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Peer and neighborhood effects: Citation analysis using a spatial autoregressive model and pseudo-spatial data

Sergio Copiello ()

Journal of Informetrics, 2019, vol. 13, issue 1, 238-254

Abstract: Spatial analysis approaches have been long since adopted in citation studies. For instance, already in the early eighties, two works relied on input-output matrices to delve into citation transactions among journals (Noma, 1982; Price, 1981). However, the techniques meant to analyze spatial data have evolved since then, experiencing a major step change starting from the turn of the century or so. Here I aim to show that citation analysis may benefit from the development and latest improvements of spatial data analysis, primarily by borrowing the spatial autoregressive models commonly used to identify the occurrence of the so-called peer and neighborhood effects. I discuss features and potentialities of the suggested method using an Italian narrow academic sector as a test bed. The approach proves itself useful for identifying possible citation behavior and patterns. Especially, I delve into the relationships between citation frequency at author level and years of activity, references, references used by the closest peers, self-citations, number of co-authors, conference papers, and conference papers authored by the nearby researchers.

Keywords: Citation analysis; Spatial analysis; Spatial scientometrics; Autoregressive models; Peer effects; Neighborhood effects (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.joi.2019.01.002

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Handle: RePEc:eee:infome:v:13:y:2019:i:1:p:238-254