Mistry, J.Sarkis, J.Dhavale, D. G.2016-02-082016-02-0820140925-5273http://hdl.handle.net/11693/26339In 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. © 2013 Elsevier B.V.EnglishBalanced scorecardE-businessGibbs samplerLatent class modelMonte Carlo simulationMultiple criteria decision makingPerformance measurementBalanced scorecardseBusinessGibbs samplersLatent class modelMultiple criteria decision makingPerformance measurementsDecision makingEquivalence classesIndustryMonte Carlo methodsElectronic commerceArticle10.1016/j.ijpe.2013.10.006