Configuring AI-guided sustainable competitive advantage for SMEs through business model innovation: A systematic literature review approach
Ariful Islam, Md Asadul Islam, Francesca Dal Mas, Justyna Fijałkowska, Mahfuzur Rahman, Maurizio Massaro
Abstract
Researchers have been exploring an effective framework for achieving competitive advantage for many years, specifically tailored to small and medium-sized enterprises (SMEs) to ensure their long-term survival. The recent surge in advanced technologies, particularly artificial intelligence (AI), has made their debates more challenging. Thus, the study proposes a conceptual framework specifically designed to leverage AI for long-term competitive advantage in SMEs, examining their business models through this lens. This study conducts a systematic literature review (SLR) to cover a broad range of relevant literature within a final sample of 69 articles. The SLR method was chosen to integrate research in a systematic, transparent, and reproducible way. For qualitative analysis and framework derivation, the study draws on a thematic ontological analysis. The study identifies multiple research streams at the intersection of advanced technology and entrepreneurship aimed at enhancing the competitiveness of SMEs. The primary outcome of this study is the development of a comprehensive business model framework, encompassing both external antecedents (namely, market and industry dynamics, technological infrastructure, government policies and support, strategic alliances, socio-cultural factors) and internal antecedents (digital leadership, dynamic capabilities/adaptability, entrepreneurial mindset, data strategy, growth/resilience), ultimately contributing to sustainable performance. Practically, the study provides a comprehensive avenue for SME owners and managers to adopt and use AI in business strategies and operations. Based on the results, SMEs can implement automation and machine learning to streamline business processes, minimize manual labor, and boost overall operational efficiency. More theoretical and practical implications, along with limitations and future directions, are also discussed, revealing multiple theoretical gateways and an agenda for subsequent empirical work.