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Measuring innovation and innovativeness: a data-mining approach

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Abstract

Despite substantial advances over the past decades, measuring innovation and innovativeness remains a challenge for both academic researchers and management practitioners. To address several key concerns with current indicators—such as their specialization and consequent one-sidedness, their frequent lack of theoretical foundations, and the fact that they may not really foster creativity and invention—this paper introduces some new metrics via one data-mining approach—formal concept analysis—which is increasingly used to represent and treat knowledge. This approach can adapt to particular needs and goals, incorporate various kinds of information (qualitative or quantitative) from different sources, and cope with several types of innovations. It also uncovers a logical route to novelty, which might enhance the generation of ideas and is used here to support the measurement of innovativeness.

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Notes

  1. Good reviews and assessments of the academic and policy literatures include Adams et al. (2006), van Beers et al. (2015), Brattström et al. (2018), Chan et al. (2008), Dziallas and Blind (2019), Gamal et al. (2011), Garcia and Calantone (2002), Gault (2013, 2018), Goldsmith and Foxall (2003), Gupta (2009), Hall and Jaffe (2018), von Hippel (2017), Kleinknecht et al. (2002), Lhuillery et al. (2017), OECD (2010), Saunila (2017) and Smith (2005).

  2. See, e.g., Dziallas and Blind (2019), Dewangan and Godse (2014), Cruz-Cázares et al. (2013), Adams et al. (2006), and the industry surveys conducted by McKinsey and the Boston Consulting Group.

  3. What complicates the matter further is that organizations often disagree on what should be measured (see Dziallas and Blind 2019, and the references therein), with the result that innovative project managers and finance department might often not use the same tools (Stefani et al. 2019). Addressing this important issue—which has to do with organizational design and incentives—is unfortunately beyond the scope of this paper.

  4. This definition fits the purpose of this paper. It is consistent with Joseph Schumpeter’s initial definition of product innovation (OECD 1997, p. 28). The literature offers several alterrnate definitions, however. For an overview and useful discussion of these, the reader may look at Rogers (1998), Baregheh et al. (2009) and Quintane et al. (2011).

  5. Formal Concept Analysis (FCA) began with Rudolf Wille (1982)'s seminal article. Extensive introductions can be found in Ganter and Wille (1996), Davey and Priestley (2002), Bĕlohlávek (2008) and Ignatov (2015). Over the past decades, numerous applications have been found, including “(…) hierarchical organization of web search results into concepts based on common topics, gene expression data analysis, information retrieval, analysis and understanding of software code, debugging, data mining and design of software engineering, internet applications including analysis and organization of documents and e-mail collections, annotated taxonomies, (…).” (Bĕlohlávek 2008, p. 4) More recently, Poelmans et al. (2013a, b) describe applications in linguistics, bioinformatics, and medicine, while Gardiner and Gillet (2015) cover many real and potential ones in chemistry. Specific uses in mechanical engineering, manufacturing and pharmaceuticals are outlined in Nanda et al. (2007), Tóth et al. (2014) and Quintero and Restrepo (2017), respectively.

  6. Owing to the German origin of FCA, sets of objects and attributes are commonly denoted G and M. These letters stand for ‘Gegenstand’ and ‘Merkmal’, the respective German words for ‘objects’ and ‘characteristics’.

  7. Considering the timing of innovation and its appraisal is out of the scope of this paper. In many cases, though, periodic reviews of innovation policies are already set a priori by contract, policy plans or rules of governance.

  8. Mathematical support for this statement can be found in subsection A.2 of the “Appendix”.

  9. Alternatively, the relation R+ could be refined by indicating value ranges, so that a new object bearing certain new attributes but losing other valuable ones would be singled out. I thank one referee for raising this issue.

  10. This outbox might be seen as the ‘creative reservoir’, or repertoire of creative opportunities, previously identified by Cohendet and Simon (2015).

  11. In the “Appendix”, γ and μ are suggestively called innovation mappings.

  12. Following Mairesse and Mohnen (2002, p. 226): “Innovativeness is conditional on a model of an innovation function (…).”.

  13. Some anticoncepts might have been discarded right away, which would make the assessments more accurate. This is the case for those exhibiting an empty set, like ({Airsparg, Bioslurp, UV oxyd, Biosparg, Nat atten},}.

  14. An exhaustive survey of the literature and debates surrounding this taxonomy can be found, for instance, in Di Stefano et al. (2012).

  15. An upper bound on the number of concepts in the context K = (G, M; R) is \(\frac{3}{2} 2^{{\sqrt {\left| R \right| + 1} }} - 1 .\) See Ganter and Wille (1996), p. 94.

  16. For surveys and explanations, see, e.g., Dias and Vieira (2015), Ignatov (2015), Valtchev et al. (2004), Kuznetszov and Obiedkov (2003) and Godin et al. (1995).

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Acknowledgements

Constructive comments from participants at the MOSAIC-HEC Montréal seminar, the CORE-UC Louvain research seminar, the 2017 Schumpeter Conference, and the 2019 KTO Workshop at Skema Business School are gratefully acknowledged. I owe special thanks to Marine Agogué, Thierry Bréchet, Patrick Cohendet, Michel Desmarais, Ludovic Dibiaggio, Soumitra Dutta, Armand Hatchuel, Pengfei Li, Paavo Ritala, Henry Tulkens, Gilda Villaran, and Isabelle Walsh for valuable conversations, remarks or suggestions. Two diligent and competent anonymous referees helped to improve significantly the content and presentation of this paper. All remaining errors and shortcomings are my own.

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Sinclair-Desgagné, B. Measuring innovation and innovativeness: a data-mining approach. Qual Quant 56, 2415–2434 (2022). https://doi.org/10.1007/s11135-021-01231-6

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