Date: 22 Jan 2008
Non-normal simultaneous regression models for customer linkage analysis
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.Get Access
Simultaneous systems of equations with non-normal errors are developed to study the relationship between customer and employee satisfaction. Customers interact with many employees, and employees serve many customers, such that a one-to-one mapping between customers and employees is not possible. Analysis proceeds by relating, or linking, distribution percentiles among variables. Such analysis is commonly encountered in marketing when data are from independently collected samples. We demonstrate our model in the context of retail banking, where drivers of customer and employee satisfaction are shown to be percentile-dependent.
Anderson, E. W., & Mittal, V. (2000). Strengthening the satisfaction-profit chain. Journal of Service Research, 3, 107–120 (Nov).CrossRef
Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science, 12, 125–143 (Spring).CrossRef
Fernandez, C., & Steel, M. F. J. (1998). On Bayesian modeling of fat tails and skewness. Journal of the American Statistical Association, 93, 359–371.CrossRef
Greene, W. H. (2003). Econometric analysis. New Jersey: Prentice Hall.
Heskett, J. L., Jones, T. O., Loveman, G. W., Earl Sasser Jr., W., & Schlesinger, L. A. (1994). Putting the service-profit chain to work. Harvard Business Review, 72, 164–170 (Mar/Apr).
Heskett, J. L., Sasser Jr., W. E., & Schlesinger, L. A. (1997). The service profit chain. New York: Free Press.
Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47, 263–291.CrossRef
Kamakura, W. A., Mittal, V., de Rosa, F., & Mazzon, J. A. (2002). Assessing the service-profit chain. Marketing Science, 21(3), 294–317.CrossRef
Koenker, R. (2005). Quantile regression. Cambridge: Cambridge University Press.
Koenker, R., & Bassett Jr., G. (1978). Regression quantiles. Econometrica, 46, 33–50.CrossRef
Kottas, A., & Krnjajic, M. (2007). Bayesian nonparametric modeling in quantile regression. Working paper, Santa Cruz: Department of Applied Mathematics and Statistics, University of California.
Morgan, N. A., Anderson, E. W., & Mittal, V. (2005). Understanding firms’ customer satisfaction information usage. Journal of Marketing, 69, 131–151.CrossRef
Mittal, V., Ross Jr., W. T., & Baldasare, P. M. (1998). The asymmetric impact of negative and positive attribute-level performance on overall satisfaction and repurchase intentions. Journal of Marketing, 62, 33–47.CrossRef
Newton, M., & Raftery, A. (1994). Approximate Bayesian interference by the weighted likelihood bootstrap. Journal of the Royal Statistical Society Series B, 56, 3–48.
Rossi, P. E., Allenby, G. M., & McCulloch, R. (2005). Bayesian statistics and marketing. New York: Wiley.
Rust, R. T., & Chung, T. S. (2006). Marketing models of service and relationships. Marketing Science, 25, 560–580.CrossRef
Streukens, S., & De Ruyter, K. (2004). Reconsidering nonlinearity and asymmetry in customer satisfaction and loyalty models: An empirical study in three retail service settings. Marketing Letters, 15, 99–111.CrossRef
Yu, K., & Moyeed, R. A. (2001). Bayesian quantile regression. Statistics and Probability Letters, 54, 437–447.CrossRef
Yu, K., & Zhang, J. (2005). A three-parameter asymmetric Laplace distribution and its extension. Communications in Statistics: Theory & Methods, 34, 1867–79.CrossRef
- Non-normal simultaneous regression models for customer linkage analysis
Volume 6, Issue 3 , pp 257-277
- Cover Date
- Print ISSN
- Online ISSN
- Springer US
- Additional Links
- Bayesian analysis
- Customer satisfaction
- Industry Sectors