Who leads the Company? - CSU Research Output

Who leads the Company?

An analysis of the Typology of the Company,

the Constellation of the Top Management Team

and the Characteristics of Managers

to determine the Dominant Coalition.

Yves Michel Serge Clerc

Bachelor of Business Administration (GSBA Zürich, Switzerland)

Master of Business Administration (Steinbeis University Berlin, Germany)

Thesis submitted in partial fulfilment of the requirements for the degree of

Doctor of Business Administration

School of Management & Marketing, Charles Sturt University, NSW Australia

December 2017

Table of Contents

Tables ...................................................................................................................... i

Figures .................................................................................................................... iv

Screenshots ................................................................................................................... v

Graphs ................................................................................................................... vii

Certificate of Authorship.............................................................................................. ix

Acknowledgements ....................................................................................................... x

Dedication ................................................................................................................... xi

Abstract .................................................................................................................. xiii

1 Chapter one: Introduction ......................................................................................... 1

1.1 Conceptual framework .................................................................................. 1

1.2 Background to the research ........................................................................... 3

1.3 Research problem and hypotheses ................................................................. 5

1.3.1 Research problem .............................................................................. 5

1.3.2 Hypotheses ........................................................................................ 5

1.4 Justification for the research ........................................................................ 10

1.4.1 Example: Application of this Dissertation (Presumption) ................. 12

1.5 Methodology ............................................................................................... 12

1.6 Outline of this thesis ................................................................................... 13

1.7 Definitions for this thesis ............................................................................ 14

1.8 Delimitations of scope and key assumptions ............................................... 15

1.9 Conclusion .................................................................................................. 16

2 Chapter two: Literature Review .............................................................................. 17

2.1 Introduction ................................................................................................ 17

2.1.1 The Literature Review Framework................................................... 19

2.2 Research Topic ........................................................................................... 20

2.2.1 Perspectives of the Research Topics ................................................ 20 2.2.2 Visualization of the four Perspectives .............................................. 21

2.2.3 The first Perspective: The Typology of the Company....................... 21 2.2.4 The second Perspective: The Top Management Team ...................... 22

2.2.5 The third Perspective: Managers’ Characteristics ............................. 22 2.2.6 The fourth Perspective: Span of Control .......................................... 22

2.3 Draft of Parent Disciplines and Problem Areas in this Dissertation.............. 23

2.4 Perspective: Typology of the Company ....................................................... 24

2.4.1 Miles & Snow Typology .................................................................. 26 2.4.2 Mintzberg’s Model of the Five Structural Configurations ................ 28

2.4.3 Porter’s Generic Competitive Strategies ........................................... 30

2.4.4 The Sixteen Detailed Business Model Archetypes ............................ 32

2.4.5 The Combined Approach: Porter / Miles & Snow ............................ 33 2.4.6 Recent Research Discourse .............................................................. 33

2.4.7 The interpretation of the Miles and Snow Typology in this thesis ..... 36

2.5 Perspective: Top Management Team ........................................................... 38

2.5.1 The influence of a TMT’s Constellation on Value Creation .............. 39

2.5.2 Summary of TMT-Constellation Aspects ......................................... 52 2.5.3 Age as a Parameter in TMT-Constellation........................................ 53

2.5.4 Education as a Parameter in TMT-Constellation .............................. 53 2.5.5 Tenure as a Parameter in TMT-Constellation ................................... 54

2.6 Perspective: Managers’ Characteristics ....................................................... 54

2.6.1 Managers’ Characteristics ................................................................ 54 2.6.2 Overview of Managers’ Characteristics ............................................ 55

2.6.3 Findings ........................................................................................... 58 2.6.4 Conclusion ....................................................................................... 60

2.7 Perspective: Span of Control ....................................................................... 61

2.7.1 Introduction ..................................................................................... 61 2.7.2 History of the concept of ‘Span of Control’ ...................................... 62

2.7.3 How Span of Control is defined in the Management Literature ........ 64 2.7.4 Literature Review on the ‘Optimum Span of Control’ ...................... 66

2.7.5 Recent discourse .............................................................................. 73 2.7.6 Collection of all Findings ................................................................. 78

2.7.7 Summary ......................................................................................... 79

2.8 A CEO’s Power .......................................................................................... 81

2.8.1 Types of Social Power ..................................................................... 82

2.8.2 Recent Discourse ............................................................................. 83 2.8.3 Conclusion ....................................................................................... 86

2.9 Summary of the Literature Review Chapter ................................................. 87

3 Chapter three: Methodology ................................................................................... 89

3.1 Introduction ................................................................................................ 89

3.1.1 Paradigm and Methodology ............................................................. 89

3.1.2 Justification for the Methodology used in the Research .................... 91 3.1.3 Role of the Researcher during this Dissertation ................................ 92

3.1.4 Reliability and Validity .................................................................... 92 3.1.5 The Unit of Analysis, the Instruments of Measurement and Sources 93

3.1.6 Instruments and Procedures used to collect Data .............................. 95 3.1.7 Administration of Procedures ........................................................... 95

3.1.8 Limitations of the Methodology ....................................................... 96 3.1.9 Software used for the Analysis ......................................................... 96

3.2 Data Collection for each Research Perspective ............................................ 97

3.3 Typology of a Company .............................................................................. 97

3.3.1 Methodology of Collecting Data ...................................................... 98 3.3.2 Introduction to Awkward Constellations .......................................... 99

3.3.3 Conditions: .................................................................................... 100

3.4 Constellation of the Top Management Team ............................................. 101

3.4.1 Age as a Parameter in TMT-Constellation...................................... 101

3.4.2 Education as a Parameter in the TMT-Constellation ....................... 101 3.4.3 Tenure as a Parameter in TMT-Constellation ................................. 102

3.4.4 Methodology of Collecting Data .................................................... 102

3.4.5 Conditions: .................................................................................... 103

3.5 Methodology to evaluate Managers’ Characteristics .................................. 105

3.5.1 Public Opinion Poll ....................................................................... 107 3.5.2 Methodology for the Data collection .............................................. 108

3.5.3 Conditions for the Perspective Characteristics................................ 109 3.5.4 Summary of all Perspectives .......................................................... 109

3.6 Methodology of the Perspective: Span of Control ..................................... 110

3.7 Ethical Considerations .............................................................................. 112

3.7.1 Consent Process ............................................................................. 112 3.7.2 Administrative Details ................................................................... 114

3.7.3 Profile of Participants .................................................................... 114 3.7.4 LOTE Subjects .............................................................................. 115

3.8 Conclusion to Chapter 3: Methodology ..................................................... 115

4 Chapter four: Data Collection ............................................................................... 117

4.1 Introduction .............................................................................................. 117

4.1.1 Online Survey ................................................................................ 118

4.1.2 Description of the Online Application ............................................ 118 4.1.3 Sample Survey-Report ................................................................... 124

4.2 Collection of Data ..................................................................................... 128

4.2.1 Synopsis of 114 Submissions ......................................................... 128 4.2.2 Partial Review of Survey-Reports .................................................. 137

4.2.3 Top Management Teams with 2 Managers plus CEO ..................... 137 4.2.4 Top Management Teams with 3, 4 and 5 Managers plus CEO........ 140

4.2.5 Teams of six and above Managers ................................................. 147

4.3 Summary .................................................................................................. 147

5 Chapter five: Conclusions and implications .......................................................... 151

5.1 Introduction .............................................................................................. 151

5.2 Conclusions from each research question and hypotheses.......................... 151

5.3 Conclusions about the research problem .................................................... 163

5.4 Implications .............................................................................................. 164

5.5 Limitations................................................................................................ 165

5.6 Further research ........................................................................................ 166

5.7 Conclusion ................................................................................................ 167

6 References ............................................................................................................ 169

7 Appendices........................................................................................................... 185

7.1 Survey Reports ......................................................................................... 185

7.2 Approval of the Business Human Research Ethics Committee .................. 243

i

Tables

Table 2.1 Different approaches for a literature review 17

Table 2.2 Stages of this thesis and the framework of a literature review from

Arksey and O’Malley (2007) 19

Table 2.3 Based on Miles & Snow (1978) ‘Dimensions of the adaptive cycle and

strategic type characteristics’ (Table 1 of Conant et al., 1990, p.367) 28

Table 2.4 The key parts of an organization, based on Mintzberg (1980), source

Lunenburg (2012, p.4) 30

Table 2.5 Porter, M. E. Competitive advantage: Creating and sustaining superior

performance, Simon and Schuster, New York, (2008, p.40‒41) 31

Table 2.6 The sixteen detailed business model archetypes from ‘Do some

business models perform better than others? A study of the 1000 largest

US firms’, Weill et al. (2006, p.31) 32

Table 2.7 ‘Combined typology of business-level competitive strategies’, Mullins

and Walker (2013), retrieved from:

www.mktgsensei.com/MBA522/Chap009.ppt 33

Table 2.8 10 parameter-questions leading to Typology. Based on Conant et al.,

(1990, p.367), author’s interpretation 36

Table 2.9 The combined Typology. Derived from Conant et al., (1990, p.367),

author’s interpretation 36

Table 2.10 Re-phrased parameter-questions to identify the Typology and therefore

the Dominant Coalition 37

Table 2.11 Framework for understanding diversity in work teams. Jackson et al.

(1995, p.212‒213) 45

Table 2.12 Smith et al.’s (1989, p.65) citation of Miles & Snow strategy continuum,

reduced to the administrative problem variables. 48

Table 2.13 Summary of parameters considered in regard to TMT’s constellation

for strategic decision-making 53

ii

Table 2.14 Summary of the findings of the literature research on Managers’

Characteristics 58

Table 2.15 Selected Managers’ Characteristics, horizontal relationship 58

Table 2.16 Selected Managers’ Characteristics, vertical relationship 59

Table 2.17 Nine limitations of Span of Control according to Hanna and Gentel

(1971, p.115) 65

Table 2.18 Company/department size versus complexity of work 66

Table 2.19 Size/complexity-grid with all findings of this literature review: on the

ideal Span of Control (numbers link to the researched literature) 79

Table 2.20 Abstracted ideal Span of Control findings with +/- tolerance indication 80

Table 2.21 Pattern in the abstracted ideal Span of Control findings 81

Table 2.22 Summary of the selected variables for the four perspectives analysed

in this thesis 87

Table 3.1 The basic characteristics of qualitative and quantitative research from

Park and Park (2016, p.2) 90

Table 3.2 Paradigm and methodology of this thesis 91

Table 3.3 Selected measurement factors of each perspective and the instruments

used in this thesis 94

Table 3.4 Re-phrased parameter-questions to isolate the Dominant Coalition

based on Miles and Snow (1978) (repeated Table 2.10) 97

Table 3.5 Constellation of Dominant Coalition based on the Miles & Snow

Typology (1978), by the author 99

Table 3.6 Ideal Span of Control findings with average and +/- delta, by the author

(repeated Table 2.20) 110

Table 3.7 Pattern in the abstracted ideal Span of Control findings, by the author

(repeated Table 2.21) 111

Table 3.8 Administrative details of this thesis 114

iii

Table 4.1 Weighting of Typology by number of participating companies 128

Table 4.2 Frequency of departments in the survey and the potential Dominant

Coalition they belong to, according to Miles & Snow (1978) 129

Table 4.3 Managers’ Characteristics by frequency and departments 129

Table 4.4 Balanced weighting of Managers’ Characteristics 130

Table 4.5 Complexity of departments with the core area indicated in yellow 130

Table 4.6 Educational level of the managers by departments in nominations 131

Table 4.7 Educational level of the managers by department as a percentage 132

Table 4.8 Occurrence of first ranking department vs perspective, split

by team size 133

Table 4.9 Departments by team size and rank according to the computer

calculation 134

Table 4.10 Departments by team size and rank according to participants

assumption 135

Table 4.11 Matching of the survey’s calculation and assumption’s result 135

Table 4.12 Number of departments having an influencer separated by the sizes of

the Top Management Teams 136

Table 5.1 Constellation of Dominant Coalition based on the Miles & Snow

Typology (1978), by the author (repeated Table 3.5) 154

Table 5.2 Confusion matrix showing calculated ranking vs assumed ranking 155

Table 5.3 Confusion matrix split by ranks into accuracy tables 156

Table 5.4 Comparison of the impact of the perspectives Typology, Team

Constellation and Manager’s Characteristics on

the final result 157

Table 5.5 Educational level of dominant Top Management Team members 161

Table 5.6 Number of departments having an influencer separated by Top

Management Team sizes 163

iv

Figures

Figure 1.1 Model of the sections of a thesis (Perry, 1998, p.65) 13

Figure 2.1 Relationship between literature review, parent disciplines, research

problem and hypotheses (Perry, 1994, p.16) 18

Figure 2.2 Parent disciplines and problem areas in this thesis 23

Figure 2.3 The five basic parts of the organization, Mintzberg (1980, p.324) 29

Figure 2.4 The key parts of an organization, abstract version of the Mintzberg

Model, source Lunenburg (2012, p.2) 29

Figure 2.5 A diagram created by Denis Fadeev (2014) of Michael Porter's three

generic strategies based on a figure from Porter M. E., Competitive

strategy: Techniques for analyzing industries and competitors, New

York: Free Press, 1980, p.39. Https://commons.wikimedia.org/

wiki/File: Michael_Porter%27s_Three_Generic_Strategies.svg 31

Figure 2.6 A common illustration of the value chain (Porter, 1985, p.36) 38

Figure 2.7 The role of strategic choice in a theory of organization,

Child (1972, p.18) 50

Figure 2.8 The relationship between one supervisor to another supervisor or

between one supervisor to a subordinate 54

Figure 2.9 Relevant Managers’ Characteristics, in horizontal and

vertical relationships 60

Figure 2.10 The limited span concept (Van Fleet and Bedeian, 1977, p.357) 63

Figure 2.11 The optimum span concept (Van Fleet and Bedeian, 1977, p.360) 64

Figure 3.1 The three major research paradigms (Johnson, Onwuegbuzie and

Turner, 2007, p.124) 90

Figure 3.2 Convergence of multiple sources of evidence, from Yin

(2017, p.117) 94

Figure 3.3 Previous Findings –Managers’ Characteristics

(repeated Figure 2.9) 106

v

Screenshots

Screenshot 2.1 Online opinion poll; Managers’ Characteristics,

author’s work from the study DBA712 55

Screenshot 3.1 Screenshot of the first online opinion poll (DBA712) to evaluate

Managers’ Characteristics 107

Screenshot 3.2 Screenshot of online opinion poll – online results (repetition of

Screenshot 2.1) 107

Screenshot 3.3 Consent form and information sheet of the online survey in a

pop up window 113

Screenshot 4.1 URL of the online survey 118

Screenshot 4.2 Instructions to start the online survey 119

Screenshot 4.3 Typology perspective I 119

Screenshot 4.4 Typology perspective II 120

Screenshot 4.5 Size of the company 120

Screenshot 4.6 Size of the Top Management Team 121

Screenshot 4.7 CEO’s age and tenure 122

Screenshot 4.8 Details about each member of the Top Management Team 122

Screenshot 4.9 Details about each Managers’ Characteristics 123

Screenshot 4.10 Ranking of the influence of the Top Management Team

members by assumptions 123

Screenshot 4.11 Sample survey-report organizational chart with percentage

values and indications of potential influencers 124

Screenshot 4.12 Sample survey-report comparing ranking by calculation

(computer) and assumption (participant) 125

Screenshot 4.13 Sample survey-report perspective Typology 126

Screenshot 4.14 Sample survey-report perspective Constellation 126

vi

Screenshot 4.15 Sample survey-report perspective Managers’ Characteristics 127

Screenshot 4.16 Survey-Report 2.7 137




Screenshot 4.20 Survey Report 3.5 141





vii

Graphs

Graph 2.1 CEO Span of Control (1986-2008). Source: Guadalupe, M., Li, H., &

Wulf, J. (2011). Who Lives in the C-Suite? In Wulf, J. (2012). The

flattened firm: Not as advertised. California Management Review.

Fall, Volume 55, Issue 1, p.10. 76

Graph 3.1 A visual representation of the Empirical (68-95-99.7) Rule based on the

normal distribution divided in 0.5 steps of Standard Deviation. Retrieved

from Math Planet, Algebra 2, Quadratic Functions and Inequalities -

https://www.mathplanet.com/education/algebra-2/quadratic-functions-

and-inequalities/standard-deviation-and-normal-distribution 104

ix

Certificate of Authorship

I hereby declare that this submission is my own work and to the best of my knowledge

and belief, understand that it contains no material previously published or written by

another person, nor material which to a substantial extent has been accepted for the

award of any other degree or diploma at Charles Sturt University or any other

educational institution, except where due acknowledgement is made in the thesis [or

dissertation, as appropriate]. Any contribution made to the research by colleagues with

whom I have worked at Charles Sturt University or elsewhere during my candidature is

fully acknowledged. I agree that this thesis be accessible for the purpose of study and

research in accordance with normal conditions established by the Executive Director,

Library Services, Charles Sturt University or nominee, for the care, loan and

reproduction of thesis, subject to confidentiality provisions as approved by the

University.

Yves Michel Serge Clerc 17th

December 2017

x

Acknowledgements

The realization of this thesis has been a lonely way throughout a significant period of

my life. Intrinsic conviction and stamina have led to this result.

per aspera ad astra

There are three people which have supported me in the finalization of this thesis and

deserve my utmost gratitude:

First and most importantly my principal supervisor Dr. Kerry Tilbrook who has

patiently accompanied this long journey with profound academic support.

Ms Honey Yam, BSc (Drake) Mathematics, from Malaysia, for translating the

researched variables into algorithms and equations as well as being supportive with the

multivariate data analyses.

Mr Yaroslav Bykov, Ph.D. (Russian Academy of Sciences) from Russia, for

programming the same algorithms and equations into an online software application

which was used for the survey.

xi

Dedication

With deep love to my wife Jrene Clerc;

by the time this work is published she has shared 20 years of her life with me.

During the realization of this doctoral dissertation she has been living and working

with me in Switzerland, Russia and Malaysia and has given birth to our children

Maurice (*2014), Nicolas (*2015) and Nathalie (*2016). Only nine months after our

daughter was born, Jrene won the Johor Bahru Triathlon (Malaysia). In every respect,

she is truly an inspiration for stamina.

xiii

Abstract

The aim of this thesis is to measure the relative share of the Top Management Team

Members’ influence on strategic decisions. Three perspectives are explored: Typology,

Team Constellation and Manager’s Characteristics and an additional dimension is added

with an in-depth analysis of the Span of Control of each Top Management Team

Manager to identify potential influencers. A model was developed which was tested

through an online survey, which showed good agreement with the theory.

Many of the factors influencing the decision-making process of the upper echelon are

difficult to specify numerically and are also hard to determine. This dissertation takes a

numerical approach to the disciplinary relationships among decision-makers within the

Top Management Team where the CEO is equipped with the power of a blocking

minority.

A predominantly quantitative methodology is applied in this thesis. Although during the

pre-empirical stage, subjective and non-measurable data is collected using an

interpretative approach, for the perspectives of Team Constellation and Manager’s

Characteristics. The research on the perspective of Span of Control is evaluated by

measurable factors and is quantitative in nature. The perspective of Typology

concentrates on the model to be applied. The empirical stage – the online survey – aims

to collect numerical data for statistical analysis. Consequently, an online survey was

developed with the objective to determine the key factors that are identified in the

literature and this resulted in 114 valid data sets, from a number of different companies.

Consideration is given to the fact that Typology originates from the organisational

structure of the business and is only influenced by the managers themselves, through

effective change management. Whereas Team Constellation is constantly changing with

increasing age and tenure of the team members; and Manager’s Characteristics can be

directly influenced by each Manager. Further research might analyse these factors by

taking an even broader approach without the use of Typology and instead considering

just the Constellation and Characteristics.

Keywords: Dominant Coalition, Top Management Team, Organizational Structure,

Typology, Team Constellation, Managers’ Characteristics, Span of Control

1

1 Chapter one: Introduction

1.1 Conceptual framework

This thesis aims to develop a systematic approach which enables an evaluation or

comparison of the Top Management Team members according to their influence on

strategic decisions by analysing and comparing organizational structures. For example,

Barrick, Bradley, Kristof-Brown and Colbert (2007, p.545) argue that “distinguishing

between team interdependence and team processes is vital to understanding how TMT

functioning relates to team and organizational outcomes” or “[i]dentifying which factors

affect firms’ performance is a critical issue in strategic management research” (Escribá-

Esteve, Sánchez-Peinado and Sánchez-Peinado, 2009, p.581). Within the same category

of statements Hambrick and Quigley’s comment is pertinent “[h]aving an accurate grasp

of whether – or how much, when and where – top executives matter is centrally

important for advancing theory and research” (2014, p.473).

In selecting a company containing complex tasks, various departments and employees;

the number of people having influence in leading the company is most probably also

larger than one. What could be described as 1 + n, where ‘1’ represents the chief

executive officer (CEO), and ‘n’ is the number of members in the Top Management

Team. Business objectives and human characteristics are variable and it can be assumed

that not everyone in the ‘n-group’ is equal. For everyone in the ‘n-group’ is as unique as

a fingerprint. This diversity should be an advantage or an added value described by

Daellenbach, McCarthy and Schoenecker as ‘Heterogeneity or diversity of the Top

Management Team (TMT)’ when mentioning that:

[w]hile heterogeneity in the TMT may increase the level of conflict within the

team, the variety of values, biases and perceptions that result from functional

background diversity should lead to a more comprehensive consideration of the

investment alternatives (1999, p.202).

Some members of this group may vary in their role in influencing the leading process,

based on criteria such as their position, participation, expertise, etc. or in “functional

background and functional experience” as defined by Daellenbach et al. (1999, p.201)

and repeated by Escribà-Esteve et al. when describing the factors such as “average age,

level of education, amount of experience, number of members or number of family

members in the TMT [that] may condition the firm’s behaviour” (2009, p.584). Talke,

Salomon and Kock describe executives’ cognitive backgrounds and values as they are

2

“a function of observable characteristics such as education or work experience” but

argue at the same time “however empirical findings on a direct relationship between

Top Management Teams diversity and firm performance are equivocal” (2011, p.821).

Whereas, Hambrick and Quigley conclude: “[i]ndeed, an understanding of executive

effects can be thought of as fundamentally important for much of organizational

science” (2014, p.473).

The aim of this thesis is to enable a pro-rated weighting of the influence of different

members in a decision-making team. The results reveal that a certain combination of

members of a group can execute more dominance in their decision-making process than

another. For example, Hambrick and Mason (1984, p.199) state that managers from

“output functions – marketing, sales and product R&D – emphasize growth and the

search for new domain opportunities” and in contrast, managers from “throughput

functions (production, process engineering, accounting)” are hypothesized to emphasize

increased efficiency and backward integration as cited in Daellenbach et al. (1999,

p.201). This will become important for this research when evaluating the value chain of

an organization. The results of this research prove that depending on the business

typology different departments do have differing levels of influence on strategic

decisions.

Hamilton, (1921), Graicunas (1933), Gulick et al. (1937) and Urwick (1956) were

among the most influential authors in the early twentieth century to write about the

principle of Span of Control and the corresponding relationships between the staff

members. The paper ‘Relationship in Organization’ which is a foundational study for

that topic, was written originally by Graicunas (1933), edited by Gulick et al. in 1937

and retyped by Nickols in 2011. From the viewpoint of this thesis, Gulick et al., have

forfeited an opportunity when they concluded that “it is not possible to assign

comparable weights to these different varieties of relationship” (Gulick et al., 1937,

p.185). This capitulation in any attempt to allocate pro-rated values to relationships was

a surprise for the author of this thesis and resulted in the initial stimulus to undertake the

research reflected in this work.

Research and in-depth analysis are conducted to discover answers to a total of seven

hypotheses covering the topic of the Dominant Coalition in Top Management Teams in

order to enrich professional research in two main fields. These are described below.

3

Referring to the aim to refute the impossibility of weighting relationships in

organizational structures it was discovered that there are specific criteria which enable a

comparison of the relationships among the members of the top management team. With

that outcome a value for the influence of each management member on strategic

decisions can be calculated.

Depending on the complexity of the tasks to solve and on the skills and experience of

staff members to solve the task, there are limits in a supervisor’s capacity to handle

organisational complexity. The British General Sir Hamilton (1853–1947) wrote in

detail about Span of Control as early as in 1921, which is elaborated in the subchapter

‘2.72 History of the concept Span of Control’. The ratio between supervisor and staff is

usually defined as the span of management, Span of Control or supervisory ratio and

work group size (Meyer, 2008, p.104).

Defining the Span of Control of a department will disclose the possibility to determine

influencers by analysing organisational structures. This is contributing to the body of

knowledge because it was found throughout the research that an overload in work or

responsibility of a manager can lead to something which is termed in this thesis ‘the

momentum of losing control’. When this happens there could be other team members

who gain influence due to the necessity of the overloaded manager having to share

responsibility.

Combining the schemata to define the power of top management team members with

the identification of their influencer results in the possibility of answering the research

question: ‘Who leads the Company?’ It is possible today to answer this question

because as Pettigrew asserts when referring to Hambrick and Mason’s ‘upper echelon’

published in 1984 that “since then a noteworthy pattern of work has emerged linking the

demographic characteristics of top management teams to a variety of organizational

outcomes such as performance” (Pettigrew, 1992, p.164).

1.2 Background to the research

What is the purpose of such a weighting system? Firstly, it provides an idea of the team

members who are necessary to lead a group of people under various circumstances.

Since a large company is not led by its thousands of employees, but by a small group of

responsible people. McManus states in ‘Losing our Span of Control’ that “the number

of people per leader should be no more than five” and suggests that “a span of control of

50:1 is ridiculous” (2007, p.22). Therefore, it makes sense to know who those people

4

are or ideally where their place in the company should be, to make sure that this group

of leading supervisors can work efficiently. A systematic approach to define those

group members and their influence in the decision-making process, could therefore also

reveal missing positions and assist in describing the profile of the incumbents who are

necessary to ensure the best performance of the company depending on its

circumstances or environment. Or as Hambrick and Mason (1984, p.200) describe it,

homogeneity can be supportive for efficient solutions, whereas heterogeneity gives

better performances for new or innovative solutions. This also means that companies

which are required to find new solutions due to their unfavourable situation can opt to

sacrifice homogeneity in their TMT to a certain degree.

But how can informal leaders and bottom-up as opposed to top-down decisions be

measured? Many factors which are influencing the decision-making process of the

upper echelon are difficult to specify numerically and are often hard to determine or as

Pettigrew puts it “research in this tradition is avowedly sociological” (1992, p.165).

Therefore it can happen that employees might ask themselves sometimes why certain

decisions were made by their company and other suggestions were unsuccessful. If they

knew a way to find and address relevant influencers (those whose names do not

necessarily appear on the senior managers’ organisational chart) employees could

contribute much better to the successful implementation of ideas. The chance that an

idea is successfully realised can be enhanced by the opportunity to communicate with

the right people in advance. “Management requires tolerance of the idea that the

meaning of yesterday’s action will be discovered in the experiences and interpretations

of today” is a valid observation by March (1991, p.100).

The systematic approach utilised in this research is useful for external people who want

to introduce their ideas into a company’s decision-making process. This follows the

perspective of Galeotti and Goyal (2009, p.510) who suggest that: “incorporating social

network information in the design of marketing and influence strategies can both reduce

waste in resources and generate greater sales” or simply as Kerr says: “[i]nfluencers are

the people who can influence the decision-makers” (2011, p.69). In Hypothesis No. 3

treated later in this study it describes for example how Sales Teams, Marketing or

Human Resources Departments or also firms located in the field of Mergers and

Acquisitions could assimilate this approach in their work.

The approach of this thesis assists in measuring who belongs to the dominant coalition

in the Top Management Teams of profit-oriented companies based on the analysis of

5

organisational structures. This systematic approach could assist in (re)organising

companies, and analyse the reasons for the success or failure of internal decisions and

also help third parties to define an effective strategy for negotiating with a company.

