Business Assignment
Please answer the questions on the ABB Supplemental questions word document using the excel spreadsheet as required. A step by step instructions is provided for NodeXL.
Due 48 hours (two days).
>Division of Labor
M 1
M 1
M 1
M 1
1
M 1
M 1
F 1 F 1
F 1
F 1
F 1
F 1
F 1
M 1 M 1
F 1
M 1
M 1
M 1
M 1
M 1
F 1
M 1
M 1
M 1
M 1
M 1
M 1
F 1
M 1
M 1
M 1
M 1
M 1
M 1
F 1 M 1
M 1
M 1
M 1
M 1
M 1
M 1 F 1
M 1
M 1
M 1
M 1
F 1
M 1
M 1
M 1
M 1 gender Power Transformers Distribution Transformers High Voltage Switchgear Insulation Component Production Galvanizing Engineering HR Paint M 1 Konrad M 1 Konrad M 1 Konrad M 1 Konrad M 1 Konrad M 1 Konrad F 1 Frank M 1 Filip M 1 Filip M 1 Filip M 1 Filip M 1 Filip M 1 Kazimierz M 1 Kazimierz M 1 Kazimierz M 1 Kazimierz M 1 Kazimierz M 1 Kazimierz F 1 Frank M 1 Kasper M 1 Kasper M 1 Kasper M 1 Kasper M 1 Kasper F 1 Frank M 1 Sebastian M 1 Sebastian M 1 Sebastian M 1 Sebastian M 1 Sebastian F 1 Frank M 1 Krzysztof M 1 Krzysztof M 1 Krzysztof M 1 Krzysztof F 1 M 1 Frank M 1 Wawrzyniec M 1 Wawrzyniec F 1 Wawrzyniec F 1 Wawrzyniec F 1 Wawrzyniec F 1 Wawrzyniec F 1 Wawrzyniec F 1 Wawrzyniec F 1 Frank M 1 Aleksy M 1 Aleksy M 1 Aleksy M 1 Aleksy M 1 5555 75
6 6
19
286
0.008547 3
0.008547 677
097
0.004292 68
42857
55556
601
0.008621 0.008696 0.008 991
0.008696 0.008547 Jakub Filip Fabian Filip Hiacynta Filip Blazej Kazimierz Mateusz Kazimierz Henryk Kazimierz Gabriel Kazimierz Zenon Konrad Marcin Krzysztof August Krzysztof Wawrzyniec Frank Czeslaw Konrad Brunon Konrad Halina Konrad Aleksy Filip 0.008403 0.007937 0.009009 0.007092 0.006944 0.006098 0.006098 0.008 0.007937 0.00625 0.006211 0.006897 0.006897 Purpose: To provide a basic level of familiarity with some of the key concepts in social network analysis and to get you thinking about how this type of analysis can be useful for decisions in organizations. There are many online resources that can give you a basic overview of key concepts and terminology for social network analysis. Here is just one example that seems sufficient for the ABB case supplemental using NodeXL Basic. 2
fname
gender
Power Transformers
Distribution Transformers
High Voltage Switchgear
Insulation Component Production
Galvanizing
Engineering
HR
Paint
Aleksy
M
1
Bernard
Dominik
Maksymilian
Tadeusz
Agnieszka
F
Brunon
Czeslaw
Doloreta
Halina
Pelagia
Petronela
Teodora
Teofila
Walburga
Wawrzyniec
August
Elzbieta
Krzysztof
Marcin
Rafal
Wiktor
Mariusz
Roza
Sebastian
Wilhelm
Zenon
Zygmunt
Albin
Franciszek
Giertruda
Kasper
Wojciech
Zachariasz
Blazej
Gabriel
Henryk
Julia
Kazimierz
Mateusz
Otto
Benedykt
Fabian
Feliks
Filip
Hiacynta
Jakub
Wladimir
Adalbert
Florjan
Ivona
Julian
Konrad
Ryszard
Wladyslaw
Formal Chart
fname
To whom do you report (Boss)?
