Asia pacific journal of information systems

The Korea Society of Management Information Systems (한국경영정보학회)
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Customer Segmentation Model for Internet Banking using Self-organizing Neural Networks and Hierarchical Gustering Method

자기조직화 신경망과 계층적 군집화 기법(SONN-HC)을 이용한 인터넷 뱅킹의 고객세분화 모형구축

  • 신택수 (연세대학교 원주캠퍼스 경영학부) ;
  • 홍태호 (부산대학교 경영학부)
  • Published : 2006.09.30
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Abstract

This study proposes a model for customer segmentation using the psychological characteristics of Internet banking customers. The model was developed through two phased clustering method, called SONN-HC by integrating self-organizing neural networks (SONN) and hierarchical clustering (HC) method. We applied the SONN-HC method to internet banking customer segmentation and performed an empirical analysis with 845 cases. The results of our empirical analysis show the psychological characteristics of Internet banking customers have significant differences among four clusters of the customers created by SONN-HC. From these results, we found that the psychological characteristics of Internet banking customers had an important role of planning a strategy for customer segmentation in a financial institution.

Keywords

References

  1. 김경규, 이정무, 김혜선, "인터넷 뱅킹 채택 행위에 있어서 신뢰와 위험의 영향," 경영학연구, 제32권 제6호, 2003년 12월, pp. 1771-1797
  2. 김대수, 신경망 이론과 응용(I), 하이테크정보, 1992
  3. 김영지, 문현정, 옥수호, 우용태, "사례기반추론 기법을 이용한 개인화된 추천시스템 설계 및 구현," 한국정보처리학회논문지D, 제9D권6호, 2002, pp. 1009-1016
  4. 김정수, 김영걸, "인터넷 뱅킹에서 고객이 인지하는 가치가 고객 충성도에 미치는 영향," 경영정보학연구, 제12권 제4호, 2002, pp. 215-235
  5. 서보밀, "Security Control and Risk Analysis under EC Environment," Ph.D. Thesis, Graduate School of Management, KAIST, 2002
  6. 염창선, 홍재범, "인터넷뱅킹의 고객만족에 영향을 미치는 요인에 대한 실증적 연구," IE Interfaces, 제17권 제3호, 2004, pp. 305-313
  7. 오상현, 김상현, "인터넷뱅킹 이용요인간 구조적 관계: 기술수용모델(TAM)의 확장을 중심으로," 마케팅연구, 제21권 제1호, 2006, pp. 1-27
  8. 유일, 김재전, 김경애, "인터넷 뱅킹 채택 결정요인에 관한 실증연구," Information Systems Review, 제6권 제1호, 2004, pp. 19-36
  9. 이건창, 정남호, 이종신, "국내 인터넷 뱅킹 사용자의 이용의도에 영향을 미치는 요인간 관계에 관한 실증연구," 경영정보학연구, 제12권 제3호, 2002, pp. 191-212
  10. 장경천, STOCKPIA 2006년 1차 은행 평가보고서, 2006(www.stockpia.com)
  11. 최준혁, 김대수, 임기욱, "군집분석과 베이지안 학습을 이용한 웹 도서 동적 추천 시스템," 한국퍼지 및 지능시스템학회논문지, 제12권 제5호, 2002, pp. 385-392
  12. 한국은행, "2005년중 국내 인터넷 뱅킹 서비스 이용현황," 한국은행 보도자료, 2006.2
  13. Aladwani, A.M., "Online Banking: A Field Study of Drivers, Development Challenges, and Expectations," International Journal of Information Management, Vol. 21, 2001, pp. 213-225 https://doi.org/10.1016/S0268-4012(01)00011-1
  14. Billsus, D. and Pazzani, M, "Learning Collaborative Information Filters," In Proceedings of 15th International Conference on Machine Learning(pp. 46-54), Morgan Kaufmann, San Francisco, CA, 1998
  15. Changchien, S.W. and Lu, T., "Mining Association Rules Procedure to Support On-line Recommendation by Customers and Products Fragmentation," Expert Systems with Applications, Vol. 20, No. 4, May 2001, pp. 325-335 https://doi.org/10.1016/S0957-4174(01)00017-3
  16. Cho, Y.H., Kim, J.K., and Kim, S.H., "A Personalized Recommender System based on Web Usage Mining and Decision Tree induction," Expert Systems with Applications, Vol. 23, 2002, pp. 329-342 https://doi.org/10.1016/S0957-4174(02)00052-0
  17. Green, P.E., "A New Approach to Market Segmentation," Business Horizons, Vol. 20, February 1977, pp. 61-73 https://doi.org/10.1016/0007-6813(77)90088-X
  18. Harrison, T.S., "Mapping Customer Segments for Personal Financial Services," International Journal of Bank Marketing, Vol. 12, No. 8, 1994, pp. 17-25
  19. Karypis, G., Han, E.-H., and Kumar, V., "Chameleon: Hierarchical Clustering Using Dynamic Modeling," IEEE Computer, Vol. 32, Aug. 1999, pp. 68-74 https://doi.org/10.1109/2.781637
  20. Khan, A.S. and Hoffmann, A., "Building a Case-Based Diet Recommendation System without a Knowledge Engineer," Artificial Intelligence in Medicine, Vol.27, No. 2, February 2003, pp. 155-179 https://doi.org/10.1016/S0933-3657(02)00113-6
