作者：ZequanXionga, XiaPenga, LiYanga, WenLoub,c,d, Star X.Zhaoe,f
作者单位：
a: Library, East China Normal University, 500 Dongchuan Road, Shanghai, China
b: Faculty of Economics and Management, East China Normal University, 500 Dongchuan Road, Shanghai, China
c: Key Laboratory of Knowledge Mining and Knowledge Services in Agricultural Converging Publishing, National Press and Publication Administration, Agricultural Information Institute, Chinese Academy of Agricultural Sciences, China
d: Key Laboratory of Advanced Theory and Application in Statistics and Data Science (East China Normal University), Ministry of Education of China, China
e: Institute of Big Data (IBD), Fudan University, 220 Handan Road, Shanghai, China
f: State Base of Intelligent Evaluation & Governance Experiment, Fudan University, 220 Handan Road, Shanghai, China
摘要：Downloads have been considered as supplemental to citations in reflecting the impact of scientific research activities and scientific output, yet the motivations to download a specific publication has not been fully explored. In scientific evaluation practice, unclear motivations could lead to difficulties for evaluating the impact of academic literature without providing a cogent interpretation of downloads as an alternative metric. To fill this gap, an expanded Technology Acceptance Model (TAM) to investigate motivations for downloading academic literature was proposed and the effectiveness verified using questionnaire data containing 480 respondents. The results show that the degree of usefulness of literature to users and the degree of relevance of literature to users were the primary factors that drive users to download specific literature. Due to the consistency between downloading and citing in reflecting the usefulness, downloads is an effective metric to supplement citation metrics in evaluating the impact of academic literature.
关键词：Motivation;Download;Usage;Technology acceptance model
来源期刊：Library & Information Science Research
发表时间：2023-03-28
DOI：https://doi.org/10.1016/j.lisr.2023.101239
收录数据库：elsevier