Konrad
Frank
Julian
Ryszard
Wladyslaw
Florjan
Adalbert
Ivona
Filip
Jakub
Fabian
Benedykt
Feliks
Wladimir
Hiacynta Filip F 1
Kazimierz Frank M 1
Henryk
Blazej
Mateusz
Gabriel
Otto
Julia
Kasper
Wojciech
Albin
Zachariasz
Franciszek
Giertruda
Sebastian
Wilhelm
Mariusz
Zygmunt
Zenon
Roza
Krzysztof
Marcin
August Krzysztof M 1
Wiktor
Rafal
Elzbieta
Wawrzyniec
Artur
Czeslaw
Brunon
Doloreta
Halina Wawrzyniec F 1
Petronela
Pelagia
Teofila
Walburga
Agnieszka
Teodora
Aleksy
Tadeusz
Maksymilian
Dominik
Bernard
Resources
fname
Who controls needed Resources?
Konrad Filip
Konrad Frank
Konrad Artur
Konrad Wawrzyniec
Julian Konrad
Julian Frank
Julian Ivona
Ryszard Julian
Ryszard Konrad
Wladyslaw Konrad
Wladyslaw Filip
Wladyslaw
Alexy
Florjan Konrad
Florjan Ivona
Adalbert Frank
Adalbert Konrad
Adalbert Filip
Ivona Frank
Ivona Wawrzyniec
Filip Konrad
Filip Wawrzyniec
Filip Krzysztof
Filip Halina
Jakub Filip
Jakub Konrad
Jakub Frank
Fabian Filip
Fabian Konrad
Benedykt Konrad
Feliks Konrad
Feliks Frank
Wladimir Konrad
Wladimir Halina
Hiacynta Filip
Hiacynta Frank
Hiacynta Konrad
Hiacynta Halina
Kazimierz Konrad
Kazimierz Frank
Kazimierz Wawrzyniec
Blazej Kazimierz
Blazej Konrad
Blazej Frank
Mateusz Kazimierz
Mateusz Frank
Henryk Kazimierz
Henryk Konrad
Henryk Frank
Gabriel Kazimierz
Gabriel Frank
Otto Kazimierz
Otto Konrad
Otto Filip
Julia Frank
Julia Wawrzyniec
Kasper Frank
Kasper Konrad
Kasper Wawrzyniec
Kasper Ivona
Wojciech Kasper
Wojciech Giertruda
Albin Kasper
Albin Giertruda
Zachariasz Kasper
Franciszek Kasper
Franciszek Giertruda
Giertruda Kasper
Giertruda Frank
Giertruda Konrad
Giertruda Wawrzyniec
Sebastian Frank
Sebastian Wawrzyniec
Wilhelm Frank
Wilhelm Wawrzyniec
Wilhelm Sebastian
Mariusz Sebastian
Zygmunt Sebastian
Zenon Sebastian
Roza Sebastian
Roza Konrad
Roza Wawrzyniec
Krzysztof Artur
Krzysztof Frank
Krzysztof Konrad
Marcin Krzysztof
Marcin Konrad
Marcin Ivona
August Krzysztof
August Konrad
August Wawrzyniec
Wiktor Wawrzyniec
Rafal Ivona
Rafal Halina
Rafal Wawrzyniec
Elzbieta Halina
Elzbieta Konrad
Elzbieta Wawrzyniec
Wawrzyniec Artur
Wawrzyniec Frank
Wawrzyniec Halina
Wawrzyniec Konrad
Czeslaw Halina
Czeslaw Konrad
Czeslaw Artur
Czeslaw Wawrzyniec
Brunon Ivona
Brunon Konrad
Brunon Frank
Brunon Wawrzyniec
Doloreta Halina
Doloreta Konrad
Doloreta Frank
Doloreta Wawrzyniec
Halina Czeslaw
Halina Konrad
Halina Frank
Halina Wawrzyniec
Petronela Ivona
Petronela Konrad
Petronela Frank
Petronela Wawrzyniec
Pelagia Halina
Pelagia Konrad
Pelagia Frank
Pelagia Wawrzyniec
Teofila Halina
Teofila Konrad
Teofila Frank
Teofila Wawrzyniec
Walburga Halina
Walburga Konrad
Walburga Frank
Walburga Wawrzyniec
Agnieszka Halina
Agnieszka Konrad
Agnieszka Frank
Agnieszka Wawrzyniec
Teodora Filip
Teodora Konrad
Teodora Frank
Teodora Wawrzyniec
Aleksy Filip
Aleksy Konrad
Aleksy Frank
Aleksy Wawrzyniec
Tadeusz Frank
Tadeusz Konrad
Maksymilian Frank
Maksymilian Konrad
Maksymilian Halina
Dominik Frank
Dominik Konrad
Bernard Frank
Bernard Konrad
Bernard Ivona
Resource Measures
Vertex
In-Degree
Betweeness Centrality
Closeness centrality
Eigenvector centrality
Adalbert
0
0.
