Organisational Work Conditions

Work conditions are the physical (lighting, noise, layout, temperature) and temporal (hours, shifts) features of the workplace. They affect performance, wellbeing and safety.

The Hawthorne studies (a crucial background point). In studies at the Hawthorne electrical plant, researchers changed illumination and other conditions to see the effect on output. Strikingly, output rose whenever conditions changed — even when lighting was made worse. The explanation: workers were performing better because they knew they were being studied and felt special/attended to. This is the Hawthorne effect.

Two big lessons:

1. Psychosocial factors (attention, being valued, group norms) can matter more than the physical conditions themselves — a foundation of organisational psychology.
2. The Hawthorne effect is a major methodological warning: in any workplace study, behaviour may change simply because people know they are being observed (a threat to validity, like demand characteristics).

Why this matters for the exam. Hold the Hawthorne effect in mind throughout: it is both a finding (attention boosts performance) and an evaluation tool (workplace studies may lack validity because participants know they're being watched).

Lighting/illumination. Adequate, appropriate lighting supports performance and reduces errors/eye strain; poor lighting harms both. (But recall the Hawthorne illumination studies — the attention, not just the light level, drove output.)

Temperature. There is an optimal range; too hot or too cold reduces concentration, comfort and performance, and can increase errors/accidents.

Noise. Loud or unpredictable noise impairs concentration (especially on complex tasks), raises stress and can harm hearing. Uncontrollable noise is worse than controllable noise.

Office design.

• Open-plan offices: + communication, collaboration, flexibility, cheaper. − more distraction/noise, less privacy, more stress and sometimes lower performance on focused work.
• Cellular/private offices: + privacy, focus. − less interaction, more costly.
• Ergonomics (chairs, desks, screen height, equipment design) reduce strain and injury and support performance.

Evaluation in brief. This research is highly applicable (workplace design) and often uses objective measures (output, error rates). But field studies have little control of extraneous variables, are vulnerable to the Hawthorne effect, and effects depend on the task (focused vs collaborative) and individual differences.

Circadian rhythms. The body has a roughly 24-hour 'body clock' (circadian rhythm) governing sleep, alertness, temperature and hormones, synchronised by light/dark. We are naturally alert by day and sleepy at night.

The problem with shift work. Working nights forces people to be awake and active when the body clock says sleep, and to sleep by day. This desynchronisation causes:

• poor, shorter sleep and chronic fatigue,
• worse health (digestive, cardiovascular problems over time),
• and impaired performance — slower reactions and more errors/accidents at night (many major industrial disasters occurred on night shifts).

Designing better rotas:

• Forward (clockwise) rotation (morning → afternoon → night) suits the body clock better than backward rotation.
• Slow rotation (or longer on each shift) lets the clock partly adjust; rapid rotation gives no time to adapt.
• Other aids: bright light at work, controlled napping, and limiting consecutive night shifts.

Evaluation in brief. Shift-work research is important and applicable (health, safety, rota design) and supported by physiological and accident data. But much evidence is correlational (other factors affect shift workers), there are large individual differences (some adapt better; 'morning' vs 'evening' types), and ethical/practical limits on controlled study.

Workplace accidents harm people and productivity, so understanding their causes is vital.

Human factors:

• Errors and lapses — slips, mistakes, forgetting steps.
• Fatigue (e.g. from shift work), stress and reduced vigilance on monotonous tasks.
• Cognitive overload or distraction, and risk-taking/complacency.
• Individual differences (some people/jobs are more accident-prone, though this idea is debated).

System/environmental factors:

• Poor equipment/ergonomic design that invites error,
• Inadequate training, unclear procedures, poor safety culture,
• Hazardous physical conditions (noise, poor lighting, heat).

The modern view. Accidents usually result from multiple causes combining — a chain of human and system failures (the 'Swiss cheese' idea: many small holes lining up). So blaming the individual alone is often too simple.

Evaluation in brief. Accident research is applied and valuable, but data are often retrospective/correlational (we study accidents after they happen), the concept of an 'accident-prone personality' is weak/ethically risky, and lab studies of error may lack ecological validity.

Reducing accidents:

• Training and procedures — teach safe behaviour and hazard awareness.
• Ergonomic/system design — design equipment and workspaces so error is less likely ('error-proofing').
• Safety signs and warnings — clear, attention-grabbing signs (though people can habituate to them).
• Reward systems / token economy — rewarding safe behaviour (e.g. accident-free periods) with tokens/bonuses, using operant conditioning to increase safe behaviour.
• Safety culture — leadership commitment, reporting near-misses, and good rota design (reducing fatigue).

Pulling it together for Paper 3 'evaluate / discuss':

• Strengths: strong real-world application (workplace design, safety, health); often uses objective data (output, accident/error rates).
• Weaknesses: field evidence has little control and risks the Hawthorne effect; much data is correlational/retrospective; effects depend on task, culture and individual differences.
• Relevant issues & debates: application to everyday life (central); individual vs situational (person vs system causes of accidents); reductionism; idiographic vs nomothetic; the use of objective vs self-report data.