Theses and Dissertations

ORCID

https://orcid.org/0000-0002-4181-0857

Advisor

Strawderman, Lesley

Committee Member

Burch, Reuben F., V

Committee Member

Carruth, Daniel W.

Committee Member

Deb, Shuchisnigdha

Date of Degree

12-12-2025

Original embargo terms

Visible MSU Only 2 Years

Document Type

Dissertation - Campus Access Only

Major

Industrial and Systems Engineering

Degree Name

Doctor of Philosophy (Ph.D.)

College

James Worth Bagley College of Engineering

Department

Department of Industrial and Systems Engineering

Abstract

A variety of potential options exist for using virtual reality head mounted displays (VR HMD) in the workplace such as training, data analysis, or design analysis. However, a common side effect impacts some VR HMD users referred to as Visually Induced Motion Sickness (VIMS). Other research continues to investigate possible mitigation options to reduce the occurrence VIMS. Investigating and understanding factors that influence acceptance could provide insights to support increased use of VR HMD in the workplace. Different theoretical technology acceptance models exist that support understanding user acceptance of VR HMD. This dissertation consists of three studies exploring the user acceptance of VR HMD in the workplace through examining prediction of the Behavioral Intention (BI) construct and associated acceptance models. The first study compared the general predictive performance of different theoretical technology acceptance models for a VR HMD workplace scenario. In this live experimentation study, participants used a VR HMD in a workplace scenario resulting in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) providing the highest predictive performance. The second study investigated developing a new acceptance model evaluating both significant factors from theoretical model frameworks as well as VIMS and mitigation related factors. This second study also involved creating a short questionnaire to allow for quick assessment without requiring a full model analysis. The generated Unified VR HMD Acceptance Model performed slightly better than the UTAUT2 while the new six item questionnaire significantly predicted BI. The third study focused on validating the Unified VR HMD Acceptance Model and questionnaire. This study used data from two sets of participants (live experiments and online survey). Results validated the three primary constructs of the Unified VR HMD Acceptance Model which also resulted in the highest adjusted R2 when compared to the other theoretical models and validated the acceptance scale questionnaire. However, a significant difference between the sets of users led to model fit indices achieving threshold (TLI, CFI) while the RMSEA fell outside of the acceptable threshold for live data, but within acceptable levels for the online data.

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