ABSTRACT
This paper presents the second part of the research conducted at Riga Technical University aimed to explore the impact of the COVID-19 pandemic on Generation X (Gen X) and Generation Y (Gen Y) consumer behavior and purchasing priorities. While the changes in consumer behavior have already been analyzed and published earlier [1], the changes in purchasing priorities which might have caused changes in consumer behavior, are going to be studied in this work. The choice of these two generations is not made randomly;on the contrary, it was an intentional selection among other consumers, as they make a very active and prominent part of buyers all over the world. The research methods used are comparative descriptive analysis, Chi-square test and qualitative content analysis of data collected in an electronic survey of respondents from Asia, Europe, and America. The findings show that statistically significant differences between the changes in purchasing priorities of both generations are found for: meat and dairy products, fruit & vegetables, non-alcoholic and alcoholic drinks, clothes & shoes, body care & cosmetics, entertainment (pay TV services, computer games, etc.) and transport. Altogether, purchasing priorities of Gen X consumers were impacted by the COVID-19 pandemic less than Gen Y consumers. Copyright 2022. © by the International Institute of Informatics and Systemics. All rights reserved.
ABSTRACT
This study analyses the changes in consumer behavior caused by the COVID-19 pandemic. The emphasis is laid on the comparative analysis of respondents of highly active purchasing ages who represent Generation X (39-58) and Generation Y (18- 38). The set of research methods used in this study consists of comparative descriptive analysis, qualitative content analysis, and Chi-square test of quantitative and qualitative data collected in an electronic survey of 982 respondents from different countries of Asia and Europe. It was revealed that the main changes in consumer behavior are conditioned by personal finances and safety & health related measures intensified during the pandemic. © by the International Institute of Informatics and Systemics.
ABSTRACT
The cost associated with employee turnover and the shortage of available workforce in the market creates a situation where employee retention is crucial for the successful operation of an organization. Working remotely, especially in a situation with COVID-19 pandemic, increases the risks of voluntary employee turnover, since it makes the judgment of employee attitudes more difficult for the managers. Voluntary employee turnover (VET) measurements are one of the key indicators for evaluating the effectiveness of personnel management practices in organizations. The paper proposes a solution to decrease the risk of voluntary employee turnover in organizations. The authors propose a machine learning based model to identify the employees prone to voluntary employee turnover based on the employee data gathered and stored by the organization. The model will allow the managers to make a prediction based on data of the risks associated with voluntary employee turnover and to adjust the decision making process based on the information. To create the proposed IT solution for predicting the voluntary employee turnover analysis of models describing it has been performed to identify the most important factors that influence it. 9 factor groups with 67 factors of VET have been identified during the analysis. In the next step, 46 data clusters relevant for the decision making have been identified in specific organization and data from the clusters retrieved for the analysis. Based on the analysis a model for machine learning will be created, developed, and validated for the use in organizations. © 2021 Latvia University of Life Sciences and Technologies. All rights reserved.