Current issues and a 'wish list' for conjoint analysis

Applied Stochastic Models in Business and Industry

Volumn 21, Issue 4-5, 2005, Pages 319-323

  Bradlow, Eric T ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Conjoint analysis; Educational testing; Massive number of attributes; Non compensatory models; Within task learning

Indexed keywords

COSTS; CUSTOMER SATISFACTION; RANDOM PROCESSES;

CONJOINT ANALYSIS; EDUCATIONAL TESTING; MASSIVE NUMBER OF ATTRIBUTES; NON-COMPENSATORY MODEL; WITHIN-TASK LEARNING;

MARKETING;

EID: 24144440397     PISSN: 15241904     EISSN: None     Source Type: Journal    
DOI: 10.1002/asmb.559     Document Type: Article

References (21)

1. Green PE, Wind Y. New ways to measure consumer judgments. Harvard Business Review 1975; 53(July-August): 107-117.
2. Green PE, Krieger AM, Wind Y. Thirty years of conjoint analysis: reflections and prospects. Interfaces 2001; 31(May-June):S56-S73.
3. Green PE, Rao V. Conjoint measurement: a new approach to quantify judgmental data. Journal of Marketing Research 1971; 8(3):355-363.
4. Lenk PJ, DeSarbo WS, Green PE, Young MR. Hierarchical Bayes conjoint analysis: recovery of partworth heterogeneity from incomplete designs in conjoint analysis. Marketing Science 1996; 15(2): 173-191.
5. Huber J, Zwerina K. The importance of utility balance in efficient choice designs. Journal of Marketing Research 1996; 33(August):307-317.
6. Toubia O, Simester DI, Hauser JR, Dahan E. Fast polyhedral adaptive conjoint estimation. Marketing Science 2003; 22(3):273-303.
7. Toubia O, Hauser JR, Simester DI. Polyhedral methods for adaptive choice-based conjoint analysis. Journal of Marketing Research 2004; 4(1): 116-131.
8. Rossi PE, Allenby GM. A Bayesian approach to estimating household parameters. Journal of Marketing Research 1993; 30:171-182.
9. Liechty JC, Fong DKH, DeSarbo WS. Dynamic models incorporating individual heterogeneity: utility evolution in conjoint analysis. Marketing Science 2005; 24:285-293.
10. Green PE, Krieger AM. A hybrid conjoint model for price-demand estimation. European Journal of Operational Research 1990; 44(October):28-38.
11. Green PE, Krieger AM. A consumer based approach to designing product line extensions. Journal of Product Innovation Management 1987; 4(1):21-32.
12. Bradlow ET, Hu Y, Ho T-H. A learning-based model for imputing missing levels in partial conjoint profiles. Journal of Marketing Research 2004; 41(4):369-381.
13. Johnson RD. Making judgments when information is missing: inferences, biases, and framing effects. Acta Psychologica 1987; 66:69-82.
14. Louviere JJ, Johnson RD. Reliability and validity of the brand-anchored conjoint approach to measuring retailer images. Journal of Retailing 1990; 66(4, Winter):359-381.
15. Meyer R. A model of multiattribute judgments under attribute uncertainty and informational constraint. Journal of Consumer Research 1981; 18(November):428-441.
16. Gilbride TJ, Allenby GM. A choice model with conjunctive, disjunctive, and compensatory screening rules. Marketing Science 2004; 23(3):391-406.
17. Marshall P, Bradlow ET. A unified approach to conjoint analysis models. Journal of the American Statistical Association 2002; 97(459):674-682.
18. Wainer H, Dorans N, Eignor D, Flaugher R, Green B, Mislevy R, Steinberg L, Thissen D. Computerized Adaptive Testing: A Primer (2nd edn.). Lawrence Erlbaum Associates: Hillsdale, NJ, 2000.
19. Krieger AM, Green PE, Umesh UN. Effect of level of disaggregation on conjoint cross validations: some comparative findings. Decision Sciences 1998; 29(Fall): 1049-1060.
20. Farquhar PH, Rao V. A balance model for evaluating subsets of multiattributed items. Management Science 1976; 22:528-539.
21. Feinberg FM, Kahn BE, McAlister L. Market share response when consumers seek variety. Journal of Marketing Research 1992; 29(May):227-237.