms”, The Academy of Management Journal, 49, 3, 544-560.
Cichoclar, A., Unbehaven, R., (1993), Neural Networks for Optimization and Signal
Processing”, John Wiley & Sons.
Delaney, J., T. and Huselid, M., A., (1996), “The Impact of Human Resource
Management Practices on Performance in For-Profit and Nonprofit Organizations”,
Academy of Management Journal, 39, 949-969.
Fey, C., F. and Björkman, I., (2000), “The Effect of Human Resource Management
Practices on MNC Subsidiary Performance in Russia”,Working Paper, Stockholm
School of Economics in St. Petersburg, SSE/EFI Working Paper Series in Business
Administration, No. 2000:6.
Haydar A., Ağdelen Z., (2006), “The Effect of Changes in Human Resource
Management Measures on the Firm Performance Throughthe Use of Nonlinear
Model”, 5th International Symposium on Intelligent Manufacturing Systems,
Sakarya.
Huang, L-C., Huang, K-S, Huang, H-P. and Jaw, B-S.,(2004), “Applying Fuzzy
Neural Network in Human Selection System”, IEEE, 169-174.
Ali HAYDAR , Zafer AĞDELEN, Pınar ÖZBEŞEKER
64
Huselid, M., A., (1995), “The Impact of Human Resource Management Practices on
Turnover, Productivity, and Corporate Financial Performance”, Academy of
Management Journal, 38, 635-672
Kent V. Rondeau, (2007), “The Adoption of High Involvement Work Practices in
Canadian Nursing Homes”, Leadership in Health Services, 20, 1, 16
Li, E. Y., (1994), “Artificial Networks and Their Business Applications”,
Information and Management, 27, 5, 303-313.
Mahesh Subramony, (2006), “Why Organizations Adopt Some Human Resource
Management Practices and Reject Others: An Exploration of Rationales”, Human
Resource Management 45, 2, 195
Patrick, M. W., Timoty, M. G., Lisa, M. M. and Mathew, R., A, (2005), “The
Relationship Between HR Practices and Firm Performance: Examining Causal
Order”, Personnel Psychology, 58, 2, 409-446.
Saraç, T., Dengiz, B. and Altıparmak, F., (2006), “Yapay Sinir Ağları ile Yazılım
Proje Süresinin Tahmini”, YA/EM 2006, Yöneylem Araştırması / Endüstri
Mühendisliği- XXVI. Ulusal Kongresi, 3-5 Temmuz, Kocaeli
Sexton, R. S. and Mcmurtey S., (2005), “Employee Turnover: A Neural Network
Solution”, Computers and Operations Research, 32, 10, 2635-2651.
Shay S. Tzafrir. (2006), “A Universalistic Perspective for Explaining the
Relationship Between HRM Practices and Firm Performance at Different Points in
Time”, Journal of Managerial Psychology 21, 2, 109
Simon H., (1999),” Neural Networks”, Second Edition, Prentice Hall.

Thank you for copying data from http://www.arastirmax.com