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METAPHORS
FOR WRONG
MANAGEMENT
ALI ANANI, PHD
I dedicate this presentation to
Rajendra Grewal for being an inspirer
and provider of ideas, relevant
published work and timely comments.
The story of an employee who reports early to
work is funny.
The DG walked early morning in an employees’
office. The employee was not in but his jacket,
lighter and eye-glasses were there. The dg
thought the employee was in the bathroom. He
sent him a congratulatory letter. It turned out that
the employee deliberately left these items in
office the night before so as to give the illusion he
was at work.
THE WRONG EMPLOYEE
My friend never smoked a cigarette and yet he
died out of cancer. So, smoking doesn’t cause
cancer
TWO WRONGS DON’T MAKE RIGHT
My friend never smoked a cigarette and yet he
died out of cancer. So, smoking doesn’t cause
cancer
A student answers a question correctly- the
new teacher draws a line that he is smart.
TWO WRONGS DON’T MAKE RIGHT
My friend never smoked a cigarette and yet he
died out of cancer. So, smoking doesn’t cause
cancer
A student answers a question correctly- the
new teacher draws a line that he is smart.
Give a questionnaire and get one response-
draw conclusion that 100% of the surveyed
approved or disapproved a certain issue.
TWO WRONGS DON’T MAKE RIGHT
A friend told me that the best time to publish
on slideshare is tuesday afternoon, based on
the success of his only one presentation.
I took his advice. I got views well below
My average
PUBLISH ON TUESDAY
Worker performance in one month
Retreated- warn him/her
Wrong assumptions based on one month
performance
WRONG DECISIONS LEAD TO
BIGGER MISTAKES
Performance has a peak curve. Little changes
near the peak cause drastic changes in
performance
Stressing the employees to assume improved
performance is misleading
Even worse is the assumption derived from
one month result is extensible to a
peak-shaped performance curve.
TIPPING POINT OF ERRORS
LEVEL OF STRESS
OPTIMAL
PERFORMANCE
PERFORMANCE
TIPPING POINT OF ERRORS
CONSEQUENCES
Wrong decisions based on one measurement
lead to wrong conclusions
Even straight lines may bifurcate
(Branch out) into two or more lines
Human performance might bifurcate into
different paths at certain tensed situation.
Assumptions of straight line
Continuity is wrong.
TIME
PERFORMANCE TENSION POINT
STABLE
STABLE
UNSTABLE
STABLE
PERFORMANCE
LINEAR PERFORMANCE
PERFORMANCE SWINGING BETWEEN TWO LEVELS
1ST LEVEL
2ND LEVEL
TWO LEVELS OF PERFORMANCE
There is no predictable correlation
between the past behaviors of the
system to its future behavior.
UNPREDICTABLE PERFORMANCE
The performance of employees does
not progress necessarily in
A linear fashion
Interaction of employees may lead to the
emergence of new behaviors and attitudes.
FURTHER CONSEQUENCES
Interaction of employees may lead to the
emergence of new behaviors and attitudes.
For example, a new team member may not
produce his best initially.
He/she has to acclimatize with the new work
environment. His/ her early performance is
below bar. Extending this performance linearly
to the future will be misleading.
FURTHER CONSEQUENCES
EXTENSION
TO TEAM
MANAGEMENT
May be we need to look at teams in a new
perspective that is not based on the forming,
storming, norming, performing and adjourning
stages (tuckman’s model)
TEAMS ARE SOCIAL PHENOMENA
A MODIFIED TUCKMAN’S MODEL
CLOSENESS
AWARENESS
CONSIDERATION
PERFORMANCE
CONVERSION
Awareness Of The New
Working Climate
Consideration
For Other Team Members
And Work Requirements
Conversion Of Attitude
Closeness Among Teams
Members Strengthens.
And So
Performance Builds Up
COMPLEXITY OF TEAMS
Interactions of team members are complex.
Some team members have the ingredients of
pulling information and sending it to others
Some are experts in pulling information, but not
in sharing it
Some are poor in pulling information, but good
at sharing the little information they pulled
Some are poor in pulling and sharing information
COMPLEXITY OF TEAMS
A
C
BC
PUSHES OUT INFORMATION C
PULLS IN
INFORMATION
Even paul allen, the cofounder of microsoft,
wrote in his autobiography, "a great programmer
can outproduce an average one by ten to one;
with a genius, the ratio might be fifty to one."
I would reinterpret this as a 'power law view of
employee value'.
Http://cavqm.Blogspot.Com/2011/12/normality-assumptions-and-power-law.Html
How team members use
their time affects individual
and overall team
performance
In a learning team,
when a team member gets smarter,
all team members can get smarter.
SMARTNESS HAS
A RIPPLING EFFECT
In a learning organization individual
performance is linked to team
performance
How team members use their time
affects individual and overall team
performance
Interruptions follow a power
Law distribution as well
TIME BETWEEN INTERRUPTIONS2 HOURS 0 SECONDS
THE TWITTER CURVE
WIDESPREADADOPTION20071980
We are entering increasingly into power law
distribution.
