Peer Review Capacity Starts With the Match

Why better reviewer targeting may matter more than sending more invitations 

Ask any editorial team what slows peer review down and the answer usually points downstream: too few reviewers, too much back and forth, too little time. But a figure from the 2025 Peer Review Week panel, Peer Review in the Age of AI: From Fatigue to Integrity, points somewhere much earlier in the process. 

At that panel, Sven Fund, Managing Director of ReviewerCredits, said that roughly three out of four review invitations are declined on average. More striking was his explanation of why. The invitation often isn’t a good match. As he put it, “This is not for me. I have no clue what I’m being asked to review here.” 

That diagnosis is consistent with other evidence. In a 2018 Publons Global Reviewer Survey, 71 percent of respondents identified invitations to review papers outside their area of expertise as a reason they decline. Workload matters too: 42 percent said they decline because they are too busy. The two figures point to an important distinction. Some capacity is lost because the right expertise isn’t matched to the right manuscript, and some is lost because reviewers simply don’t have the time. The first problem is not necessarily easier, but it is more directly within a publisher’s control. 

The industry often talks about reviewer capacity as a supply problem: there aren’t enough people willing to review. But there is another dimension that is easier to overlook: access. An editor may have thousands of potential reviewers in a database and still struggle to identify the five people whose expertise genuinely fits a particular manuscript. 

That’s exactly the kind of gap this year’s Peer Review Week theme, “Peer Review Capacity: Volume, Speed, and Quality,” asks the industry to confront. Better matching cannot solve reviewer scarcity, but it can prevent publishers from wasting the scarce capacity they already have. 

A Decline Isn’t a Neutral Outcome 

It’s tempting to treat a declined invitation as a routine part of the process. An editor sends a few extra requests, and someone eventually says yes. At that rate, decline isn’t the exception; it is effectively the default. Every mismatched invitation means another editor cycle, another wait, and another unnecessary request competing for a reviewer’s limited attention. 

It’s also a wider risk than inconvenience. Poor matching doesn’t just create delay. It can create a quality and integrity problem when reviewers accept work outside their expertise. The same mismatch can therefore become both a capacity problem and a quality problem. 

The Problem Starts Before the Invitation Is Sent 

Molly Cranston, Editorial Content Manager at Taylor & Francis, made a related point on the same panel: the opportunity to address some of these problems sits at the top of the funnel, well before a reviewer ever opens an email. Her team has seen the clearest gains from AI-assisted support at pre-submission and desk assessment, catching manuscripts that are incomplete, out of scope, or simply not ready before they ever generate a reviewer request in the first place. 

Good reviewer targeting isn’t simply about finding someone who works in the same discipline. It means matching the manuscript to the reviewer’s specific methods, sub-specialty, and current research activity. Good reviewer matching requires answering three questions: 

Subject fit → Expertise fit → Recency 

1. Is this the right subject area? 
2. Does this person have the specific expertise or methodological knowledge required? 
3. Are they actively working in this area now?

That last question matters more than it sounds. Refreshing the reviewer database regularly isn’t just good hygiene; it’s a question of recency. Someone who published on a topic five years ago may look like a perfect match on paper while no longer working actively in that area. 

Historical relevance is not the same thing as current expertise. 

It’s also worth naming a quieter cause of the same problem: over-reliance on a small group of highly visible researchers. When editors default to familiar names under time pressure, those names get invited regardless of whether the manuscript actually sits in their current expertise, which is its own route to a decline. The problem isn’t always that editors don’t have enough reviewers. Sometimes they don’t have enough reviewers they already know and trust. 

Recent authors and citers can be useful discovery signals, not automatic reviewer recommendations, for widening the pool beyond the same shortlist every time. Technology can make this process faster and more scalable, but the underlying principle isn’t technological: targeting has to be treated as a first-class editorial step, not an afterthought squeezed in before an invitation goes out. 

Getting the Match Right Isn’t Enough 

Better matching solves only the first half of the problem. Once a reviewer accepts, the process itself still has to be easy enough to navigate. 

Sven’s sharpest point on the panel wasn’t about AI at all. It was about workflow. Reviewers, he argued, have to relearn peer review and submission processes for every single journal they work with, even within the same publisher, a problem that predates AI and has more to do with user experience than with technology. That’s part of why even technically capable submission systems can still create friction. “It’s not a technology challenge,” he said. “It’s a user challenge.” 

Good targeting gets the right person into the process. Standardized workflows make it easier for that person to stay in it. Better AI-assisted matching can narrow the gap, but if the underlying workflow is inconsistent from journal to journal, targeting improvements will only go so far. The fix has to work on both levels: smarter scope matching upstream, and a more consistent, predictable process once a reviewer is actually invited. 

The Bottom Line 

A high decline rate isn’t simply a reviewer engagement problem to be solved with better outreach. It is, at least in part, a targeting problem, and one that begins well before an invitation ever reaches a reviewer’s inbox. 

If the industry wants to build peer review capacity, the first question shouldn’t be “How do we get more reviewers to say yes?” It should be “Are we asking the right reviewers in the first place?” Peer-review capacity is not simply a headcount problem. It is a matching problem, a workflow problem, and ultimately a system-design problem.