A Bayesian Generalized Rank Ordered Logit Model : An Empirical Application

Researchers typically use rank ordered logit regression to analyze consumer preference data collected with Best-Worst Scaling (BWS) surveys. We propose a generalized rank ordered logit (GROL) model that accommodates scale and preference heterogeneity. Both the GROL and mixed rank ordered logit model (MROL) accommodate scale and preference heterogeneity, but the GROL allows one to model scale heterogeneity as a function of demographic or environmental variables. A Monte Carlo experiment compares the small sample properties of the GROL with the MROL specification. Simulation results suggest that the GROL model performs comparatively well when either the GROL or the MROL is the true data generating process. An empirical application analyzes a BWS survey on consumer preferences for single-use eating-ware (SUEW) products made from biobased materials. Findings suggest that consumers value most product degradability and the use of non-plastic materials to fabricate SUEW. Consumers valued most the rapidity of product degradability and the use of non-plastic materials to make SUEW plates. Respondent attentiveness also affected willingness-to-pay (WTP) estimates across attributes. Results suggest that attentive respondents were willing to pay for biodegradable SUEW about $3.00 more than inattentive respondents