Top 10 Common Reasons Reviewers Reject Research Papers

Journal Selection | Impact Factor | Peer Review | Paper Rejections | Rebuttal

You’ve poured countless hours into designing your study, analyzing data, and polishing your manuscript, only to receive the dreaded rejection email. If you’ve been through this, you’re not alone. Whether you’re a student, early-career researcher, or even a seasoned academic, facing rejection from a peer-reviewed journal can feel frustrating and sometimes mysterious.

But here’s the good news: most rejections aren’t personal. They often stem from a common set of issues that editors and reviewers encounter repeatedly. Understanding these recurring pitfalls can dramatically improve your chances of getting published.

In this blog, we’ll break down the top ten reasons research papers get rejected. Whether preparing your first manuscript or revising for resubmission, knowing what not to do is just as important as knowing what to include. Let’s demystify the review process, looking at the red flags you want to avoid.

1. Editorial reasons

Not all rejections come from peer reviewers—some papers don’t make it that far. Before your article reaches the hands of external experts, it must first pass an initial editorial screening. This early-stage gatekeeping ensures that submissions align with the journal’s scope, standards, and expectations.

1.1. Out of scope for the journal

Surprisingly, many rejections occur at this point for reasons unrelated to the quality of the research itself. One common reason? The topic just doesn’t fit the journal. Let’s look at a real-world journal, Journal of Information Systems, to better understand. The Journal of Information Systems focuses specifically on the intersection of accounting and information systems, not just general IT or IS.

✖ It is rejected because it does not fall within the narrow scope of accounting information systems literature.

A researcher submits a well-executed study on “AI in e-commerce fraud detection.”

✔ This article is within the journal’s scope, so it passes editorial review!

A researcher submits an article titled: “Evaluating the effectiveness of Blockchain-based audit systems in enhancing financial reporting transparency: Evidence from mid-sized accounting firms.”

Tip: The scope of many journals can’t be known from the journal’s title. As an author, carefully review the journal’s “Aims and Scope” section and the recently published articles.

1.2. Research ethics ignored

There are several reasons why research ethics are being ignored. Let’s examine these.

Tip: Even with compelling methods and findings, ethical integrity is non-negotiable in scholarly publishing. Always secure IRB or ethics committee approval when human data is involved, and ensure transparency about data collection, consent, and risk mitigation in your submission.

1.3. Violation of publication ethics

Let’s examine several existing publication ethics that might be violated intentionally or unintentionally.

Tip: Respecting publication ethics is essential not just for getting your work published, but for maintaining the integrity of the academic community.

2. Editorial reasons

Even if a paper passes the editorial screening, it can still be rejected during peer review due to technical shortcomings. These issues often relate to the soundness of the research design, data analysis, or interpretation of results. Peer reviewers are trained to evaluate whether the study is methodologically rigorous, replicable, and logically argued. When these elements are weak or inconsistent, even an interesting topic or novel idea may not be enough to warrant publication.

In this section, we’ll explore the most common technical reasons peer reviewers cite when recommending rejection—and how authors can address them before submission.

2.1. Outdated topic

A paper may be rejected during peer review if the topic is considered outdated or irrelevant to current academic discussions or real-world challenges. Even if the methodology is sound, reviewers look for research that contributes new insights or addresses emerging issues. Submitting a study on a topic that has already been thoroughly explored—especially without offering a fresh perspective—can signal to reviewers that the paper adds little value to the field.

This doesn’t mean historical or foundational topics are off-limits, but they must be framed in a way that connects to ongoing debates or contemporary applications.

✖ The topic is outdated, the data is no longer relevant, and it does not reflect current business technology trends.

A study analyzing the adoption of Windows XP in small businesses, using data collected in 2009, was submitted to a 2025 technology journal.

✔ Although the paper discusses older systems, it connects the historical context to present-day challenges and offers comparative insight that is relevant and valuable.

A paper revisiting early ERP adoption in the 2000s, comparing it to modern cloud-based ERP migration strategies in today’s hybrid IT environments.

2.2. Weak research motive

A common technical reason for rejection is a weak or unclear research motive—the underlying why behind the study. Reviewers expect a compelling justification explaining the research problem’s significance and why it matters now. A weak research motive may appear in papers that “fill a gap” without showing why that gap is essential, or that restate what’s already known without challenging, extending, or applying it meaningfully.

✖ The topic is too broad and has been studied extensively previously. No problem is indicated, and no explanation is given as to why this study is necessary.

