Σύγκριση μεθόδων

	Choice-Based Conjoint	Adaptive Conjoint Analysis
Πεδίο	Marketing Research	Marketing Research
Οικογένεια	Regression model	Process / pipeline
Έτος προέλευσης	1983	1987
Δημιουργός	Jordan J. Louviere & George Woodworth; Sawtooth Software (Bryan Orme)	Richard M. Johnson (Sawtooth Software)
Τύπος	Discrete-choice experiment for product preference and part-worth utilities	Computer-adaptive conjoint combining self-explication and paired comparisons
Θεμελιώδης πηγή	Louviere, J. J., & Woodworth, G. (1983). Design and Analysis of Simulated Consumer Choice or Allocation Experiments: An Approach Based on Aggregate Data. Journal of Marketing Research, 20(4), 350-367.	Green, P. E., Krieger, A. M., & Agarwal, M. K. (1991). Adaptive Conjoint Analysis: Some Caveats and Suggestions. Journal of Marketing Research, 28(2), 215-222.
Εναλλακτικές ονομασίες	CBC, Discrete-Choice Conjoint, Choice Experiment Conjoint, Choice-Based Conjoint Analysis	ACA, Adaptive Conjoint, Computer-Adaptive Conjoint, Self-Explicated and Paired-Comparison Conjoint
Συναφείς	4	4
Σύνοψη	Choice-based conjoint analysis (CBC) measures how consumers value the features of a product by observing the choices they make among competing, attribute-defined profiles rather than by asking them to rate attributes directly. Each respondent completes a series of choice tasks, picking the single most preferred alternative (often with a 'none' option) from a small set, and the pattern of choices across many tasks reveals the implicit trade-offs people make. The method grew out of Louviere and Woodworth's 1983 integration of conjoint measurement with discrete-choice theory, which showed that controlled choice experiments could be analyzed with the multinomial logit model. Because the choice task mimics a real purchase decision, CBC has become the dominant form of conjoint in commercial marketing research, popularized by Sawtooth Software. Estimation recovers part-worth utilities for every attribute level, either at the aggregate level or, more commonly today, individually through hierarchical Bayes. Those utilities then feed market simulators that predict shares of preference for new or hypothetical product configurations.	Adaptive Conjoint Analysis (ACA) is a hybrid, computer-administered conjoint method that builds each respondent's part-worth utilities by combining a self-explicated priors stage with a sequence of adaptively chosen paired-comparison trade-offs. Developed by Richard Johnson at Sawtooth Software in the mid-1980s, ACA was designed to handle many more attributes than a respondent could realistically evaluate in full-profile or choice tasks. The interview first asks people to rate the desirability of attribute levels and the importance of attributes, then uses those answers to generate paired product comparisons that are roughly balanced in utility, which are the most informative trade-offs. Respondents indicate graded preference between each pair, and the program updates the utilities in real time, focusing later questions where uncertainty is greatest. Green, Krieger, and Agarwal's 1991 evaluation in the Journal of Marketing Research documented both ACA's strengths and important caveats about its self-explicated component and attribute-importance estimates. ACA produces individual-level utilities that can drive purchase-likelihood calibration and market simulation.