1.3 Research problem and hypotheses

1.3.1 Research problem

The approach of this thesis will assist in recognizing the influence of the different

members working in a decision team. The results show how a certain combination of

members of a group may be more effective in their decision-making process than

another, and also that depending on the Typology of the company, different departments

have varying influences on strategic decisions. The importance of choice was described

by March in an arresting way: “[it] is the process that gives meaning to life, and

meaning is the core of life” (1991, p.111).

1.3.2 Hypotheses

The following section describes the hypotheses, the research background and their

contribution to the immediate field of research.

Hypothesis 1: The impossibility to allocate numerical values to relationships of

decision-makers in organizational structures as Gulick et al. have written in 1937 can

be refuted.

Research background: The research knowledge developed after 1937 makes it

possible to select specific criteria to facilitate the comparison of the members of the Top

Management Team and then to charge these criteria with different weightings. Four

perspectives are analysed to consider and validate this:

1. The Typology of a company. In respect to the nature of the business it is defined

which departments will directly or indirectly belong to the Dominant Coalition.

2. The Constellation of the Top Management Team members could give more

insights. Through the analysis of the team constellation in selected Top

Management Teams it should be possible to define those people who hold a

stronger position in the team and therefore exert more influence on the CEO

than others.

3. Managers’ Characteristics It is assumed that it is necessary to know which

characteristics are of importance in a horizontal relationship (between manager

6

and manager) on the organizational chart and which characteristics are of

importance in a vertical relationship (leader to subordinate).

4. Span of Control. Regarding the size of the organization and also the complexity

of tasks it is explored how many subordinates are effectively managed by one

person. If that value is exceeded, the responsible manager is losing control and

then the next logical step would be that another person in the team beside the

leader gains importance in supporting/influencing the leader – because this

manager can no longer handle the task of leading in isolation.

The above perspectives describe in descending order the following factors:

the environment of the company

the Top Management Team and

the individual Manager

and finally the Span of Control of each manager.

Collecting data on these four perspectives results in enough information to rank the

members of the Top Management Team by their influence on the decision-making

process and to indicate if there is a high probability for a hidden force, or an influencer,

in any or several of the departments.

Hypothesis 2: Members of the top management team with an identical hierarchical

level have differing amounts of influence on strategic decisions.

Research Background: This research theorises that the official leader of a group has

the power to say ‘no’ to a proposal because s/he has the final responsibility for that

decision. Hambrick and Quigley (2014) describe the influence of the CEO on Return on

Assets with 35.5% where they differentiate “low- (28.3), medium- (35.0), and high-

discretion (42.4) industry subsamples” (p.484). In this research the power of a CEO is

described as the blocking minority, which is 34% because each decision is assumed to

be a vote for which a two-thirds majority is necessary to succeed. Pettigrew succinctly

states that “power lies with those at the strategic apex of the organization” (1992,

p.163). From a mathematical point of view, however, if any other value such as for

example 25% is defined as the CEO’s part of decision power it would not lead to

another outcome. The only relevant question is how the reciprocal value of the CEO’s

power, the blocking minority, is divided among the other team-members. The easiest

answer is that in our case the remaining 66% of the decision power is equally divided

among the number of team members in the top management team. But it seems

7

self-evident that depending on different factors the amount of influence may differ

among the members of the top management. Nevertheless, there are some uniform

patterns because as Talke et al. suggest “TMT candidates often exhibit similar

characteristics: most of them are male, older and have a business background” (2011,

p.829). March’s comment on hierarchies reinforces this observation more strongly in

that they “fit a mostly male world view of human order … organized around relations of

domination and subordination” (1991, p.108). If Hypothesis No 1, that influence is

measurable, can be verified, then it should also be possible to allocate this influence

among the Top Management Team members through finding differences amongst them.

Hypothesis 3: An algorithm can be used to evaluate the members of the Dominant

Coalition.

Research Background: Despite the question of whether the interdependence of Top

Management Team members in regards to the company and team can be measured it is

important to understand to what degree this process can be done without human

interference and just by feeding a system with a minimal set of data. If this is achievable

such an evaluation can be done without having personal knowledge of either the

company or the Top Management Team members. The potential value of such a

possibility is apparent for many different applications such as:

For organizational design in an attempt to reduce dysfunctional aspects

The strategic approach of a sales team towards a potential customer could be

adjusted when knowing who is most influential in the targeted company

Marketing could use this tool to stimulate the right channels

Merger and Acquisition companies may find such a tool interesting because it

could detect hidden influencers

Human Resources companies, departments or even individuals who are

approaching new employers could use such a tool for a better prediction of

whether cooperation is likely to result.

These are just to name a few possibilities. The question is how with a little information

(relevant set) and grade of accuracy (in percentage) a prediction of the constellation of

the Dominant Coalition is given just based on the perspectives of Typology,

Constellation and Manager’s Characteristics.

8

Exploration 3a: Which of the analysed perspectives, Typology, Team Constellation or

Managers’ Characteristics, has most influence on the constitution of the Dominant

Coalition?

Research Background: After selecting three perspectives as important to the

constitution of the Dominant Constellation it is evaluated if one of these perspectives is

determining the result more than the others. It could be that three perspectives are

evaluated but actually the outcome is established with just one or two of them. Are there

factors in one of the perspectives of such importance that there is just no way to

compensate this factor by substituting any other or a combination of these? One

difference among the perspectives of Typology, Constellation and Characteristics which

needs to be remembered is that the perspective of Typology is pre-determined by the

design and structure of the company and is not influenced by the individual manager.

Depending on the Typology of a company a dominant coalition is already determined

(Miles & Snow, 1978), however, most likely a company is not classified into one

Typology only but rather is a mixture of several Typologies so that the Dominant

Coalition might oscillate among various departments.

Exploration 3b: If the Dominant Coalition is predetermined through the Typology what

influence does Team Constellation have on the final setting?

Research Background: Another aspect related to the three perspectives which is

analysed is whether the right person in a suitable company might fail if the constellation

of the Top Management Team is unsuitable for them. Certainly, a positive team

constellation is beneficial to the individual manager but the question is how much

weight (importance) is implied in the Team Constellation. Understanding this could be

beneficial for the career planning of job movers; one person may be the successful

candidate having found a compatible company but later becomes a disruptive element

within the Team Constellation and therefore is not suitable.

Exploration 3c: If the Dominant Coalition is predetermined through Typology what is

the influence of Managers’ Characteristics on the final setting?

Research Background: Allied with the perspective of Team Constellation’s influence,

and the possibility of one department being in the Dominant Coalition, we explore to

what extent the Characteristics of each Top Management Team manager is influencing

the constellation of the Dominant Coalition. Typology representing the nature or

organisational structure of the company is the superordinate perspective as it is or at

9

least should be in most cases a stable perspective. The Constellation of the Top

Management Team is a comparably dynamic (fast or slowly) changing perspective

depending on the time line and the associated changes of Top Management Team

members. Both of those perspectives cannot or can only partially be influenced by the

individual Manager whereas the perspective Characteristics focuses on the individuality

of each Manager. A person may analyse a Top Management Team and evaluate its

weaknesses with the aim of taking advantage of such weakness. For example, if

someone can capitalize on a certain personal strength which by coincidence is a

weakness of the remaining Top Management Team members then this person

potentially could secure a place in the Dominant Coalition.

Hypothesis 4: The head of Finance is predominantly the hidden deputy (number two) in

the Top Management Team

Research Background: It is unsurprising that the Chief Financial Officer has an

important role especially when companies declare themselves as a profit oriented entity.

This research also investigates to what extent the head of Finance is the shadow of the

CEO and also what are the circumstances that could reward another member of the Top

Management Team (other than the head of Finance) with such an influential deputy

position. The perspective of Typology usually finds Finance most often in the Dominant

Coalition. Nevertheless, there is room for the assumption that depending on the nature

of the business, other Top Management Team Members are also important or even the

most important directly after the CEO. Although Finance is important for a small

laboratory developing a treatment against cancer it is obvious that the role of the head of

R&D of the same company is of utmost importance because the results of this

department will have a direct impact on the laboratory’s future existence.

Hypothesis 5: Dominance in a Management Team is related to Education and the

Complexity of Tasks of the dominant team members.

Research Background: This research analyses data from 100 companies which

captures information from an estimated 400 individual managers including data in

respect of their education and the complexity of their role within the context of the

whole company. This information is useful if the complexity of work (difficulty of the

tasks and numbers of supervised staff) and educational status of the manager serves as

an entitlement for membership of the Dominant Coalition within the company.

10

Exploration 5a: So who leads the company? Where are the influencers hidden?

Research Background: The hypothesis is that if the number of subordinates exceeds

the Span of Control of a manager then there is a requirement for a hidden number two to

emerge in that team or department. Because the manager is overloaded with tasks, this

number two or deputy will take over partially and make proposals to the manger. The

manager will or chooses to trust these proposals from the influencers due to time

constrains and then by osmosis gradually includes them in his or her contributions to the

overall top management team. This means that the contribution to the top management

team comes not only from the managers themselves but also indirectly from their

influencers. This may result in the situation of bottom up leadership where a hidden (or

not hidden) influencer is steering the company to a certain extent.

1.4 Justification for the research

This dissertation will allocate numeric values to disciplinary relationships among

decision makers in the Top Management Team by analysing the Company’s Typology,

the Team Constellation and Manager’s Characteristics simultaneously. For example,

Escribà-Esteve et al. encouraged the author to undertake this research when they

lamented that “few studies have provided a framework that jointly analyses the

managerial characteristics, the mediating processes and attitudes by which managers

shape the direction of their firms” (2009, p.582). Also Hambrick and Quigley (2014,

p.474) asked “how much influence do CEOs in general have on organizational

performance?” when referring to Lieberson and O’Connor (1972) as well as Mackey

(2008).

Any individual or group whether internal or external to a particular company could ask

themselves why certain proposals were accepted and others were unsuccessful. Who

belongs to the team which is taking the decision and who is influencing this team might

be unclear. “The question of the relative power of managerial elites and others is a

crucial empirical issue” according to Pettigrew (1992, p.163). The name of a potential

influencer does not even have to appear on the organisational chart. The chance that a

proposal is successfully accepted is enhanced by the opportunity to understand the Top

Management Team members and how they form coalitions in working together. As

recommended by Sabatier and Weible “in order to have any prospect of success, they

[policy participants] must seek allies, share resources, and develop complementary

strategies” (2007, p.196). What is new in this approach is the opportunity of isolating

11

the internal influencers through the analysis of organizational structures. Firstly, a

relevant set of questions is defined where answers are weighted according to an

algorithm and will result in a ranking of the evaluated Top Management Team members

according to their dominance. Secondly, the possibility of managers having lost their

Span of Control is analysed because this may lead to the support of subordinates who

finally could function as influencers.

To demonstrate the importance of the proposed model a situation from the author’s own

work environment is described which is representative of similar situations in other

companies within the private sector. The author of this research is employed by a

German manufacturer of hydraulic accessories, a globally leading supplier for industries

such as Oil & Gas, Heavy Machinery, Railway Systems or Mining. The Shipbuilding

industries offered a large market to sell the products of this hydraulic accessory

producer. Yet it was difficult to tackle that potential market. Due to the fact that quite

often the sales representatives liaised with the wrong people. Whenever a contact with

the shipyards was established bargaining commenced for every single small order

between the purchasing or engineering departments. After considerable time, it was

realized that it was necessary to liaise with the design departments of the ship owners

who produce the drawings of the whole ship. These departments or in some cases

separate companies, are more worried about topics such as the safety, long-time

corrosion prevention or the overall weight reduction than the shipbuilder itself and have

a different agenda of priorities compared with the procurement department. It was

discovered that although the design department would not buy our products or install

them they could specify those products in their drawings which makes the selection of

them mandatory for all following parties involved. Jungmeister (2006, p.2) captures the

same principle when stating:

[a]s early compliance with the regulation often is a key differentiating factor,

this means, that companies that are quicker to get official approval for their

products, have an important and usually lasting competitive advantage and

therefore have a sustainable source of profit.

This awareness has saved the hydraulic accessory producer a significant amount of

money due to reduced travel and visiting costs and has increased financial turnover. If

there was a tool like the one which is developed with this dissertation – this awareness

could have been gained earlier. This tool can assist in reorganizing companies when

analysing the reasons for the success or failure of an internal decision system that might

12

expose an unbalanced accumulation of power in the Top Management Team. “The

decision processes we observe seem to be infused with strategic actions and politics at

every level and every point” (March, 1991, p.104). Due to these considerations the

author is convinced that it is worthwhile to undertake this research in order to develop a

tool which assists companies to understand the dominant coalition of Top Management

Teams and their influencers.

1.4.1 Example: Application of this Dissertation (Presumption)

In the following example an imaginary case demonstrates how the outcome of this

research is applied. It is unnecessary for the reader to understand every part of the

calculation at this stage because it is explained later in this thesis.

1.5 Methodology

For this thesis a predominantly quantitative methodology is applied, although during the

pre-empirical stage subjective and non-measurable data is collected in an interpretative

approach for the perspectives of Team Constellation and Managers’ Characteristics. The

Given Assumption: A small company in the health care business develops and distributes complex hearing aid

devices. The CEO has, through his position, a blocking minority for decisions defined with 34% (in value of the

whole decision process). The other 66% of the decision power is to be shared among the Top Management Team.

In this fictional case study there are four departments with 6 employees in each department; R&D, Production,

Finance and Sales.

The Calculation: Through the analysis of the Typology of the company, the Constellation of the Top

Management Team and the Characteristics of each Manager the following findings are calculated:

R&D is three times more dominant than sales and six times more important than finance and production

(6/2/1/1)

In a small department with complex tasks, the maximum span of control is 5

The Outcome

The manager of R&D will possess an allocated decision power of 39.6%

As the maximum span of control is 5 the manager of the R&D department achieved the ‘momentum of losing

control’ and therefore at least 1 influencer is assumed in this department.

The Conclusion: The target of this dissertation is to understand who is leading the company and to understand

the dominance of Top Management Team members. With the small case study above it is demonstrated that in

this case the R&D department accrues the biggest amount of power (dominance) in regards to strategic decisions;

even more than the CEO. Additionally, it was found, that there must be at least one influencer in the R&D

department. This knowledge could be crucial to understand the decision process of that specific company. If it is

the goal to sell a production machine to this company, a sales presentation should therefore not only be held for

the CEO and the head of the Production but also the head of R&D and maybe even a search for the influencer of

the R&D department is necessary to be successful.

13

research concerning the perspective Span of Control is evaluated by measurable factors

and is of a quantitative nature. The perspective Typology was found neither with

quantitative nor qualitative research because this perspective involved the selection of

the model which is applied and already included some of the factors that we were

investigating. The empirical stage – the online survey – is based on a quantitative

process with the aim to collect numerical data for statistical analysis.

1.6 Outline of this thesis

The structure of this study follows the model for a thesis described by Perry (1998) who

outlined the structure of a thesis as comprising five main chapters. These are an

Introduction, Model and Hypotheses, Methodology of data collection, Analysis of the

collected data and Contribution to the body of Knowledge. According to Perry “this five

section structure can be justified” because “it starts with the description of the problem

at the beginning and finishes with the solution for the same problem in the final

chapter” (1998, p.66).

The pre-empirical stage includes the description of the research, the definition of the

problem area and the research questions discussed in this introductory chapter.

The second chapter informs the reader about the research undertaken through literature

reviews and the systematic approach as to how the parent disciplines were narrowed

down to the corresponding research areas consisting of Typology, Team Constellation,

Managers’ Characteristics and Span of Control.

Then the third chapter discusses the methodology chosen, the process of data collection

and outlines the approach of the analysis to finally resolve the research questions. The

first section commences by defining which variables will be considered and then

discusses the methodology used for the data collection for that perspective. During this

Body of Knowledge

Candidate’s research

Chapter 1 Introduction

Chapter 2 Model and Hypotheses

Chapter 3 Methodology of data collection

Chapter 4 Analysis of

collected data

Chapter 5 Contribution to

body of Knowledge

Figure 1.1: Model of the sections of a thesis (Perry, 1998, p.65)

14

empirical stage a positivist approach in collecting and comparing all the findings

is taken.

Data collection is obtained in the fourth chapter. Initially, the developed online survey

and its related data collection process are described before the collected data is

presented. In this chapter, however, the findings are not analysed in detail but rather just

enumerated and potential trends are briefly highlighted.

In the final chapter there is an in-depth discussion of the results and thematic patterns of

the findings where deductive logic (deductive from the findings) is applied to answer

and resolve each of the hypotheses. The findings assist in improving the online survey

and also to suggest future research directions.

1.7 Definitions for this thesis

Echelon: Describes the level of management

Team Constellation: In this thesis the Team Constellation is measured with Age,

Tenure and Education because these factors were evaluated through the research

literature to significantly impact on the functionality of the team.

Top Management Team: Generally, this includes the CEO plus the first layer of

Managers the HOD’s (Head of Departments). In this research, however, the focus is on

the Top Management Team (TMT) that is the first layer of managers only. Atypically,

this excludes the CEO, due to the circumstances that a numerical value for each of the

team members is allocated whereas the CEO has (in this thesis) a fixed value and

therefore is irrelevant.

Managers’ Characteristics: The literature review paired with an online opinion poll

with 525 valid participations resulted in a core of 21 personal Characteristics which

were reduced to the most relevant set of 6 Characteristics. These are Reliability,

Archetype (role model), Communication, Competence, Social Competence and

Network so as to compare Managers’ Characteristics. Throughout the thesis this term is

also mentioned as Characteristics.

Span of Control: This is the principle where depending on the size of a company

paired with the complexity of the corresponding team a limitation is given to where one

team leader can retain an overview. This limitation is termed the momentum of losing

15

control which for this research is of great importance because this is the moment when

the leader’s dependency on some of the team members will increase.

Typology: This term was defined by Miles & Snow in 1978 and describes four essential

natures of business entities: Prospector, Defender, Analyser and Reactor.

1.8 Delimitations of scope and key assumptions

The thesis aims to determine the constellation of the Dominant Coalition in the Top

Management Team as well as potential influencers among the subordinates in profit-

oriented companies. This limits the research to profit-orientated companies to exclude

variables that could occur due to the nature of different businesses. Furthermore, the

discussed target group is limited to the Top Management Team (TMT) echelon with the

exception of potential influencers among subordinates. The remaining staff as well as

board members are not included in this research. It is also clear, that this dissertation

will not result in one fixed formula which can be applied to any potential Team

Constellation or for all situations. Rather this thesis results in one possible approach

exploring how evaluations are narrowed down from collecting a specific set of data to

describe the relationships among the Top Management Team members. This process

may reveal room for improvements which is documented along with the results in the

final chapter.

This dissertation aims to find a method to answer the initial research question ‘Who

leads a company?’ and to provide a proposed way to measure interdependencies among

Top Management Team members. It is assumed that the literature review with topics

such as Typology, Team Constellation, Managers’ Characteristics and Span of Control

results in enough criteria to describe at least the following points:

The nature of a business in regards to its Typology which may not solely be

dominated by one of the Typologies of Defender, Prospector, Analyser or

Reactor but is more likely a less distinctive or hybrid mixture of them

Team Constellation in regards to Age, Tenure with the company and Education

as the most important factors to express one’s proportion of dominance in a

management team

Selection of the most important Managers’ Characteristics relevant in horizontal

professional relationships (Manager towards Manager) and in vertical

professional relationships (Manager towards Subordinates)

16

Ideal Span of Control regarding the Team/Company size and complexity of

work to define the momentum of losing control and becoming dependant on

influencers

Undertaking this research is also justified by the perspectives of other researchers such

as Talke, Salomo and Kock (2011, p.820) who for example complain that “whether

TMT diversity has a direct impact on firm performance or whether this relationship is in

fact mediated by a firm’s innovation strategy and subsequent innovation outcomes

remains largely unanswered”. Pettigrew (1992, p.163) also emphasises the necessity “to

draw together aspects of the sociological, organizational and managerial literature,

which in the past have not talked to one another”.

In the following chapter a major literature review (consisting of several smaller reviews)

is undertaken to isolate key factors in all four perspectives which this research uses as a

foundation to construct an algorithm to measure the allocation of dominance in Top

Management Teams.

1.9 Conclusion

In this initial chapter, the conceptual framework is described and an explanation of what

was the initial trigger in commencing this research is provided. The interest of the

author in organisational structures and understanding the evolution of the Dominant

Coalition was confronted with the prejudice in historical research assuming that the

relationship among managers cannot be measured. Questioning this preconception

became the first of seven hypotheses. The research in its entirety is elaborated with a

pre-dominantly quantitative methodology within the standard five chapter outline. This

chapter concludes with an explanation of the terminologies used frequently in this thesis

and the delimitations of the scope and key assumptions.

The second chapter covers the literature review or rather the four small literature

reviews outlining the different perspectives used in this thesis to capture the main topic.

It concludes with a discussion of the CEO’s share of dominance when it comes to

decision making in the Top Management Team.

17

2 Chapter two: Literature Review

2.1 Introduction

“A Literature review is a story about a journey”, introduces Robert Brown’s ‘How to

prepare a literature review’ (2002, p.57). After having worked for a long time on this

literature review, the author identifies with Dr Brown’s analogy. The development went

several times through a process of interest, satiation and disaffirmation. Why is hard to

say. Maybe the best explanation is “The Problem of too few sources and the problem of

many sources” as Knopf aptly describes it (2006, p.130). For this thesis a major

problem was the process of gathering the data and composing and combining parts of

different puzzles into one larger picture. This differed from the logical stages of how to

conduct a literature review according to authors such as Arksey and O’Malley (2007),

Cronin, Ryan and Coughlan (2008) or Randolph, (2009) as described in the table below.

How to Conduct a

Literature Review

Methodological

Framework

Undertaking a literature

review, a step-by-step

approach

1. Problem formulation

2. Data collection

3. Data evaluation

4. Analysis and

interpretation

5. Public presentation

1. Identifying the research

question

2. Identifying relevant

studies

3. Study selection

4. Charting the data

5. Collating, summarizing

and reporting the

results

1. Selecting a review topic

2. Searching the literature

3. Gathering, reading and

analysing the literature

4. Writing the review

5. References

Randolph

(2009, p.4)

Arksey and O’Malley

(2007, p.22)

Cronin, Ryan and Coughlan

(2008, p.39)

Table 2.1: Different approaches for a literature review

By comparing the three variants on how to undertake literature reviews, the author

considers step number four ‘charting the data’ as described by Arksey and O’Malley

(2007) an important stage which is less emphasized in the other two frameworks. This

was exactly what was ignored at the beginning of this literature review; after defining

the research topics whatever literature was available was gathered in the aim to

synthesise this together. It is as if one would try to push several pieces of furniture

through a door at the same time. For as Gall, Borg and Gall warned: “[y]ou cannot

simply read all these documents, take casual notes, and then write a literature

18

review…you might need to develop a coding scheme, apply it to the documents, revise

it based on this experience, and re-apply it” (1996, p.159).

An illustration of the relationship between the literature review, parent disciplines,

research problems and hypotheses is given by Perry (1994, p.16) in the figure below.

This shows the importance of understanding that the literature review does not only

cover the research problem area itself. A literature review overlays the research problem

area into parent (or neighboring) disciplines.

Figure 2.1: Relationship between literature review, parent disciplines, research problem and hypotheses (Perry, 1994,

p.16)

Furthermore, it is important to distinguish if the research question itself has an

exploratory or an explanatory character. As this definition implies, exploratory is

sourced from the word exploring and explanatory from explaining or as Perry defined it:

“…if the [PhD] research is exploratory…then the literature will unearth research

questions (like ‘what’) that will be answered in the research”, and on the other hand if

the research is explanatory “then the literature review will unearth testable hypotheses

that can be answered with a ‘yes’ or ‘no’”, Perry (1994, p.20).

There are different reasons to undertake a literature review; to introduce a short journal

article/research report or to prepare an integrative literature research piece on a topic of

significance. According to Bragget, Jarratt and Bamberry (2008, p.19) we can

distinguish the following types of literature analysis:

historical research (data collection to determine and interpret its validity)

survey research (conducting a survey by using a questionnaire)

experimental/empirical research (controlling variables in experiments)

Literature Review Including Parent Discipline Research Problem Area

Boundaries of Research Problem

Parts of the Research Problem studied in previous Research

Research Questions or Hypotheses not answered in Previous Research

19

qualitative research (usually the interpretation of a particular group of

individuals)

evaluative research (determination of how well something is being performed).

The literature reviews for this thesis have an exploratory character offering a mixture of

historical research where data was collected to determine the validity of the research

topics, and also an evaluative aspect to find out how well the research topics are sourced

from the contemporary literature. Because the majority of research topics of this thesis

were introduced during the last century each of the four short literature reviews is

discussing historical papers which are explaining the foundations of each topic or are

written based on the original documents published during an earlier period. To balance

this, each of the four literature reviews employs a selection of recent research to assess

any new developments or determine if the principles of each topic remain unchanged.

2.1.1 The Literature Review Framework

After the results from the earlier study the author follows the procedure as discussed in

Table 2.2 and shown in the framework below which leads through the review like an

imaginary red thread. This framework will assist in maintaining the focus of the

literature review. Each research topic is re-named later as a ‘perspective’ and is

introduced and then elaborated through the analysis of historical and recent articles and

finally summarized when the findings are tabulated. The goal of each short literature

review is to find the key-arguments of one perspective (for example Managers’

Characteristics) in order to weight them according to their significance and popularity in

academic research.

Table 2.2: Stages of this thesis and the framework of a literature review from Arksey and O’Malley (2007)

Introduction of Research Topic

Main Results of Prior Findings

Finding gaps through Literature Review

Conclusions and summary

Methodological Framework Literature

Review

1. Identifying the Research Question

2. Identifying Relevant Studies

3. Study Selection 4. Charting the Data 5. Collating,

summarizing and

reporting the Results

Arksey and O’Malley (2007, p.22)

20

After a review of available literature which encompassed a broader perspective of the

research area including the parent disciplines, relevant papers to the specific research

problem areas were selected and collated in order to frame the research questions and

hypotheses. This was done with the intention;

a) to detect gaps in the existing literature

b) of being led to new findings

c) of obtaining answers on questions from the author’s former research

d) to formulate and verify hypotheses.

In this thesis four short literature reviews are conducted. These are covering the

Typology of the Company, Team Constellation of Top Management Teams, Managers’

Characteristics and Span of Control. At the conclusion of each of the literature reviews

the main results and findings are summarized in tables. Then the findings are discussed

and the remaining open questions are explored to detect gaps which could provide

insight into the answers this thesis is investigating.

2.2 Research Topic

2.2.1 Perspectives of the Research Topics

In this section the framework is described through which the influence of Top

Management Team members on strategic decisions will be evaluated. The complexity

contained in the title of this thesis makes it unlikely to provide a valid answer by

analysing the topic from just one perspective. In the research conducted before

commencing this study it was concluded that a systematic approach of four perspectives

would result in one possible way to examine the research topic by incorporating a 360°

view which should help to make any outcome as accurate as possible, repeatable and

reproducible. In analysing the dominance of Top Management Team members it was

decided to approach this phenomena from the perspectives of the:

Typology of the Company

Team Constellation of the Top Management Team

Managers’ Characteristics

Span of Control.

Certainly, it is worth mentioning that instead of perspectives the research topics could

be called different viewpoints, sub-systems, or levels; however, it was decided to call

them perspectives because it seemed the most appropriate description. At the end of this

chapter a scheme of a square depicting one of the literature / research topics on each

21

side is shown to visualize the complete research with its four perspectives. Rethinking

this scheme and replacing it with other forms like circles, lists, or three-dimensional

structures resulted in unsatisfactory outcomes because it always appeared that one of the

perspectives would be superior to the others. For example, in viewing the Typology of

the Company as the main perspective and visualizing it at the top then the perspective of

Span of Control has no link or logic or at least it is perceived to be of only minor value.

