About two-thirds of EU economy is driven by innovation, hence making the production of high value-added technologies a major driver of advanced economies. Enterprises belonging to this sector are dynamic, flexible and open to foreign markets, and through regular innovation, they contribute to GDP growth and employment. Nevertheless, in today’s context, companies should not only stimulate sustainable economic growth but also contribute to stable socio-cultural development and the reduction of environmental pollution. Hence, it is relevant to analyse the efficiency of high-tech companies in the context of overall country’s sustainable development. At present, usage of databases is no longer sufficient for accurately capturing hidden complex relationships, trends, and draw timely conclusions – specialized econometric modelling techniques and tools are necessary. Therefore, an integration of artificial intelligence and big data analytics would enable to utilize much larger amounts of data, increase an analytical productivity and accuracy of results. The first phase of the project will include the analysis of the current performance of high-tech companies, incorporating the elements of innovation, economic growth, social responsibility and environmental pollution. Later, factors which determine operational efficiency as well as efficiency assessment methodologies will be reviewed in the most recent scientific literature. When the analysis of the current methodologies for assessing the sustainable growth of the national economy is done, econometric modelling and artificial intelligence (canonical correlation, data envelopment, and principal component analyses) will be applied. This will help to develop a comprehensive tool for assessing the efficiency of high-tech companies in the context of country’s sustainable development.
Project funding:
KTU Research and Innovation Fund
Project results:
On average HTM (high-tech manufacturing) companies tend to outperform HTKIS (high-tech knowledge-intensive services) on all indicators, except for profit which was hard to compare due to abnormal distribution. The highest variability revealed by PCA was primarily due to all socio-eco aspects followed by financial health (debt and equity) and HTM companies scored slightly better on average in these directions. Correlations showed that socio-eco indicators are strongly interlinked and somewhat independent from the financial situation. Only a weak positive reaction of socio-environmental aspects could be noticed to high debt or bad equity for HTM companies and high capital expenditures for HTKIS. The most significant canonical correlation structure showed that an undesirable socio- environmental situation reduces sales and return on assets for HTM companies. The remaining insights were mostly finance-related and large similarities between the sectors (HTM and HTKIS) were noticeable. Similarities aside, the HTM sector was found to be not only more stable and better performing, but also a bit more sensitive to socio-eco aspects. Socio-eco-efficiency by NACE categories in HTM sector varied smoothly from -1.84% for chemicals and chemical products to 2.72% for pharmaceuticals, medicinal chemicals and botanical products, but was mostly around zero in HTKIS sector with a spike of 7.27% for scientific R&D and a drop to -3.68% for renewable energy. Technological changes improved with time indicating market recovery after the global financial crisis. Together with this recovery competition among companies increased simultaneously, resulting in negative socio-eco-efficiency changes during last years and suggesting that for an average high-tech company it became harder to catch-up to market frontiers. The outcomes of this study may act as recommendations for political strategists, investors, and company executives who not only seek to increase the financial efficiency of their enterprises but also care for social responsibility and sustainable development in the long run.
Period of project implementation: 2020-04-14 - 2020-12-31
