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9990 · 4.1.3

Motivators at work — practice questions

Practice and worked examples for 9990 Motivators at work. Short previews only — attempt the full question in MarkScheme against the official scheme.

Worked example 1

A car parts factory replaces its traditional assembly line with 'manufacturing cells'. Previously, workers performed single, repetitive tasks. Now, teams of 8 are responsible for assembling, testing, and packaging a complete fuel pump system. Before the change, the daily absenteeism rate was 12%. After three months, the rate dropped to 4%. Team output increased from 80 to 104 units per day.

  1. Explain these improvements using Hackman & Oldham's Job Characteristics Model.
  2. Calculate the percentage decrease in absenteeism and the percentage increase in output.
Show solution outline

1. Explanation using JCM: The new cell-based system enriches the jobs according to Hackman & Oldham's model:

  • Skill Variety: Increased, as workers now perform multiple tasks (assembly, testing, packaging) instead of one.
  • Task Identity: Increased significantly. Workers build a complete product ('a whole fuel pump system') rather than an anonymous part.
  • Task Significance: Increased, as teams see the final product and are responsible for its quality, making their work feel more important.
  • Autonomy: Increased, as teams are given responsibility for their work, likely including scheduling and problem-solving.
  • Feedback: Increased, as teams test their own products, providing immediate, direct knowledge of results. These core characteristics lead to critical psychological states: experienced meaningfulness, experienced responsibility, and knowledge of results, which in turn drive positive outcomes like lower absenteeism and higher performance.

2. Calculations:

Percentage Decrease in Absenteeism:

  • Step 1: Find the absolute decrease in the rate. 12% - 4% = 8 percentage points
  • Step 2: Calculate the percentage change relative to the original rate. (Decrease / Original Rate) × 100 (8 / 12) × 100 = 66.7% decrease

Percentage Increase in Output:

  • Step 1: Find the absolute increase in units. 104 units - 80 units = 24 units
  • Step 2: Calculate the percentage change relative to the original output. (Increase / Original Output) × 100 (24 / 80) × 100 = 30% increase

Worked example 2

A company redesigns the role of its customer service agents from a highly scripted job to a 'Customer Advocate' role with more autonomy. A consultant measures the job's characteristics on a 7-point scale before and after the change.

Job Ratings (1=Very Low, 7=Very High):

  • Before: Skill Variety=2, Task Identity=1, Task Significance=3, Autonomy=2, Feedback=3
  • After: Skill Variety=5, Task Identity=4, Task Significance=6, Autonomy=5, Feedback=6

Using Hackman & Oldham's formula, calculate the Motivating Potential Score (MPS) for the job both before and after the redesign and comment on the significance of the change.

Show solution outline

Formula: The Motivating Potential Score (MPS) is calculated as: MPS = [(Skill Variety + Task Identity + Task Significance) / 3] × Autonomy × Feedback

Step 1: Calculate the MPS Before the Redesign

  • First, calculate the average score for the meaningfulness components (Variety, Identity, Significance). (2 + 1 + 3) / 3 = 6 / 3 = 2.0
  • Now, apply the full MPS formula using the 'Before' scores for Autonomy (2) and Feedback (3). MPS (Before) = 2.0 × 2 × 3 = 12

Step 2: Calculate the MPS After the Redesign

  • First, calculate the average score for the meaningfulness components using the 'After' scores. (5 + 4 + 6) / 3 = 15 / 3 = 5.0
  • Now, apply the full MPS formula using the 'After' scores for Autonomy (5) and Feedback (6). MPS (After) = 5.0 × 5 × 6 = 150

Step 3: Comment on the Significance The MPS increased dramatically from 12 to 150. This indicates a substantial improvement in the job's potential to intrinsically motivate employees. The redesign successfully enhanced all five core characteristics, but the multiplicative effect of the large increases in Autonomy and Feedback was particularly powerful. According to the model, this higher MPS is predicted to lead to improved employee outcomes, such as higher job satisfaction, better work quality, and reduced turnover.

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