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«ABSTRACT Conjoint Analysis (CA) is a very popular class of methods for measuring consumer preferences, both in research and practice. However, since ...»

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Several empirical studies have shown that AHP at least can keep up with traditional CA. To increase the practical impact of such empirical comparisons, we run AHP (with slight modifications with regard to the hierarchy, the scale and the number of pairwise comparisons conducted) against the popular Choice-Based Conjoint Analysis in an online survey. The empirical study focuses on the comparison in a real online consumer research setting with non-academic respondents to investigate the applicability as well as predictive and convergent validity of the two approaches.

The measures used in this study show that AHP is a good alternative for estimating market shares.

AHP outperformed CBC in market share prediction and was almost as accurate as CBC with respect to first choice hit rates. This is an important finding which suggests AHP as a promising tool for preference measurement. Moreover, the results show that AHP is slightly better with respect to face validity as well as the subjective evaluation of the approaches by the respondents. The AHP survey was rated as being more realistic, less difficult and more enjoyable. No significant differences were found concerning the average interview length.

M. Meißner, R. Decker/ Measuring Consumer Preferences with CBC and AHP As outlined in the theoretical part of the paper, Harker’s (1987) technique for incomplete pairwise comparison matrices can be used to reduce respondents’ burden and with that the total interview length. In this context future research should investigate how many pairwise comparisons are needed to ensure a high predictive validity of the results.

Hierarchical structuring of decision problems has been found to be one common theme in the way humans deal with complexity (Forman and Gass, 2001). In this study, we used a simple 3-level hierarchy for modeling the product evaluation problem. However, further empirical investigations are needed to determine the influence different hierarchical structures have on preference measurement.

By using hierarchies with more levels, the number of pairwise comparisons required could be further reduced. Albeit, in consumer online surveys respondents might have problems with the comparison of product categories consisting of multiple attributes. We leave this aspect for future research.

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Andrews, R.L., Ansari, A., & Currim, I.S. (2002). Hierarchical bayes versus finite mixture conjoint analysis models: A comparison of fit, prediction and partworth recovery, Journal of Marketing Research, 39, 87-98.

Chrzan, K., & Orme, B. (2000). An overview and comparison of design strategies for choice-based conjoint analysis, Sawtooth Sequim.

Forman, E.H. (1990). Random indices for incomplete pairwise comparison matrices. European Journal of Operational Research, 48, 153-155.

Forman, E.H., & Gass, S.I. (2001). The analytic hierarchy process: An exposition, Operations Research, 49, 4, 469-486.

Gass, S.I., & Standard, S.M. (2002). Characteristics of positive reciprocal matrices in the analytic hierarchy process. Journal of the Operational Research Society, 53, 1385-1389.

Green, P.E., & Srinivasan, V. (1990). Conjoint analysis in marketing: New developments with implications for research and practice. Journal of Marketing, 54, 3, 3-19.

Green, P.E., Krieger, A.M., & Wind, Y. (2001). Thirty years of conjoint analysis: Reflections and prospects. Interfaces, 31, 3, 56-73.

Haaijer, R., & Wedel, M. (2007). Conjoint choice experiments: General characteristics and alternative model specifications. In A. Gustafsson et al. (Eds.), Conjoint Measurement (pp.199-229), Berlin: Springer.

Harker, P.T. (1987). Incomplete pairwise comparisons in the analytic hierarchy process. Mathematical Modelling, 9, 11, 837-848.

Helm, R., Scholl, A., Manthey, L., & Steiner, M. (2004a). Measuring customer preferences in new product development: Comparing compositional and decompositional methods. International Journal of Product Development, 1, 1, 12-29.

Helm, R., Steiner, M., Scholl, A., & Manthey, L. (2004b). A comparative empirical study on common methods for measuring preferences. Jenaer Schriften zur Wirtschaftswissenschaft, from http://www.wiwi.uni-jena.de/Papers/wp-sw2004.pdf.

