Solutions for Relaunching Art Consumption After COVID-19 - From the Perspective of Consumers with Higher Education

Iuliana Coman ()
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Iuliana Coman: The Bucharest University of Economic Studies, Romania

Database Systems Journal, 2022, vol. 13, issue 1, 35-46

Abstract: The Covid-19 pandemic brought major changes to most areas of activity. Art was no exception and faced significant changes in both consumer behavior and the behavior of art producers who had to adapt to the difficulties of this period. The paper aims to present the image of art consumer behavior, including the socioeconomic context generated by the coronavirus pandemic in Romania, and to analyze the possible relaunching measures that can be taken for the restoration of the art market after the coronavirus pandemic. Another goal of the paper is to open this subject for future analysis, underlining the influences that art manifests in society. The analysis uses macroeconomic indicators provided by the National Institute of Statistics, Eurostat, estimates of companies playing in the Romanian market, and a survey conducted in the first week of May 2020, during the COVID-19 crisis, on a sample of 200 persons from the south of Romania. The survey goal was to capture the image of art consumer behavior, the influence that art has on the lives of respondents as well as the respondents' attitude towards the possible relaunching measures that can be taken for the restoration of art consumption after the coronavirus pandemic. Art continued to influence the lives of individuals and society during the COVID-19 period, with a wide range of roles played in the evolution of society. The online promotion of all art forms was the relaunch measure that was best received by most of the respondents. In the assessment of the possible relaunch measures, an important role is played by the presence of art in the respondents' lives and their convictions regarding the influences of art upon society.

Keywords: Big Data; Machine Learning; Romania; Air quality; MLR; SARIMA; C5.0; Random Forest; ANN (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:aes:dbjour:v:13:y:2022:i:1:p:35-46

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