Conjoint Analysis vs Preference Analysis: A Comparison

1987 ◽  
Vol 60 (3_part_2) ◽  
pp. 1063-1068
Author(s):  
Thomas R. Schori ◽  
H. Lee Meadow
Keyword(s):  
Conjoint Analysis ◽  
The Other ◽  
Optimal Configuration ◽  
Preference Analysis

Conjoint and Preference Analyses were used in an attempt to identify a potential new product's optimal configuration. From each analysis, the same conclusions were drawn, that is, there was no analytic advantage of one analysis over the other. Since the Preference Analysis was more simple and less costly to use, this procedure seems more desirable to use in optimizing a new product's design.

CONJOINT ANALYSIS VS PREFERENCE ANALYSIS: A COMPARISON

1987 ◽  
Vol 60 (3c) ◽  
pp. 1063-1068
Author(s):  
THOMAS R. SCHORP ◽  
H. LEE MEADOW
Keyword(s):  
Conjoint Analysis ◽  
Preference Analysis

Improving the External Validity of Conjoint Analysis: The Essential Role of Profile Distribution

2021 ◽  
pp. 1-27 ◽  
Author(s):  
Brandon de la Cuesta ◽  
Naoki Egami ◽  
Kosuke Imai
Keyword(s):  
Conjoint Analysis ◽  
Real World ◽  
External Validity ◽  
Causal Effect ◽  
The Other ◽  
Estimation Methods ◽  
Social Scientists ◽  
Actual Distribution ◽  
Open Source Software Package ◽  
Profile Distribution

Abstract Conjoint analysis has become popular among social scientists for measuring multidimensional preferences. When analyzing such experiments, researchers often focus on the average marginal component effect (AMCE), which represents the causal effect of a single profile attribute while averaging over the remaining attributes. What has been overlooked, however, is the fact that the AMCE critically relies upon the distribution of the other attributes used for the averaging. Although most experiments employ the uniform distribution, which equally weights each profile, both the actual distribution of profiles in the real world and the distribution of theoretical interest are often far from uniform. This mismatch can severely compromise the external validity of conjoint analysis. We empirically demonstrate that estimates of the AMCE can be substantially different when averaging over the target profile distribution instead of uniform. We propose new experimental designs and estimation methods that incorporate substantive knowledge about the profile distribution. We illustrate our methodology through two empirical applications, one using a real-world distribution and the other based on a counterfactual distribution motivated by a theoretical consideration. The proposed methodology is implemented through an open-source software package.

scholarly journals Preference analysis of determining jobs using conjoint analysis

2020 ◽  
Vol 1442 ◽  
pp. 012040
Author(s):  
R D Karima ◽  
R Setiadi ◽  
T Siswantining
Keyword(s):  
Conjoint Analysis ◽  
Preference Analysis

scholarly journals An Examination of Consumer Willingness to Pay for Local Products

2015 ◽  
Vol 44 (3) ◽  
pp. 253-274 ◽  
Author(s):  
Aaron Adalja ◽  
James Hanson ◽  
Charles Towe ◽  
Elina Tselepidakis
Keyword(s):  
Willingness To Pay ◽  
Conjoint Analysis ◽  
Random Sample ◽  
Local Food ◽  
Food Products ◽  
The Other ◽  
Local Production

We use data from hypothetical and nonhypothetical choice-based conjoint analysis to estimate willingness to pay for local food products. The survey was administered to three groups: consumers from a buying club with experience with local and grass-fed production markets, a random sample of Maryland residents, and shoppers at a nonspecialty Maryland supermarket. We find that random-sample and supermarket shoppers are willing to pay a premium for local products but view local and grass-fed production as substitutes. Conversely, buying-club members are less willing to pay for local production than the other groups but do not conflate local and grass-fed production.

A Conjoint Analysis of Five Factors Influencing Heavy Vehicle Drivers Perceptions of Workload

1995 ◽  
Vol 39 (17) ◽  
pp. 1102-1106 ◽  
Author(s):  
Thomas E. Nygren
Keyword(s):  
Conjoint Analysis ◽  
The Other ◽  
Traffic Density ◽  
Heavy Vehicle ◽  
Comparison Analysis ◽  
Scaling Technique ◽  
Factors Influencing ◽  
Five Factors ◽  
Perceived Workload ◽  
Conjoint Scaling

