Multi-criteria analysis using latent class cluster ranking: An investigation into corporate resiliency

Abstract

a b s t r a c tIn this paper, we introduce a multi-stage multiple criteria latent class model within a Bayesian framework that can be used to evaluate and rank-order objects based on multiple performance criteria. The latent variable extraction in our methodology relies on Bayesian analysis and Monte Carlo simulation, which uses a Gibbs sampler. Ranking of clusters of objects is completed using the extracted latent variables. We apply the methodology to evaluate the resiliency of e-commerce companies using balanced scorecard performance dimensions. Cross-validation of the latent class model confirms a superior fit for classifying the e-commerce companies. Specifically, using the methodology we determine the ability of different perspectives of the balanced scorecard method to predict the continued viability and eventual survival of e-commerce companies. The novel methodology may also be useful for performance evaluation and decision making in other contexts. In general, this methodology is useful where a ranking of elements within a set, based on multiple objectives, is desired. A significant advantage of this methodology is that it develops weighting scheme for the multiple objective based on intrinsic characteristics of the set with minimal subjective input from decision makers.

Addressing preference heterogeneity in public health policy by combining Cluster Analysis and Multi-Criteria Decision Analysis: Proof of Method

“…However, their combined use in health-related research, as we propose, is innovative. We could only locate one other application of the idea, in production economics, where it was used to evaluate e-commerce enterprises [ 19 ]. Before turning to these techniques, we describe the data.…”