Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Managerial Network Clusters

5,262 views

Published on

Managers interact in different ways. This presentation is extracted from a real case study in which different managerial-impacting factors are analyzed. Performing risk-type analysis reshuffles the performance clusters and the results point to areas that warrant attention

Published in: Business, Technology, Education

Managerial Network Clusters

  1. 1. Managerial Networks Clusters<br />Ali Anani, PhD<br />
  2. 2. Workers interact- These are actions that generate different outcomes.<br />The boundaries of an organizational network are usually very well-defined by its organizational structure.<br />Employees may engage in a myriad of different interactions. Patti Anklam published several interesting ideas on this subject<br />Interactions among Workers<br />
  3. 3. Information exchange- the more people know and trust each other the more they exchange information<br />Awareness of what people know- the more people are aware of what co-workers know, the more likely they will exchange information<br />Types of Information Exchange and Their Determinants<br />
  4. 4. Knowledge Seeking- People seek information from knowledgeable people to solve problems if no barriers exist. Barriers limit the free flow of consultation on issues related to problem-solving and decision-making<br />Motivational Energy- Knowledgeable staff may affect people positively or negatively. People tend to consult experts who motivate them while avoiding people who are de-motivating (Unless they have no option but to consult with them)<br />Types of Information Exchange and Their Determinants- 2<br />
  5. 5. A sample of twenty one managers was taken randomly for a middle-sized company<br />Face to face interviews and written surveys produced the results shown in the next table<br />The dimensions taken are the same as discussed in the preceding slides<br />A Case Study<br />
  6. 6. Survey Results<br />
  7. 7. NeuroXL Classifier from was used to analyze the results as explained in an earlier publication by the author<br />The first analysis was designed to produce two clusters. (See next slide)<br />Analysis Methodology<br />
  8. 8. The Two Clusters<br />
  9. 9. The previous slide shows two clearly differentiated clusters.<br />The meeting point between the two clusters is the frequency of receiving information; otherwise the two clusters diverge<br />The Two Clusters- 2<br />
  10. 10. In Cluster 1, managers are familiar with who has information and the skills to consult with. However; peer managers are hesitant to seek their advise because these managers do not either motivate their peers or save them time. This realization apparently led to the low frequency of information exchange<br />The Two Clusters- 3<br />
  11. 11. Cluster 2 compromises managers whose common profile is being low in the familiarity of the knowledge and skills of their peers and are not seekers of opinion to make decision. However; they benefit from the time savings they get from their peers and of the enthusiasm they get from these activities<br />The Two Clusters- 4<br />
  12. 12. The Two Clusters- 5<br />The Cluster Radar<br />The previous clusters are redrawn in a radar graph<br />
  13. 13. The segmentation of managers into three clusters produced the following clusters with cluster 1 having the highest weight<br />Three Managerial Clusters<br />
  14. 14. The line graph is as shown below. Cluster 2 (14.29% by weight) scores lowly in both dimensions pertaining to other managers being either familiar with their knowledge or skills. The culture and communication levels are low indeed. <br />Three Managerial Clusters- 2<br />
  15. 15. It was decided to take out the dimension of frequency of decision consulting to see the outcome. The resulting clusters had the following weights. A more balanced cluster weights resulted than the original case <br />Three Managerial Clusters- 3<br />
  16. 16. This time cluster 3 realized time savings from consultation after removal of the decision consultancy dimension . Apparently, the benefits are lost because of indecisiveness. However; the issue of time value is critical in both clusters 1 and 2 as time savings from consultation show negative values<br />Three Managerial Clusters- 3<br />
  17. 17. This time cluster 3 realized time savings from consultation. Apparently, the benefits are lost because of indecisiveness. However; the issue of time value is critical in both clusters 1 and 2 as time savings from consultation show negative values<br />Three Managerial Clusters- 4<br />
  18. 18. The impact of time savings resulting from consultation prompted repeating the cluster analysis without this factor. The weights of the three clusters emerged as follows. Interestingly, the same weights for leaving decision data, but are reshuffled <br />Three Managerial Clusters- 5<br />
  19. 19. Cluster 1 shows radical performance difference than both cluster 2 and, to a lesser extent, cluster 3<br />Three Managerial Clusters- 5<br />
  20. 20. The managers were divided into four clusters to form four quadrants. <br />Four Clusters<br />
  21. 21. The managers were divided into four clusters to form four quadrants. The clusters show four different type of managers. Clusters 1 and 3 are the least beneficial from time savings<br />Four Clusters<br />
  22. 22. The value of cluster risk analysis becomes evident if we take one dimension out. In this example the decision consulting frequency was left out. The results are shown in this slide and the next one<br />Four Clusters<br />
  23. 23. The problem areas of each cluster may be identified. Satisfying or leaving out one factor leads to reshuffling of the weights of new clusters<br />Four Clusters<br />

×