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

Jamshed Mistry, Joseph Sarkis*, Dileep G. Dhavale

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Peer-reviewed scientific journalInternational Journal of Production Economics
Issue numberFebruary
Pages (from-to)1-13
Number of pages13
Publication statusPublished - 01.02.2014
MoE publication typeA1 Journal article - refereed


  • 512 Business and Management
  • Multiple criteria decision making
  • Performance measurement
  • Latent class model
  • Gibbs sampler
  • Monte Carlo simulation
  • E-business
  • Balanced scorecard


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