5
6
0.00
8
4
0.01588
Agnieszka 0 0
0.008
9
0.022
3
Albin 0 0
0.006
25
0.004292
Aleksy 1
9.222222
0.008772
0.02246
Artur 4 2
0.008547
0.015927
August 0 1
0.008475
0.0
14
Benedykt 0 0
0.007752
0.006976
Bernard 0
1.144444
0.015283
Blazej 0 0 0.008475
0.015
33
Brunon 0 1.144444
0.008696
0.020763
Czeslaw 1
0.666667
0.0
17
Doloreta 0 0 0.008696
0.022319
Dominik 0 0
0.008403
0.0
13
Elzbieta 0 0 0.008475
0.016198
Fabian 0 0
0.007813
0.00976
Feliks 0 0 0.008403
0.013097
Filip 9
57.242857
0.009174
0.029984
Florjan 0 1.144444
0.007937
0.009162
Franciszek 0 0
0.00625
Frank 33
861.25
39
0.012658
0.065933
Gabriel 0 0
0.007576
0.008357
Giertruda 3
14
7.9
0.009259
0.02202
Halina 13
82.169841
0.009615
0.040303
Henryk 0 0 0.008475
0.015333
Hiacynta 0
0.5
0.008621
0.019622
Ivona 8
68.919048
0.009091
0.023543
Jakub 0 0.555556 0.008475 0.01588
Julia 0 0 0.008
0.0
11
Julian 1
9.477778
0.016069
Kasper 5
275.868254
0.009434
0.024211
Kazimierz 5
48.666667
0.009009
0.024089
Konrad 39
1257.826984
0.013514
0.075152
Krzysztof 3
12.288889
0.019705
Maksymilian 0 0 0.008547
0.016838
Marcin 0
2.344444
0.0
10
Mariusz 0 0
0.005814
0.001342
Mateusz 0 0 0.007576 0.008357
Otto 0 1 0.008
0.011996
Pelagia 0 0 0.008696 0.022319
Petronela 0 1.144444 0.008696 0.020763
Rafal 0
0.833333
0.007634
0.011407
Roza 0
16.533333
0.013799
Ryszard 0 0
0.007874
0.008468
Sebastian 5
326.466667
0.014457
Tadeusz 0 0 0.008403 0.013097
Teodora 0 0.555556 0.008696
0.021361
Teofila 0 0 0.008696 0.022319
Walburga 0 0 0.008696 0.022319
Wawrzyniec 25
603.47619
0.011765
0.059036
Wiktor 0 0
0.007143
0.00548
Wilhelm 0 0
0.008264
0.012943
Wladimir 0 0 0.007874
0.010718
Wladyslaw 0 0 0.007874
0.011845
Wojciech 0 0 0.00625 0.004292
Zachariasz 0 0
0.006211
0.002248
Zenon 0 0 0.005814 0.001342
Zygmunt 0 0 0.005814 0.001342
Trust
fname
Who do you Trust?
