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A threshold model for respondent heterogeneity

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Abstract

Continuous models of respondent heterogeneity assume the existence of a response function where variables of interest are continuously related to explanatory variables. In many situations this assumption may not be true. In this paper we propose an approach of modeling respondent heterogeneity that identifies abrupt changes in the distribution of response coefficients around a threshold specification. Our model differs from traditional threshold models by introducing the threshold effect to describe across-unit behavior as opposed to within-unit behavior. We illustrate our proposed Bayesian threshold model for survey data from a large national retail bank that examines the effects of service wait times on customer satisfaction. We find evidence of a threshold effect where long in-process wait times are associated with bank branches characterized by weak associations between service quality drivers and overall perceptions of service quality. Branches with wait times below the threshold are found to have much stronger associations.

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Notes

  1. We employ the Newton–Raftery approximation using draws of the posterior distribution to evaluate the model likelihood as described by Rossi et al. (2005) on p. 166 (cf., Lenk 2009).

References

  • Allenby, G. M., & Ginter, J. L. (1995). Using extremes to design products and segment markets. Journal of Marketing Research, 32, 392–403.

    Article  Google Scholar 

  • Allenby, G. M., Arora, N., & Ginter, J. L. (1998). On the Heterogeneity of Demand. Journal of Marketing Research, 35, 384–389.

    Article  Google Scholar 

  • Bauwens, L., Lubrano, M., & Richard, J. F. (1999). Bayesian inference in dynamic econometric models. New York: Oxford University Press.

    Google Scholar 

  • Brady, M. K., & Cronin, J. P. (2001). Some new thoughts on conceptualizing perceived service quality: A hierarchical approach. Journal of Marketing, 65, 34–49.

    Article  Google Scholar 

  • Clemmer, E., & Schneider, B. (1993). Managing customer dissatisfaction with waiting: Applying Social-Psychological Theory in a service setting. Advances in Services Marketing and Management, 2, 213–229.

    Google Scholar 

  • Davis, M. M., & Maggard, M. J. (1990). An analysis of customer satisfaction with waiting times in a two-stage service process. Journal of Operations Management, 9(3), 324–334.

    Article  Google Scholar 

  • Fruhwirth-Schnatter, S. (2006). Finite Mixture and Markov Switching Models. Springer Series in Statistics (pp. 274–275). Springer: New York.

    Google Scholar 

  • Gilbride, T. J., & Allenby, G. M. (2004). A choice model with conjunctive, disjunctive and compensatory screening rules. Marketing Science, 23(3), 391–406.

    Article  Google Scholar 

  • Haynes, P. (1990). Hating to wait: Managing the final service encounter. Journal of Services Marketing, 4(4), 20–26.

    Article  Google Scholar 

  • Hui, M., & Tse, D. K. (1996). What to tell consumers in waits of different lengths: An integrative model of service evaluation. Journal of Marketing, 60, 81–90.

    Article  Google Scholar 

  • Hui, M., Thakor, M. V., & Gill, R. (1998). The effect of delay type and service stage on consumers’ reactions to waiting. Journal of Consumer Research, 24, 469–479.

    Article  Google Scholar 

  • Hui, M., Zhao, X., Fan, X., & Au, K. (2004). When does the service process matter? A test of two competing theories. Journal of Consumer Research, 31, 465–475.

    Article  Google Scholar 

  • Lenk, P. (2009). Simulation pseudo-bias correction to the harmonic mean estimator of integrated likelihoods. Journal of Computational and Graphical Statistics, 18(4), 941–960.

    Article  Google Scholar 

  • Lenk, P. J., & DeSarbo, W. S. (2000). Bayesian inference for finite mixtures of generalized linear models with random effects. Psychometrika, 65(1), 93–119.

    Article  Google Scholar 

  • Maister, D. H. (1985). The Psychology of Waiting Lines, the Service Encounter (pp. 113–123). Lexington Books: Lanham.

    Google Scholar 

  • Meyer, T. (1994). Subjective importance of goal and reactions to waiting in line. Journal of Social Psychology, 134(6), 819–827.

    Article  Google Scholar 

  • Parasuraman, A., Berry, L. L., & Zeithaml, V. A. (1991). Refinement and reassessment of the SERVQUAL scale. Journal of Retailing, 67(4), 420–450.

    Google Scholar 

  • Rossi, P. E., Allenby, G. M., & McCulloch, R. (2005). Bayesian Statistics and Marketing. New York: Wiley.

    Book  Google Scholar 

  • Sureshchandar, G. S., Chandrasekharan, R., & Anantharaman, R. N. (2002). Determinants of customer-perceived service quality: a confirmatory factor analysis approach. Journal of Services Marketing, 16(1), 9–34.

    Article  Google Scholar 

  • Swait, J. (2001). A non-compensatory model incorporating attribute cutoffs. Transportation Research. Part B, 35, 903–928.

    Article  Google Scholar 

  • Teas, R. K. (1993). Expectations, performance evaluation, and consumers' perceptions of quality. Journal of Marketing, 57(4), 18–34.

    Article  Google Scholar 

  • Terui, N., & Ban, M. (2007). Modeling heterogeneous effective advertising stock using single-source data. Mimeograph, presented at The Frank M. Bass Conference for Marketing Science.

  • Wedel, M., & Kamakura, W. (2000). Market Segmentation Conceptual and Methodological Foundations (2nd ed.). Boston: Kluwer Academic Press.

    Google Scholar 

  • Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60, 31–46.

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Kelley School of Business, Indiana University, Bloomington, IN, USA

    Sandeep R. Chandukala

  2. College of Business Administration, The University of Toledo, Toledo, OH, USA

    Sylvia Long-Tolbert

  3. Fisher College of Business, Ohio State University, Columbus, OH, USA

    Greg M. Allenby

Authors
  1. Sandeep R. Chandukala
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  2. Sylvia Long-Tolbert
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  3. Greg M. Allenby
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Correspondence to Sandeep R. Chandukala.

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Chandukala, S.R., Long-Tolbert, S. & Allenby, G.M. A threshold model for respondent heterogeneity. Mark Lett 22, 133–146 (2011). https://doi.org/10.1007/s11002-010-9115-0

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  • DOI: https://doi.org/10.1007/s11002-010-9115-0

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