Neural network modeling in cross-cultural research: A comparison with multiple regression

Organizational Research Methods

Volumn 7, Issue 4, 2004, Pages 389-399

  Palocsay, Susan W ^a   White, Marion M ^a,b  

^a JAMES MADISON UNIVERSITY   (United States)

^b James Madison University ^*

Author keywords

Cross cultural research; Multiple regression; Neural networks; Organizational justice

Indexed keywords

EID: 4944221221     PISSN: 10944281     EISSN: None     Source Type: Journal    
DOI: 10.1177/1094428104268030     Document Type: Review

References (42)

1. 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, 11-32.
2. Bond, H. M., Leung, K., & Wan, K. C. (1982). How does cultural collectivism operate? The impact of task and maintenance contribution on reward distribution. Journal of CrossCultural Psychology, 23, 186-200.
3. 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, 503-524.
4. Condon, E. M., Golden, B. L., & Wasil, E. A. (1999). Predicting the success of nations at the summer Olympics using neural networks. Computers & Operations Research, 26, 1243-1265.
5. Conover, W. J. (1980). Practical nonparametric statistics (2nd ed.). New York: John Wiley & Sons.
6. DeTienne, K. B., DeTienne, D. H., & Joshi, S. A. (2003). Neural networks as statistical tools for business researchers. Organizational Research Methods, 6, 236-265.
7. Dorfman, P. W., & Howell, J. P. (1988). Dimensions of national culture and effective leadership patterns: Hofstede revisited. Advances in International Comparative Management, 3, 127-150.
8. Duliba, K. A. (1991). Contrasting neural networks with regression in predicting performance in the transportation industry. Proceedings of the 24th Annual Hawaii International Conference on Systems Sciences, 4, 163-170.
9. Garson, G. D. (1991). A comparison of neural network and expert systems algorithms with common multivariate procedures for analysis of social science data. Social Science Computer Review, 9, 399-434.
10. Gorr, W. L., Nagin, D., & Szczypula, J. (1994). Comparative study of artificial neural network and statistical models for predicting student grade point averages. International Journal of Forecasting, 10, 17-34.
11. Goss, E. P., & Ramchandani, H. (1998). Survival prediction in the intensive care unit: A comparison of neural networks and binary logit regression. Socio-Economie Planning Sciences, 32, 189-198.
12. Greenberg, J. (1990). Organizational justice: Yesterday, today, and tomorrow. Journal of Management, 16, 399-432.
13. Hinton, G. (1992). How neural networks learn from experience. Scientific American, 267, 145-151.
14. Hofstede, G. (1980). Culture's consequences: International differences in work-related values. Beverly Hills, CA: Sage.
15. Kiang, M. Y. (2003). A comparative assessment of classification methods. Decision Support Systems, 35, 441-454.
16. Liang, T., Chandler, J. S., Han, L, & Roan, J. (1992). An empirical investigation of some data effects on the classification accuracy of probit, ID3, and neural networks. Contemporary Accounting Research, 9, 306-328.
17. Meindl, J. R., Cheng, Y. K., & Jun, L. (1990). Distributive justice in the workplace: Preliminary data on managerial preferences in the PRC. Research in Personnel and Human Resources Management, pp. 221-236.
18. Nguyen, N., & Cripps, A. (2001). Predicting housing values: A comparison of multiple regression analysis and artificial neural networks. Journal of Real Estate Research, 22, 313-336.
19. Palocsay, S., Stevens, S., Brookshire, R., Sacco, W., Copes, W., Buckman, R., et al. (1996). Using neural networks for trauma outcome evaluation. European Journal of Operational Research, 93, 369-386.
20. Palocsay, S., Wang, P., & Brookshire, R. (2000). Predicting criminal recidivism using neural networks. Socio-Economic Planning Sciences, 34, 271-284.
21. Philipoom, P. R., Rees, L. P., & Wiegmann, L. (1994). Using neural networks to determine internally-set due date assignments for shop scheduling. Decision Sciences, 25, 825-851.
22. Riedmiller, M., & Braun, H. (1993). A direct adaptive method for faster backpropagation learning: The RPROP algorithm. Proceedings of the IEEE International Conference on Neural Networks. San Francisco: IEEE.
23. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. In D. E. Rumelhart, J. L. McClelland, & the PDP Research Group (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 1, pp. 318-459). Cambridge, MA: MIT Press.
24. Rumelhart, D. E., Widrow, B., & Lehr, M. A. (1994). The basic ideas in neural networks. Communications of the ACM, 37, 87-91.
25. Salchenberger, L. M., Cinar, E. M., & Lash, N. A. (1992). Neural networks: A new tool for predicting thrift failures. Decision Sciences, 23, 899-916.
26. Sharda, R. (1994). Neural networks for the MS/OR analyst: An application bibliography. Interfaces, 24, 116-130.
27. Somers, M. J. (2001). Thinking differently: Assessing nonlinearities in the relationship between work attitudes and job performance using a Bayesian neural network. Journal of Occupational and Organizational Psychology, 74, 47-61.
28. St. John, C. H., Balakrishnan, N., & Fiel, J. O. (2000). Modeling the relationship between corporate strategy and wealth creation using neural networks. Computers & Operations Research, 27, 1077-1092.
29. SubbaNarisimha, P. N., Arinze, B., & Anandarajan, M. (2000). The predictive accuracy of artificial neural networks and multiple regression in the case of skewed data: Exploration of some issues. Expert Systems With Applications, 19, 117-123.
30. Subramanian, V., Hung, M. S., & Hu, M. Y. (1993). An experimental evaluation of neural networks for classification. Computers & Operations Research, 20, 769-782.
31. Tam, K. Y., & Kiang, M. Y. (1992). Managerial applications of neural networks: The case of bank failure predictions. Management Science, 38, 926-947.
32. Veiga, J. R, Lubatkin, M. H., Calori, R., Very, P., & Tung, Y. A. (2000). Using neural network analysis to uncover the trace effects of national culture. Journal of International Business Studies, 31, 223-238.
33. Warner, B., & Misra, M. (1996). Understanding neural networks as statistical tools. American Statistician, 50, 284-293.
34. Weiss, S. M., & Kulikowski, C. A. (1991). Computer systems that learn. San Mateo, CA: Morgan Kaufman.
35. Werbos, P. J. (1974). Beyond regression: New tools for prediction analysis in the behavioral sciences. Unpublished doctoral dissertation, Harvard University, Cambridge, MA.
36. Werbos, P. J. (1988). Generalization of backpropagation with application to arecurrent gas market model. Neural Networks, 1, 339-356.
37. White, H. (1990). Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings. Neural Networks, 3, 535-549.
38. White, M. M., Tansky, J. A., & Baik, K. (1995). Linking culture and perceptions of justice: A comparison of students in Virginia and South Korea. Psychological Reports, 77, 1103-1112.
39. Wilson, R. L., & Hardgrave, B. C. (1995). Predicting graduate student success in an MBA program: Regression versus classification. Educational and Psychological Measurement, 55, 186-195.
40. 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.
41. 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.
42. Wu, F. Y., & Yen, K. K. (1992). Applications of neural network in regression analysis. Computers and Industrial Engineering, 23, 93-95.