Konrad Ivona
Konrad Halina
Julian Ryszard
Julian Wladyslaw
Julian Florjan
Julian Adalbert
Ryszard Wladyslaw
Ryszard Florjan
Ryszard Adalbert
Ryszard Julian
Wladyslaw Julian
Wladyslaw Ryszard
Wladyslaw Florjan
Wladyslaw Adalbert
Florjan Adalbert
Florjan Wladyslaw
Florjan Ryszard
Florjan Julian
Adalbert Florjan
Adalbert Wladyslaw
Adalbert Ryszard
Adalbert Julian
Ivona Julian
Ivona Florjan
Ivona Agnieszka
Ivona Aleksy
Filip Jakub
Filip Florjan
Filip Agnieszka
Filip Aleksy
Jakub Fabian
Jakub Benedykt
Jakub Florjan
Fabian Florjan
Fabian Agnieszka
Fabian Aleksy
Benedykt Filip
Benedykt Ivona
Benedykt Ryszard
Benedykt Pelagia
Feliks Florjan
Feliks Agnieszka
Feliks Aleksy
Feliks Filip
Wladimir Florjan
Wladimir Agnieszka
Wladimir Aleksy
Wladimir Pelagia
Hiacynta Pelagia
Hiacynta Feliks
Hiacynta Wladimir
Kazimierz Blazej
Kazimierz Mateusz
Kazimierz Henryk
Kazimierz Gabriel
Blazej Mateusz
Blazej Henryk
Blazej Gabriel
Mateusz Blazej
Mateusz Henryk
Mateusz Gabriel
Henryk Blazej
Henryk Mateusz
Henryk Gabriel
Gabriel Blazej
Gabriel Mateusz
Gabriel Henryk
Otto Kazimierz Otto Mateusz
Otto Henryk
Otto Gabriel
Julia Blazej
Julia Kazimierz
Julia Henryk
Julia Gabriel
Kasper Kazimierz
Kasper Filip
Kasper Agnieszka
Kasper Aleksy
Wojciech Kazimierz
Wojciech Albin
Wojciech Agnieszka
Wojciech Kasper
Albin Kazimierz
Albin Filip
Albin Kasper
Albin Aleksy
Zachariasz Kazimierz
Zachariasz Filip
Zachariasz Kasper
Zachariasz Aleksy
Franciszek Kasper
Franciszek Filip
Franciszek Zachariasz
Franciszek Aleksy
Giertruda Kazimierz
Giertruda Kasper
Giertruda Zachariasz
Giertruda Aleksy
Sebastian Kasper
Sebastian Filip
Sebastian Wilhelm
Sebastian Roza
Wilhelm Mariusz
Wilhelm Sebastian
Wilhelm Wilhelm
Wilhelm Roza
Mariusz Ivona
Mariusz Halina
Mariusz Konrad
Zygmunt Mariusz
Zygmunt Sebastian
Zygmunt Wilhelm
Zygmunt Roza
Zenon Konrad
Zenon Halina
Zenon Wilhelm
Roza Kasper
Roza Filip
Roza Wilhelm
Roza Sebastian
Krzysztof Sebastian
Krzysztof Aleksy
Krzysztof Filip
Krzysztof Elzbieta
Marcin Wiktor
Marcin Rafal
Marcin Elzbieta
August Wiktor
August Rafal
August Elzbieta
Wiktor Krzysztof
Wiktor August
Wiktor Rafal
Wiktor Elzbieta
Rafal Krzysztof
Rafal Wiktor
Rafal August
Rafal Elzbieta
Elzbieta Krzysztof
Elzbieta Wiktor
Elzbieta Rafal
Elzbieta August
Wawrzyniec Artur
Wawrzyniec Konrad
Wawrzyniec Halina Czeslaw Wawrzyniec
Czeslaw Ivona
Czeslaw Halina Brunon Wawrzyniec
Brunon Mariusz
Brunon Halina
Doloreta Pelagia
Doloreta Teofila
Doloreta Walburga
Doloreta Agnieszka
Halina Wawrzyniec
Halina Czeslaw
Halina Brunon
Petronela Pelagia
Petronela Teofila
Petronela Walburga
Petronela Agnieszka
Pelagia Kasper
Pelagia Teofila
Pelagia Walburga
Pelagia Agnieszka
Teofila Pelagia
Teofila Sebastian
Teofila Walburga
Teofila Agnieszka
Walburga Pelagia
Walburga Teofila
Walburga Aleksy
Walburga Agnieszka
Agnieszka Pelagia
Agnieszka Teofila
Agnieszka Walburga
Agnieszka Aleksy
Teodora Pelagia
Teodora Teofila
Teodora Walburga
Teodora Agnieszka
Aleksy Agnieszka
Aleksy Sebastian
Aleksy Tadeusz
Tadeusz Aleksy
Tadeusz Maksymilian
Tadeusz Dominik
Tadeusz Bernard
Maksymilian Aleksy
Maksymilian Dominik
Maksymilian Bernard
Maksymilian Tadeusz
Dominik Aleksy
Dominik Maksymilian
Dominik Tadeusz
