Exploring the Citation and Impact Advantages of Open Access Papers in Hybrid Journals: A Case Study of Biochemistry Publications

Abstract

Open Access (OA) has emerged as a pivotal driver shaping the dissemination scope and academic impact of research findings. To clarify the impact of publishing models such as open access on the citation performance of biochemical papers, this study selects 177,745 biochemistry professional papers included in the core collection of the Web of Science (WoS CC) as the research data; we conduct an analysis of citation and impact advantages in biochemistry research. Employing correlation analysis, baseline regression modeling, and two-way ANOVA, our analysis indicates that: OA publications in biochemistry exhibit notable citation and impact advantages, which are positively correlated with the degree of openness, and the key determinants of the OA advantage encompass funding sources, reference count, and publication region. At present, China accounts for a disproportionately small proportion of OA papers in this field. In the context of the open-science paradigm, Chinese academic journals must systematically address their developmental bottlenecks and formulate publication innovation strategies to enhance the quality of academic publishing.

1. Introduction

In the realm of academic publishing, the dissemination scope of papers has been restricted by subscription fees and publication channels. With the rapid advancement of information technology, the OA publishing model has presented novel opportunities for the extensive dissemination and sharing of academic achievements (Shen et al., 2018). Open Access, as delineated by the Budapest Open Access Initiative (BOAI), facilitates unconstrained digital access to academic outputs via public internet platforms, thereby fundamentally transforming global knowledge dissemination models (BOAI, n.d.). In the context of open science, countries across the globe and regional communities are continuously exploring novel models and pathways for scientific development. With the continuous growth of research investment, research output has been significantly enhanced, and the number of papers published in OA journals has also witnessed rapid growth. However, beneath this substantial increase in quantity, some scholars have put forward their considerations and doubts.

Scholars contend that due to the open nature of OA papers, they can be readily accessed, read, and cited by a larger number of individuals (Xie et al., 2022). Concurrently, some scholars opine that with the increase in the number of gold OA journals, the quality of OA papers cannot be ensured, and their citation advantages have started to be questioned by the academic community (Eve, 2017). Moreover, there are constantly emerging “low-quality and high-priced” journals with low acceptance thresholds but high charges for OA papers, which has disrupted the normal academic ecosystem (McCabe & Langer, 2024).

Hybrid journals represent one of the transitional forms from traditional subscription-based journals to gold OA ones. Different from gold OA journals and flipped journals, hybrid journals still levy a certain amount of subscription fees (X. Zhang, 2024). The “hybrid openness and diverse charging” approach adopted by hybrid journals not only mitigates the risks of traditional subscribed journals transitioning to gold OA journals but also guarantees the quality of paper output to a certain degree (Stojanovski & Andonovski, 2023; Onjia, 2025).

Consequently, this study selected hybrid journals as samples. While controlling for journal quality, it compared the citation rates of open-access (OA) papers and toll-access (TA) papers published in hybrid journals and examined the relevant factors influencing article citation. This approach mitigates the interference stemming from the disparity in journal quality between OA journals and traditional subscription-based journals in previous research. It further delves into whether OA papers possess citation advantages over TA papers in journals of equivalent quality, with the aim of offering guidance for the development of domestic academic journals, particularly in the selection of publication models for hybrid journals.

2. Literature Review

Based on the extant studies, the research regarding the citation advantage of OA publications primarily centers on three aspects: disciplinary disparities, influencing factors, and article influence. A multitude of scholars have delved into this topic from diverse dimensions and attained substantial research outcomes.

2.1. Disparities in OA Citation Advantage Prevalence

Domestic and foreign scholars have carried out multi-dimensional studies on the development trend of OA papers, the citation advantages of papers and journals brought about by the OA model, and research methods (Basson et al., 2021; Delikoura & Kouis, 2021). Nevertheless, scholars have not yet reached a consensus on whether OA papers possess citation advantages, and the research on the citation advantages of OA papers remains in the exploratory phase (Zheng et al., 2024; Sotudeh & Estakhr, 2018). Currently, there are primarily three viewpoints regarding the citation advantages of OA papers: (1) The accessibility of OA papers encourages a larger number of individuals to read and cite them. (2) Most OA papers can be accessed and read prior to publication, thus commencing the citation accumulation process earlier than traditional subscription-based articles. (3) More prominent authors may be more prone to publishing OA papers and are more willing to contribute high-quality OA papers. Some scholars’ research has demonstrated that OA papers do exhibit relatively significant citation advantages.

Empirical studies have verified the OA citation advantage in several disciplines. For instance, in the field of human electrophysiology, OA papers have approximately an 18% “OA advantage” in citation counts compared to non-OA papers; in clinical medicine, the citation advantage of OA papers published in hybrid journals is apparent (Saravudecha et al., 2023); and in library and information science (LIS), OA papers tend to have higher citation counts (Eysenbach, 2006; Huang et al., 2019). These findings confirm the existence of the OA citation advantage in certain subject areas.

