Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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Estimation of a Structural Equation Model Including Brand Choice Probabilities

브랜드 선택확률 분석을 위한 구조방정식 모형

  • Lee, Sang-Ho (Department of Industrial and Management Engineering POSTECH) ;
  • Lee, Hye-Seon (Department of Industrial and Management Engineering POSTECH) ;
  • Kim, Yun-Dae (Department of Industrial and Management Engineering POSTECH) ;
  • Jun, Chi-Hyuck (Department of Industrial and Management Engineering POSTECH)
  • 이상호 (포항공과대학교 산업경영공학과) ;
  • 이혜선 (포항공과대학교 산업경영공학과) ;
  • 김윤대 (포항공과대학교 산업경영공학과) ;
  • 전치혁 (포항공과대학교 산업경영공학과)
  • Received : 2010.02.09
  • Accepted : 2010.05.13
  • Published : 2010.06.01
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Abstract

The partial least squares (PLS) method is popularly used for estimating the structural equation model, but the existing algorithm may not be directly implemented when probabilities are involved in some constructs or manifest variables. We propose a structural equation model including the brand choice as one construct having brand choice probabilities as its manifest variables. Then, we develop a PLS-based algorithm for the structural equation model by utilizing the multinomial logit model. A case is introduced as an application and simulation studies are performed to validate the proposed algorithm.

Keywords

References

  1. Bagozzi, R. P. (1980), Causal Models in Marketing, John Wiley and Sons, New York.
  2. Berger, P. D. and Nasr, N. I. (1998), Customer lifetime value : marketing models and applications, Journal of Interactive Marketing, 12(Winter), 17-30. https://doi.org/10.1002/(SICI)1520-6653(199824)12:1<17::AID-DIR3>3.0.CO;2-K
  3. Blattberg, R. C. and Deighton, J. (1996), Manage marketing by the customer equity test, Harvard Business Review, 74(4), 136-144.
  4. Fornell, C. and Bookstein, F. L. (1982), Two structural equation models : LISREL and PLS applied to consumer exit-void theory, Journal of Marketing Research, 19, 440-452. https://doi.org/10.2307/3151718
  5. Fornell, C., Johnson, M. D., Anderson, E. W., Cha, J., and Bryant, B. E. (1996), The American customer satisfaction index : nature, purpose, and findings, Journal of Marketing, 60, 7-18. https://doi.org/10.2307/1251898
  6. Fornell, C. and Larcker, D. F. (1981), Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research, 18, 39-50. https://doi.org/10.2307/3151312
  7. Guinot, C., Latreille, J. and Tenenhaus, M. (2001), PLS path modeling and multiple table analysis, Application to the cosmetic habits of women in Ile-de-France, Chemometrics and Intelligent Laboratory Systems, 58, 247-259. https://doi.org/10.1016/S0169-7439(01)00163-0
  8. Hellier, P. K., Geursen, G. M., Carr, R. A., and Rickard, J. A. (2003), Customer repurchase intention : a general structural equation model, European Journal of Marketing, 37, 1762-1800. https://doi.org/10.1108/03090560310495456
  9. Hulland, J. (1999), Use of partial least squares (PLS) in strategic management research : a review of four recent studies, Strategic Management Journal, 20, 195-204. https://doi.org/10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7
  10. Joreskog, K. A. and Sorbom, D. (1989), LISREL 7 User's Reference Guide, 1st ed., Scientific Software, Mooresville, IN, USA.
  11. Martensen, A., Gronholdt, L., and Kristensen, K. (2000), The drivers of customer satisfaction and loyalty : cross-industry findings from Denmark, Total Quality Management, 11, S544-553. https://doi.org/10.1080/09544120050007878
  12. Noonan, R. and Wold. H. (1982), "PLS path modeling with indirectly observed variables : A comparison of alternative estimates for the latent variable," In Joreskog, K., and H. Wold, (Eds.), Systems under indirect observation : Causality, structure, prediction, Amsterdam : North Holland Publishing, 1-54.
  13. Rust, R. T., Lemon, K. N., and Zeithaml, V. A. (2004), Return on marketing : using customer equity to focus marketing strategy, Journal of Marketing, 68, 109-127. https://doi.org/10.1509/jmkg.68.1.109.24030
  14. Tenenhaus, M., Vinzi, V. E., Chatelin, Y., and Lauro, C. (2005), PLS path modeling, Computational Statistics and Data Analysis, 48, 159-205. https://doi.org/10.1016/j.csda.2004.03.005