Managers’ Performance- A Fibonacci Clusters Approach
Ali Anani, PhD
Introduction
The article of Craig Brown entitled “ The Commune, The Mission And The Social” and the stimulating comments of Bas de Baar inspired the idea of
this presentations. I dedicate this presentation to their creative insights.
The Masterpiece Article of Craig Brown ď‚Ž
In his inspiring article, Craig proposes a four-quadrant to study the performance of projects through people.
ď‚Ž
The two dimensions used for constructing the quadrant are sociability and solidarity
Project Performance Quadrant
Sociability is whether people like working together – in particular, whether they are communicating and co-operating freely.
Solidarity at its extremes is ONE goal or NO goal. The solidarity axis is the mission focus.
Project Performance Quadrant- 2 ď‚Ž
Craig Project Performance Quadrant
A Modified Way of Drawing the Quadrants ď‚Ž
Craig’s work and my previous publications on docstoc inspired me with the idea of developing four performance clusters and use these clusters as the building block for the performance quadrants. This approach gives a quantitative approach to the four quadrants forming the grand quadrant. This way the four quadrants do not have to be equally weighted.
The Performance of Managers
In an extension of Craig’s work, the performance of managers is again (at least, in part) is judged by their solidarity and sociability
It was decided in this work to define Sociability as a function of trust and communication intensity.
Trust equation is proportional to reliability, credibility and intimacy and inversely proportional to selfishness
The Performance of Managers- 2
The Trust Equation is Trust = (Reliability*Credibility*Intimacy )/Selfishness
The more a manager acts and fulfills what he promises, the more trustful he will become.
The less a manager talks about himself and the less he uses I, the more trustful he will become
Increased intimacy increases trust and enhances communication accordingly
The Performance of Managers- 3
The two solidarity attributes chosen for this study are: goal understanding and goal ownership. The importance of vision in making future work graspable and owned by employees was discussed by the author previously. See the presentation entitled “The Cost of Poor Vision on Companies” and “Balancing the Balanced Scorecard”
The Performance of Managers- 4
The data provided for the managers are modified so as not to reveal the identity of any person
The data are given for thirty managers
The analysis of data was done using the same procedure reported by the author previously. See “Employee Performance Clustering”
Summary of Data and Their Clusters The figure shows the data and their division into four clusters. Each row represents one manager. The total sample is 30 managers
The Characteristics of the Four Clusters ď‚Ž
The weight of each cluster is shown in the figure below
Clusters weights 35.00%
33.33%
30.00%
30.00%
25.00%
23.33%
20.00% 15.00%
13.33%
10.00%
5.00% 0.00% Cluster 1 weight (%)
Cluster 2 weight (%)
Cluster 3 weight (%)
Cluster 4 weight (%)
The Characteristics of the Four Clusters-2 ď‚Ž Clusters profiles
The definition of each cluster is shown in the figure below Cluster 1
Cluster 2
Cluster 3
Cluster 4
80.00%
60.00%
40.00%
20.00%
0.00% Trust Magnitude
-20.00%
-40.00%
-60.00%
Communication Intensity
Goal Understanding
Goal Ownership
The Characteristics of the Four Clusters-3 ď‚Ž
Cluster 1 is characterized by being the lowest in all dimensions (trust magnitude, communication intensity, goal understanding and goal ownership). This is equivalent to the Fragmented Quadrant by Craig.
Clusters profiles
Cluster 1
Cluster 2
Cluster 3
Cluster 4
80.00%
60.00%
Solidarity Zone
Sociability Zone
40.00%
20.00%
0.00% Trust Magnitude
-20.00%
-40.00%
-60.00%
Sociability Zone
Communication Intensity
Goal Understanding
Goal Ownership
Solidarity Zone
The Characteristics of the Four Clusters-4 ď‚Ž
Cluster 2 is characterized by being rether low in the sociability dimensions (trust magnitude and communication intensity) and positively high on the solidarity dimensions (goal understanding and goal ownership), but to a lesser degree than cluster 1. This is closest to the Networked Quadrant by Craig.
