Plagiarism and AI Policy

Plagiarism and AI Policy

Corporate Governance & Audit Archive Review (CGAAR)

1. Introduction

The Corporate Governance & Audit Archive Review (CGAAR) is committed to maintaining the highest standards of academic integrity and ethical research conduct. As a scholarly journal focusing on corporate governance, auditing practices, financial accountability, risk assessment, regulatory compliance, and organizational transparency, CGAAR requires authors to adhere to rigorous ethical standards throughout the research and publication process.

This policy outlines CGAAR’s position on plagiarism, self-plagiarism, and the responsible use of Artificial Intelligence (AI) tools. All authors submitting manuscripts to CGAAR must comply with the guidelines described herein.

2. Plagiarism Policy

2.1 Definition of Plagiarism

Plagiarism is the appropriation of another person’s ideas, words, data, or analytical work without proper acknowledgment. Within corporate governance and auditing scholarship, plagiarism includes, but is not limited to:

  • Copying text verbatim from articles, books, reports, or online materials without citation.

  • Paraphrasing another scholar’s theories, governance models, audit frameworks, or interpretations without attribution.

  • Using previously published study designs, risk assessment models, compliance matrices, or audit methodologies without acknowledgment.

  • Presenting others’ corporate data, financial reports, audit findings, or case studies as original.

  • Replicating figures, tables, diagrams, organizational charts, or conceptual frameworks without permission or citation.

  • Utilizing unpublished dissertations, working papers, review drafts, or internal documents without acknowledgment.

Plagiarism, whether deliberate or unintentional, is considered a serious ethical violation.

2.2 Types of Plagiarism Recognized by CGAAR

a. Direct Plagiarism

Copying text word-for-word from any source without quotation marks or citation.

b. Mosaic / Patchwork Plagiarism

Rewriting content by mixing, rearranging, or slightly altering the language of the original authors.

c. Paraphrasing Plagiarism

Rephrasing another researcher’s ideas or analysis without proper attribution.

d. Data or Evidence Plagiarism

Using corporate financial statements, audit samples, governance assessment data, or confidential organizational records without attribution.

e. Structural Plagiarism

Replicating the structure, methodology, or reasoning of existing studies without acknowledgment.

f. Source Plagiarism

Failing to cite primary sources and instead using uncredited secondary interpretations.

2.3 Self-Plagiarism

Self-plagiarism refers to reusing one’s previously published content without disclosure. This includes:

  • Reusing literature review sections, theoretical discussions, or conceptual models.

  • Using previously published audit case studies or governance analyses without citation.

  • Republishing the same dataset, tables, or analytical results.

  • Submitting substantially similar manuscripts to multiple journals.

Self-plagiarism is only permissible when:

  • Reused content is minimal and appropriately referenced.

  • The manuscript offers substantial new contributions.

  • Prior publications are clearly disclosed during submission.

Duplicate or redundant publication is strictly prohibited.

2.4 Plagiarism Detection and Similarity Assessment

All submitted manuscripts undergo plagiarism screening using professional similarity detection software.

  • A similarity index below 15% (excluding references, quotations, and routine methodological language) is generally acceptable.

  • High similarity in literature review, conceptual framework, analysis, or discussion is not tolerated.

  • Reproduction of tables, figures, or datasets without citation results in automatic rejection.

  • Authors may be asked to revise or justify any questionable overlaps.

The final decision regarding plagiarism lies solely with the editorial board.

2.5 Consequences of Plagiarism

If plagiarism is detected at any stage (submission, review, or post-publication), CGAAR may implement the following actions:

  • Immediate rejection of the manuscript.

  • Retraction of the published article with a public retraction statement.

  • Notification to the author’s institution, funding body, or professional organization.

  • Suspension or permanent ban on future submissions.

  • Reporting of misconduct to relevant regulatory or academic bodies.

These actions align with COPE (Committee on Publication Ethics) guidelines.

3. Artificial Intelligence (AI) Use Policy

3.1 Guiding Principle

AI tools may be used to support, but not replace, the scholarly work of authors. Intellectual contributions, analytical reasoning, interpretation, and academic conclusions must be generated solely by the human authors.

CGAAR values academic integrity and requires transparent, responsible use of AI during manuscript preparation.

3.2 Acceptable Uses of AI Tools

The following uses of AI are permitted, provided they are properly disclosed:

  • Language editing: grammar, spelling, and clarity improvement.

  • Technical editing: formatting citations, references, headings, and tables.

  • Data visualization: generating charts, graphs, and figures using author-supplied datasets.

  • Statistical support: assisting with computations, provided results are manually verified.

  • Qualitative coding assistance: AI may assist in identifying patterns, but authors must confirm and interpret findings independently.

AI must not influence or generate conceptual, theoretical, or analytical content.

3.3 Prohibited Uses of AI Tools

The following uses are strictly forbidden:

  • Generating literature reviews, conceptual frameworks, hypotheses, or theoretical discussions.

  • Drafting sections of the manuscript, including introduction, analysis, discussion, or conclusion.

  • Producing fabricated data, audit findings, governance assessments, or financial records.

  • Creating fake references, citations, or unverifiable sources.

  • Interpreting research results, making governance recommendations, or drawing audit conclusions.

  • Analyzing sensitive organizational or audit data in ways that compromise confidentiality.

Any AI use that replaces human scholarly contribution is unethical and unacceptable.

3.4 AI Disclosure Requirement

All authors must include a separate AI Use Disclosure Statement in their manuscript, detailing:

  • The specific AI tools used.

  • The purpose of AI assistance.

  • Assurance that AI did not generate or influence the research ideas, analysis, or conclusions.

Sample Disclosure:
“AI-assisted tools were used solely for language editing and formatting. All conceptualization, data analysis, and scholarly interpretations were completed independently by the author(s).”

Non-disclosure of AI use may result in rejection or retraction.

3.5 Author Responsibility

Authors are fully responsible for:

  • The accuracy, validity, and reliability of AI-assisted content.

  • Ensuring no AI-generated content compromises academic rigor.

  • Verifying all numerical, analytical, and governance-related outputs.

  • Maintaining confidentiality of sensitive data, audit findings, and corporate information.

AI tools cannot be listed as authors or contributors.

4. Editorial Responsibilities

The editorial board of CGAAR is responsible for:

  • Evaluating manuscripts for plagiarism and improper AI use.

  • Ensuring adherence to global research ethics standards (COPE, ICMJE, APA).

  • Requesting raw data, audit papers, governance permissions, or supplementary documents when necessary.

  • Handling ethical concerns impartially and transparently.

  • Updating policies according to technological and ethical developments.

5. Revision and Updating of Policy

CGAAR reviews and updates this policy regularly to address:

  • Advances in AI technologies.

  • Emerging ethical challenges in corporate governance and auditing research.

  • Global best practices in academic publishing.

Revisions will be clearly communicated through the journal’s official platform.