Artificial intelligence marketing and leadership commitment: a solution for resource-constrained e-commerce firms

Purpose This study aims to investigate how implementation barriers impede the development of AI-driven marketing agility in e-commerce SMEs, with a particular focus on the moderating role of leadership commitment. Drawing on dynamic capabilities theory (DCT), we examined the relationships between technical complexity, cost implications, talent gaps, organizational resistance and marketing agility while exploring how leadership commitment moderates these relationships in the context of Chinese e-commerce SMEs. Design/methodology/approach We employed a mixed-methods approach combining two state-of-the-art methods: partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) analysis. Data were collected through a structured questionnaire from 317 marketing managers in Chinese e-commerce SMEs. The proposed research model was first validated using PLS-SEM to test the hypothesized relationships, followed by ANN analysis to explore nonlinear patterns and the relative importance of the implementation barriers. Findings Largely in line with existing understanding, technical complexity and organizational resistance demonstrate the strongest negative effects on marketing agility development. However, leadership commitment significantly moderates all hypothesized relationships, weakening the negative impacts of implementation barriers. Triangulated ANN analysis has confirmed the hierarchical importance of these barriers and revealed nonlinear patterns in their relationships with marketing agility, with technical complexity emerging as the most influential factor. Originality/value By reconceptualizing dynamic capabilities in the AI era, the research has revealed that the entire capability development process operates under different laws in technologically discontinuous contexts and introduced a new theoretical lens for understanding digital leadership in technology adoption contexts. Our theoretical advances enable precision interventions for organizations navigating AI implementation challenges in marketing operations.