Some scholars still maintain a skeptical stance regarding the causal relationship between OA and the citation advantage of papers. They contend that the citation advantage of OA papers is indeterminate and exhibits variations across different fields. For example, OA papers in hybrid engineering–biology journals do not generally possess a citation advantage (Tian et al., 2024); in oncology, there is a negative correlation between citation frequency and the open-access type of the journal (Heyard & Hottenrott, 2021).

The controversy surrounding the aforementioned research could stem from deficiencies in sample selection during the early investigations into the citation advantage of OA papers. Prior studies have indicated that the citation advantage of OA papers might result from researchers’ inability to control article quality (McCabe & Snyder, 2015). Consequently, this paper undertakes research on hybrid journals, as they publish both OA papers and TA papers concurrently. These papers exhibit high comparability in aspects such as journal quality, subject scope, and review process and can more effectively control these crucial variables that may influence citation rates.

2.2. Determinants of OA Citation Impact

From existing studies, OA citation advantage is influenced by a number of factors, such as the quality of the paper and the popularity of the authors (Sattari et al., 2022), enhanced social media exposure, which may also increase the citation rate of an article, and whether the paper is funded or not, as scientific funding can effectively promote the output of high-quality papers (Fu et al., 2025; Wu et al., 2020). OA papers have different temporal evolution patterns compared to non-OA papers; in some areas OA papers are quickly viewed after publication and their citation advantage is more pronounced with time. In addition, it has also been argued that an increase in the number of authors can also increase the citation frequency of a paper (Whitehead et al., 2020).

2.3. Scholarly Influence Assessment of Open Access Publications

Influence represents a crucial aspect of academic paper evaluation. Academic paper influence can be classified into academic, social, and technological influence (Sattari et al., 2022). Research on paper influence primarily centers around bibliometric indicators such as citation frequency, h-index, and impact factor (Eysenbach, 2006; Davis, 2009). It is a prevalent approach to integrate the impact factor of journals and paper citation frequency indicators to access the academic influence of the paper (Fu et al., 2025). Some scholars have analyzed the effects of citation frequency and citation time on the value of academic papers and introduced elements like the academic paper citation factor, publication time window, (i.e., annual index), and journal impact factor to establish a domestic evaluation system for academic paper influence (Wu et al., 2020).

As an interdisciplinary domain, biochemistry is capable of generating a substantial volume of raw data, and its methodologies undergo rapid updates. Open access facilitates the swift dissemination of novel technologies, mitigates redundant research, and enhances the efficiency of resource utilization. Furthermore, biochemistry journals are at the vanguard of the shift from traditional subscription-based models to open access. For example, premier journals like Nature Chemistry have implemented hybrid models (e.g., the “Gold Open Access” option), and their policy adjustments exert a demonstrative influence on other disciplines (Whitehead et al., 2020).

Previous studies have demonstrated that journal impact factors are significantly correlated with the number of citations and academic impact of papers in this field (Didegah & Thelwall, 2013). However, evidence regarding the OA citation advantage in biochemistry remains scarce and controversial (Sud & Thelwall, 2015; Whitehead et al., 2020). In terms of research methodology, some scholars have employed observational studies and randomized controlled trials that failed to adequately control for differences between OA journals and non-OA journals. Others have analyzed paper citations across multiple fields without conducting a more in-depth investigation of a specific field.

Therefore, in this paper, when studying the OA advantage in the field of biochemistry, we will focus on the roles of different influencing factors on the OA advantage, combining linear regression modeling and two-way ANOVA to explore the various factors affecting the advantages of OA papers in biochemistry publications.

More specifically, this study proposes to answer the following:

RQ1: Do OA papers in hybrid journals have citation advantages?

RQ2: What are the specific factors that influence the citation advantages of a thesis?

RQ3: What are the differences in the publication and citation of OA papers in different countries and regions?

3. Data and Method

3.1. Data Extraction

The criteria for journal screening in this study are derived from the Journal Citation Reports (JCRs). Papers in the field of Biology & Biochemistry are selected as the research object. Journals in this discipline with 10–90% of OA papers in the years 2018, 2019, and 2020 in the JCR database are retrieved, and a total of 8147 journals are obtained. Based on the Journal Impact Factor (JIF) Quartile of the journals in JCR, the journals are classified into four grades: Q1, Q2, Q3 and Q4. To ensure the quality of the journals, 348 hybrid journals with a journal grade of Q1 are selected. After a meticulous one-by-one examination of these journals, it was discovered that a total of 26 of the journals had transitioned to OA journals within these three years. Finally, 287 journals are screened out. The number of articles published in 287 biochemical journals is shown in Appendix A. In this paper, only papers with the document type “Article” are chosen, and a total of 177,745 papers are acquired. Among them, 50,952 are gold OA papers, 73,302 are other OA papers, and 53,491 are non-OA papers. Flowchart of data screening is shown in Figure 1.

