Green, P.E. and Srinivasan, V. 1978, Conjoint Analysis in Consumer Research: Issues and Outlook, Journal of Consumer Research, 5: 103-123.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....generating linear constraints of the coe#cient of the value function can easily be accommodated. This idea has been often exploited, see [15] We present below two techniques using it. It should be noticed that rather di#erent techniques have been proposed in the literature on Marketing [32, 92, 93, 101, 118]. 4.2.1 UTA [99] UTA ( UTilite Additive , i.e. additive utility in French) is one of the oldest technique belonging to this family. It is supposed in UTA that there is a subset Ref X of reference alternatives that the decision maker knows well either because he she has experienced them or ....

P. E. Green and V. Srinivasan. Conjoint analysis in consumer research: issues and outlook. Journal of Consumer Research, 5:103--152, 1978.

....in the previous section justify their use as a tool for assigning an order to patients on a waiting list. To design a point system that could be used to manage waiting lists for cataracts, and which would take societal preferences into account, the technique of conjoint analysis (CA) was used. [12] Although CA has been widely used in market research since the mid 1970s, its use in the . eld of health economics has been minimal. 13] 14] CA is used to elicit individuals preferences for sets of multi attribute alternatives (products, services, etc. The technique is based on obtaining ....

....The attributes and levels described above de. ne 384 (42344)patienttypes. Given the nature of the attributes chosen, the property of separability is as15 sumed. This in turn allows the number of patient types to be evaluated to be reduced to 16, using an orthogonal fractional factorial design. [12], 22] This design ensures the absence of collinearity. C) Choosing a data collection method. A variety of dierent preference elicitation methods exist. They include asking respondents to compare a series of two alternatives; to choose one alternative from a set of alternatives; to rate the full ....

Green, P., V. Srinivasan. Conjoint analysis in consumer research: issues and outlook. Journal of Consumer Research 1978; 5:103-122.

.... in the stimulus is relatively small, the full profile model is likely to perform better in terms of predictive validity; on the other hand, if the correlation between attributes is low and the number of attributes included in the design is large, the pairwise trade off model is likely to be better (Green and Srinivasan, 1978). The Hybrid Conjoint Model Hybrid conjoint models were developed in the early 1980s, ten years after conjoint analysis was first introduced into market research, expressly for the purpose of reducing the complexity of the data collection process of the traditional conjoint model. Since the ....

Green, Paul E. and V. Srinivasan (1978), "Conjoint Analysis in Consumer research: Issues and Outlook," Journal of Consumer Research, Vol. 5, (September) , 103-123.

....Full profile conjoint data collection involves presenting consumers with a set of product descriptions which contain information about each attribute to be ranked. Ranking all potential product descriptions is prohibitive in practice, and a fractional factorial orthogonal table of profiles is used [10]. There exists several computer software packages that design a set of orthogonal tables. Conjoint analysis measurement procedures take the consumer preference rankings and calculate part worth utility functions, the details of which can be found in numerous sources, for example Green et al. [9] ....

P.E. Green and V. Srinivasan. Conjoint Analysis in Consumer Research: Issues and Outlook, Journal of Consumer Research, 5: 103 -- 123, 1978.

....Preferences tend to change as consumer s beliefs and desires change, or as consumers acquire more information about a particular product. Modeling consumer preferences for a particular product class involves the collection of data and culminates in a preference ranking. Conjoint analysis [Green and Srinivasan, 1978] is then used to estimate the willingness of consumers to trade off varying levels of product attributes on the basis of their preferences. This is achieved by determining utility functions for each of the attributes of a product which are then used to design products with the optimum appeal. In ....

Green, P.E. and Srinivasan, V., Conjoint Analysis in Consumer Research: Issues and Outlook, Journal of Consumer Research, 5: 103 -- 123, 1978.

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Green, P.E. and Srinivasan, V. 1978, Conjoint Analysis in Consumer Research: Issues and Outlook, Journal of Consumer Research, 5: 103-123.

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