Journal of the Operational Research Society

, Volume 57, Issue 3, pp 231–240 | Cite as

Knowledge acquisition and revision using neural networks: an application to a cross-national study of brand image perception

Case-Oriented Paper

Abstract

A three-tier knowledge management approach is proposed in the context of a cross-national study of car brand and corporate image perceptions. The approach consists of knowledge acquisition, transfer and revision using neural networks. We investigate how knowledge acquired by a neural network from one car market can be exploited and applied in another market. This transferred knowledge is subsequently revised for application in the new market. Knowledge revision is achieved by re-training the neural network. Core knowledge common to both markets is retained while some localized knowledge components are introduced during network re-training. Since the knowledge acquired by a neural network can be expressed as an accurate set of simple rules, we are able to compare the knowledge extracted from one network with the knowledge extracted from another. Comparison of the originally acquired knowledge with the revised knowledge provides us with insights into the commonalities and differences in car brand and corporate perceptions across national markets.

Keywords

neural networks knowledge revision knowledge transfer global brand image perceptions 

References

  1. Charalambous C, Charitou A and Kaourou F (2000). Comparative analysis of articial neural network models: application in bankruptcy predictions. Ann Opns Res 99: 403–425.CrossRefGoogle Scholar
  2. Piramuthu S, Ragavan H and Shaw MJ (1998). Using feature construction to improve the performance of neural networks. Mngt Sci 44: 416–430.CrossRefGoogle Scholar
  3. Sung TK, Chang N and Lee G (1999). Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction. J Mngt Inf Systems 16: 63–85.CrossRefGoogle Scholar
  4. Koskivaara E (2000). Artificial neural network models for predicting patterns in auditing monthly balances. J Opl Res Soc 51: 1060–1069.CrossRefGoogle Scholar
  5. Zhang GP and Berardi VL (2001). Time series forecasting with neural network ensembles: an application for exchange rate prediction. J Opl Res Soc 52: 652–664.CrossRefGoogle Scholar
  6. Brocket PL, Cooper WW, Golden LL and Xia X (1997). A case study in applying neural networks to predicting insolvency for property and casualty insurers. J Opl Res Soc 48: 1153–1162.CrossRefGoogle Scholar
  7. Hansen JV and Nelson RD (2003). Forecasting and recombining time-series components by using neural networks. J Opl Res Soc 54: 307–317.CrossRefGoogle Scholar
  8. St John CH, Balakrishnan N and Fiet JO (2000). Modeling the relationship between corporate strategy and wealth creation using neural networks. Comput Opns Res 27: 1077–1092.CrossRefGoogle Scholar
  9. Wong BK, Bodnovich TA and Lai VS-K (2000). The use of cascade-correlation neural networks in University fund raising. J Opl Res Soc 51: 913–920.CrossRefGoogle Scholar
  10. Baesens B, Van Gestel T, Viaene S, Stepanova M, Suykens J and Vanthienen J (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. J Opl Res Soc 54: 627–635.CrossRefGoogle Scholar
  11. Baesens B, Setiono R, Mues C and Vanthienen J (2003). Using neural network rule extraction and decision tables for credit risk evaluation. Mngt Sci 49: 312–329.CrossRefGoogle Scholar
  12. Lisboa PJ, Vellido A and Edisbury B (eds) (2000). Business Applications of Neural Networks. World Scientific: Singapore.CrossRefGoogle Scholar
  13. Wong BK, Lai VS and Lam J (2000). A bibliography of neural network business applications research: 1994–1998. Comput Opns Res 27: 1045–1076.CrossRefGoogle Scholar
  14. Fletcher J and Obradovic Z (1993). Combining prior symbolic knowledge and constructive neural network learning. Connection Sci 5 (3–4): 365–375.CrossRefGoogle Scholar
  15. Shultz TR and Rivest F (2001). Knowledge-based cascade-correlation: using knowledge to speed learning. Connection Sci 13 (1): 43–72.CrossRefGoogle Scholar
  16. Towell GG and Shavlik JW (1993). The extraction of refined rules from knowledge-based neural networks. Machine Learning 13: 71–101.Google Scholar
  17. Cooper LG and Giuffrida G (2000). Turning datamining into a management science tool: new algorithms and empirical results. Mngt Sci 46: 249–264.CrossRefGoogle Scholar
  18. Setiono R, Pan S-L, Hsieh M-H and Azcarraga A (2005). Automatic knowledge extraction from survey data: learning M-of-N constructs using a hybrid approach. J Opl Res Soc 56: 3–14.CrossRefGoogle Scholar
  19. Hertz J, Krogh A and Palmer RG (1991). Introduction to the Theory of Neural Computation. Addision-Wesley: Redwood City, CA.Google Scholar
  20. Setiono R (1997). A penalty function approach for pruning feedforward neural networks. Neural Comput 9: 185–204.CrossRefGoogle Scholar
  21. Dennis Jr JE and Schnabel RB (1983). Numerical Methods for Unconstrained Optimization and Nonlinear Equations. Prentice-Hall: Englewood Cliffs, NJ.Google Scholar
  22. Pratt L and Jennings B (1996). A survey of transfer between connectionist networks. Connection Sci 8: 163–184.CrossRefGoogle Scholar

Copyright information

© Palgrave Macmillan Ltd 2005

Authors and Affiliations

  1. 1.National University of SingaporeSingapore
  2. 2.Yuan Ze UniversityChung-LiTaiwan
  3. 3.De La Salle UniversityManilaPhilippines

Personalised recommendations