MS Proposal by Lucas Provine

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Name: Lucas Provine

Master’s Thesis Proposal Meeting

Date: Thursday, June 15, 2023

Time: 10:00am EST

Location: Microsoft Teams Meeting

Click here to join the meeting

Meeting ID: 253 934 152 278 

Passcode: oBs66o 



Ruth Kanfer, Ph.D. (Georgia Tech)


Thesis Committee Members:

Ruth Kanfer, Ph.D. (Georgia Tech)

Phillip Ackerman, Ph.D. (Georgia Tech)

Rick Thomas, Ph.D. (Georgia Tech)


Title: Effects of Individual Differences in Personality Traits and Self-Concept of Abilities on Willingness to Adopt AI Tools


Abstract: Artificial intelligence (AI) is increasingly being used to automate and augment tasks in a variety of domains from the workplace to daily life. While much research examines characteristics of technology such as design that improve user adoption, less is known about how individual differences in personality traits and self-concept of abilities affect adoption intentions. The objective of this study is to determine how personality traits and self-concept of abilities in several domains contribute to one’s willingness to adopt AI for decision-making purposes in various contexts. This study leverages the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) and incorporates personality traits (i.e., extraversion, neuroticism, and propensity to trust) and self-concept of abilities (i.e., verbal, math, spatial, and organizational domains) to improve predictive validity beyond the model’s core factors: performance expectancy, effort expectancy, and social influence. This study seeks to evidence two primary questions: (1) How much do individual differences in personality traits and self-concept of abilities influence people’s intentions to adopt AI for decision-making? (2) How willing are people to adopt AI in domains for which they perceive they have a high (or low) ability? This cross-sectional, vignette study will be conducted via an online survey administered to a sample of students and employees (full- and part-time) recruited on Prolific and SONA. Multiple regression analyses will be used to test the incremental predictive validity of personality traits and self-concept of abilities on intentions to adopt AI tools in general and specific use domains. The results of this study will have theoretical implications for the extension and broader use of UTAUT as well as the mechanisms by which individual differences in personality and self-evaluations influence the acceptance or rejection of AI. These results may also be used to better understand and predict AI adoption at the individual level across contexts.


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