Interactions of Publication Volume, Journal Impact, and Article Processing Charges: Comparative Study of China and Global Practices in Nature Portfolio is a research paper published in Publications (2024). On theSindex it has a DataRank of 0.104. It has been cited 1 time.
In the commercial realm, the annual publication volume (PUB) significantly influences the business models of article processing charge (APC)-based open access (OA) journals, though it may negatively impact journal reputation. Despite this, the interactions among APC, PUB, and the Journal Citation Indicator (JCI)—a key marker of journal reputation—have not been thoroughly examined. The objective of this study is to reveal the interactions among APC, PUB, and JCI, determine if there are differences between the interactions inside and outside of China, and uncover the possible mechanisms enabling dominant publishers to set APC prices without compromising their market position. Through cross-correlation and linear regression analyses, our findings reveal distinct APC business models between China and the rest of the international OA journal landscape. Specifically, while both cases demonstrate a proportional relationship between APC and JCI, China exhibits an inverse relationship between APC and PUB, contrasting with the global trend. This suggests that the business model in China sets an “optimized” PUB for Chinese APC-based OA journals, which would pose challenges for journal management and the expansion of the domestic APC-based OA market volume. In the rest of the international context, by contrast, the business model supports a proactive annual increase in APC list prices driven by the proportional relationships between APC and PUB. These insights underscore the need for more nuanced APC business models that can adapt to regional variations in funder requirements and policy expectations.
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Base Score Contribution
0.104
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.