The citation advantage of author-pays model: the case of Springer and Elsevier OA journals.

Autor/inn/en	Sotudeh, Hajar; Ghasempour, Zahra; Yaghtin, Maryam
Titel	The citation advantage of author-pays model: the case of Springer and Elsevier OA journals.
Quelle	In: Scientometrics, (2015) 2, S.581-608 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-015-1607-5
Schlagwörter	Open access; Author-pays model; APC; Article processing charge; Citation; Springer; Elsevier
Abstract	Abstract Aiming to investigate the citation advantage of author-pays model, the present communication compares open access (OA) and Toll Access (TA) papers recognition in author-pays OA journals in 2007–2011. This is the first large scale study concentrating on all APC-funded OA journals published by Springer and Elsevier as the two greatest publishers authorizing and embracing the model. According to the research findings, the OA papers have been exponentially increased in recent years. They are, also, found to outperform the TA ones in their impacts whether in the annual comparisons or across disciplines. The annual OA citation advantages range from 21.36 % for 2009 to 49.71 % for 2008. Social Sciences and Humanities (with 3.14 %) and Natural Sciences (with 35.95 %) gain the lowest and the highest advantages, respectively. The citation advantage can be attributed to the higher visibility of the OA articles, implying the popularity and usefulness of the OA author-pays model to their readership. It may, also, have roots in the selectivity of the authors in choosing the author-pays outlet to publish their high-quality papers, signifying the overall prestige of the OA papers published in the model. Whatever may be the ultimate interpretation, i.e. correlation or causation, the OA citation advantage may encourage the authors who are willing to support OA movement, while seeking to get published in the well-established traditional journals. This may help approach the not-yet-achieved critical mass necessary to evaluate the success of the model.
Erfasst von	OLC
Update	2023/2/05