The Opposite of Satisfaction is NOT Disatisfaction

Motivational theory is separated into two camps, the Agency Theory (Incentive Theory) camp and the Two Factor Theory camp.  Every performance incentive or bonus program is an example of the Agency Theory at work. Agency theory was popularized by two economists (Jensen & Meckling) in 1976 who concluded that managers do not always act in the best interest of shareholders (OMG!). Jensen and Meckling argued if you want managers to behave differently, compensate them according to the behavior you are trying to elicit. This theory effectively delegated management to a formula (Source: Clayton).

Steven Levitt, co-creator of Freakonomics, famously put Jensen & Meckling’s theory to work when trying to potty train his three year old daughter, Amanda. After struggling to achieve the desired behavior, Levitt provided Amanda  with a bag of her favorite treat (M&M’s) every time she successfully used the potty. After 3 days Levitt’s daughter was able to tinkle on command to collect her favorite treat at will (see Youtube video below).

The example of a three old exploiting her father’s incentive system to rob him of her favorite  treats is cute, but is an example of what occurs everyday in the corporate world. Clever individual’s will exploit incentive systems to maximize their personal gain (see this post). I like to refer to this as the “Ship it! We’ll worry about it when it comes back” attitude, where quality is often tossed aside in an effort to maximize short term gains. When the incentive system is applied to safety goals, injuries will go unreported and the game can become increasingly dangerous.

Herzberg’s Two Factor Theory takes a more complex approach to motivation. Herzberg concluded that satisfaction and dissatisfaction are not on the same continuum. Herzberg found that there are motivating factors which lead to satisfaction and Hygiene Factors which lead can lead to dissatisfaction. The best case scenario for hygiene factors is “no dissatisfaction”. Thus, the opposite of satisfaction is not dissatisfaction.

Examples of Motivating  factors include:

  • Challenging Work
  • Recognition
  • Personal Growth
  • Responsibility

Examples of Hygiene factors include:

  • Compensation
  • Job Security
  • Work Conditions

What we can learn from studying Herzberg’s theory is that motivation is more complex than simply incentivizing the right behavior.  Though the hygiene factors must be “right” to prevent dissatisfaction,  motivation requires individuals to have a mission and purpose. My favorite example of this lesson is the story of John F. Kennedy and the NASA janitor. When asked by the President what he “did for NASA”, the janitor’s response, “I am putting a man on the moon” (link).

Click the Link Above to Check out Clayton Christensen's, "How Will You Measure Your Life" 

Are You Living in a Red Bead Experiment?

The red bead experiment was created as a gift for W. E. Deming as a demonstration of the Illusion of Management. The Red Bead Experiment consists of a paddle with 50 slots and a group of “willing workers”. The workers are instructed to dip the paddle into the pan of beads (white & red) which contains 20% red beads & 80% white beads. The goal of is to produce white beads, as the customer will not accept red beads. The workers are required to read the detailed work instructions and extract the beads from the pan using the paddle. A trained quality inspector counts the beads and records the results. “Management” provides encouragement and administers discipline to “poor performers”. An example of the kit is provided below (source).


To illustrate the experiment I’ve typed names in an excel spreadsheet selected 6 “willing workers” who have graciously applied. Each worker randomly generates carefully scoops 50 beads each week and the total number of red beads is diligently recorded by the inspector. After 4 weeks the average number of defects (red beads) is calculated for each “willing worker”. The quality manager presents the results to the staff at the monthly management review with the following chart.


The conversation likely goes as follows:

Quality Manager: “Here is the average weekly scrap performance for each operator. As you can see they all missed the target of 4 red beads per week; however, Brad was quite close.

Human Resources Manager:”Tom was the worst performer. We need to consider putting him on a performance improvement plan“.

Production Manager: “We need to create a quality mindset and start doing things right the first time

Engineering Manager: “I’ll have the process engineers watch Brad and try to glean any Best Practices

Next Management Review:


Quality Manager: ” The results from the previous month have not improved. We need to make quality a priority

Human Resources: “Tom’s performance has clearly not improved. I recommend we move to terminate him. Brad’s performance has also been impacted by everyone else’s. And Tessa is only getting worse!

Production Manager: “We must drive accountability down to the operators

Engineering Manager: “Clearly they [operators] are not following the standard work”

Final Management Review:


Quality Manager: “The results are not improving…. We need to hold the supervisors accountable”

Human Resources: “The New Girl is an improvement over Tessa and Brad is heading in the right direction now. I don’t know what else you want me to do!

Production Manager: “I keep emphasizing quality in the kick off meetings… these people are just not getting it!

If the fictitious scenario created using a random number generator in excel hits close to home, you have my condolences. This example and Deming’s Red Bead experiment are intended to help managers think about the system and processes which generate the results. They also illustrate how “data driven” thinking with a touch of confirmation bias can get out of hand quickly as managers perpetuate the Illusion of Management.


For more info on the Red Bead Experiment check out this Youtube video here . Also for an additional resource on Dr. Deming, please check out the book linked below.

