Can open access increase LIS research’s policy impact? Using regression analysis and causal inference.

Autor/inn/en	Zong, Qianjin; Huang, Zhihong; Huang, Jiaru
Titel	Can open access increase LIS research’s policy impact? Using regression analysis and causal inference.
Quelle	In: Scientometrics, (2023) 8, S.4825-4854 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-023-04750-1
Schlagwörter	Policy impact; Open access; Evidence-based policy-making; Policy-makers; Policy citation counts; Unpaywall; Dimensions
Abstract	Abstract The relationship between open access and academic impact (usually measured as citations received from academic publications) has been extensively studied but remains a very controversial topic. However, the effect of open access on policy impact (measured as citations received from policy documents) is still unknown. The purpose of this study was to examine the effect of open access on the policy impact, which might initiate a new controversial topic. Research articles in the field of library and information science (LIS) were selected as the data sample (n = 48,884). Negative binomial regression models were used to examine the dataset. Furthermore, propensity score matching (PSM) analysis, a causal inference approach, was used to estimate the effect of open access on the policy impact based on a selected LIS journal (Scientometrics, n = 4019) that received the most policy citations among the LIS journals. Linear regression models, logit regression models, four other matching methods, open access status provided by different databases, and different sizes of data samples were used to check the robustness of the main results. This study revealed that open access had significant and positive effects on the policy impact.
Erfasst von	OLC
Update	2024/1/01