Dominik Bernard
Bernard Aleksy
Bernard Maksymilian
Bernard Dominik
Bernard Tadeusz
Trust Measures
Vertex In-Degree
Betweenness Centrality
Closeness Centrality
Eigenvector Centrality
Konrad 6
247.339683
0.006098
0.005647
Filip 13
517.448618
0.060093
Frank 1 0
0.003745
0.000245
Artur 1 0 0.003745 0.000245
Wawrzyniec 3
218.666667
0.004717
0.001958
Julian 5
38.522894
0.005988
0.009062
Ivona 4
775.906921
0.024377
Ryszard 5
10.230159
0.005405
0.008381
Wladyslaw 4 0
0.005208
0.006347
Aleksy 17
989.886289
0.066105
Florjan 10
287.360653
0.007092
0.026856
Adalbert 4 0 0.005208 0.006347
Krzysztof 5
510.5
0.007042
0.023439
Halina 6
19.316667
0.004808
0.002933
Jakub 1
1.5
0.00641
0.016753
Fabian 1
24.205578
0.006944
0.028469
Benedykt 1
86.131136
0.018257
Feliks 1
25.656768
0.028735
Wladimir 1
37.554459
0.006897
0.025724
Hiacynta 0
3.95
0.018877
Kazimierz 11
604.833333
0.02417
Blazej 5 0.5
0.004651
0.006209
Mateusz 5 0.5 0.004651 0.006209
Henryk 6
1.666667
0.004673
0.006824
Gabriel 6 1.666667 0.004673 0.006824
Otto 0 0
0.00463
0.005518
Julia 0 0 0.00463 0.005518
Kasper 8
404.670269
0.053555
Wojciech 0
53.788889
0.006536
0.020453
Giertruda 0
55.566667
0.006803
0.02199
Albin 1
114.685714
0.007194
0.02812
Zachariasz 2
115.72674
0.007246
0.031633
Franciszek 0 0
0.006849
0.026492
Sebastian 6
257.952381
0.033905
Wilhelm 5
102.25
0.010478
Mariusz 3
141.807143
0.006061
0.00685
Zygmunt 0
30.43254
0.009045
Zenon 0
14.81746
0.005155
0.002389
Roza 3
65.488095
0.020939
Marcin 0 0 0.005155
0.005046
August 3 0 0.005155 0.005046
Wiktor 4 0.5
0.005181
0.005608
Rafal 4 0.5 0.005181 0.005608
Elzbieta 5 0.5 0.005181 0.005608
Czeslaw 1
157.78254
0.005882
0.004376
Brunon 1 7.9
0.004762
0.002179
Doloreta 0 0
0.005952
0.018895
Petronela 0 0 0.005952 0.018895
Pelagia 9
101.17376
0.036068
Teofila 6
41.790476
0.006452
0.02688
Walburga 6
38.488889
0.006623
0.030466
Agnieszka 14
414.835281
0.008197
0.057351
Teodora 0 0 0.005952 0.018895
Tadeusz 4 0
0.006135
0.013276
Maksymilian 3 0 0.006135 0.013276
Dominik 3 0 0.006135 0.013276
Bernard 3 0 0.006135 0.013276
The excel spreadsheet I have provided contains 3 lists of names which represent the vertices we will analyze using a directed graph. Here is what I want you to do:
1. Install NodeXL Basic (free) using the link provided in the assignment.
2. Open NodeXL
3. Open the additional Excel spreadsheet I provided in the assignment “ABB Social Network dataset”
4. Click on the bottom Tab labeled Trust, Copy the names listed in columns A&B
5. Paste those names into columns A&B of a NodeXL template file
6. In the NodeXL tab at the top of the sheet, click Workbook Columns > Show ALL.
7. In the graph menu, change graph Type to Directed
8. In the analysis menu, click Graph Metrics, check mark “Overall Graph Metrics” and “Vertex In-Degree” then click Calculate Metrics
9. On the Vertices worksheet tab, copy all names in column A, and paste those names into the column titled “Labels”
10. Click Autofill Columns, Set Vertex Size to “In-Degree,” click Options > Vertex Size Options > … On the right side, where it says “To this vertex size” increase the number from 10 to 40. Click OK. Click Autofill. Click Close.
