Plagiarism Screening

Finance, Accounting and Business Analysis (FABA) applies a rigorous plagiarism screening procedure to ensure the originality, transparency, and integrity of all submitted manuscripts.

This policy should be read in conjunction with the journal’s Publication Ethics & Malpractice Statement (see: https://faba.bg/index.php/faba/pem).

Mandatory Similarity Screening

All submissions undergo plagiarism detection screening prior to peer review using professional similarity-check software.

Screening is conducted:

• upon initial submission (before desk review);
• after major revision, where substantial changes have been introduced;
• prior to final acceptance, when deemed necessary.

Manuscripts are not sent to peer review before passing similarity screening.

Similarity Score Interpretation

The journal does not rely solely on numerical similarity percentages. All similarity reports are manually reviewed by the editorial team.

Important:
Similarity percentages exclude references, bibliographies, properly formatted quotations, and standard methodological descriptions.

General internal guidance:

• Below 15% similarity (excluding references and quotations) – typically acceptable;
• 15–25% – requires editorial assessment;
• Above 25% – may result in rejection unless clearly justified.

The nature, source, context, and distribution of overlap are more important than the raw percentage.

Types of Plagiarism Considered

The journal screens for:

• verbatim plagiarism;
• mosaic plagiarism;
• self-plagiarism;
• redundant or duplicate publication;
• salami slicing;
• inappropriate citation manipulation.

Editorial Actions

If minor overlap is detected, authors may be required to revise and properly attribute sources.

If substantial or intentional plagiarism is identified, the manuscript will be rejected without peer review.

If plagiarism is discovered post-publication, the journal may issue:

• correction;
• expression of concern;
• retraction.

Author Responsibility

By submitting a manuscript, authors confirm that:

• the work is original;
• all sources are properly cited;
• reused material is clearly identified;
• the manuscript complies with the journal’s Publication Ethics & Malpractice policy.

Intentional plagiarism may result in sanctions, including submission bans.

Redundant Publication & Self-Plagiarism Policy

Finance, Accounting and Business Analysis (FABA) prohibits redundant publication and improper reuse of previously published material.

This policy operates under the broader framework of the journal’s Publication Ethics & Malpractice Statement.

Definition

Redundant publication occurs when authors publish substantially similar content in more than one journal or venue without proper disclosure or justification.

Self-plagiarism occurs when authors reuse significant portions of their own previously published work without proper citation, acknowledgment, or editorial disclosure.

What Constitutes Redundant Publication

The following practices are considered unethical:

• submitting the same manuscript to multiple journals simultaneously;
• republishing previously published data without citation;
• splitting one study into multiple minimally different publications (“salami slicing”);
• translating and republishing the same article without disclosure.

Acceptable Reuse

Limited reuse may be acceptable when:

• prior publication is clearly cited;
• the new manuscript offers substantial new analysis or interpretation;
• conference papers are expanded into full articles with significant additional content;
• explicit disclosure is made at submission.

Authors must inform the editorial office of any prior dissemination of the work.

Editorial Evaluation

When overlap with prior publications is detected, editors will:

• request clarification from the authors;
• assess the degree of novelty;
• determine whether the manuscript constitutes redundant publication.

Substantial duplication may result in rejection.

Sanctions

Confirmed redundant publication may lead to:

• manuscript rejection;
• retraction (if already published);
• notification of institutions (in severe cases);
• temporary or permanent submission ban.

AI & Plagiarism Interaction Policy

Finance, Accounting and Business Analysis (FABA) recognizes the increasing use of artificial intelligence (AI) tools in academic writing. The journal permits responsible AI-assisted language editing but strictly prohibits unethical AI-related practices.

This policy complements the Plagiarism Screening Policy and Publication Ethics & Malpractice Statement.

Permissible AI Use

Authors may use AI tools for:

• language editing;
• grammar improvement;
• formatting assistance;
• summarization for internal drafting.

