Abstract

Augmented Reality (AR) projects a virtual overlay onto real space so that the user can see a superimposed image over the real-world background. Although AR has advanced recently and a breadth of applications can be found in practice, they are focused on simple tasks with few examples of more complex work tasks. One area that could benefit from advancing AR technology is operations management, specifically operational performance measurement (OPM); however, a brief review of the literature reveals that this potential application area has not yet been explored. Therefore, the purpose of this work is to investigate the application of AR technology to OPM to improve real-time decision-making and management practice. A systematic literature review was conducted to evaluate the current application areas related to management practices. This review did not identify any studies related to using AR to support OPM, but did identify many applications relevant to management activities that empirically demonstrate the benefit of adoption. The review analyzed the current development in this research area and how it has matured including evaluating the applications discussed in the identified publications to demonstrate the existing gap in the research related to OPM applications. An expert study was then conducted to explore potential challenges and benefits of such a device as well as to operationally define effective decision-making for operations managers. The results of the expert study were leveraged to develop a Design of Experiments based laboratory study to empirically test the effects of an AR supported environment on decision-making effectiveness and operational performance. The results showed that the AR device supported improved operational performance, but did not show a significant effect on participants' perceived decision-making effectiveness. This study contributes to the academic literature on technology-enabled OPM and managerial decision-making as well as providing insights for industry professionals interested in adopting AR to support management functions.

Notes

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Graduation Date

2020

Semester

Fall

Advisor

Keathley, Heather

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

CFE0008357; DP0023794

URL

https://purls.library.ucf.edu/go/DP0023794

Language

English

Release Date

December 2020

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

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