3.2. Description of Variables

Based on a comprehensive review and in-depth analysis of the extant literature (Cheng et al., 2021), citation frequency, immediacy index, and impact factor are selected as explanatory variables, while the degree of OA of the paper serves as the dependent variable. Simultaneously, in order to eliminate the influence of other factors on the frequency of citations, variables such as the country of origin of the paper, the number of references, and the number of grants are incorporated into the control variables, referring to the research of Robson B J (Robson & Mousquès, 2016), Wang J (Wang et al., 2024) and Heyard R (Heyard & Hottenrott, 2021).

3.2.1. Dependent Variable

Citation frequency can reflect the stages of the impact life cycle of an academic paper and is widely used to measure the status of research results in a particular field (Saravudecha et al., 2023). The immediacy index can reflect the popularity and attention of the paper in a short period of time (Shin, 2009; Huang et al., 2024), and the impact factor is the most direct indicator to present the quality level of the journal. Consequently, a substantial number of scholars have incorporated these three factors into their research (Guo et al., 2018; Huang et al., 2019). In this paper, the citation frequency, immediacy index, and impact factor of the paper are utilized as explanatory variables.

3.2.2. Independent Variable

In the research on OA, scholars have adopted diverse approaches to classify the open access status of papers. The following classifications are commonly encountered: firstly, OA and non-OA; secondly, OA, Gold OA and Green OA (Huang et al., 2024); and thirdly, Instant OA and Delayed OA (G. Zhang et al., 2021), among others. Given that hybrid journals integrate both OA and non-OA publishing models, and OA can be classified into gold OA and other OA based on the extent of open access, this paper classifies the open access types of papers into gold OA, other OA, and non-OA.

3.2.3. Control Variables

Control variables, as an essential component of the theoretical model design, can effectively enhance the accuracy and reliability of the experiment. In this study, a series of control variables are introduced to more precisely analyze the relationship between the degree of OA and the citation frequency of papers. Based on the variables in the theoretical model and referring to the research of existing scholars, the selected control variables encompass the country of paper origin, the amount of funding, the number of keywords, the number of authors, the number of references, the publication year of the paper, and the journal citation index. The names and definitions of each variable are presented in

Table 1. List of variable definitions.

Variable Type	Variable Name	Notation	Definition
Dependent variable	citation frequency	cite	Number of times the paper has been cited by other papers since it was published or publicly released
	immediacy index (Tsay & Chen, 2005)	II	Number of times a paper published in a journal in a given year was cited in that year/total number of papers published in the same year
	impact factor (Z. Liu et al., 2025)	IF	The total number of citations of papers published in the first two years of the journal’s existence in the statistical year divided by the total number of papers published by the journal in those two years.
Independent variable	open access status	OA	Difficulty of making the results of scientific knowledge publicly available on the Internet and the range of 0 = non-OA papers, 1 = other OA papers, 2 = gold OA papers
Control Variables	country to which the paper belongs (Robson & Mousquès, 2016)	country	Country of the corresponding author of the paper
	the number of funds (Heyard & Hottenrott, 2021)	fund	Number of fund-supported projects for a paper
	the number of keywords (Uddin & Khan, 2016)	key	The number of keywords in a paper
	the number of authors (Wang et al., 2024)	author	Total number of people involved in researching, writing and contributing to a paper
	the number of references (Judge et al., 2007)	ref	Number of other papers cited in academic papers
	the time of the paper’s publication (Gnewuch & Wohlrabe, 2017)	year	Year in which the paper was formally accepted and published in a journal or conference
	journal citation index (Y. Liu et al., 2025)	JCI	Average of CNCIs for papers and reviews published in the first three years of the year.

.

3.3. Model Construction

A regression model is constructed, with the citation frequency, immediacy index and impact factor serving as explanatory variables, the degree of OA of the paper as an explanatory variable, and the country to which the paper belongs, the number of funds, the number of authors, the number of references, the publication time of the paper and the journal citation index as control variables.

The regression equation is presented as follows:

I_cite,i = α₀ + α₁I_OA,i + α₂I_c,i + α₃I_f,i + α₄I_k,i + α₅I_a,i + α₆I_r,i + α₇I_y,i + α₈I_JCI,i + ε_i

(1)

In this equation, I_cite,i, I_OA,i, I_c,i, I_f,i, I_k,i, I_a,i, I_r,i, I_y,i and I_JCI,i, respectively, denote the citation frequency of the i-th paper, the degree of open access, the country to which the paper belongs, the number of funds, the number of keywords, the number of authors, the number of references, the publication year, and the journal citation index. α₀ represents the intercept, namely the constant term, while α₁~α₈ signify the influence coefficients of the degree of OA, the country, the number of funds, the number of keywords, the number of authors, the number of references, the publication time, and the journal citation index on the citation frequency; ε_i represents the error term.

Based on the regression results of the model, the magnitude and direction of the influence of each variable on the citation frequency of papers can be analyzed. For instance, if α₁ is positive and statistically significant, it implies that an increase in OA levels of papers has a positive impact on the citation frequency.

In addition, since the citation frequency does not fully reflect the OA paper advantage, the impact factor and immediacy index, as important indicators of the influence advantage of the paper, need to be studied further. Therefore, the impact factor and immediacy index are included as explanatory variables in Equation (1) to explore the extent to which factors such as the degree of a paper’s open access, the number of grants, and the number of references impact the paper’s influence advantage.