Paradoxically, some base their evaluations on
one measurement- and draw conclusions from
one point.
This is management by wrong assumptions
Thanks to
MAR AL-HUSSENI
For his help in the graphics

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Metaphors for wrong management

  • 2. I dedicate this presentation to Rajendra Grewal for being an inspirer and provider of ideas, relevant published work and timely comments.
  • 3. The story of an employee who reports early to work is funny. The DG walked early morning in an employees’ office. The employee was not in but his jacket, lighter and eye-glasses were there. The dg thought the employee was in the bathroom. He sent him a congratulatory letter. It turned out that the employee deliberately left these items in office the night before so as to give the illusion he was at work. THE WRONG EMPLOYEE
  • 4. My friend never smoked a cigarette and yet he died out of cancer. So, smoking doesn’t cause cancer TWO WRONGS DON’T MAKE RIGHT
  • 5. My friend never smoked a cigarette and yet he died out of cancer. So, smoking doesn’t cause cancer A student answers a question correctly- the new teacher draws a line that he is smart. TWO WRONGS DON’T MAKE RIGHT
  • 6. My friend never smoked a cigarette and yet he died out of cancer. So, smoking doesn’t cause cancer A student answers a question correctly- the new teacher draws a line that he is smart. Give a questionnaire and get one response- draw conclusion that 100% of the surveyed approved or disapproved a certain issue. TWO WRONGS DON’T MAKE RIGHT
  • 7. A friend told me that the best time to publish on slideshare is tuesday afternoon, based on the success of his only one presentation. I took his advice. I got views well below My average PUBLISH ON TUESDAY
  • 8. Worker performance in one month Retreated- warn him/her Wrong assumptions based on one month performance WRONG DECISIONS LEAD TO BIGGER MISTAKES
  • 9. Performance has a peak curve. Little changes near the peak cause drastic changes in performance Stressing the employees to assume improved performance is misleading Even worse is the assumption derived from one month result is extensible to a peak-shaped performance curve. TIPPING POINT OF ERRORS
  • 11. CONSEQUENCES Wrong decisions based on one measurement lead to wrong conclusions
  • 12. Even straight lines may bifurcate (Branch out) into two or more lines
  • 13. Human performance might bifurcate into different paths at certain tensed situation. Assumptions of straight line Continuity is wrong. TIME PERFORMANCE TENSION POINT STABLE STABLE UNSTABLE STABLE PERFORMANCE
  • 15. PERFORMANCE SWINGING BETWEEN TWO LEVELS 1ST LEVEL 2ND LEVEL TWO LEVELS OF PERFORMANCE
  • 16. There is no predictable correlation between the past behaviors of the system to its future behavior.
  • 18. The performance of employees does not progress necessarily in A linear fashion
  • 19. Interaction of employees may lead to the emergence of new behaviors and attitudes. FURTHER CONSEQUENCES
  • 20. Interaction of employees may lead to the emergence of new behaviors and attitudes. For example, a new team member may not produce his best initially. He/she has to acclimatize with the new work environment. His/ her early performance is below bar. Extending this performance linearly to the future will be misleading. FURTHER CONSEQUENCES
  • 22. May be we need to look at teams in a new perspective that is not based on the forming, storming, norming, performing and adjourning stages (tuckman’s model)
  • 23. TEAMS ARE SOCIAL PHENOMENA A MODIFIED TUCKMAN’S MODEL CLOSENESS AWARENESS CONSIDERATION PERFORMANCE CONVERSION Awareness Of The New Working Climate Consideration For Other Team Members And Work Requirements Conversion Of Attitude Closeness Among Teams Members Strengthens. And So Performance Builds Up
  • 24. COMPLEXITY OF TEAMS Interactions of team members are complex. Some team members have the ingredients of pulling information and sending it to others Some are experts in pulling information, but not in sharing it Some are poor in pulling information, but good at sharing the little information they pulled Some are poor in pulling and sharing information
  • 25. COMPLEXITY OF TEAMS A C BC PUSHES OUT INFORMATION C PULLS IN INFORMATION
  • 26. Even paul allen, the cofounder of microsoft, wrote in his autobiography, "a great programmer can outproduce an average one by ten to one; with a genius, the ratio might be fifty to one." I would reinterpret this as a 'power law view of employee value'. Http://cavqm.Blogspot.Com/2011/12/normality-assumptions-and-power-law.Html
  • 27. How team members use their time affects individual and overall team performance
  • 28. In a learning team, when a team member gets smarter, all team members can get smarter. SMARTNESS HAS A RIPPLING EFFECT
  • 29. In a learning organization individual performance is linked to team performance
  • 30. How team members use their time affects individual and overall team performance
  • 31. Interruptions follow a power Law distribution as well TIME BETWEEN INTERRUPTIONS2 HOURS 0 SECONDS THE TWITTER CURVE WIDESPREADADOPTION20071980
  • 32. We are entering increasingly into power law distribution. Paradoxically, some base their evaluations on one measurement- and draw conclusions from one point. This is management by wrong assumptions
  • 33. Thanks to MAR AL-HUSSENI For his help in the graphics