“This study examines customer satisfaction with online shopping platforms.”

✔ A specific current problem is identified, and a relevant and emerging demographic is targeted. Both of these describe a meaningful contribution to theory and practice.

“This study investigates how real-time AI chatbots influence customer satisfaction in mobile commerce among Gen Z consumers – a segment rapidly shaping online retail but underrepresented in current satisfaction models.”

Tip: When developing your research motive, ask: Why does this study matter now? Who benefits from the findings? What gap does it fill – and why is that gap meaningful?

2.3. Lack of necessary details

Another frequent technical reason for rejection is the lack of sufficient detail in the research methodology, data analysis, or results reporting. Peer reviewers need to clearly understand how the study was conducted to evaluate its validity, reliability, and replicability. When crucial elements—such as sample selection, data collection procedures, statistical methods, or analytical tools—are missing or vaguely described, reviewers may question the integrity and usefulness of the research.

Providing too little detail can leave reviewers with unanswered questions, even if the study itself was sound.

✖ Who were the participants? How many were there? What survey instrument was used? Which statistical tests were used? Many details are missing to fully assess or replicate the research.

“Participants were surveyed, and the results were analyzed using appropriate statistical methods.”

✔ This version provides specific details about the sample, instrument, analysis method, and threshold for statistical significance allowing the reviewers to evaluate the study’s rigor.

“A total of 312 university students from three campuses were surveyed using a validated 20-item questionnaire on cybersecurity awareness. Responses were analyzed using multiple regression to examine the relationship between awareness and reported risky behaviors, with significance set at p < .05.”

2.4. Lack of up-to-date references/too much self-citation

Peer reviewers often flag papers that rely too heavily on outdated sources or are overly reliant on self-citation. Using current, relevant literature demonstrates that you’re aware of recent developments in your field and that your work builds upon the latest research. Conversely, citing mostly older studies can make your work appear disconnected from ongoing scholarly conversations.

Similarly, while citing your prior work is appropriate when relevant, excessive self-citation—especially when it sidelines other key voices—can be biased or self-promotional. Reviewers expect a balanced, current, and well-rounded literature review that situates your research in a broader body of knowledge.

✖ The author only cites themselves, and the references are almost a decade old, suggesting a narrow and outdated literature base.

“This study builds on our previous findings (Smith, 2014; Smith, 2015; Smith, 2016).”

✔ Here, the author incorporates both recent and foundational sources, cites other scholars, and shows an awareness of ongoing developments.

“Recent studies have explored the role of data privacy in mobile health apps (Chen & Lee, 2022; Ahmad et al., 2025), building of foundational work in digital ethics (Smith, 2015).”

2.5. Poorly presented data

Effective data presentation highlights key insights, maintains accuracy, and supports the overall narrative of your research. Tables and figures should be labeled clearly, formatted consistently, and directly tied to the discussion in the text.

Reviewers often reject papers where data is confusing, cluttered, or misleadingly displayed—whether in tables, charts, or written descriptions. Poor data presentation makes it difficult to interpret findings, assess statistical validity, or understand the significance of the results. Even if your data is solid, how you visualize and explain it can make or break a reviewer’s impression

2.6. Not enough impact for the journal or topic advancement

Even well-written and methodologically sound papers can be rejected if they don’t demonstrate sufficient impact or contribution to the field. Reviewers and editors seek research that offers new insights, addresses important questions, or pushes the boundaries of existing knowledge. If a study is too narrow, confirms what’s already well established, or lacks a clear implication for practice or theory, it may be deemed unsuitable, especially for competitive, high-impact journals.

✖ This finding is already widely known, lacks specificity, and doesn’t offer new theoretical or practical insights.

“This study confirms that social media use is common among college students.”

✔ This statement targets a specific, relevant issue, focuses on an underserved group, and contributes to social media and civic engagement research.

“This study explores how algorithmic content moderation on social media platforms influences the political engagement of first-generation college students, a population underrepresented in digital civic participation research.”

Tip: Impact doesn’t have to mean ground-breaking innovation; it can also come from applying known ideas in novel contexts, addressing underserved populations, or refining existing theories in meaningful ways. The key is to show how your work matters.

2.7. Inaccurate conclusions not supported by the data

One of the most critical red flags for peer reviewers is when a paper’s conclusions go beyond what the data can reasonably support. This can include overstating findings, making causal claims from correlational data, or drawing broad generalizations from a limited sample. Even if the rest of the paper is strong, inaccurate or exaggerated conclusions can undermine credibility and lead to rejection. A well-written conclusion accurately reflects the scope and limitations of the research, offering insight without overstating certainty.

