Knowledge Intensive Business Sector in Latvian National Economy: Random Effect Factor outlook

Abstract

Global experience shows that sustainable economic development takes place in countries with economies focused on the creation and intensive use of knowledge. Entrepreneurs are interested in investing in knowledge, using obtained findings in the company’s development. Investment knowledge strengthens company’s market position, thus increasing the probability of successful implementation of its new products and services. Based on the general idea of tailor-made mix of content, structure, and functioning mechanism of market relations, it can be stated that knowledge is necessary for market participants in order to reach broader market share, take business advantage from innovations, increase competitiveness and uptake new markets, as well as ensure higher satisfaction regarding both goods and services for their customers. Investing in large-scale research projects enables opportunity to accumulate knowledge is a power for large corporations, which further determines their dominance in the global market. However, knowledge in terms of disruptive services is still more important among owners and managers of small and medium-sized enterprises (SMEs). The expansion of knowledge in the medium and especially in the small business environment promoted the emergence of a specific business niche known as the knowledge intensive business. The knowledge-based economy is gradually “displacing” the resource-based economy, stimulating entrepreneurs to put more focus on the use of information resources as a feature of the knowledge-intensive economy, thus pacing overall growth dynamics of segment. This article focuses on the identification and analysis of factors affecting the knowledge intensive business development business sector in Latvian national economy with random effects regression model. Random effects regression was used since it best suited for panel data. Compiled available repeated observations on the same units allowing to enrich the model by inserting an additional term in the regression, capturing individual-specific, time-invariant factors affecting the dependent variable.