The manuscript addresses a potentially important topic and could, in principle, make a useful contribution by mapping the development of scholarship on entrepreneurial drivers and inhibitors. The article also presents a relatively broad set of bibliometric outputs, including descriptive statistics, citation analyses, collaboration analyses, thematic maps, and keyword networks. However, the current version does not yet provide a coherent conceptual domain, a consistent and reproducible dataset, or a sufficiently grounded theoretical contribution. These concerns are substantial because they affect the validity of the reported findings rather than only the presentation of the paper.
The manuscript’s central construct requires much sharper definition. The paper uses entrepreneurial drivers, inhibitors, motivations, challenges, startups, entrepreneurship, entrepreneurial activity, entrepreneurial ecosystems, and business management in ways that suggest they belong to one unified literature. These concepts overlap but are not interchangeable. Entrepreneurial drivers can include motives, opportunity perceptions, self efficacy, skills, prior experience, social networks, capabilities, access to finance, institutional support, and market conditions. Inhibitors can include uncertainty, resource constraints, fear of failure, lack of legitimacy, discrimination, adverse institutional conditions, weak capabilities, and financing barriers. Startup research also cannot be treated as synonymous with entrepreneurship research generally.
This lack of conceptual precision creates a problem throughout the manuscript. The introduction promises a review of drivers and inhibitors, whereas the search strategy appears to emphasize startup related terms, motivation, motivational factors, and challenges. The results then identify very broad terms such as entrepreneurship, innovation, business, and startups. These results cannot establish the intellectual structure of research on drivers and inhibitors unless the authors first explain precisely what counts as a driver, what counts as an inhibitor, and which level of analysis is relevant.

The authors should begin by defining the theoretical domain of the review. They should state clearly whether the paper focuses on individual level motivations and barriers, venture level resources and constraints, ecosystem level conditions, or a theoretically justified integration of these levels. The title, abstract, introduction, search strategy, inclusion criteria, results, discussion, and conclusion should then be aligned with this definition. Without this revision, the study risks becoming a broad bibliometric overview of entrepreneurship rather than a review of a clearly specified theoretical domain.
The literature review should also become more selective and theoretically integrated. At present, the introduction refers to lean startups, uncertainty, accelerators, intrapreneurship, artificial intelligence, entrepreneurial ecosystems, strategic decision making, and business model adaptation. All of these topics may be relevant to entrepreneurship, but the manuscript does not explain how they collectively define the theoretical object under review. The result is breadth without a clear theoretical argument.

The authors should organize the literature around a coherent explanation of entrepreneurial action. A useful structure could distinguish individual antecedents, venture level conditions, and contextual influences. The paper could then use the bibliometric analysis to examine whether these three streams have developed independently, converged over time, or remained disconnected. Such a structure would provide a basis for a more meaningful theoretical contribution than a simple description of publication counts, authors, countries, journals, and keywords.

Several relevant studies should be considered because they would help the authors develop this conceptual structure. Shane and Venkataraman’s 2000 article is important for clarifying the relationship between entrepreneurial opportunities and entrepreneurial action. McMullen and Shepherd’s 2006 article is important for explaining why uncertainty is not simply another challenge but a central condition shaping entrepreneurial judgment and action. Welter’s 2011 article is particularly useful because it explains why entrepreneurship must be understood within business, social, institutional, and spatial contexts. Nambisan’s 2017 article would be relevant if the manuscript retains its discussion of technology, digitalization, and changing entrepreneurial environments.

Chernyak Hai and Solodoha’s 2026 study should also be considered if the authors retain social entrepreneurship within the scope of the review or seek to develop the individual and psychosocial driver stream more carefully. The article examines the contribution of personal traits and socio psychological factors to social entrepreneurial ventures, and it can help the authors distinguish individual level drivers from venture and ecosystem level explanations. This reference should not be presented as evidence for all forms of entrepreneurship, but it is theoretically relevant to the manuscript’s stated interest in entrepreneurial drivers.

Gabay, Solodoha, Kagan, and Shafran Tikva’s 2026 article may be relevant only if the authors explicitly broaden the manuscript to include organizational entrepreneurial behavior or intrapreneurship. The paper examines the role of entrepreneurship and collaboration values in innovative behavior among nurses. It would not be essential in a review that remains limited to startup formation and venture creation. The authors should therefore include it only if they make the broader theoretical move and explain its relevance.

