"Scientific research" Almanac

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  STUDYING THE COUNTERPARTY RISK IN INDUSTRIAL ENTERPRISES (ALONG THE LINES OF THE BREWING INDUSTRY)

  The purpose of the present study is to investigate the manifestation of counterparty risk in the operation of Bulgarian breweries and to highlight specific business solutions and practices for its effective management. In this regard, the following tasks are formulated and implemented: studying and summarizing the theoretical formulations of ... The purpose of the present study is to investigate the manifestation of counterparty risk in the operation of Bulgarian breweries and to highlight specific business solutions and practices for its effective management. In this regard, the following tasks are formulated and implemented: studying and summarizing the theoretical formulations of counterparty risk in industrial enterprises; analyzing and evaluating the current state and identifying the development trends of the brewing industry in the European Union and in Bulgaria, in particular; investigating the risky circumstances and situations arising from the actions and responses of corporate counterparties; summarizing and presenting the results of the conducted empirical survey; highlighting the best practices and initiatives for counterparty risk management in the surveyed business establishments. The main research thesis is that identifying, maintaining and developing partner relationships and interactions with certain counterparties (suppliers, clients, distributors and other physical persons and/or legal entities and the subsequent change in their actions and responses lead to unforeseen and unexpected risk situations in the breweries. As a result of the survey, the study identifies the current situation, the specificity and dynamics in the development of the brewing industry within the European Union and Bulgaria, as well as the bearers of counterparty risk in the brewing enterprises in the country and the most problematic situations and weaknesses in working with them, and outlines the guidelines and priorities for reducing, limiting, avoiding and eliminating the counterparty risk in the surveyed respondents.

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  MEASURING POVERTY IN BULGARIA: STATISTICAL ASPECTS

  Fighting poverty and social exclusion is among the biggest issues of modern social development and a priority of the social policy at both national and European level. The main goal of this paper is to explore the statistical dimensions of poverty in Bulgaria via theoretical justification and empirical analysis of the level, dynamics and ... Fighting poverty and social exclusion is among the biggest issues of modern social development and a priority of the social policy at both national and European level. The main goal of this paper is to explore the statistical dimensions of poverty in Bulgaria via theoretical justification and empirical analysis of the level, dynamics and determining factors of poverty for the period 2008–2017. The research thesis of the study is that the Gini coefficient and the multiple binary logistic regression models are appropriate analytical tools for analyzing the relationships and identifying the part of the population that is at risk of poverty and social exclusion. In this paper, we present the conceptual basis of poverty as a subject of the statistical analysis. We clarify the organizational and methodological aspects of the data provision. We have performed empirical analysis of the dynamics and factor relationships of poverty in Bulgaria on the basis of the statistical information, obtained from NSI and Eurostat. We have developed multiple regression models and estimated the socio-demographic conditions of poverty. The main results of the study can be summarized in several areas. The application of the statistical approach is a necessity for identifying the level and the factor relationships of poverty. The quantitative measure of poverty is based on comprehensive statistical information that is collected in accordance with the Eurostat methodology and adapted at a national level by the NSI. The dynamics of the Gini coefficient reveals that income inequality of the population is rising. The multiple logistic regression model states that the impact of the variables “gender”, “age” and “region” are statistically significant. The derived probabilities and odds ratios can be used in developing policies for poverty reduction.

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  MEASURING CONSUMER SATISFACTION WITH THE OMNI-CHANNEL EXPERIENCE

  Most consumer brands today are available in both physical and online stores, and marketing specialists are constantly seeking new ways to contact and engage their customers. More and more companies are trying to adapt to the digital world and create multiple channels to satisfy consumers' requirements for an optimal buying experience. These ... Most consumer brands today are available in both physical and online stores, and marketing specialists are constantly seeking new ways to contact and engage their customers. More and more companies are trying to adapt to the digital world and create multiple channels to satisfy consumers' requirements for an optimal buying experience. These channels include physical stores, websites, mobile apps, social media and more. The present study presents the results of an empirical study aimed at offering measurement models of consumer satisfaction with and expectation of the omni-channel customer experience. The study has also sought an answer to the question: how much the expectations of the different aspects of omni-channel experience affect the degree of customer satisfaction. The research tasks can be summarized as follows: (1) analysis of scientific literature in the field of omni-channel marketing, customer experience and satisfaction in an omni-channel setting; (2) collection and analysis of data about customer’s expectations and satisfaction with their experience in an omni-channel buying environment, as well as validation of their measurement models and a structural model of their relationship. The partial least squares method has been used in order to test the measurement and structural models. It has been found that customers are satisfied with their omni-channel experience if companies: offer them relevant offers, synchronize their promotions across channels, offer the "Click & Collect" option, integrate data on user devices and enhance the return options for products purchased through different channels. The results also show that only the expectations for integrating data from different devices used by consumers to contact the brand have a small effect on the level of consumer satisfaction