Authors:
Dominik Forner
1
;
Sercan Ozcan
1
and
David Bacon
2
Affiliations:
1
Faculty of Business and Law, University of Portsmouth, Portland Street, Portsmouth and U.K.
;
2
Institute of Cosmology and Gravitation, University of Portsmouth, Burnaby Road, Portsmouth and U.K.
Keyword(s):
Innovation Performance, Neural Networks, Machine Learning, National Innovation System, Economic Growth, Innovation Policy, Decision Support System.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Business Analytics
;
Business Intelligence
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Analytics
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Software Engineering
;
Symbolic Systems
Abstract:
National innovation performance is essential for being economically competitive. The key determinants for its increase or decrease and the impact of governmental decisions or policy instruments are still not clear. Recent approaches are either limited due to qualitatively selected features or due to a small database with few observations. The aim of this paper is to propose a suitable machine learning approach for national innovation performance data analysis. We use clustering and correlation analysis, Bayesian Neural Network with Local Interpretable Model-Agnostic Explanations and BreakDown for decomposing innovation output prediction. Our results show, that the machine learning approach is appropriate to benchmark national innovation profiles, to identify key determinants on a cluster as well as on a national level whilst considering correlating features and long term effects and the impact of changes in innovation input (e.g. by governmental decision or innovation policy) on inno
vation output can be predicted and herewith the increase or decrease of national innovation performance.
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