Adaptive Conjoint
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Adaptive Applications
Adaptive Conjoint Analysis

The difference from the other Conjoint analyses is that respondents make judgments on the degree of preference between a pair, starting from the  levels of an attribute and eventually step-by-step building  their judgmental preference of products/services.  

ACA was developed specifically for the situation, where there are many attributes and levels. Most ACA questions present only small subsets of attributes, so questions  do not necessarily become more complex, when there are many attributes in the study.

With more than six attributes,  ACA  is likely to be more appropriate than the full profile method. Like most full profile applications, ACA is a "main effects only" model, and assumes there are no interactions among attributes.

Products or Services are thought of as possessing specific levels of defined attributes and a respondent's "liking" for a product is modeled as the sum of respondent's "utilities" for each of the attribute levels. For example, attributes for preserved cured sardines could include: Type of package, color of package, price. Levels of the price attribute might include: GRD 300,GRD 420, GRD 480; levels of the type of package attribute might include: cylinder, orthogonal, cube, triangle and levels of color might include: red, blue, yellow. It would be time consuming and difficult for respondents to evaluate all possible product combinations in order to provide information on their values for the various product features. 

Adaptive Conjoint offers a more efficient way to obtain such information: only a carefully computerized chosen set of hypothetical product concepts are presented to respondents for evaluation. For example, a respondent might be asked to choose between the following two concepts:

red, cylinder,  tin package Vs
blue, orthogonal, tin package

The answer to this and successive questions would be used to determine the respondent's utility for each of the attribute levels. Once utilities have been determined, the respondent's overall utility (preference) for a given product can be estimated by summing the utilities for each attribute level that describes the product/service.

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