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REVIEW 4 major objections 6 minor 13 references

GenAI in Entrepreneurship: a systematic review of generative artificial intelligence in entrepreneurship research: current issues and future directions

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper claims to be the first systematic literature review of generative AI and large language models in entrepreneurship research, and it organizes 83 peer-reviewed papers into five thematic clusters.

desk verdict Useful orientation map of GenAI-entrepreneurship research, but the clustering pipeline is not reproducible as reported and the 'first systematic review' claim overreaches. read the letter →

arxiv 2505.05523 v1 pith:T2LDAU34 submitted 2025-05-08 econ.GN cs.AIq-fin.EC

Anna Kusetogullari , Huseyin Kusetogullari , Martin Andersson , Tony Gorschek This is my paper · ORCID
classification econ.GNcs.AIq-fin.EC
keywords GenAIinEntrepreneurshipLLMsSystematicLiteratureReviewClusteringDigitalTransformationGenerativeArtificialIntelligenceLargeLanguageModels
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to establish that research on generative AI and large language models in entrepreneurship has matured enough to be mapped, and that it organizes into five recognizable themes. A sympathetic reading takes the central claim to be that the 83 peer-reviewed articles retrieved from Web of Science and Scopus form five clusters: digital transformation and behavioural models, GenAI-enhanced education, sustainable innovation and strategic AI impact, business models and market trends, and data-driven technological trends. If the mapping is right, it gives entrepreneurship scholars a baseline of what has been studied and a concrete agenda of what has not, especially macro-level questions about GenAI as an external enabler and about regulation that permits business experimentation.

What carries the argument

The mechanism that carries the argument is an unsupervised text-analysis pipeline: TF-IDF vectorization turns the textual content of each paper into a numerical vector; Principal Component Analysis reduces the dimensionality of those vectors; hierarchical clustering then merges similar papers into a tree, and the tree is cut to produce five groups. The paper also uses the notion of external enablers from entrepreneurship theory as the interpretive lens for why GenAI and LLMs matter for entrepreneurship. The pipeline is what generates the five cluster labels; the external-enabler lens is what turns the clusters into a research agenda.

What would settle it

Re-run the same TF-IDF, PCA, and hierarchical clustering recipe on the same 83 papers with different text inputs or similarity settings, have independent entrepreneurship scholars label the resulting groups, and compare; if the five themes do not reappear or the labels cannot be reproduced, the five-cluster map is an artifact of the method.

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Extended reading notes

Core claim

The paper's central claim is that this is the first systematic literature review devoted specifically to GenAI and LLMs in entrepreneurship research, and that the field's current intellectual landscape consists of five thematic clusters. The largest cluster, Business Models & Market Trends, contains 21 of the 83 papers; Sustainable Innovation & Strategic AI Impact contains 20; Data-Driven Technological Trends contains 18; GenAI-Enhanced Education & Learning Systems contains 13; and Digital Transformation & Behavioural Models contains 11. The authors argue that GenAI and LLMs act as external enablers that change the preconditions for entrepreneurship in two ways: by helping entrepreneurs do current work more effectively and by enabling new ventures, products, and business models. They conclude that existing research has concentrated on micro-level and firm-level effects, and they call for more macro-level research on scope, mechanisms, and roles of GenAI as an external enabler, and on regulatory frameworks that balance ethics and risk with experimentation and innovation.

Load-bearing premise

The five themes are genuine divisions in the literature rather than artifacts of the clustering setup, because the number of clusters was chosen from a dendrogram and no stability check or independent expert validation is reported.

Editorial extensions

If this is right

  • If the five-cluster map is correct, future literature reviews in this area can use it as a starting point rather than starting from scratch.
  • The cluster sizes imply that business-model and sustainability questions currently dominate, while digital-transformation and behavioural research is comparatively thinner.
  • The paper's gap analysis says the field needs longitudinal and experimental designs, more diverse samples beyond students in emerging economies, and more attention to macro-level external-enabler and regulatory questions.
  • The steep growth in publications from about three per year in 2020-2022 to 47 in 2024 suggests the review captures a field in rapid expansion, so the baseline may date quickly.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the clustering is robust, a natural next test would be to compare these keyword-derived themes with a citation-network or co-authorship map of the same 83 papers; topic clusters and community structures do not always coincide.
  • The paper's own observation that publication counts surged only after 2023 suggests the five clusters may be a snapshot of a pre-paradigmatic field, and later reviews will likely find new clusters or splits.
  • The external-enabler framing could be made operational by extracting, paper by paper, which specific enabling mechanism the authors credit to GenAI, such as cost reduction, speed, or new opportunity recognition, and testing whether those mechanisms differ across the five clusters.
  • One testable extension is to apply the same TF-IDF, PCA, and hierarchical clustering recipe to a larger corpus that includes preprints and conference papers; if the five themes persist, the map generalizes beyond peer-reviewed journals.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper presents a systematic literature review of research on generative artificial intelligence (GenAI) and large language models (LLMs) in entrepreneurship. The authors searched Web of Science and Scopus, retained 83 peer-reviewed articles after screening, and applied TF-IDF vectorization, PCA, and hierarchical clustering to identify five thematic clusters. The paper reports descriptive statistics by cluster and year, summarizes the literature within each cluster, discusses ethical concerns, and proposes future research directions organized around the five clusters. The central claims are that these five thematic clusters accurately organize the field and that this is the first systematic literature review of GenAI and LLMs in entrepreneurship research.

