Date

12-4-2025

Department

Graduate School of Business

Degree

Doctor of Philosophy in Business Administration (PhD)

Chair

Clifton G Howell

Keywords

artificial intelligence, governance, frameworks, organizational readiness

Disciplines

Leadership Studies

Abstract

This convergent-parallel mixed-methods study examined how the absence of a standardized framework for integrating generative artificial intelligence (AI) affected leadership alignment, organizational consistency, and ethical governance within enterprises. Grounded in the pragmatic research paradigm, the study combined quantitative and qualitative data to evaluate factors that shaped AI framework effectiveness and to identify insights that could guide future framework development. Quantitative data were collected from 137 full-time professionals representing executive, technology, employee, and regulatory roles across industries. Using validated constructs derived from the Technology Acceptance Model (TAM) and Adaptive Structuration Theory (AST), statistical analyses including ANOVA, chi-square, and regression were conducted to explore relationships between framework type and key organizational outcomes. Qualitative data from 20 semi-structured interviews were coded to identify dominant themes of leadership influence, framework ambiguity, and ethical responsibility. Findings revealed that leadership behaviors, rather than framework type alone, were the primary drivers of alignment, governance effectiveness, and organizational readiness. Leadership role significantly predicted alignment outcomes, with executives and technology leaders reporting higher coherence in AI integration than employees. Regression analyses indicated that Attitude Toward AI, Effort Expectancy, and Perceived Threat collectively predicted ethical governance outcomes, delineating that readiness and perception shaped success. Qualitative results supported these findings, demonstrating that visible leadership engagement, clear communication, and reinforcement of ethical expectations promoted adoption confidence, while ambiguous frameworks and diffuse accountability weakened governance and trust. Triangulation of both strands demonstrated convergence, complementarity, and divergence across perspectives. The study concluded that leadership commitment and organizational readiness exerted greater influence on successful AI integration than the existence of a standardized framework; however, the findings offered foundational insight for future efforts to design consistent, ethically grounded frameworks for responsible AI adoption.

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