Conjoint analysis, a multi-factor subjective scaling technique, was used in a study of heavy vehicle drivers to obtain a measure of their perceived workload demands under different driving conditions. These included combinations of low and high levels of traffic density, lighting, roadway type, visibility, and traction. A tradeoff comparison analysis was used to collect the conjoint scaling data from a subset of the complete 2−2−2−2−2 design. Results indicated that an additive factor representation fit the data very well, but that the five factors had very different importance weights. The drivers’ orderings of perceived demand appeared to be inversely related to their control over the conditions. The two most important factors (traction and visibility) are effectively environmental factors that cannot be easily controlled by the driver. The other three factors (traffic density, highway type, and lighting) can, at least to some extent, come under the control of the driver. Implications of these results and the use of conjoint scaling methodology are discussed.

scholarly journals Consumer Preferences for Peanut Butter (Mamba) Products in Haiti: A Conjoint Analysis

2003 ◽  
Vol 30 (2) ◽  
pp. 99-103 ◽  
Author(s):  
R. G. Nelson ◽  
C. M. Jolly ◽  
M. J. Hinds ◽  
Y. Donis ◽  
E. Prophete
Keyword(s):  
Cluster Analysis ◽  
Conjoint Analysis ◽  
Logit Model ◽  
Consumer Preferences ◽  
Peanut Butter ◽  
Multinomial Logit Model ◽  
Demographic Variables ◽  
The Other ◽  
Relative Importance ◽  
Market Segments

Abstract Haitian consumers were surveyed to determine their preferences for three attributes of peanut butter: form (spicy, sweet, plain), origin (Haiti, U.S.), and price (lowest, most common, highest). Conjoint analysis was used to calculate relative importance and strengths of preferences for these attributes, which showed that price had more than twice the importance in the buying decision as either of the other attributes. Cluster analysis was used to identify market segments of like preferences, such as those strongly favoring Haitian products, or strongly disliking the plain form, or strongly sensitive to price. A multinomial logit model was used to evaluate the effect of various demographic variables on the probability of membership in a segment. A market share simulation determined that a new, sweet peanut butter product would increase domestic revenues most if priced at the highest level because a segment of the population would purchase the product and increase total peanut consumption.

scholarly journals Consumer Preference Analysis on Attributes of Milk Tea: A Conjoint Analysis Approach

Foods ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1382
Author(s):  
Ardvin Kester S. Ong ◽  
Yogi Tri Prasetyo ◽  
Ma. Althea Deniella C. Libiran ◽  
Yuanne Mae A. Lontoc ◽  
Joyce Anne V. Lunaria ◽  
...  
Keyword(s):  
Conjoint Analysis ◽  
Consumer Preferences ◽  
Orthogonal Design ◽  
Consumer Preference ◽  
Analysis Approach ◽  
Cream Cheese ◽  
Preference Analysis ◽  
Price Range ◽  
Consumer Preference Analysis

Milk tea is a famous drink that has been heavily consumed since 2011. This study aimed to determine the combination of milk tea attributes that were most preferred using a Conjoint Analysis Approach. Specifically, this study utilized different attributes such as the size of tapioca pearls, sugar level, price range, brands, type of milk tea, cream cheese inclusion, and the amount of ice. Conjoint analysis with the orthogonal design was utilized to evaluate the preference of milk tea among consumers. The results showed that pearl size was the attribute most considered by consumers (29.137%), followed by sugar level (17.373%), the amount of ice (17.190%), the type of drink (13.421%), price (11.207%), and the least considered were cream cheese inclusion (9.525%) and the brands (2.147%). The findings of this study will be beneficial to milk tea firms about consumer preferences regarding the various attributes of milk tea. Finally, the result of this study could be applicable to different beverage-focused studies worldwide.

An Empirical Comparison of Ratings-Based and Choice-Based Conjoint Models

1992 ◽  
Vol 29 (3) ◽  
pp. 368-377 ◽  
Author(s):  
Terry Elrod ◽  
Jordan J. Louviere ◽  
Krishnakumar S. Davey
Keyword(s):  
Conjoint Analysis ◽  
Traditional Approach ◽  
Choice Task ◽  
The Other ◽  
Logit Models ◽  
Empirical Comparison ◽  
Aggregate Model ◽  
Individual Level ◽  
Multinomial Logit Models ◽  
Better Than

The authors compare two approaches to conjoint analysis in terms of their ability to predict shares in a holdout choice task. The traditional approach is represented by three models fit to individual-level ratings of full profiles, whereas the other approach is represented by four multinomial logit models fit to choice shares for sets of full profiles. Both approaches predict holdout shares well, with neither the ratings-based nor the choice-based approach dominant, though some models predict better than others. Particularly promising is a new aggregate model that captures departures from independence of irrelevant alternatives (IIA).

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