Name: Lucas Provine

Master’s Thesis Defense Meeting

Date: Tuesday, March 12, 2024

Time: 11:15am EST

Location: Microsoft Teams Meeting

Click here to join the meeting

Meeting ID: 290 022 267 955 

Passcode: zCqkJq 

Advisor:

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

Artificial intelligence (AI) is increasingly being used to automate and augment tasks in a variety of domains from the workplace to daily life. However, little is known about the influence that individual differences in personality and ability self-concept have on people’s attitudes and adoption of AI technology to assist with tasks. The objective of this study was to determine how select personality traits (e.g., extraversion, neuroticism, and propensity to trust) and ability self-concept (e.g., verbal, math, spatial, and organizational) contribute to one’s willingness to adopt AI for decision-making purposes in various contexts. I leveraged the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003) to do so. To accomplish this, 231 working adults (126 females and 105 males) were recruited from Prolific to participate in a vignette study that involved assessment of attitudes and behavioral intentions to use AI in 22 scenarios. The results indicated that: (1) the personality and self-concept variables do not contribute additional meaningful variance in predicting behavioral intentions to use AI over and above UTAUT’s performance expectancy, effort expectancy, and social influence variables; (2) one’s general propensity to trust others is associated with more positive expectations of AI performance; (3) higher ability self-concept is positively associated with perceiving AI as requiring less effort to use; and (4) attitudes and intentions toward using AI are significantly lower when individuals perceive personal situational liability for the consequences of errors that might occur while using the AI. Future researchers are encouraged to further explore how salient situational factors and stable individual difference variables might interact to inform people’s attitudes and intentions toward using AI.