2.2.2 Visualization of the four Perspectives

The drawings on the left illustrate all four Perspectives which are

selected to collect data for the research topics. The order of the

perspectives is not representing importance; however, it follows a

certain logic from large to small. Larger does not mean more

complex and smaller does not mean simpler either. The drawing

represents a) the company b) the top management team c) the TMT member and d) the

Span of Control in each Top Management Team member’s department. For each of

these research topics a literature review is conducted to gather information which will

be assigned with a numeric value. All the results are then aligned in a set of formulas

used to create an online tool which is based on a set of questions where the percentage

of influence or dominance of each Top Management Team member can be evaluated.

2.2.3 The first Perspective: The Typology of the Company

An evaluation of a Top Management Team and its members

should involve the environment the Top Management Team

works within. A start-up company for fruit juices certainly is

facing different challenges in the form of external and internal

factors than a long standing private bank. The questions related to

the research topic for the perspective Typology could involve the:

Nature of the Company

Financial situation of the Company

Maturity of the Company

Competitive Environment of the Company

Stage of Innovation of the Industry the Company operates within.

22

2.2.4 The second Perspective: The Top Management Team

Every Top Management Team comprises two main roles: the role

of the CEO and the role of Top Management Team members.

Although the CEO is a member of the Top Management Team

through their given mandate to say ‘No’ to any proposal s/he is

different from the other team members. The main focus of the

literature review about the perspective of the Top Management Team is to identify those

factors which are important to define how the remaining power (besides the CEO’s) is

allocated among the other Top Management Team members.

2.2.5 The third Perspective: Managers’ Characteristics

It was found that there are some characteristics more important in

horizontal relationships (the same hierarchical level; Manager to

Manager) and some characteristics that are more important in

vertical relationships (supervisor towards subordinates). Some of

the characteristics are found to be relevant in both directions. The

categorization of horizontal and vertical descriptors illustrates how the relationship from

the point of view of a manager is described on an organizational chart. The selection of

the relevant characteristics will enable a comparison and determine the ranking

accordingly.

2.2.6 The fourth Perspective: Span of Control

This perspective is only indirectly influencing the amount of

influence a Top Management Team member may have in a

decision-making process. The Span of Control, however, has an

importance in the momentum of losing control. This momentum

is achieved when the complexity of work and team size is

unbalanced. In such cases a manager will inevitably lose control and either appoints

officially one or several deputies or indeed one or several deputies will evolve

unofficially because the Managers cannot handle their tasks anymore. This phenomena

is important because the manager is accumulating a high share in the influencing power

on strategic decisions but on the other hand has delegated some power to deputies. This

combination would lead to the fact that deputies might be responsible for proposals

introduced to the Top Management Team.

23

2.3 Draft of Parent Disciplines and Problem Areas in this Dissertation

Typology, Top Management Team value creation, Team Roles and Span of Control are

selected in the context of this thesis to be evaluated as the parent disciplines. The figure

below has to be read from each side towards the centre. The parental discipline of Span

of Control discusses the concept of how much individuals can be controlled under

certain conditions by one supervisor and in the second layer what happens if that Span

of Control is exceeded. In Value Creation the focus is on the differentiation of primary

and secondary departments and then the focus is on the Team Constellation. The

analysis of the Typology of a company leads us to the definition of the dominant

coalition that runs a company. Team Roles are discussed in the broader context of

Managers’ Characteristics and the selection of the most relevant ones in relationship

towards another manager (horizontal relationship on an organizational chart) or towards

a subordinate (vertical relationship on an organizational chart).

Figure 2.2: Parent disciplines and problem areas in this thesis

24

2.4 Perspective: Typology of the Company

The questions related to the research topic for the perspective

Typology of the Company concern the nature, competitiveness,

development or strategy of a company to list a few factors

describing the business environment a Top Management Team

works within. Child suggests that:

[i]t is not possible to abstract from the environment when considering the

strategic choices available to organizational actors. This is partly because the

environment presents threats and opportunities for the organization which

establish the parameters of choice. It is also because the ways in which

organizational actors understand the environment affect the extent to which they

believe they enjoy autonomy of choice between alternatives. (1997, p.53)

The combination of these environmental factors could result in too many answers or

variables which then make it necessary to combine and categorize information.

“Typologies are a key way of organizing complex webs of causal relationships”

according to Delbridge and Fiss (2013, p.329) and Snow and Ketchen (2014, p.231)

prescribe the use of typologies for “description and prediction”.

The literature review about the nature of the company is initiated with Delbridge and

Fiss because these two authors are elaborating and simultaneously critically evaluating

the importance of Typologies in their article ‘Editors’ comments: Styles of theorizing

and the social organization of knowledge’ (2013). Delbridge and Fiss have analyzed the

number of publications of the Academy of Management Review over the timespan from

1976 and 2012 regarding papers related to Typology. The authors found that there is a

clear decline in publications and have questioned that phenomena:

[b]y their very nature typological theories tend to be holistic and therefore do not

lend themselves as easily to a focus on individual direct net effects. As

interdependent webs of relationships, typologies more frequently involve

complex causal relationships involving interaction, substitution, and

bidirectional causality. (Delbridge and Fiss, 2013, p.330)

The authors, however, agree that correlational methods can be used for typological

theories but underline “that the methodological training and habits, as well as norms and

expectations that come with correlational analysis, may subtly bias us against

25

typological theorizing” (p.330). Another reason for a decline in research related to

typologies could be that “there are fewer new phenomena that could be addressed by

means of a typology” and this would lead to the assumption that “typologies have

somewhat outlived their usefulness since there is nothing new to classify” (Delbridge

and Fiss, 2013, p.329).

A rebuttal to this assumption was written one year later in 2014, when Snow and

Ketchen published their paper on ‘Typology-driven theorizing: A response to Delbridge

and Fiss’ in the same journal where these authors confirm that “sciences would benefit

from a renewed emphasis on typology-driven theorizing” (2014, p.231). Snow and

Ketchen give several reasons to Delbridge and Fiss why fewer new models may be

developed by stating that:

[a] typology is valuable when its ideal types are comprehensive and mutually

exclusive, the types can be validly and reliably measured, and the theoretical

foundation underlying the typology is clearly articulated. (2014, p.231)

Similarly, Snow and Ketchen (2014, p.232) question if a Typology still has validity

after more than three decades and explain a possible reason with the existence of a

theoretical framework (in this case the adaptive cycle) which accompanies the Miles

and Snow (1978) Typology. In answering Delbridge and Fiss’ question as to why there

are fewer new papers written about typologies, Snow and Ketchen analyse authors of

various works (Snow and Hambrick, 1980; Conant, Mokwa and Varadarajan, 1990; and

Desarbo, Di Benedetto, Song and Sinha, 2005) amongst others who have developed

quantitative measurement methods based on the Miles and Snow Typology. These

studies are giving researchers today “an array of measurement techniques at their

disposal when using the Miles-Snow Typology” (Snow and Ketchen, 2014, p.232).

This thesis concludes that there are three basic models widely accepted in academic

literature describing the typology of companies; Miles, Snow, Meyer and Coleman’s

‘Organizational strategy, structure, and process’ from 1978, Mintzberg’s model from

1980 outlined in ‘The structure in 5’S: A synthesis of the research on organization

design’ and Porter’s Competitive strategy: Techniques for analysing industries and

competitors also from 1980. Less popular, but more recent models, are found in Weill,

Malone, D’Urso, Hermann and Woerner’s paper ‘Do some business models perform

better than others?’ (2006) and an approach of Mullins & Walker (2013) titled

‘Combined typology of business-level competitive strategies’ combining Porter’s and

26

Miles et al.’s models. In the following literature review of Typology (the first

perspective) the focus is on explaining the principles of all the models described above

to select the most suitable one for the thesis and to assess how the selected model assists

in classifying companies analysed in this context.

2.4.1 Miles & Snow Typology

The Miles & Snow Typology (1978) is the oldest one in this comparison. It has not lost

its premier position, however, as the tool for company classifications pertaining to their

organizational strategy. Although, only Miles and Snow are usually mentioned, which

could give the false impression that those two authors are the sole developers of this

classification tool; the original article ‘Organizational strategy, structure, and process’

was co-written by Miles, Snow, Meyer and Coleman (1978). Miles et al. (1978)

summarize that:

[p]roponents of the strategic-choice perspective argue that organizational

behavior is only partially preordained by environmental conditions and that the

choices which top managers make are the critical determinants of organizational

structure and process (p.548).

The model of Miles et al. (1978) is building on previous models from Chandler (1962),

Child (1972), Cyert and March (1963), Drucker (1954/1974), Thompson (1967) and

Weick (1969/1977). Dahlan, Auzair and Ibrahim (2007, p.84) conclude that “typology

focuses on the rate of change in products or markets”. Miles et al. (1978) categorize the

complexity of problems and their consequential choices into three categories:

The entrepreneurial problem

The engineering problem

The administrative problem.

They suggest that in “mature organizations, management must solve each of these

problems simultaneously, but for explanatory purposes, these adaptive problems can be

discussed as if they occurred sequentially” (Miles et al., 1978, p.549).

Miles et al. emphasize the position of the strategic orientation of the organizations

because “each type has its own unique strategy for relating to its chosen market(s) and

each has a particular configuration of technology, structure, and process that is

consistent with its market strategy” (1978, p.550). These typologies are the; Defenders,

Analysers, Prospectors and Reactors.

27

There are a large number of representations of the Miles & Snow Typology which are

available in the research literature. The one which was chosen to use further is derived

from Conant, Mokwa and Varadarajan (1990). The reason for this selection is that

Conant et al. have in comparison to the others included the dominant coalition initially

described by Miles and Snow. This is important for this research because the

classification of the organization to analyze is to pair the typology with the aim to know

how this impacts on the Top Management Teams’ dominant coalition.

The qualifying arguments identifying which typology a company belongs to can be

evaluated among other criteria through their company strategy, the environment a

company operates within and their organizational characteristics. A Prospector may be

described as a creative and innovative organization in a dynamic environment following

a progressive strategy to find new opportunities. A Defender is set up with a more

centralized organization focused on tight control and efficient overhead planning,

surrounded by a stable environment protecting their current market. The Analyser

represents more of a mix in respect to its organization with an efficient but creative

point; their environment could change slightly as the main focus is on their existing

market with only moderate innovation. Finally, the Reactor will have no structured

organization and will just adjust to whatever needs appear which is also reflected in

their strategy (Conant et al. 1990, pp.381–383).

One of the questions elaborated further in Chapter 3 of this thesis is the quantification of

such findings. Even if it is defined for example that a chosen company belongs to the

Typology Defender where it is clear that finance and production will achieve a

dominant coalition it will be part of the methodology discussed in this thesis to quantify

such advantages. It is unlikely that an organizational typology is the sole parameter

giving any department a strategic advantage in comparison to others. The approach of

the author is that there are different parameters accumulating in the strengths of Top

Management Team members. As mentioned above Snow and Hambrick (1980), Conant,

Mokwa and Varadarajan (1990) and Desarbo, Di Benedetto, Song and Sinha (2005)

amongst others were named by Snow and Ketchen (2014, p.232) as researchers who

have developed quantitative measurement methods based on the Miles and Snow

Typology which are providing researchers today with “an array of measurement

techniques at their disposal when using the Miles-Snow Typology”. From those articles

the table by Conant, Mokwa and Varadarajan (1990) proved very helpful as it compiles

all parameters together in one summary.

28

Table 2.3: Based on Miles & Snow (1978), Dimensions of the adaptive cycle and strategic type characteristics

(Table 1 by Conant et al., 1990, p.367)

2.4.2 Mintzberg’s Model of the Five Structural Configurations

Two years after Miles & Snow and also in the same year as Michael Porter, Henry

Mintzberg published his ‘Structure in 5’s: A synthesis of the research on organization

design’ (1980). According to Mintzberg an organizational strategy is seen as a logical

output depending on five structural configurations. These are the:

Simple Structure

Machine Bureaucracy

Professional Bureaucracy

Divisionalized Form

Adhocracy.

This model is based on five core typologies of an organisation which is illustrated in

different versions as shown in Figure 2.3, the original from Mintzberg, and as an

example Figure 2.4, a ‘refined’ version by Lunenburg.

29

Figure 2.3: The five basic parts of the organization, Figure 2.4: The key parts of an organization,

Mintzberg (1980, p.324) abstract version of the Mintzberg Model, source

Lunenburg (2012, p.2)

The five core typologies of Mintzberg (1980) are influenced by the single or combined

influence of a) five mechanisms as to how those core parts are coordinated by

supervision or standardization levels b) the design of organizational structures (whereas

Mintzberg enumerates nine parameters as the most commonly found) and finally c) the

contingency factors which are adding another four parameters. The influence of a

tension on each core part individually or on several core parts simultaneously would

then result in a specific orientation of the company’s strategy. For example tension on

the strategic apex would result in more centralisation.

Lunenberg (2012, p.4) summarizes the Mintzberg model in a table which was helpful in

deciding whether the model is useful for this thesis (Table 2). The answer is ‘most

likely no’ because the Mintzberg-model is from the author’s point of view more of a

holistic approach with too many unmeasurable parameters. Or as Doty, Glick and Huber

stated: “because the typology and underlying theory have received little or no

systematic empirical examination, in large-scale comparative studies, there is little

support for the theory” (1993, p.1197). Mintzberg’s model is based on several levels of

influencing factors and the assumption that any changing variable may originate from

inside the organization or could be an external influence and these factors will have an

impact on the different core typologies which could become hard to measure. The

approach used in this thesis requires a system which is more rigid, knowing that flexible

parameters will be sacrificed in order to make the collected data measurable.

Although it is helpful to have an indication of the ‘Key Parts of Organization’ the

Mintzberg-model does elaborate all parts of an organization (the different echelons)

which means utilizing both the horizontal and vertical differentiations. Whereas, the

30

focus of this thesis is purely on the echelon of the Top Management team or to label it

in the terminology of the Mintzberg model; the horizontal differentiation of the

‘Strategic Apex’.

Table 2.4: The key parts of an organization, based on Mintzberg (1980), source Lunenburg (2012, p.4)

2.4.3 Porter’s Generic Competitive Strategies

The difference between Porter’s (1980) model, which is certainly among the most

popular ones, is that it is less of a classification model like the one from Mintzberg

(1980), Miles & Snow (1978), Weill et al. (2006) or Mullins and Walker (2013). Each

of those models considers all possible kinds of strategies and also the less optimum

performing strategies such as for example the Miles & Snow strategic type of the

Reactor. Porter’s Generic competitive strategies are restricted to delineating the attempt

of the ‘strategic approaches to outperforming other firms in an industry’ (2008, p.35).

Consequently, as Peng, Tan & Tong claim the “[s]trategic group has become an

attractive middle ground between firm- and industry-level analyses in strategic

management research” (2004, p.1105). Porter also admits the existence of organizations

not falling into any of his three generic strategic approaches: “the firm failing to develop

its strategy in at least one of the three directions – a firm that is ‘stuck in the middle’ –

is in an extremely poor strategic situation”. Furthermore, that:

[t]he firm stuck in the middle is almost guaranteed low profitability. It either

loses the high-volume customers who demand low prices or must bid away its

profits to get this business away from low-cost firms. (2008, p.41–42)

Porter’s (2008) model is mapping two axes; the strategic advantage and the strategic

target. In a four field matrix the targets specified are ‘industrywide’ or a ‘particular

segment only’ versus the strategic advantages of ‘uniqueness’ or ‘low cost’. In the

original diagram from 1980 ‘stuck in the middle’ is not part of the graph; however, later

illustrations of this model included it.

31

Figure 2.5: A diagram created by Denis Fadeev (2014) of Michael Porter's three generic strategies based on a figure

from Porter M. E., Competitive strategy: Techniques for analyzing industries and competitors (New York: Free Press,

1980), page 39. Https://commons.wikimedia.org/wiki/File:Michael_Porter%27s_Three_Generic_Strategies.svg

In the table below the model is described showing the three Generic Strategies in

respect to their commonly required skills and resources as well as the main

organizational requirements. Although we could interpret the meanings and try to

stretch the interpretation until it fits, however, overall in the context of this research it

was concluded that Porter’s model will not suit the needs and approach for this thesis in

developing a tool to classify a Top Management Team by departments due to the

model’s too general classification. This perception is shared with Smith, Guthrie and

Chen who criticized Porter’s model as being “described in relatively general terms”

(1989, p.63). In comparison Miles & Snow offer a classification using 10 parameters

whereas Mintzberg’s model is almost too complex with three dimensions containing

five, nine and four parameters respectively.

Table 2.5 : Porter, M. E. (2008).

Competitive advantage:

Creating and sustaining superior

performance, Simon and

Schuster, New York, pp.40–41).

32

2.4.4 The Sixteen Detailed Business Model Archetypes

In ‘Do some business models perform better than others? A study of the 1000 largest

US firms’, by Weill, Malone, D’Urso, Herman and Woerner (2006) the authors have

studied the business model archetypes and set them in a grid with the type of asset

which is involved in different types of businesses or as they describe it “what a

company does and how the business makes money doing these things” (p.2). The result

is a table of 16 different business models from which in the authors’ point of view

Table 2.6: The sixteen detailed business model archetypes from ‘Do some business models perform better than

others? A study of the 1000 largest US firms’, Weill et al. (2006, p.31)

organizations can be classified. In their study, the top 1000 companies of the United

States’ economy in the year 2000 were analysed in order to compare their performances.

Weill et al. (2006) conclude that “business models are a better predictor of financial

performance than industry classifications and that some business models do, indeed,

perform better than others” (p.1) or similarly “a company’s business model[s] are

substantially better predictors of its operating income than its industry classification and

other control variables alone” (p.21).

Although this classification model from Weill et al. is interesting, and a new approach

in 2006, after most other models were established decades before; it was decided not to

select this classification table to be the parameter for this thesis. The classification itself

is very clear and straight forward, however, the aim behind it is focused on the financial

performance and it omits additional parameters which would obtain further conclusions

about a potential dominant coalition of the Top Management Team. For example, a

‘Distributor of Physical goods’ is just not specific enough for this research.

33

2.4.5 The Combined Approach: Porter / Miles & Snow

Mullins and Walker’s (2013, p.5) ‘Combined typology of business-level competitive

strategies’, is an interesting approach in melding Miles & Snow’s Typology together

with Porter’s Three Generic Strategies. This approach, however, is comparatively young

in relation to the other models and is not be found widely in the research literature

which suggests a lack of proven applicability.

The Mullins and Walker model is shown in the context of this chapter for the sake of

covering this topic as comprehensively as possible because it is definitely an interesting

approach, however, a further analysis of this model will not be undertaken.

Table 2.7: Combined typology of business-level competitive strategies, Mullins and Walker (2013, Chapter 9, p.5),

retrieved from: www.mktgsensei.com/MBA522/Chap009.ppt

2.4.6 Recent Research Discourse

The discussion of how to categorize the strategies of companies in an application of any

of the Typologies discussed before has not lost its importance today. The model of

Miles & Snow (1978) is widely accepted and contemporary research is based on this

typology’s definition. A few examples of recent research using the Miles & Snow

typology as an integral part of it are discussed here.

Hussin, King and Cragg (2002) analysed typologies in ‘IT alignment and firm

performance in small manufacturing firms’ using a comparable process to that

undertaken for this thesis. For Hussin et al.’s study an e-mail questionnaire was sent out

34

to participants similar to an online survey which was the option chosen for this thesis.

Hussin et al. (2002) have based their categorization of seven company strategy types

based on different previous studies and they requested the 50 randomly contacted

managers of small firms to indicate their company strategy on Likert-scales, the same

procedure that was undertaken for this thesis. Ghezal (2015) used Confirmatory Factor

Analysis (CFA) to investigate the reliability of the seven business strategies used by

Hussin et al (2002) and although Ghezal (2015, p.86) concludes that “this confirmatory

factor analysis confirmed the multidimensionality of the small business strategy

instrument developed by Cragg et al. (2002)” Ghezal encourages other researchers to

replicate his research which should be focused on additional considerations such as

“sample size and deeper interpretations of absolute values” (2015, p.86).

Taran and Boer (2015) were aiming to develop a business model ‘innovation typology’

in their paper. Taran and Boer suspect that “innovation based solely on new products

and aimed at local markets is no longer sufficient to sustain competitiveness and

survival” (2015, p.301). For this reason the authors searched for the business model

typology which in terms of innovation is most successful or more successful than its

competitors and they analyzed case studies of flourishing innovative companies.

Chung, Jung, Baek and Lee researched ‘The impacts of strategic orientation and HRM

systems on firm performance’ (2008). The authors were looking for a correlation

between human resource strategies and improved company performance and they

conduct regression analysis in order to compare Miles & Snow (1978) typologies and

found their hypothesis confirmed in that “prospectors adopt more high involvement

HRM systems as compared to analysers and defenders” (Chung et al., 2008, p.87).

In ‘Cloning an industry: Strategy typologies of Shanghai biotechnology companies’

Malone, Hales, Chan, Love and Rayner (2008) analysed 19 biotechnology companies

and categorized them according to the Miles & Snow Typology (1978) and Porter’s

Generic Strategy Type (1980) with the finding that the majority of the investigated

companies (Shanghai based and active in biotechnology) are applying Porter’s cost

leadership strategy (Malone et al., 2008, p.38) and are either Analyser or Reactor types

(Malone et al., 2008, p.40).

Castro Pinto, Altinsoy and Santos António (2014) use the Miles & Snow (1978)

typology to explore the performance of military organizations; especially the alignment

of the divisions of strategic and operational military. The authors conclude that in terms

35

of their sample group “there is no significant overall performance difference among the

Prospector, Analyser and Defender groups” (2008, p.14) but “all the fit factors related

with Miles and Snow (1978) adaptive cycle dimensions (entrepreneurial, engineering,

administrative and environmental) have a significant impact on the organizational

performance” (2008, p.18).

Zinn, Spector, Weimer and Mukamel (2008) explored the Miles & Snow (1978)

typology to compare the response of nursing homes to quality measurement

publications in relation to the nursing homes’ strategic orientation. The survey was

undertaken with a comparatively large sample size by addressing 10% of nursing homes

nationwide of which 48% have participated (724 nursing homes responded). Among

their outcomes they found that “Prospectors are almost twice as likely and Analysers 67

percent more likely to change priorities of existing quality programs when compared

with Defenders” (Zinn et al., 2008, p.610).

Song, Di Bendetto and Mason compare “the relationship between M-S strategic type

and four firm capabilities (technology, information technology, market-linking, and

marketing capabilities)” (2007, p.28). The authors see the strength of prospectors in

Technology and IT capabilities whereas defenders were found to be the weakest in

those two fields. Defenders on the other hand are assumed to be strongest in market-

linking and marketing capabilities with prospectors vice versa being the weakest in this

discipline. The authors found that “the best way to measure capabilities was to ask the

informant to rate their firms on the various capabilities, relative to their “top three”

competitors in the industry” (Song et al., 2008, p.22). The hypothesis was supported

with defenders being strongest in market-linking and marketing capabilities as well as

prospectors having “significantly” greater capabilities in IT but it only “partially

supported” that the prospectors have better capabilities in technology (p.24). To find

verification, in this thesis the participants are also asked to assign their own ranking,

however, this will be in relation to the most influential top management team members.

36

2.4.7 The interpretation of the Miles and Snow Typology in this thesis

The Miles & Snow Typology is

selected for this thesis to

create a tool to measure who belongs

to the dominant coalition in Top

Management Teams. This Typology-

model is among the most established

and it also suits the aim of this

thesis in regard to its establishment

of 11 parameters under the four

typologies; Defender, Prospectors,

Analysers and Reactors. Although

typology itself is not a primary

concern, however, the fact that each

typology has a given dominant

coalition makes the model of

Miles & Snow the perfect match

with this thesis. Thus in this research

10 of the parameters are used to find

out which typology an organization

most likely belongs to which is then

automatically described in the 11th

parameter (red frame) which

departments are most likely to

appear in the dominant coalition. Of

course it is very likely that an

organization does not 100% fall

into one typology. It is also

possible that an organization might

have the majority of weight

in one Typology but to some

extent also experiences aspects of

the other typologies. For example

Entrepreneurial and Engineering

Table 2.8: 10 parameter-questions leading to Typology. Based on Conant et al., (1990, p.367), authors’ interpretation

Table 2.9: The combined Typology. Derived from Conant et al.,

(1990, p.367), author’s interpretation

37

problems could be allocated under the Prospector typology whereas the administrational

problems are more suitable in the Reactor typology. This will be helpful to weigh and

compare the influence of departments. In order to make the parameters understandable

for participants in the study the parameters are rephrased slightly without changing their

meaning. The participant will be asked the Questions from the table below – with four

answers given on the right to choose from. An interesting finding was done by Smith et

al. on the correlation between company size and Typology, “[a]nalysers are primarily

large firms and Reactors are small firms” whereas Defenders and Prospectors “are

generally smaller than Analysers and larger than Reactors” (1989, p.75).

Defenders Prospectors Analysers Reactors Parameters/Questions

1. The Market

environment of

our company is:

We focus on a narrow

market segment

We continuously

expand our market

focus

We continuously adjust

our market focus

Our market focus

can change

2. Our company

achieves Success

because…

…we are prominent in

our market

…we are permanently

pushing for new

solutions

…we adjust to market

needs

…we exploit

chances

3. Our company

conducts

Observations…

…of our market and

our organization

…wherever possible,

aggressive search

…we observe our

competition

…depending on

actual need

4. Growth is

experienced

because of…

…we focus on a

narrow market

segment with

advanced technology

…permanently

accessing new markets

/ new developments

…focused penetration

and careful product

selection

…our flexibility

5. Technology in

our company

needs to…

…be cost-efficient …flexible …synchronised …adjusted to our

actual needs

6. Our company

invests in

Technology…

…of our core market …of different and new

developments

…which is compatible to

our infrastructure

…when necessary

7. Technology in

our company

works because of:

…standardization and

maintenance

…of the people behind

it

…planning and

synergies

…we are open for

experiments

8. Planning is for

our company

…a fundamental …related to problems

and opportunities

…comprehensive and

permanently improved

…crisis oriented

9. The Structure of

our company is…

…functional/line

authority

…product and/or

market centered

…staff dominated /

matrix

…tight formal

authority

10. Control in our

company is…

…centralized and

backed up by finance

…depending on

market performance

…calculated based on

risks

…handling

problems

Dominant

Coalition

Finance, Production Marketing, Sales,

Research &

Development

Planning Staff / Support

Activities acc. to Porter

Trouble-Shooter

(depending on the

issue)

The weighting of the typology of the organization together with the Team Constellation

of the Top Management Team and the Characteristics of the individual member of the

Top Management Team combined together results in a comparison among all members

of the Top Management Team in order to evaluate their importance in the Dominant

Coalition. The weighting approach used is discussed in Chapter 3: Research

Methodology.

