Premium
Moving forward quantitative research on innovation management: a call for an inductive turn on using and presenting quantitative research
Author(s) -
Faems Dries
Publication year - 2020
Publication title -
randd management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.253
H-Index - 102
eISSN - 1467-9310
pISSN - 0033-6807
DOI - 10.1111/radm.12406
Subject(s) - quantitative analysis (chemistry) , quantitative research , inductive reasoning , counterintuitive , point (geometry) , complement (music) , computer science , data science , qualitative property , management science , knowledge management , epistemology , sociology , economics , social science , philosophy , chemistry , biochemistry , geometry , mathematics , chromatography , artificial intelligence , complementation , gene , phenotype , machine learning
Whereas innovation scholars have mainly relied on survey designs, secondary data and experiments to engage in deductive theory‐testing research, I highlight that quantitative data can also be viable sources to induce theoretical insights into emerging innovation phenomena. In this paper, I discuss how scholars can use quantitative data for inductive innovation management research. First, I point to quantitative data as viable complements to enrich qualitative inductive research. Second, I point to the presence of alternative methods (e.g., cluster analyses and fsQCA) that allow using quantitative data as the core data for inductive research. Finally, I highlight the need to reduce the existing gap between how quantitative research on innovation management is executed and how it is presented in paper publications. In particular, I advocate an alternative mode of reporting that embraces the surprises and counterintuitive insights, which often emerge as scholars engage in a quantitative research journey. Together, my arguments aim to stimulate an inductive turn on quantitative research in innovation management, which can complement the existing deductive research tradition.