Predicting customer choice in services using discrete choice analysis

2008 ◽  
Vol 47 (1) ◽  
pp. 179-191 ◽  
Author(s):  
R. Verma ◽  
G. R. Plaschka ◽  
B. Hanlon ◽  
A. Livingston ◽  
K. Kalcher
Keyword(s):  
Discrete Choice ◽  
Discrete Choice Analysis ◽  
Customer Choice ◽  
Choice Analysis

A Comparative Study of Data-Intensive Demand Modeling Techniques in Relation to Product Design and Development

2009 ◽  
Author(s):  
Conrad S. Tucker ◽  
Christopher Hoyle ◽  
Harrison M. Kim ◽  
Wei Chen
Keyword(s):  
Decision Tree ◽  
Comparative Study ◽  
Product Design ◽  
Discrete Choice ◽  
Classification Model ◽  
Discrete Choice Analysis ◽  
Analysis Model ◽  
Customer Choice ◽  
Choice Analysis ◽  
C4.5 Decision Tree

This paper presents a comparative study of choice modeling and classification techniques that are currently being employed in the engineering design community to understand customer purchasing behavior. An in-depth comparison of two similar but distinctive techniques — the Discrete Choice Analysis (DCA) model and the C4.5 Decision Tree (DT) classification model — is performed, highlighting the strengths and limitations of each approach in relation to customer choice preferences modeling. A vehicle data set from a well established data repository is used to evaluate each model based on certain performance metrics; how the models differ in making predictions/classifications, computational complexity (challenges of model generation), ease of model interpretation and robustness of the model in regards to sensitivity analysis, and scale/size of data. The results reveal that both the Discrete Choice Analysis model and the C4.5 Decision Tree classification model can be used at different stages of product design and development to understand and model customer interests and choice behavior. We however believe that the C4.5 Decision Tree may be better suited in predicting attribute relevance in relation to classifying choice patterns while the Discrete Choice Analysis model is better suited to quantify the choice share of each customer choice alternative.

scholarly journals Determination of Additional Willingness to Pay for Socially Responsible Technical Products Using Discrete Choice Analysis

2016 ◽  
Vol 6 (1) ◽  
pp. 45
Author(s):  
Florian Vincent Haase ◽  
Maria Kohlmeyer ◽  
Beatrice Rich ◽  
Ralf Woll
Keyword(s):  
Social Responsibility ◽  
Willingness To Pay ◽  
Discrete Choice ◽  
Discrete Choice Analysis ◽  
Socially Responsible ◽  
Choice Analysis ◽  
Primary Goods ◽  
Technical Products

<p>Previous studies examined additional willingness to pay for socially responsible primary goods. However, technical products have not been considered. Therefore, the purpose of this study is to estimate additional willingness to pay for socially responsible technical products. Within an overview of given methods for measuring willingness to pay, the discrete choice analysis was applied to this study. As technical products, computer mice were chosen exemplary, since there is a partially fair mouse available. It was found that two of three fair labeled mice have a negative willingness to pay. Only consumers of the fair produced and labeled mouse has a positive willingness to pay. The consumers pay perhaps more attention to the aspect of social responsibility, if presented brands are comparatively unknown. In this connection, consumers allocate a higher value to social responsibility.</p>

scholarly journals Research: Discrete Choice Analysis in Hospitality Management Research

1997 ◽  
Vol 21 (1) ◽  
pp. 28-47 ◽  
Author(s):  
Rohit Verma ◽  
Gary M. Thompson
Keyword(s):  
Conjoint Analysis ◽  
Discrete Choice ◽  
Logit Model ◽  
Multinomial Logit Model ◽  
Theoretical Background ◽  
Hospitality Management ◽  
Discrete Choice Analysis ◽  
Management Research ◽  
Customer Preferences ◽  
Choice Analysis

This article focuses on discrete choice analysis (DCA), which offers an effective approach for incorporating customer preferences into operating decisions in hospitality businesses. First the theoretical background of DCA is presented, including a discussion of how DCA compares to conjoint analysis. The authors then present a guide to designing and conducting a DCA study. Conducting a discrete choice study involves identifying the attributes relevant to customers'choices and the appropriate levels of these attributes, designing an experiment, collecting data and estimating parameters using a multinomial logit model. Finally, the strategic implications of DCA in hospitality management research are discussed.

scholarly journals Health warnings on wine labels: a discrete choice analysis of Italian and French Generation Y consumers

2019 ◽  
Vol 8 (1) ◽  
pp. 81-90 ◽  
Author(s):  
Azzurra Annunziata ◽  
Lara Agnoli ◽  
Riccardo Vecchio ◽  
Steve Charters ◽  
Angela Mariani
Keyword(s):  
Discrete Choice ◽  
Generation Y ◽  
Discrete Choice Analysis ◽  
Health Warnings ◽  
Choice Analysis
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