Table 2.10: Re-phrased parameter-questions to identify the Typology and therefore the Dominant Coalition

38

2.5 Perspective: Top Management Team

The following short literature review was conducted with the aim

to segregate the relevant attributes of managers which seems of

importance in describing one’s connections in the Top

Management Team. In order to analyse “the importance of

competitive behaviours to overall firm strategy and performance,

an understanding of the determinants, or influencers of those behaviours is essential”

according to Hambrick, Cho, & Chen (1996, p.659)

The concept of the value chain or a chain of activities (Porter, 1985, p.36ff) is an

illustration to describe each single process in a company or system. The value chain

is often visualised as in the figure below. In this case the value chain

Figure 2.6: A common illustration of the value chain (Porter, 1985, p.36)

diagram is helpful to describe the structure of the company. Describing all the positions

in the value chain which are coordinated by members of the Top Management team

allows us to consider which position is adding value, and accordingly to enable the

ranking of the positions/managers in descending order. This is necessary to understand

who is responsible for the most valuable process steps. It is clear that valuable in this

context is not a solely monetary value. Depending on the nature of a business the

creation of ideas or strategies may be valuable as well. A company could either

integrate or outsource some positions of their value chain depending on the decision

whether a competence is a core competence or not. Prahalad and Hamel (1990, pp.79–

91) provide three definitions: “[a] core competence allows potential access to a

multitude of markets, a core competence contributes to a considerable customer benefit

regarding the final product, and a core competence should be hard to copy by the

competition”. Normann and Ramírez argue that Porter’s value chain (see the previous

figure) is a ‘traditional’ way of looking at it and that “every company occupies a

Firm Infrastructure

Human Resource Management

Technology Development

Procuration

Logis

tics

Op

erati

on

s

Mark

etin

g

S

ale

s

Logis

tics

Ser

vic

es

Pro

fit

Pri

mary

Act

ivit

ies S

up

port

Act

ivit

ies

39

position on a value chain” as this understanding is still focused on an “assembly line”

whereas the authors are convinced that “global competition, changing markets, and new

technologies are opening up qualitatively new ways of creating value” (1993, p.65).

2.5.1 The influence of a TMT’s Constellation on Value Creation

Andrew Kakabadse highlights the importance of reducing tensions in the Top

Management Team Constellation in this comment:

[i]t is important to create an environment, where the pronouncing of views the

asking of questions, the entering into deep debate and emerging with the

declaring of intent, is a vital process to experience in order to shift towards a

cohesive team who could effectively lead the organization. (2000, p.11–12)

This section provides an overview of the findings of different authors in respect of what

kind of parameters in the Top Management Team constellation are resulting in some

kind of influence on the value creation. These may be positive or negative factors. All

the parameters found are enumerated and if possible weighted according to the

frequency or popularity of their occurrence. In a second step a selection of the most

important factors and also research on how these impact decisions related to the value

chain is done.

Agnihotri (2014) is describing educational level, total organizational tenure and

functional heterogeneity collectively as “the top management traits” (p.250).

Agnihotris’ analysis of ‘The role of the upper echelon in the value chain management’

is referenced again in this chapter but it can already be stated that throughout the

research of the available literature those three factors seem among the crucial ones and

were repeatedly confirmed when sourcing related papers. In ‘The influence of top

management team heterogeneity on firms’ competitive moves’, Hambrick, Cho and

Chen (1996) define top management teams as “the aggregate informational and

decisional entity through which competitive moves are made.” Hambrick et al. (1996,

p.659) found top management teams to show a “great propensity for action” if they

were diverse “in terms of functional backgrounds, education and company tenure”

compared with more homogeneous top management teams who were considered as

comparatively “slower”. This research paper compares the relationship of team

heterogeneity of the top management team in regard to acting and responding. Whereas

Hambrick et al. (1996, p.661) cite the studies of Bantel and Jackson documenting that

“young, short-tenure, highly educated teams... [are] relatively innovative”. In contrast

40

Hambrick et al. (1996, p.662) also suggest that the authors Finkelstein and Hambrick

(1990), Grimm and Smith (1991), Wiersema and Bantel (1992) have found that

“organizational tenure of top management team members was found to be strongly

associated with strategic persistence”.

Hambrick et al. reviewed the work of Wagner, Pfeffer, and O’Reilly (1984) and Jackson

et al. (1991) who both found that if the firm tenure of an individual top management

team member is too different from the top management team members’ average firm

tenure then: “[t]he more likely he or she was to depart” (Hambrick et al. 1996, p.663).

This means that a heterogeneous firm tenure might increase innovation but also implies

the risk of more frequent changes of top management team members for those team

members who are too diverse in terms of firm tenure. Therefore, a converse argument

may be that an ideal firm tenure dispensation for an innovative team would be if all or

most team members were different from the average firm tenure. The author of this

thesis regrets that Hambrick et al. (1996) do not discuss the influence of top

management team members’ education and functional background to the extent which

was expected from reading their introduction. A hint is found in regard to functional

background; however, as a more diverse top management team is more likely to have a

“broader potential repertoire for generating actions” although the number of internal

conflicts may arise because of this diversity. Hambrick et al. propose that “top

management team heterogeneity is positively related to the firm’s action propensity”

(1996, p.665).

Support for diversity in top management teams is apparent in Jackson, May & Witney’s

report ‘Understanding the dynamics of diversity in decision-making teams’ (1995,

p.218) where the authors state that “diversity within a decision-making team is

recognized as important primarily because it is associated with the resources available

during the decision-making process”. Usually, diversity in Top Management Teams in

the research literature is related to an increased number of actions which improve the

company’s result or as Ferrier and Lee (2002, p.162) describe it: “overall, this stream of

research suggests that aggressive competitive behaviour – more actions, innovative and

radical actions, quick response, complex and differentiated action repertoires – is related

to better organizational performance”. Both Hambrick et al. (1996) and Jackson et al.

(1995) are presenting a variety of studies which substantiate that broadening team

diversity (educational background, gender, age) may lead to communication errors and

reduced team cohesiveness. Since this thesis is interested in a snap-shot method of the

41

allocation of decision-making power in a top management team, the potential negative

mid- and long-term aftermath of team diversity are neglected. An interesting point in

regards to age and tenure is raised by Jackson et al. who are convinced that employees

can compensate for age and firm tenure with education and “consequently, within each

level of the organizational hierarchy, age diversity is replacing the homogeneity

associated with traditional age-based stratification” (1995, p.206).

Knight et al. (1999) in ‘Top management team diversity, group process and strategic

census’ selected the variables of interpersonal conflict, agreement seeking, strategic

consensus, location, functional diversity, age diversity, educational diversity and

employment tenure diversity to study the influence of demographic diversity on

strategic consensus. It was unusual to see ‘location’ as a variable in Knight et al.’s paper

but an explanation is given by the authors with the comment that “in order to test for

systematic differences between the two subsamples, location was included as a control

variable” (1999, p.454). Furthermore, as the relation concerned parameters that are

discussed in the literature review about relevant Managers’ Characteristics for the

moment the control variable ‘location’ will be skipped and also interpersonal conflict,

agreement seeking and strategic consensus which leaves function, age, education and

tenure. In their conclusion Knight et al. explain that they were assuming that all

diversity variables have a negative impact on strategic consensus, however, they found

that “employment tenure diversity had a positive effect on strategic consensus” (1999,

p.459) and although “diverse values and perspectives may bring challenges… [but it]

…is also likely to have positive effects such as increasing the environmental scanning

capacity or [to] increase the potential actions”.

Richard and Shelor (2011) provide a focused paper linking age and top management

team’s performance. In their introduction to the term cultural diversity, the authors cite

Cox and Blake (1991), when distinguishing between visible attributes such as “race,

gender and age” from non-visible visible attributes such as “marital status, educational

level and work experience” (2011, p.958). Richard and Shelor (2011, p.959) reviewed

various work of other authors (Hambrick, 1994; Hambrick et al. 1996; Jackson, 1992)

and found age to serve as a “proxy for perspectives, belief systems and network and

affiliations” while also referring to social identity theory when affirming that belonging

to an (age) group “creates a psychological state that confers social identity or a

collective representation of self-identity and behaviour”.

42

According to Eagly and Wood (1991) there is “little scientific evidence for sex

differences in any social behaviour except for aggression” and that if differences occur

“they are very small in magnitude” (p.2). After this assertion, Eagly and Wood (1991)

recap the widely accepted stereotypes of men being more task-oriented and women

being stronger on interpersonal disciplines (p.7). Reynolds, Fisher and Nelson (1996,

p.87) argue that female leaders have a preference for being led by feelings which is not

necessarily negative because they acknowledge that “executives with this style of

(feeling-) decision-making will be recognized and promoted to positions where they can

make a significant strategic contribution regardless of gender” (1996, p.87).

In more recent work by Eagly and Carli such as ‘The female leadership advantage: An

evaluation of the evidence’ (2003) the authors reject previous research which expressed

little difference in gender roles in Top Management Teams with a collection of evidence

that suggests the qualities of the female leadership actually results in the winning

margin. For example, when introducing newer research they comment that “[a]rticles in

newspapers and business magazines reveal a cultural realignment in the United States

that proclaims a new era for female leaders” (Eagly and Carli, 2003, p.808). This is a

result of a transformational process where “modern characterizations of effective

leadership have become more consonant with the female gender role” (p.810). The

authors see evidence in the transformation of different parameters because of a) women

having changed in general, b) leadership roles having changed, c) organizational

practices have changed and d) culture is changing, resulting in the numbers of women in

managerial positions are rising (Eagly and Carli, 2003, pp.826–827).

By 2005, Eagly is extending those findings further in ‘Achieving relational authenticity

in leadership: Does gender matter?’ She states that the dominance of men is a manmade

phenomenon and that “[w]hen leadership is defined in masculine terms, the leaders who

emerge are disproportionately men, regardless of the sex composition of the community

of followers” (Eagly, 2005, p.463). The author, however, also acknowledges that:

[r]esearch has shown that men and women differ ideologically to some extent,

especially in terms of the twin themes of women’s greater social compassion and

men’s more non-traditional morality and greater tolerance of ethical lapses.

(Eagly, 2005, p.467)

Or as Sinclair (2008) declares “critical and feminist theorists show that most leadership

research, including studies of transformational leadership, continue to present

43

prescription – heroic or post-heroic – as if they were gender neutral” (p.32) but

“[l]eaders who are highly emotionally literate become able to demand new levels of

emotional labour from employees” (pp.32–33).

More pertinent to this study is the question of whether gender diversity has any

influence in regards to the Top Management Team constellation. Ruiz-Jimenez,

Fuentes-Fuentes and Ruiz-Arroyo explore this topic in ‘Knowledge combination

capability and innovation: The effects of gender diversity on Top Management Teams

in technology-based firms’ (2016). The authors assume that companies have better

innovation performance if they have the capability to combine knowledge and that this

is augmented by gender diversity in the Top Management Team (pp.505–506). A

sample size of 205 firms was analysed and all results were supportive of the claim that

“for higher levels of gender diversity, innovation performance increases as the level of

knowledge combination capability increases” (Ruiz-Jimenez et al., 2016, p.510). This

leads these authors to conclude that:

[i]ncorporating a greater number of woman at the levels of top management in

technology sectors is not only a question of moral justice or social equity, but

also contributes positively to the quality of the decisions made by the Top

Management to stimulate the development of organizational capabilities. (Ruiz-

Jimenez et al., 2016, p.513)

Opstrup and Villadsen (2015) have researched the influence of gender diversity in Top

Management Teams in regards to financial performance. The authors provide three

factors suggesting why gender diversity in Top Management Teams is positive and

these are promoting a) a better understanding of the organization’s environment, b)

increased creativity and innovative output and c) more effective problem solving

(Opstrup and Villadsen, 2015, p.292). Unfortunately, in this case their hypothesis that

more gender diverse Top Management Teams will result in better financial performance

was not supported through their analysis (p.297).

‘The more, the merrier? Women in Top-Management teams and entrepreneurship in

established firms’ by Lyngsie and Foss (2017) is a paper exploring gender diversity in

Top Management Team pertaining to entrepreneurial performance. They argue that

“while increasing female representation in the TMT may make the firm more

entrepreneurial, this effect depends on how female top managers are perceived by lower

level employees” (p.488). Lyngsie and Foss (2017 p.492) evaluated “the existence of a

44

direct positive association of female top managers and entrepreneurial outcomes”.

Finally, Kakabadse et al. (2015, p.268) conclude the discussion about gender diversity

with a clear statement that “corporate governance theories support board diversity on

the basis that a diverse board operates to reduce agency costs, facilitates access to

untapped resources and networks, and improves performance”.

Richard and Shelor (2011) in a meta-analysis compare different approaches as to

whether the span of age in a Top Management Team has a negative, positive or no

effect at all on company performance. The authors also differentiate if the heterogeneity

of age could have a different impact on lower or higher levels of management. The

proposition that age heterogeneity might have a negative impact on group performances

received “marginal” support (p.965) whereas the proposition that age heterogeneity may

have a positive impact on group performances depending on the level of the

management received “significant” (p.967) support through the authors’ analysis where

the result is described as an inverted U-shape. Meaning that at the lowest level of

management the heterogeneity of age is less relevant than on a medium level of

management whereas it turns into a “marginal” negative when it comes to the highest

level of management.

In ‘Understanding the dynamics of diversity in decision-making teams’ Jackson, May &

Withney (1995) undertook research exploring how the diversity of decision-making

teams is influencing their dynamics. Jackson et al. see the mix of genders in the

workforce as “maximally diverse” and resulting in a decline in the gender based

segregation over the time. Although the percentage of women in the boardroom is not as

high as men this proportion is “substantial” (p.205). In regards to age the authors

unsurprisingly conclude that the average age is increasing and therefore the

“distribution of ages (variance) represented in the workforce is also changing” (p.206).

Jackson et al. (1995, p.212–213) have created an overview about the taxonomy of

diversity in teams by grouping the aspects of diversity where a confirmation of the

previous findings throughout the literature is evident. These aspects are grouped as task-

related (i.e. tenure and educational level) and relational-related aspects (gender,

ethnicity or culture).

45

Table 2.11: Framework for understanding diversity in work teams (Jackson et al., 1995, p.212‒213).

Regarding tenure which was discussed previously, Jackson et al. (1995, p.214) portray

tenure in the context of decision agendas that might be influenced by politically

motivated agendas with increased tenure of the decision-makers and conversely that

shorter tenure or higher turnover rates will provide more diversity in a team and could

improve the innovation performance. Homogeneity in a team is strengthening

operations whereas heterogeneity is more helpful to break with existing models and

allow new options to be considered.

There is no ideal position on the proportion of homogeneity or heterogeneity in a team

since the positive or negative attributes related to it are finally related to the tasks to be

solved by the same team. Consequently, it is most likely that a finance department

requires more streamlined actions than a research and development department where it

is a more routine part of the work to find new ways of achieving things. Also Jackson et

al. (1995, p.230) state that there is “clear support for a relationship between diversity

and creativity”, however, it should be considered that “if team members are so

46

heterogeneous that there is no basis for similarity, then they may be unable to work

together” (p.231). The positives and negatives of a heterogenic composition in the role

of a top management team depend largely on the circumstances of the company at the

time of the analysis. Factors like the financial status of the company, expansion or

consolidation phase could be pivotal. “Diversity [heterogeneity] within a decision-

making team is recognized as important primarily because it is associated with the

resources available during the decision-making process” (Jackson et al. 1995, p.218)

and in contrast to this “positive affect [homogeneity] is likely to be particularly

beneficial for improving performance” (p.244).

The hypothesis of this thesis that the influence of Top Management Team members

towards a dominant coalition can be calculated by looking at parameters describing that

specific manager in horizontal and vertical relationship to his or her environment is

gaining support from Hambrick and Mason’s statement that “the theory states that

organizational outcomes – strategic choices and performance levels – are partially

predicted by managerial background characteristics” and “organizational outcome –

both strategies and effectiveness – are viewed as [a] reflection of values and cognitive

bases of powerful actors in the organization. It is expected that, to some extent, such

linkages can be detected empirically” (1984, p.193).

Value dissimilarity in top management teams is also discussed by Lankau, Ward,

Amason, Ng, Sonnenfeld and Agle (2007) and the authors see satisfaction, commitment,

and effectiveness as important factors between team members (p.13). Furthermore,

Lankau et al., (2007, p.23) mention age and education level as important factors. Also,

they can claim, that perceived and actual disagreements between a member of the top

management team and the CEO, influence the employee’s attitudes and satisfaction

(Lankau et al., 2007, p.28).

Hambrick and Mason (1984, p.196) admit that their research is limited to “observable

managerial characteristics” for which they chose as examples age, tenure in the

organisation, functional background, education, socioeconomic roots, and financial

position. Socioeconomic roots or socioeconomic status (SES) defines the collective

measurement of social and economic factors of one person in relation to others, which

from another point of view can be seen as the accumulation of the characteristics of age,

tenure in the organisation, functional background, education and financial position. It

should be seen as a summary of those factors and not as an additional one. The term is

used in this research to describe one TMT member in relation to another. Regarding age

47

Hambrick and Mason (1984, p.199) propose that: “[f]irms with young managers will be

more inclined to pursue risky strategies than will firms with older managers. Specific

forms of risk include unrelated diversification, product innovation and financial

leverage”.

In this thesis it was analysed earlier that tenure could be described as an inverted U-

shape whereas a short and a too long tenure would not be positively associated with the

efficiency of a decision-making team or as Agnihotri (2014, p.250) concludes “as the

tenure of a TMT increases, actions taken by that team become more homogenous in

nature”. Kakabadse, Kakabadse and Khan (2014, p.642) discuss the importance of

company secretaries because the authors are convinced that the company secretaries

outstay the management: “[n]on-executive directors have been the focus of much

attention post-financial crisis, but their positions can be transient”. Hambrick and

Mason also differentiate the financial climate that the company finds itself. For

organizations which are exploring new terrain or are forced to change their strategy,

longer tenure of managers is not positively associated. For education, the authors

summarize in their research that the type of education is less relevant than the amount of

education, however, more education is only required with more complex tasks (1984,

pp.200–201). Agnihotri concludes that an “average educational level” is contributing

positively to a company’s value chain action intensity and value chain action

heterogeneity” (2014, pp.243).

Finally, Hambrick and Mason (1984) also consider the impact on performance of team

diversity depending on the financial possibilities of the company. It can be summarized

that homogeneity seems to be supportive for efficient solutions whereas heterogeneity

gives better performances for new or innovative solutions which also means that

companies which are forced by a crisis to find new solutions will need to sacrifice

homogeneity in their TMT to a certain degree (Hambrick and Mason, 1984, pp.200).

Agnihotri has found evidence for this when concluding that “value chain action

heterogeneity is positively impacted by the top management team’s functional

heterogeneity” (2014, p.250).

Smith, Guthrie and Chen (1989) in their field study of the Typologies from Miles &

Snow (1978) summarize Top Management Team similarities in respect to

Entrepreneurial, Engineering and Administrative problems. For this research, only the

administrative part was extracted. Besides quantifying ‘age’ and ‘tenure’ as a parameter,

Smith et al. also use the variable of educational background in the corresponding

48

typologies of Defender, Analyser and Prospector. The typology of Reactor was omitted,

most likely as this typology is constituted as having no planned structure. An additional

aspect which Smith et al. (1989) have added and which we have not found throughout

other research discussed in this chapter is the variable of whether top management

people are recruited from inside or from outside of the company. This is most likely,

however, a logical implication of Prospector-Organizations having on average younger

Top Management Team Members with shorter tenure.

Table 2.12: Smith et al.’s (1989, p.65) citation of Miles & Snow strategy continuum, reduced to the administrative

problem variables.

In regards to identifying who is most or least replaceable in industrial value chains a

recent article in the Harvard Business Review written by Jacobides and MacDuffie

portrays the system integrators; those companies that are combining the products of

many other companies and distributing them finally to the consumer, as being those

which are least likely to be replaced (2013, p.94). The authors describe four things of

importance in order to ensure irreplaceability. These are: “controlling the assets least

likely to be commoditized; being the guarantor of quality to the end customer; staying

in close touch with changing customer needs; and balancing the imperatives of growth

and strategic control of the value chain” (Jacobides and MacDuffie, 2013, p.95). Due to

the unlimited number of different business objectives, however, there is no general rule

applicable to determine who is irreplaceable because this is dependent on the

environment, stage of development and as mentioned above the business objectives a

company finds itself at the time of such a statement. Given the assumption that those

variables are accessible then a ranking could be done.

In a fictitious case where a company’s survival is dependent on an expected

breakthrough development from the R&D department in the next twelve months, it is

most likely that this situation is strengthening the position of the responsible head of

department. The important factors outlined by Jacobides and MacDuffie (2013, p.95)

such as being in charge of the quality control and facing the customer are giving the

49

impression that throughput departments and their responsible leaders might be more

likely to be replaced than output departments. Support for this belief is found for

example in ‘The role of the upper echelon in the value chain management’ a meta-study

based on a secondary data collection by Agnihotri (2014) which analyses the impact of

the TMT’s members and constellation in regards to value chain action intensity and

value chain activity heterogeneity. Agnihotri focuses on the educational level, the tenure

with the company and the heterogeneity of TMT’s in regards to the value chain. The

assumption made in this thesis that throughput departments are of less importance than

output departments finds support when Agnihotri (2014, p.242) concludes that:

[f]or example in the airline industry, competitive moves that were considered

included price cuts, promotions, service improvements and acquisitions and co-

promotion (referring to Chen and MacMillan, 1992) but human resource

management practices, like changes in compensation policies or outsourcing of

aircraft management maintenance, repair and operations services were not

considered in any of the studies.

Baig and Akhtar (2011) dedicated part of their Value Configuration Analysis Approach

to differentiate the primary activities versus the support activities in Porter’s Value

Chain Model from 1985. Whereas Porter called logistics, operations, marketing & sales

and service primary activities and infrastructure such as human resources, information

technology and procurement the support activities. Baig and Akhtar (2011, p.253) view

the primary activities as mediators which “rely on a mediating technology to handle and

coordinate in standardized ways operations involving multiple clients who are

distributed in time and space”. Hambrick and Mason (1984) classify the primary and

support activities as output and throughput departments when positioning of importance

(in terms of value creation) is related to or dependent on the industry and developmental

stage of a company. The relationship of the functional background of a TMT member is

always related to growth and profitability. Yet the difference is that throughput

functions are better in commodity-like stable industries and output functions are more

positively related to turbulent, differentiable industries (Hambrick and Mason, 1984,

p.199). The primary and support activities of a company are not only important but also

the environmental and actual situation a company is in during the time of the analysis.

Consequently, finance may be a supportive department in an expanding company, yet

the same department might gain an importance in times where the financial resources

are limited. Child suggests that:

50

[the] choice of markets to be served can considerably affect the performance of

an enterprise because the return available from different markets and industries

varies considerably and because some markets are expanding while others are

not – a poor choice here leads to market inefficiency. (1972, p.12)

It is difficult to undertake research about the traits and characteristics of managers in

the upper echelon without studying the highly regarded and pioneering studies of John

Child (1972) who was amongst those who introduced the term ‘strategic choice’.

‘Strategic choice’ describes the situation where the increasing complexity of the

environment or the permanent change of conditions influences the number of possible

decisions from which the manager must select a preferred option. These external

influencing factors are categorized as environmental variability, encompassing the

frequency of change and environmental complexity, as well as the heterogeneity and the

concept of environmental illiberality to describe the “degree of threat that faces

organizational decision-makers in the achievement of their goals from external

competition, hostility or even indifference” (Child, 1972, p.4). Internal influencing

factors such as ‘technology’ and ‘size’ are nominated. The factor of technology covers

the development of technology or the invention of accessible technology or the

knowledge about existing technology. Size as an influencing factor is seen from

different perspectives whereas the larger size of operations could give access to more

opportunities, however, this also increases the number of organizational problems

connected with more administration necessary to manage size (Child, 1972, pp.1–7).

Internal factors of technology and size are often interconnected because according to

Child “there is in fact a considerable debate as to the type of constraints which size and

technology may each and both imply for organizational structure” (1972, p.7).

Figure 2.7: The role of strategic choice in a theory of organization (Child, 1972, p.18).

51

This study concludes from the influencing factors that there are two main categories.

Those directed externally from the company implying “that organizational decision-

makers do take positive steps to define and manipulate their own corners of the

environment” and those influencing factors directed inwards used and/or abused by

those who have created a territorial area within a certain workspace. “[T]hose in control

of an organization may in practice be able to exercise a degree of authority… over other

organizations or individuals which are nominally independent” says Child (1972, p.9).

Finally, taking all those influencing factors together the most important issue among the

relationship between individuals and organizations is “the degree of influence which the

controllers of one organization can exert over their counterparts in other organizations,

and vice versa” (Child, 1972, p.10). Furthermore, Child compares the individual

performance of decision-makers with the performance of their organization. Child is

convinced that if the performance of the organization reaches the required level a

decision holder might adjust their decision-making pattern to accommodate their

performance according to personal preferences, even to the extent that this might imply

additional costs for the organization (1972, p.11). In a second paper published in 1997,

Child reviews the model of strategic choice under a more contemporary lens and

concludes that:

[w]ithout an attempt to draw upon, and even to reconcile, the insights offered by

its various perspectives, organization studies will run a serious risk of becoming

little more than an arena of ‘clashing cymbals’ (or indeed symbols) making little

real theoretical advance and having nothing useful to say for practice either.

(1997, p.44)

He further suggests that “a contemporary contribution of strategic choice analysis

derives from its potential to integrate some of the different perspectives in organization

studies” (1997, p.44).

Also Child confirms that age and educational level are among the factors to be

considered when analyzing the constellation of a Top Management Team:

[a]ge and education, although they locate people within social categories which

can generate common identities and beliefs, are likely to affect action

determinism not only through the medium of ideology but also through

competence. (1997, p.51)

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Child (1997, p.59) also sees common identities in the Top Management Team

Member’s social network which extends his earlier work on strategic choice where

organization and environment were previously viewed as two separate clusters. Child

refers to the Miles & Snow Typology which was written in 1978, well after his own

initial article on strategic choice (1972) and admits that the Miles & Snow

“categorization was an important refinement of the strategic choice concept”. In the

same article Child is even citing Miles & Snow (1978) in regard to his own strategic

choice approach that “the effectiveness of the organizational adaption hinges on the

dominant coalition’s perceptions of environmental conditions and the decisions it makes

concerning how the organization will cope with these conditions” (1997, p.59). Another

salient point is where Child argues that it is important to distinguish in the strategic

choice analysis if one wants to view the market as the influencing unit or just the

organization itself. From Child’s perspective it requires “an understanding of how

actors engage with both internally sedimented structures (‘routines’) and external

institutionalized structures in the process of furthering organizational evolution” (1997,

pp.66–67).

The review of Child’s papers about strategic choice concludes this short literature

review. This chapter’s role is to find evidence to decide which parameters are

considered by analyzing the Top Management Teams’ constellation of attributes in

regard to strategic decision-making. Chapter 3 provides a summary of these findings

and investigates how the selected factors are developed.

2.5.2 Summary of TMT-Constellation Aspects

The table below illustrates the parameters considered by different authors in respect to

key factors of the Top Management Team members in regard to their performance in

strategic decision-making. Overall it can be shown that the overwhelming majority of

authors use Age, Education and Tenure as the key parameters. Not as frequently cited,

but also quite often mentioned, is the importance of Functional Background. Also for

this study the Typology of the Organization is important because it already defines

under which classification the department would belong to the dominant coalition. It is

understood, that there may also be cases where employees have a different functional

background than their departmental attachment. The exploration of gender diversity in

Top Management Teams has not resulted in an advantage for either gender because the

stereotypes discussed within the gender related debate have also crystalized the pros and

53

Table 2.13: Summary of parameters considered in regard to TMT’s constellation for strategic decision-making

cons of both stereotypes and overall only gender diversity is related positively to firms’

performance. Consequently, this thesis does not pursue this issue further. MOVE Down

2.5.3 Age as a Parameter in TMT-Constellation

The literature review has shown that age has an impact on the functionality of top

management teams under different pre-conditions. What all findings have in common is

that age is not having a positive affect if it is completely different from all other group

members which is similar to the heterogeneity / homogeneity discussion. It results in a

positive impact on firms’ performance to have a group where all staff are in the same

age group but it is also acceptable to have a group which is homogenous in regards to an

equally diversified age constellation. Whatever could be considered as a norm in that

specific group – should not be challenged with something which is completely out of

that norm. In Chapter 3: Research Methodology, this parameter will be measured by

standard deviation, applied to the group which is to be analyzed, and then calculating

how the Top Management Team members are in or out of the norm of their specific

team.

