Moving forward quantitative research on innovation management: a call for an inductive turn on using and presenting quantitative research

Abstract

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.

Exploring the Open COVID Pledge in the fight against COVID‐19: a semantic analysis of the Manifesto, the pledgors and the featured patents

“…In this study, modern Innovation Management methods are used, according to the recent calls of top journals, to contribute to methodological diversity (Tseng et al, 2007; Choi et al, 2013; Ghazinoory et al, 2013; Lee et al, 2014; Han and Sohn, 2015; Arts et al, 2017; Moehrle et al, 2017; Antons et al, 2020; Faems, 2020; Ritala et al, 2020). More specifically, this study implements semantic methods to leverage the hidden potential of unstructured data to unveil original and novel data insights (Faems, 2020; Ritala et al, 2020). For what concerns the unstructured data related to patents, we applied IP Analytics, with specific references to text mining approaches and semantic methods (Aristodemou & Tietze, 2017, 2018).…”

Establishing Merger Feasibility Simulation Model Based on Multiple-Criteria Decision-Making Method: Case Study of Taiwan’s Property Management Industry

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Innovation management research methods: embracing rigor and diversity

“…As a result, some of the articles featured here seek to enhance IM researchers’ ability to leverage both well‐established research methods such as case study approaches (Elsahn et al, 2020) and more recent methods like text mining that are increasingly applied when dealing with large‐scale sets of digitised text (Antons et al, 2020). Other papers in this Special Issue challenge prevailing understandings, perceptions, and assumptions about when and how to use quantitative research methods (Faems, 2020) or encourage researchers to focus closely on the temporal construction of innovation phenomena (Ellwood and Horner, 2020). Finally, a number of articles discuss how complementary methodologies and approaches can enrich IM scholars’ toolset by highlighting practical relevance and academy‐industry integration, including two articles on action research (Guertler et al, 2020; Ollilla and Yström, 2020) and one on design research in IM (Auernhammer, 2020).…”

“…Data becomes increasingly accessible through digital platforms, repositories, and interfaces, and research methods utilise increasingly diverse and advanced computational approaches. This facilitates more accurate analysis (see Antons et al, 2020), as well as helps to generate new insights from the data (as advocated by Faems, 2020).…”