I_if,i = β₀ + β₁I_OA,i + β₂I_c,i + β₃I_f,i + β₄I_k,i + β₅I_a,i + β₆I_r,i + β₇I_y,i + β₈I_JCI,i + ε_i

(2)

I_ii,i = γ₀ + γ₁I_OA,i + γ₂I_c,i + γ₃I_f,i + γ₄I_k,i + γ₅I_a,i + γ₆I_r,i + γ₇I_y,i + γ₈I_JCI,i + ε_i

(3)

Herein, I_if,i denotes the impact factor of the paper, β₀ represents the intercept, i.e., constant term, and β₁~β_8, respectively, signify the impact coefficients of each variable on the paper’s impact factor, I_ii,i represents the immediacy index of the paper, γ₀ represents a constant term, and γ₁~γ_8, respectively, denote the impact coefficients of each variable on the paper’s immediacy index.

4. Analysis of Empirical Results

4.1. Descriptive Statistics

Gold OA theses signify the highest level of openness within the OA publishing model. The sample size for the statistics in this paper amounts to 50,952. The average citation frequency is 48.60 times. Among them, the highest citation was 5882 times, which was for an article published by Stuart in CELL (Stuart et al., 2019). The average immediacy index of the journals to which these theses belong is 2.75, and the average impact factor is 8.75. The maximum values of the immediacy index and the impact factor are 14.77 and 38.81, respectively.

The sample size of other OA papers totals 73,302, with an average citation frequency of 31.72 times. Among them, the highest citation was 11,733 times, which was for an article published by Hoffmann in CELL (Hoffmann et al., 2020). The average immediacy index of the journals to which these papers belong is 2.00, and the average impact factor is 6.26.

The sample size of non-OA papers totals 53,491, with an average citation frequency of 27.51 times. The average immediacy index of the journals to which these papers belong is 1684 and the average impact factor is 6.03. The maximum values of the immediacy index and the impact factor are 11.18 and 34.02, respectively.

Overall, the average citation frequency, average journal impact factor, and average journal immediacy index of different types of OA papers exhibit significant differences. In the descriptive statistics of the sample, all three variables demonstrate a numerical relationship of gold OA > other OA > non-OA. The advantage of other OA over non-OA dissertation data is relatively minor, while the advantage of gold OA over other OA and non-OA dissertation data is substantial, yet the standard deviation values are also large. Evidently, there is considerable variability in the data of gold OA papers. The descriptive statistics of the sample are presented in

Table 2. Descriptive statistics of variables.

Variables	Gold OA				Other OA				Non-OA
	Mean	SD	Maximum	Minimum	Mean	SD	Maximum	Minimum	Mean	SD	Maximum	Minimum
cite	48.60	104.45	5882	0	31.72	78.87	11,733	0	27.51	45.55	2531	0
II	2.75	2.80	14.77	0.25	2.00	1.56	14.77	0.25	1.68	1.16	11.18	0.25
IF	8.75	7.18	38.81	1.78	6.26	3.54	38.81	1.78	6.03	3.98	34.02	1.78
fund	4.11	4.41	160	0	3.26	3.89	208	0	2.46	2.39	52	0
key	8.43	2.71	10	0	8.51	2.64	10	0	8.40	2.68	10	0
author	9.36	11.45	728	1	7.80	8.34	552	1	6.53	5.33	362	1
ref	55.47	33.12	2934	0	53.82	31.38	3196	0	55.02	31.03	1053	0
year	5.77	1.38	8	2	5.98	1.37	8	1	5.77	1.26	8	2
JCI	2.09	1.30	7.19	0.53	1.63	0.71	7.19	0.53	1.55	0.79	6.36	0.53
sample size	50,952				73,302				53,491

.

4.2. Correlation Analysis

Linear regression necessitates the existence of a linear relationship between the independent variable (X) and the dependent variable (Y). Prior to performing linear regression analysis, correlation analysis is primarily conducted to validate the strength and direction of the linear relationship between variables, screen for valid variables, diagnose multicollinearity issues, and optimize the rationality of model assumptions. This, in turn, improves the accuracy and explanatory power of regression analysis.

To examine the correlation among the main variables, a Pearson correlation test is conducted on the main variables within the research model. The Pearson correlation coefficients among the variables indicate that the level of open access exhibits a positive correlation with the citation advantage and impact advantage of the paper at the 1% significance level. The correlation coefficients and corresponding significance levels among the variables are presented in

Table 3. Pearson correlation analysis table.