Clusters profiles 80.00%
60.00%
Cluster 1
Cluster 2
Cluster 3
Sociability Zone
Cluster 4
Solidarity Zone
40.00%
20.00%
0.00% Trust Magnitude
-20.00%
-40.00%
-60.00%
Sociability Zone
Communication Intensity
Goal Understanding
Goal Ownership
Solidarity Zone
The Characteristics of the Four Clusters-5 ď‚Ž
Cluster 3 is characterized by being slightly low on sociability (trust magnitude and communication intensity) but rather high on solidarity (goal understanding and goal ownership). This is equivalent to the Mercenary Quadrant by Craig.
Clusters profiles
Cluster 1
Cluster 2
Cluster 3
Cluster 4
80.00%
60.00%
Solidarity Zone
Sociability Zone
40.00%
20.00%
0.00% Trust Magnitude
-20.00%
-40.00%
-60.00%
Sociability Zone
Communication Intensity
Goal Understanding
Goal Ownership
Solidarity Zone
The Characteristics of the Four Clusters-6 ď‚Ž
Cluster 4 is characterized by being slightly high on sociability (trust magnitude and communication intensity) and solidarity (goal understanding and goal ownership). This is equivalent to the Communal Quadrant by Craig.
Clusters profiles 80.00%
60.00%
Cluster 1
Cluster 2
Cluster 3
Sociability Zone
Cluster 4
Solidarity Zone
40.00%
20.00%
0.00% Trust Magnitude
-20.00%
-40.00%
-60.00%
Sociability Zone
Communication Intensity
Goal Understanding
Goal Ownership
Solidarity Zone
The Characteristics of the Four Clusters-6
The four clusters (equivalent to the four quadrants of Craig) do not have equal weights, In this study cluster 1 has 33.3% weight, followed by cluster 3 ( 30%), cluster 4 (23.3%) and last cluster 2 (23.3%)
Cluster 1 (The Fragmented Quadrant) and cluster 2 (The Mercenary Quadrant) represent about 66% of the total weight.
Cluster 4 (The Communal Quadrant) represent only 23.3% of the weight. Is Pareto operating again in which about 20% of the managers are communal? Hard work is awaiting us!
Fibonacci Analysis ď‚Ž
It was decided to run simulations with 3, 5 and 8 clusters. These are Fibonacci Numbers and it would be interesting to run them. Summary results are given in the following slides
Fibonacci Analysis 3-Clusters Clusters weights
Clusters profiles
45.00% 40.00%
Cluster 1
Cluster 2
Cluster 3
80.00% 40.00% 60.00%
35.00%
33.33% 40.00%
30.00% 26.67% 25.00%
20.00%
20.00% 0.00% Trust Magnitude
15.00% -20.00%
10.00% 5.00%
-40.00%
0.00% Cluster 1 weight (%)
Cluster 2 weight (%)
Cluster 3 weight (%)
-60.00%
Communication Goal Understanding Intensity
Goal Ownership
Fibonacci Analysis 5-Clusters Cluster 1 Clusters profiles Cluster 3
Clusters weights
Cluster 2 Cluster 4
Cluster 5
35.00%
ď‚Ž
Three clusters 33.33%
30.00%
80.00%
60.00%
25.00%
40.00%
23.33% 20.00%
20.00% 16.67% 15.00% 13.33%
13.33%
0.00% Trust Magnitude Communication Goal Goal Ownership Intensity Understanding
10.00% -20.00% 5.00%
-40.00% 0.00% Cluster 1 weight (%)
Cluster 2 weight (%)
Cluster 3 weight (%)
Cluster 4 weight (%)
Cluster 5 weight (%)
-60.00%
Fibonacci Analysis 8-Clusters 8- Clusters 18.00% 16.00% 14.00% 12.00%
10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Cluster 8 weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%) weight (%)
Fibonacci Analysis 8-Clusters Clusters profiles
Cluster 1
Cluster 2
Cluster 3
Cluster 4
Cluster 5
Cluster 6
Cluster 7
Cluster 8
80.00% 60.00% 40.00% 20.00% 0.00% Trust Magnitude
-20.00% -40.00% -60.00%
-80.00%
Communication Intensity
Goal Understanding
Goal Ownership
Fibonacci Wave ď‚Ž Does
Fibonacci wave operate in management systems so that 3 + 5 clusters generate an 8wave structure even in performance systems? See next summary slide
Fibonacci Wave- 2