Identifying entrepreneurial discovery processes with weak and strong technology signals: a text mining approach

Levan Bzhalava; Jari Kaivo-Oja; Sohaib S Hassan; Wolfgang Dieter Gerstlberger

doi:10.12688/openreseurope.14499.2

Identifying entrepreneurial discovery processes with weak and strong technology signals: a text mining approach

Open Res Eur. 2022 Nov 1:2:26. doi: 10.12688/openreseurope.14499.2. eCollection 2022.

Authors

Levan Bzhalava^{1

2}, Jari Kaivo-Oja^{1

3}, Sohaib S Hassan⁴, Wolfgang Dieter Gerstlberger²

Affiliations

¹ Finland Futures Research Centre, Turku School of Economics, University of Turku, Turku, 20500, Finland.
² Department of Business Administration, TalTech School of Business and Governance, Akadeemia tee 3, Tallinn, 12611, Estonia.
³ Kazimieras Simonavičius University, Vilnius, 02189, Lithuania.
⁴ SME Management Graduate School, University of Siegen, Siegen, 57072, Germany.

Abstract

This study aims to propose methods for identifying entrepreneurial discovery processes with weak/strong signals of technological changes and incorporating technology foresight in the design and planning of the Smart Specialization Strategy (S3). For this purpose, we first analyse patent abstracts from 2000 to 2009, obtained from the European Patent Office and use a keyword-based text mining approach to collect weak and strong technology signals; the word2vec algorithm is also employed to group weak signal keywords. We then utilize Correlation Explanation (CorEx) topic modelling to link technology weak/strong signals to invention activities for the period 2010-2018 and use the ANOVA statistical method to examine the relationship between technology weak/strong signals and patent values. The results suggest that patents related to weak rather than strong signals are more likely to be high-impact innovations and to serve as a basis for future technological developments. Furthermore, we use latent Dirichlet allocation (LDA) topic modelling to analyse patent activities related to weak/strong technology signals and compute regional topic weights. Finally, we present implications of the research.

Keywords: Entrepreneurial Discovery Process (EDP); Smart Specialization Strategy (S3); innovations; patents; technology foresight; text mining; topic modelling; weak signals; word2vec..

Associated data

figshare/10.6084/m9.figshare.19130729.v2

Grants and funding

This research was financially supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 832862. Dr. Jari Kaivo-oja notes that this study is directly linked to the project “Platforms of Big Data Foresight (PLATBIDAFO)”, which has received funding from European Regional Development Fund (project No 01.2.2-LMT-K-718-02-0019) under grant agreement with the Research Council of Lithuania (LMTLT).