  21. Kohonen, T., "Self-organized Formation of Topologically Correct Feature Maps," Biological Cybernetics, Vol. 43, 1982, pp. 53-69
  22. Kotler, P., Marketing Management Analysis, Planning, Implementation, and Control, 9th ed., Prentice-Hall International, 1997, pp. 257-660
  23. Lee, C.H., Kim, Y.H., and Rhee, P.K., "Web Personalization Expert with Combining Collaborative Filtering and Association Rule Mining Techniques," Expert Systems with Applications, Vol. 21, 2001, pp. 131-137 https://doi.org/10.1016/S0957-4174(01)00034-3
  24. Lee, L.C. and Feber, R., "Use of Time as a Determinant of Family Market Behavior," Journal of Business Research, Vol. 5, 1977, pp. 75-91 https://doi.org/10.1016/0148-2963(77)90027-3
  25. Liao, Z. and Cheung, M.T., "Internet- based e-banking and Consumer Attitudes: An Empirical Study," Information & Management, Vol. 39, No. 1, 2002, pp. 283-295 https://doi.org/10.1016/S0378-7206(01)00097-0
  26. Lin, C.F., "Segmenting Customer Brand Preference: Demographic or Psychographic," Journal of Product & Brand Management, Vol. 11, No. 4, 2002, pp. 249-268 https://doi.org/10.1108/10610420210435443
  27. Luttrell, S.P., "Hierarchical Self-Organizing Neural Networks," in Proceedings of 1st IEE Conference on Artificial Neural Networks, London, U.K., 1989, pp. 2-6
  28. Machauer, A. and Morgner, S., "Segmentation of Bank Customers by Expected Benefits and Attitudes," International Journal of Bank Marketing, Vol. 19, No. 1, 2001, pp. 6-17 https://doi.org/10.1108/02652320110366472
  29. McDougall, G.H.G. and Levesque, T.J., "Benefit Segmentation Using Service Quality Dimensions: An Investigation in Retail Banking," International Journal of Bank Marketing, Vol. 12, 1994, pp. 15-23 https://doi.org/10.1108/02652329410052946
  30. Meidan, A., Bank Marketing Management, Macmillan, New York, NY, 1984
  31. Ravald, A. and Gronroos, C., "The Value Concept and Relationship Marketing," European Journal of Marketing, Vol. 30, No. 2, 1996, pp. 19-30 https://doi.org/10.1108/03090569610106626
  32. Sathye, M., "Adoption of Internet Banking by Australian Consumers: An Empirical Investigation," International Journal of Bank Marketing, Vol. 17, No. 7, 1999, pp. 324-334 https://doi.org/10.1108/02652329910305689
  33. Shergill, G.S. and Li, B., "Internet Banking- An Empirical Investigation of a Trust and Loyalty Model for New Zealand Banks," Journal of Internet Commerce, Vol. 4, No. 4, 2005, pp. 101-118 https://doi.org/10.1300/J179v04n04_07
  34. Suh, B. and Han, I., "Effect of Trust on Customer Acceptance of Internet Banking," Electronic Commerce Research and Applications, Vol. 1, 2002, pp. 247-263 https://doi.org/10.1016/S1567-4223(02)00017-0
  35. Suh, B. and Han, I., "The Impact of Customer Trust and Perception of Security Control on the Acceptance of Electronic Commerce," International Journal of Electronic Commerce, Vol. 7, No. 3, 2003, pp. 135-161
  36. Ultsch, A. and Siemon, H., "Exploratory Data Analysis: Using Kohonen's Topology Preserving Maps," Technical Report 329, Univ. of Dortmund, Dortmund, Germany, 1989
  37. Varfis, A. and Versino, C., "Clustering of Socio-economic Data with Kohonen Maps," Neural Network World, Vol. 2, No. 6, 1992, pp. 813-834
  38. Vellido, A., Lisboa, P.J.G., and Meehan, K., "Segmentation of the On-line Shopping Market Using Neural Networks," Expert Systems with Applications, Vol. 17, No. 4, 1999, pp. 303-314 https://doi.org/10.1016/S0957-4174(99)00042-1
  39. Vesanto, J. and Alhoniemi, E., "Clustering of the Self-Organizing Map," IEEE Transactions on Neural Networks, Vol. 11, No. 3, 2000, pp. 586-600 https://doi.org/10.1109/72.846731
  40. Wang, F. and Shao, H., "Effective Personalized Recommendation based on Time- framed Navigation Clustering and Association Mining," Expert Systems with Applications, Vol. 27, No. 3, October 2004, pp. 365-377 https://doi.org/10.1016/j.eswa.2004.05.005