A high level of “environmental awareness” in the participating countries will likely raise the success of the Kyoto Protocol negotiated in Paris in 2015. In this context it is of interest to investigate the diffusion of environmental awareness, and also the (economic) factors, on which this diffusion depends. This paper addresses these questions for the regions of the Russian Federation, which are sufficiently diverse regarding cultural and economic issues. From a formal point of view, a “Multiple-Indicator-Multiple- Causes” (MIMIC) approach, based on a variety of “indicators” for environmental awareness, derived from search entries in c Yandex, and a variety of “causes”, economic and socio-economic factors, is applied. The empirical results point first to a strong dependence of environmental awareness on the level of GRP per capita. Moreover, the diffusion seems to spread from the eastern part of Russia towards the western regions.

Šešėlinę ekonomiką daug kas supranta kaip nelegalų verslą ar kontrabandą. Tai nėra tik vogtų daiktų realizavimas, prostitucija ar narkotikai, kuriuos privalo kontroliuoti policija ir kitos valstybės institucijos. Tačiau ir legalus verslas taip pat tampa šešėlinės ekonomikos dalimi, kai jos veiklos rezultatai falsifikuojami siekiant išvengti mokesčių. Tai neteisėti sandoriai, nerealios jų vertės ir panašiai. Iš tiesų, tai sudėtingas reiškinys, iškreipiantis oficialiąją statistiką, stabdantis ekonomikos augimą. Šio darbo tikslas nustatyti ekonominius faktorius, reikšmingai įtakoja Lietuvos šešėlinę ekonomiką. Darbo eigoje apibrėžiama šešėlinė ekonomika, jos sudedamosios dalys bei vertinimo metodikos. Tyrimo metu sudaromas Lietuvos šešėlinės ekonomikos MIMIC (Multiple Indicator Multiple Causal) modelis. Tikrinamas jo suderinamumas su turimais statistiniais duomenimis. Taip pat pateikiamos trys skirtingos MIMIC modelio specifikacijos, remiantis statistiniais kriterijais jos palyginamos ir randama priimtiniausia. / There is considerable agreement internationally about the factors that determine the relative size of the underground economy and that evidence of underground activity will be captured in several economic indicators. Until recently, however, the methods that have been employed to measure the underground economy focused on only a few causal factors, one indicator, and only produced an estimate for one particular point in time. This paper presents the modeling technique that threats the underground economy as an unobservable or latent variable and incorporates multiple indicator and multiple causal variables – MIMIC model. Moreover this technique allows us to estimate the significance of each causal variable as well as each indicator. First of all the theoretical definition of the shadow economy is proposed. Further the MIMIC modeling technique is described and the emphasis is laid on the most common variables used for latter method. Subsequently, the model is defined and the corresponding economical variables of Lithuanian are chosen. Whereupon the model is estimated and respecified while nonsignificant variables and relations been removed. Finally the conclusions about the most suitable model and statistically significant variables are maid.

This thesis focuses on one key theme, which is to understand the construct of organizational networking behaviors in business-to-business markets. It is concerned with two main issues, which are built into the research program of three empirical studies. The first issue is concerned with a systematic understanding of organizational behaviors in response to the embeddedness and interconnectedness of the network of business relationships that every organization has to deal with. Study 1 of the research program explores the concept termed ‘organizational networking behaviors’. This study adopts an industrial network approach, the central tenet of which is that firms undertake a continuous process of interaction with their important partners in the embedding context of the business network. A multi-informant approach, using semi-structured interviews, was used to collect qualitative data from thirty-one executive managers (in fifteen manufacturing firms in the UK). This study identified information acquisition, opportunity enabling, strong-tie resource mobilization and weak-tie resource mobilization as four types of organizational networking behaviors, which are reflected by their respective sub-types. The resulting conceptualization of organizational networking forms the basis for the operationalization of the construct in Study 2. While Study 1 takes an exploratory qualitative approach, Study 2 deploys a confirmatory quantitative approach since it is necessary to confirm/refute the resulting conceptualization and its types from Study 1. A rigorous scale construction and validation process was followed in this study. The conceptualization of the measurement model was carefully considered based on its theoretical underpinning. A second-order formative measurement structure was conceptualized, which required the employment of a multiple indicators and multiple causes (MIMIC) model for the validation of such a measurement model. A dataset of 603 responses was collected and analyzed to confirm the structure of the four types of organizational networking behaviors, which is in line with the results of Study 1.The second issue that the thesis is concerned with is the influences of such organizational networking efforts, which are examined from a firm’s behavioral perspective. Study 3 examines how organizational networking behaviors serve as the driver of a firm’s customer-oriented, competitor-oriented and relationship-oriented behaviors due to the sensing and seizing aspects of networking. It was also hypothesized that a firm’s customer-oriented, competitor-oriented and relationship-oriented behaviors positively affect firm performance. The test of these hypotheses required survey data collection, which was done through an on-line questionnaire. A dataset of 354 responses was collected from UK managers, whose organizations operate in business-to-business markets in either the manufacturing or services sectors. The use of statistical modeling techniques is similar to that of Study 2. The research results indicate that a firm’s network-oriented behaviors positively impact on the development of customer-oriented and competitor-oriented behaviors. They also foster relationship coordination with its important business partners within the network. In addition, the effective management of the firm’s portfolio of relationships is found to mediate the positive impact of network-oriented behaviors on firm profitability.