Date of Award

Fall 2019

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Engineering Management & Systems Engineering

Committee Director

Holly Handley

Committee Member

Charles Daniels

Committee Member

Pilar Pazos-Lago

Committee Member

Anthony Dean

Abstract

One of the issues organizations face is identifying the required skills needed for a position and then evaluating whether their personnel have these skills or if there is a “skills gap”. The skills gap is the distance between the position requirements and the skills currently possessed by the worker in that position. While multiple models have been created over the years to address facets of the problem, none of them provide a comprehensive framework to clearly identify the required skills and worker qualifications and then evaluate the degree of similarity. A composite skills gap model has been developed using the Design Science Research Method to combine elements of previous models and to ensure that the resulting model met a set of established criteria. The Skills Gap Analysis Model (SGAM) was evaluated using demonstration data to ensure that it provided a single taxonomy for both position requirements and the worker qualifications, the resulting descriptions were quantifiable and comparable, the data was accurate and actionable, the model framework is adaptable to any domain, and that it is easy to use and not time consuming. The framework provided by the model establishes a theoretical foundation for skills gap analyses that allows for more analytical research in this area. By utilizing the SGAM to identify position requirements and worker qualifications, organizations can move personnel to better suited positions or utilize needed training in the specific areas identified. As technology moves towards increasing automation, robotics and artificial intelligence, this type of model can identify what skills are necessary for “re-tooling” the workforce to meet the needs to support these systems.

DOI

10.25777/39p3-8w95

ISBN

9781392448694

ORCID

0000-0001-7415-0819

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