In 1924, engineers at the Hawthorne Works factory in Illinois had a simple question:

“Does better lighting improve worker productivity?”

They increased the lighting. Productivity went up. Success!

Then they decreased the lighting. Productivity went up again.

They tried different lighting levels—bright, dim, even back to the original. Productivity kept increasing.

The lighting didn’t matter. What mattered was that workers knew they were being watched.

This phenomenon—where people change their behavior simply because they’re being observed—became known as The Hawthorne Effect.

The Original Study

From 1924 to 1932, researchers conducted experiments at Western Electric’s Hawthorne Works factory outside Chicago.

The initial goal was straightforward: optimize working conditions to increase productivity.

The Illumination Studies (1924-1927)

Researchers varied lighting levels for workers assembling electrical relays.

Expected result: Optimal lighting would yield maximum productivity.

Actual result: Productivity increased regardless of whether lighting went up, down, or stayed the same.

Even when lighting was reduced to moonlight levels, productivity improved.

The Relay Assembly Test Room (1927-1932)

Researchers took six female workers and placed them in a separate room to conduct more controlled experiments.

They varied:

  • Work hours
  • Break frequency
  • Break duration
  • Incentive pay
  • Free lunches
  • Work environment temperature

Every change increased productivity.

Then they removed all the improvements and returned to original conditions.

Productivity remained high.

The researchers were baffled.

The Real Discovery

Harvard researcher Elton Mayo analyzed the results and realized:

It wasn’t the changes that improved productivity—it was the attention.

The workers knew they were special. They were:

  • Selected for the study
  • Observed by researchers
  • Asked for feedback
  • Treated as valued participants

They felt important. So they worked harder.

This wasn’t about lighting, breaks, or incentives. It was about human psychology.

What Is the Hawthorne Effect?

The Hawthorne Effect is the phenomenon where individuals modify their behavior in response to being observed.

Key characteristics:

1. Observation Changes Behavior

People perform better when they know they’re being watched.

2. The Effect Is Temporary

Once observation ends, behavior often reverts.

3. Attention Matters More Than Conditions

Feeling valued and noticed drives behavior more than physical environment.

4. It’s Often Subconscious

People don’t deliberately try to impress—they unconsciously adjust their behavior.

Modern Examples

The Hawthorne Effect appears everywhere:

In Schools

Classroom Observations

  • Teachers perform better when administrators observe their classes
  • Students behave better when visitors are present
  • Performance often drops once observation ends

In Healthcare

Hand Hygiene Compliance

  • Doctors wash hands more frequently when auditors are present
  • Compliance drops when monitoring stops
  • Studies show 30-50% improvement during observation periods

In Workplaces

Productivity Monitoring

  • Employees work faster when managers are watching
  • Call center workers handle calls more professionally when monitored
  • Remote workers increase activity when tracking software is enabled

In Research

Clinical Trials

  • Patients report better outcomes when they know they’re being studied
  • Placebo effect is partially Hawthorne effect
  • Self-reported data is often skewed by observation awareness

In Tech and Software

The Hawthorne Effect is rampant in software development:

Code Metrics

Lines of Code Tracking

Team: "Management is tracking LOC as a productivity metric."
Result: Developers write more verbose code, add unnecessary comments
Productivity metric becomes meaningless

Standups and Status Updates

Daily Accountability

Manager starts attending standups regularly
Team suddenly has more "progress" to report
Actual productivity may not increase, but visibility does

Pull Request Reviews

Review Activity Monitoring

Leadership starts tracking code review metrics
Developers approve PRs faster, but less thoroughly
Metric improves, quality decreases

Monitoring and Analytics

User Behavior Tracking

Users know they're being tracked
They behave differently than they would naturally
A/B tests get skewed results

Performance Reviews

The Sprint Before Review Season

Developers know reviews are coming
Suddenly everyone's committing more code
Activity spikes, then drops after reviews

The Dark Side: Gaming the System

The Hawthorne Effect often leads to metric manipulation rather than genuine improvement:

GitHub Contribution Graphs

Developers obsess over “green squares”:

  • Commit trivial changes daily
  • Split meaningful work into tiny commits
  • Contribute to vanity projects

The graph looks good. The value doesn’t increase.

Productivity Theater

Remote workers:

  • Keep Slack status “active”
  • Send messages at odd hours to appear hardworking
  • Move their mouse to prevent “idle” status

Appearing productive becomes more important than being productive.

Test Coverage Obsession

Teams chase 100% coverage:

  • Write meaningless tests that assert true === true
  • Test getters and setters
  • Focus on coverage metric, ignore actual quality

Coverage goes up. Bug count doesn’t go down.

