Avalilação PREreview Estruturada de RESEARCH MODEL PROPOSAL ON COGNITIVE OVERLOAD, ANXIETY, COGNITIVE FATIGUE, AVOIDANCE BEHAVIOR, AND DATA LITERACY IN BIG DATA ENVIRONMENTS: RESULTS OF A SYSTEMATIC LITERATURE REVIEW

Does the introduction explain the objective of the research presented in the preprint?

Yes

The introduction (and abstract) clearly presents the research objective: to propose a preliminary conceptual model that investigates the associations between cognitive overload, anxiety, cognitive fatigue, avoidance behavior, and data literacy in Big Data environments. The variables and purpose of the systematic review are explicitly stated, as highlighted in the previous analysis.

Are the methods well-suited for this research?

Highly appropriate

As detailed in the previous evaluation, the methods are fundamentally flawed for a systematic literature review: No description of databases searched, search strings, or selection criteria. No PRISMA flowchart or quality assessment of included studies. Insufficient transparency to allow replication. The review does not follow established best practices for SLRs, severely limiting the validity and usefulness of the proposed conceptual model.

Are the conclusions supported by the data?

Somewhat unsupported

The authors conclude that their proposed model applies to Big Data environments, but the data they collected come almost entirely from studies on information, social media, COVID-19, and news – not Big Data. The authors themselves admit (page 19) that few studies investigate these associations in Big Data settings. Nevertheless, they adapt the model to Big Data without providing theoretical justification or empirical evidence that the relationships hold when moving from “information” to “Big Data.” Descriptive conclusions (e.g., that Cognitive Overload is a central variable) are supported, but the main claim of relevance to Big Data overgeneralizes. Therefore, the conclusions partially address the data but are not fully supported.

Are the data presentations, including visualizations, well-suited to represent the data?

Highly inappropriate or unclear

Missing PRISMA flowchart (Figure 1) – The text repeatedly refers to “Figure 1. Search and selection process” (pages 11, 13), but the figure is not present in the extracted text or visible in the manuscript. A PRISMA flowchart is essential for any systematic literature review to transparently show the number of records at each stage. Its absence makes it very challenging to interpret the screening and inclusion process. Missing conceptual model diagram (Figure 3) – The authors describe “Figure 3. Preliminary research model proposal” (page 19) and the model includes nine propositions (P1–P9). However, the figure is not provided in the manuscript. The reader cannot see the visual representation of the proposed relationships, which is a major barrier to understanding. Co‑occurrence “network” presented as a table (Table 4) – The authors claim to present a “co‑occurrence network” but show only a table listing keywords, betweenness degree, and a cluster number. No actual network visualization (nodes, edges, clusters visually) is provided. A table is not appropriate for representing a network structure; it does not communicate the connections between keywords effectively. Tables are adequate but insufficient – Tables 1, 2, 3, and 5 are clear and correctly formatted. However, the absence of the two key figures (PRISMA flowchart and conceptual model diagram) and the inappropriate tabular representation of a network make the overall data presentation highly inadequate. The data presentations do not follow basic accessibility and clarity standards for a systematic literature review. Major visual elements are missing, and a network is poorly represented. This makes it very challenging to interpret the data correctly.

How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research?

Somewhat clearly

The authors write in a clear, organized manner. They explain their findings (e.g., Overload is the central variable, the nine associations, the predominance of studies on information/social media/COVID-19) and propose specific next steps (empirical testing of the model, meta‑analysis, expanding the search strings, including other formats). However, the clarity is compromised by a fundamental contradiction that they do not clearly resolve: They repeatedly state that most findings come from non‑Big‑Data contexts (information, social media, news, health). Yet they present their model and conclusions as directly applicable to Big Data environments without a clear, explicit justification or a transparent discussion of the limitations of this extrapolation. The discussion of next steps is reasonable (e.g., “the model should be tested empirically”), but they do not clearly acknowledge that the first logical next step would be to validate whether the same associations even hold in Big Data before proposing a model for it.

Is the preprint likely to advance academic knowledge?

Not at all likely

The preprint has fundamental flaws that prevent it from advancing academic knowledge: Misaligned evidence – The model is proposed for Big Data environments, but the data come almost entirely from studies on information, social media, COVID-19, and news. The authors acknowledge this but still extrapolate without justification. Methodologically unsound systematic review – The search strategy lacked synonyms, used a biased free‑access criterion, performed no quality assessment of included studies, and synthesized associations only as a list of references (no direction, magnitude, or consistency). Conclusions not supported – The central claims (e.g., Data Literacy inversely associated with Overload, Anxiety, and Avoidance in Big Data) are not evidenced by the data presented. Missing key figures – The PRISMA flowchart and the conceptual model diagram are absent, making the review non‑transparent and the model unvisualized. Because of these flaws, the preprint offers no significant, reliable advancement to knowledge. Any potential insights are undermined by poor methodology and overgeneralization.

Would it benefit from language editing?

Yes

“We identified 93 articles for analysis, and we found nine direct associations between these variables. These results served as a basis for us to appropriate their theoretical backgrounds…” – “Appropriate” is used incorrectly (should be “adopt” or “adapt”). “It is the responsibility of individuals to collect the right data, prepare it, analyze it, and interpret it in a way that gives meaning according to the business context and share it with stakeholders…” – Missing parallel structure and a subject shift. “Table 1 shows the results of the search and selection process of articles.” – Repetitive and slightly unnatural. “1t was therefore seen that the phenomena…” – Typo (“1t” instead of “It”) on page 17. “In this sense, the following proposition arises:” – Repeated formulaic phrasing that could be streamlined. While the preprint is understandable, the language is not consistently fluent or professional. A language edit would improve clarity and readability without changing content.

Would you recommend this preprint to others?

No, it’s of low quality or is majorly flawed

Based on the detailed evaluation above, the preprint suffers from fundamental methodological problems (biased free‑access inclusion, no synonym search, no quality assessment), a severe misalignment between the data (mostly on information/social media) and the claimed Big Data context, unsupported conclusions, and missing key figures (PRISMA flowchart, conceptual model diagram). These flaws make it unreliable and not suitable for recommending to others in its current form.

Is it ready for attention from an editor, publisher or broader audience?

No, it needs a major revision

The preprint has fundamental issues that prevent it from being ready for an editor, publisher, or broader audience: Methods are highly inappropriate for the stated research objective (biased inclusion criteria, no synonym search, no quality assessment). The data do not support the main conclusion (model for Big Data is based on non‑Big‑Data studies). Key elements (PRISMA flowchart, conceptual model figure) are missing. Language editing is needed. A major revision – including rethinking the scope, redoing or transparently reframing the review, and providing proper visualizations – is required before it can be considered for publication or public dissemination.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.