Universities rely on metrics, whether that is to allocate resources, assess performance or to demonstrate accountability, metrics are used to provide checks and balances, and to also provide a some feeling of being in control.

In large and complex institutions, where activity spans hundreds, even thousands, of staff, across multiple disciplines, standardised measurement provides structure. Without structured information, governance risk increases and institutional judgement becomes fragile.

Yet metrics do not merely measure behaviour.
Over time, they shape it.

This distinction matters. In higher education, performance indicators are rarely neutral. They act as signals, and signals influence decisions.

To understand modern universities, we must therefore examine not only what metrics report, but what they encourage.

Let us consider this through two complementary lenses.

The Governance Lens

From a governance perspective, indicators simplify complexity. Research income can be tracked, publication volume can be counted, citation performance can be benchmarked, and doctoral completions can be reported. Dashboards allow governing boards and executive teams to observe trends, compare performance and set targets.

There is nothing inherently problematic with this. Universities require accountability. They must be able to evidence performance to regulators, funders and stakeholders.

The difficulty arises when metrics begin to substitute for judgement.

Indicators are proxies. They approximate quality, impact or contribution, but they do not fully capture them. When embedded within performance frameworks, however, they acquire influence. Resource allocation may follow them. Promotion criteria may reflect them. Institutional reputation may be interpreted through them. For example, if internal funding models reward publication volume, behaviour will naturally steer in that direction.

Complex scholarly activity can be compressed into a narrow set of measurable outputs. Institutions may converge around similar optimisation strategies if they are competing on comparable external benchmarks. Improvement in one visible metric can displace effort from activity that is harder to measure, but equally valuable.

These outcomes are rarely the result of deliberate policy. They are the consequence of incentive design.

Through a governance lens, the central question is therefore not whether to use metrics. It is how to design and interpret them so that they inform judgement without distorting behaviour.

The Scholar’s Lens

The same system looks different from the perspective of an individual academic.

Academic careers are increasingly shaped by measurable outputs. Hiring panels review publication records. Promotion committees examine authorship position and citation patterns. Funding bodies assess track record against quantifiable indicators. Institutional benchmarks provide internal comparison points.

Researchers respond rationally to these signals. They make choices about where to publish, the visibility of collaborators, and the balance between quality and quantity. Adaptation to this landscape is not cynical. It is strategic.

The challenge arises when adaptation becomes over-optimisation. Short-term gains in measurable performance can obscure longer-term questions about coherence, depth and intellectual identity. A publication list may expand while a research programme fragments. Collaboration may broaden while intellectual focus weakens.

When metrics become dominant drivers, a research career can be shaped more by evaluation criteria than by scholarly intent.

From a scholar’s perspective, more strategic questions begin to matter. What kind of programme of work am I building? How will the pattern of my publications be interpreted over time? Do my collaborations reinforce depth within a field, or simply serve to optimise the metrics? Am I responding thoughtfully to performance signals, or merely reacting to them?

These are not questions about gaming a system. They are questions about understanding how evaluation systems function, and how scholarly trajectories are read by others.

Scholars need to be wary of chasing short-term objectives rather than thinking long term about how decisions made now will shape their future career.

The Feedback Loop Between Governance and Scholarship

There is a feedback loop between institutional governance and individual behaviour.

Institutions design performance frameworks. Scholars respond to those frameworks. Aggregated behaviour reshapes institutional data. That data then informs future governance decisions.

If publication volume is rewarded, volume increases. If volume increases, dashboards show growth. If dashboards show growth, existing frameworks appear validated. The system stabilises around its chosen indicators.

This loop can produce positive outcomes when incentives reinforce intellectual ambition and coherent programme development.

However, it can also produce fragility and unintended consequences, if the metrics are not well designed and/or not aligned with institutional goals.

Rapid expansion of measurable outputs may enhance external perception in the short term. However, if underlying research lacks depth or coherence, longer-term reputation can suffer. Citation performance may lag behind publication growth. Recruitment and promotion decisions may be influenced by headline numbers rather than sustained contribution.

The issue is not the existence of metrics, but the subtle ways in which they shape collective behaviour.

The Amplifying Role of Artificial Intelligence

Artificial intelligence introduces a further dimension to this dynamic. AI tools increase productivity, assist with drafting and analysis, and lower certain barriers to output. As output becomes easier to generate, publication volume may rise.

Evaluation frameworks built primarily around scale may increasingly struggle to distinguish quality from quantity. Governance systems may appear to record improvement, while underlying quality becomes more difficult to interpret.

Technology does not remove incentive effects. It intensifies them.

Alignment as Deliberate Design

The purpose of examining metrics through both lenses is not to reject measurement. Universities require accountability and scholars require recognition. The challenge is alignment.

When governance frameworks reinforce genuine scholarly ambition, recognise depth as well as scale, and support coherent programme development, institutions and individuals strengthen one another.

A word of caution, institutions rarely become metric-driven overnight. They drift there, gradually. Therefore, the metrics need regular review and updates to ensure that they remain aligned with the strategic goals of the institution.

Scholars do not become reactive overnight. They respond to signals and the university leaders should spend some time considering the consequences (unintended, or not) of the metrics they implement.

Recognising these dynamics early allows for better design, both of governance frameworks and of academic careers.

Questions for Leaders

Do your dashboards inform judgement, or are they gradually replacing it?

What behaviours are your incentive systems rewarding, even unintentionally?

Are you measuring what matters, or measuring what is easiest to measure?

Questions for Scholars

Is your publication pattern building a coherent programme of work?

If your CV were assessed ten years from now, what intellectual narrative would it reveal?

Are you responding strategically to evaluation signals, or reacting tactically to short-term targets?

Continuing the Conversation

If your institution is reviewing its performance frameworks, or if you are reflecting on how evaluation systems shape academic careers, these are conversations worth having early. I welcome dialogue with leaders and scholars who are thinking about incentive design and long-term alignment.