Graduation Year

2026

Document Type

Dissertation

Degree

D.B.A.

Degree Granting Department

Business

Major Professor

Dirk Libaers, Ph.D.

Co-Major Professor

Daniel Acheampong, DBA.

Committee Member

Christos Pantzalis, Ph.D.

Committee Member

Ehsan Sheybani, Ph.D.

Keywords

artificial intelligence governance, digital transformation, organizational climate, personality, technology utilization

Abstract

This dissertation examines the gap between professionals’ intentions to use Generative Artificial Intelligence (AI) and their actual adoption behavior in workplace settings. Although Generative AI offers substantial potential to improve efficiency and performance, concerns about control, misuse and organizational risk have led to inconsistencies between intended and enacted use. Motivated by this misalignment, the study investigates the psychological, behavioral and social mechanisms that shape the intention-implementation divide. Drawing on an integrated framework combining the Theory of Planned Behavior and the Technology Acceptance Model, alongside personality traits, the research conceptualizes adoption as a phase-specific process involving evaluative, motivational and volitional components. Quantitative methods are employed, including structural equation modeling supplemented by regression-based robustness checks and bootstrap mediation analysis, to examine direct, indirect and conditional relationships within the model. Through survey data collected from 378 working professionals across industries and organizational roles, the findings indicate that attitudes and subjective norms play a central role in intention formation while perceived behavioral control supports the transition to behavioral enactment. Personality traits influence early evaluative perceptions but do not alter the core pathways linking intention to behavior. The study contributes to technology adoption research by clarifying boundary conditions of traditional acceptance models and emphasizing the importance of post-intentional processes in professional Generative AI use. Practical implications emphasize aligning adoption strategies with routine work practices, strengthening perceived control, and reinforcing social legitimacy to support responsible, sustained implementation.

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