2.5.4 Education as a Parameter in TMT-Constellation

Education can compensate for age and/or experience for younger staff, however, there

was no indication that education is compensated when considering this in reverse. The

reviewed authors seem to have a common consensus that the type of education is of less

importance than the amount of education. This parameter is steadily increasing in

importance from NO education until MORE education.

Author Age Education Tenure Various

Hambrick et al., 1996 Age Education Tenure Functional Background

Finkelstein and Hambrick, 1990 Tenure

Wagner, Pfeffer and O’Reilly, 1984 Tenure

Jackson et al., 1991 Age Education Tenure Gender

Knight et al., 1999 Age Education Tenure Interpersonal Conflict, Agreement seeking, Strategic

Consensus, Location, Functional Background

Richard and Shelor, 2011 Age Education Gender, Experience, Race, Marital Status

Jackson and Withney, 1995 Age Education Tenure Gender

Hambrick and Mason, 1984 Age Education Tenure Functional Background, Socioeconomic Roots, Financial

Position

Smith, Guthrie and Chen, 1989 Age Education Tenure

Agnihotri 2014 Education Tenure Functional Background

Hambrick, Cho and Chen, 1996 Education Tenure Functional Background

Child, 1997 Age Education Socioeconomic Roots

Lankau, 2007 Age Education

54

2.5.5 Tenure as a Parameter in TMT-Constellation

Tenure will also be described using standard deviation so as to allocate values to each

top management team member. Due to tenure being considered as an inverted U-shape

where a certain amount of tenure in the company is increasing the job holder’s

importance but after exceeding the peak point the tenure is then reducing in its

importance. Certainly, this parameter also has to be set in proportion to the other Top

Management Team Members.

2.6 Perspective: Managers’ Characteristics

Merely estimating which characteristics of one’s personality may

assist people in increasing their influence on decision-making

processes would be purely subjective. Everyone consulted would

give a different answer. One person could consider factors like

networking, contribution to profit or knowledge as very

important. Someone else might view other characteristics as important. That is why a

separate literature research was undertaken in this thesis concerning the key personality

and attributes or characteristics of managers. On the following pages the key

characteristics between a supervisor to another supervisor (horizontal) or between

supervisors to a subordinate are explored (vertical).

Figure 2.8: The relationship between one supervisor to another supervisor or between one supervisor to a subordinate

2.6.1 Managers’ Characteristics

In regards to Managers’ Characteristics the initial research undertaken for this thesis

was the screening of a database of interviews with senior executives who are in leading

positions in reputable companies in Switzerland. The interviews were collected from the

newspaper Neue Zürcher Zeitung, which is one of the foremost quality broadsheets

servicing the German-speaking part of Europe.

The second research for this thesis was undertaken to evaluate the key Characteristics of

Managers using an online opinion poll conducted during the study prior to this thesis. In

cooperation with an IT-Specialist, a web site was set-up on which visitors were invited

55

to rate the importance of the key factors which were found in the first research activity

described above. This was both in the horizontal and vertical directions demonstrating

the direction of relationships from the point of view of the individual manager

appearing on an organizational chart. The directions were illustrated by two small icons,

symbolising a manager (wearing a suit) and a subordinate (wearing a safety helmet). For

each direction of the relationship, the relevant set of key factors was listed and next to

each feature an evaluation scale was shown. Through Likert-scales, each participant was

able to allocate points and vote according to their preference.

In the picture above the result of the online survey is shown. The upper part shows the

key Characteristics for a manager in the relationship between supervisor and

subordinate and the lower portion shows the key Characteristics for a manager in the

relationship between supervisor and supervisor. With the following short literature

review, the author of this thesis is aiming for verification of the findings from this

earlier research.

2.6.2 Overview of Managers’ Characteristics

Commencing with Mayer and Gavin, (2005, p.878), trust, performance and the ability to

focus attention are key factors which should be characteristics of a successful manager.

Furthermore, the paper states that trust is a result of ability, benevolence and integrity.

Screenshot 2.1: On-line opinion poll; key characteristics of managers, author’s work from the study DBA712

56

Mayer and Gavin, (2005, p.878) conclude that “[...] a lack of trust in management may

undermine attempts to direct employees’ attention”.

Barrick et al. (2007) researched the influence on companies’ performance if members of

the top management team (TMT) pursued common goals. Referring to Pulitzer Prize-

winning author James McGregor, who wrote that “[t]he function of leadership is to

engage followers, not merely to activate them”, Barrick et al. state that leaders were

rated by followers as more effective when communicating organisational goals. In the

results of their study, Barrick et al. (2007, p.89) conclude that contrary to popular

assumptions, a similar prioritisation of goals is not related to individual performance.

‘Feeling at the Top’ is a study by Reynolds Fisher and Nelson (1996). The article

focuses on female leadership qualities. They argue that “females are more likely to

prefer the feeling decision-making style associated with affiliative leadership than the

thinking style preferred by males and associated with authoritarian leadership”

(Reynolds Fisher and Nelson, 1996, p.77) and conclude their research with the result

that strategic value is enhanced when feeling decision-making styles are adopted by

executive teams (1996, p.87). Unfortunately, this study also perpetuates the dichotomies

between thinking and feeling, logic and intuition.

Medcof (2008, p.412) begins his paper on Chief Technology Officers’ power, with the

description of personal characteristics such as strong personal relationships, networks

inside and outside the company and ownership positions in the firm. Medcof (2008,

p.415) claims that these can be more important than traditional definitions. He describes

monitoring, updating and planning as the most time-consuming activities for the

members of the top management team.

In 1997, Katzenbach wrote a paper about ‘The Myth of the Top Management Team’.

The title indicates the author’s critique of the term ‘team’ because he views top

management not as a group of people working together on the basis of free will but

rather because of organisational necessity. For example, Katzenbach states that the

“team at the top is a badly misused term that obscures both what teams can accomplish

and what makes them work” (1997, p.84). He explains that because members of the top

management normally have to lead the team below them, team leaders therefore have

different characteristics compared to team members. Real teamwork becomes difficult

when a team, in this case a top management team, only consists of team leaders

(Katzenbach, 1997, pp.84–87). Katzenbach highlights several characteristics of people

57

with leadership qualities and describes them as important and / or counterproductive in

the relationship between managers. These include performance, skills, commitment,

accountability, overachiever and a sense of urgency.

In ‘The New Deal at the Top’ Doz and Kosonen (2007) are convinced that members of

the top management team “have a very clear idea of their roles and responsibilities and

of how they relate to one another” (p.98). The authors conclude that collective

responsibility, interdependent strategy and value creation, coupled with transparent

measurement, overlapping experience and responsibility and also value-oriented control

should be the characteristics of modern top management team members (Doz and

Kosonen, 2007, p.101). Doz and Kosonen, however, confirm that “authoritative CEOs

find the new deal particularly difficult” (2007, p.100) due to their inflexibility.

‘Environment, Strategy and Power within Top Management Teams’ is the title of a

study conducted by Hambrick in 1981. In this study, Hambrick (1981, pp.253–254)

mentions that “intra-organisational power” results from the combination of formal

authority, coping behaviour and personal characteristics which strengthens the

assumption that formal authority is not the sole factor of the decision-making process.

From a strategic point of view, the author distinguishes between prospectors and

defenders (Hambrick (1981, p.256). This means, that some of the executives intend to

guard their field of responsibilities (defenders) and others are permanently aiming to

expand their influence (prospectors).

Barrick et al. (2007, p.547) investigated top management team interdependence. The

team mechanism comprises communication and cohesion between the team members,

and the authors argue that “... the more that team members need to coordinate their work

to achieve collective tasks, goals, and rewards, the more team performance should be

influenced by team communication and cohesion” (Barrick et al, 2007, p.546). As

already concluded by Katzenbach (1997), Barrick et al, (2007, p.555) also suggest that

an organisation does not always need a “real team at the top” but that communication

and cohesion between employees generate increased value.

The last paper examined has the humorous title ‘Surviving Your New CEO’, written by

Coyne and Coyne in 2007. These authors give advice on the key characteristics

supporting productive relationships between managers and managers. They believe the

most important characteristics described in their study are: trust, vision, honesty,

performance and commitment (Coyne and Coyne, 2007, pp.66 – 68).

58

2.6.3 Findings

For the research on Managers’ Characteristics this thesis builds upon the results from:

a) the analysis of 50 interviews from a database

b) an online opinion poll with 525 valid individual votes

c) the summarized key characteristics of managers according to the literature review

which are shown in the table below:

Author Selected Managers’ Characteristics

Barrick, Bradley, Kristof-Brown and

Colbert

2007

Communication and cohesion between team members

Coyne and Coyne

2007 Trust, vision, honesty, performance and commitment

Doz and Kosonen

2007 Responsibility, transparency, experience, control

Hambrick

1981 Formal authority, coping behaviour and personal characteristics

Katzenbach

1997

Performance, skills, commitment, accountability, overachiever, sense

of urgency

Lankau et al.

2007 Satisfaction, commitment, effectiveness, age, education level

Mayer and Gavin

2005

Trust, performance and the ability to focus attention. Trust is a result

of ability, benevolence and integrity

Medcof

2008

Strong personal relationship, networking, ownership, monitoring,

updating, planning

Reynolds Fisher and Nelson 1996 Feeling decision-making

Affiliative Leadership

Table 2.14 Summary of the findings of the literature research on Managers’ Characteristics

To provide a better overview, the keywords were clustered into groups and subgroups.

Reliability Archetype (Role Model) Competence Network

Trust (2)

Ability

Benevolence

Integrity

Accountability

Transparency

Honesty

Commitment (2)

Engagement

Overachiever

Satisfaction

Responsibility

Skills

Age

Education level

Experience

Sense of urgency

Authority

Coping behaviour

‘Feeling’ decisions

Affiliative leader

Relationship

Network

Cohesion

Communication Authority Foresight Hard Facts

Communication (2) Monitoring

Control

Visionary

Performance (3)

Ownership

Table 2.15 Selected Managers’ Characteristics, horizontal relationship

59

In a comparison of the same groups and subgroups (with the number of mentions for

each Characteristic in brackets) these results were found through the analysis of the

published interviews:

Reliability Archetype (Role Model) Communication Conveyor

Reliability (15) Openness (9) Honesty (9) Fairness (6) Transparency (4)

Trust (4) Respect (3) Objectivity (2) Clarity

Archetype (Role Model) (8) Commitment (5) Authenticity (4) Worker (3) Integrity (3)

Responsibility (2) Engagement Fighter Loyalty

Communicator (12) Listener (6)

Conveyor (14) Supporter (2) Motivator Feedback

Foresight Authority Competence Social Competence

Foresight (12) Targets (2) Innovation Priorities

Authority (8) Decider (2)

Competence (7) Social competence (6)

Humour Humility Cooperation Generosity

Humour (3) Humility(3) Cooperation (2) Team player

Generosity

Table 2.16 Selected Managers’ Characteristics, vertical relationship

The keywords were ranked through an online survey with 525 valid individual

respondents to allocate a numerical value representing its importance. The participants

were eligible to give a value from 1 (least important) to 10 (most important) and the

participants could give a value for the keywords for the relationship between Manager –

Manager (horizontal) and Manager – Subordinate (vertical). Regarding the purely

horizontal factors (manager – manager), networking, holding shares in the company and

contribution to profit were the outcomes. Those resulting from the survey were the

relevant factors. To hold shares, however, is not seen as much influence on the

relationship among managers. Networking and contribution to profit were higher in

percentage indicating their importance. This might differ depending on the size of the

company. For example, in a small or medium sized company a Top Management Team

member may also be the company owner. In such a case it is most likely that a

shareholder could use their power to overrule other Top Management Team members.

Whereas, in larger companies the percentage of shares held by executives could be a

minor percentage or maybe a part of their salary remuneration.

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2.6.4 Conclusion

Combining all the findings to derive Managers’ Characteristics within horizontal and

vertical relations, there are six major factors (although they could be divided into more

different sub groups) that are relevant in both horizontal and vertical directions. The

factors mainly required in horizontal relationships among managers are networking,

shares in the company and contribution to turnover. On the other hand social

competence, the ability to convey ideas, humour, humility, cooperation and generosity

are skills that are mainly beneficial for the vertical relationships between managers and

their subordinates. These are the findings of the previous research and the result of more

than 700 participants of whom 525 are used for this thesis as shown below:

Figure 2.9: Managers’ Characteristics, in horizontal and vertical relationships

Important factors in both directions

Reliability Archetype (Role Model) Communication Competence Authority

Foresight

Horizontal factors

Network Shares in the company Contribution to turnover

Vertical factors

Social competence Conveyor Humour Humility Cooperation Generosity

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2.7 Perspective: Span of Control

2.7.1 Introduction

The last perspective of Span of Control is not directly related to

the evaluation of dominance of each Top Management Team

member but is more of an evaluation of whether any of the Top

Management Team members are likely to be influenced by

someone in their team when measured on the assumption that

they must have reached the momentum of losing control after exceeding a certain

number of direct reports. The aim of the following short literature review is to gather

knowledge about what are the criteria necessary to define the maximum Span of

Control. This is because this study requires a formula to base the development of the

research tool to be designed.

Span of control as defined at the beginning of this thesis is the limitation of the amount

of subordinates a manager can control at one time. If a group which is under the control

of one manager exceeds a certain team size, one or more members of the group

members will be officially or unofficially involved in the group leadership to support

the manager. This may be a formal deputy or a person who comes into this role for

whatever reason. There could also be more than one person involved in assisting the

manager. It is clear that a manager, who leads a group that exceeds a group size which

makes it unfavourable to lead it alone, also has to handle a certain amount of difficulties

which makes it hard to be respected by everybody and to control everything alone.

Based on the consideration that if one person in a management team is accumulating a

reasonable amount of power and this person is supported by one or more official or

unofficial influencers it is reasonable to assume that those influencers indirectly control

the manager through their role in the top management team. This is why it is important

to know what are the factors limiting the Span of Control.

This section starts with a description of the history of Span of Control and how the

managerial literature defines this concept. Following this, different approaches of how

to define the maximum Span of Control are explored. Similar to the earlier literature

reviews the findings are summarized in a grid with several fields representing the size of

a group or company in relation to the maximum Span of Control. The definition of the

fields results from the literature review as the same parameters were found to recur. The

fields of the grid will assist in separating the findings visually.

62

2.7.2 History of the concept of ‘Span of Control’

Generally, the term ‘Span of Control’ refers to the number of subordinates a supervisor

oversees or can effectively control or as Meyer commented “Span of Management is

typically measured as a ratio” (2008, p.104). But the assumed optimal span differs.

Napoleon, for example, mentioned a span of five people, Clausewitz spoke of ten and

the British General Sir Ian Hamilton proposed a span of three to six people (Van Fleet

and Bedeian, 1977, p.357). As can be seen by several sources, the term ‘Span of

Control’ has its origin in the military. The British general Sir Hamilton (1853–1947)

was one of the first who wrote in detail about the term within a military context in his

book The Soul and Body of an Army (1921). Hamilton stated that: “[a]s to whether the

groups are three, four, five or six it is useful to bear in mind; the smaller the

responsibility of the group member, the larger may be the number of [the] group – and

vice versa” (as cited in Gulick and Urwick, 1937, p.183). In addition, Gulick and

Urwick cited Hamilton (1921, p.183) regarding the ideal sizes of groups. For example,

an ideal size of a group at a higher hierarchical level is described as three group

members (for example the management team) and an ideal group size on a lower

hierarchical level is considered to have around six group members as cited by Urwick

(1956, p.39). Also Entwisle and Walton (1961, p.522) mention Hamilton (1921) but

they also claim that the concept of Span of Control might be much older. Most probably

the idea did not come from theoretical models but rather from the “intuitive belief” to

increase the effectiveness when forming tight groups (Entwisle and Walton, 1961,

p.522). According to Van Fleet and Bedeian (1977, p.357), Span of Control is therefore

an historical concept which was used by the Romans, Egyptians or Greeks, and then

later mainly in a military context.

Besides Hamilton (1921), Gulick and Urwick (1937), Entwisle and Walton (1961) and

also Graicunas (1933) took an important role in providing guidelines for the theory of

the Span of Control model. In his ‘Relationship in Organization’ (1933), a paper on the

science of administration which later was edited by several other writers such as Gulick

et al. in (1937), Graicunas wrote that “one of the surest sources of delay and confusion

is to allow any superior to be directly responsible for the control of too many

subordinates” (as cited in Nickols, 2011, p.2). Urwick, who was a friend of Graicunas,

remembers a day in Paris when Graicunas asked him to discuss a “mathematical proof”

that the numbers of subordinates reporting to a supervisor should be limited (Urwick,

1956, p.40). After analysing the work of Graicunas and Urwick, Nickols (2011, p.2)

63

provides six pragmatic reasons for increasing the Span of Control in organisations.

These are that:

people want to report to the supervisor

they aim to build empires

cost reduction as well as the minimising of management overheads

the desire to shorten command chains

to flatten the hierarchy

the existence of companies which work successfully with a higher Span of Control than the

proposed five or six.

It is apparent here that some of these reasons are unethical or even specious such as

building up empires because they occur at the expense of the company’s welfare.

Some years later Urwick wrote a paper about Span of Control and saw the complexity,

duties and problems of top executives as having increased “commensurately” (Urwick.

1956, p.39). This is probably the reason why Urwick concludes that it is one of the main

tasks of an executive to reduce his or her “overload of less important daily duties” to

gain more time for strategic tasks and facilitate better relationships with his or her staff

and the organisation which ultimately makes the executive a high performing “business

leader” (Urwick, 1956, p.39).

The figure below by Van Fleet and Bedeian, (1977, p.357), illustrates the relationship

between effectiveness of supervision and Span of Control. Therefore, it can be stated

that when the number of supervised staff increases the effectiveness of the individual

supervision decreases. The following illustration shows the limits where no more

effectiveness is evident.

Figure 2.10: The limited span concept (Van Fleet and Bedeian, 1977, p.357)

Van Fleet and Bedeian (1977) concluded that the limit of Span of Control greatly

depends on the supervisor and that this limit most probably differs from the optimum

Increasing effectiveness

per person supervised

Decreasing effectiveness per person supervised

Constant effectiveness

per person supervised

Span of Management

Eff

ecti

ven

ess

of

Su

per

vis

ion

64

effectiveness. Van Fleet and Bedeian (1977, p.360) visualized the optimum span of

management. The figure below shows, that the effectiveness is not the highest where

one might naively assume by having a ratio from 1:1 for supervisor to subordinate

because it would be a waste of resources rather than the ideal constellation which is the

crossing point of the two lines representing “available managing power” and

“supervision needed”. After that point the effectiveness is decreasing until a point which

is defined as the limit.

Figure 2.11: The optimum span concept (Van Fleet and Bedeian, 1977, p.360)

2.7.3 How Span of Control is defined in the Management Literature

According to Van Fleet and Bedeian (1977, p.356), Span of Control is “one of the most

discussed single concepts in classical, neo-classical or modern management theory”.

Schroeder, Lombardo and Strollo (2000) rationalize this and argue that Span of Control

in reality is “determined more by budgetary considerations than by management theory”

(p.35). Yassine, Goldberg and Yu see the concept of Span of Control as rather simplistic

when summarizing their review of Koontz and O’Donnell (1959), Bell (1967),

Williamson (1967) and Perrow (1986) when stating: “[t]he span of control is a simple

managerial construct which identifies or regulates the amount of direct supervision that

exists between a superior and his direct subordinates within an organization” (Yassine,

Goldberg and Yu, 2005, p.1). On the other hand there are authors who believe that Span

of Control is one of the “crucial” factors concerning the effectiveness of a company

(Van Fleet and Bedeian, 1977, p.356).

For this study, it is important to understand that in any group that exceeds a certain size

(maximum Span of Control) the group leader is at risk of losing control or of becoming

dependent on other group members. Abramson et al. (1958, p.17) states that power

cannot be measured through the amount of people controlled by a leader. In addition, it

can be said that the power of a group leader is limited by the size of the group, and

depends on what the group does. Consequently, this research looks at different

65

perspectives in order to describe the minimum, ideal and optimum Span of Control

depending on the size of the group and what the group does to a) build an overview b)

search for interconnections and c) visualize the results.

McManus (2007, p.22) criticises today’s trend to eliminate the middle management, so

that the upper management receives an even bigger Span of Control, which in return

significantly reduces the effectiveness of supervision. McManus (2007, p.22) asks: “if

we reduce the number of supervisors per employee, aren’t we also moving toward the

need for more self-direction?” His opinion parallels the conclusion drawn by Gittel

(2001, p.471), in a study of Continental airline, where he states that performance

improves with a small Span of Control through “positive effects on group processes”. In

this respect Hanna and Gentel (1971, p.115) went a step further when arguing that Span

of Control should not exceed the number of people “that can feasibly and effectively be

coordinated and directed” which in turn depends upon nine circumstances:

the nature of the task the area involved the time involved

the nature of the instructions the abilities of the

subordinates the material used

the amount of authority

delegated the abilities of the supervisor

the harmony of the

subordinates

Table 2.17: Nine limitations of Span of Control according to Hanna and Gentel (1971, p.115)

This is confirmed by the work of Wilson and McLaren (1977) where the authors argue

that there is no perfect rule for a standard Span of Control and that it depends on the

level of authority. For example, “the height and width of each pyramid will be

determined by conditions ... such as time ... competence ... reliability ... and the ability

of the head to delegate authority” (Wilson and McLaren, 1977, p.68). Souryal (1977,

p.161) views efficiency as the main outcome which has to be increased through Span of

Control. Usually, a larger Span of Control results in a lack of supervision which again

reduces efficiency. Yassine, Goldberg and Yu (2005, p.1) comment that “organizational

theorizing has become either overly complex, computational and mathematical”.

Therefore, the trend is towards ‘simple’ quantitative modelling (Yassine et al, 2005,

p.1). This chapter concludes the literature compilation with Michel (2007, p.40) who

states that the executive becomes “the limiting factor” when the company is growing

and therefore an executive “should use a scorecard to observe where there is a

slowdown in [the]company’s control”.

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2.7.4 Literature Review on the ‘Optimum Span of Control’

In the following section different research papers regarding the optimal Span of Control

are critically reviewed. For example Graicunas (1933) who was primarily focused on

researching the maximum Span of Control. These findings will be summarised in a grid

and again we will attempt to find similarities. The literature review in this section also

contains historical articles which are based on the early days of the development of the

concept of Span of Control. In addition, a selection of recent academic research is

discussed to observe if there are significant changes over time.

When researching literature on this topic it was soon recognised that there are several

variables which influence the Span of Control. Most obvious are the variables of

hierarchy, size of the company or department and the complexity of work which is done

by the selected group. The risk in bringing these factors together in one table is the

danger of over simplification. Nevertheless, it seems plausible that for non-exceptional

business cases such a simplified system can be applied.

Size of the Company/Department

Small Medium Large

Com

ple

xit

y High

Medium

Low

Table 2.18: Company/department size versus complexity of work

After examining the available literature we understood that hierarchy can also be seen

as an indication of complexity. Therefore, hierarchy and complexity were merged in the

vertical axis described as being very complex, complex and less complex. In the

Horizontal Axis: the size of the company or department is visualised in the horizontal

axis as small, medium and large. This was done to mirror an organizational top down

chart where the vertical position represents the complexity of the work and the

horizontal dimension represents the number of staff in one department/company. The

left upper corner will thus symbolise the smallest company/department with the most

complex tasks. This could be the CEO with his/her top management team or, for

example, a small group of scientists who are highly educated experts undertaking

research in newly emergent technologies. The corner on the right lower edge illustrates

a larger group of staff with less complex responsibility for example repetitive work

where no higher education level or specialised knowledge is required. The review was

67

undertaken wherever possible in the sequence of the date of publication, as it has been

recognized that the values given by the authors reviewed for a maximum Span of

Control became more precise over the years and also have increased numerically.

Similarly, to the previous literature reviews, this review begins with the analysis of

papers and articles dating around the time when research about Span of Control was at

its peak. Elevated numbers (1, 2, 3

) in this chapter are used to index authors in a table at

the end of this short literature review.

In The Manager’s Span of Control by Urwick (1956, p.39) there is an early citation of

Hamilton who stated, that the “average human brain finds its effective scope in handling

from three to six other brains”1 and “if he delegates to three heads he will be kept fairly

busy whilst six heads of branches will give most bosses a ten hours day” (as cited by

Urwick, 1956, p.39)1. Later Urwick (1956) confirms the structure of the newly created

matrix in this research by citing Hamilton again, saying “the smaller the responsibility

of the group members, the larger may be the number of the group and vice versa” (as

cited by Urwick, 1956, p.40). Urwick (1956) also makes a cross reference to a paper he

had written roughly twenty years before where he reduces part of the work of Graicunas

to a concise statement: “No superior can supervise directly the work of more than five

or, at most, six subordinates whose work interlocks” (Urwick, 1956, p.8), referring to

Urwick’s (1938) Scientific Principles and Organization)2. Reading Urwick’s study

(1956, p.42) it is apparent that “social and administrative distance” as well as a great

number of subordinates create a certain “looseness of supervision”. Furthermore,

Urwick (1956) gives examples from the army where a Chief Officer had eleven officers

under his immediate control and a total force of 2,500 people which illustrates how the

Span of Control can increase in large organisations.3 Urwick (1956) also analyses the

organisation of Sears, a large U.S. corporation, by discussing different levels of

complexity. In this business case where a standardised pattern is used Urwick does not

see any reason why 20 stores “should not be controlled effectively by a single chief”4

(Urwick, 1956, p.45). He even mentions a director who controlled 30 business units –

he was not involved in the daily business any longer but only had to supervise that

developments went “according to plan” (Urwick, p.1956, 45)5. Another example by

Urwick (1956) reports a divisional commander who was coordinating the work of 18

people directly which was beyond his personal maximum Span of Control. To return to

his personal optimum Span of Control, the commander limited the right of people

contacting him directly to six instead of the initial 18 people (Urwick, 1956, p.46)6.

68

Entwisle and Walton (1961) undertook a broad survey of organisations with different

sizes according to the number of people involved and they distinguished three

companies/departments (small, medium and big) which supports the layout of this

thesis’ framework for Span of Control. In the survey executives explain how many

people report directly to them and also, at one level lower, how many people are

involved at this level. By distinguishing the results of the smaller and the medium-sized

organisations, Entwisle and Walton (1961, p.524)7

state that the medium Span of

Control should be as large as four to seven for smaller organisations and five to seven

for medium-sized organisations. Entwisle and Walton (1961) emphasise that they only

examined “the positions of chief executives or other positions only to the extent that

they are related to the chief executive position”. Entwisle and Walton (1961) compared

their work with Dale8

(1952), who advocated “a median span between eight and nine for

large companies, and a median span between six and seven for medium companies” and

argues that the Span of Control is identical when looking at businesses or universities

(Dale, 1952, cited in Entwisle and Walton, 1961, p.528). Also discussed by Entwisle

and Walton (1961, p.529) is the term ‘span of attention’ which is pertinent to the

discussion in this thesis, especially in connection with networking factors between

managers. ‘Span of attention’ counts the direct contacts between members of a group

and also compares the number of contacts between people.

Meyer (1968, p.945) introduces the factor of expertise into our discussion. Experts gain

their knowledge outside organisations or corporations and thus have knowledge which

is difficult to supervise for managers. This leads to the assumption that groups of

experts could ask for less supervision – which would influence the Span of Control. In

each level of hierarchy “goals are further divided into sub goals, until at the non-

supervisory level work is divided so that each employee can be expected to complete

alone the tasks assigned to him” (March and Simon,1958, as cited by Meyer, 1968,

p.945). This confirms the approach of this thesis to merge together hierarchy and

complexity in the vertical axis of the matrix. An interesting input is given by Meyer

when he states that “the greatest number of expert employees tends to occur in divisions

that combine complex financial functions” (1968, p.947). This assists in understanding

the influence of external consultants as they obtain their expertise by analysing different

organizations and situations. Meyer (1968, p.948) notices that departments have fewer

experts if a department has less than 20% of subordinates holding a college degree.

