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To divide or not to divide? The impact of partitioned pricing on the informational and sacrifice effects of price

Abstract

Firms often partition a product’s price into two mandatory parts (e.g., the base price of a mail-order DVD and the surcharge for shipping and handling) instead of charging one all-inclusive price. This study examines whether and to what extent partitioned pricing (compared to one all-inclusive price) influences the informational and sacrifice effects of price. We empirically show that partitioned pricing oppositely affects these two distinct roles of price: the informational effect of price (i.e., price as an indicator of quality) increases, while the sacrifice effect (i.e., price as a measure of sacrifice) becomes more negative. In product categories with substantial price–quality inferences, the positive impact of partitioned pricing on the informational effect can overcompensate for its negative impact on the sacrifice effect, making partitioned prices the preferable strategy.

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Notes

  1. Several studies provide empirical evidence that a scenario in which the respondent has to pay for the product and a second scenario in which someone else is paying can decompose the total effect of price in its positive and negative component in a conjoint analysis setting (e.g., Rao and Sattler 2003; Völckner 2008; Theysohn et al. 2011).

  2. The choice share of the most expensive alternative ranges between 23.3 % and 40 %. In other words, when consumers get the wine for free, they did not automatically choose the most expensive alternative, which provides further confidence in the measurement of the two price response components.

  3. To identify potential outliers with regard to the mean total and informational effects of price, we calculated the lower and upper quartiles (LQ and UQ) and the interquartile range (IQR = UQ − LQ). Cases beyond the quartiles by 1.5 IQRs are considered as outliers (e.g., Tukey 1977). Table 1 (lower half) reports the sample sizes with the outliers excluded (n = 318, which equals 88.8 % of the final effective sample).

  4. Because the two conjoint scenarios (total effect and informational effect of price) can be interpreted as a within-subject factor, we also estimated a repeated measures ANOVA with the pricing method as a between-subject factor and the total/informational effect of price as a within-subject factor. We again found a significant increase of the informational effect of price in the partitioned pricing conditions compared to the all-inclusive condition.

References

  • Allenby, G. M., & Lenk, P. J. (1994). Modeling household purchase behavior with logistic normal regression. Journal of the American Statistical Association, 89(428), 1218–1231.

    Article  Google Scholar 

  • Alpert, M. I. (1971). Identification of determinant attributes: A comparison of methods. Journal of Marketing Research, 8(2), 184–191.

    Article  Google Scholar 

  • Arora, N., Huber, J., Mick, D. G., & Kamakura, W. (2001). Improving parameter estimates and model prediction by aggregate customization in choice experiments. Journal of Consumer Research, 28(2), 273–283.

    Article  Google Scholar 

  • Bertini, M., & Wathieu, L. (2008). Research note: Attention arousal through price partitioning. Marketing Science, 27(2), 236–246.

    Article  Google Scholar 

  • Bornemann, T., & Homburg, C. (2011). Psychological distance and the dual role of price. Journal of Consumer Research, 38(3), 490–504.

    Article  Google Scholar 

  • Brucks, M., Zeithaml, V. A., & Naylor, G. (2000). Price and brand name as indicators of quality dimensions for consumer durables. Journal of the Academy of Marketing Science, 28(3), 359–374.

    Article  Google Scholar 

  • Burman, B., & Biswas, A. (2007). Partitioned pricing: Can we always divide and prosper? Journal of Retailing, 83(4), 423–436.

    Article  Google Scholar 

  • Carlson, J. P., & Weathers, D. (2008). Examining differences in consumer reactions to partitioned prices with a variable number of price components. Journal of Business Research, 61(7), 724–731.

    Article  Google Scholar 

  • Cheema, A. (2008). Surcharges and seller reputation. Journal of Consumer Research, 35(1), 167–177.

    Article  Google Scholar 

  • Cronley, M. L., Posavac, S. S., Meyer, T., Kardes, F. R., & Kellaris, J. J. (2005). A selective hypothesis testing perspective on price-quality inference and inference-based choice. Journal of Consumer Psychology, 15(2), 159–169.

    Article  Google Scholar 

  • Darke, P. R., & Chung, C. M. J. (2005). Effects of pricing and promotion on consumer perceptions: It depends on how you frame it. Journal of Retailing, 81(1), 35–47.

    Article  Google Scholar 

  • Diehl, K., Kornish, L. J., & Lynch, J. G. (2003). Smart agents: When lower search costs for quality information increase price sensitivity. Journal of Consumer Research, 30(1), 56–71.

    Article  Google Scholar 

  • Erickson, G. M., & Johansson, J. K. (1985). The role of price in multiattribute product evaluations. Journal of Consumer Research, 12(2), 195–199.

    Article  Google Scholar 

  • Forsythe, S., & Shi, B. (2003). Consumer patronage and risk perceptions in Internet shopping. Journal of Business Research, 56(11), 867–875.

    Article  Google Scholar 

  • Greene, W. H. (2008). Econometric analysis (6th ed.). New Jersey: Pearson Education.

    Google Scholar 

  • Hamilton, R. W., & Srivastava, J. (2008). When 2 + 2 is not the same as 1 + 3: Variations in price sensitivity across components of partitioned prices. Journal of Marketing Research, 45(4), 450–461.

    Article  Google Scholar 

  • Huber, J., & Zwerina, K. (1996). The importance of utility balance in efficient choice designs. Journal of Marketing Research, 33(3), 307–317.

    Article  Google Scholar 

  • Huber, J., Wittink, D. R., Fiedler, J. A., & Miller, R. (1993). The effectiveness of alternative preference elicitation procedures in predicting choice. Journal of Marketing Research, 30(1), 105–114.

