Opportunity recognition in the tension field of knowledge and learning: The case of converging industries

Knowledge-based and learning perspectives alone explain opportunity recognition insufficiently. While knowledge forms the base, learning may help entrepreneurs in novel contexts. Interactions between them add complexity to the analysis of opportunity recognition. Even though many contexts require combinations of knowledge and learning, research on opportunity recognition in these contexts remains scarce. We address this gap with fuzzy-set qualitative comparative analysis (fsQCA) using data from 107 corporate entrepreneurs from converging industries. Converging industries offer a unique context to explore these complexities, requiring entrepreneurs to merge knowledge and learn from new fields. We identify three types with high levels of opportunity recognition: the “broad experienced adapter”, the “specific experienced adapter”, and the “experimenter”. Unlike a simple knowledge-based view suggests, we argue that knowledge is not always necessary. Entrepreneurs compensate for knowledge deficits by combining several learning capabilities. Configurational analysis enriches the theory of how multi-domain knowledge and learning contribute to opportunity recognition. • This study explores the complex relationship between knowledge and learning. • Converging industries offer a unique context to explore this relationship. • We identify recipes of knowledge and learning that enable opportunity recognition. • Our study introduces learning capabilities that compensate for knowledge deficits.