"Scientific research" Almanac

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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

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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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  IMPACT OF CONSUMER PROMOTIONS ON CUSTOMER-BASED BRAND EQUITY

  The present research paper outlines the effects of consumer promotions. Its subject is the type and frequency of effects of consumer promotions on customer-based brand equity dimensions. The leading role of consumer promotions within the communication mix, both nationally and globally, along with the lack of unambiguous evidence about their effect ... The present research paper outlines the effects of consumer promotions. Its subject is the type and frequency of effects of consumer promotions on customer-based brand equity dimensions. The leading role of consumer promotions within the communication mix, both nationally and globally, along with the lack of unambiguous evidence about their effect on brand equity as a top intangible asset, underlie the motivation of the current research. The present work aims to confirm the research thesis that consumer promotions can generate both positive and negative effects on customer-based brand equity depending on their type and frequency. The negative effects of monetary promotions, the positive effects of non-monetary promotions and promotions frequency on perceived quality and brand loyalty are estimated by structural equation modelling and finite mixture approach. The segmentation analysis provides estimates of the effects according to the degree of brand loyalty. According to the results, monetary promotions are more appropriate for the non-loyal segment, while non-monetary promotions are more appropriate for the loyal segment.