6,240 research outputs found
Predicting long-term publication impact through a combination of early citations and journal impact factor
The ability to predict the long-term impact of a scientific article soon
after its publication is of great value towards accurate assessment of research
performance. In this work we test the hypothesis that good predictions of
long-term citation counts can be obtained through a combination of a
publication's early citations and the impact factor of the hosting journal. The
test is performed on a corpus of 123,128 WoS publications authored by Italian
scientists, using linear regression models. The average accuracy of the
prediction is good for citation time windows above two years, decreases for
lowly-cited publications, and varies across disciplines. As expected, the role
of the impact factor in the combination becomes negligible after only two years
from publication
When does centrality matter? Scientific productivity and the moderating role of research specialization and cross-community ties
The present study addresses the ongoing debate concerning academic scientific productivity. Specifically, given the increasing number of collaborations in academia and the crucial role networks play in knowledge creation, we investigate the extent to which building social capital within the academic community represents a valuable resource for a scientist's knowledge-creation process. We measure the social capital in terms of structural position within the academic collaborative network. Furthermore, we analyse the extent to which an academic scientist's research specialization and ties that cross-community boundaries act as moderators of the aforementioned relationship. Empirical results derived from an analysis of an Italian academic community from 2001 to 2008 suggest academic scientists that build social capital by occupying central positions in the community outperform their more isolated colleagues. However, scientific productivity declines beyond a certain threshold value of centrality, hence revealing the existence of an inverted U-shaped relationship. This relationship is negatively moderated by the extent to which an academic focuses research activities in few scientific knowledge domains, whereas it is positively moderated by the number of cross-community ties established
Predicting the impact of academic articles on marketing research: Using machine learning to predict highly cited marketing articles
The citation count of an academic article is of great importance to researchers and readers.
Due to the large increase in the publication of academic articles every year, it may be difficult
to recognize the articles which are important to the field. This thesis collected data from
Scopus with the purpose to analyze how paper, journal, and author related variables performed
as drivers of article impact in the marketing field, and how well they could predict highly cited
articles five years ahead in time. Social network analysis was used to find centrality metrics,
and citation count one year after publication was included as the only time dependent variable.
Our results found that citations after one year is a strong driver and predictor for future
citations after five years. The analysis of the co-authorship network showed that closeness
centrality and betweenness centrality are drivers of future citations in the marketing field,
indicating that being close to the core of the network and having brokerage power is important
in the field. With the use of machine learning methods, we found that a combination of paper,
journal, and author related drivers perform better at predicting highly cited articles after five
years, compared to using only one type of driver.nhhma
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