Application of DEA vs. PLSPM in organizational performance measurement

C. Kalpani Dissanayake, Jennifer A. Cross, S. Torabzadehkhorasani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The concept of performance measurement is vital to the industry since managers frequently make crucial organizational decisions based on their findings. Data Envelopment Analysis (DEA) technique has being a leading application in organizational performance measurement, partly due to the quantitative approach it uses to generate 'efficiency indices'. This study entailed comparing key features of performance measurement with DEA to that incorporating Partial Least Squares Path Modeling (PLSPM), a technique recently exploited for its cited nonparametric and exploratory powers. Both techniques can analyze the relationship between a set of independent and dependent variables; and they both can be used to generate performance indices using quantitative data from organizations. However, in literature, there are also several criticisms and disadvantages highlighted in both these techniques. Given the claims of their capabilities and presuming the accuracy of such this study is to understand the capabilities and limitations of performance measurement in organizations using DEA and PLSPM with the help of a published case study.

Original languageEnglish (US)
Title of host publication67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
EditorsHarriet B. Nembhard, Katie Coperich, Elizabeth Cudney
PublisherInstitute of Industrial Engineers
Pages31-36
Number of pages6
ISBN (Electronic)9780983762461
StatePublished - Jan 1 2017
Event67th Annual Conference and Expo of the Institute of Industrial Engineers 2017 - Pittsburgh, United States
Duration: May 20 2017May 23 2017

Publication series

Name67th Annual Conference and Expo of the Institute of Industrial Engineers 2017

Other

Other67th Annual Conference and Expo of the Institute of Industrial Engineers 2017
Country/TerritoryUnited States
CityPittsburgh
Period5/20/175/23/17

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

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