Proceedings of the International Symposium on the Analytic Hierarchy Process 2009 Ishizaka, A., Balkenborg, D., & Kaplan, T. (2005). AHP does not like compromises: The role of measurement scales. Joint-Workshop on Decision Support Systems, Experimental Economics & e-Participation 2005, Graz, 46-57.

Ji, P., & Jiang, R. (2003). Scale transitivity in the AHP. Journal of the Operational Research Society, 54, 896-905.

Johnson, R. (1997). Including holdout choice tasks in conjoint studies. Sawtooth software research paper series, Sequim.

Meißner, M., Scholz, S.W., & Decker, R. (2008). AHP versus ACA – An empirical comparison. In C.

Preisach et al. (Eds.), Data analysis, machine learning, and applications (pp. 447-454), Berlin:

Springer.

Miyake, C., Satomi, H., Keikichi, O., & Shinohara, M. (2003). 2-cyclic design in AHP. Journal of the Operations Research Society of Japan, 46, 4, 429-447.

Morera, O.F., & Budescu, D.V. (2001). Reduction of random error in analytic hierarchies: A comparison of holistic and decompositional decision strategies. Journal of Behavioral Decision Making, 14, 223-242.

Mulye, R. (1998). An empirical comparison of three variants of the AHP and two variants of Conjoint Analysis. Journal of Behavioral Decision Making, 11, 263-280.

Myers, J.H., & Alpert, M.I. (1968). Determinant buying attitudes: Meaning and measurement. Journal of Marketing, 32, 4, 13-20.

Orme, B.K., Alpert, M.I., & Christensen, E. (1997). Assessing the validity of conjoint analysis – Continued. Sawtooth Software Research Paper Series, Sequim.

Orme, B.K. (2002). Formulating attributes and levels in conjoint analysis, Sawtooth Software Research Paper Series, Sequim.

Pöyhönen, M., Hämäläinen, R.P., & Salo, A.A. (1997). An experiment on the numerical modelling of verbal ratio statements. Journal of Multi-Criteria Decision Analysis, 6, 1-10.

Rao, V.R. (2007). Developments in conjoint analysis. In B. Wierenga (Ed.), Handbook of marketing decision models (pp. 23-53), New York: Springer.

Saaty, T.L. (1980). The Analytic Hierarchy Process, New York: McGraw Hill.

Saaty, T.L. (2003). Decision-making with the AHP: Why is the principal eigenvector necessary?

European Journal of Operational Research, 145, 85-91.

Salo, A.A., & Hämäläinen, R.P. (1997). On the measurement of preferences in the analytic hierarchy process. Journal of Multi-criteria Decision Analysis, 6, 309-319.

Sawtooth Software (2005a). Report on conjoint analysis usage among saw tooth software customers, from http://www.sawtoothsoftware.com.

Sawtooth Software (2005b). The CBC/HB system for hierarchical bayes estimation. Technical paper series – Sawtooth Software Inc.

Sawtooth Software (2008). The CBC system for choice-based conjoint analysis. Technical paper series – Sawtooth Software Inc.

M. Meißner, R. Decker/ Measuring Consumer Preferences with CBC and AHP Schwartz, R.G., & Oren, S.S. (1988). Using analytic hierarchies for consumer research and market modeling. Mathematical and Computer Modeling, 11, 266-271.

Tscheulin, D.K. (1991). Ein empirischer Vergleich der Eignung von Conjoint-Analyse und Analytic Hierarchy Process (AHP) zur Neuproduktplanung. Zeitschrift für Betriebswirtshaft, 61, 1267Vargas, L.G. (1990). An overview of the analytic hierarchy process and its applications. European Journal of Operational Research, 48, 2-8.

Von Nitzsch, R., & Weber, M. (1993). The effect of attribute ranges on weights in multiattribute utility measurements. Management Science, 39, 8, 937-943.

Wind, Y., & Saaty, T.L. (1980). Marketing applications of the Analytic Hierarchy Process.

Management Science, 26, 641-658.



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