Mapping the Use of Bibliometric Software and Methodological Transparency in Literature Review Studies: A Comparative Analysis of China-Affiliated and Non-China-Affiliated Research Communities (2015–2024)
Abstract
1. Introduction
2. Methodology
2.1. Study Design
2.2. Data Source and Search Strategy
2.3. Search Strategy, Screening, and Final Sample Selection
2.4. Comparative Analysis of Methodological Practices (RQ1–RQ3)
- Whether the software version was explicitly reported;
- Whether threshold parameters (e.g., minimum citation counts and keyword frequency) were defined;
- Whether the inclusion and exclusion criteria for source documents were clearly articulated;
- Whether data preprocessing procedures—such as name unification, abbreviation handling, or data cleaning—were described;
- Whether the source database (e.g., Scopus or Web of Science) was identified and justified.
- Software functionality constraints (e.g., limited clustering algorithms or visualization tools);
- Data limitations (e.g., incomplete citation coverage or disciplinary bias);
- Analytical subjectivity (e.g., author decisions in parameter selection);
- Issues related to citation normalization or field variance (e.g., differing citation behaviors across disciplines).
3. Results
3.1. Most-Cited Studies by China-Affiliated and Non-China-Affiliated Authors
3.2. Comparative Use of Bibliometric Software Tools and Their Analytical Purposes (RQ1)
3.2.1. Bibliometric Software Use in China-Affiliated Studies
3.2.2. Bibliometric Software Use in Non-China-Affiliated Studies
3.2.3. Cross-Group Comparison and Reflections
3.3. Methodological Reporting and Transparency (RQ2)
3.3.1. Clarity and Transparency in Methodological Reporting in China-Affiliated Studies
- Reporting of software version: Among the 25 China-affiliated studies examined, a significant number failed to report the version of the bibliometric software employed explicitly. Only 6 out of 25 studies (24%) mentioned the version used, which includes the explicit reporting of Microsoft Excel 2016 (Ren et al., 2018) and 2018 (Jin et al., 2021), CiteSpace v3.8.R5 (L. Chen et al., 2017), IBM SPSS Statistics (23) (van Nunen et al., 2018), and Gephi 0.8.2 (Zhang et al., 2019; Zhao et al., 2020). In most studies (76%), the absence of version reporting raises concerns about reproducibility, particularly when different versions of tools like VOSviewer or CiteSpace can yield varying visualizations or clustering outputs due to algorithmic updates.
- Specification of threshold settings: A more encouraging pattern emerges regarding threshold reporting. Fifteen studies (60%) specified threshold parameters relevant to their analyses. These thresholds included citation count cutoffs for co-citation analysis (Ampah et al., 2021; X. Ding & Yang, 2022; Feng et al., 2017; van Nunen et al., 2018; S. Xu et al., 2020; X. Xu et al., 2018), and statistical analysis of countries and regions, as well as organizations, based on a minimum threshold of published papers (Song et al., 2022). For term selection in topic analysis, a term frequency–inverse document frequency (TF-IDF) with a threshold of 0.05 (X. Chen et al., 2022), frequency filters for co-word or keyword analyses (Ampah et al., 2021; X. Ding & Yang, 2022; B. Wang et al., 2021; H. Wang et al., 2019; S. Xu et al., 2020), and co-authorship analysis (Liu et al., 2020) were used. For the statistical analysis of countries and regions, a threshold of “at least 5 Mg publications in 2022” was applied (Liang et al., 2017), and parameters for clustering or term pruning were used (Yang et al., 2023). Such detail provides critical transparency for understanding how data was filtered or visualized. However, in 10 studies (40%), threshold settings were either omitted or vaguely referenced without quantitative justification (Y. Ding et al., 2021; Jin et al., 2021; Koseoglu et al., 2016; Li & Wang, 2019; Mao et al., 2018; Ren et al., 2018; Tlili et al., 2022; Q. Wang & Su, 2020; Zhang et al., 2019; Zhao et al., 2020).
- Definition of inclusion and exclusion criteria: There is strong consistency across the sample in defining inclusion and exclusion criteria. All 25 studies (100%) clearly articulated the parameters for selecting relevant literature. Typical inclusion criteria encompassed publication date ranges, document types (e.g., peer-reviewed articles and reviews), database scope (e.g., Web of Science Core Collection), and topical relevance (e.g., the presence of keywords in titles, abstracts, or the full text). Exclusion criteria often targeted conference papers, non-English texts, irrelevant disciplines, or duplicate records (Koseoglu et al., 2016; Liu et al., 2020; X. Xu et al., 2018; Zhang et al., 2019).
- Description of data preprocessing procedures: Similarly, all 25 studies (100%) offered some description of their data preprocessing workflows. These descriptions ranged from simple keyword merging and duplicate removal (Ren et al., 2018; H. Wang et al., 2019) to more sophisticated processes involving multi-stage screening, Python-based data transformation, or normalization of institutional names and terms (Ampah et al., 2021; X. Chen et al., 2022; X. Xu et al., 2018). While not all studies elaborated on every step of preprocessing (e.g., stop-word removal and stemming), most outlined how raw bibliographic data were transformed into analyzable formats, enhancing procedural transparency.
