K

Kaciak, E. & Louviere, J. (1990). Multiple correspondence analysis of multiple choice experiment data. Journal of Marketing Research, 27, 455-465.

Kaciak, E. & Louviere, J. (1992). Multiple correspondence analysis of multiple choice experiment data. Journal of Marketing Research, 29, 455-465.

Kalish, S. & Nelson, P. (1991). A comparison of ranking, rating and reservation price measurement in conjoint analysis. Marketing Letters, 2 (4), 327-335.

Kamakura, W. (1988). A least sqares procedure for benefit segmentation for conjoint experiments. Journal of Marketing Research, 25, 157-167.

Kamakura, W. & Ozer, M. (2000). A multi-trait multi-method validity test of partworth estimates. In A. Gustafsson, A. Herrmann & F. Huber (Eds.), Conjoint measurement - methods and applications (pp. 225-251). Berlin: Springer.

Kamakura, W.A., Wedel, M. & Agrawal, J. (1994). Concomitant variable latent class models for conjoint analysis. International Journal of Research in Marketing, 11 (5), 451-465.

Kantor, J. (1984). The valuation of unlisted shares. An empirical study examining, by using conjoint analysis and other methods, the variables affecting the valuation of Canadian Unlisted Shares. University of Bradford.

Kaplan, R.M., Bush, J.W. & Berry, C.C. (1979). Health status index: Category rating versus magnitude estimation for measuring levels of well-being. Medical Care, 17, 501-523.

Kara, A. (1996). The effectiveness of hybrid conjoint models and the adaptive analytic hierarchy process in predicting choice. Conference Paper, Integration in Marketing: 1996 AMA Summer marketing Educators' Conference, August 3~6, San Diego, CA.

Kara, A., Kaynak, E. & Kucukemiroglu, O. (1994). Credit card developement strategies for the youth market: The use of conjoint analysis. International Journal of Bank Marketing, 12 (6), 30-36.

Karni, R., Sanchez, P. & Tummala, V.R. (1990). A comparative study of multiattribute decision making methodologies. Theory and Decision, 29 (3), 203-222.

Karson, M.J. & Mullet, G.M. (1989). Conjoint utility limits as affected by conjoint design and estimating program. Marketing Research, December, 27-32.

Karson, M.J. & Mullet, G.M. (1991). The effect of design and estimation program on conjoint utility limits: A reply. Marketing Research, March, 50-54.

Katzenstein, H., Kavil, S., Mummalaneni, V. & Dubas, K. (1994). Design of an ideal direct marketing course from the students’ perspective. Journal of Direct Marketing, 8 (2), 66-72.

Kedia, P.K., Green, P.E. & Goldberg, S.M. (1981). A zero-one integer programming algorithm for conjoint analysis. In K. Bernhardt, I. Dolich, M. Etzel, W. Kehoe, T. Kinnear, W. Jr. Perreault & K. Roering (Eds.), 1981 AMA Educators’ Proceedings: The Changing Marketing Environment – New Theories and Applications (No. 47, pp. 326-329). Chicago, IL: American Marketing Association.

Keeney, R.L. & Raiffa, H. (1976). Chapter 10: Aggregation of individual preferences. In R.L. Keeney & H. Raiffa (Eds.), Decisions with multiple objectives: Preferences and value-tradeoffs. New York: Wiley.

Kempe, P. (1992a). Applications of conjoint analysis: Segmentation and new product development. In Marketing Opportunities with Advanced Research Techniques: Proceedings of the second SKIM Seminar (pp. 27-32). Rotterdam: SKIM Market and Policy Research.

Kempe, P. (1992b). Advantages of combining conjoint analysis and perceptual mapping. In Marketing Opportunities with Advanced Research Techniques: Proceedings of the second SKIM Seminar (pp. 27-32). Rotterdam: SKIM Market and Policy Research.

Kersten, G.E. & Noronha, S.J. (1999). WWW-based negotiation support: design implementation, and use. Decision Support Systems, 25 (2), 135-154.

Keuchel, S. (1994). Wirkungsanalyse von Maßnahmen zur Beeinflussung des Verkehrsmittelwahlverhaltens: Eine empirische Untersuchung am Beispiel des Berufsverkehrs in Münster/Westfalen. Göttingen: Vandenhoek & Ruprecht.

Kienast, P., MacLachlan, D., McAlister, L. & Sampson, D. (1983). Employing conjoint analysis in making compensation decisions. Personnel Psychology, 36 (2), 301-313.

Kinnucan, M.T. (1994). Modeling user‘s preferences for document delivery. OCLC Systems and Services, 10 (2), 93-98.

Klein, M. (2002). Wählen als Akt expressiver Präferenzoffenbarung: Eine Anwendung der Conjoint-Analyse auf die Wahl zur Hamburger Bürgerschaft vom 21. September 1997. Frankfurt a. M.:Lang.

Klein, M. (2002). Die Wahrnehmung und Bewertung von Wahlplattformen durch die Wähler: Conjoint-Measurement zur Analyse von Policy-Präferenzen. Planung & Analyse, 20 (1), 52-57.

Klein, N.M. (1986). Assessing unacceptable attribute levels in conjoint analysis. In M. Wallendorf & P. Anderson (Eds.), Advances in consumer research (No. 14, pp. 154-158). Provo, UT: Association for Consumer Research.

Klenosky, D.B. & Perkins, W.S. (1992). Deriving attribute utilities from consideration sets: An alternative to self-explicated utilities. In J.F. Sherry & B. Sternthal (Eds.), Advances in consumer research (No. 19, pp. 657-663). Provo, UT: Association for Consumer Research.