Variables	Gold OA			Other OA			Non-OA
	Cite	II	IF	Cite	II	IF	Cite	II	IF
country	−0.083 ***	−0.094 ***	−0.121 ***	−0.049 ***	−0.061 ***	−0.077 ***	−0.073 ***	−0.049 ***	−0.086 ***
fund	0.195 ***	0.198 ***	0.261 ***	0.116 ***	0.091 ***	0.187 ***	0.103 ***	0.082 ***	0.120 ***
key	0.083 ***	0.084 ***	0.144 ***	0.056 ***	−0.011 ***	0.060 ***	0.077 ***	−0.007 *	0.012 ***
author	0.245 ***	0.265 ***	0.280 ***	0.176 ***	0.244 ***	0.257 ***	0.092 ***	0.178 ***	0.139 ***
ref	0.119 ***	0.052 ***	0.122 ***	0.126 ***	−0.029 ***	0.091 ***	0.143 ***	0.032 ***	0.052 ***
year	0.189 ***	0.020 ***	0.027 ***	0.182 ***	−0.044 ***	−0.065 ***	0.139 ***	−0.099 ***	−0.066 ***
JCI	0.505 ***	0.860 ***	0.959 ***	0.338 ***	0.603 ***	0.824 ***	0.419 ***	0.631 ***	0.863 ***

Note: *, **, *** indicate significance at 10%, 5% and 1% level of significance, respectively.

.

When variables are correlated, it is essential to exclude the possibility that excessive inter-variable correlation affects the study results. Consequently, the independence of variables must be verified through a multicollinearity test. We investigated the Variance Inflation Factor (VIF) values of the primary variables, as presented in

Table 4. Analysis of multicollinearity between independent variables of the paper.

Variables	Gold OA		Other OA		Non-OA
	VIF	1/VIF	VIF	1/VIF	VIF	1/VIF
ref	1.490	0.669	1.470	0.679	1.370	0.728
key	1.470	0.680	1.460	0.684	1.360	0.737
author	1.240	0.805	1.170	0.853	1.120	0.889
fund	1.230	0.813	1.140	0.879	1.090	0.920
JCI	1.150	0.867	1.070	0.93	1.050	0.955
year	1.060	0.943	1.050	0.954	1.030	0.973
country	1.020	0.985	1	0.996	1.010	0.988
Mean	1.240		1.18		1.150

. The mean VIF values of the variables were 1.24, 1.18, and 1.14, respectively, and the VIF values of each variable were significantly lower than the critical value of 10, further demonstrating that there is no severe multicollinearity among the variables. The test results are shown in Table 4.

4.3. Benchmark Regression Results

Benchmark regression analyses were carried out on citation frequency, immediacy index, and impact factor. Group regressions were also performed on the overall sample, gold OA papers, other OA papers, and non-OA papers, and the results are presented in

Table 5. Regression analysis of citation frequency.

Variables	Base Regression Analysis	Citation Frequency of Papers
		All Papers	Sig.	Gold OA	Sig.	Other OA	Sig.	Non-OA	Sig.
OA	Coefficient	2.245 ***	<0.001	-	-	-	-	-	-
	T-value	−0.11		-		-		-
country	Coefficient	−0.140 ***	<0.001	−0.167 ***	<0.001	−0.137 ***	<0.001	−0.144 ***	<0.001
	T-value	−0.009		−0.025		−0.013		−0.011
fund	Coefficient	0.355 ***	<0.001	0.282 ***	<0.001	0.260 ***	<0.001	0.526 ***	<0.001
	T-value	−0.031		−0.069		−0.042		−0.057
key	Coefficient	−0.594 ***	<0.001	−1.363 ***	<0.001	−0.701 ***	<0.001	0.054	0.281
	T-value	−0.037		−0.1		−0.05		−0.05
author	Coefficient	0.746 ***	<0.001	1.000 ***	<0.001	0.781 ***	<0.001	0.345 ***	<0.001
	T-value	−0.017		−0.035		−0.024		−0.031
ref	Coefficient	0.202 ***	<0.001	0.293 ***	<0.001	0.208 ***	<0.001	0.130 ***	<0.001
	T-value	−0.004		−0.011		−0.005		−0.005
year	Coefficient	5.669 ***	<0.001	8.669 ***	<0.001	5.083 ***	<0.001	3.924 ***	<0.001
	T-value	−0.062		−0.165		−0.081		−0.093
JCI	Coefficient	17.56 ***	<0.001	20.780 ***	<0.001	15.580 ***	<0.001	15.310 ***	<0.001
	T-value	−0.090		−0.183		−0.18		−0.149
constant term	Coefficient	−44.92 ***	<0.001	−61.860 ***	<0.001	−37.130 ***	<0.001	−30.080 ***	<0.001
	T-value	−0.484		−1.25		−0.678		−0.722
sample size		177,529		50,899		73,236		53,394

Note: Values in brackets below the regression coefficients are t-values, and *, **, and *** indicate significance at the 10%, 5%, and 1% significance levels, respectively.

,

Table 6. Regression analysis of immediacy index.