- Identification of bibliographic databases: Every study in the China-affiliated group identified the source(s) of bibliographic data, fulfilling this reporting criterion. The WoS emerged as the most frequently used database, cited in nearly all cases. Some studies employed CNKI (Li & Wang, 2019), Scopus (X. Xu et al., 2018), or ERIC (X. Chen et al., 2022) as complementary sources. The clarity in database selection allows readers to assess the scope of coverage, disciplinary bias, and indexing limitations of the analyses.
3.3.2. Clarity and Transparency in Methodological Reporting in Non-China-Affiliated Studies
- Software version reporting: In contrast to best practices for reproducibility, only 5 out of 25 studies (20%) explicitly stated the version of the bibliometric software or tools used. Examples include studies that report the use of the analyzed version for each software tool. For instance, CRExplorer 1.9 (2018), Publish or Perish 7 (2019), ScientoPyUI 1.4.0 (2019), BibExcel 2017 (2017), Biblioshiny (2019), BiblioMaps 3.2 (2018), CiteSpace 5.5.R2 (2019), CitNetExplorer 1.0.0 (2014), SciMAT 1.1.04 (2016), Sci2 Tool 1.3 (2018), and VOSviewer 1.6.13 (2019) are listed. Similarly, library versions like Bibliometrix R-3.6.1 (2019) are provided (Moral-Muñoz et al., 2020). The version of the Shiny package is reported as 1.2.0. For R, the study implicitly refers to ‘R Core Team, 2019’ and ‘RStudio Team, 2019’ for RStudio, suggesting the latest stable versions available at the time of writing (Linnenluecke et al., 2020). VOSviewer (last accessed 11 December 2017) from http://www.vosviewer.com/ (Faust et al., 2018), VOSviewer 1.6.16 (Rejeb et al., 2022), and UCINET 6 (Bugge et al., 2016) were also mentioned. The vast majority—20 studies (80%)—referred to tools such as VOSviewer, Gephi, or Bibliometrix without detailing their version, making it difficult for future researchers to replicate results with precision. The lack of versioning documentation remains a significant barrier to reproducibility, especially considering software updates often alter algorithmic behavior and visual output.
- Threshold settings specification: A strong performance is observed regarding threshold transparency: 19 studies (76%) specified threshold settings used in their bibliometric analyses. These thresholds include minimum citation counts for co-citation inclusion, keyword co-occurrence frequencies, clustering or visualization cutoffs, etc. Despite some variation in how thresholds are justified, their explicit reporting enhances transparency and allows others to understand the basis for thematic and structural delineations. Only six studies (24%) omitted threshold details entirely (Ben-Daya et al., 2019; Di Vaio et al., 2020; Donthu et al., 2021; Geissdoerfer et al., 2017; Moral-Muñoz et al., 2020; Mukherjee et al., 2022).
- Inclusion and exclusion criteria: The definition of inclusion and exclusion criteria was particularly robust. All 25 studies (100%) offered clearly defined criteria, often outlining publication timeframes, language filters, source types (e.g., peer-reviewed journals), and domain relevance. These criteria were frequently detailed in either the methodology sections or supplementary tables. A few studies applied more complex criteria, such as snowballing techniques or multi-stage filtering, reinforcing methodological thoroughness.
- Data preprocessing procedures: A large majority of studies, 23 out of 25 (92%), described their data preprocessing steps, such as duplicate removal, keyword normalization, author disambiguation, and transformation of RIS/BibTeX files into analyzable formats. In some cases, studies also detailed the creation of thesaurus files or the use of stemming methods to harmonize terminology before keyword co-occurrence analyses. Only two studies (8%) failed to describe preprocessing steps beyond general references to data “cleaning” or “filtering” (Geissdoerfer et al., 2017; Pranckutė, 2021).
- Bibliographic database identification: All 25 studies (100%) explicitly identified the bibliographic databases used for data collection, demonstrating full compliance with this fundamental reporting standard. The most frequently cited databases included Web of Science, Scopus, and occasionally Google Scholar or ERIC, depending on the domain, etc. Several studies justified their database selection based on indexing scope, citation quality, or disciplinary relevance.
3.3.3. Comparative Analysis of Methodological Transparency in China-Affiliated and Non-China-Affiliated Studies
- Software version reporting: One of the most striking differences between the two groups lies in the reporting of software versions. Only 24% (6/25) of the China-affiliated studies explicitly reported the version of bibliometric software used, compared to an even lower 20% (5/25) in the non-China-affiliated group. Although both groups fall short of best practices, the slight edge for China-affiliated studies suggests some awareness of the importance of version control, albeit inconsistently applied. In both cohorts, the widespread omission of version reporting reflects a significant barrier to replicability, especially when bibliometric software tools (e.g., CiteSpace, VOSviewer, and BibExcel) are regularly updated with altered algorithms and interface changes.