    Article  Google Scholar 

  • Iyengar, R., Jedidi, K., & Kohli, R. (2008). A conjoint approach to multipart pricing. Journal of Marketing Research, 45(2), 195–210.

    Article  Google Scholar 

  • Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.

    Article  Google Scholar 

  • Kardes, F. R., Cronley, M. L., Kellaris, J. J., & Posavac, S. S. (2004). The role of selective information processing in price-quality inference. Journal of Consumer Research, 31(September), 368–374.

    Article  Google Scholar 

  • Kardes, F. R., Cronley, M. L., & Kim, J. (2006). Construal-level effects on preference stability, preference-behavior correspondence, and the suppression of competing brands. Journal of Consumer Psychology, 16(2), 135–144.

    Article  Google Scholar 

  • Lal, R., & Sarvary, M. (1999). When and how is the internet likely to decrease price competition? Marketing Science, 18(4), 485–503.

    Article  Google Scholar 

  • Lee, Y. H., & Han, C. Y. (2002). Partitioned pricing in advertising: Effects on brand and retailer attitudes. Marketing Letters, 13(1), 27–40.

    Article  Google Scholar 

  • Lichtenstein, D., & Burton, S. (1989). The relationship between perceived and objective price-quality. Journal of Marketing Research, 26(4), 429–443.

    Article  Google Scholar 

  • Liu, M. W., & Soman, D. (2008). Behavioral Pricing. In C. P. Haugtvedt, P. M. Herr, & F. R. Kardes (Eds.), Handbook of consumer psychology (pp. 659–681). New York: Psychology Press.

    Google Scholar 

  • Lynch, J. G., & Ariely, D. (2000). Wine online: Search costs affect competition on price, quality, and distribution. Marketing Science, 19(1), 83–103.

    Article  Google Scholar 

  • Morwitz, V. G., Greenleaf, E. A., & Johnson, E. J. (1998). Divide and prosper: Consumers’ reactions to partitioned prices. Journal of Marketing Research, 35(4), 453–463.

    Article  Google Scholar 

  • Orme, B. (2006). SSI Web v5: Software for web interviewing and conjoint analysis. Sequim: Sawtooth Software, Inc.

    Google Scholar 

  • Orme, B. (2004). The CBC/HB system for hierarchical Bayes estimation version 3.2. Sequim: Sawtooth Software, Inc.

    Google Scholar 

  • Rao, A. R., & Monroe, K. B. (1988). The moderating effect of prior knowledge on cue utilization in product evaluations. Journal of Consumer Research, 15(2), 253–264.

    Article  Google Scholar 

  • Rao, V. R. & Sattler, H. (2003). Measurement of price effects with conjoint analysis. Separating informational and allocative effects of price. In: Conjoint measurement: methods and applications. Eds. Gustafsson, A., Herrmann, A., & Huber, F., 3rd ed., Berlin et al., Springer, 47–66.

  • Sheng, S., Bao, Y., & Pan, Y. (2007). Partitioning or bundling? Perceived fairness of the surcharge makes a difference. Psychology & Marketing, 24(12), 1025–1041.

    Article  Google Scholar 

  • Shiv, B., Carmon, Z., & Ariely, D. (2005). Placebo effects of marketing actions: Consumers may get what they pay for. Journal of Marketing Research, 42(4), 383–393.

    Article  Google Scholar 

  • Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4(3), 199–204.

    Article  Google Scholar 

  • Theysohn, S., Klein, K., Völckner, F., & Spann, M. (2011). Dual effect-based market segmentation and price optimization. Journal of Business Research, available online 16. December 2011 (http://dx.doi.org/10.1016/j.jbusres.2011.11.007).

  • Tukey, J. W. (1977). Exploratory data analysis. Reading: Addison Wesley.

    Google Scholar 

  • Völckner, F. (2008). The dual role of price: Decomposing consumers’ reactions to price. Journal of the Academy of Marketing Science, 36(3), 359–377.

    Article  Google Scholar 

  • Völckner, F., & Hofmann, H. (2007). Perceived price–quality relationship. A meta–analytic review and assessment of its determinants. Marketing Letters, 18(3), 181–196.

    Article  Google Scholar 

  • Ward, M. R., & Lee, M. J. (2000). Internet shopping, consumer search and product branding. Journal of Product & Brand Management, 9(1), 6–20.

    Article  Google Scholar 

  • Xia, L., & Monroe, K. B. (2004). Price partitioning on the internet. Journal of Interactive Marketing, 18(4), 63–73.

    Article  Google Scholar 

  • Yan, D., & Sengupta, J. (2011). Effects of construal level on the price-quality relationship. Journal of Consumer Research, 38(2), 376–389.

    Article  Google Scholar 

Download references

Acknowledgment

The authors would like to thank Henrik Sattler for his helpful comments on previous versions of this manuscript and Sven Theysohn for his help with the data collection. The authors are also grateful for the constructive feedback received during the presentation of this paper at the European Marketing Academy Conference in Nantes.

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Correspondence to Franziska Völckner, Alexander Rühle or Martin Spann.

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Völckner, F., Rühle, A. & Spann, M. To divide or not to divide? The impact of partitioned pricing on the informational and sacrifice effects of price. Mark Lett 23, 719–730 (2012). https://doi.org/10.1007/s11002-012-9174-5

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  • DOI: https://doi.org/10.1007/s11002-012-9174-5

Keywords

  • Partitioned pricing
  • Price response of demand
  • Dual role of price
  • Price-perceived quality inference