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

Jamshed Mistry, Joseph Sarkis and Dileep G. Dhavale

International Journal of Production Economics, 2014, vol. 148, issue C, 1-13

Abstract: In 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.

Keywords: Multiple criteria decision making; Performance measurement; Latent class model; Gibbs sampler; Monte Carlo simulation; E-business; Balanced scorecard (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:148:y:2014:i:c:p:1-13

DOI: 10.1016/j.ijpe.2013.10.006

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