- Threshold settings specification: Threshold transparency appears more robust in the non-China group, with 76% (19/25) of studies specifying threshold settings, compared to 60% (15/25) in the China group. These thresholds—such as minimum citation counts for inclusion in co-citation networks, frequency filters in keyword co-occurrence, or conditions for cluster inclusion—are essential to understanding and reproducing the analytical logic of the study. The higher reporting rate among non-China-affiliated researchers may reflect greater adherence to emerging standards in science mapping, or it may suggest more frequent engagement with advanced tools that require parameter customization.
- Inclusion and exclusion criteria: Encouragingly, both groups demonstrate exemplary performance in defining inclusion and exclusion criteria, with 100% of studies in both cohorts clearly articulating how publications were selected and filtered. This uniformity underscores a shared recognition of the importance of delimiting the study corpus—an essential step for reducing bias and ensuring thematic coherence. Criteria typically included publication type (e.g., peer-reviewed articles), language restrictions (primarily English), topical relevance, and timeframes. In more advanced studies, snowballing techniques and journal rankings (e.g., AJG and ABDC) were also employed as inclusion mechanisms.
- Data preprocessing procedures: Data preprocessing was also robustly described in both groups, though the non-China-affiliated studies exhibited a slight advantage: 92% (23/25) versus 100% (25/25) in the China group. Both groups frequently detailed steps such as duplicate removal, keyword normalization, author name disambiguation, and conversion of RIS/BibTeX formats for input into tools like Gephi or VOSviewer. However, China-affiliated studies often provided more procedural specificity—for example, outlining multi-stage screening strategies or manually validating abstracts. In contrast, a few non-China-affiliated studies mentioned data cleaning only in general terms.
- Bibliographic database identification: On this indicator, both groups again performed flawlessly, with 100% of studies identifying their data sources. Web of Science was the most frequently used database in both groups, followed by Scopus. Some studies in the non-China group also drew on Google Scholar, ERIC, or subject-specific repositories, whereas China-affiliated studies occasionally incorporated domestic databases such as CNKI. The consistency across both groups indicates a solid understanding of the need to document data provenance, a crucial aspect of methodological rigor.
3.4. Methodological Limitations and Recommendations
3.4.1. Methodological Limitations and Recommendations in China-Affiliated Studies
- Software functionality: Most China-affiliated studies did not explicitly acknowledge limitations related to bibliometric software functionality. Many papers applied bibliometric methods without specifying the tools used or evaluating their constraints (L. Chen et al., 2017; Li & Wang, 2019; Ren et al., 2018; S. Xu et al., 2020). However, a few studies offered implicit or explicit reflections. One study (Feng et al., 2017) highlighted the limitations of keyword-based maps and recommended broader content inclusion. Another study (Koseoglu et al., 2016) underscored the underuse of relational statistical tools due to a lack of researcher familiarity. Another study (X. Chen et al., 2022) criticized the inefficiency of manual content analysis in handling large datasets. Technical integration challenges between BIM and GIS systems were reported by (H. Wang et al., 2019), highlighting interoperability issues. More direct critiques came from (Yang et al., 2023) and (Ampah et al., 2021), which described the incompatibility of HistCite with Scopus, as well as the complexity of BibExcel and CiteSpace’s merging limitations. These insights collectively call for the development of more accessible, interoperable, and scalable bibliometric software to enhance analytical precision and usability in future research.
- Data availability and quality: A considerable number of studies (Ampah et al., 2021; L. Chen et al., 2017; Y. Ding et al., 2021; Koseoglu et al., 2016; Li & Wang, 2019; Liang et al., 2017; Liu et al., 2020; Mao et al., 2018; Song et al., 2022; Tlili et al., 2022; van Nunen et al., 2018; Q. Wang & Su, 2020; X. Xu et al., 2018; Yang et al., 2023; Zhang et al., 2019; Zhao et al., 2020) explicitly acknowledged limitations about data sources and quality. Common issues included the following: database restriction—most studies relied solely on the Web of Science Core Collection, leading to potential exclusion of relevant documents from Scopus, CNKI, or non-English literature (L. Chen et al., 2017; Tlili et al., 2022; S. Xu et al., 2020; Yang et al., 2023); incomplete indexing— several authors noted delays in database updates, causing recent articles (e.g., from 2021–2022) to be underrepresented at the time of data collection (Song et al., 2022; van Nunen et al., 2018; Q. Wang & Su, 2020); keyword and scope constraints—limitations in search strategies, including narrow keyword scopes, were commonly cited as contributing to possible omissions of relevant studies (L. Chen et al., 2017; Feng et al., 2017; X. Xu et al., 2018; Zhang et al., 2019); and data noise and errors—a few studies reported challenges with duplicate records, author disambiguation, and inconsistent metadata (Y. Ding et al., 2021; van Nunen et al., 2018).