According to Meyer’s analysis (1968, p.948), small groups in small companies have an

69

optimal Span of Control of about 2.88–3.34 in groups with many experts and of about

4.67–5.56 in groups with fewer experts9. Larger groups in large companies have an

optimal Span of Control of about 6.25–8.05 in groups with many experts and of 10.7–

11.27 in groups with few experts10

. Furthermore, Meyer (1968) emphasises specialised

departments which are placed in the matrix as small groups. He ranks them according to

complexity with 6.08, 6.89 and 8.38 as the average Span of Control11

(Meyer 1968,

p.948). According to Meyer (1968, p.949)12

, the average Span of Control of a first-line

supervisor lies between 6.25 and 11.27 in large departments. As most of the literature

reviewed describes the possible Span of Control only with one digit after the comma, in

this thesis Meyer’s findings will be placed in the matrix rounded to the next tenth to

ensure ease of understanding.

Ouchi and Dowling (1974, p.358) add the time spent on each subordinate by the

supervisor as an important variable and remind us that not only the figure of Span of

Control should be considered when analysing organisational structures. Furthermore,

Ouchi and Dowling confirm that “as the subordinate’s task becomes more complex, he

requires more contact and thus causes his superior’s span to narrow” (1974, p.358).

According to Ouchi and Dowling it is a simple equation which shows the relationship of

span and supervision. To supervise four people who work ten hours a week is equal to

supervising one person who works 40 hours a week. The author considers this

calculation to be a little too simplistic even if the principle can be supported, however,

not to consider the complexity of the work makes every comparison useless in the

author’s opinion. Yet, the argument of Ouchi and Dowling (1974, p.361) advances

further when they add that workload in administration may change according to the

subordinates supervised. Ouchi’s and Dowling’s (1974) survey was undertaken with

124 retail department stores that could be considered as medium-sized organisations.

From the results of the survey an average span of 12.9 for executives who supervise

managers and 23.3 as a Span of Control for executives who supervise salespeople can

be concluded (Ouchi and Dowling, 1974, p.361) 13

.

‘A History of the Span of Management’ from Van Fleet and Bedeian (1977) is another

article which was analysed in the context of this literature review. In a grid of

chronological statements, the authors Van Fleet and Bedeian (1977, p.358) list different

authors; some of them give valuable information for this research: Fayol (1916)

recommends no more than six subordinates in small teams and for 20–30 subordinates

“when the work is simple”14

, which is similar to the findings of Jones (1925)15

who

70

mentions five and 25 under the same circumstances. White (1926)16

sees seven as the

maximum span for an administrative executive, and Dennison (1931)17

thinks that for

people who have to deal with more than “simple or uniform mechanical work” between

six and 12 is the right Span of Control. For the executive level, Alford (1940)18

sees

“preferably [a] limitation” of five or six subordinates which Brech (1946)19

agrees to

with the additional proviso “if their activities interlock”19

. Balderston, Karabasz and

Brecht (1949)20

state that four to five at the upper level of authority, ten at a lower level

of authority and up to 50 in “the case of the supervision of common labourers” are

appropriate, whereas Davis (1954)21

considers a range from three to nine on an

executive level and ten to 30 on an operative level suitable. Van Fleet and

Bedeian (1977, p.358) have chronically collected all of the previous mentioned authors

into one table.

Where Van Fleet and Bedeian (1977, p.361)22

refer to General Electric, a Span of

Control of 50+ is mentioned. Woodward (1959)23

concludes that the median number of

persons reporting to the top executive should vary from four to ten and of those

reporting to first-line supervisors from fifteen to twenty-three as cited in Van Fleet and

Bedeian (1977, p.361). Hair (1959) 24

finds three types of averages of Span of Control;

five for chief executives, 13 for first-line supervisors and 20 for the lowest level in the

management hierarchy (which we allocate in the ‘lowest level of difficulty’) as cited by

Van Fleet and Bedeian (1977, p.361). Viola and Najjar (1970, 1971)25

undertook

research into the life insurance industry – which is categorised in this thesis as

representing a large company – and found an average of 6.75 subordinates (as cited in

Van Fleet and Bedeian, 1977, p.362). In their conclusion Van Fleet and Bedeian

emphasise that the theory of Span of Control has experienced an evolution in two steps.

In the beginning the limitations of Span of Control were mainly researched and later the

focus was more concentrated on approximating the optimum Span of Control (Van

Fleet and Bedeian 1977, p.365).

In ‘Span of control on nursing inpatient units’, Pabst (1993) compares two medical

centres which are classified as large organisations. Pabst studies the distribution of

managers and caregiving staff, which will be categorized in the medium difficult section

of the grid because although it is repetitive work, however, it also involves care giving

for people. The average ratio of supervisor to subordinate is 27.5 in one medical centre

and 22.9 in the other (Pabst 1993, p.89)26

. Also originating from the nursing sector,

Altaffer (1998, p.36), examined the Span of Control of first-line managers, who are

71

positioned between staff members and higher level managers in the organisational chart.

Altaffer (1998, p.37) distinguishes between first-line managers with nursing and non-

nursing tasks – which are categorized as medium and low complexity in large

companies in our matrix. The optimum Span of Control for nursing first-line managers

is 24.45–38.47 and for non-nursing first-line managers it is 25.78–27.93 subordinates

per manager (Altaffer., 1998, p.37)27

.

Funkhouser (2002, p.1) conducted a performance audit to assess the organisational

structures of city departments. He researched only departments which are organised

according to the free market economy and therefore excluded police and fire

departments “which use hierarchical, military-like structures”. Funkhouser (2002, p.8)28

discovered that the smallest city department has a Span of Control of 3.2 and that the

largest Span of Control with 12.8 is found in a medium-sized department. Funkhouser

(2002, p.15) also observes that there is a trend towards increasing the Span of Control

through “eliminating middle management” with the target to save costs. This makes

sense from a monetary and short-term point of view but because the attention of a

supervisor to their team is reduced by increasing the Span of Control, subordinates

would find themselves increasingly in an unobserved situation which would most

probably influence their satisfaction and quality of work or even compromise their

safety. Haslam et al., argue that some of the causes of fatal accidents at work are poor

maintenance, defective materials and poor supervision (2005, p.402).

In their research paper ‘Span of control matters’, Cathcart et al., (2004) discuss the

influence of Span of Control on employees’ engagement. Cathcart et al., (2004) provide

cases from the nursing field which were discussed earlier in the research by Pabst

(1993) and Altaffer (1998). These mainly fit into medium-sized organisations with

medium and low hierarchical levels for which Cathcart et al. (2004) suggest eight to

twelve subordinates or 20–30 in cases where “an area was defined as having ‘simple’

operations” (Cathcart et al., 2004, p.396)29

.

Cathcart et al.’s paper again questions the issue of ‘how big is too big’ and the authors’

state that “employee engagement scores declined fairly consistently as work group size

increased” (Cathcart et al., 2004, p.398). The results of their research finally answered

the question of when a group size exceeds a successful level. In their conclusion they

identify that the performance of work groups is diminishing in team sizes of 15 and 40

(Cathcart et al., 2004, p.398). Furthermore, the authors strengthen their research results

72

with the argument that “the relationship between employee engagement and span of

control was real ... across all conditions and was independent of all other demographic

variables” (Cathcart et al., 2004, p.398).

Yassine, Goldberg and Yu (2005) also discussed Span of Control in their research

‘Simple models of hierarchical organizations’. At the beginning of their study, the

authors emphasise that “simple models of hierarchy can help to understand or compress

empirical results” and that “communication time ratio between and within hierarchies

and communication topology ratio are important factors in determining span of control”

(Yassine et al., 2005, p.1). Additionally, Yassine et al., (2005, p.2) suggest that “the

optimal Span of Control depends on the nature, scale, topic of work and tasks

processed”, which again supports our grid and also the conclusion of Yassine et al.,

(2005, p.2) that simpler models may be less accurate than expensive and complicated

computing models but are easier to “construct and analyse”. Mackenzie (1974) like

Graicunas (1933) also searched for a calculation of the maximum Span of Control

instead of the more modern aim to find the optimum Span of Control (as cited in

Yassine et al., 2005, p.4). Mackenzie (1974) has undertaken more sophisticated research

in visualising relationships between subordinates and supervisors because he assumed

“that any person in a hierarchical organization spends time either working on his/her

own task or interacting with others” (as cited in Yassine et al., 2005, p.4). In their

research Yassine et al. found that “the total communication time is composed of two

components ... the time between teams ... and the time within teams” (Yassine et al.,

2005, p.7), which has another influence on our grid, as the time is increasing according

to the complexity of work/tasks. Through such statements, this research is gaining

support that more difficult tasks require smaller teams.

A comprehensive paper about ‘Losing our span of control’ was written by McManus

(2007). In this work McManus enumerates three levels of Span of Control: at a higher

level he recommends a span of 3, for a medium level a span of 15 and for the lowest

level a span of up to 50. McManus (2007, p.22)30

argues that a Span of Control of 50 is

“ridiculous”. To enlarge the Span of Control “might be trendy” and it “looks a lot better

on the short-term-budget bottom line” but it also correlates directly to a decline of

“work force skill level” according to McManus (2007, p.22). Furthermore, McManus

(2007) criticises the reduction of middle level managers because he cannot see who else

would do their work (p.22). There are small or medium sized companies in which the

available amount of management capacity is low. Yet, there are of course also

73

companies, which are spoiled by success and have an oversupply of managers. Many of

whom were hired in successful years to reduce the workload of the hardworking

managers. In such companies there are certainly also hierarchical levels of supervisors

that are unnecessary if it comes to accurate calculations in times where margins are

becoming smaller and organisational structures need to be reconsidered.

In the ‘Span of management: Concept analysis’ by Meyer (2008) a list of conceptual

approaches is described which includes a lot of the papers which were also reviewed

such as Altaffer (1998), Cathcart et al. (2004), Gulick et al. (1937), Ouchi and Dowling

(1974), Pabst (1993) and Van Fleet & Bedeian (1977). It could be critiqued, however,

that well known and important papers such as those from Graicunas (1933, 1937),

Urwick (1937, 1956), Entwisle and Walton (1961) or Mackenzie (1974) are missing. In

addition, although Meyer (2008) has analysed a broader collection of research papers

(51) there is not much new information compared with what is already summarized

throughout the more classical literature review. This could be considered as a sign that

the information and results regarding the theory of Span of Control have somehow

reached their natural limitations.

Unfortunately, the paper by Meyer (2008) does not give numerical indications about

Span of Control. As the paper compares different concepts it would have been helpful

for this thesis if the results of the research would deliver something measurable. On the

other hand, Meyer’s work is beneficial in framing the problem and does help to support

the findings of this thesis. One citation which refers to Cathcart et al. (2004) is the

identical one which was selected in the analysis of the same paper for this research

which states that “employee engagement declines if group sizes exceed 15 and/or 40

group members (as cited in Meyer, 2008, p.108)”.

2.7.5 Recent discourse

The following section discusses how more recent academic research is building on

knowledge concerning the model of Span of Control. As indicated at the beginning of

the literature review about Span of Control the work of Graicunas has contributed

significantly to the topic overall and is discussed in conjunction with the analysis of

‘The span of control and the formulas of V.A. Graicunas’ written by Nickols (2011).

This is one of the latest research papers focussing on Graicunas’ work in connection

with Span of Control. After an introduction to Graicunas’ curriculum vitae, the author

discusses the term ‘maximum span of control’ and connects Graicunas’ research with

74

more up-to-date discussions about the topic of the ‘optimum span of control’. Also

Nickols (2011) discusses the modern pressure to enlarge the Span of Control and

speculates about the consequences when he asks “how many is too many?” (Nickols,

2011, p.3). Nickols concludes by reviewing the papers of Graicunas who suggested

“that the maximum number of subordinates should be five and probably four in most

cases” (as cited in Nickols, 2011, p.3) and that this is more dependent on the

environment and factors like “the scope and scale of the work”. Nickols then goes a step

further and provides an example: “a group of six factory workers reporting to a

supervisor presents a less complex problem than six division presidents reporting to the

CEO of a large company” (Nickols, 2011, p.3). Then Nickols examines Graicunas’

work more closely and argues that “as the number of subordinates increase the

complexity of the relationships increases exponentially” (2011, p.4). Nickols (2011,

pp.3–5) explains how Graicunas merged the:

direct single relationship between superior and individual subordinates

cross relationships between individual subordinates and

direct group relationships between superior and combinations of subordinates.

In ‘Exploring the performance effects of visible attribute diversity: the moderating role

of Span of Control and organizational life cycle’ by Richard, Ford and Ismail (2007),

the authors examine racial and gender diversity with Span of Control and explore these

factors also in relation to the organizational life cycle. The hypothesis regarding Span of

Control is that a narrow Span of Control with diverse racial or gender backgrounds will

increase the team’s performance. This sample group was taken from the banking

industry and organizational structure was measured with the proportion of managers to

staff. The outcome of the research is that the racial background received only marginal

support whereas the gender diversity of the team showed some significance related to

firm’s performance under a narrow Span of Control, however, the authors explain that

the topic is complex and has to be set within the context of the individual company. For

example when stating “[w]hile organizations in earlier stages benefit from more

diversity, firms in later stages experienced economic performance losses with more

diversity” (Richard et al., 2007, p.2101).

Mena (2012) makes a huge claim in describing the span of management as simply the

“most discussed single concept in classical, neo-classical or modern management

theory” (p.181). In his research the author attempts to create a model which helps to

75

identify the right group sizes sourced from the organizational structure on a case by case

basis for make-to-order enterprises. The findings reveal some correlations from group

sizes “when the number of jobs and groups were less than or equal to 30 and 10

respectively, the run time was reasonable. When the number of jobs increased beyond

40, the run time increased exponentially” (Mena, 2012, p.190)31

. In relation to optimal

group size and effort this is claimed to be 5 staff (p.191)31

.

In The determination of span of control, Hopej and Martan (2006) compare the methods

of Stieglitz (1962) and Graicunas (1933) in an attempt to find a specific indication for

Span of Control. The authors see some limitations, however, in both methods. The

Graicunas method involves the measurement of relationships with an exponential

character “which means that even a small change in the number of relationships

between superior and subordinates influences in a fierce way the degree of the load on

the superior” (Hopej and Martan, 2006, p.60). They suggest that in the Stieglitz method

“one may claim that it does not take into account the greater number of factors, such as

time pressure or organisational culture” or that “the way of assigning values to

individual factors is also debatable” (p.61). Hopej and Martan, (2006) conclude with an

insight which they admit is to some extent separate from most of the literature, claiming

that “span of control is an endless process” (p.61) which requires regular review.

A critical review of the trend to reduce hierarchical levels is offered in Wulf’s, ‘The

flattened firm: Not as advertised’ (2012) where the author lists several factors

such as enhanced competition, deregulation and higher efficiency in

combination with the belief “that firms were eliminating layers in their internal

structures and pushing decisions down to lower-level managers” (Wulf, 2012,

p.7). She explains the changes in firm hierarchies over the last decades where

hierarchical layers were taken out and the span of control was increased and

queries “however was this delayering or restructuring?” (Wulf, 2012, p.9). There

is also a direct correlation between the increased Span of Control, the reduced

number of layers and the increased financial compensation for the managers in

charge (Wulf, 2012, pp.10-13). The author concludes that “flattening is not just

about structural change. It affects the role of the CEO, how decisions are made

and managerial incentives” (Wulf, 2012, p.18).

In a graph sourced from an earlier work by Wulf the increase of the CEO Span of

Control is shown to have doubled during the last two decades.

76

Graph 2.1: CEO Span of Control (1986-2008). Source: Guadalupe, M., Li, H., & Wulf, J. (2011). Who lives in the C-

Suite? In Wulf, J. (2012). The flattened firm: Not as advertised. California Management Review. Fall, Volume 55,

Issue 1, p.10.

In another interesting paper where Wulf was co-author ‘How many direct reports?’

(Neilson and Wulf, 2012) the same increase in Span of Control as per the previous

paper by Wulf is emphasised:

[t]he leap in the chief executive’s purview is all the more remarkable when you

consider that companies today are vastly more complex, globally dispersed, and

strictly scrutinized than those of previous generations. (p.113)

Here the authors discuss five relevant fields encompassing not only the CEO’s purvue

but also being valid for up to two hierarchical levels lower. First of all senior-executives

should consider their own position in regards to their tenure because the authors see the

Span of Control shrinking in the future for in an ideal case “the span of control is

typically highest at the start” (Neilson and Wulf, 2012, p.115). Furthermore, one should

consider its involvement in cross-organizational tasks for “the more highly related the

business activities, the more time a leader is likely to spend on integration issues,

working with colleagues at the next level down in committees or one-on-one” (p.117).

Thirdly, a manager should observe their own time allocation for non span-of-control

related activities:

[f]irst, be aware of how you’re spending your days and how that meshes with the

needs of the business; awareness is the starting point of any adjustment that may

help you in the longer run. (Neilson and Wulf, 2012, p.117)

The fourth consideration for a manager is to reflect on the scope of their role. Whereas

“dividing the roles may allow you to take on more functional responsibility” and should

be compared with the advantages of having not appointed a deputy “you’ll have more

on your hands” according to Neilson and Wulf (2012, p.118). Finally, the fifth point is

77

regarding the team’s composition. Also the authors note that during the last 20 years

80% of the CEO positions added to the Fortune 500 are for CEOs who are functional

specialists which is possibly creating the opportunity for CEOs “to have more time to

devote to business strategy” (Neilson and Wulf, 2012, p.119).

This thesis will not discuss in detail the case study of El-Khalil and El-Kassar (2016)

describing different departments in the car manufacturing industry. It is worth

mentioning, however, that after the 2009 financial crisis in the United States the authors

found a sharp increase in the Span of Control in different departments among the three

largest car manufacturers examined in this case study. They stated that “the increase in

the span of control from pre 2009 to post 2009 was not a standard increase. Some

facilities increased by 67% (lowest increase) and others by 271% (highest increase)”

(El-Khalil and El-Kassar, 2016, p.1730), which is demonstrating the dynamic

connection of Span of Control to the external environment and market flucuations.

Graves (2013) refers to the Span of Control and is convinced that “no perfect ratio

likely exists” when explaining that a narrow Span of Control gives a CEO the advantage

to dedicate more time with his/her subordinates in contrast to a flat hierarchy where

subordinates could enjoy more independent activity (p.118). In order to determine the

optimum Span of Control, Graves recommends considering four factors: 1st

Organizational Size, 2nd

Skill Level, 3rd

Organizational Culture and 4th

Manager

Responsibilities (2013, p.118).

A short article presented by Zoltners, Sinha and Lorimer (2014) in the Harvard

Business Review asks in its title ‘Does your company have enough sales managers?’ An

average for the United States sales force of a span of 10–12 sales people is mentioned

which is relevant to this research and it is brought into the context of complexity of

work where “sales teams work with customers to deliver technically complex, custom

solutions; sales managers each supervise an average of 6–8 strategic account managers”.

It goes further in describing the part-time merchandisers who “perform activities such as

stocking shelves, setting up displays and conducting inventories” (Zoltners et al., 2014,

p.2)32

to reach a Span of Control up to 50. After recapping what kind of tasks sales

people usually undertake the authors finish with the reminder that costs should not

define the Span of Control but rather the efficiency of the management:

[a] company needs enough sales managers to ensure that all key people,

customers, and business management tasks get executed well. At the same time,

78

a company must ensure that non-critical administrative tasks aren’t polluting the

sales managers’ role. (Zoltners et al., 2014, p.4)

Another dimension of the topic Span of Control is explored by Teuber, Backes-Gellner

and Ryan (2016) who analyse the fundamentals necessary for a broader Span of Control

when they outline three key-factors: a) qualified employees b) coordinated wages to

increase employees’ tenure and loyalty and c) trust between staff and management

(Teuber et al., 2016, pp.258–259). The authors then sample 22 companies throughout

central Europe and also some US companies. Indeed, the results are convincing and it

reveals that the Span of Control is the narrowest in the US (average 7.1) and broader

among the European countries such as the United Kingdom (10.3), Switzerland (13.6)

and Germany (26) (Teuber et al., 2016, p.264). The difference is that “German

companies show the same configuration of practices, namely high-quality vocational

training, strong internal labour markets, wage coordination and employee

representations” whereas “all [the analysed] US companies have no high-quality

vocational training, no wage coordination and no employee representation” (Teuber et

al., 2016, p.267).

The literature review covering Span of Control is concluded and is followed by a

summary of the results in a matrix which depicts all the findings about the Span of

Control within small, medium and large companies. It also illustrates the high, medium

and low hierarchical levels and the complexity of work.

2.7.6 Collection of all Findings

In the following grid all the findings of this literature research on span of control are

brought together. In the horizontal axis three columns segregate the sizes of the

company or departments into small / medium / large. In the vertical axis the

differentiation of the complexity of work is structured into high / medium / low.

Therefore, this grid results in nine fields into which the findings from the literature

review are allocated. The values describe the number of team members and the footnote

refers to the author discussed in the literature review. Several grouped footnotes denote

that there were several authors mentioning that this same number of team members may

be adequate for the corresponding size of the company/department and the

corresponding complexity of work.

79

For example: in the field in the upper left corner (yellow background) the first number

is indicated as: 5-62, 18, 19, 31

where it means that Urwick (1938)2, Alford (1940)

18, Brech

(1946)19

and Mena (2012) 31

see preferably a limitation of five to six subordinates on a

complex level of work in a small entity or department.


Small Medium Large C

om

ple

xit

y/H

iera

rch

y

High

5-62, 18, 19, 31,

4-77,

2.9 – 3.49,

6.111, 614, 515,

4-520, 3.228,

330

5-77, 6-78,

6.2 – 810,

3 – 921,

4 – 1023,

524

5-832

113, 8-98,

6.3 – 11.312,

6.7525

Medium

3-61,

4.7 – 5.69,

6.911, 715

6-186,

10.7 – 11.310,

12.313, 10-1232

6 – 1217, 1020,31,

1324, 12.828,

8-1229, 1530

204, 15-2323,

22.9 – 27.526,

25.8 – 27.927

Low

8.411, 23.313,

10 – 3021,

2024,

20 – 3029,

305,31,

20 – 3014, 2515,

5020, 22,

24.5 – 38.527,

5030,32 Table 2.19: Size/complexity-grid with all findings of this literature review on the ‘ideal Span of Control’ (the

numbers link to the researched literature)

2.7.7 Summary

For a better overview of the findings and to improve readability the average Span of

Control is calculated for every field in the matrix. Furthermore, an indication of a

possible range based on all the literature reviewed indicates the approximate range of

tolerance to be applied for departments belonging in this field. Because of this the

results are just indications possibly having an unpredictable deviation from unknown

variables, depending on the culture, complexity of the task and disposable time to solve

the task. The definition of the possible range is calculated so that it includes 50% of the

range of all references from the different papers in a specific field of the matrix. For

example, if we have the following ‘cited’ values: 1, 2, 3, 4, 5, 6 as the recommended

number of subordinates in one field, the average would be 3.5 and the range is 5 where

50% equals 2.5, which means, that the indication in the sample field would be

Ø3.5 ±1.25. This means, that the recommended number of subordinates for those

intersections of company/department size and complexity level would be 3.5, whereas

±1.25 subordinates would be still in the range. Indications are given with two digits

80

after the comma because in later work using this matrix there could be part-time as well

as full time positions being considered. The star at the deviation number in the field of

small group/low complexity is set because there was only one reference which fitted

into this field – but, as it can be recognised, there are certain visual patterns in the

diagonal axis (discussed in more detail below the table) and one can find the closest

number to 8.4 for the field small Company/low complexity and 8.73 for the field large

Company/high complexity. Based on that consideration the same deviation for both

fields was applied.


Small Medium Large

Com

ple

xit

y/H

iera

rchy

High Ø 4.86

± 1.03

Ø 6.38

±1.75

Ø 8.73

±1.25

Medium Ø 5.38

± 1.00

Ø 11.32

± 3.00

Ø 23.16

± 3.26

Low Ø8.4

± 1.25*

Ø 22.22

± 5.00

Ø 35.33

± 6.38

Table 2.20: Abstracted ideal Span of Control findings with +/- tolerance indication

This is the matrix where this thesis consolidates the further discussion about Span of

Control. Viewing the matrix and ignoring the mathematical details of the figures for a

moment, after looking at them from some distance, one realises that the figures

resemble each other in a diagonal direction. With the aim to simplify them by rounding

up or down, a pattern is becoming visible. For the matrix of nine fields, separating

complexity and company size, one could assume only four recommended Span of

Control recommendations 5, 10, 20 and 35 in order to simplify it. To verify this

phenomena further study through the literature available has resulted in finding a related

statement from Cathcart et al. (2004, p.398) who is answering the question about

maximum Span of Control when he concludes “there were 2 points at which

engagement scores dropped most noticeably: as work group sizes grew larger than 15,

and then again as work group sizes grew larger than 40”, which the author of this thesis

then has illustrated with two black arrows. Considering all the readings, a third arrow

has been added at the level of six for the maximum Span of Control as found in Urwick

(1956, p.39); Graicunas (1938, p.8); Entwisle and Walton (1961, p.524); Meyer (1968,

p.948); Van Fleet and Bedeian (1977, p.358, citing Fayol (1916); Alford (1940); Brech

81

(1946) and also Viola and Najjar (1970, 1971, as cited in Van Fleet and Bedeian, 1977,

p.362). Next to the first matrix the final matrix below shows now a combination of

optimum Span of Control (in colours) and maximum Span of Control (arrows) in

relation to company size and complexity level to show a pattern in diagonal axis.


Small Medium Large C

om

ple

xit

y/H

iera

rchy

High 5 5 10

Medium 5 10 20

Low 10 20 35

Table 2.21: Pattern in the abstracted ideal Span of Control findings

2.8 A CEO’s Power

The following brief summary does not represent any of the perspectives explained

earlier. Nevertheless, the aim is to give a brief introduction to the topic of the CEO’s

power to elaborate the considerations for the allocation of dominance in the Top

Management Team which were undertaken for this thesis.

In terms of organisational charts, this thesis shall draw a model of power of the top

management team when it comes to decision-making. In the past the CEO or Managing

Director was seen as the primary and single source of power to decide the company’s

direction, however, today “the emphasis changed from the one person clearly

highlighting the pathways forward, to a group based view of leadership” and in this

group consensus “understanding and being responsive to context became the

predominant concern” (Kakabadse, 2000, pp.14–15). The top management team is

assumed to contain two major fields of power which differ according to their

hierarchical level; the power of the CEO and the power of the top management team.

In general, for this thesis it is considered that every single decision is conducted similar

to a vote by the people involved in this specific decision process. Full acceptance would

mean 100%. Expecting the total decision power to be 100% and searching for the

6 15 40

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influence of each participant of the deciding group (which is absolutely unclear and not

possible without further information) the power of the CEO and the power of the rest of

the Top Management Team can be differentiated because this is the only given variable

at present. Finding an answer to this question, which is the aim of this thesis, will assist

in replicating a real decision-making situation among a few people by allocating each

member of the decision team a numerical value representing their power of influence on

the final decision. For example, if a CEO’s power is equal to 50%, the other 50% of the

decision power can be allocated amongst the other members of the Top Management

Team. How to define who is holding how much of the decision-making power is the

main topic of this thesis.

To introduce the subject of the CEO’s power a short introduction of the definition of

power is provided and then a brief literature sample is undertaken to understand if there

is any rule or commonality regarding a CEO’s power in a decision-making process.

2.8.1 Types of Social Power

French and Raven (1959) developed the well-known classification of five main types of

social power, plus two additional types. The two additional forms are firstly

informational power which concerns the possession of information others do not have.

