Authors : Xu Wang, Dongyang Shi, Zeeshan Muhammad, Yufei Xue, Junping Qiu
In an open scientific environment, multidimensional feature analysis and influence comparison of Sleeping Beauty papers can fully tap their potential scientific research value. This study selected management and computer science as the representative disciplines. The data sources are Web of Science (WoS) and altmetric.com, from which the study obtained a large amount of bibliographic data, citation data, and altmetrics data.
Firstly, the Sleeping Beauty papers are recognized by the K-values recognition method combined with three indicators. This study then analyzes and compares the sleep characteristics, journal distribution characteristics, awakening mechanisms, and impact of Sleeping Beauty papers across the two disciplines.
The empirical results show that. (1) computer science disciplines are more prone to producing Sleeping Beauty papers with more prominent citation features. (2) Papers published in top-tier management journals may also experience delayed recognition after years of dormancy, whearase Sleeping Beauty papers in the field of computer science often appear in low-level journals. (3)
Both disciplines focus on the innovation and value of articles in theoretical aspects, whearase the computer science discipline pays more attention to the application and improvement of previous algorithms or technologies. (4) The sleep and awakening of Sleeping Beauty papers are associated with both academic and social influences.