- Analytical subjectivity and researcher bias: Although fewer in number, some studies openly addressed concerns about subjective judgment in coding or interpretation: For instance, (X. Xu et al., 2018) and (X. Ding & Yang, 2022) acknowledged that keyword selection, thematic clustering, and coding processes might introduce bias, despite efforts to ensure objectivity through expert input or consensus. Other studies implied subjectivity by noting that bibliometric indicators alone cannot reveal underlying qualitative nuances, thus encouraging the use of complementary content analysis (X. Chen et al., 2022; Tlili et al., 2022; van Nunen et al., 2018).
- Citation normalization and disciplinary differences: Only a minority of studies explicitly addressed limitations related to citation normalization or disciplinary variance. One study (van Nunen et al., 2018) highlighted that older publications naturally accumulate more citations, potentially biasing impact analyses. The study (Koseoglu et al., 2016) emphasized that interdisciplinary topics—such as tourism, marketing, and psychology—have divergent citation behaviors, complicating normalization efforts. Other studies (Mao et al., 2018; B. Wang et al., 2021) suggested that conceptual overlaps or translation ambiguities (e.g., between “smart” and “intelligent”) can also distort thematic interpretation across fields and regions.
Recommendations for Improving Future Bibliometric Studies in China-Affiliated Studies
- Expand and diversify data sources: Many studies (L. Chen et al., 2017; Yang et al., 2023; Zhao et al., 2020) called for the inclusion of multiple databases (e.g., Scopus, CNKI, PubMed, and Google Scholar), and broader document types (e.g., books, conference papers, and dissertations) to enhance coverage.
- Improve keyword strategy and taxonomy development: Several authors (van Nunen et al., 2018; X. Xu et al., 2018; Zhang et al., 2019) suggested the need for more robust keyword taxonomies and iterative refinement of search terms to improve comprehensiveness and replicability.
- Promote transparency and reporting standards: Studies (X. Ding & Yang, 2022; Koseoglu et al., 2016; Yang et al., 2023) emphasized the importance of better documentation of software tools, database search strategies, and inclusion/exclusion criteria to ensure reproducibility and cross-study comparability.
- Incorporate complementary methods: Several studies recommended combining bibliometric approaches with qualitative content analysis, expert interviews, or case-based reasoning to deepen insights (Tlili et al., 2022; van Nunen et al., 2018; S. Xu et al., 2020).
- Enhance interdisciplinary and cross-cultural collaboration: Encouraging multinational and multidisciplinary research collaborations was highlighted in several studies (Mao et al., 2018; Q. Wang & Su, 2020; Yang et al., 2023) to bridge knowledge gaps and enrich citation networks. Addressing author and institutional disambiguation, the adoption of researcher identifiers, such as ORCID, and organizational standardization is recommended to improve accuracy in author-level metrics (van Nunen et al., 2018).
3.4.2. Methodological Limitations and Recommendations in Non-China-Affiliated Studies
- Software functionality: Several studies explicitly acknowledged software-related limitations. For example, (Donthu et al., 2021; Fahimnia et al., 2015; Leung et al., 2017; Moral-Muñoz et al., 2020; Mukherjee et al., 2022; Pranckutė, 2021) described constraints associated with widely used tools such as VOSviewer, HistCite, Pajek, Gephi, and Bibliometrix. Common issues included difficulties with merging synonymous keywords, limited compatibility with specific databases (e.g., HistCite’s inability to support Scopus), file format constraints (e.g., Pajek), and steep learning curves that may deter novice users. Additionally, the studies (Donthu et al., 2021; Goyal & Kumar, 2021; Moral-Muñoz et al., 2020) cautioned against over-reliance on software-generated outputs without deeper theoretical engagement, warning that such reliance may not meet the standards of high-impact journals. Several studies (Arsad et al., 2022; Faust et al., 2018; Verma et al., 2021a) also noted performance limitations in algorithmic tools such as PSO and ANFIS due to convergence speed and computational expense.
- Data availability and quality: Data-related limitations were among the most acknowledged. Many studies (Blettler et al., 2018; Donthu et al., 2021; Fahimnia et al., 2015; Geissdoerfer et al., 2017; Goodell et al., 2021; Meerow et al., 2016; Mora et al., 2017; Moral-Muñoz et al., 2020; Pranckutė, 2021; Rejeb et al., 2022; van der Have & Rubalcaba, 2016; Verma et al., 2021b) pointed to incomplete or biased bibliographic databases as a key issue. Frequent challenges included the following: limited access to non-English or gray literature (Leung et al., 2017; Meerow et al., 2016; van der Have & Rubalcaba, 2016); errors in metadata, duplicate records, and inconsistencies in indexing (Blettler et al., 2018; Goodell et al., 2021; Pranckutė, 2021); search date limitations causing underrepresentation of recent publications (Arsad et al., 2022; Fahimnia et al., 2015); and structural limitations in export capacity from platforms like Scopus and Web of Science (Moral-Muñoz et al., 2020; Pranckutė, 2021). These issues raised concerns about comprehensiveness, particularly in emerging or multidisciplinary fields such as sustainability, circular economy, and IoT.
