Authors Are Not Asking Journals To Move Faster

A useful survey result can be uncomfortable because it removes an excuse. Journal leaders often talk as if authors mainly want one thing from the publishing process: speed. Faster triage, faster reviewer invitations, faster first decisions, faster production, faster indexing. The pressure is real, but a new community survey from Astronomy & Astrophysics suggests that speed is not the whole mandate authors are giving journals.

On June 29, 2026, A&A reported results from its community survey on trends and challenges in scientific publishing. Nearly 3,000 astronomers from 69 countries responded, and the published editorial says the survey explored journal selection, peer review, open access, research assessment, and artificial intelligence: https://www.aanda.org/news/3206-a-a-community-survey-reveals-strong-support-for-quality-fairness-and-human-oversight-in-scientific-publishing. The arXiv record for the editorial gives the more precise survey count: 2,944 responses from 69 countries after distribution to 28,787 A&A authors and co-authors: https://arxiv.org/abs/2606.27447.

The headline is not subtle. The A&A summary says researchers value credibility over speed, fairness over profit, and human judgment over automation. That should land in publisher planning meetings as an operations signal, not a slogan. If a journal keeps optimizing the visible dashboard while authors are worried about fairness, expertise, cost, and accountability, the journal may get faster and still lose trust.

The Survey Is A Governance Signal

A single discipline survey should not be treated as a referendum for all scholarly publishing. Astronomy has its own infrastructure, preprint culture, funding patterns, and community governance. Still, the pattern is useful because it is coherent. Respondents put journal quality and reputation ahead of publication costs, impact metrics, and publication speed. They raised peer-review concerns around reviewer expertise, fairness, and the quality of feedback. They supported open access as a principle while objecting to author-pays models when APCs block the preferred venue. They welcomed AI for assistance, not for replacing editorial judgment.

That combination matters because it cuts across the usual internal silos. Editorial teams own peer-review quality. Finance teams own pricing and waiver models. Product and platform teams own workflow instrumentation. Production teams own publication outputs. Policy teams own AI rules and transparency language. Authors experience all of those decisions as one journal.

Speed Is Not The Top Complaint

The survey does not say publication speed is irrelevant. Researchers still notice delays, and journals that let manuscripts sit without communication damage author confidence. The more interesting finding is that respondents were generally willing to accept moderate delays for more thorough, constructive, and expert evaluation. That should change how journal leaders read turnaround metrics.

A median time-to-first-decision number can hide a weak review process. A journal can reject quickly with thin triage notes, assign available reviewers instead of qualified reviewers, or accelerate decisions by leaning too heavily on desk rejection. Those changes may improve a dashboard while leaving authors with the sense that their work was not evaluated fairly.

The better operating question is not only "how fast did we decide?" It is "what kind of decision did the author receive?" Editors need to know whether reviewer expertise matched the manuscript, whether reports were substantive, whether conflicts and bias risks were handled, whether appeals reveal recurring fairness concerns, and whether author communication explained the decision clearly enough to be trusted.

Cost Is Now Part Of Journal Reputation

The A&A report is also blunt about APC anxiety. The news summary says many respondents reported that high article processing charges had prevented them from publishing in preferred journals, and that researchers preferred collective funding approaches such as governmental support and institutional agreements over author-pays models. A&A connects that finding to its own Subscribe to Open model, which it presents as an alternative to APC-funded publishing.

For journal managers, this means price cannot be separated from editorial reputation. A journal may have a respected board and rigorous peer review, but if the payment route excludes unfunded authors, early-career researchers, or researchers outside well-supported institutions, the quality claim begins to look incomplete. Waiver policies help only if they are easy to find, consistently applied, and not socially costly for authors to request.

This is an area where boards should ask for evidence, not anecdotes. How often are APCs waived or discounted? Which countries, institutions, career stages, or article types are affected? How many accepted papers stall because of payment? How often do authors decline transfer or publication because the cost path is unclear? If those answers are not available, the journal is making equity claims without operational visibility.

AI Gets A Narrower Mandate From Researchers

The A&A respondents took a practical position on AI: useful for language, administrative work, technical support, and some editorial assistance, but not acceptable as a substitute for human peer-review or editorial decision-making. That aligns with the direction of wider guidance. The 2026 ICMJE recommendations say journals should have AI policies, should make editors, reviewers, and authors aware of them, and should not upload submitted manuscripts into AI systems where confidentiality cannot be assured without permission: https://www.icmje.org/icmje-recommendations.pdf.

The operational distinction is important. Authors may accept AI that checks submission completeness, flags missing references, improves accessibility of language, or helps editors manage workloads. They are far less likely to accept a process where an opaque system influences reviewer selection, novelty assessment, rejection wording, or integrity judgment without disclosure and human accountability. Journals that use AI quietly may believe they are avoiding controversy. They may instead be creating a future trust problem.

What Journal Boards Should Measure

• Review quality, not only review duration: track substantive feedback, reviewer expertise, editor overrides, and appeal themes.
• Fairness signals: monitor conflicts, repeated reviewer-author proximity, geographic or institutional concentration, and complaint patterns.
• Cost friction: measure waived APCs, delayed invoices, withdrawn accepted papers, and author support requests by region and article type.
• AI transparency: list which workflow steps use AI assistance, what data is processed, who reviews the output, and what authors are told.
• Community feedback: run periodic author and reviewer surveys, then connect results to board decisions rather than leaving them in a slide deck.

None of these measures replaces editorial judgment. They make judgment easier to govern. A board that sees only volume, acceptance rate, impact metrics, and turnaround time is looking at a narrow version of journal health. The A&A survey is a reminder that authors judge journals through a wider lens.

Where Publicator Fits In This Work

This is the kind of governance problem where platform design matters. Publicator can support AI-assisted submission checks, reviewer matching and governance, role-scoped access, audit trails, DOI and Crossref-ready metadata, JATS/PDF/HTML production, journal hosting, analytics, SSO, integrations, data residency choices, and multi-journal management in one workflow. The point is not to make every journal more automated. It is to give editors and publishers enough structured evidence to decide where automation helps, where human judgment must stay visible, and where author trust is being earned or strained.

Practical Takeaway For Journal Leaders

The practical takeaway is to stop treating speed as the master metric. Keep measuring it, but place it beside review quality, fairness, cost accessibility, AI transparency, and author confidence. A journal that can explain why a decision took time, who evaluated the work, how conflicts were handled, what costs applied, and where AI did or did not assist will be better positioned than a journal that simply moves the manuscript faster.

Authors are not asking journals to become slower for its own sake. They are asking journals to protect the things that make publication worth waiting for: expert evaluation, fair treatment, accountable use of technology, and a business model that does not turn access to publication into a proxy for institutional wealth. Journal leaders should treat that as a design brief for the next operating cycle.