In Jobs, Race Cars, and the Designer's Paradox I wrote about the nature of job design and how we should think of jobs like Formula 1 cars:
"...the safety and well-being of the driver is an ancillary consideration to winning the race. In other words, you can't win a race with a dead driver."
Most of the time though, we won't be doing the designing in the first place; like being given a hunk of scrap metal and asked to find someone to get in the cockpit.
The problem is, we've committed to this race. No matter how bad the job, we still need to fill it.
To this regard, we ought to be fixing up that hunk of scrap metal as best we can. Given the opportunity, we should work to craft jobs that are rewarding and provide people with the tools they need to succeed. When we do this well, we maximize motivating potential.
Back in the 1970s, Richard Hackman and Greg Oldham developed a protocol for measuring a job's capacity to motivate: the job diagnostic survey. A parallel assessment to their job characteristics model, which remains one of the most widely accepted theories in I/O psychology.
Based on the idea that the task itself is the key to motivation, the job characteristics model outlines five job characteristics that influence three psychological states contributing to the employee experience. Ultimately these psychological states mediate desired outcomes, such as performance and organizational commitment.
The five job characteristics:
- skill variety: the range of tasks performed
- task identity: the degree to which the whole job is completed from start to finish [i.e., are they building the whole car or just desiging the tires]
- task significance: the impact the job has on others
- feedback: the degree to which knowledge of performance is learned
- autonomy: the degree of discretion and freedom over the work
It is the designer's job to moderate the connections between job characteristics and psychological states.
The psychological states:
experienced meaningfulness: the degree to which the worker experiences the work to be inherently meaningful or valuable to them
experienced responsibility: the degree to which the worker experiences a feeling of responsibility for the work
knowledge of results: the level of clarity and direction the worker has about outcomes of a response in relation to a goal
These psychological states mediate the outcomes on the right hand of the figure above. Said differently, they are the reason those outcomes happen. This is an important aspect to understand because the job characteristics themselves do not result in these outcomes. Rather, the worker's psychological state does.
This implies that different workers produce different outcomes [and have different needs as well].
To maximize the likelihood that these psychological states are reached, we need a way to measure our baseline. Luckily, Hackman and Oldham, yet again, give us just the thing:
Motivating Potential Score [MPS]
The Motivation Equation
We can use MPS to assess the results of the job diagnostic survey which measures each of the five job characteristics individually, while the motivation equation lumps them all together to produce a single score for a jobs motivating potential.
MPS can be thought of as the average of skill variety, task identity, and task significance, multiplied by feedback and autonomy. It is the key aspect of the job characteristics model and we can use it to effectively diagnose and optimize a job.
By tweaking with a job's design, you moderate it's characteristics which, in turn, influence the three psychological states. When you are able to achieve a high MPS, you maximize the employee experience, which in turn maximizes desired outcomes.
In practice, the job optimization cycle looks like this:
- Step 1: Worker fills out the job diagnostic survey
- Step 2: Individual scores for each job characteristic are measured
- Step 3: MPS is calculated from individual scores
- Step 4: Based on results, adjustments are made to job design
- Step 5: Repeat [giving ample time to adjust before surveying again]
Running the Diagnostics
If we want to improve the motivating potential of a job, we need to focus on the individual characteristics first. Because each person who works a job has different motivational needs, we should do this on a case by case basis.
I recognize, however, that this isn't as straight forward as it sounds. Even more so for those with little to no training in the area of psychological assessments.
In effort to provide greater access to practitioners, I have embedded the job diagnostic survey into my toolbox.
From there, you can take the survey and receive results via email. Since each question is marked with an abbreviation for its respective job characteristic. You can simply sum up your scores for each and plug them into the motivation equation.
[Alternatively, you can use the embedded repl in my toolbox. By clicking run, you will be prompted to either take the full survey or enter the scores you calculated for each job characteristic. With either option, the program will calculate MPS automatically for you. Give it a try!]
Understanding your results, on the other hand, can be somewhat confusing. MPS using this version of the survey results in an output in the thousands. However, it is important to understand that MPS is meant to be used comparatively. That is, there is no "good" or "bad" scores. Only room for improvement.
To be sure I was thinking about this correctly, I turned to Paul Spector, author, researcher, and professor of I/O psychology at the University of South Florida:
All the items are written in a positive direction (high scores, higher characteristic), so you just need to sum the scores on the 3 items per subscale. With a 7-point scale scores are 3 to 21 (or 1-7 if you divide by number of items).— Paul E. Spector (@PaulESpector) November 8, 2020
I would compute MPS in the way Hackman & Oldham suggested. You will get large numbers because it is multiplicative. The scores though are used comparatively (e.g., one job is higher than another), so just use consistently.— Paul E. Spector (@PaulESpector) November 9, 2020
This is why it is just as important to look at individual scores for each job characteristic as well as the overall MPS.
The motivation equation is a comprehensive measure used to assign a score for a job's potential to motivate. You should compare MPS among teams of both similar and varying R&R to maximize your understanding of job design at your organization. You should not make any assumptions about a score for any one individual.
It is important to remember that individual psychological differences play a significant role here–just because one person is highly motivated in a job does not mean another person will be too.
Ultimately, the individual scores are where you will want to focus when trying to improve the motivating potential for a particular job. And if you do [and you should] assess MPS across teams, the results from one job will help you understand another.
- What about role x grants a high skill variety score, while role y's is so low?
- What aspects of role x could I bring into role y to improve skill variety?
This process of assessing, adjusting, and re-assessing is the job optimization cycle I mentioned earlier. As you iterate through the job design/redesign process, you eventually find yourself at a place of balance.
Of course, you may not be optimizing for balance. Maybe you want to improve turnover for entry level roles, so you implement cross training and new diverse responsibilities to keep new hires engaged. Maybe you are more concerned about maximizing the motivating potential of more senior roles, because productivity of senior members is 10x of junior members. Either way, whatever you are optimizing for, you will need a way to measure your baseline.
- In a 2016 study of 181 healthcare workers, MPS had a significant positive correlation with organizational commitment.
- Comparing MPS to similar jobs helps you generate a baseline. Comparing jobs with varying roles & responsibilities [R&R] helps you to identify factors that influence job characteristics and psychological states.