- Analytical subjectivity and researcher bias: A majority of the reviewed studies acknowledged the inherent subjectivity involved in bibliometric interpretation. For example, (Donthu et al., 2021; Fahimnia et al., 2015; Geissdoerfer et al., 2017; Linnenluecke et al., 2020; Meerow et al., 2016; Mukherjee et al., 2022) pointed to selection bias introduced through keyword choices, manual coding, or interpretation of thematic clusters. The role of researcher judgment in defining inclusion/exclusion criteria, coding conceptual frameworks, and interpreting network visualizations was often emphasized. While some studies (Donthu et al., 2021; Fahimnia et al., 2015) suggested that advanced software tools can mitigate subjectivity, most agreed that qualitative insight remains essential to meaningful interpretation of bibliometric results.
- Citation normalization and disciplinary field differences: Compared to China-affiliated studies, non-China-affiliated publications more frequently addressed citation normalization issues. Several studies (Fahimnia et al., 2015; Goyal & Kumar, 2021; Linnenluecke et al., 2020; Mora et al., 2017; Moral-Muñoz et al., 2020; Mukherjee et al., 2022; Pranckutė, 2021) acknowledged that citation behaviors vary significantly across disciplines, affecting impact measures like the h-index and Local vs. Global Citation Scores. Studies also emphasized that citation-based indicators often disadvantage newer publications and disciplines that value book chapters or non-journal outputs. Some researchers (Goyal & Kumar, 2021; Sarkodie & Strezov, 2019) argued for the development of context-specific metrics or multi-metric frameworks to account for disciplinary diversity.
Recommendations for Improving Future Bibliometric Studies in Non-China-Affiliated Studies
- Software integration and methodological triangulation: Several studies (Donthu et al., 2021; Fahimnia et al., 2015; Moral-Muñoz et al., 2020) advocated combining tools such as VOSviewer, Gephi, and Bibliometrix to balance strengths and overcome individual limitations. Additionally, authors encouraged integrating bibliometric methods with systematic reviews, content analysis, or theory-driven frameworks to enhance depth and validity (Ben-Daya et al., 2019; Donthu et al., 2021; Goyal & Kumar, 2021).
- Enhanced data cleaning and preprocessing: The importance of meticulous data cleaning was repeatedly emphasized (Donthu et al., 2021; Goodell et al., 2021; Moral-Muñoz et al., 2020; Pranckutė, 2021), including removing duplicates, resolving author disambiguation issues, and verifying metadata accuracy.
- Improved database strategies: To reduce selection bias and broaden the scope of analysis, researchers recommended using multiple databases (e.g., WoS, Scopus, SSRN, and Google Scholar) and incorporating gray literature and non-English sources (Meerow et al., 2016; Pranckutė, 2021; Rejeb et al., 2022; van der Have & Rubalcaba, 2016).
- Disciplinary sensitivity and normalization techniques: Scholars urged caution when comparing indicators across fields, suggesting the use of field-normalized citation metrics and the inclusion of contextual metadata (Moral-Muñoz et al., 2020; Mukherjee et al., 2022; Pranckutė, 2021).
- Capacity building and accessibility: Recognizing steep learning curves, some studies (Faust et al., 2018; Moral-Muñoz et al., 2020) proposed developing user-friendly bibliometric platforms and training resources to support broader adoption among novice researchers.
- Ethical and inclusive research practices: Some studies, especially those focusing on sustainability and social sciences (Kent Baker et al., 2020; Meerow et al., 2016; van der Have & Rubalcaba, 2016), highlighted the need for more inclusive, participatory, and ethically grounded bibliometric research that addresses underrepresented regions, languages, and communities.
3.4.3. Comparative Analysis of Methodological Limitations and Recommendations
- Software functionality: China-affiliated studies vastly underreport software-related limitations. Many did not identify the tools used or engage with their operational constraints, suggesting a general lack of reflection on how software choice might shape findings (L. Chen et al., 2017; Li & Wang, 2019; Ren et al., 2018; S. Xu et al., 2020). Where mentioned, limitations were often peripheral or implied, with only a few studies acknowledging tool-related barriers such as interface complexity or challenges in data integration (Feng et al., 2017; H. Wang et al., 2019; Yang et al., 2023). In contrast, non-China-affiliated studies demonstrated a deeper engagement with software functionality. Several studies provided explicit critiques of widely used tools such as HistCite, VOSviewer, Pajek, and Bibliometrix, acknowledging limitations in merging keyword variants, database compatibility, scalability, and learning curves (Donthu et al., 2021; Fahimnia et al., 2015; Moral-Muñoz et al., 2020; Mukherjee et al., 2022). Furthermore, multiple studies emphasized that bibliometric outputs alone are insufficient and must be accompanied by theoretical insight and content analysis. There was also a clear trend toward combining tools to compensate for individual limitations, a practice scarcely observed in China-affiliated work.
