Journal of Service Science and Management

Volume 8, Issue 1 (February 2015)

ISSN Print: 1940-9893   ISSN Online: 1940-9907

Google-based Impact Factor: 1.24  Citations  h5-index & Ranking

Bayesian Conjoint Analysis in Water Park Pricing: A New Approach Taking Varying Part Worths for Attribute Levels into Account

HTML  XML Download Download as PDF (Size: 1546KB)  PP. 46-56  
DOI: 10.4236/jssm.2015.81006    4,821 Downloads   6,035 Views  Citations

ABSTRACT

Nowadays, the application of conjoint analysis for measuring customers’ preferences for goods and services is wide-spread in marketing. A sample of customers is confronted with fictive offers and asked for evaluations. From these responses part worths for attribute levels of the offers are estimated and used to develop an optimal design and pricing for an offer. However, especially in tourism, it can be observed that attribute importance not only differs between customers but also varies over a single customer’s usage situations and her/his mood. In this paper, we propose a measurement approach that respects this variation. Part worths are stochastically modeled and estimated using Bayesian procedures. The approach is applied to design and price a water park.

Share and Cite:

Löffler, S. and Baier, D. (2015) Bayesian Conjoint Analysis in Water Park Pricing: A New Approach Taking Varying Part Worths for Attribute Levels into Account. Journal of Service Science and Management, 8, 46-56. doi: 10.4236/jssm.2015.81006.

Cited by

[1] Simulation und Optimierung auf Basis der Conjointanalyse
2021
[2] Conjointanalyse: Verbreitung und Validität kommerzieller Anwendungen im Zeitverlauf
2021
[3] Kapitel 4: Der Einfluss von Begleitpersonen als ein sozialer Kontext
2020
[4] Kapitel 5: Der Einfluss der Reisezeit als ein zeitlicher Kontext
2019
[5] Die Ermittlung von individuellen Zahlungsbereitschaften mittels Präferenzmessung: Die Price-Adapted Choice-Based Conjointanalyse
2018
[6] The benefits of incorporating utility dependencies in finite mixture probit models
2017
[7] Supervised Machine Learning for Plants Identification Based on Images of Their Leaves
International Journal of Agricultural and Environmental Information Systems (IJAEIS), 2016
[8] Hierarchical Bayes Conjoint Choice Models–Model Framework, Bayesian Inference, Model Selection, and Interpretation of Estimation Results

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.