The most serious methodological concern is the inconsistency in the description of the dataset. The abstract states that the data were extracted from Scopus and Web of Science between 1991 and 2020. Section 3.1 instead states that the authors searched across Elsevier, Web of Science, SAGE, Springer, Wiley Online, Taylor and Francis Online, and other publishers. The same section refers to a search period from 1985 to 2021. Elsewhere, the manuscript presents 1991 to 2020 as the final period. Section 5 again refers to analyses from 1985 to 2021.
These inconsistencies are fundamental rather than cosmetic. A bibliometric study is only as valid as its corpus. Different databases, sources, time periods, search fields, and document type restrictions produce different samples and potentially different intellectual maps. It is currently impossible to determine what exact set of records generated the tables and figures.

The authors should provide one definitive and internally consistent account of the study corpus. The revised paper should report the exact database or databases used, the date of each search, the full search strings, the Boolean logic, the searched fields, language restrictions, subject area filters, document type restrictions, inclusion and exclusion criteria, deduplication procedure, screening process, and final inclusion count. The authors should also provide a full list of included records in the online materials, including title, authors, year, source, abstract, keywords, DOI, and database identifier where available.
The search strategy itself does not clearly operationalize the stated theoretical domain. The reported query appears to rely mainly on startup terms, motivation, motivational factors, and challenges. It is unclear why this would capture research on entrepreneurial drivers and inhibitors more broadly. Important inhibitor related terms seem absent or insufficiently represented, including barriers, constraints, obstacles, deterrents, fear of failure, institutional conditions, resource limitations, financing constraints, discrimination, legitimacy, and uncertainty.

The authors should redesign the search strategy after defining the focal construct. They should explain how every group of terms maps onto the theoretical definition. If the corpus is primarily about startup motivation and challenges, the title and theoretical claims should be narrowed accordingly. If the authors intend to review the broader literature on drivers and inhibitors, the search strategy must be expanded and validated.
There are substantial inconsistencies in the reported descriptive statistics. The abstract reports an average of 30.24 citations per document. Section 3.2 also reports 30.24. Table 1 reports 27.27, while Section 4.1 reports 27.91. These figures should not differ if they were calculated from the same verified corpus and citation date.
There is also an inconsistency in the composition of the sample. Section 3.2 states that the corpus contains 259 articles and 31 review papers, totaling 290 records. Table 1 instead reports 387 articles and three review or conference proceeding documents, totaling 390 records. This is not a small reporting error. It indicates that either different analyses were conducted using different datasets or that the manuscript contains substantial errors in data processing and reporting.

Table 2 also appears to include the same Hessels et al. 2008 article twice, with the same title, DOI, and citation count but different author labels. This raises further concerns regarding duplicate records or metadata errors. The authors should undertake a complete audit of the master dataset. They should inspect duplicate titles, duplicate DOIs, title variants, source name variants, incomplete author fields, inconsistent document types, and records that may have been counted more than once.

The authors should then regenerate every result from one verified master file. This file should be deposited alongside the raw database exports, a codebook, data cleaning decisions, and the complete scripts used to generate the descriptive statistics, tables, figures, and bibliometric maps.
Source normalization also appears to require revision. Table 5 seems to include multiple variants of International Journal of Entrepreneurial Behaviour and Research as separate sources. If these records refer to the same journal, the Bradford analysis and the conclusions concerning the most productive journals are distorted. This issue matters because journal rankings are central to the paper’s descriptive claims.

The authors should standardize journal titles using source identifiers where possible. They should retain both raw and normalized titles in the shared dataset and explain the normalization procedure. All journal level analyses should be rerun after this data cleaning process.

The authors should also reconsider the decision to stop the analysis in 2020. The explanation that three complete decades permit a balanced comparison is reasonable if the article is explicitly framed as a historical analysis. However, this choice is not consistent with the manuscript’s claims about current research priorities, recent developments, and emerging themes. The revised introduction includes studies from 2025 and 2026, but these studies are not part of the bibliometric corpus.
The authors should choose one of two approaches. They may update the corpus to a recent and clearly specified cutoff date, while using comparable time periods in the longitudinal analysis. Alternatively, they may retain the 1991 to 2020 period but reframe the manuscript as a historical bibliometric account of the literature through 2020. If they choose the historical approach, the paper should substantially temper claims about current themes, emerging research priorities, and contemporary practical implications.

