AllenbyGreg M., AroraNeeraj, and GinterJames L. (1995), “Incorporating Prior Knowledge into the Analysis of Conjoint Studies,”Journal of Marketing Research, 32(May), 152–62.
2.
AllenbyGreg M., and GinterJames L. (1994), “Using Extremes to Design Products and Segment Markets,” Working Paper Series 94–41, College of Business, Ohio State University.
3.
AndersonD. A., and WileyJames B. (1992), “Efficient Choice Set Designs for Estimating Cross-Effects Models,”Marketing Letters, 3(October), 357–70.
4.
BatsellRichard R., and LouviereJordan J. (1991), “Experimental Choice Analysis,”Marketing Letters, 2(August), 199–214.
5.
CarmoneFrank J.Jr., GreenPaul E., and JainArun K. (1978), “Robustness of Conjoint Analysis: Some Monte Carlo Results,”Journal of Marketing Research, 15(May), 300–303.
6.
CarmoneFrank J.Jr., and SchafferCatherine M. (1995), “Review of Conjoint Software,”Journal of Marketing Research, 32(February), 113–20.
7.
CarrollJ. Douglas (1969), “Categorical Conjoint Measurement,” paper presented at Meeting of Mathematical Psychology, Ann Arbor, MI, (August).
8.
CarrollJ. Douglas (1973), “Models and Algorithms for Multidimensional Scaling, Conjoint Measurement, and Related Techniques,” in Multiattribute Decisions in Marketing, GreenP. E., WindY., eds. Hinsdale, IL: Dryden Press, 335–37; 341–48.
9.
CarrollJ. Douglas, PruzanskySandy, and KruskalJoseph B. (1979), “Candelinc: A General Approach to Multidimensional Analysis of Many-Way Arrays with Linear Constraints on Parameters,”Psychometrika, 45(March), 3–24.
10.
CarsonRichard T. (1994), “Experimental Analysis of Choice,”Marketing Letters, 5(October), 351–68.
11.
CattinPhilippe, and WittinkDick R. (1976), “A Monte Carlo Study of Metric and Nonmetric Estimation Methods for Multiattribute Models,” Research Paper No. 341, Graduate School of Business, Stanford University.
12.
DeSarboWayne S., Douglas CarrollJ., LehmannDonald R., and O'ShaughnessyJohn (1982), “Three-Way Multivariate Conjoint Analysis,”Marketing Science, 1(Fall), 323–50.
13.
DeSarboWayne S., GelfandAlan E., and DanesJeffrey (1983), “A Simple Bayesian Procedure for Estimation in a Conjoint Model,”Journal of Marketing Research, 20(February), 29–35.
14.
DeSarboWayne S., and GreenPaul E. (1984), “Choice-Constrained Conjoint Analysis,”Decision Sciences, 15, 297–323.
15.
DeSarboWayne S., OliverRichard L., and RangaswamyArvind (1989), “A Simulated Annealing Methodology for Clusterwise Linear Regression,”Psychometrika, 54(4), 707–36.
16.
DeSarboWayne S., WedelMichel, VriensMarco, and RamaswamyVenkatram (1992), “Latent Class Metric Conjoint Analysis,”Marketing Letters, 3(July), 273–88.
17.
De SoeteGeert, and Douglas CarrollJ. (1983), “A Maximum Likelihood Method for Fitting the Wandering Vector Model,”Psychometrika, 48, 553–66.
18.
DillonWilliam R. (1994), “Issues in the Estimation of Latent Structure Models of Choices,”Marketing Letters, 5(October), 323–34.
19.
ElrodTerry, LouviereJordan J., and DaveyKrishnakumar S. (1992), “An Empirical Comparison of Ratings-Based and Choice-Based Conjoint Models,”Journal of Marketing Research, 24(August), 368–77.
20.
GreenPaul E. (1984), “Hybrid Models for Conjoint Analysis: An Expository Review,”Journal of Marketing Research, 21(May), 155–69.
21.
GreenPaul E., and DeSarboWayne S. (1979), “Componential Segmentation in the Analysis of Consumer Tradeoffs,”Journal of Marketing, 43(Fall), 83–91.
22.
GreenPaul E., GoldbergStephen M., and MontemayorMila (1981), “A Hybrid Utility Estimation Model for Conjoint Analysis,”Journal of Marketing, 45(Winter), 33–41.
23.
GreenPaul E., and HelsenKristiaan (1989), “Cross-Validation Assessment of Alternatives to Individual-Level Conjoint Analysis: A Case Study,”Journal of Marketing Research, 26(August), 346–350.
24.
GreenPaul E., and KriegerAbba M. (1993), “Conjoint Analysis with Product-Positioning Applications,” in Handbooks in OR & MS, Vol. 5, EliashbergJ., and LilienG. L., eds. New York: Elsevier Science Publishers.