Variables	Base Regression Analysis	Immediacy Index of Affiliated Journals
		All Papers	Sig.	Gold OA	Sig.	Other OA	Sig.	Non-OA	Sig.
OA	Coefficient	0.104 ***	<0.001	-	-	-	-	-	-
	T-value	−0.004		-		-		-
country	Coefficient	−0.002 ***	<0.001	0.002 ***	<0.001	−0.005 ***	<0.001	0	0.504
	T-value	−0.000		−0.001		0		0
fund	Coefficient	−0.010 ***	<0.001	−0.024 ***	<0.001	−0.014 ***	<0.001	0.005 **	0.01
	T-value	−0.001		−0.002		−0.002		−0.002
key	Coefficient	0.021 ***	<0.001	0.017 ***	<0.001	0.015 ***	<0.001	0.004 ***	<0.001
	T-value	−0.001		−0.003		−0.002		−0.002
author	Coefficient	0.023 ***	<0.001	0.009 ***	<0.001	0.032 ***	<0.001	0.022 ***	<0.001
	T-value	−0.001		−0.001		−0.001		−0.001
ref	Coefficient	−0.004 ***	<0.001	−0.006 ***	<0.001	−0.004 ***	<0.001	−0.001 ***	<0.001
	T-value	−0.000		0		0		0
year	Coefficient	−0.034 ***	<0.001	−0.022 ***	<0.001	−0.039 ***	<0.001	−0.065 ***	<0.001
	T-value	−0.002		−0.005		−0.003		−0.003
JCI	Coefficient	1.521 ***	<0.001	1.860 ***	<0.001	1.278 ***	<0.001	0.900 ***	<0.001
	T-value	−0.003		−0.005		−0.007		−0.005
constant term	Coefficient	−0.497 ***	<0.001	−0.826 ***	<0.001	0.067 *	0.05	0.540 ***	<0.001
	T-value	−0.017		−0.036		−0.025		−0.024
sample size		177,529		50,899		73,236		53,394

Note: Values in brackets below the regression coefficients are t-values, and *, **, and *** indicate significance at the 10%, 5%, and 1% significance levels, respectively.

and

Table 7. Regression analysis of impact factors.

Variables	Base Regression Analysis	Impact Factor of Affiliated Journals
		All Papers	Sig.	Gold OA	Sig.	Other OA	Sig.	Non-OA	Sig.
OA	Coefficient	0.015 **	0.015	-	-	-	-	-	-
	T-value	−0.006		-		-		-
country	Coefficient	−0.009 ***	<0.001	−0.008 ***	<0.001	−0.012 ***	<0.001	−0.011 ***	<0.001
	T-value	−0.000		−0.001		−0.001		−0.001
fund	Coefficient	0.044 ***	<0.001	0.024 ***	<0.001	0.041 ***	<0.001	0.077 ***	<0.001
	T-value	−0.002		−0.003		−0.002		−0.004
key	Coefficient	0.086 ***	<0.001	0.100 ***	<0.001	0.057 ***	<0.001	0.064 ***	<0.001
	T-value	−0.002		−0.004		−0.003		−0.004
author	Coefficient	0.004 ***	<0.001	−0.006 ***	0.001	0.033 ***	<0.001	−0.012 ***	<0.001
	T-value	−0.001		−0.001		−0.001		−0.002
ref	Coefficient	−0.004 ***	<0.001	−0.003 ***	<0.001	0.001 *	0.077	−0.008 ***	<0.001
	T-value	−0.000		0		0		0
year	Coefficient	−0.093 ***	<0.001	−0.031 ***	<0.001	−0.110 ***	<0.001	−0.127 ***	<0.001
	T-value	−0.004		−0.007		−0.004		−0.007
JCI	Coefficient	4.830 ***	<0.001	5.258 ***	<0.001	3.645 ***	<0.001	4.381 ***	<0.001
	T-value	−0.005		−0.007		−0.01		−0.011
constant term	Coefficient	−1.561 ***	<0.001	−2.737 ***	<0.001	0.097 ***	<0.001	−0.136 **	0.012
	T-value	−0.028		−0.05		−0.036		−0.054
sample size		177,529		50899		73,236		53,394

Note: Values in brackets below the regression coefficients are t-values, and *, **, and *** indicate significance at the 10%, 5%, and 1% significance levels, respectively.

.

The influence direction of each variable on the explanatory variables is determined by the positive or negative values of the regression coefficients. The regression coefficient values of all variables are positive, indicating that all these variables have a positive impact on the explanatory variables. As can be observed from the results of the overall regression of citation frequency, all variables in the paper’s overall regression are significant at the 1% level (Table 5). That is, the OA degree of the paper, the number of funds, the number of authors, the number of references, the publication time of the paper, and the citation indicator have a positive influence on the paper’s citation frequency. The OA degree of the paper, the number of keywords, the number of authors, and the citation indicator have a positive effect on the paper’s immediacy index (Table 6). The OA degree of papers, the number of grants, the number of authors, the number of keywords, and citation metrics have a positive impact on the journal impact factor (Table 7).