Significance. If the clustering analysis is reliable and the novelty claim is accurate, the paper would provide a useful map of a rapidly growing research area and a structured research agenda. The study has several strengths: it follows a documented systematic-review protocol, reports the search and screening flow, gives a worked-out future research directions table, and explicitly addresses ethical dimensions. The unsupervised clustering approach is a reasonable and potentially valuable complement to purely narrative reviews. However, the load-bearing methodological step is not currently reproducible: Section 3.2 omits key parameters, contains an internal inconsistency about whether clustering was performed on TF-IDF vectors or keyword lists, and reports no validation of cluster stability or label validity. Several cited studies are also not clearly about GenAI, which raises questions about corpus composition. These issues must be resolved before the five-cluster taxonomy can be accepted as the paper's main contribution.

major comments (4)
  1. [Section 3.2] The clustering pipeline is not reproducible as described. The authors do not report the number of PCA components retained, the linkage criterion (e.g., Ward, complete, average), the distance metric, or the dendrogram height at which the five-cluster cut is made. Because the number of clusters is selected by inspecting the dendrogram, the five themes in Figure 2 and the cluster distribution in Figure 3 could change under alternative, equally plausible parameter choices. The text also shifts between two different inputs: it first says TF-IDF vectorization transforms textual content, then says hierarchical clustering groups articles 'based on their keyword lists.' If the clustering was actually performed on keyword lists rather than on TF-IDF representations of titles/abstracts/full texts, the method and results would differ substantially. I request that the authors specify the exact input representation, all preprocessing steps, all hyperparameters, the cluster-cut rule, and a stability or validation analysis (e.g., silhouette scores, bootstrap resampling, or independent expert labeling of cluster membership).
  2. [Section 6 (and Abstract)] The claim that this is 'the first systematic literature review of GenAI and LLMs in research on entrepreneurship' is contradicted by the paper's own references. Section 4.3 cites Dwivedi (2025) as 'A systematic review' and Section 4.4 cites López-Solís et al. (2025) as 'a systematic literature review.' Both are described as covering GenAI in entrepreneurship-adjacent domains. The authors should either demonstrate how their scope, corpus, or analytical approach differs from these existing reviews, or qualify the novelty claim accordingly.
  3. [Section 3.1 (Figure 1) and Sections 4.3–4.6] The search strategy appears over-inclusive relative to the stated focus on GenAI. The keyword list includes models and methods such as BERT, RoBERTa, GANs, and diffusion models, which are not necessarily generative AI in the sense used in the paper, and several included papers do not appear to be about GenAI at all, e.g., Karim et al. (2022) on ICT use, Knieps (2021; 2024) on 5G networks, Sajter (2024) on Croatian economic science, and Basilico and Graf (2023) on regional knowledge spaces. The screening stage is described only as an abstract screening with 27 exclusions; the authors should report inclusion/exclusion criteria in sufficient detail to allow replication and should consider a sensitivity analysis that shows whether the five clusters remain stable when questionable papers are removed.
  4. [Figure 3 and Section 4.1.1] The cluster-level results cannot be verified because the paper does not provide a full list of which papers are assigned to which cluster. Figure 2 is a low-resolution dendrogram and Figure 3 gives only cluster sizes. I recommend that the authors include, as a supplementary table or appendix, the cluster membership for all 83 papers, together with the keywords or representative terms used to label each cluster. Without this, the qualitative summaries in Sections 4.2–4.6 cannot be checked against the actual clustering output.
minor comments (6)
  1. [Abstract] The sentence 'not least because of its impact on the preconditions for entrepreneurship' contains a singular/plural mismatch; 'its' should agree with 'GenAI and LLMs.'
  2. [Section 3.1] The phrase 'international mall and medium-sized enterprises' is a typo; it should read 'small and medium-sized enterprises.'
  3. [Section 4.2 and References] In-text citations and reference entries are inconsistent: 'Abaddi, 2023' in the text corresponds to 'Abaddi, S. (2024)' in the references, and 'Thottoli et al. (2023)' in the text corresponds to a 2025 reference entry. These mismatches should be corrected throughout.
  4. [References] The reference list contains duplicate entries, including two entries for Maarouf et al. (2025) and two entries for Duong, C. D. (2024a/2024b) that are not consistently cited. A full bibliography cleanup is needed.
  5. [Section 3.2 and Figure 5] Figure 5 is referenced but not explained in the main text; the authors should describe how the common keywords were computed and what the figure is intended to show.
  6. [Section 4.4] The paper says 'López-Solís et al. (2025) underscores the continued importance of human oversight' and 'a study by López-Solís et al. (2025)' but the reference list identifies it as a systematic literature review; the text should make clear that this is a review rather than a primary empirical study.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the clustering is descriptive, no fitted value is recycled as evidence, and there is no load-bearing self-citation chain.

