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An Empirical Comparison Between Discrete Choice Experiment and Best-worst Scaling: A Case Study of Mobile Payment Choice

Qinxin Guo and Junyi Shen ()
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Qinxin Guo: Graduate School of Economics, Kobe University, JAPAN

No DP2019-14, Discussion Paper Series from Research Institute for Economics & Business Administration, Kobe University

Abstract: As an alternative method to discrete choice experiments, best-worst scaling provides additional information about consumers, slightly lessens the burden of mental process, and shows better quality. However, its advantages were ambiguous in previous literature, since each case of the best-worst scaling contained distinct information, and results from comparisons with discrete choice experiment varied with different data. In this study, we applied a goodness of fit statistic named count R square in evaluating the best-worst scaling profile case, the discrete choice experiment, and the best-worst scaling multi-profile case by using data from a survey of preference for mobile payment. The results suggest that the best-worst multi-profile case surpasses other methods. We also compared the mixed logit model and the latent class model using three non-nested tests. The results indicate that the mixed logit model is superior to the latent class model in all three tests.

Keywords: Discrete Choice Experiment; Best-worst Scaling; Goodness of Fit; Latent Class Model; Mixed Logit Model (search for similar items in EconPapers)
Pages: 27 pages
Date: 2019-07
New Economics Papers: this item is included in nep-dcm and nep-pay
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