11. On the Graph, Change Fruchterman-Reingold to Harel-Koren Fast Multiscale. Click Refresh graph.
12. This should give you a visual graph of the social network indicating who is most (and least) trusted to help get the organization on the right track. If the graph you see is a bit muddled, you can click Refresh a few times until you find clearer version.
13. You can repeat this set of procedures using names from the Formal Chart Sheet and names from the Resources Sheet.
14. You can also change colors of selected nodes (for example, you can highlight the department heads who report to Frank), and just play around with the data.
In case you are having trouble, I have included some example graphs you can use in the supplemental Questions document.
I know that NodeXL basic has very limited abilities, but it is sufficient to make the point that social network analysis is a very useful tool for analyzing qualitiative data and making personnel decisions for organizations. This is the main purpose of the exercise.
*Supplemental material created by professor
According to the case, in approximately March of 1996, the ABB Elta organization had a total of 932 employees. At this time, David Hunter was still the country manager (served from 1990 through the end of 1996). As the case discusses, this was a difficult time because of several key factors:
1. Wage pressures were rising and exacerbating the productivity and profitability problems facing ABB Elta.
2. Employees were not accustomed to showing initiative and leadership for various cultural legacy reasons, largely stemming from mistrust and risk aversion during the Soviet era.
3. Efforts at leading the change toward more productive management practices were failing to make the desired impact, possibly due to an entrenched resistance to change as the prior leadership sought to defend their powerful positions in the organizational hierarchy.
4. Frank wanted to consider whether restructuring might be necessary and who might be the right people to lead a change initiative.
Frank Duggan entered this new leadership position in Feb 1996. As he entered that position, in February, he asked his good friend, Artur Czynczyk, to conduct a survey of the workforce leaders and managers. In all, there were 55 employees in various leadership, management, and supervisory roles at ABB Elta. Each of these 55 leaders had their own team of lower-level employees who worked for them. As indicated in the case, the organization was structured with several divisions: Power Transformers, Distribution Transformers, High Voltage Switchgear, Insulation Component Production, Galvanizing, Engineering, HR, and Paint.
The survey Artur administered contained only 3 questions:
1. To whom do you report? (Who do you consider to be your #1 Boss?) [name one person]
2.
Who controls the resources needed for you to do your job better?
[name up to 4 people]
3. Who do you trust most to help get our organization back on track? [name up to 4 people]
The responses are available in the excel spreadsheet under the tabs: Formal Chart, Resources, and Trust.
After receiving the responses, Artur began analyzing the data using NodeXL in order to look for patterns and to help Frank interpret the meaning of the data. NodeXL is an excel-based software program that can analyze social network analysis data. The following diagrams provide a graphical representation of the organizational networks for perceived formal authority, resource control, and trust. The nodes consist of names of managers and team leaders who were asked to indicate which other managers and team leaders satisfied the above (3) questions. The lines are actually directional arrows which indicate who selected whom as the most significant boss, controller of resources, and most trusted person. The individuals marked in Pink are actually the formal leaders of their respective departments within the organization. Finally, the size of the nodes are proportionately scaled to indicate the “in-degree.” This means that the people who are most often selected by others will have a node with larger diameter.
To whom do you report? (Who do you consider to be your #1 Boss?)
Who controls the resources needed for you to do your job better?
Who are the people who you trust most to help get our organization back on track?
Based on this information, and the associated info in the spreadsheet…
1. What are the main conclusions you would draw from looking at the first diagram showing the formal organization structure?
2. What is surprising about the workforce’s perception of who controls the resources that are most needed for people to increase their work performance?
3. What is surprising about who are the people most trusted to get the organization back on track?
4. After carefully looking at the graphical diagrams and the data, can you identify any individuals who may be representative of the entrenched resistance?
5. Based on the diagrams and data, who do you think should be selected as change agent(s)?
6. Frank would really like to have a person on the inside who could report back to him secretly about the progress of the cultural change and status of the resistance (a.k.a. as an informant with access to info about morale and the culture change progress). Can you identify anyone who may be trusted by people represented by both the entrenched resistance group and the potential change agents who could be such a well-positioned informant?