This form of power is closely related to the power by expertise, but weakens as soon as

the information is shared (French and Raven, 1959). On the other hand there is

connectional power, concerning the influence actors possess due to their network or

contacts, which is reflected in the support they receive from others (French and Raven,

1959). The basic five forms of power are:

reward power: power exerted by a supervisor in rewarding a subordinate,

regardless of the form of the reward (monetary, hierarchical, social, etc.)

legitimate power: power given through a position or an authority which makes a

subordinate believe that the supervisor has a legitimate right to perform certain

actions

coercive power: power delegated from the government to the police in a

democratic country. In the corporate environment it is the power to penalize the

subordinate by taking away something like status, position, job, etc.

referent power: this form of power refers to the identification and respect which

is connected with a supervisor. In this case subordinates identify with the

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personality of the supervisors and feel satisfaction by accepting and/or imitating

them.

expert power: special or additional knowledge gives people a privileged and

special status which finally results in a powerful position and the dependency of

those who do not share this knowledge

(based on French & Raven, 1959, pp.156–163).

Docherty (2004) adds an interesting component which is social time, to the above-

mentioned power factors. Social time is not just a matter of elapsed time – social time is

the trajectory of a human life; it is valuable to us because of what we can do or want to

do while time passes (Docherty, 2004, p.863). This could provide managers with

interesting input, as they could attempt to motivate subordinates to try to achieve as

much as possible in their social time. Conversely, subordinates always weigh effort

against output. For example, a staff member can ask them self whether it is worthwhile

to follow the instructions of a supervisor or should a change (e.g. of the workplace) be

considered?

Becker argued that “People rationally evaluate the benefits and costs of activities, such

as education, training, expenditures on health, migration, and formation of habits that

radically alter the way they are” (1992, p.51). Motivation is discussed widely in the

current management literature but derives mainly from the work of motivational

theorists such as Douglas McGregor (1906–1964), Abraham Maslow (1908–1970),

David C. McClelland (1917–1998), Frederick Herzberg (1923–2000), John Adair

(*1934) and Victor H. Vroom (*1932). This thesis is based on the generalised

assumption that people are free to decide for themselves at any time if they would like

to follow their leaders or not. This freedom sometimes results in employees resigning

and leaving their positions.

2.8.2 Recent Discourse

According to Morgan (2006) the question how managers lead, is answered frequently

by exploring the: “authority, power and the superior – subordinate relationship” (1997,

p.154). Shin (2008, p.9) sees the power of a CEO evolving in three processes: through

board co-optation, resource dependence and informational advantage. Consequently,

“power is inherently a relational concept; power is meaningful only when it refers to the

actual or ability to control the behaviour of others” (Emerson 1962, as cited in Shin

2008, p.9). Alvarez and Svejenova, authors of Sharing Executive Power: Roles and

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Relationships at the Top (2007) believe that “the life of top managers is now so

complex that they cannot cope with the demands” as cited in Hanson (2008, p.54).

Despite this observation, Kakabadse is not surprised that the sharing of CEO positions

often fails: “It's impossible to share the same vision of where you want to take the

business”, as cited in Hanson (2008, p.54). Also Kakabadse and Kakabadse see a

potential area of conflict regarding the CEO’s power, between the roles of the

chairperson and the CEO: “it is not clear who is in charge of the corporation - for many

people, the role of corporate leader has become synonymous with the CEO” (2007,

p.60). ‘Sense making’ and ‘deep friendship’ are the two key elements according to

Kakabadse, Kakabadse and Knyght (2010) in the constellation between the Chairman

and the CEO. “If either component is lacking, the study respondents narrated bonding

deficits, but specified the relationship as workable as long as both parties acknowledge

‘the need to work at it’” (p.293). Against this backdrop complexity is always apparent

because “[c]onflict and power plays inevitably arise whenever interests collide and

therefore, we may regard them as an inevitable part of organizational life” (Neumann

and Hirschorn, 1999, p.685).

In ‘The Perils of Power’ Kwak (2002) suggests that the power related to the CEO,

depends on whether s/he is the owner of the company. Founder-owned companies tend

to have fewer outside directors, but Kwak (2002, p.13) argues that “giving [a] founder-

CEO too much power can be a recipe for disaster”, because the CEO might be

“overconfident of [his/her] own power” which Kwak calls the “Icarus Paradox” (Kwak,

2002, p.13). To lessen the power of a CEO, Kwak proposes to set up a “strong and

independent” board because “truly visionary leaders understand the value of sometimes

binding their hands” (Kwak, 2002, p.13).

“The company that wins today is the one that makes the best decisions and is able to act

on them quickly”, says Michel (2007, p.33) who, citing Bass (1990) and Cannella and

Monroe (1997), thinks that decision-making processes are executed through “various

professionals” and these factors also influence a company`s performance. Porter, Lorsch

and Nohria start their paper ‘Seven surprises for new CEOs’ (2004) with the statement

that a CEO is “bearing full responsibility for a company’s success or failure, but … [is]

unable to control most of what will determine it” (Porter et al., 2004, p.62), because

running the business is just “a small part” of a CEO’s work (Porter et al., 2004, p.64).

Also Porter et al. (2004, p.65) state that it is not possible for a CEO to personally

control everything and that finally “control shifts from direct to indirect” which means

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in this context, that a “CEO often ends up knowing less about the operational details of

their companies than they did in their previous positions” and that is why s/he will run

the company through “the right senior management team to share the burden”.

Shen (2003) discusses a number of different views focussed on understanding the

CEOs’ power. These include those of Daily and Dalton (1994) where power plays a

central role in corporate governance. Pfeffer (1981) succinctly suggests that power

refers to the capacity of social actors to exert their will and to achieve their goal, and

finally Pfeffer and Salanick (1978, cited in Shen 2003, p.468) state that the CEO’s

power is reflected in their ability to hold on to the job.

Generally, Shen (2003, p.468) observes that the power of a CEO increases over time.

Shen (2003), however, does not analyse the dependence between a CEO’s power and

members of the top management team. There are only slight hints of this when Shen

cites Westphal and Zajac (1995) in saying that “the CEO may try to strengthen his or

her power by selecting directors who are sympathetic to management or passive in

governance” (Shen, 2003, p.469) and also when he cites Pfeffer (1981) “the CEO may

try to promote executives who are loyal and force out those who challenge his or her

authority” (Shen, 2003, p.469).

Conversely, to Shen (2003), Combs, Ketchen, Perryman and Donahue (2007, p.1302)

believe that CEO power decreases over time due to “political obstacles arising from an

increasing number of enemies and rivals as one rises in the firm”. According to their

study on ‘The moderating effect of CEO power on the board composition-firm

performance relationship’, Combs et al. (2007, p.1302) believe that periods of political

stability within a top management team are only transitory. They suggest that other

executives are highly motivated to detect and react to the shortcomings of the CEO

because each of them may have the potential to become CEO (as cited in Combs et al.,

2007, p.1302). Also the discussion of power, is repeated by Combs et al. (2007), for

example when they cite Pearce (1995) who alludes to the political instability that results

in CEOs gaining sources of “formal and reward power” to enhance “their ability to

maintain a dominant coalition and reduce the probability of a power contest” (Pearce,

1995, cited in Combs et al., 2007, p.1303). Beyond that, Combs et al. (2007) discuss

different situations of a CEO’s power but unfortunately do not venture deeper into our

research topic of how the top management team shares the amount of power necessary

to enforce strategic decisions. Some explanations regarding the CEO’s power are

numerical and appear to be what this thesis is searching for, but after precise analysis it

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is concluded that this is merely a discussion about measuring the impact of events on a

company depending on a CEO’s power (Combs, 2007, p.1309).

2.8.3 Conclusion

Based on the analysis of the research undertaken the main conclusion is that the CEO

has a very powerful position when it comes to decision-making. What Porter et al.

(2004, p.65) even describe as the ‘most’ powerful position in an organisation. But the

CEO’s power might be different depending on the decision which s/he has to take

together with their top management team. The CEO does not always enjoy the same

power of influencing a decision. This thesis is based on the assumption that the CEO

has the exclusive right to refuse and to say ‘no’ or what is described by Porter et al. as

the use of the CEO’s power to “reject” proposals (Porter, et al., 2004, p.65). Since a

CEO has the power to reject a proposal, it means that in “negative” cases the CEO has

100% of the decision power bundled in their vote. In the case of a positive decision, the

CEO is depending on their team and therefore is just one part of the positive decision

which is again confirmed by Porter et al. (2004, p.65) when they suggest that a CEO by

implementing their plans (being committed to a decision) “reduces his real power”. In

the context of this thesis this reduction of power is expressed in a blocking minority

which represents 34% to express the share of a CEO in a constructive process of finding

solutions. Consequently, 34% is seen in the context of this thesis as the ideal value to

describe a CEO’s power as 34% does contribute significantly to a positive scenario but

at the same time is enough to block or reject any proposal in a negative scenario.

Consequently, the percentage of the influence of the CEO is described in three possible

values (when describing it in percentages):

100% influence in the case that the CEO would be the only one in the

management of a Company

51% if the CEO has only one more person in his/her team and they accept each

other to be acting as a team but with the CEO as the supervisor

and finally 34% if the CEO has two or more members in her team accepting

each other acting as a team but where the CEO has the power to say ‘no’ which

is interpreted in this thesis with a blocking minority (which is technically 1/3+1n

whereas n means anything: 33.4 as well as 33.333334 but in order to keep it with

the numbers without decimals it will be expressed as 34%.

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2.9 Summary of the Literature Review Chapter

With the aim to unpack the necessary variables to construct an equation which will

enable a ranking of Top Management Team members according to their influence when

it comes to strategic decision taking, four perspectives have been researched in

individual short literature reviews with the following findings:

Perspective Variables

Typology of a Company

- Defenders

- Analysers

- Prospectors

- Reactor

Top Management Team Constellation

- Age

- Tenure

- Education


Horizontal Factors:

Network, Shares in the company, Contribution to Turnover

Bi-Directional Factors

Reliability, Archetype (Role Model), Communication, Competence,

Authority, Foresight

Vertical Factors

Social Competence, Conveyor, Humour, Humility, Cooperation,

Generosity

Span of Control

Table 2.22: Summary of the selected variables for the four perspectives analysed in this thesis

Those variables are set into an algorithm and allocated weightings to profile the Top

Management Team members in regard to their participation in the dominant coalition.

The four Typologies cannot be influenced by the manager directly but depending into

which Typology a company’s strategy is more oriented it will influence the power of

the departments benefitting from each Typology. The Team Constellation changes its

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parameter of age and tenure with time; which means that the conditions may change for

each individual manager when personal age and tenure fit into the Top Management

Teams’ configuration or also may diverge from it. Managers’ Characteristics is certainly

the perspective where each manager can potentially have most influence by adjusting

their personal habits and behaviours to confirm to the strategic direction of the firm. The

matrix of Span of Control categorizes companies and departments in terms of

complexity and size, whereas the number in each field indicates the momentum of

losing control.

In Chapter 3: Research Methodology the selection, explanation and justification of the

research methodology is discussed. This chapter, describes the role of the researcher,

the instruments and procedures used to collect data including their respective

limitations. The process of data collection for each perspective will be outlined. For the

perspective of Typology the definition of which departments belong to the Dominant

Coalition is discussed although there are situations of awkward constellations and these

are explained further and shown how they are handled. Team Constellation explores

why the factor of education was easier to quantify than age and tenure. After this, the

selection of 21 Managers’ Characteristics is revised to compile a relevant set. In

addition, it is outlined how the participants were selected along with the consent process

obtained from each participant, as well as the corresponding ethics approval which was

a requirement for this research.

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3 Chapter three: Methodology

3.1 Introduction

In this chapter the methodology used for this thesis is explained in detail following

Chad Perry’s model outlined in ‘A structured approach to presenting theses’ (2012)

which is applied in the research. This introduction is followed by subchapters

explaining the justification for the methodology chosen, the units, the sources,

procedures, limitations, software used and ethical issues related to the research. Then

this chapter explores the research methodology used for each of the four perspectives

analysed (Typology, Team Constellation, Managers’ Characteristics and Span of

Control) and reveals how all the results are consolidated into one ranking to express the

dominance of each member of the Top Management Team.

3.1.1 Paradigm and Methodology

Thomas Samuel Kuhn, a scientific theorist, popularised the term ‘paradigm’ with his

publication of ʻThe Structure of Scientific Revolution’, but according to Wray (2011)

Kuhn did not invent the term. Wray (2011, p.380) states that Kuhn claims in the preface

of ‘The Structure of Scientific Revolutionsʼ to have discovered the concept of a

paradigm, but as Wray has researched “Daniel Cedarbaum (1983) has traced the first

use of the term ‘Paradigm’ in discussions of science to the late 1700s” (Wray, 2011,

p.382).

Guba and Lincoln (1994), define a paradigm as:

[a] set of basic beliefs (or metaphysics) that deals with ultimate or first

principles. It represents a worldview that defines, for its holder, the nature of the

‘world’, the individual's place in it, and the range of possible relationships to that

world and its parts, as, for example, cosmologies and theologies do (p.107)

The four categories of research paradigms defined by Guba and Lincoln (1994) describe

the pattern regarding ontology (the meaning of being or reality), epistemology (how and

why do we know something) and methodology (how will we find out something).

Regarding the research methodology there is a fundamental question about the selection

of the design for a research undertaking when defining the aim of the research and the

strategy of how to achieve this. The thesis writer has to ask himself if the aim of the

research is to discover or to justify (Park and Park, 2016, p.1) when deciding between

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qualitative or quantitative research methodologies. Park and Park (2016) illustrate the

basic characteristics of qualitative and quantitative research in this helpful table for

researchers to identify into which category the research fits.

Table 3.1: The basic characteristics of qualitative and quantitative research from Park and Park (2016, p.2)

According to Park and Park qualitative research is used to “explore the descriptive

accounts and similarities and differences of various social events” whereas the

“objectives of the quantitative method in social science are to predict and control social

phenomena” (2016, p.4). Given that from the beginning this is not always a clear or

easy decision, understandingly the author was uncertain as to which methodology

would be most appropriate for this research. Because of this difficulty, Park and Park

highlight that “some researchers have suggested the integration of both qualitative and

quantitative research methods” (2016, p.3). According to Johnson, Onwuegbuzie and

Turner (2007, p.123) “mixed research can be viewed as incorporating several

overlapping groups of mixed methods researchers or types of mixed methods research”.

In the figure below from Johnson et al. (2007) the transition from pure qualitative to

pure mixed to pure quantitative is shown with the intermediate steps of qualitative

mixed and quantitative mixed.

Figure 3.1: The three major research paradigms (Johnson, Onwuegbuzie and Turner, 2007, p.124)

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Or as Onwuegbuzie and Hitchcock (2017, p.58) describe it the research can be called a

“Mixed Methods Impact Evaluation” when both approaches, quantitative and

qualitative, are used in a significant proportion for one research project. Onwuegbuzie

and Hitchcock recommend mixed methods mainly because the logic applied can slide

between inductive and deductive, and because of the possibilities to intersect different

sentiments, structures and influences as well as the improvement in quality due to the

fusion of quantitative and qualitative approaches (Onwuegbuzie and Hitchcock, 2017,

p.58). Harrison is incisive with his definition of exploratory design in regards to mixed

methods where he states “in exploratory designs, researchers first collect qualitative

data, analyse the qualitative data, and then build on the qualitative data for the

quantitative follow-up” (2013, p.2156) and this describes the process of development

that was applied in this thesis.

3.1.2 Justification for the Methodology used in the Research

Ontology Epistemology Method

Pre-Empirical Stage Interpretative Interpretative

Literature Reviews Team Constellation and

Characteristics with Qualitative Approach,

subjective, non-measurable factors

Positivism Positivism

Literature Review Span of Control with

Quantitative approach of selecting objective and

measurable factors and Literature Review of the

Typology where the selection of the model

results in one outcome

Empirical Stage Positivism Positivism Sampling / use of Survey and Measuring or

Statistical comparisons of all findings with Quantitative research approach

Table 3.2: Paradigm and methodology of this thesis

The methodology used for this thesis is of a mixed nature but is predominantly

quantitative. This is due to the procedure of applying mixed methodologies as per

Harrison (2013, p.2156) when firstly collecting subjective and non-measurable data in

an interpretative approach during the pre-empirical stage. The empirical stage is then

solely quantitative because the goal is to do statistical analysis of the data collected

through the online survey to which participants were invited by e-mail and through

human resources-related social network groups.

This thesis follows the description of Harrison above because the evaluation of

Managers’ Characteristics and Team Constellation results in collecting subjective and

non-measurable data. The other two perspectives Span of Control and Typology are

treated differently because to identify the key factors of Span of Control is purely

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quantitative based research with measurable, objective values obtained from the

literature review. The perspective of Typology did not require much data collection

because the literature review conducted for the Typology mainly provided a foundation

for a decision on which model to apply. The decision to use the typology of Miles &

Snow (1978) pre-determines the selection of dominant coalition, at least in a case where

a firm would fit into only one Typology.

Overall this thesis belongs in the category of pre-dominant quantitative methodologies

which is evident by the overall aim of this thesis to express each Top Management

Team members’ share of the Dominant Coalition which yields a requirement for a

measurable quantitative outcome where all data collected and evaluated is in statistical

form.

3.1.3 Role of the Researcher during this Dissertation

The outcome of a research project is a result of factors, such as interpretation, meaning,

individual points of views, and understanding and experiences and because of this it is

necessary to define where researchers state their position in this process. Four roles,

which are traditionally held by a researcher are those of complete participation,

participant as observer, observer as participant or complete observation as per the

definition of Junker (1960, p.223). The method applied to find the ‘truth’ also depends

on the paradigm which is used in the research. For quantitative research, Hinds,

Scandrett-Hibden and McAulay (1990) note that all research must undergo critical

appraisal, especially regarding objectivity, reliability and validity (p.431). In this thesis,

the author will act as a pure observer when gathering data from existing and already

published data.

3.1.4 Reliability and Validity

Emden and Sandelowski (1998) state that “in order for an experiment to be without

error, it must be replicable (that is, be reliable), measure those things that it purports to

measure (that is, be internally valid), and enables generalizability (that is, be externally

valid)” (p.207). On the other hand Wolcott (1990) provides a more cynical definition of

validation when he suggests “whatever validity is, I apparently ‘have’ or ‘get’ or

‘satisfy’ or ‘demonstrate’ or ‘establish’ it …” (p.121). Morse et al. (2002) also describe

this by stating that “verification is the process of checking, confirming, making sure and

being certain” p.9). The reliability and validity of this thesis is in the pre-empirical stage

based on data from publications collected in the field of research which is then verified

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by the comparison of supporting or dissenting publications and then argued based on

their commonality. The empirical stage finds its verification in the statistical direct

comparison between the assumptions of the participants on the online survey with the

prediction of the developed algorithm based on the previous research.

3.1.5 The Unit of Analysis, the Instruments of Measurement and Sources

An outcome of this research is the percentage value indicating the dominance of Top

Management Team members in the dominant coalition. The final result is an

accumulation of shares which each Top Management Team member can accumulate in

each sub-category of the three perspectives (Typology, Team Constellation and

Managers’ Characteristics). The fourth sub-category or perspective Span of Control is

not contributing to the percentage of dominance. Span of Control is measured separately

to monitor the momentum of losing control.

The table below details for each perspective, the measurement factors collected in the

pre-empirical stage through literature reviews, and the measurement instruments applied

and developed in the online survey. For each perspective data was collected in different

ways. For example, Typology had 10 dimensions laid out as multiple choice questions

with 4 answers according to the nature of Typology. Team Constellation collected

manual input data about age and tenure for each Top Management Team member

(which was complemented with additional collected information about the same

parameters for the CEO) and then it offered a multiple choice selection for the section

on education.

Information about the Managers’ Characteristics was collected with Likert-scales

following the recommendation from Neumann (2014) to ask the participants if they

approved or did not approve so as to give at least 2 categories (p.231). The survey

requests the participants to rate the capability of a Manager from 0 (non-existent or

neutral) and then with index scores from 1 (lowest) to 10 (best) in any of the 6

Characteristics. It was decided to use index scores to avoid potential cultural confusions

by different perceptions in regard to wording (such as weak or strong) depending on the

participants’ backgrounds because “zero implies neutrality” and “index scores give a

more precise quantitative measure of a person’s opinion” according to Neumann (2014,

p.234). In Span of Control on the other hand the number of staff per department is set in

context to the complexity of work (low, medium, high) and the size of the company

(small, medium, large).

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Overall aim Perspective Measurement factors Measurement instrument

Percentile

Ranking of Top

Management

Team members

participation in

the Dominant

Coalition

Typology

Identification of preferred Typology

in regards to a firm being an

Analyser, Defender, Prospector or

Reactor

Typology has 10 dimensions which

result in dominant coalitions in

favour of different departments;

each dominant coalition results in a

percentage allocation for each

department; the total value of all 10

dimensions is counted

Team

Constellation

Fit of a Manager in a team’s

homogeneity or heterogeneity in

terms of Age, Tenure and

Education

Age and Tenure are measured with

Standard Deviation whereas

Education is related to gaining

points for more Education

Managers’

Characteristics

Comparison of each Top Managers’

Profile in terms of: Reliability,

Role-Model (Archetype)*,

Communication and Competence,

Network and Social Competence

The total score received through

Likert-scales of each member of the

Top Management Team is weighted

against the total score of all

managers

Table 3.3: Selected measurement factors of each perspective and the instruments used in this thesis

*The Characteristic Role-Model was named Archetype during the time of the usage of the survey for this thesis but later renamed Role-Model to avoid confusion with C.G. Jung’s Archetype’s

Yin (2017) details six valid sources of evidence: Documentation, Archival Records,

Interviews, Direct Observations, Participant-Observation and Physical Artifacts (p.102).

Although “any of the preceding sources of evidence can and have been the sole basis for

entire studies”, however, the author recommends the use of multiple sources as the

“finding or conclusion is likely to be more convincing and accurate if it is based on

several different sources” (Yin, 2017, pp.114–116).

Figure 3.2: Convergence of multiple sources of evidence, from Yin (2017, p.117)

For this study the selection of the sources of evidence follows the principle of collecting

data through the research of documents and archival records during the pre-empirical

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stage and applying a survey with a sample group for the data collection during the

empirical stage to quantify the reliability.

The target group for which the survey of this thesis focuses on is any employee attached

to a profit oriented company. The sample group was selected by the equal probability

sampling method (EPSEM) by a simple random sample from professional business

networks in Xing and LinkedIn used by the author. The criterion of the participants’

selection was limited to employed individuals aged between 20 and 65 years old,

working in profit oriented companies. There were no restrictions on gender or ethnicity

or any orientation of belief. The pool of sampling elements contained 461 derived from

Switzerland, Germany, France, United Kingdom, Russia, United States, Thailand,

Malaysia and Vietnam comprising of 76% males and 24% females. Data was collected

from 114 participants whereas for the first 9 submissions the analysis of standard

deviation for the perspective Team Constellation omitted collecting the data of the CEO

which was amended and then applied for the remaining 105 participants. All

submissions are printed in the appendix of this thesis.

3.1.6 Instruments and Procedures used to collect Data

The background of how each perspective has been measured is explained in detail after

this general overview of the methodologies applied for this thesis.

3.1.7 Administration of Procedures

Data was collected for the period of one month between 11th of January 2017 and 3

rd of

February 2017. An invitation to participate in the testing of an online calculation tool

was sent out to 461 individuals of whom 114 (24.7%) followed the invitation (response

rate). Then 342 (74.2%) ignored the invitation and 5 (1.1%) declined to answer either

due to confidentiality reasons or based on the opinion that the organisational structure of

the company exceeded the capability of levels to be captured by the developed tool. The

response rate of 24.7% achieved within this research is lower than an average value of

33% for online surveys which Nulty (2008, p.302) is indicating in his meta survey of

eight reported online surveys. Nulty (2008, p.302) concludes that “in general, online

surveys are much less likely to achieve response rates as high as surveys administered

on paper”. A plausible reason for obtaining a lower response rate in respect to this thesis

survey could be the language barrier where the social environment/background of the

author and therefore most of the potential participants are from German speaking

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Switzerland and the survey was solely available in English because it was created in the

context of this thesis for a University in Australia.

3.1.8 Limitations of the Methodology

As mentioned above the online calculation tool is only available in English which is not

the native language of about 60% of the participants, however, this may only have led to

difficulties of understanding. Throughout most of the survey there is no requirement to

write (with the exception of numbers for age and tenure).

The size of the Top Management Team which was analysed is limited from 2–9

members as fewer members would not qualify to form a Dominant Coalition and more

members minimize the measurable difference among the members so that any

calculation applied becomes redundant with the chosen methodology.

Entries of Age are limited from 20 to 65 years and the tenure from 1 to 45 years in order

to reflect working generations only.

The list of Departments is limited to those enumerated in Porter’s (1980) illustration of

the Value Chain. For the selection in the survey, however, Finance is mentioned

additionally (although it is not explicitly mentioned in the Value Chain) because it is

hidden amongst ‘infrastructure’ and this was assumed to lead to confusion.

3.1.9 Software used for the Analysis

The development of a customised online tool to enable addressing random participants

involved the author working together with two different specialists. As a first step the

findings of the literature research had to be set in equations for each perspective

(Typology, Team Constellation, Managers’ Characteristics and Span of Control) and

then as a summary into a fifth equation. This was done in cooperation with Ms Honey

Yam, a mathematician from Kuala Lumpur, and generated in Excel for a fast review and

testing of the set of algorithms. In a second step, Dr. Yaroslav Bykov with his company

AvantLab from St. Petersburg, was engaged to undertake the programming. For the

development on side of the server AvantLab has chosen LAMP stack which is a

combination of Linux, Apache, MySQL and PHP. Mostly it is PHP scripting language

and MySQL database which are comparable easy and well known tools. For the client-

side the choice was only one: HTML and JavaScript. It is implemented in XHTML 1.0

Strict, which is a subset of HTML and supported by most browsers, including those for

mobile phones.

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3.2 Data Collection for each Research Perspective

In the following section the methodology of data collection for each perspective

(Typology, Team Constellation, Managers’ Characteristics and Span of Control) is

elaborated individually without repeating what was described in the previous

subchapters where an overview of the methodology of the data collection was

described.

3.3 Typology of a Company

Defining the typology of a company according to the Miles & Snow (1978) typology

structure will assist in identifying the center of gravity in regards to the constellation of

the dominant coalition. The table below illustrates the 10 perspectives in which Miles

and Snow have categorized their typologies of Defenders, Prospectors, Analysers and

Reactors.

Defenders Prospectors Analysers Reactors Parameters/Questions 1. The Market

environment of

our company is:

We focus on a narrow

market segment

We continuously expand

our market focus

We continuously adjust

our market focus

Our market focus

can change

2. Our company

achieves Success

because...

…we are prominent in

our market

…we are permanently

pushing for new

solutions

…we adjust to market

needs

…we exploit

chances

3. Our company

conducts

Observations…

…of our market and

our organization

…wherever possible,

aggressive search

…we observe our

competition

…depending on

actual need

4. Growth is

experienced

because of…

…we focus on a

narrow market

segment with

advanced technology

…permanently accessing

new markets / new

developments

…focused penetration

and careful product

selection

…our flexibility

5. Technology in

our company

needs to…

…be cost-efficient …flexible …synchronised …adjusted to our

actual needs

6. Our company

invests in

Technology…

…of our core market …of different and new

developments

…which is compatible

to our infrastructure

…when necessary

7. Technology in

our company

works because of:

…standardization and

maintenance

…of the people behind it …planning and

synergies

…we are open for

experiments

8. Planning is for

our company

…a fundamental …related to problems

and opportunities

…comprehensive and

permanently improved

…crisis oriented

9. The Structure of

our company is…

…functional/line

authority

…product and/or market

centered

…staff dominated /

matrix

…tight formal

authority

10. Control in our

company is…

…centralized and

backed up by finance

…depending on market

performance

…calculated based on

risks

…handling

problems

Dominant

Coalition

Finance, Production Marketing, Sales,

Research &Development

Planning Staff / Support

Activities according to

Porter

Trouble-Shooter

(depending on the

issue)

Table 3.4: Re-phrased parameter-questions to isolate the Dominant Coalition based on Miles and Snow (1978)

(repeated Table 2.10)

During the online survey the participant is guided through the 10 questions. Four

possible answers are given and shown simultaneously as a multiple choice selection.