✖ The word “prove” is too bold, and “teenagers worldwide” is overly broad unless a truly global population and sample was used.

“Our findings prove that social media causes anxiety in teenagers worldwide.”

✔ The conclusion is appropriately cautious, reflects the sample used, and avoids making unsupported causal claims.

“Our findings suggest a significant association between frequent social media use and reported anxiety symptoms among U.S. high school students, warranting further investigation into potential causal pathways.”

2.8. Inappropriate statistical tests or lack of statistics

Statistical analysis is a cornerstone of empirical research, and reviewers are quick to reject papers that either use inappropriate statistical tests or lack statistical analysis altogether when it’s clearly needed. Using the wrong test—for example, applying a parametric test to non-normally distributed data—can lead to invalid results. Likewise, failing to report any statistics in a study that involves numerical data raises questions about the rigor and reliability of the findings.

Reviewers expect authors to demonstrate not only what was found, but how it was analyzed—and why that method was appropriate. Without this, the study’s conclusions cannot be trusted.

✖ The authors applied a statistical test without verifying whether its assumptions were met, possibly invalidating the results.

“We compared the results using a t-test, but we did not check for normality or variance assumptions.”

✔ The authors selected a statistical test based on the nature of the data and reported how assumptions were tested, demonstrating statistical rigor.

“We conducted a Mann-Whitney U test to compare group medians, as the data did not meet the assumptions of normality (Shapiro-Wilk, p<.05).”

Tip: Choose statistical methods that fit your data type and distribution and always report how assumptions were checked!

2.9. Not transparent about limitations

No study is perfect—and peer reviewers know that. What they expect is honest and thoughtful reflection on a study’s limitations. Failing to acknowledge constraints such as sample size, generalizability, methodological choices, or potential biases can make your research appear overly confident or incomplete. Reviewers may interpret this as a lack of critical thinking or, worse, as an attempt to obscure weaknesses.

By clearly stating limitations, you not only demonstrate scholarly integrity but also help others understand the context and boundaries of your findings. Transparency builds credibility and invites further inquiry.

✖ Every study has limitations. Claiming otherwise appears unrealistic and may raise red flags for reviewers.

“This study has no major limitations.”

✔ The authors identified a specific limitation and provided a constructive suggestion for future research.

“One limitation of this study is the use of a convenience sample from a single university, which may limit the generalizability of the findings. Future research should include a more diverse population from across multiple institutions.”

3. Poor language quality

Poor language quality is a frequent reason for rejection, especially in international journals where clarity and precision are essential. If an article is riddled with grammar errors, awkward phrasing, or inconsistent terminology, reviewers may struggle to understand the core ideas, even if the research is solid. In extreme cases, poor writing can make a paper unreadable, causing editors to reject it without sending it out for review.

Clear, professional language helps communicate complex ideas effectively and reflects the author’s attention to detail. Reviewers are not expected to serve as copy editors, so ensuring your manuscript is polished and readable before submission is essential. This may require careful self-editing, peer feedback, or professional editor assistance—especially for non-native English speakers.

✖ Poor grammar and awkward phrasing.

“Due to the reason that the sample was small, so the results might not generalize.”

✔ Concise, precise wording, proper grammar.

“Because the sample size was small, the findings may not be generalizable to larger populations.”

Tip: Before submitting, read your article aloud, run it through spell and grammar check tools, and get feedback from colleagues. Strong writing doesn’t just support your research – it ensures it gets the attention it

4. Summary

Rejection from a peer-reviewed journal can be disheartening, but it is often preventable. This guide demystifies the top 10 reasons research papers are rejected, drawing from both editorial and peer review perspectives. From submitting a paper outside the journal’s scope to using inappropriate statistical methods or overstating conclusions, each section outlines what goes wrong and how to fix it.

The blog covers:

• Editorial reasons, such as ethics violations or topic mismatch
• Technical flaws, including weak research motives, outdated topics, poor data presentation, and methodological oversights
• Language quality, showing how unclear writing can hinder even strong studies
• Common misconceptions, like the rules about simultaneous submission or the fear of idea theft

With clear examples of what not to do and examples of good practice, this blog helps authors navigate the publication process with confidence and clarity. Whether you’re a graduate student or early-career researcher, this is a must-read before hitting “submit.”