- Data availability and quality: Both groups acknowledged limitations in data quality and availability, but the depth and specificity differed. China-affiliated studies often pointed to database restrictions (e.g., reliance on WoS or CNKI), delayed indexing, or narrow keyword selection. However, these acknowledgments were usually brief and seldom accompanied by corrective strategies. Non-China-affiliated studies, by contrast, offered more comprehensive critiques. They highlighted metadata inconsistencies, limitations in export capacity, exclusion of non-English and gray literature, and underrepresentation of Social Sciences and Humanities in mainstream databases (Moral-Muñoz et al., 2020; Pranckutė, 2021; Rejeb et al., 2022; van der Have & Rubalcaba, 2016). Importantly, these studies frequently provided targeted recommendations—such as multi-database use, inclusion of non-journal sources, and metadata validation protocols—to address these issues. This signals a more proactive and systematic approach to data reliability.
- Analytical subjectivity and researcher bias: Subjectivity in coding and interpretation was more frequently acknowledged in non-China-affiliated studies. Researchers openly discussed how thematic clustering, keyword selection, or classification of conceptual tensions involve human judgment (Geissdoerfer et al., 2017; Goyal & Kumar, 2021; Linnenluecke et al., 2020; Meerow et al., 2016). Several authors addressed this limitation by proposing collaborative coding, triangulation methods, and integration with qualitative techniques such as content analysis or expert interviews. In China-affiliated studies, analytical subjectivity was less frequently acknowledged and rarely problematized. When mentioned, it was often framed as a byproduct of broader research limitations rather than a specific concern of bibliometric analysis. This suggests a less developed awareness of the interpretive dimensions of bibliometric methodology.
- Citation normalization and disciplinary differences: Non-China-affiliated studies were more attuned to the challenges of comparing citation metrics across disciplines. Several papers explicitly noted that metrics like the h-index or citation counts are not normalized and may be skewed by disciplinary norms, publication age, or source type (Fahimnia et al., 2015; Goyal & Kumar, 2021; Linnenluecke et al., 2020; Mora et al., 2017; Moral-Muñoz et al., 2020; Mukherjee et al., 2022; Pranckutė, 2021). Recommendations included using field-normalized indicators, triangulating metrics, and acknowledging citation biases against novel or interdisciplinary work. Conversely, China-affiliated studies infrequently addressed citation normalization. Only a few studies noted differences in citation accumulation or scope across research areas, and these were generally discussed in passing rather than as core methodological concerns. This indicates a gap in the analytical sensitivity to bibliometric disparities rooted in disciplinary cultures.
Recommendations for Methodological Improvement in Both China-Affiliated and Non-China-Affiliated Research Communities
4. Discussion
4.1. Tool Preferences Reflect Regional Infrastructures and Methodological Cultures
4.2. Transparency Gaps Undermine Reproducibility
4.3. Reflexivity and Limitations: A Divided Practice
4.4. Conceptual Orientations Shape Methodological Choices
4.5. Toward Methodological Convergence and Future Standards
- Requiring explicit reporting of software versions and threshold settings.
- Promoting the use of standardized methodological checklists.
- Encouraging integration of bibliometric and qualitative methods.
- Supporting training in open-source tools to reduce regional inequalities.
