Keywords

Quality, efficiency, analytic hierarchy process, delphi, data envelopment analysis, dea, ahp

Abstract

In a time of strained resources and dynamic environments, the importance of effective and efficient systems is critical. This dissertation was developed to address the need to use feedback from multiple stakeholder groups to define quality and assess an entity’s efficiency at achieving such quality. A decision support model with applicability to diverse domains was introduced to outline the approach. Three phases, (1) quality model development, (2) input-output selection and (3) relative efficiency assessment, captured the essence of the process which also delineates the approach per tool applied. This decision support model was adapted in higher education to assess academic departmental efficiency at achieving stakeholder-relative quality. Phase 1 was accomplished through a three round, Delphi-like study which involved user group refinement. Those results were compared to the criteria of an engineering accreditation body (ABET) to support the model’s validity to capture quality in the College of Engineering & Computer Science, its departments and programs. In Phase 2 the Analytic Hierarchy Process (AHP) was applied to the validated model to quantify the perspective of students, administrators, faculty and employers (SAFE). Using the composite preferences for the collective group (n=74), the model was limited to the top 7 attributes which accounted for about 55% of total preferences. Data corresponding to the resulting variables, referred to as key performance indicators, was collected using various information sources and infused in the data envelopment analysis (DEA) methodology (Phase 3). This process revealed both efficient and inefficient departments while offering transparency of opportunities to maximize quality outputs. Findings validate the potential of the ii Delphi-like, analytic hierarchical, data envelopment analysis approach for administrative decision-making in higher education. However, the availability of more meaningful metrics and data is required to adapt the model for decision making purposes. Several recommendations were included to improve the usability of the decision support model and future research opportunities were identified to extend the analyses inherent and apply the model to alternative areas.

Notes

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Graduation Date

2013

Semester

Summer

Advisor

Sepulveda, Jose

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Industrial Engineering and Management Systems

Degree Program

Industrial Engineering

Format

application/pdf

Identifier

CFE0004921

URL

http://purl.fcla.edu/fcla/etd/CFE0004921

Language

English

Release Date

August 2014

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Open Access)

Subjects

Dissertations, Academic -- Engineering and Computer Science, Engineering and Computer Science -- Dissertations, Academic

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