Abstract

This paper analyzes the effects of the co-authorship and bibliographic coupling networks on the citations received by scientific articles. It expands prior research that limited its focus on the position of co-authors and incorporates the effects of the use of knowledge sources within articles: references. By creating a network on the basis of shared references, we propose a way to understand whether an article bridges among extant strands of literature and infer the size of its research community and its embeddedness. Thus, we map onto the article – our unit of analysis – the metrics of authors' position in the co-authorship network and of the use of knowledge on which the scientific article is grounded. Specifically, we adopt centrality measures – degree, betweenneess, and closeness centrality – in the co-authorship network and degree, betweenness centrality and clustering coefficient in the bibliographic coupling and show their influence on the citations received in first two years after the year of publication. Findings show that authors' degree positively impacts citations. Also closeness centrality has a positive effect manifested only when the giant component is relevant. Author's betweenness centrality has instead a negative effect that persists until the giant component - largest component of the network in which all nodes can be linked by a path - is relevant. Moreover, articles that draw on fragmented strands of literature tend to be cited more, whereas the size of the scientific research community and the embeddedness of the article in a cohesive cluster of literature have no effect.

Highlights

  • Generating scientific knowledge is as a social activity in which scientists find problems to tackle, become aware of connections between elements, elaborate on existing ideas to produce new or refined answers

  • We focus on two types of networks: the coauthorship network (CA), and the bibliographic coupling network (BC)

  • Most articles are written by 3 authors (m = 3.404, s = 3.021, min = 1, max = 57), they have on average 48.65 references (s = 34.537, min = 1, max = 398), and receive on average 4.95 citations in the first two years after the publication (s = 9.890, min = 0, max = 273), the distribution of citations is skewed to the right

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Summary

Introduction

Generating scientific knowledge is as a social activity in which scientists find problems to tackle, become aware of connections between elements, elaborate on existing ideas to produce new or refined answers. It is a recipe with both social and knowledge ingredients. Studies on co-authorship networks have gathered the attention of many scholars, not just because of their descriptive and synthetic power to describe the evolution of research communities, and because social networks play a significant role on the generation of knowledge [1,2,3]. Coauthorship networks (hereafter CA) are a type of social network based on co-author relationships that are built over time by scientists

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