Authors remain fully responsible for all content submitted.

Prohibited AI Practices

The following are not permitted:

• submitting AI-generated content without critical human oversight;
• fabricating data, references, or citations using AI tools;
• generating fictitious peer review suggestions;
• producing fabricated experimental results.

AI-generated text that reproduces copyrighted or previously published material without attribution constitutes plagiarism.

Disclosure Requirement

If AI tools were used in drafting or editing the manuscript, authors must disclose this in the manuscript (e.g., in an Acknowledgment section).

Example statement:

“AI-based language assistance tools were used for grammar refinement. All intellectual content and analysis are the sole responsibility of the authors.”

AI and Similarity Screening

AI-generated content is subject to the same plagiarism detection screening as all submissions.

Similarity percentages exclude references and properly formatted quotations, but AI-generated unattributed overlap will be treated as plagiarism.

Editorial Authority

Editors reserve the right to request:

• clarification regarding AI use;
• raw data;
• additional documentation of authorship and originality.

Failure to comply may result in rejection.

DATA AVAILABILITY POLICY

Finance, Accounting and Business Analysis (FABA) supports transparency, reproducibility, and responsible research practices. Authors are encouraged to make underlying research data available where ethically and legally permissible.

This policy operates in alignment with the journal’s Publication Ethics & Malpractice Statement.

Scope

This policy applies to empirical research, quantitative studies, qualitative datasets, computational models, surveys, and experimental results where underlying data are necessary to validate findings.

Data Availability Statement (Mandatory)

All submitted manuscripts must include a Data Availability Statement placed before the References section.

The statement must specify one of the following:

• Data openly available in a public repository (include link and DOI).
• Data available upon reasonable request from the corresponding author.
• Data not publicly available due to legal, ethical, or confidentiality restrictions (with explanation).
• No new data were generated or analyzed in this study.

Recommended Repositories

Authors are encouraged to deposit data in:

• institutional repositories;
• recognized subject repositories;
• general repositories (e.g., Zenodo, Figshare, OSF).

Deposited datasets should include sufficient metadata and documentation.

Ethical and Legal Considerations

Where human subjects are involved, data sharing must comply with:

• informed consent agreements;
• institutional review board (IRB) approvals;
• national and international data protection regulations.

Sensitive or confidential data must not be disclosed improperly.

Editorial Verification

Editors may request:

• confirmation of data accessibility;
• anonymized datasets;
• documentation supporting ethical compliance.

Failure to provide a Data Availability Statement may delay review.

Post-Publication Integrity

If concerns arise regarding the validity of published data, authors may be required to provide raw data for verification.

AI IN PEER REVIEW POLICY

Finance, Accounting and Business Analysis (FABA) acknowledges the evolving role of artificial intelligence in scholarly workflows. The journal establishes clear rules regarding AI use within the peer review process to protect confidentiality and academic integrity.

This policy complements the journal’s AI & Plagiarism Interaction Policy and Publication Ethics framework.

Confidentiality Principle

Manuscripts under review are confidential documents. Reviewers must not upload or share manuscript content with external AI tools or platforms that store, train on, or retain submitted data.

Uploading confidential manuscripts to public AI systems may constitute a breach of confidentiality.

Limited AI Assistance

Reviewers may use AI tools only for:

• grammar refinement of their review report;
• improving clarity of their written feedback.

AI tools must not be used to:

• generate substantive review content;
• analyze confidential data;
• summarize manuscripts for automated evaluation;
• replace independent expert judgment.

Responsibility of Reviewers

Reviewers remain fully responsible for:

• the accuracy and originality of their reports;
• maintaining confidentiality;
• ensuring that AI tools do not compromise manuscript privacy.

Editors and AI Use

Editors must not rely solely on AI-generated assessments when making editorial decisions. AI tools may assist in administrative checks (e.g., similarity detection), but editorial judgment must remain human-led.