full rationale

The paper's central claim is that TF-IDF vectorization, PCA, and hierarchical clustering produce five thematic clusters from 83 articles. This is an unsupervised, descriptive analysis over a fixed corpus; the cluster labels are assigned after inspecting the dendrogram, which is interpretation rather than a prediction derived from fitted inputs. No parameter is fit to a subset and then used to predict a closely related quantity, and no equation or result reduces to an input by construction. The method section is under-specified and contains an internal inconsistency (it says clustering groups articles 'based on their keyword lists' after describing TF-IDF vectorization of textual content), and the 'first systematic review' claim is weakened by the paper's own citations of Dwivedi (2025) and Lopez-Solis et al. (2025) as systematic reviews. However, these are reproducibility and accuracy concerns, not circularity: the clustering does not assume the clusters it reports, the authors do not rely on their own prior work as load-bearing evidence, and no uniqueness theorem or ansatz is smuggled in via self-citation. The derivation chain is therefore self-contained in the relevant sense, and the appropriate circularity score is 0.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The review rests on an assumed representative corpus, an assumed valid text representation, and hand-chosen clustering settings. No parameters are fit to a target outcome, so circularity burden is low, but the unspecified clustering hyperparameters and the choice of five clusters are unvalidated degrees of freedom. No invented entities are introduced.

free parameters (2)
  • Number of clusters = 5
    The five-cluster solution is chosen by visual inspection of the dendrogram (Figure 2); no formal selection criterion or robustness check is provided, and the entire thematic map depends on this choice.
  • PCA and clustering hyperparameters
    The number of principal components, distance metric, linkage criterion, and TF-IDF settings are not reported in Section 3.2; these choices affect cluster assignments and make the result non-reproducible.
assumptions (3)
  • domain assumption The 83 articles retrieved from Scopus and WoS collectively represent the relevant literature on GenAI and entrepreneurship.
    Conclusions about the intellectual landscape and gaps presume the search and screening process did not omit major work; however, search syntax, screening rules, and inclusion decisions are only partially reported.
  • domain assumption TF-IDF vectors, PCA reduction, and hierarchical clustering produce thematic groups that correspond to meaningful research topics.
    The method is applied in Section 3.2 with no stability analysis, no comparison to expert coding, and no report of hyperparameters, so the validity of the five cluster labels is assumed.
  • domain assumption The papers in the five clusters concern generative AI rather than adjacent technologies.
    The search query includes encoder-only models such as BERT and RoBERTa and general terms such as chatbot, and Cluster 5 contains papers on 5G and IoT, so the boundary between GenAI and broader AI or digital technologies is assumed rather than established.

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Cite this review

Pith. "Pith review of GenAI in Entrepreneurship: a systematic review of generative artificial intelligence in entrepreneurship research: current issues and future directions." pith.science (2026). https://pith.science/paper/T2LDAU34

@misc{pith2026250505523,
  author       = {Pith},
  title        = {Pith review of: GenAI in Entrepreneurship: a systematic review of generative artificial intelligence in entrepreneurship research: current issues and future directions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T2LDAU34}},
  note         = {Machine review of arXiv:2505.05523}
}
read the original abstract

Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are recognized to have significant effects on industry and business dynamics, not least because of their impact on the preconditions for entrepreneurship. There is still a lack of knowledge of GenAI as a theme in entrepreneurship research. This paper presents a systematic literature review aimed at identifying and analyzing the evolving landscape of research on the effects of GenAI on entrepreneurship. We analyze 83 peer-reviewed articles obtained from leading academic databases: Web of Science and Scopus. Using natural language processing and unsupervised machine learning techniques with TF-IDF vectorization, Principal Component Analysis (PCA), and hierarchical clustering, five major thematic clusters are identified: (1) Digital Transformation and Behavioral Models, (2) GenAI-Enhanced Education and Learning Systems, (3) Sustainable Innovation and Strategic AI Impact, (4) Business Models and Market Trends, and (5) Data-Driven Technological Trends in Entrepreneurship. Based on the review, we discuss future research directions, gaps in the current literature, as well as ethical concerns raised in the literature. We highlight the need for more macro-level research on GenAI and LLMs as external enablers for entrepreneurship and for research on effective regulatory frameworks that facilitate business experimentation, innovation, and further technology development.

Figures

Figures reproduced from arXiv: 2505.05523 by the authors.

Figure 1
Figure 1. Procedure for paper search, collection and clustering research articles [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Dendrogram Showing the Distribution of 83 Research Papers Using PCA-Based Hierarchical Clustering [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Distribution of research articles across five different clusters. 4.1.2 Distribution of Articles Published by Year [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Number of articles published per year. 4.2 Cluster 1: Digital Transformation & Behavioural Models Advanced AI models such as GenAI and ChatGPT, are reshaping the entrepreneurial landscape by transforming not only the mechanics of business creation but also the cognitiv…

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Reference graph

Works this paper leans on

13 extracted references · 12 canonical work pages

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Reviewed August 15, 2026 · model on record in the stance chip above.