US academic libraries: understanding their web presence and their relationship with economic indicators

作     者：Orduna-Malea, Enrique Regazzi, John J. 

作者机构：Polytech Univ Valencia UPV Dept Audiovisual Commun Documentat & Hist Art Valencia 46022 Spain Long Isl Univ Palmer Sch Lib & Informat Sci Brookville NY 11552 USA Long Isl Univ Dept Comp Sci & Management Engn Brookville NY 11552 USA 

出 版 物：《SCIENTOMETRICS》 (科学计量学)

年 卷 期：2014年第98卷第1期

页      面：315-336页

核心收录：

学科分类：1205[管理学-图书情报与档案管理] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Academic libraries Webometrics Web-based indicators Economic variables Repositories Digital collections Online catalogs Universities United States 

摘      要：The main goal of this research is to analyze the web structure and performance of units and services belonging to U.S. academic libraries in order to check their suitability for webometric studies. Our objectives include studying their possible correlation with economic data and assessing their use for complementary evaluation purposes. We conducted a survey of library homepages, institutional repositories, digital collections, and online catalogs (a total of 374 URLs) belonging to the 100 U.S. universities with the highest total expenditures in academic libraries according to data provided by the National Center for Education Statistics. Several data points were taken and analyzed, including web variables (page count, external links, and visits) and economic variables (total expenditures, expenditures on printed and electronic books, and physical visits). The results indicate that the variety of URL syntaxes is wide, diverse and complex, which produces a misrepresentation of academic libraries web resources and reduces the accuracy of web analysis. On the other hand, institutional and web data indicators are not highly correlated. Better results are obtained by correlating total library expenditures with URL mentions measured by Google (r = 0.546) and visits measured by Compete (r = 0.573), respectively. Because correlation values obtained are not highly significant, we estimate such correlations will increase if users can avoid linkage problems (due to the complexity of URLs) and gain direct access to log files (for more accurate data about visits).