In the analysis of research results, a negative correlation is observed between the number of keywords and citation frequency for OA papers (including Gold OA and Other OA), whereas a positive correlation is identified for Toll Access (non-OA) papers. This divergent pattern can be explained by the distinct publishing ecosystems, target readership characteristics, and retrieval mechanisms associated with OA and non-OA models, rather than arbitrary differences:

For OA papers, the core advantage of open access lies in breaking down access barriers, enabling a broader and more diverse readership (including researchers from interdisciplinary fields, practitioners, and early-career scholars) to discover and access the work. To leverage this openness and expand search coverage across multiple databases, authors often tend to incorporate a larger number of keywords—including both core terms and peripheral, interdisciplinary concepts. However, an excessive accumulation of keywords can dilute the specificity of the research topic: when a paper is tagged with too many loosely related terms, it risks being “lost in retrieval”—i.e., failing to stand out among highly relevant literature in its core field. For specialized researchers seeking targeted knowledge, such overgeneralized keyword lists reduce the paper’s relevance signal in database searches, thereby lowering the likelihood of being cited by scholars in its primary research community (Lu et al., 2020; Uddin & Khan, 2016). This mechanism ultimately leads to the observed negative correlation between keyword count and citation frequency for OA papers.

In contrast, non-OA papers are typically published in subscription-based journals with well-defined thematic scopes and a relatively fixed, highly specialized readership (predominantly researchers deeply engaged in the journal’s focus area). In this closed-access ecosystem, the primary audience is already focused on the journal’s niche field, and retrieval is often confined to databases or platforms accessible to subscribed institutions. Here, an appropriate increase in the number of keywords—especially terms that capture sub-themes, methodological nuances, or key findings of the research—enhances the paper’s visibility within its specialized retrieval landscape. Since the readership is inherently interested in the field, additional relevant keywords do not dilute the paper’s relevance but rather help it align with more specific search queries from specialized scholars. This strengthens the paper’s discoverability among its target audience, directly contributing to higher citation potential and explaining the positive correlation between keyword count and citation frequency for non-OA papers.

Collectively, these two distinct mechanisms—driven by OA’s broad accessibility vs. non-OA’s specialized subscription model—account for the overall divergent correlations between keyword number and citation frequency across the two publishing types.

Subsequently, group regressions were conducted for papers with different OA degrees, and it was discovered that the conclusions were basically consistent with the overall regression results. Consequently, it can be demonstrated that the constructed models (1), (2), and (3) are all valid.

4.4. Analysis of Issuing Region

From the aforementioned correlation analysis and regression results (Table 3, Table 5, Table 6 and Table 7), it can be observed that, within the overall regression results, all factors, excluding the issuing region, are capable of exerting an impact on the explanatory variables. Given that the issuing region is an unordered grouping variable, this study is unable to explore the degree of its influence on the explanatory variables in the regression results. Consequently, this paper conducts a two-way analysis of variance on the top ten regions in terms of the number of papers issued by OA type, without taking into account the effects of other variables; the analysis results are presented in

Table 8. Results of two-way ANOVA for author’s region and OA type.

Explained Variables	Variant	Sum of Squared Deviations	Degrees of Freedom	F-Statistic	p-Value	Sig.
cite	OA	1,668,421.50	2	123.50	0.0000	***
	country	1,904,428	9	31.33	0.0000	***
	country × OA	845,500.40	18	6.95	0.0000	***
II	OA	22,488.12	2	409.67	0.0000	***
	country	22,103.50	9	89.48	0.0000	***
	country × OA	16,222.94	18	32.84	0.0000	***
IF	OA	4123.58	2	514.76	0.0000	***
	country	2586.61	9	71.75	0.0000	***
	country × OA	2829.18	18	39.24	0.0000	***

Note: *, **, *** indicate significance at 10%, 5%, and 1% level of significance, respectively.

. Overall, disparities in the regions of authors have a significant influence on citation frequency, immediacy index, and impact factor.

An analysis of the affiliations of corresponding authors unveils distinct national disparities in the performance of open access publications. The United States retains its preeminence in publication quantity with 58,429 papers, followed by China, which has 21,374 entries. Notably, the United Kingdom demonstrates the highest proportion of gold OA publications, reaching 59.95%, thereby positioning itself as a vanguard in the adoption of open science.

Citation metrics indicate that China achieves a superior mean citation rate, with 39.85 citations per article, slightly surpassing the U.S. average of 38.94. Nevertheless, gold OA papers from the U. S display the highest citation intensity, at 57.41 citations per paper, with China closely following at 57.26. The most remarkable citation outlier stems from the work of German researcher Hoffmann in Cell (Howarth et al., 2017), which has amassed 11,733 citations—the highest recorded frequency within the dataset. A comparison of the top ten countries in terms of the total number of published papers is presented in

Table 9. Number of papers and citations in the top 10 countries in terms of number of papers.

