TY - JOUR
T1 - Opportunity recognition in the tension field of knowledge and learning
T2 - The case of converging industries
AU - Ohlert, Simon
AU - Laibach, Natalie
AU - Harms, Rainer
AU - Bröring, Stefanie
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2025/1
Y1 - 2025/1
N2 - 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.
AB - 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.
KW - causal complexity
KW - converging industries
KW - knowledge
KW - learning
KW - opportunity recognition
UR - http://www.scopus.com/inward/record.url?scp=85205425259&partnerID=8YFLogxK
U2 - 10.1016/j.jbusres.2024.114993
DO - 10.1016/j.jbusres.2024.114993
M3 - Article
AN - SCOPUS:85205425259
SN - 0148-2963
VL - 186
JO - Journal of business research
JF - Journal of business research
M1 - 114993
ER -