Date of Conferral

1-14-2026

Date of Award

January 2026

Degree

Ph.D.

School

Management

Advisor

Jean Gordon

Abstract

Artificial intelligence (AI) technologies have been rapidly integrated into U.S. project management workflows; however, the level of trust in AI-enabled automated tools among project managers remains unknown. The purpose of this quantitative correlational cross-sectional study was to examine the relationship between U.S. project managers’ attitudes toward AI and their trust in AI-enabled automated systems used in project management, controlling for the influence of industry sector. This study is grounded in the trust in automation framework. The participants consisted of 180 U.S.-based project managers experienced in AI, who completed the Artificial Intelligence Attitude Scale and Trust of Automated Systems Test. The bivariate regression indicated that attitudes toward AI significantly predicted trust, F(1, 178) = 91.10, p < .001, accounting for 33.9% of the variance (R² = .339). Extending the model with industry sector yielded a statistically significant multiple linear regression, F(6, 173) = 19.01, p < .001, explaining 39.7% of the variance in trust (R² = .397). Attitudes toward AI emerged as the strongest predictor across all models (β = .550, p < .001), while the information technology, healthcare, and manufacturing sectors displayed higher levels of trust. The findings suggest that cognitive perceptions and the industry context have a significant impact on trust in AI technologies within project management workflows. These insights can help project-based leaders to foster environments that build trust and support effective adoption of AI systems. The implications for positive social change include the potential for organizational leaders and policymakers to foster more ethical and inclusive AI adoption, mitigate resistance to innovation, and enhance workforce adaptability during digital transformation.

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Available for download on Thursday, January 14, 2027

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