Country of First Author	Total Publications	Gold OA (%)	Other OA (%)	Non-OA (%)	Mean Citations	Gold OA Citation Mean	Other OA Citation Mean	Non-OA Citation Mean	Max Citations
US	58,429	29.70%	51.44%	18.87%	38.94	57.41	32.50	27.43	5882
China	21,374	15.57%	30.27%	54.16%	39.85	57.26	37.99	36.89	5737
Germany	11,112	32.86%	31.24%	35.91%	35.88	45.43	34.84	25.43	11,733
UK	10,429	59.95%	33.17%	6.88%	38.73	44.40	31.01	26.50	3957
France	6984	26.23%	49.08%	24.68%	29.71	38.43	28.54	22.77	2294
Canada	6902	21.56%	40.92%	37.53%	30.83	45.43	28.92	24.51	1461
Australia	6406	16.70%	46.14%	37.15%	32.53	47.77	31.64	26.77	1923
Japan	5228	25.99%	37.72%	36.29%	25.2	38.93	22.12	18.57	3870
Spain	4695	18.69%	47.06%	34.25%	27.18	36.54	27.58	21.52	1144
Italy	4651	21.58%	39.42%	39.00%	30.68	40.44	31.85	24.08	1455

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5. Discussion

This paper examines the citation and impact advantages of OA papers in hybrid journals within the biochemistry domain. It also explores the influence of factors such as the degree of open access on the citation frequency of papers, the impact factor of journals, and the immediacy index. A benchmark regression was performed on the overall data. The degree of OA was employed as the explanatory variable, while citation frequency, immediacy index, and impact factor were used as the explained variables, respectively. After confirming the absence of multicollinearity among variables through correlation analysis, this study conducted baseline regression analyses. The level of OA adoption was set as the explanatory variable, with citation frequency, immediacy index, and journal impact factor as the dependent variables. The regression results demonstrate statistically significant positive effects of OA adoption on these metrics at the 1% significance level. From the baseline regression analysis, it is evident that the citation and impact of a paper are also affected by numerous other factors, including the authors, title, abstract, keywords, etc. These elements play a crucial role in the utilization of scholarly resources (Abramo et al., 2022), providing standardized information that enables the effective retrieval of scholarly resources.

Most existing studies have demonstrated that OA papers possess citation advantages (Samuel & Lucivero, 2020; Abdin & De Pretis, 2024). However, this paper’s research reveals that 28.67% of papers in the biochemistry field adopt a gold OA publishing model, with a certain percentage using other OA publishing models, and 30.09% being non-OA papers. It can be inferred that, although OA can better enhance the visibility and impact of a paper, authors still tend to submit to other OA or non-OA modes. The OA publishing model emerged as a means to facilitate the dissemination of scholarly output and provide the public and researchers with convenient access to high-quality scholarly resources (Sud & Thelwall, 2015; Mahony, 2024). However, some publishers have raised Article Processing Charges (APCs) and lowered the acceptance criteria for OA papers to increase their quantity, thereby obtaining more funds for development. The inappropriate use of APCs by publishers directly impacts the average quality of OA papers, resulting in a decline in the recognition of OA papers (McCabe & Langer, 2024; Copiello, 2020).

To address the core issues of OA publishing—such as quality degradation caused by profit-driven publisher behaviors and the consequent low academic recognition—targeted measures are essential. Firstly, aiming at the root cause of uneven OA paper quality, it is necessary to establish a unified and rigorous academic review mechanism and quality evaluation standards for OA journals, with clear requirements for peer review procedures, review expert qualifications, and revision norms to prevent the lowering of acceptance thresholds for the sake of quantity. Secondly, aiming at the profit model of some journals that relies solely on charges, it is necessary to strengthen the supervision of OA journal operation mechanisms: on the one hand, standardize the setting of APCs, establish a transparent pricing mechanism linked to service quality (such as review efficiency and dissemination support) to avoid arbitrary price increases; on the other hand, strictly rectify journals that prioritize profit over quality and reduce the number of such non-standard publications through industry certification and elimination mechanisms. These targeted measures are the key to reflecting the true value of OA—facilitating knowledge sharing, enhancing the academic recognition of OA papers, and promoting the wider dissemination of high-quality papers at the societal level.

Researchers should also be fully aware of the existence of OA advantages when writing papers for publication and can attempt to achieve higher citations for their papers through a higher degree of OA. However, it should be clear that relying solely on other factors does not genuinely improve the citation and impact of the paper. The quality of the paper, as the core competency of the paper, is the guiding principle for scholars to conduct research and refine their findings. The essence of choosing OA papers is to enable more individuals to quickly access high-quality and outstanding papers through open access, thereby truly realizing the sharing and dissemination of knowledge and promoting the progress and innovation of academic research.

6. Summary

This study is subject to several practical constraints. Only OA journals indexed in the Journal Citation Reports (JCRs) within the biochemistry subject category were selected. The research characteristics in the field of biochemistry, including high reproducibility of experimental data and frequent international collaboration, may render its OA dissemination effect more favorable than that of other disciplines. Therefore, the results should be generalized to other disciplines related to biochemistry with caution. At the same time, it is necessary to deeply explore the feasibility of performing logarithmic transformation on the dependent variable and using robust standard errors to effectively address the non-normality issue of the dependent variable and enhance the robustness of the research conclusion.

The study identified influencing factors such as the number of funds and references, yet it is possible that key variables were overlooked. In subsequent research, we will continue to conduct in-depth investigations, refine the research framework by integrating additional variables, and conduct an advantage analysis of papers from more high-quality journals in other fields to expand the scope and depth of research in this domain. Furthermore, quality control of OA papers will be reinforced to further enhance the advantages and influence of OA papers.