文章基本信息

标题：Research Productivity and Its Relationship to Library Collections
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作者：Sandra L. De Groote ; Beyza Aksu Dunya ; Jung Mi Scoulas 等
期刊名称：Evidence Based Library and Information Practice
印刷版ISSN：1715-720X
电子版ISSN：1715-720X
出版年度：2020
卷号：15
期号：4
页码：16-32
DOI：10.18438/eblip29736
出版社：University Of Alberta
摘要：Objective – The purpose of this study was to explore in the current academic library environment, the relationship between library collections data (collections’ size, expenditures, and usage) and faculty productivity (scholarly output). The researchers also examined the degree to which new and existing library metrics predict faculty productivity. Methods – Demographic data (e.g., faculty size, student size, research and development expenditures), library budget data (e.g., collection expenditures), collection use data (e.g., full-text article requests and database searches), and publication output for 81 doctoral granting universities in the United States were collected to explore potential relationships between research productivity, collection use, library budgets, collection size, and research expenditures using partial correlations. A hierarchical multiple regression was also used to ascertain the significance of certain predictors of research productivity (publications). Results – A correlation existed between the number of publications (research productivity) and library expenditures (total library expenditures, total library material expenditures, and ongoing library resource expenditures), collection size (volumes, titles, and ebooks), use of collection (full-text article requests and total number of references in the articles), and research and development expenditures. Another key finding from the hierarchical multiple regression analysis showed that full-text article requests were the best predictor of research productivity, which uniquely explained 10.2% of the variation in publication. Conclusion – The primary findings were that full-text article requests, followed by library material expenditures and research expenditures, were found to be the best predictor of research productivity as measured by articles published.