Measuring Change: Readiness and Adoption

A change is not finished when a system goes live or a new structure takes effect. It is finished when people work the new way and the expected benefits appear. Measurement tells the change team how close it is to that point and where to act.

Measures serve three purposes:

• Steering: showing which groups need more support while there is still time to give it.
• Deciding: informing go/no-go decisions before a launch.
• Accounting: showing the sponsor whether the change is delivering what it promised.

Without measures, progress is judged by activity, such as training held or emails sent.

Readiness and adoption metrics therefore shift the conversation from what the project did to what people are actually doing.

Readiness describes whether people are prepared to change. Achilles Armenakis and colleagues described it as the beliefs, attitudes and intentions people hold about whether a change is needed and whether the organization can carry it out. Bryan Weiner later framed organizational readiness as shared commitment to the change and shared confidence in the ability to deliver it.

Adoption describes whether people actually use the new way of working, and how well.

Readiness and adoption measures answer different questions, so a change needs both.

A leading indicator gives early warning of a future result. A lagging indicator confirms a result after the fact.

Measures in a change form a chain:

1. Activity: briefings held, training delivered.
2. Readiness: understanding, confidence, intention to use the new way.
3. Adoption: use, proficiency, the old way retired.
4. Benefits: the business outcomes the change was meant to deliver.

Each link leads the one after it and lags the one before. Benefits matter most but arrive last, often too late to steer by; leading indicators are less conclusive but arrive in time to act.

Readiness and adoption metrics are most useful when the team tracks several links of the chain, not just the last one.

Larchfield Logistics is replacing paper pick lists with a handheld scanning app in its warehouses.

Readiness, before go-live:

• share of supervisors briefed and able to explain why the change is happening;
• training completion and practice-session results by shift;
• a short pulse survey on understanding and confidence;

Adoption, after go-live:

• share of picks recorded in the app rather than on paper;
• requests for paper lists or other workarounds;
• scanning error rates and help-desk tickets by type;

Benefits, later: picking accuracy and on-time dispatch.

Walking the floor and talking to pickers explain what the figures mean.

Readiness and adoption metrics work best as a mix of data and conversation.

• Correlation is not causation. If adoption and customer satisfaction both rise, seasonality, staffing or other changes may explain the pattern.
• Measures can be gamed. When a number becomes a target, people improve the number rather than the behavior. Donald Campbell warned that the more a quantitative indicator is used for social decision-making, the more it is subject to corruption pressures. A similar idea is known as Goodhart's law.
• Easy is not the same as important. Logins and attendance are easy to count; proficient use and changed behavior matter more.
• Averages hide groups. An overall adoption figure can conceal a team that has not started.

Change readiness and adoption metrics are therefore evidence to interpret, not verdicts to report.

• Start from the objectives and the behaviors the change requires, not from available data.
• Keep the set small, and cover readiness, adoption and benefits.
• Take a baseline before the change, or there is nothing to compare against.
• Break results down by the groups identified in the impact assessment.
• Agree in advance what action each result will trigger, and who owns it.

Validated instruments exist, such as the readiness scale by Daniel Holt and colleagues, covering appropriateness, management support, change-specific efficacy and personal valence. Simple, well-chosen questions often serve a project better than long surveys.

Good change readiness and adoption metrics are few, linked to decisions and read alongside what people say.