This paper is devoted to the analysis of evolution of innovation concepts, aspect and types. First emergence and evolution of different aspects and concepts of innovation are analysed, then the development of innovation concepts from a historical perspective and finally an overview given of types of innovation classifications developed in the literature. Surrounding this different definitions of innovation are described and analyzed in detail. The main goal of the article is to define the trend of development of innovation conceptualization and understanding over time.JEL Classification: B10, B20, O31, O32, O33, Q55

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The main aim of this study is to compare Russian regions according to their ability to create new technologies efficiently and to identify factors that determine these differences over a long period of time. We apply data envelopment analysis (DEA) to assess the relationship between the results of patenting and resources of a regional innovation system (RIS). Unlike previous studies, we apply the DEA method over a long period, comparing regions to one another and over time. In general, RIS efficiency in Russia increased during the period, especially in the least developed territories. There was significant regional differentiation. The most efficient RIS were formed in the largest agglomerations with leading universities and research centers: the cities Moscow and Saint Petersburg and the Novosibirsk, Voronezh, and Tomsk regions. Econometric calculations show that RIS efficiency was higher in technologically more developed regions with the oldest universities and larger patent stock. Time is a crucial factor for knowledge accumulation and creating links between innovative agents within RIS. Entrepreneurial activity was also a significant factor because it helps to convert ideas and research into inventions and new technologies and it enhances the interaction between innovative agents. It is advantageous to be located near major innovation centres because of more intensive interregional knowledge spillovers. Public support of more efficient regions can lead to a more productive regional innovation policy.

This paper explores the relationship between a firm's knowledge sourcing strategy and green innovation. The data are taken from Organisation for Economic Cooperation and Development REGPAT database, February 2016, relative to the European Patent Office firms' patent applications published up to December 2015.The study contributes to the literature by focusing on the moderating role of integration between different activities related to environmental performance on the relationship between knowledge sources and green innovations. The results from 240 firms indicate a shift in the focus from internal knowledge to external knowledge when developing environmental innovations. Government policies promoting more knowledge complementarity and coordination between environmental fields will help to promote more knowledge transfer, allowing more sustainable development.

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The paper reviews the application of the data envelopment analysis (DEA) method for measuring the efficiency of national innovation systems (NIS). The paper firstly visualizes the logic of DEA method and briefly summarizes the key advantages and main limitations of the DEA method. Further, this paper provides a comprehensive review of 11 empirical studies on cross-country analysis of NIS efficiency with DEA technique. In its main part the paper analyses the specifications of DEA models used in the reviewed studies, the content of the country samples, sets of input and output variables used and the resulting lists of efficient countries. The review detects general trends and differences in the sets of variables and the content of country samples. Moreover, this paper highlights the problem of "small countries bias" in the reviewed studies: situation when "small" (in terms of national innovation system scope and the level of development) countries (like Venezuela, Kyrgyzstan etc.) are included in the country sample, these "small" countries become the efficient ones. In general, empirical studies on cross-country analysis of national innovation systems efficiency using DEA method pay little attention to profound analysis of previous relevant studies. Therefore, this paper is among the first papers with deep review of such empirical studies.

This study contributes to existing literature on the relationship between pro- ductivity and innovation through the knowledge spillover effects. To this end, we consider both a theoretical model and an empirical analysis in Russia. The investigation is based upon a dataset composed of 85 Russian regions for the period 2010–2014. In particular, the effect of R&D Spillovers are analysed through the use of spatial econometric techniques. In so doing, we have allowed the productivity of each region to be affected by the productivity of nearby regions. Results show that R&D significantly affects Russian regions productivity and that productivity spillover across regions matter