4.6. Limitations of the Study
4.7. Practical Implications and Research Directions
Looking Ahead, Several Avenues for Future Research Emerge from This Study
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Stage | China-Affiliated Studies | Non-China-Affiliated Studies | Description |
---|---|---|---|
Identification | 17,727 records retrieved | 17,727 records retrieved | Initial records were identified from the Scopus database using the same keyword query, focused on literature reviews employing bibliometric analysis: (“bibliometric analysis”) and (“literature review” OR “review”). |
Screening | 3715 studies retained | 9202 studies retained | Records were filtered to include only English-language journal articles, reviews, and conference papers. Editorials, letters, notes, and non-peer-reviewed documents were excluded. China includes (China-affiliated) and excludes (non-China-affiliated). |
Eligibility—citation ranking | Top 2000 most-cited studies | Top 2000 most-cited studies | For conceptual structure analysis (RQ4), the 2000 most-cited studies in each group were selected based on total citations. |
Inclusion—content analysis | Top 25 most-cited studies | Top 25 most-cited studies | For qualitative content analysis (RQ1–RQ3), the top 25 most-cited studies in each group were reviewed in full text to extract data on software tools, methodological reporting, and methodological limitations and recommendations. |
Top 25 Most-Cited Studies Authored by China-Affiliated Researchers | Top 25 Most-Cited Studies Authored by Non-China-Affiliated Researchers | ||||||
---|---|---|---|---|---|---|---|
Rank | TC 1 | Study Title | Reference | Rank | TC 1 | Study Title | Reference |
1 | 725 | Comprehensive utilization and environmental risks of coal gangue: A review | (Li & Wang, 2019) | 1 | 6538 | How to conduct a bibliometric analysis: An overview and guidelines | (Donthu et al., 2021) |
2 | 560 | Supply chain finance: A systematic literature review and bibliometric analysis | (X. Xu et al., 2018) | 2 | 5200 | The Circular Economy—A new sustainability paradigm? | (Geissdoerfer et al., 2017) |
3 | 552 | A comprehensive review on food waste anaerobic digestion: Research updates and tendencies | (Ren et al., 2018) | 3 | 1955 | Defining urban resilience: A review | (Meerow et al., 2016) |
4 | 461 | A bibliometric analysis on green finance: Current status, development, and future directions | (Zhang et al., 2019) | 4 | 1558 | Green supply chain management: A review and bibliometric analysis | (Fahimnia et al., 2015) |
5 | 425 | Supply chain collaboration for Sustainability: A literature review and future research agenda | (L. Chen et al., 2017) | 5 | 1375 | Web of Science (WoS) and Scopus: the titans of bibliographic information in today’s academic world | (Pranckutė, 2021) |
6 | 413 | Bibliometric analysis of safety culture research | (van Nunen et al., 2018) | 6 | 1108 | Software tools for conducting bibliometric analysis in science: An up-to-date review | (Moral-Muñoz et al., 2020) |
7 | 410 | Research advances of magnesium and magnesium alloys worldwide in 2021 | (Song et al., 2022) | 7 | 1004 | Internet of things and supply chain management: a literature review | (Ben-Daya et al., 2019) |
8 | 399 | Corporate social responsibility for supply chain management: A literature review and bibliometric analysis | (Feng et al., 2017) | 8 | 976 | Conducting systematic literature reviews and bibliometric analyses | (Linnenluecke et al., 2020) |
9 | 396 | Is Metaverse in education a blessing or a curse: a combined content and bibliometric analysis | (Tlili et al., 2022) | 9 | 822 | Deep learning for healthcare applications based on physiological signals: A review | (Faust et al., 2018) |
10 | 387 | Bibliometric studies in tourism | (Koseoglu et al., 2016) | 10 | 738 | Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review | (Di Vaio et al., 2020) |
11 | 384 | Two Decades of Artificial Intelligence in Education: Contributors, Collaborations, Research Topics, Challenges, and Future Directions | (X. Chen et al., 2022) | 11 | 671 | Financial literacy: A systematic review and bibliometric analysis | (Goyal & Kumar, 2021) |
12 | 366 | Smart Manufacturing and Intelligent Manufacturing: A Comparative Review | (B. Wang et al., 2021) | 12 | 655 | Guidelines for advancing theory and practice through bibliometric research | (Mukherjee et al., 2022) |
13 | 357 | Knowledge mapping of platform research: a visual analysis using VOSviewer and CiteSpace | (X. Ding & Yang, 2022) | 13 | 583 | Social innovation research: An emerging area of innovation studies? | (van der Have & Rubalcaba, 2016) |
14 | 332 | Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse | (Zhao et al., 2020) | 14 | 582 | A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems | (Verma et al., 2021a) |
15 | 293 | Disruption risks in supply chain management: a literature review based on bibliometric analysis | (S. Xu et al., 2020) | 15 | 574 | Conducting systematic literature review in operations management | (Thomé et al., 2016) |
16 | 291 | Anaerobic digestion: An alternative resource treatment option for food waste in China | (Jin et al., 2021) | 16 | 553 | What is the bioeconomy? A review of the literature | (Bugge et al., 2016) |
17 | 291 | Integration of BIM and GIS in sustainable built environment: A review and bibliometric analysis | (H. Wang et al., 2019) | 17 | 493 | Freshwater plastic pollution: Recognizing research biases and identifying knowledge gaps | (Blettler et al., 2018) |