Sprint Velocity Games

Teams inflate velocity:

  • Increase story point estimates
  • Cherry-pick easy tickets
  • Avoid refactoring and tech debt

Velocity improves on paper. Actual delivery doesn’t.

Why the Hawthorne Effect Is Dangerous

1. False Positives

You think you’ve improved something, but you’ve just increased observation:

  • New productivity tool → People use it because you’re watching
  • New process → People follow it while you’re monitoring
  • Once you look away, behavior reverts

2. Goodhart’s Law

“When a measure becomes a target, it ceases to be a good measure.”

The Hawthorne Effect amplifies this:

  • Measure commits → People optimize for commits, not value
  • Measure test coverage → People game coverage, ignore quality
  • Measure response time → People close tickets fast, ignore solutions

3. Unsustainable Behavior

Performance gains from observation aren’t sustainable:

  • Workers can’t maintain “being watched” intensity forever
  • Metrics degrade once monitoring decreases
  • Burnout increases

4. Distrust and Resentment

Constant observation creates:

  • Feeling of being micromanaged
  • Loss of autonomy
  • Toxic culture of surveillance

How to Account for the Hawthorne Effect

1. Acknowledge It Exists

When you introduce monitoring:

  • Expect initial behavior changes
  • Wait for the novelty to wear off
  • Measure long-term trends, not short-term spikes

2. Use Control Groups

Run experiments with:

  • Observed group
  • Unobserved control group
  • Compare results to isolate the observation effect

3. Measure What Matters

Focus on outcomes, not activities:

  • ❌ Lines of code written
  • ✅ Features shipped and adopted
  • ❌ Hours worked
  • ✅ Problems solved
  • ❌ Commits per day
  • ✅ Customer value delivered

4. Make Observation Normal

If everything is always monitored, the Hawthorne Effect diminishes:

  • Continuous integration is always watching code quality
  • Automated tests always check correctness
  • Becomes baseline, not special attention

5. Value Autonomy Over Surveillance

Trust-based cultures perform better long-term than surveillance-based ones:

  • Set clear goals
  • Give autonomy to achieve them
  • Measure outcomes, not activity

The Programmer’s Perspective

As engineers, we’re both observers and observed:

When You’re Observed

Your manager starts tracking your commit frequency.

Ask yourself:

  • “Am I changing my behavior because I’m being watched?”
  • “Am I optimizing for the metric instead of the goal?”
  • “Is this sustainable?”

When You’re the Observer

You implement a new monitoring dashboard.

Ask yourself:

  • “Will this change behavior in ways I don’t intend?”
  • “Am I measuring activity or outcomes?”
  • “What will people game?”

Real-World Software Examples

Microsoft’s “Stack Ranking”

Microsoft ranked employees against each other. The Hawthorne Effect kicked in:

  • Employees optimized for visibility, not collaboration
  • People avoided helping others (made peers look better)
  • Internal competition destroyed teamwork

Result: Microsoft abandoned stack ranking.

Amazon’s Warehouse Monitoring

Amazon tracks every worker movement. The Hawthorne Effect manifests as:

  • Workers skip bathroom breaks to maintain metrics
  • Productivity “increases” at the cost of health
  • High turnover and burnout

Result: Unsustainable, inhumane working conditions.

Google’s 20% Time

Google announced “20% time” for side projects. Initial results were amazing—Gmail, Google News, AdSense.

Why? Googlers knew leadership was watching. They wanted to show results.

Over time, as observation decreased, participation in 20% time plummeted.

The Irony

The Hawthorne Effect was itself affected by the Hawthorne Effect.

Recent reanalysis of the original data suggests:

  • The productivity gains were overstated
  • Researchers may have cherry-picked data
  • Workers knew their performance was being studied for publication

The very discovery of the Hawthorne Effect may have been amplified by the Hawthorne Effect.

The Uncomfortable Truth

You are already being affected by the Hawthorne Effect.

  • Your GitHub profile? You’re aware others see it.
  • Your Slack activity? You know your team is watching.
  • Your standups? You’re conscious of being evaluated.

The question isn’t whether the Hawthorne Effect applies to you.

The question is: Are you optimizing for what matters, or just for what’s being watched?

Key Takeaways

  • ✅ Being observed changes behavior—often unconsciously
  • ✅ Attention and feeling valued improve performance
  • ✅ The effect is temporary and can lead to gaming metrics
  • ✅ Surveillance culture can backfire long-term
  • ✅ Measure outcomes, not activity
  • ✅ Acknowledge the Hawthorne Effect when interpreting data

The workers at Hawthorne Works didn’t care about lighting.

They cared about being seen, heard, and valued.

When you track metrics, deploy monitoring, or evaluate performance—remember:

People don’t just respond to the system. They respond to being watched.

Design your systems accordingly.