Search results

This research paper aims to contribute to the field of innovation performance benchmarking by identifying appropriate benchmarking groups and exploring learning opportunities and integration directions.

The study employs a multi-dimensional innovation-driven clustering methodology to analyze data from the 2019 edition of the Global Innovation Index (GII). Hierarchical and K-means Cluster Analysis techniques are applied using various sets of distance matrices to uncover and analyze distinct innovation patterns.

This study classifies 129 countries into four clusters: Specials, Advanced, Intermediates and Primitives. Each cluster exhibits strengths and weaknesses in terms of innovation performance. Specials excel in the areas of institutions and knowledge commercialization, while the Advanced cluster demonstrates strengths in education and ICT-related services but shows weakness in patent commercialization. Intermediates show strengths in venture-capital and labour productivity but display weaknesses in R&D expenditure and the higher education quality. Primitives exhibit strength in creative activities but suffer from weaknesses in digital skills, education and training. Additionally, the study has identified 35 indicators that have negligible variance contributions across countries.

The study contributes to finding the relevant countries’ grouping for the enhancement of communication, integration and learning. To this end, this study highlights the innovation structural differences among countries and provides tailored innovation policies.

Digital transformation has gained particular interest among academics and policymakers in recent years. However, the empirical quantification of digital transformation stages and their impact on innovation in small and medium-sized enterprises (SMEs) remains understudied. Therefore, this study aims to investigate the impact of digital transformation stages on a differentiated measurement of innovation performance in SMEs.

The authors propose a simplified one-dimensional digital maturity path to estimate the stages of digital transformation in SMEs. The authors validate their approach with a cluster analysis and perform an ordered logistic regression to estimate the impact of digital transformation stages on SMEs' innovation performance.

The authors' results show that digital transformation in general has a positive impact on SMEs' innovation performance. More precisely, the authors find that the early stage of digital transformation has a detrimental effect on innovation performance, while significant and positive effects can be expected from the experimental stage onward. Furthermore, the advanced stage of digital transformation significantly increases the probability of producing radical innovations.

This study contributes to the ongoing discussion about the relationship between digital transformation and innovation in SMEs by presenting an approach to quantify digital transformation stages in SMEs. Additionally, this study provides new insights into the specific dynamics of the relationship between different stages of digital transformation and their impact on a differentiated measurement of innovation performance, including technological, non-technological and radical innovation.

Introduction: Today the ability of nations to develop and implement innovations is core for their international competitiveness. Ukraine is striving for innovation progress; however, its innovation performance is relatively low. The research problem is to find the bottlenecks, affecting Ukraine’s innovation capability.

Purpose: This study aims to research the national innovation capability profiles, based on cluster analysis, to develop an understanding of drivers and threats for the innovation capability of Ukraine.

Need of the study: The knowledge-based economy, which had already turned into one of the most efficient developmental models of the 21st century, became a key driver of international competitiveness for the leading developed countries due to their progressive structural shifts towards the growth of high-technology manufacturing and knowledge-intensive sectors. These trends are significant to capture for the sake of increasing the innovation capability of the economy of Ukraine.

Methodology: The study is based on the K-means clustering method, which is employed for identifying 10 country clusters based on the indicators of their R&D and innovation activities, which allowed us to assess the innovation capability of Ukraine in comparison with 140 countries of the world. Data selection and normalisation were based on the 2019 Global Competitiveness Report indicators.

Findings: The study showed that Ukraine’s innovation capability problems are typical for most developing countries and are prevalently connected to low R&D expenditures, patent applications, and international co-invention activities. Most countries, except for the technologically developed ones, follow the so-called ‘passive technological learning’ strategies, which usually result in low economic productivity.

Practical implications: Several innovation policy implications have been developed for the government of Ukraine based on the cluster analysis results and accounting for the problems of the national innovation system (NIS).