Search results

The purpose of this paper is to explore the extent to which customers evaluate inferior product presentations on a local shopping platform based on the retailer (small and medium-sized enterprise [SME] vs retail chain).

The authors conducted a between-subject online study (N = 728) with a 2 (SME vs retail chain) x 2 (high vs low-quality product presentation) between-subject experimental design. The authors conducted several analyses of variance to analyze the hypotheses and analyses of covariance for a mediating effect.

This study's results show that customers tend to overlook inaccuracies from a retail chain more often than they overlook inaccuracies from SME retailers. The authors show that the perceived competence of the retailer mediates the impact of the retailer's quality on customer purchasing intentions, not only for the presented product but also for the retailer itself.

Based on this study's results, the authors give implications for retailer cross-channel strategies and hint at the importance of emphasizing the retail size.

This paper provides important contributions to the literature on resource-advantage theory by explaining that consumers perceive quality differences within product presentations on online shopping platforms differently with regard to retail chains vs SME retailers. Moreover, the information processing theory helps to provide a theoretical foundation concerning differences in online store quality (via engineered low- vs high-quality product presentations in this study).

The study aims to help epidemiologists identify new patterns and trends in spreading infections on the example of the current coronavirus disease 2019 (COVID-19) pandemic using data from search engines. The study identified the types of thematic search of Russian Internet users and queries that have a mathematically confirmed correlation with public health indicators: mortality and morbidity from COVID-19. The study aims to determine digital epidemiology search trends to the current COVID-19 pandemic. The study identified the types of thematic search of RuNet users and queries that have a mathematically confirmed correlation with public health indicators: mortality and morbidity from COVID-19.

The authors explored two types of data: (1) the monthly datasets of keywords relevant to COVID-19 extracted from the Yandex search engine and (2) officially published statistics data. Alongside, the authors searched for associations between all variables in this dataset. The Benjamin–Hochberg correction for multiple hypothesis testing was applied to the obtained results to improve the reliability of the results. The authors built a unique website with opportunities to update datasets and designed dashboards to visualize the research outcomes using PHP and Python.

The research results show the number of significant relationships that the authors interpreted in epidemiology as a new instrument in Public Health research. There are 132 data combinations with a correlation higher than 75%, making it possible to determine a mathematically reliable relationship between search statistics trends and mortality/morbidity indicators. The most statistically significant effects identified in bundles “query” – “query”, “query” – “morbidity”, “query” – “mortality”.

The authors developed a new approach in analyzing outbreaks of infections and their consequences based on a comprehensive analysis of epidemiological and infodemic data. The research results are relevant to public health as other decision-making and situational analysis tools for citizens and specialists who want to receive additional confirmation for the indicators of the official statistics of the headquarters for control and monitoring of the situation with coronavirus and others infections.