The manuscript’s claimed theoretical contribution also needs significant development. The authors state that the study provides a framework connecting venture creation stages, cognitive aspects of startups, and temporal dynamics. However, the framework is not clearly specified, visually presented, or directly derived from the analyses. Most results concern publication growth, productive authors, highly cited articles, countries, affiliations, journals, keywords, and collaboration patterns. These are useful descriptive outputs, but they do not automatically constitute a theoretical contribution.

The paper would make a stronger contribution if it used the bibliometric analysis to identify a specific conceptual tension in the literature. For example, the authors could examine whether research has treated entrepreneurial action mainly as an outcome of individual motivations, venture capabilities, or contextual resource conditions. They could then assess whether these streams have become more integrated over time or whether they remain disconnected. This would require a theoretically grounded coding structure and more careful interpretation of the clusters, themes, and temporal evolution.

At present, the discussion goes beyond the evidence in several places. Publication counts, citation patterns, keyword frequencies, and coauthorship networks can describe the visibility and structure of a research field. They cannot demonstrate that particular countries produce better research, that collaboration with certain countries will necessarily improve research quality, that specific journals are more prestigious, or that practitioners can derive effective entrepreneurial strategies from bibliometric maps.
The discussion should distinguish clearly between descriptive findings and theoretical inference. It should avoid causal language unless the analysis supports causal claims. It should also reduce the practical implications. A bibliometric review can help readers identify visible themes, influential sources, and areas for future research. It cannot establish which interventions, policies, or entrepreneurial strategies are effective unless the authors examine the substantive empirical evidence in the underlying studies.

The figures and tables require stronger methodological documentation. Several network figures are difficult to read because the labels are small and the captions do not fully explain the analytical choices. Each figure should report the number of records analyzed, the unit of analysis, the threshold for inclusion, the normalization method, the clustering procedure, the software package and version, and the meaning of node size, link strength, colors, and distance.
The authors should also consider whether each visualization adds unique value. Some visualizations may repeat information already available in the tables or text. The revised manuscript should retain only figures that materially improve the reader’s understanding of the intellectual structure, thematic evolution, or collaboration patterns of the field.

The Zenodo data availability statement is a positive feature, but meaningful reproducibility requires more complete documentation. The authors should share the raw database exports, the cleaned analytic dataset, a codebook, all data cleaning decisions, the software environment, package versions, and the exact scripts used to generate every table and figure. This is especially important given the current inconsistencies in corpus description and descriptive statistics.
The manuscript also requires extensive language editing. Several passages are repetitive, grammatically unclear, or excessively broad. Terms such as “prestigious,” “high quality,” “maximum contribution,” and “significant utility” should either be defined through explicit criteria or removed. The conclusion should be rewritten after the core methodological issues are resolved. It should summarize only findings that are supported by the validated corpus and analyses, rather than claiming a theoretical framework that has not been fully developed.

The manuscript has a potentially worthwhile objective, but it requires fundamental reconstruction before the results can be evaluated as reliable. The three issues that most directly prevent indexing are the absence of a clearly defined and consistently operationalized focal construct, the unresolved contradictions in the data sources and reported corpus, and the gap between descriptive bibliometric findings and the claimed theoretical contribution. Addressing these issues would have far greater value than adding further visualizations or expanding the descriptive results.

References
Chernyak‐Hai, L., & Solodoha, E. (2026). Psychosocial Drivers in Social Entrepreneurship Ventures: Exploring the Contribution of Personal Traits and Socio‐Psychological Factors. Global Business and Organizational Excellence.‏
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of business research, 133, 285-296.‏
Gabay, P. G., & Solodoha, P. E. (2026). Innovative behavior in nurses: The interactive effects of managerial role, entrepreneurship, and collaboration values. Nursing Outlook, 74(5), 102855.‏
McMullen, J. S., & Shepherd, D. A. (2006). Entrepreneurial action and the role of uncertainty in the theory of the entrepreneur. Academy of Management review, 31(1), 132-152.‏
Nambisan, S. (2017). Digital entrepreneurship: Toward a digital technology perspective of entrepreneurship. Entrepreneurship theory and practice, 41(6), 1029-1055.‏
Shane, S., & Venkataraman, S. (2000). The promise of entrepreneurship as a field of research. Academy of management review, 25(1), 217-226.‏
Welter, F. (2011). Contextualizing entrepreneurship—conceptual challenges and ways forward. Entrepreneurship theory and Practice, 35(1), 165-184.‏