Theses and Dissertations
ORCID
https://orcid.org/0009-0000-4875-5689
Advisor
Strawderman, Lesley
Committee Member
Smith, Brian
Committee Member
González-Vargas, Jessica
Committee Member
Tian, Wenmeng
Date of Degree
5-15-2026
Original embargo terms
Embargo 2 years
Document Type
Dissertation - Open Access
Major
Industrial & Systems Engineering
Degree Name
Doctor of Philosophy (Ph.D.)
College
James Worth Bagley College of Engineering
Department
Department of Industrial and Systems Engineering
Abstract
Hourly manufacturing production workers represent a large and critical segment of the US workforce, yet little research exists on their technology acceptance behaviors despite increasing investment in Industry 4.0 and AI enabled tools. Understanding the factors influencing AI and technology adoption in manufacturing will help counter the effects of attrition and reliance on subject matter experts, enhance on-boarding, and support better communication across increasingly diverse workforces so the value of investment can be realized. These benefits of improved technology adoption present a strong need for research. A comprehensive literature review of models and methods was completed to understand previous research approaches. The review identified three research needs: a better understanding of the hourly production worker attitudes and preferences, a new understanding of which models correlate best to adoption measures, and identification of learnings from an actual technology implementation in manufacturing. Three studies were conducted to address these research gaps. The first study utilized a generalizable method for profiling end user populations using the Five Factor Model (FFM), General Attitudes toward Artificial Intelligence Scale (GAAIS), and Computer Self-Efficacy (CSE) models. The survey results from workers across four US regions were used to create a persona characterization. The results were used in the second study to identify several statistically significant correlations to Technology Acceptance Model (TAM) constructs. The results show that attitudes toward AI and confidence in one’s technology skills outweigh personality in shaping adoption intent. A final case study reviews the implementation of a generative AI chatbot implementation within a Fortune 200 manufacturing firm to draw conclusions on the impact of first two studies on adoption and identify other barriers. Collectively, these studies advance understanding of technology acceptance among hourly production workers and provide actionable guidance for improving adoption of AI enabled tools in manufacturing settings.
Recommended Citation
Gratton, Andrew Robert, "Understanding technology acceptance behavior of manufacturing workers" (2026). Theses and Dissertations. 6901.
https://scholarsjunction.msstate.edu/td/6901
