Graduation Year

2026

Document Type

Dissertation

Degree

D.B.A.

Degree Granting Department

Business

Major Professor

Daniel Achempong, DBA.

Co-Major Professor

Ehsan Sheybani, Ph.D.

Committee Member

Dirk Libaers, Ph.D.

Committee Member

Christos Pantzalis, Ph.D.

Keywords

artificial intelligence, market organization, market result, market team productivity and artificial intelligence

Abstract

Marketing organizations face intensifying pressure to sustain creative output while managing constrained resources and accelerating digital content cycles. The integration of artificial intelligence into marketing brainstorming processes presents a promising response to this challenge; however, prior to this investigation, no comprehensive quantitative synthesis had established the overall effectiveness of human-AI collaboration in marketing creativity contexts or identified optimal implementation strategies.

This dissertation presents a systematic meta-analytic investigation of AI-enhanced brainstorming effectiveness in marketing contexts. Following PRISMA guidelines, 42 independent empirical studies (N = 2,166 participants) published between 2018 and 2025 were identified across four primary academic databases, coded for implementation approach, study quality, and creativity outcomes, and synthesized using random-effects meta-analysis with Hedges' g effect size estimation and modified Cochrane Risk of Bias quality assessment criteria.

Random-effects meta-analysis yielded a pooled effect of Hedges' g = 0.702 (95% CI [0.613, 0.791], z = 15.46, p < .001), representing a moderate-to-large positive effect of AI-enhanced brainstorming on marketing creativity outcomes. Augmentation approaches, in which AI assists rather than replaces human creative leadership, produced a pooled effect of g = 0.729, while replacement approaches yielded g = 0.603; however, the difference was not statistically significant (Δg = 0.127, p = .294). Significant publication bias was detected, and bias correction reduced the estimate to g = 0.607, which remained substantive and statistically significant. Study quality was a strong moderator, accounting for 75.38% of between-study variance.

Findings provide the first meta-analytic benchmark confirming that human-AI collaboration reliably enhances marketing creativity, regardless of implementation approach. Results support updated theoretical frameworks integrating AI as a collaborative partner in creative processes and offer evidence-based organizational guidance for AI creativity tool adoption, strategy selection, and return-on-investment estimation

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