This study aims to explore the role of marketing using generative artificial intelligence (GenAI) in the competitive marketing performance of small businesses (SMBs), utilising the Resource-Based View (RBV), Dynamic Capabilities (DC), and the Technology-Organisation-Environment (TOE) framework.
This research examines the existing literature on the relationship between Generative Artificial Intelligence (GenAI) and marketing, with a particular focus on its role in competitive marketing performance in SMBs, following the PRISMA guidelines. This analysis aims to enhance understanding of how SMBs can leverage GenAI in their marketing strategies to improve their competitive edge, while underscoring the significance of limited budgets, niche-market focus, the development of AI competencies and infrastructure, and ethical considerations.
This research develops a theoretical framework that proposes eight key research propositions for GenAI marketing. Using this framework, chatbots, multimodal GenAI, large language models, and deep learning in GenAI can significantly improve competitive marketing performance by increasing revenue, reducing costs, maintaining business continuity, and enhancing positive customer experiences and retention, particularly when applied through personalised customer interactions, high-quality marketing content creation, and data analytics for market insights. Additionally, factors such as limited budgets, niche market focus, AI expertise, infrastructure development, and ethical considerations may shape the interaction between GenAI and marketing in small businesses.
Drawing on the Resource-Based View (RBV), Dynamic Capabilities (DC), and the Technology-Organisation-Environment (TOE) framework, together with existing literature on GenAI-enabled marketing and competitive marketing performance, this study develops a theoretical framework that explains the relationships among GenAI, marketing, and the competitive marketing performance of SMBs. The research emphasises the link between GenAI and marketing, examining its effect on competitive marketing performance, while also considering influencing factors such as limited budgets, a focus on niche markets, improvements in AI skills and infrastructure, and ethical concerns.