Driving Out Fear: The Hidden Risk

W. Edward Deming was an American statistician, management consultant, and professor credited  with influencing the Toyota Production System (TPS) and lean manufacturing (subjects which will be explored on this blog at some point). Along with the Plan-Do-Check-Act cycle and advocacy for statistical quality control (e.g. control charts), Deming developed his 14 key management principles. In this post we will explore #8, “Drive out Fear”.


“Drive out fear, so that everyone may work effectively for the company”

– W.Edward Deming

I found myself pondering this concept most recently while reading “A Random Walk Down Wall Street” by B.G. Malkiel.  A book first published ~40 years ago which explores the efficient market hypothesis, wisdom or lack thereof of investment gurus, and the madness of crowds in great detail.  In his book, Malkiel discussed the work of Kahnemann & Tversky (critics of the efficient market hypothesis) whose behavioral experiments discovered that people are extremely loss averse and in situations where loss was assured, individuals exhibit risk-seeking behavior. One such example is provided below:

Consider the below options for yourself:

Option 1: Guaranteed loss of $750

Option 2: 75% chance of a $1000 loss & 25% change of no loss

When faced with the two options provided above, Kahnemann & Tversky found that  90% of subjects selected option #2, despite the fact that the expected value of both decisions is the same. How could this concept apply to organizations?

In organizations where individuals are held “accountable”, often a euphemism for a perform or “you’re fired” management philosophy, individuals are put in situations where loss may be assured. Based on the work of Kahnemann & Tversky it is the tendency of the vast majority of people to exhibit risk seeking behavior. In the world of business this may result in shipment of nonconforming product, hiding injuries, violation of environmental laws & policies, etc.

In my own experience I’ve witnessed individuals “manipulating” on-time delivery numbers, supervisors aborting lengthy processes early to meet delivery commitments,  and overriding safety features to reduce cycle times. One organization had a philosophy uttered jokingly by employees on the production floor, “Ship it! We’ll worry about it when it comes back”. As a young engineer I found myself chastising the individuals while seemingly oblivious to the fact that the behavior was human nature and a response to the management system.

For years, quality professionals have been focused on developing detailed “processes”  to the point of annoying everyone within the organization. The switch of ISO 9001(Quality Management System used across many countries & industries as the minimum requirements of a quality management system) from process thinking to risk based thinking has quality professionals around the world preparing SIPOC’s (Supplier-Input-Process-Output-Customer) diagrams and performing FMEA (failure modes and effects analysis) activities throughout their organizations in an attempt to map out organizational risk. I find myself wondering how we eliminate the greatest risk, FEAR in our organizations?


“Well, 99% of things done in the world good or bad is to pay a mortgage”

-Nick Naylor, Thank You for Smoking


W. Edward Deming:

14 Points for Total Quality Management:

“A Random Walk Down Wall Street”:

ISO 9000:

Performance Improvement Lessons from a 6 Year Old

One of the pivotal experiences which provided the catalyst for starting this blog occurred in Fall of 2012 when I was living with my soon to be first wife  and helping to raise her son.  He was in his 1st grade and was struggling with a behavior to a degree where the school put him on a daily behavior report card system.

The report card consisted of him receiving a score card every day with each school subject and recess. Under each subject he would receive either a smiley face or frown face for behavior and work and a single face for recess.   The report card allowed for a maximum of 13 Smiley faces though he typically received around 3-4.

After begging, bribing, scolding, pleading,  and any other “Hail Mary” attempt to try to get him to behave I had a Eureka moment. After each day we attempted a different reward (bribe) or punishment in desperation to try and correct the behavior. Being an experienced process engineer I figured there would be no way to determine which input variable would lead to the desired output if we were consistently turning the proverbial knobs on a daily basis. I recognized the school had gifted us with a measurement system allowing us to trial a single incentive system and evaluate its effectiveness over a period of time and use statistics to measure the difference. This would prevent us from chasing “noise” and ensuring we would be able to identify real progress. Thus, we set a goal of 10 Smiley Faces per day and worked out an incentive program with his after school day care teacher.

Lo and behold the number of smiley faces increased (see the example graph below) and he drastically reached a point where he was averaging nearly 10 smile faces per day. Being the obnoxious data savvy engineer, I performed a t-test and the resulting p-value was significantly below 0.05 indicating the change in performance was statistically significant. For the next several days I practiced my interview with Oprah and Dr. Oz while brushing my teeth as I had successfully deployed process engineering and management fundamentals to solve the fundamental challenge in parenting.

A few days (or maybe a couple weeks), I ended up picking up the boy from school and ran into his teacher. Proud of my accomplishments I had to ask, “How has his behavior been?”. Waiting in anticipation for an “Atta Boy” moment his teacher paused and said, “he is receiving a lot more smiley faces; however, by the end of the day he is a real terror”. Note: She likely put it more politically correct, but you get the gist.

Defeated, I had my first real world lesson in Goodhart’s law: “When a measure becomes a target, it ceases to be a good measure”. Children 1, Engineer 0.

Smiley Faces



Goodhart’s Law:


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