25.
GreenPaul E., and KriegerAbba M. (1994), “Hybrid Models in Conjoint Analysis: An Update,” working paper, Wharton School, University of Pennsylvania.
26.
GreenPaul E., and KriegerAbba M. (1995), “Attribute Importance Weights Modification in Assessing a Brand's Competitive Potential,”Marketing Science, in press.
27.
GreenPaul E., KriegerAbba M., and SchafferCatherine M. (1993a), “A Hybrid Conjoint Model with Individual Level Interaction,”Advances in Consumer Research, 20, 1–6.
28.
GreenPaul E., KriegerAbba M., and SchafferCatherine M. (1993b), “An Empirical Test of Optimal Respondent Weighting in Conjoint Analysis,”Journal of the Academy of Marketing Science, 21(Fall), 345–55.
29.
GreenPaul E., and SrinivasanV. (1978), “Conjoint Analysis in Consumer Research: Issues and Outlook,”Journal of Consumer Research, 5(September), 103–23.
30.
GreenPaul E., and SrinivasanV. (1990), “Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice,”Journal of Marketing, 54(October), 3–19.
31.
HagertyMichael R. (1985), “Improving the Predictive Power of Conjoint Analysis: The Use of Factor Analysis and Cluster Analysis,”Journal of Marketing Research, 22(May), 168–84.
32.
HermanSteve (1988), “Software for Full-Profile Conjoint Analysis,” in Proceeding of the Sawtooth Conference on Perceptual Mapping, Conjoint Analysis, and Computer Interviewing., MetegranoM., ed. Ketchum, ID: Sawtooth Software, 117–30.
33.
HuberJoel, WittinkDick R., JohnsonRichard M, and MillerRichard (1992), Proceedings of the Sawtooth Software Conferences, MetegranoM., ed. Ketchum, ID: Sawtooth Software.
Intelligent Marketing Systems, Inc. (1993b), “Ntelogit Version 2.0,”Edmonton, Aberta: Intelligent Marketing Systems.
36.
JohnsonRichard M. (1987), “Adaptive Conjoint Analysis,” in Sawtooth Software Conference on Perceptual Mapping, Conjoint Analysis, and Computer Interviewing., MetegranoM., ed. Ketchum, ID: Sawtooth Software, 253–65.
37.
KamakuraWagner (1988), “A Least Squares Procedure for Benefit Segmentation with Conjoint Experiments,”Journal of Marketing Research, 25(May), 157–67.
38.
KamakuraWagner, WedelMichel, and AgrawalJagdish (1994), “Concomitant Variable Latent Class Models for Conjoint Analysis,”International Journal of Research in Marketing, 11, 451–64.
39.
KriegerAbba M., and GreenPaul E. (1991), “Designing Pareto Optimal Stimuli for Multiattribute Choice Experiments,”Marketing Letters, 2, 337–48.
40.
KrishnamurthiLakshman, and WittinkDick R. (1989), “The Part-Worth Model and Its Applicability in Conjoint Analysis,” working paper, College of Business Administration, University of Illinois.
41.
KruskalJoseph B. (1965), Analysis of Factorial Experiments by Estimating Monotone Transformations of the Data,”Journal of the Royal Statistical Society, Series B, 27, 251–63.
42.
KuhfieldWarren F., TobiasRandall D., and GarrattMark (1994), “Efficient Experimental Designs with Marketing Research Applications,”Journal of Marketing Research, 31(November), 545–57.
43.
LazariAndreas G., and AndersonDonald A. (1994), “Designs of Discrete Choice Set Experiments for Estimating Both Attribute and Availability Cross Effects,”Journal of Marketing Research, 31(August), 375–83.
44.
LenkPeter J., DeSarboWayne S., GreenPaul E., and YoungMartin R. (1994), “Hierarchical Bayes Conjoint Analysis: Recovery of Part-Worth Heterogeneity from Incomplete Designs in Conjoint Analysis,” working paper, School of Business Administration, University of Michigan.
45.
LouviereJordan J. (1988), Analyzing Decision Making: Metric Conjoint Analysis.Beverly Hills, CA: Sage Publications, Inc.
46.
LouviereJordan J., and WoodwardGeorge (1983), “Design and Analysis of Simulated Consumer Choice or Allocation Experiments,”Journal of Marketing Research, 20(November), 350–67.
47.
LuceDuncan R. (1959), Individual Choice Behavior: A Theoretical Analysis.New York: John Wiley & Sons, Inc.
48.
LuceDuncan R., and TukeyJohn W. (1964), “Simultaneous Conjoint Measurement: A New Type of Fundamental Measurement,”Journal of Mathematical Psychology, 1, 1–27.
