Peer Review Policy

The International Journal of Business Operations and Management Studies enforces a rigorous, independent peer-review architecture engineered to uphold absolute scholarly objectivity, methodological precision, and empirical validity. By maintaining a stringently managed double-blind system, the journal eliminates implicit bias, ensuring that manuscript selection is governed purely by intellectual merit, research integrity, and domain contribution.

1. Core Review Model: Double-Blind Evaluation

To ensure a completely impartial evaluation, the journal utilizes a double-blind peer review process.

  • Anonymity of Authors: Reviewers do not receive any information regarding the identity, institutional affiliation, country of origin, or gender of the authors.

  • Anonymity of Reviewers: Authors do not receive any identifying information regarding the referees assigned to evaluate their work.

To maintain this system, authors must strictly anonymize their main manuscript files prior to submission, removing direct self-citations, institutional ethics committee names, and document metadata properties.

2. Structural Steps in the Review Sequence

The editorial workflow follows a linear, highly trackable path managed entirely through the journal's automated electronic editorial platform. This ensures transparency, clear timeline management, and definitive accountability at every milestone.

       [Manuscript Submission]
                 │
                 ▼
     [Initial Editorial Screening] ──► (Fails Compliance/Scope) ──► [Desk Rejection]
                 │
          (Passes Screen)
                 │
                 ▼
   [Double-Blind Peer Evaluation] ──► (Minimum 2 External Expert Reports)
                 │
                 ▼
    [Editorial Board Assessment]
                 │
                 ▼
     [Final Executive Decision] ──► (Accept / Revision / Rejection)

2.1 Initial Editorial Screening

Every submitted manuscript undergoes a mandatory administrative pre-check by the managing editor. This phase includes automated plagiarism screening using Crossref Similarity Check (powered by iThenticate) and a technical compliance check to ensure adherence to formatting rules, British English language standards, and full anonymization.

Manuscripts that clear the pre-check are forwarded to the Editor-in-Chief or a designated Associate Editor for an initial editorial evaluation. Papers that lack sufficient academic depth, violate core ethical standards, or display fragmented methodologies are desk-rejected at this stage without entering external review.

2.2 External Peer Evaluation

Manuscripts passing the initial screening are assigned to an Associate Editor, who secures a minimum of two independent external reviewers. These reviewers are selected based on their verified expertise within the specific methodological domain (e.g., empirical operations management, stochastic modeling, or supply chain logistics) and their active publication record in international scholarly literature.

Reviewers are allocated a standard period to submit their detailed, analytical reports. If the independent reports yield conflicting conclusions, the handling editor will secure a third independent evaluation to ensure an equitable consensus.

2.3 Synthesis and Executive Decision

Once all requested external reports are submitted, the Associate Editor synthesizes the assessments, scrutinizes the reviewer commentary for bias or non-constructive critiques, and submits a formal publishing recommendation. The final administrative decision is rendered exclusively by the Editor-in-Chief.

3. Editorial Decision Framework

The final decision communicated to the corresponding author falls under one of four distinct publishing pathways:

Decision Status Operational Criteria and Next Steps
Accept As Is The manuscript is deemed conceptually complete, empirically rigorous, and methodologically sound. It is forwarded to the production department for XML typesetting, final copyediting, and digital object identifier (DOI) assignment.
Minor Revision The core architecture of the paper is sound, but specific refinements are required. Authors must address localized textual ambiguities, clarify specific data presentations, or update peripheral literature citations. These revisions are typically verified internally by the handling editor.
Major Revision The manuscript displays significant potential, but requires fundamental structural, theoretical, or analytical overhauls. This may involve re-estimating statistical models, clarifying variables, or expanding the discussion section. Resubmissions under this category are routinely routed back to the original external reviewers for a second round of evaluation.
Reject The manuscript exhibits irreparable flaws in its research design, insufficient contribution to the field of operations and management studies, or critical ethical non-compliance. Rejection decisions are final and not subject to appeal unless a demonstrable procedural error occurred during review.

4. Ethical Obligations and Integrity Safeguards

4.1 Conflict of Interest Recusal

Reviewers and editors must recuse themselves from the evaluation process immediately if they detect a conflict of interest. A conflict arises if an evaluator shares a current or recent institutional affiliation with the authors, has co-authored research with them within the past three years, or possesses direct commercial or financial stakes in the outcome of the research.

4.2 Confidentiality of the Scholarly Record

All manuscripts under review are treated as privileged, legally confidential intellectual property. Editors, administrative staff, and external reviewers are strictly prohibited from sharing, discussing, copying, or distributing any text, data sets, or underlying arguments contained within an unpublished submission.

4.3 Generative AI Restrictions

In compliance with international publishing security protocols, reviewers are forbidden from inputting any portion of a submitted manuscript, data file, or graphic into third-party generative Artificial Intelligence (AI) tools or Large Language Models (LLMs). Uploading proprietary text into public or private AI platforms breaks confidentiality mandates, violates author copyright, and breaches data security laws.