18 | 278 | Nanomaterials for treating emerging contaminants in water by adsorption and photocatalysis: Systematic review and bibliometric analysis | (Zhao et al., 2018) | 18 | 491 | The First Two Decades of Smart-City Research: A Bibliometric Analysis | (Mora et al., 2017) |
19 | 263 | Smart logistics based on the internet of things technology: an overview | (Y. Ding et al., 2021) | 19 | 480 | A review on Environmental Kuznets Curve hypothesis using bibliometric and meta-analysis | (Sarkodie & Strezov, 2019) |
20 | 256 | Integrating blockchain technology into the energy sector—From theory of blockchain to research and application of energy blockchain | (Q. Wang & Su, 2020) | 20 | 459 | Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis | (Goodell et al., 2021) |
21 | 255 | Study of acupuncture for low back pain in recent 20 years: A bibliometric analysis via CiteSpace | (Liang et al., 2017) | 21 | 445 | Artificial intelligence in marketing: Systematic review and future research direction | (Verma et al., 2021b) |
22 | 255 | Research advances of magnesium and magnesium alloys worldwide in 2022 | (Yang et al., 2023) | 22 | 426 | A bibliometric analysis of board diversity: Current status, development, and future research directions | (Kent Baker et al., 2020) |
23 | 249 | Reviewing two decades of cleaner alternative marine fuels: Towards IMO’s decarbonization of the maritime transport sector | (Ampah et al., 2021) | 23 | 416 | Bibliometrics of social media research: A co-citation and co-word analysis | (Leung et al., 2017) |
24 | 247 | 12 years roadmap of the sulfur cathode for lithium sulfur batteries (2009–2020) | (Liu et al., 2020) | 24 | 377 | Drones in agriculture: A review and bibliometric analysis | (Rejeb et al., 2022) |
25 | 240 | Research on biomass energy and environment from the past to the future: A bibliometric analysis | (Mao et al., 2018) | 25 | 377 | Hydrogen energy storage integrated hybrid renewable energy systems: A review analysis for future research directions | (Arsad et al., 2022) |
(a) | ||
Reporting Item | Yes (Count, %) | No (Count, %) |
Software version explicitly reported | 6 (24%) | 19 (76%) |
Threshold settings specified | 15 (60%) | 10 (40%) |
Inclusion and exclusion criteria are defined | 25 (100%) | 0 (0%) |
Data preprocessing described | 25 (100%) | 0 (0%) |
Bibliographic database identified | 25 (100%) | 0 (0%) |
(b) | ||
Reporting Indicator | Yes (Count, %) | No (Count, %) |
Software version explicitly reported | 5 (20%) | 20 (80%) |
Threshold settings specified | 19 (76%) | 6 (24%) |
Inclusion and exclusion criteria are defined | 25 (100%) | 0 (0%) |
Data preprocessing procedures are described | 23 (92%) | 2 (8%) |
Bibliographic database identified | 25 (100%) | 0 (0%) |
(c) | ||
Indicator | China-Affiliated (Yes %) | Non-China-Affiliated (Yes %) |
Software version explicitly reported | 24% (6/25) | 20% (5/25) |
Threshold settings specified | 60% (15/25) | 76% (19/25) |
Inclusion and exclusion criteria are defined | 100% (25/25) | 100% (25/25) |
Data preprocessing procedures are described | 100% (25/25) | 92% (23/25) |
Bibliographic database identified | 100% (25/25) | 100% (25/25) |
(d) | ||
Dimension | China-Affiliated Studies | Non-China-Affiliated Studies |
Most-used tools | Predominantly VOSviewer, CiteSpace, and occasionally Gephi | Primarily Gephi, VOSviewer, and Bibliometrix, with more frequent use of hybrid tools |
Reporting transparency | Strong in inclusion/exclusion criteria and preprocessing, but weak on software versioning and thresholds | Strong emphasis on threshold settings and justification of data sources, but weak on software versioning |
Limitations acknowledged | Focused on data scope (e.g., database limitations) and publication types; limited attention to software constraints or interpretive bias | Broad coverage of limitations, including software functionality, data quality, analytical subjectivity, and field-normalization issues |
Recommendations | Emphasis on expanding content scope and database coverage; relatively fewer methodological suggestions | Advocates for tool triangulation, mixed-method approaches, and improvements in citation normalization |
Conceptual themes | Dominated by AI applications, sustainability, public health, and traditional Chinese medicine | Focused on Sustainable Development Goals (SDGs), digital transformation, innovation, and education systems |
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Ali Abaker Omer, A.; Dong, Y. Mapping the Use of Bibliometric Software and Methodological Transparency in Literature Review Studies: A Comparative Analysis of China-Affiliated and Non-China-Affiliated Research Communities (2015–2024). Publications 2025, 13, 40. https://doi.org/10.3390/publications13030040
Ali Abaker Omer A, Dong Y. Mapping the Use of Bibliometric Software and Methodological Transparency in Literature Review Studies: A Comparative Analysis of China-Affiliated and Non-China-Affiliated Research Communities (2015–2024). Publications. 2025; 13(3):40. https://doi.org/10.3390/publications13030040
Chicago/Turabian StyleAli Abaker Omer, Altyeb, and Yajie Dong. 2025. "Mapping the Use of Bibliometric Software and Methodological Transparency in Literature Review Studies: A Comparative Analysis of China-Affiliated and Non-China-Affiliated Research Communities (2015–2024)" Publications 13, no. 3: 40. https://doi.org/10.3390/publications13030040
APA StyleAli Abaker Omer, A., & Dong, Y. (2025). Mapping the Use of Bibliometric Software and Methodological Transparency in Literature Review Studies: A Comparative Analysis of China-Affiliated and Non-China-Affiliated Research Communities (2015–2024). Publications, 13(3), 40. https://doi.org/10.3390/publications13030040