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Why Performance Metrics Undermine Adoption

By Alexandra Salamis M.A., M.S.W., ICF, EMCC, CEC, CCMP Article C6.2

Performance metrics are designed to focus effort. They clarify priorities, enable comparison, and create accountability. In stable operating environments, they are indispensable. During change, however, the same metrics often work against the very behaviours organisations are trying to introduce.1

This is not because metrics are wrong. It is because they continue to reward yesterday’s logic while tomorrow’s way of working is still fragile.

Metrics don’t just measure behaviour. They shape it.

Organisations often treat metrics as neutral indicators. In practice, metrics are behavioural signals. They tell people what matters, what is safe, and what will be noticed.2 When metrics remain unchanged during transformation, they anchor behaviour to the old operating model. People quickly learn that:

  • experimentation carries downside but little upside
  • learning curves are penalised as underperformance
  • stabilisation work is invisible
  • short-term results outweigh long-term value

Under pressure, people respond rationally.

The collision between adoption and performance

Most change initiatives require people to work differently before they work better.

There is a learning period. Productivity dips. Errors surface. Work slows as judgement is recalibrated. When performance metrics do not account for this transition, they create an impossible trade-off. People must choose between:

  • meeting targets and following the new process
  • protecting results and investing in adoption
  • surfacing issues and avoiding scrutiny

In these moments, metrics almost always win.3

Why “temporary tolerance” rarely works

Many organisations attempt to address this by offering informal tolerance. Leaders reassure teams that dips in performance are expected. They encourage patience. They promise that metrics will be reviewed later. These assurances help at the margins, but they rarely override formal systems. When targets are still tracked, reported, and escalated, people behave accordingly. Informal permission is no match for formal consequence. This is why adoption often lags even when leaders are supportive and understanding.

How metrics create unintended consequences

When metrics are misaligned with change goals, unintended consequences emerge quickly. Organisations see:

  • selective use of new processes
  • reversion to legacy tools under pressure
  • data manipulation to protect targets
  • reluctance to surface early problems
  • uneven adoption across teams

None of this requires bad intent. It is the predictable result of conflicting signals. Metrics resolve ambiguity faster than any communication.

Why metrics are hard to change midstream

Performance systems are deeply embedded. They are tied to compensation, reporting cycles, and external commitments. Changing them can feel risky, complex, or politically sensitive. As a result, organisations often defer metric changes until after adoption is “complete.” This sequencing is backwards. By the time metrics are adjusted, behaviour has already settled into patterns that are difficult to undo.

Metrics as an early warning system

Well-designed metrics can support change rather than undermine it. They can: - distinguish between learning-related variance and true underperformance

  • make stabilisation work visible
  • reward issue surfacing rather than suppression
  • signal which behaviours are non-negotiable during transition When metrics evolve alongside change, they reduce uncertainty rather than amplify it.

Reframing performance management during change

Performance management during change is not about lowering standards. It is about aligning measures with what success actually requires at different stages.4 This means being explicit about:

  • what good performance looks like during transition
  • which metrics will temporarily carry less weight
  • how learning and stabilisation will be recognised
  • when expectations will tighten again

Without this clarity, people default to protecting themselves.

A more honest view of adoption risk

Adoption does not fail because people resist change. It fails because performance systems punish the behaviours change requires. Until metrics are treated as part of change design rather than background infrastructure, organisations will continue to see the same patterns repeat. This is one way of thinking about why change succeeds or fails. Other pieces go deeper into how incentives and unintended consequences shape behaviour during transformation.

  1. Hackman, J. R., & Oldham, G. R. (1976). “Motivation Through the Design of Work: Test of a Theory.” Organizational Behavior and Human Performance, 16(2), 250–279. https://doi.org/10.1016/0030-5073(76)90016-7. Hackman and Oldham demonstrate that task feedback — direct information about the results of work — is a core determinant of task performance and motivation. During change, the feedback that performance metrics provide is misaligned with the new processes: people doing the right thing receive neutral or negative performance feedback because the metrics still measure the old way of working. Metrics effectively teach people that the new behaviours are underperforming even when they are correct, actively working against the adoption the change requires. ↩︎

  2. Latham, G. P., & Locke, E. A. (1979). “Goal Setting — A Motivational Technique That Works.” Organizational Dynamics, 8(2), 68–80. https://doi.org/10.1016/0090-6461(79)90032-9. Latham and Locke’s goal-setting theory establishes that specific, measurable goals direct attention, effort, and persistence toward behaviours that contribute to the goal — and away from behaviours that do not. Applied to performance metrics in change contexts: metrics are not passive measures; they are active attentional signals that tell people what matters, what will be noticed, and what is safe to prioritise. When the metrics remain calibrated to the pre-change operating model, they direct attention away from adoption behaviours regardless of what leadership communication says is important. ↩︎

  3. Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. Kahneman’s research on loss aversion establishes that losses loom larger than equivalent gains — people weight the risk of a bad outcome roughly twice as heavily as the prospect of an equivalent good one. Under time pressure during change, this asymmetry becomes acute: the near-certain cost of missing a performance target outweighs the uncertain benefit of adoption compliance. Metrics win not because people are indifferent to change, but because the loss associated with metric failure is more immediate and more salient than the gain associated with doing the new thing correctly. ↩︎

  4. Simons, R. (1995). Levers of Control: How Managers Use Innovative Control Systems to Drive Strategic Renewal. Harvard Business School Press. Simons argues that diagnostic control systems — which monitor critical performance variables — must be calibrated to the strategic variables that matter now, not those that mattered previously. During transformation, the strategic variables change: adoption fidelity, stabilisation progress, and learning-curve management become critical. When diagnostic systems continue measuring pre-change variables instead, they create governance misalignment — reporting accurately on the wrong things while the variables that actually determine transformation success go unmeasured. Aligning performance measures to what success requires at each stage is not a concession; it is a governance requirement. ↩︎

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