Klenosky, D.B., Benet, S.B. & Chadraba, P. (1996). Assessing czech consumers´ reactions to western marketing practices: A conjoint approach. Journal of Business Research, 36 (2), 189-198.

Knapp, F.D. (1998). Determinanten der Verkehrsmittelwahl. Berlin: Duncker & Humblot.

Knight, G.A. (1999). Consumer preferences for foreign and domestic. Journal of Consumer Marketing, 16 (2), 151-162.

Köcher, W. (1997). Die MaiK-Conjoint-Analyse. Ein neues Verfahren zur computergestützten Ermittlung von Kundenpräferenzen. Marketing ZFP, 3, 141-152.

Kohli, R. & Krishnamuri, R. (1987). A heuristic approach to product design. Management Science, 33 (12), 1523-1533.

Kohli, R. & Mahajan, V. (1991). A reservation-price model for optimal pricing of multiattribute products in conjoint analysis. Journal of Marketing Research, 28 (3), 347-354.

Kohli, R. & Sukumar, R. (1990). Heuristics for product-line design using conjoint analysis. Management Science, 36 (12), 1464-1478.

Kohli, R. (1988). Assessing attribute significance in conjoint analysis: Nonparametric tests and empirical validation. Journal of Marketing Research, 25, 123-133.

Koo, L.C., Tao, F.K.C. & Yeung, J.H.C. (1999). Preferential segmentation of restaurant attributes through conjoint analysis. International Journal of Contemporary Hospitality Management, 11 (5), 241-249.

Kopel, P.S. & Kever, D. (1991). Using adaptive conjoint analysis for the development of lottery games - An Iowa lottery case study. In M. Metegrano (Ed.), 1991 Sawtooth Software Conference Proceedings (pp. 143-154). Ketchum, ID: Sawtooth Software.

Kotler, P. (1973). Mathematical models of individual buyer behavior. In H.H. Kassarjian & T.S. Robertson, Perspectives in consumer behavior (pp. 541-560). Glenview, IL: Scott, Foreman and Company.

Krantz, D.H. & Tversky, A. (1971). Conjoint-measurement analysis of composition rules in psychology. Psychological Review, 78, 151-169.

Krantz, D.H. (1964). Conjoint measurement: The Luce-Tukey axiomatization and some extensions. Journal of Mathematical Psychology, 1, 248-277.

Krantz, D.H., Luce, R.D., Suppes, P. & Tversky, A. (1971). Foundations of measurement. I: Additive and polynomial representations. New York, NY: Academic Press.

Kress, G.J. & Snyder, J. (1994). Analyzing markets — five emerging techniques. In Forecasting and market analysis techniques: A practical approach (pp. 257-282). Westport, CT: Quorum Books.

Krieger, A.M., Green, P.E. & Umesh, U.N. (1998). Effect of level disaggregation on conjoint cross validations: Some comparative findings. Decision Sciences, 29 (4), 1047-1058.

Krishnamurthi, L. & Wittink, D.R. (1991). The value of idiosyncratic functional forms in conjoint analysis. International Journal of Research in Marketing, 8, 301-313.

Krishnamurthi, L. (1983). The salience of relevant others and its effect on individual and joint preferences: An experimental investigation. Journal of Consumer Research, 10, 62-72.

Krishnamurthi, L. (1988). Conjoint models of family decision making. International Journal of Research in Marketing, 5, 185-198.

Krogh, H. (1993). Wer wägt, gewinnt. Manager Magazin, 7, 124-130.

Krupnick, A. & Cropper, M.L. (1992). The effects of information on health risk valuations. Journal of Risk and Uncertainty, 5, 29-48.

Kruskal, J.B. & Carmone, F.J. (1969a). MONANOVA: A FORTRAN IV program for monotone analysis of variance. Behavioral Science, 14, 165-166.

Kruskal, J.B. & Carmone, F.J. (1969b). MONANOVA: A FORTRAN IV program for monotone analysis of variance. Journal of Marketing Research, 6, 497.

Kruskal, J.B. (1965). Analysis of factorial experiments by estimating monotone transformations of the data. Journal the Royal Statistical Society, Series B, 27, 251-263.

Kucher, E. & Hilleke, K. (1993). Value pricing through conjoint measurement: A practical approach. European Management Journal, 11, 283-290.

Kucher, E. & Simon, H. (1987). Durchbruch bei der Preisentscheidung – Conjoint-Measurement: eine neue Technik zur Gewinnoptimierung (USW-working paper (Nr. 2)).

Kucher, E. (1985). Conjoint-Measurement bei Pharmazeutica. Pharma-Marketing Journal, 4, 112-117.

Kuhfeld, W.F. (1996). Multinomial logit, discrete choice modeling. An introduction to designing choice experiments, collecting, processing, and analyzing choice data with the SAS system (Technical Report No. 273). SAS Institute.

Kuhfeld, W.F. (1997). Efficient experimental designs using computerized searches. In Proceedings of the Sawtooth Software Conference (No. 6, pp. 71-85). Seattle, WA: Sawtooth Software.

Kuhfeld, W.F., Tobias, R.D. & Garrat, M. (1994). Efficient experimental design with marketing research applications. Journal of Marketing Research, 31 (November), 545-557.

Kumar, V. & Gaeth, G.J. (1991). Attribute order and product familiarity effects in decision tasks using conjoint analysis. International Journal of Research in Marketing, 8, 113-124.