Measuring Innovation and Innovativeness - A Data-Mining Approach

34 Pages Posted: 28 Oct 2016 Last revised: 9 Jan 2020

See all articles by Bernard Sinclair-Desgagne

Bernard Sinclair-Desgagne

SKEMA Business School - Sophia Antipolis Campus

Date Written: January 8, 2020

Abstract

Despite significant advances over the past decades, the measurement of innovation and innovativeness remains a challenge for both academic researchers and management practitioners. This paper introduces some metrics that might address several key concerns, via a systematic and comprehensive, yet intuitive and practical, approach which is currently used in data-mining to represent and treat knowledge. In principle, this approach can cope with various types of innovations: product or process, services, technological, organizational and social. It can simultaneously incorporate information from different sources: objective and subjective assessments, quantitative and qualitative data files, as well as input, process, financial and environmental/social indicators. It can adapt to the particular needs and strategies of a country, region, industry, firm, entrepreneur or household. It can deal with radical, incremental or even disruptive innovations. Most importantly, it can also reveal an innovation logic and support the search for novelty.

Keywords: Knowledge representation and measurement; Formal concept analysis; Innovation indicators; Out-of-the-box thinking

JEL Classification: C80; O32

Suggested Citation

Sinclair-Desgagne, Bernard, Measuring Innovation and Innovativeness - A Data-Mining Approach (January 8, 2020). Available at SSRN: https://ssrn.com/abstract=2857721 or http://dx.doi.org/10.2139/ssrn.2857721

Bernard Sinclair-Desgagne (Contact Author)

SKEMA Business School - Sophia Antipolis Campus ( email )

60 rue Dostoïevski
Sophia Antipolis, 06902
France

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