Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models

Giesen, Joachim; Mueller, Klaus; Taneva, Bilyana; Zolliker, Peter

doi:10.1007/978-3-642-14125-6_14

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…The function estimates two issues basically; first, the importance of each attribute has been examined relative to the other ones and the partworth utility that is the level of preference. The utility value of a given item would be calculated based on the maximum amount of the utilities of all the possible options ( 58 ). Therefore, the MNL modeling process aims to determine the utility value of deterministic and random components of an item ( 59 ).…”

Section: Methodsmentioning

confidence: 99%

Willingness to Pay for Advanced Safety Features in Vehicles: An Adaptive Choice-Based Conjoint Analysis Approach

Nickkar

Lee

2022

Transportation Research Record: Journal of the Transportation R

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Advanced safety features in vehicles are emerging features that car manufacturers design and produce to improve the safety of driving a vehicle. In recent years, car manufacturers increasingly have provided different safety features as options to their customers. Understanding consumers’ interests and priorities relating to these features in vehicles has motivated researchers to conduct market analysis studies. This study aims to analyze the willingness to pay (WTP) for the advanced safety features in vehicles by adults resident in the U.S.A. Implementing adaptive choice-based conjoint analysis (ACBC), which is a special market analysis technique, and using comprehensible and realistic attributes in the survey are the main innovations of this study. The results of this study show that surround monitoring and collision avoidance systems are the most important attributes for respondents in their next vehicle. After that, the vehicle’s connectivity system was the third most important factor. Respondents distinctly preferred a vehicle with fully automated crash avoidance features. Moreover, the vehicle with safety features having the highest utilities may capture almost 61% of the market.

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Section: Methodsmentioning

confidence: 99%

Willingness to Pay for Advanced Safety Features in Vehicles: An Adaptive Choice-Based Conjoint Analysis Approach

Nickkar

Lee

2022

Transportation Research Record: Journal of the Transportation R

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“…The utility functions under consideration may be as simple as a (weighted) sum of criteria, or may be selected within a parameterized class of functions whose parameters are to be determined. This type of approach has been extensively investigated, in particular, by researchers interested in multiple criteria problems with discrete alternatives (MCDA) (see, e.g., , and in particular Bouyssou and Pirlot (2016); Dyer (2016); Moretti et al (2016); Siskos et al (2016) for recent surveys of closely related topics; see also Corrente et al (2016) for extensions), or in conjoint analysis (see, e.g., Giesen et al (2010); Gustafsson et al (2007);Rao (2014)). More recently, similar questions have also been investigated in preference learning, a subfield of artificial intelligence (see, e.g., Corrente et al (2013);Fürnkranz and Hüllermeier (2010)).…”

Section: Preference Modeling and Utility Theorymentioning

confidence: 99%

Revealed preference theory: An algorithmic outlook

Smeulders

Crama

Spieksma

2019

European Journal of Operational Research

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Revealed preference theory is a domain within economics that studies rationalizability of behavior by (certain types of) utility functions. Given observed behavior in the form of choice data, testing whether certain conditions are satisfied gives rise to a variety of computational problems that can be analyzed using operations research techniques. In this survey, we provide an overview of these problems, their theoretical complexity, and available algorithms for tackling them. We focus on consumer choice settings, in particular individual choice, collective choice and stochastic choice settings.

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