49.
MahajanVijay, GreenPaul E., and GoldbergStephen M. (1982), “A Conjoint Model for Measuring Self-and Cross-Price Demand Relationships,”Journal of Marketing Research, 19(August), 334–42.
50.
McFaddenDaniel (1974), “Conditional Logit Analysis of Qualitative Choice Behavior,” in Frontiers in Econometrics, ZarembkaP., ed. New York: Academic Press, 105–42.
51.
MooreWilliam L., and SemenikRichard J. (1988), “Measuring Preferences with Hybrid Conjoint Analysis: The Impact of Different Numbers of Attributes in the Master Design,”Journal of Business Research, 16, 261–74.
52.
OgawaK. (1987), “An Approach to Simultaneous Estimation and Segmentation in Conjoint Analysis,”Marketing Science, 6, 66–81.
53.
OliphantKaren, EagleThomas G., LouviereJordan J., and AndersonDon (1992), “Cross-Task Comparison of Ratings-Based and Choice-Based Conjoint,” in 7992 Sawtooth Software Conference Proceedings, MetegranoM., ed. Ketchum, ID: Sawtooth Software.
54.
PekelmanDov, and SenSubrata L. (1979), “Improving Prediction in Conjoint Analysis,”Journal of the American Statistical Association, 75(December), 801–16.
55.
RoskamE. C. I. (1968), Metric Analysis of Ordinal Data in Psychology.Netherlands: Vam Voorschoten.
ShockerAllan D., and SrinivasanV. (1977), “Linmap (Version II): A FORTRAN IV Computer Program for Analyzing Ordinal Preference (Dominance) Judgments Via Linear Programming Techniques for Conjoint Measurement,”Journal of Marketing Research, 14(February), 101–103.
59.
SrinivasanV. (1988), “A Conjunctive-Compensatory Approach to the Self-Explication of Multiattributed Preferences,”Decision Sciences, 19(Spring), 295–305.
60.
SrinivasanV., JainArun K., and MalhotraNaresh K. (1983), “Improving the Predictive Power of Conjoint Analysis by Constrained Parameter Estimation,”Journal of Marketing Research, 20(November), 433–38.
61.
SrinivasanV., and WynerGordon A. (1989), “Casemap: Computer-Assisted Self-Explication of Multi-Attributed Preferences,” in New Product Development and Testing, HenryW., MenascoM., and TakadaH., eds. Lexington, MA: Lexington Books, 91–111.
62.
SteckelJoel H., DeSarboWayne S., and MahajanVijay (1991), “On the Creation of Feasible Conjoint Analysis Experimental Designs,”Decision Sciences, 22, 435–42.
63.
SteenkampJan-Benedict E. M., and WedelMichel (1992), “Fuzzy Clusterwise Regression in Benefit Segmentation: Application and Investigation into its Validity,”Journal of Business Research, 26(March), 237–49.
64.
ThursoneL. L. (1927), “A Law of Comparative Judgement,”Psychological Review, 34, 276–86.
65.
van der LansIvo A., and HeiserWillem H. (1992), “Constrained Part-Worth Estimation in Conjoint Analysis Using the Self-Explicated Utility Model,”International Journal of Research in Marketing, 9, 325–44.
66.
VriensM., WedelM., and WilmsT. J. (1994), “Metric Conjoint Segmentation Methods: A Monte Carlo Comparison,” working paper, Faculty of Economics, University of Groningen.
67.
WedelMichel, and SteenkampJan-Benedict E. M. (1989), “Fuzzy Clusterwise Regression Approach to Benefit Segmentation,”International Journal of Research in Marketing, 6, 241–58.
68.
WedelMichel, and KistemakerCor K. (1989), “Consumer Benefit Segmentation Using Clusterwise Linear Regression,”International Journal of Research in Marketing, 6, 45–49.
69.
WedelMichel, and DeSarboWayne (1993), “A Latent Binomial Logit Methodology for the Analysis of Paired Comparison Choice Data,”Decision Sciences, 24(6), 1157–1170.
70.
WittinkDick, and CattinPhilippe (1989), “Commercial Use of Conjoint Analysis: An Update,”Journal of Marketing, 53(July), 91–96.
71.
WittinkDick, VriensMarco, and BurhenneWim (1994), “Commercial Use of Conjoint in Europe: Results and Critical Reflections,”International Journal of Research in Marketing, 11, 41–52.
72.
YoungForrest W. (1972), “A Model for Polynomial Conjoint Analysis Algorithms,” in Multidimensional Scaling: Theory and Applications in the Behavioral Sciences, Vol. 1, ShepardR. N., RomneyA. K., and NerloveS., eds. New York: Academic Press, 69–104.