Comparative efficiency of California State University system by DEA

Hur, Mansik; Way, Michael H.

doi:10.1504/ijssci.2020.113197

Cited by 1 publication

(1 citation statement)

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“…The first type of application measures the efficiency of different universities which were treated as DMUs. Some of these works include evaluating the efficiency of Australian Government-owned universities (Abbott and Doucouliagos, 2003), higher learning institutions in England (Johnes, 2006), British universities (Flegg et al , 2004), public universities in Greece (Katharaki and Katharakis, 2010), universities in Chile (Ramírez-Correa et al , 2012), Polish universities (Nazarko and Šaparauskas, 2014; Chodakowska, 2015), European universities (Klumpp, 2018), research universities in China (Yang et al , 2018), public higher education institutions (HEIs) in Argentina (Quiroga-Martínez et al , 2018), Chinese universities (Fan and Qu, 2019; Han, 2020) and campuses within the California State University system (Hur and Way, 2020). On the other hand, some works evaluated a specific discipline among various universities.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy preference programming formulation in data envelopment analysis for university department evaluation

Mirasol-Cavero

Ocampo

2021

JM2

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Purpose University department efficiency evaluation is a performance assessment on how departments use their resources to attain their goals. The most widely used tool in measuring the efficiency of academic departments in data envelopment analysis (DEA) deals with crisp data, which may be, often, imprecise, vague, missing or predicted. Current literature offers various approaches to addressing these uncertainties by introducing fuzzy set theory within the basic DEA framework. However, current fuzzy DEA approaches fail to handle missing data, particularly in output values, which are prevalent in real-life evaluation. Thus, this study aims to augment these limitations by offering a fuzzy DEA variation. Design/methodology/approach This paper proposes a more flexible approach by introducing the fuzzy preference programming – DEA (FPP-DEA), where the outputs are expressed as fuzzy numbers and the inputs are conveyed in their actual crisp values. A case study in one of the top higher education institutions in the Philippines was conducted to elucidate the proposed FPP-DEA with fuzzy outputs. Findings Due to its high discriminating power, the proposed model is more constricted in reporting the efficiency scores such that there are lesser reported efficient departments. Although the proposed model can still calculate efficiency no matter how much missing and unavailable, and uncertain data, more comprehensive data accessibility would return an accurate and precise efficiency score. Originality/value This study offers a fuzzy DEA formulation via FPP, which can handle missing, unavailable and imprecise data for output values.

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