«Мягкая сила» России на постсоветском пространстве
Conference Article Compilation of II International scientific-practical conference of young scientists and students “Time of challenges and opportunities: problems, solutions and perspectives” .
The results of cross-cultural research of implicit theories of innovativeness among students and teachers, representatives of three ethnocultural groups: Russians, the people of the North Caucasus (Chechens and Ingushs) and Tuvinians (N=804) are presented. Intergroup differences in implicit theories of innovativeness are revealed: the ‘individual’ theories of innovativeness prevail among Russians and among the students, the ‘social’ theories of innovativeness are more expressed among respondents from the North Caucasus, Tuva and among the teachers. Using the structural equations modeling the universal model of values impact on implicit theories of innovativeness and attitudes towards innovations is constructed. Values of the Openness to changes and individual theories of innovativeness promote the positive relation to innovations. Results of research have shown that implicit theories of innovativeness differ in different cultures, and values make different impact on the attitudes towards innovations and innovative experience in different cultures.
We address the external effects on public sector efficiency measures acquired using Data Envelopment Analysis. We use the health care system in Russian regions in 2011 to evaluate modern approaches to accounting for external effects. We propose a promising method of correcting DEA efficiency measures. Despite the multiple advantages DEA offers, the usage of this approach carries with it a number of methodological difficulties. Accounting for multiple factors of efficiency calls for more complex methods, among which the most promising are DMU clustering and calculating local production possibility frontiers. Using regression models for estimate correction requires further study due to possible systematic errors during estimation. A mixture of data correction and DMU clustering together with multi-stage DEA seems most promising at the moment. Analyzing several stages of transforming society’s resources into social welfare will allow for picking out the weak points in a state agency’s work.