Work Design Simulator

Job profile inputs · 1–7

Edit a design

How the score is built

Redesign, step by step

Feedback means knowing the results of your own work. In v2, Before AI uses manual baselines (no AI), turnover is a yearly % from a 15% base, and redesign has a short learning dip at 3 months.

Model settings

Weights and constants

v1 values are the default. Change them to test other theories or to enter weights estimated from your own data.

JCM strength in Motivation40%

JCM (MPS) 0.40SDT 0.60

Motivation = 100 × (w × MPS/343 + (1 − w) × SDT/7). v1 uses w = 0.40.

All model constants

Save or load a scenario

Paste a scenario

Scores → office

What each activity means

Who does what comes from the job design. How many leave and how rushed the office looks come from the scores.

Role	Where	Why, in theory
Before Letter & form processor	Desks	Handles each case manually end to end. Low autonomy (3).
A Exception firefighter	Desks, AI console	Task identity 2: only fragments the AI rejected.
A Escalation runner	Meeting	Autonomy 2: decisions pushed up to the Team Lead.
B AI rule tuner	Rules board	Autonomy 6, Feedback 6: decides what the AI may send.
B Case owner	Desks, meeting	Task identity 6: owns exceptions start to finish.
B Peer coach	Lounge	SDT relatedness 5: peer coaching pairs.
Leaving	Exit door	v2: expected leavers = turnover % × 6 people. A walker appears only when about 1 person is expected to leave.

Read before using for prediction

Model assumptions and limits

All inputs are illustrativeThe 1–7 scores are reasoned estimates, not survey data. The shaded band on each bar shows the range if every score were half a point higher or lower.
Weights are not estimated from datav2 effect sizes follow published research directions (job characteristics link strongly to motivation, weakly to objective performance; satisfaction links modestly to turnover), but the exact numbers are set by reasoning. Calibrate with real survey scores and outcomes before using it to predict.
MPS multiplies scoresHackman & Oldham's formula exaggerates gaps (16 vs 192). Research finds a simple additive index predicts about as well. It is kept because it is the classic model.
Autonomy appears in both theoriesJob autonomy (what the role allows) and felt autonomy (what people experience) are separate inputs and have different values in v2.
What the model leaves outHeadcount decisions, training cost, governance of AI rule changes, job insecurity, pay and the job market. Each can change real outcomes.

About this project

Built by Viann

Concept, case design, framework choices and scoring logic by Viann (MSocSc Industrial-Organisational Psychology). Interface and 3D office built with AI assistance (Claude). A teaching and decision-support prototype, not a validated forecasting tool.

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.
Deci, E. L., & Ryan, R. M. (2000). The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268.
Parker, S. K., & Grote, G. (2022). Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Applied Psychology, 71(4), 1171–1204.

Work Design Simulator

The trap: early wins fade

How each job feels

Diagnosis

What this model leaves out