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Variations in Citations Across Databases: Implications for Journal Impact Factors

The Journal Impact Factor (JIF) is a widely used metric for ranking journals based on the number of citations garnered by papers published over a specific timeframe. To assess the accuracy of JIF values, I compared citation counts for 30 of my own publications across six major bibliography databases: CrossRef, Web of Science, Publisher records, Google Scholar, PubMed and Scopus. The analysis revealed noteworthy variations in citation counts, ranging from 10% to over 50% between the lowest and highest citation counts. Google Scholar records the highest citation numbers, while PubMed reported the lowest. Notably, Web of Science, whose citation data are used in JIF calculations, tend to underestimate citation counts compared to other databases. These observations raise concerns about the accuracy of JIF calculation based on Web of Science's citation data. The real JIF values for most journals would differ from those annually reported by Clarivate's journal citation reports (JCR). These citation discrepancies underscore the importance of comprehensive data collection and the necessity to include additional citation sources. Not because a paper is cited in one journal rather than another should it have a less or more citation weight. Ultimately, one citation remains one citation, regardless of its origin. Clarivate Analytics may thus need to consider integrating all citation sources for more accurate JIF values. Alternatively, Google Scholar could potentially develop its own journal or citation impact based on its extensive journal citation records. However, while making adjustments to how the Journal Impact Factor is calculated can make it more mathematically precise, it doesn't address the fundamental biases built into the metric. Even with refinements, the Journal Impact Factor will remain skewed due to how it's defined and used.

Mobility restrictions and their implications on the rental housing market during the COVID-19 pandemic in China's large cities

The COVID-19 pandemic has caused significant mobility restrictions and generated profound impacts on global socio-economic development. Mobility restrictions can generate significant impacts on the demand and supply sides of the rental housing market. By taking 77 large Chinese cities as cases, this research establishes a stepwise mediation effect test to evaluate the impacts of the pandemic on the rental housing market during Q1 2020. The results show that the confirmed cases were negatively associated with rental unit transactions, and the inter-city and intra-city movement played a significant role of mediating effects. Meanwhile, the impact of pandemic on rents lagged behind rental transaction in China's large cities, and the strict mobility controls caused the high vacancy rate of rental housing, leading to the bankruptcy of many housing rental agencies. Our research add to the burgeoning literature examining the mediating effect of mobility control between confirmed case and housing rental market. It demonstrates that the change of housing rental market induced by pandemic in China is the short-term influence on rental unit transaction, which is different from western countries. In China, a country with the most strict mobility control, the challenges come from the impact of pandemic on housing rental agencies.

Satisfaction and Demands of Indoor Space in the High-Density Residential Areas in the COVID-19 Era

The last few years have witnessed a change in residents’ demand for indoor space due to the COVID-19 pandemic. From the perspective of residential satisfaction in the urban areas in various levels of COVID-19 severity, the household survey was conducted to explore the changing residential demands. The IBM SPSS Statistics was employed to analyze the survey data with a focus on the relationship between pandemic severity and residents’ satisfaction, as well as the future influence of COVID-19 on indoor space and the varying demands. Correlation analysis was performed. The variables included in the correlation analysis were the following: urban epidemic severity, number of confirmed cases, density of confirmed cases, regional pandemic severity and satisfaction of different indoor spaces. This study revealed that the hallway, bathroom, living room and master bedroom are key areas in which the residential demands are concentrated. These should be paid attention to in the future residential design.