Toward a Theoretical Framework for Generative Artificial Intelligence in Marketing

Generative Artificial Intelligence (AI) has emerged as a transformative force in marketing, transitioning from traditional technological tools designed to execute predefined tasks to intelligent agents capable of learning, creating, deciding and adapting autonomously. Unlike other technologies, AI possesses the ability to analyse data, generate insights, and autonomously determine optimal courses of action in real time. However, this technological progress is accompanied by a significant gap in understanding AI’s full potential and limitations, leading to widespread misconceptions on how AI agents can be used effectively and ethically. These uncertainties highlight the need for a comprehensive framework to guide Generative AI integration into marketing practices.
The Primary challenge addressed in this dissertation is driven by the lack of a marketing-specific theoretical framework to guide the integration of AI logic into marketing practices. To bridge this gap, this study proposes a novel theoretical framework based on Hunt’s inductive realist approach, specifically designed for AI-driven marketing practices. By positioning AI as both a creative and decision-making agent, the framework highlights the necessity of iterative refinement and ethical alignment to ensure AI applications resonate with societal values and address evolving consumer expectations.
This research adopts a novel two-step methodology, grounded in the indigenous theory development inductive realist approaches to construct an initial theoretical framework for AI in marketing. The approach emphasizes foundational premises and iterative propositions, providing a structured yet adaptable model ideal for addressing the complexities of emerging research domains. Further an empirical study is designed to identify perceptions of AI, uncovering key themes above. Cognitive maps are constructed to visualize the relationships among these themes, providing insights into how they interact and influence marketing outcomes. This empirical analysis designed to further assist theoretical advancements, offering a robust foundation for future research and practice of AI-driven marketing strategies.
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