Big-data AI analytics in value-chain innovation and international marketing strategy: insights from SMEs in cultural and creative industries

Abstract

Purpose Despite great consensus on the positive impact of big-data-driven artificial intelligence (AI) analytics (BDAI) on a firm’s performance, it still appears to be a black box mechanism through which small and medium-sized enterprises (SMEs) strengthen their dynamic competencies to innovate and expand their global footprint. To fill this theoretical and empirical gap we examine the relationship between BDAI affordances, digital marketing capabilities (DMCs), value-chain innovation and international market goals. Design/methodology/approach The study incorporates the dynamic capability view an extension of the resource-based view and the knowledge-based view to empirically examine the primary data collected from marketing managers and executives of SMEs in cultural and creative industries utilizing Structural Equation Modeling (SEM) analysis. Findings The study highlights the significant role of BDAI affordances such as intelligent process recommendations, customer intelligence and market intelligence on DMCs, where DMCs significantly affect value-chain innovation and international market strategy both directly and indirectly. Research limitations/implications The study minimizes the gap in identifying the BDAI affordances to drive innovation and international market strategy in the context of SMEs in cultural and creative industries. Marketing managers can incorporate these findings to enhance their digital capabilities for competitive advantages in international markets. Originality/value The study proposes a holistic framework of BDAI affordances for the strategic use of digital resources and knowledge to transform digital capabilities into new forms of value to expand in the international market. These insights are robust and grounded in findings provided by marketing practitioners.