Answering a question will add on points to the corresponding typology whereas a 100%

typology specific result would mean that the determined dominant coalition is awarded

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51% of the maximum ‘points’ which can be collected in this part of the survey. It is

more complicated when there is not a 100% match in one specific typology – and it is

more probable that this will be the case most of the time.

Now the question is how to create an algorithm to allocate the pointing systems among

the typologies depending on how often a participant has selected it. The variety of

combinations could be calculated through a grid of 4 questions in 10 categories,

whereas the participant is required to answer each question and not more than one

answer per category is allowed. The total number of permutations is 104 (10’000). A

preference for one or the other typology will show a preference towards certain

departments. It is interesting if for example the answers are distributed among

Defenders and Analysers as in such a case the department of Finance would feature

twice among the beneficiaries and accordingly its share on the dominant coalition

would increase significantly. Attention is given to answers which fall into the category

of the Reactors because it cannot be identified which department is carrying the role

defined by Miles & Snow (1978) as a trouble shooter. Consequently, points resulting

out of a selection for this Typology will be allocated equally to all departments available.

3.3.1 Methodology of Collecting Data

The creation of a pointing system for Typology was not done in one attempt as there

were several obstacles. Firstly, it was necessary to make the main differentiation about

Dominant groups and Secondary groups. According to the Miles & Snow Typology it is

a given which Typology will result in which constellation of a dominant coalition. For

the Typology of Defender a Dominant Coalition results with Finance and Production (2

Departments). For the Typology of Prospector a Dominant Coalition is resulting among

Marketing, Sales and R&D (3 Departments). The Typology of Analyser is putting

Finance, HR, Infrastructure and R&D together into a Dominant Coalition (4

Departments) and in the Typology of Reactor all existing Departments are falling into

the group of the dominant coalition according to Porter’s Value Chain (1980). As

previously mentioned, Finance was added because this department is hidden in Porter’s

Infrastructure and could be missed by participants. The table below is depicting all the

possible Dominant Coalitions depending on which Typology is chosen by the

participant at each of the 10 strategic directions of the company analysed.

What is apparent from viewing the table, before starting with any calculation, is that

Finance is represented in three out of four Typologies. This implies that the department

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of Finance is counted more often to the Dominant Coalition than any other department.

This result is surprising because Finance is only a secondary activity according to

Porter’s Value Chain.

Defender Prospector Analyser Reactor

Finance X X X

Production X X

Marketing X X

Sales X X

R & D X X X

HR X X

Infrastructure X X

Logistics X

Service X

Table 3.5: Constellation of Dominant Coalition based on the Miles & Snow Typology (1978), by the author

In general the allocation of percentages for the Dominant and Secondary groups is that

the Dominant Group gains 51% whereas the Secondary group gets 49%. Unfortunately,

this does not work out in all constellations.

3.3.2 Introduction to Awkward Constellations

Relying on the basic assumption that the Group of Dominant Departments always

possesses 51% of power versus 49% for the Secondary Departments will not eventuate

as demonstrated in the examples below in cases where there are more Dominant

Departments than Secondary Departments. This is because the shared percentage would

become less for a Dominant in comparison to a Secondary Department. In such

situations, the Dominant Departments would not be dominant anymore:

1 Dominant Department (51%) vs 1 Secondary Department (49%)

2 Dominant Departments (25.5% each) vs 1 Secondary Department (49%)

3 Dominant Departments (17% each) vs 2 Secondary Departments (25.5% each)

Therefore, in a constellation of more Dominant Departments than Secondary

Departments a Dominant Department would have a smaller percentage than a

Secondary Department! Since a Coalition per se consists of more than one (1) and Miles

& Snow have defined in their Typologies that the Defender, Prospector and Analyser

have two (2), three (3) and four (4) departments in its Dominant Coalitions for each

Typology this leads to awkward constellations whenever there are companies having

fewer Secondary Departments in such a specific case. The exception is for companies in

the Typology of Reactors as in this case all Departments are in the Dominant Coalition.

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The problem will appear in the following situations (Dominant vs Secondary) 2:1, 3:1,

3:2, 4:1, 4:2, 4:3. One way to counteract this issue is to view it from the smallest

possible constellation where a smaller group is dominant over a larger group by the

percentage allocated. Two (2) is given as this is the minimum required to build a

coalition. Three (3) is therefore the next bigger value. Thus 2:3 is taken as the smallest

ideal example where therefore the proportional factor is 1.5 in order to be equal and

1.5+1n to be dominant. Therefore, 1n is representing just about anything. A majority is

at the end not only 51% but also 50.1 or 50.001 or 50.0001% … it is just 50% +

anything, which is defined in this thesis as 1n. So in awkward constellations we chose to

describe the value for the Dominant Department to be the value of the Secondary

Department multiplied by 1.5 + adding 1n, a microscopic value which at the end gives

the amplitude. So the smallest possible constellation was taken as a pattern to be applied

to all other situations where the same unfavourable constellation will appear.

3.3.3 Conditions:

1. Ten Typology questions are asked and the answer for each question will determine

which department is in the Dominant Coalition.

2. In the cases where the number of Departments in the Dominant Group (d) is lower

than the number of Departments in the Secondary Group (s), the percentage of

influence assigned to each Department of the Dominant Group is 0.51/d, whereas the

percentage of influence assigned to each Department of the Secondary Group is (1-

0.51)/s.

3. In cases where the number of Departments in the Dominant Group (d) is higher than

the number of Departments in the Secondary Group (s) for example 3d:2s the

percentage of influence assigned to each Department of the Dominant Group is

[1.00/(d+s)]+0.01 whereas the percentage of influence assigned to each Department

of the Secondary Group is the proportional remaining value/s.

4. If there is only one Department named in the Dominant Group (in cases where there

are not all departments available according to the Miles & Snow Typology) then this

department receives 51% by default.

In the conditions set above with all possible variations the percentage of influence of a

Department or the whole Group in the Dominant Coalition is always higher than the

percentage of influence of a Department or the Group in the Secondary Coalition.

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At the end of the section on Typology the accumulated percentages through all ten

questions for each Manager (Department) are weighted in comparison to all other

Managers (Departments).

3.4 Constellation of the Top Management Team

The findings of the literature review have resulted in three main parameters defining

how well members of the Top Management Team fit into that team. These are the Age,

Education, and Tenure with the company. The three parameters are to some extent

independent, however, it is clear that a certain age is required to gain education. The

same follows for the qualifications and expertise necessary for being a Member of the

Top Management Team. Finally, this might be different from company to company or

be industry related, and only gains its importance when compared to other members of

the same team.

3.4.1 Age as a Parameter in TMT-Constellation

Age can be seen as a positive factor under different pre-conditions. What all findings

have in common is that age is not having a positive affect if it is completely different

from all others in a group. It is preferable, to have a group where all are in the same age

group but it is also fine to have a group which is homogenous in regards to an equally

diversified age constellation. Whatever could be considered as a norm in that specific

group – should not be challenged with something which is completely outside of that

norm. Standard deviation is applied to the group which is analyzed to calculate if all

team members are in or out of the norm. If all other members of the Top Management

are 50 years old then most likely it is a disadvantage being the only person who is 40

years old in that team. If in another group, however, all members are equally diversified

in the age group between 40 and 50 then to be any age in that range does not matter.

There is very little ambiguity in how points are allocated to Top Management Team

Members for the parameter age-constellation. The question is just whether someone fits

IN the age pattern of a group or NOT; meaning one or zero as a result.

3.4.2 Education as a Parameter in the TMT-Constellation

Education potentially can compensate for age and or experience for younger staff,

however, throughout the literature review no indication was found that education could

be compensated when considering this the other way around. A manager could qualify

in a higher age group with the help of additional knowledge but it does not matter how

educated one is, s/he will not get younger. In some cases, if for example the complete

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Top Management Group is constituted with younger members, this might be a

disadvantage. The authors who were reviewed seem to have common agreement that the

kind or type of education is less important than the amount of education. This parameter

is steadily increasing in importance from NO education until MORE education. In the

online survey form each member of the Top Management Team has to be classified by

the participants according to their levels of education from No Tertiary Education,

Bachelor’s, Master’s until Doctorate. The points which each member of the Top

Management Team can earn are dependent on their education only, but not in

proportion to the other Top Management Team Members unlike Age and Tenure. This

is because according to the literature review being over- or under qualified is not a

direct problem once one is in the job, however, it may be a hindrance in being

considered for a position in the first place.

3.4.3 Tenure as a Parameter in TMT-Constellation

Tenure is considered as an inverted U-shape where a certain amount of tenure to the

company is increasing the job holder’s importance but if exceeding the peak point then

tenure is losing its importance. Likewise with age, this parameter also has to be set in

proportion to the other Top Management Team Members with standard deviation used

for this sub perspective because it is a requirement to fit the group, whatever the group

segregation may appear. Clearly, the nature of the company does play a role in this. For

an organization which is permanently exploring new markets an ideal tenure is certainly

defined differently from the ideal tenure of members of an organization that is trying to

retain the same values over time. Where the nature of the company is defined by its

Typology the analogies in regards to the composition of the Top Management Team are

then organically given. Therefore, in the attempt to collect primary data, tenure can be

monitored exclusively for one Top Management Team at a time and the results might

not be comparable with any other company’s Top Management Team. In further studies

where a lot of data would be available it is most likely that one can identify similarities

in comparable fields such as on an industry wide basis for example.

3.4.4 Methodology of Collecting Data

This chapter delineates the main factors qualifying a manager’s personal fit into the Top

Management Team which is measured in this thesis through Age, Education and

Tenure. All three parameters are considered to be equally important and therefore a

similar maximum amount of points is given for each.

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3.4.5 Conditions:

1. The online survey is created to capture a total of a maximum of 10 Top Management

Team Members

2. The CEO possess by default 34% of the decision-making power because s/he is the

only one who can say ‘no’ to anything and for this s/he needs to hold the blocking

minority which is given as 1/3+1n (33.4 or 33.34 or 33.334 etc.). To avoid decimals

in this research the CEO’s power is expressed as 34%.

3. Tenure is measured in this thesis in a value curve like an inverted U shape. The

contribution of a Top Management Team Member to the company is increasing if

one stays a certain time with the company, however, it is then again decreasing if that

person stays too long. The benchmark for what is considered a short, right or long

term tenure can only be given when the collected data for all Top Management Team

Members is from the same company. The highest peak of contribution lies in the

average for that set of top management team members. By using standard deviation

the sample data set is analysed so that 68% of all samples are falling within 1

standard deviation, 95% will fall within the range of 2 standard deviations and 99.7%

are covered in the range of 3 standard deviations of the mean.

After the first few tests using standard deviation to measure and judge Tenure, the

findings were that the outcome was not really meaningful. The reason for this is that

the usual age to enter into professional life would be somewhere in a person’s

twenties whereas a standard age to become retired is then in a person’s sixties which

gives only forty years of professional life. Typically, people do not commence their

professional life in the Top Management Team, however, membership of this

enclave can last until retirement. In this simple example of a team consisting of two

members where the younger member is 30 years old and the older one 60, the

average would be 45 and therefore the standard deviation is 15. So by a normal use

of standard deviation both members would fall into +/- 1 standard deviation and

anyone who could be imagined working in the same group would fall into a +/- 2

standard deviation (as this covers the age of 15 up to 75). If an example with a

smaller bandwidth such as a range of 35 to 55 years is considered then the average

would be 45 with a standard deviation of +/-10 years. Also in the second example,

the majority of Top Management Team members would fall into the same group and

any differentiation becomes obsolete. For this reason smaller steps were considered

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necessary to recognize differences in age and tenure patterns. So for Age and Tenure

a minimum of 1 and maximum of 4 points have been defined and separated in steps

of 0.5 standard deviations:

4 points = within +/- 0.5 standard deviation from average

3 points = within +/- 1 standard deviation from average

2 points = within +/- 1.5 standard deviation from average

1 point = more than +/- 1.5 standard deviation from average

4. Age is also measured similarly to Tenure with standard deviation and rated in steps

of 0.5 standard deviations. There is no right or wrong in regards to a person’s age;

the only thing that matters is if it fits to the group or not. One’s age might fit to a

group if there is no pattern of age in this group or conversely if there is a pattern of

age. In other words, the homogeneity or heterogeneity of the age-pattern of an

existing group is defining if an additional person fits into that group. Of course not to

fit in that group is not the mistake of the additional person but rather a disadvantage

for that person because their age may fit well into another team.

In conclusion, for Age and Tenure standard deviation was applied in bandwidths of

half standard deviations to receive more details. The goal is to differentiate how far

from the average age-and tenure-pattern the individual manager is.

5. Education Level: It can be said that the level of education has a significant relevance

to a member of the Top Management Team. Yet the type of one’s education does not

matter according to academic literature when it comes to the performance analysis of

Top Management Teams. Hambrick and Mason, (1984, pp.200–201) or Jackson et

Graph 3.1: A visual representation of the empirical (68-95-99.7) rule based on the normal distribution divided in

0.5 steps of Standard Deviation. Retrieved from Math Planet, Algebra 2, Quadratic Functions and Inequalities -

https://www.mathplanet.com/education/algebra-2/quadratic-functions-and-inequalities/standard-deviation-and-

normal-distribution

105

al. (1995, p.206) are convinced that age and firm tenure can be compensated by

education, however, the focus is on the level of the education (Jackson, 1995, p.212).

For as Talke, Salomo, Kock, (2011, p.826) describe it “[e]ducational background is

the maximum educational level of each TMT member within the categories high

school degree, bachelor’s degree, master’s degree, and doctoral degree”. Important

issues are the amount of education, which is serving as a filter to select the right

candidates and secondarily the comparison in regards to the amount of education of

the other Top Management Team Members. If all possess a bachelor’s degree then

the similarity is already given. A question which is not answered in this thesis would

be if for example someone has done two different bachelor’s degrees, would that

have a comparable value with someone who completed one master degree? This

scenario is not evaluated in this survey. This perspective was standardized to have

for Age, Tenure and Education always as a minimum 1 point, maximum 4 point

because the literature review has not resulted in any preference.

4 points = Doctoral degree or equivalent

3 points = Master’s degree or equivalent

2 points = Bachelor’s degree or equivalent

1 point = No Tertiary degree or equivalent

In the section Team Constellation in the online survey the maximum points which

can be collected or awarded to one member of the Top Management Team is 12

points (age 4, tenure 4, education 4) and the minimum to be received is 3 points

(each with 1).

3.5 Methodology to evaluate Managers’ Characteristics

To find the ideal Characteristics of a Manager, Yin’s Convergence of Evidence (2017)

was applied in creating evidence through the research from different sources. Firstly,

archival records from over 50 interviews with members of top management teams were

studied in order to segregate key Characteristics which were then compared with the

findings of a classical literature review and thirdly quantified in a public opinion poll.

The figure below (which is repeated) illustrates the outcome of this initial research

about the Managers’ Characteristics where this was separated into a horizontal

(relationship between two or more people on the same hierarchical level) and in a

vertical (relationship between a supervisor to his or her subordinates) direction.

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Figure 3.3: Previous Findings – Managers’ Characteristics (repeated Figure 2.9)

There are six major factors which are relevant in both directions, horizontal and vertical.

Factors like networking, shares in the company and contribution to turnover are mainly

important between managers (horizontal). Shares in the company and contribution to

turnover are not Characteristics per se as they are not influenced by personality. Shares

can be bought or inherited for example and certainly do have an influence in the

company’s strategic direction, however, holding shares and its relationship to legitimate

power is more associated with the Constellation of the Board Members. Contribution to

Turnover is another Characteristic which should be related to someone’s field of action

rather than personality. The head of Sales and Marketing most likely has more influence

on the turnover than the head of the R&D department, although the developments from

R&D will lead to turnover once new developments/products are launched. This will be

discussed in Typology in regards to the strategic orientation of a company. Social

competence, conveyor (of ideas), humour, humility, cooperation and generosity are

skills mainly important between managers and subordinates (vertical relationships) and

are related more to a manager’s personality.

Important Factors in both Directions

Reliability

Role-Model (Archetype) Communication Competence Authority Foresight

Horizontal Factors

Network Shares in the company Contribution to turnover

Vertical Factors

Social competence Conveyor Humour Humility Cooperation Generosity

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3.5.1 Public Opinion Poll

In order to allocate to each characteristic a numeric value to emphasise its particular

importance, a public opinion poll on the Internet was conducted prior to this research

where participants were requested to rate the importance of the Managers’

Characteristics, both in horizontal and vertical directions. The directions were illustrated

by two small icons, symbolising a manager (wearing a suit) and a subordinate (wearing

a safety helmet). For each direction of relationship, the relevant set of Characteristics

was listed and next to each a Likert-scale was shown.

After the rating, a second screen appeared where the outcome of the poll was visualized.

So each participant immediately received an overview of the general results and was

able to compare the public opinion to his/her own.

Screenshot 3.2: Screenshot of online opinion poll – online results (repetition of Screenshot 2.1)

From 678 votes those responses were eliminated which were incomplete or duplicates.

After this initial cull 525 votes remained.

Screenshot 3.1: Screenshot of the first

online opinion poll (DBA712) to evaluate


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3.5.2 Methodology for the Data collection

The data collection for this thesis about the perspective Managers’ Characteristics was

influenced by three main considerations:

Would the participant of the survey lose motivation if they were requested to

evaluate each Top Management Team member for all 21 Characteristics?

Does achieving a good result in one Characteristic compensate for a poor

performance in another Characteristic?

Are the Characteristics comparable in terms of importance?

In the research prior to this thesis it was analysed if the different Characteristics have

anything in common and it was concluded that this is not the case. No commonalities or

dependences among any Characteristic were identified. Because participants of the

online survey are requested to rate each Top Management Team member it seemed

problematic to ask a participant to rate all Managers by 21 separate Characteristics.

Another point would be that having so many Characteristics to choose from would

make the impact of one Characteristic irrelevant. Because of these considerations, as a

first step, the two Characteristics ‘shares in the company’ and ‘contribution to turnover’

have been sorted out as per the explanation above. Furthermore, those six

Characteristics which were named twice, (and also in the horizontal and vertical

relationships), have been sorted out as it was considered difficult to determine in an

online survey whether a member of a Top Management Team is more reliable towards

colleagues on the same hierarchical level than towards colleagues on a lower hierarchy

level for example. Finally, those Characteristics were chosen which obtained a mode of

10 and a median higher than eight out of ten.

With these few steps a selection of all Characteristics was received which first of all

have scored highest in the opinion poll. Four were named to be important in both

horizontal and vertical relationships (Reliability, Role-Model, Communication and

Competence) plus another Characteristic which is only named in the vertical (Network)

and one which is only named in the horizontal (Social Competence). With these

modifications the author believes that a balanced selection was chosen out of the 21

Characteristics making it more user-friendly for participants.

As the preliminary research prior to this thesis has shown that none of the

Characteristics has anything in common with another one, it was then necessary to

ensure that the calculation model for this thesis included the proviso that performing

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well in one Characteristic would not compensate for underperforming in another

Characteristic. To achieve this it was decided to weight each Characteristic in

proportion to how the participants from the first public poll have voted for those

Characteristics. It was counted how many people have rated these Characteristics with a

10, 9 or an 8 and so on. Adding up all the results gave an overall score for each

Characteristic.

In the end the value of a Characteristic was set in proportion to the overall value of all

Characteristics; to rate someone in Reliability (for example) with a value of 8 does not

have the same impact as rating someone on Competence also with 8. Furthermore, a cap

on the value of each Characteristic median was set to ensure, someone could not

achieve more points than the ideal. Also people should not be punished for scoring

better in a specific Characteristic. It is not relevant having too much of a certain

Characteristic (for example if Reliability is chosen by the public poll to have an

importance of 7, then a manager is not considered over-reliable if rated with an 8).

3.5.3 Conditions for the Perspective Characteristics

Assuming that the total score for all six Characters is 400 and Reliability scored 100,

Role-Model 50, Competence 75 then their weight is:

Reliability = 100/400 = 025%

Role-Model = 50/400 = 013%

Competence = 75/400 = 019%

…and so forth until the Total = 100%

Then if assuming manager A scored in Reliability 10 points, in Role-Model 8 points and

Competence 6 points then the score would be (10 points x 25%) + (8 points x 13%) + (6

points x 19%). Similarly, to the method applied for Typology and also in the selection

of Characteristics, the weight of the total score of one member is weighted against the

total score of all managers to quantify their share in that section.

3.5.4 Summary of all Perspectives

The result is that the proportion of each member of the management team counts

together as 100%. For all perspectives these 100 percentages are reflecting the influence

on decision-making power in that specific perspective, ignoring the proportion for the

CEO. Once the CEO’s value is deducted (34%) the remainder is then weighted as 1/3

since there are three perspectives: Typology, Team Constellation and Managers’

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Characteristics. So the combined total of all three perspectives is (100 - 34) = 66% or

22% per each perspective.

3.6 Methodology of the Perspective: Span of Control

The last perspective of this research, Span of Control, should be understood separately

from the previous three perspectives of Typology, Team Constellation and Managers’

Characteristics. The perspective of Span of Control is only added after the evaluation of

each manager’s dominance to evaluate if there is a solid reason to assume if there are

one or more additional influencers in any of the departments of the Top Management

Team Members.

The approach used for this research builds on the principle of Span of Control, where

depending on the size of a company, paired with the complexity of the corresponding

team, limitations exist on whether a team leader can retain an overview. Once this

limitation is reached, there are reasonable grounds to assume that a second force, an

influencer, is officially or unofficially installed to support the team leader. Given the

common situation that Managers are overloaded with their assigned tasks, this ‘second’

voice may be the source for the reports or proposals given to the CEO. If the CEO is

following the advice of his/her most influential Top Management Team Member then at

the end an ‘official decision of the CEO’ is possibly being steered by a regular staff

member.


Small Medium Large

Com

ple

xit

y/H

iera

rchy

High Ø 4.86

± 1.03

Ø 6.38

±1.75

Ø 8.73

±1.25

Medium Ø 5.38

± 1.00

Ø 11.32

± 3.00

Ø 23.16

± 3.26

Low Ø8.4

± 1.25*

Ø 22.22

± 5.00

Ø 35.33

± 6.38

Table 3.6: Ideal Span of Control findings with average and +/- delta, by the author (repeated Table 2.20)

The results are illustrated in a grid of nine fields where from left to right the three

classifications of size, and in the vertical direction the three classifications of the

complexity of work are shown. Both tables show the same results, however, in the first

table the exact average figures with the +/- Delta are shown, and the second table

illustrates the results in rounded numbers to show the similarity in the diagonal axis.

111


Small Medium Large

Com

ple

xit

y/H

iera

rchy

High 5 5 10

Medium 5 10 20

Low 10 20 35

Table 3.7: Pattern in the abstracted ideal Span of Control findings, by the author (repeated Table 2.21)

As an example, in a small company a head of department dealing with a high

complexity of tasks will ideally have not more than a Span of Control of 5 people (Ø

4.86 ± 1.03). Should the data analysis of the online survey result in a situation in which

the Span of Control is more than 6 for the department described above, then it is

assumed that the head of this department is losing control and delegating some of their

work to an influencer.

The survey is aligning three parameters to evaluate if the corresponding head of

department has lost control over their staff. These factors are: a) the size of the

company, where this information is relative to the industry the company is in and b) the

complexity of work, which on the other hand is set in relation to the other departments

and the number of staff in the department itself. These two parameters will define the

field in the table above whereas the third question then quantifies if the maximum Span

of Control is exceeded. Wherever this is the case, it will be indicated with (+influencer)

that the head of this department is unlikely to manage the whole department by herself

and therefore has to delegate some leadership tasks or bases her leadership on the

influence of some team members.

It is fundamental to understand that this perspective to identify a potential influencer of

a department is only relevant for departments which achieve more than 16% in the

overall dominance. Only these departments are (in theory) strong enough to form a

dominant coalition with the CEO (34%).

6 15 40

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In a situation where a CEO is deciding on a recommendation from her strongest

department and that department is headed by someone who has exceeded his Span of

Control, it would mean that the decision taken by the CEO is based ultimately on the

influencer of that specific department.

3.7 Ethical Considerations

In the National Ethics Application Form (NEAF) it was described that this research is

“an approach to allocate numeric values to the professional network relationships

among decision-makers in the same organizational structure”. The aim is to determine,

measure and compare the internal influencers among the Top Management Team

members who might have final responsibility for strategic decisions in profit-oriented

companies. The main part of this thesis – especially in preparation for the data

collection – will be informed through several short literature reviews. Detailed literature

reviews were carried out in the fields of Typology, Team Constellation, Managers’

Characteristics and Span of Control.

In order to collect data from participants and also to justify if the theoretical approaches

can withstand verification in a real environment a survey will be made accessible

online. The Ethics approval was necessary because of the involvement of participants in

this research. It has to be understood, however, that the survey will not be conducted in

a form where the principle investigator requests a participant to communicate details of

their own experience. The participant will be invited as an evaluator of the developed

system. The aim is to reach 100 submissions in total as a mix of all hierarchical levels:

CEO, Top Management Team and Staff. It may be that not every hierarchical level will

be reached equally. The selection process of the candidates will be done in a way that

there is no restriction in regards to the age, gender or ethnicity as long as the participant

is part of the workforce. Results will be treated anonymously where the participants

decide to submit anonymously; however, the IP address as well as time and date is

captured in order to have an anchor of differentiation for internal purposes.

A scan of the approval of the Business Human Research Ethics Committee under which

the survey of this research with participants is covered is printed in the appendices.

3.7.1 Consent Process

There will be no data collection without obtaining the consent from every participant.

Informed consent is obtained from the participants before going through the survey

which results from the compilation of the outcome of our research to evaluate the

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percentage of influence per Top Management Member regarding strategic decisions.

The candidate will see on their computer screen a window explaining that no data will

be recorded for the sake of identifying the candidate. There will be a second notice at

the end of the application where the participant can decide whether the transmitted data

shall be anonymously transmitted or not. The participant has to confirm before and after

the application with a mouse click that s/he agrees to that procedure prior to proceeding

and/or then finishing the evaluation.

The participants cannot be identified from the obtained data except if the participant has

decided that it is not necessary to keep the collected information anonymously or if

someone undertakes the effort to track back an IP number.

Screenshot 3.3: Consent form and information sheet of the online survey in a pop up window

There is no incentive or payment given to any participant. The participant will see at the

end of the survey an organizational chart which includes a percentage/weighting the

corresponding influence next to each position of the Top Management Team. In

addition there will be a number or sign which indicates potential influencers that are

working in this department. Furthermore, there are several tables enumerating all the

values keyed in by the participants linked with our formulas. Next to each table there is

a brief explanation about the meaning of that specific section as well as an icon linked

to a more detailed document providing an explanatory background.

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3.7.2 Administrative Details

Name: Mr Yves Clerc

Address: 10 Jalan Bukit Pantai, Condo ZEHN A-22-3 59100 Kuala Lumpur, Malaysia

Organisation: Charles Sturt University School of Management and Marketing

Position: Student

Email: [email protected]

Summary of qualifications and relevant expertise BBA, MBA

Site for which this principal researcher / investigator is responsible.

School of Management and Marketing Faculty of Business, Charles Sturt University

What training or experience does the student have in the relevant research methodology?

Coursework Assignments DBA 711.1 / 711.2 / 711.3 DBA 712.1 / 712.2 / 712.3 DBA 713 DBA 714.1 / 714.2 / 714.3

Supervision Dr. Kerry Tilbrook, BA (Hons) MA (Hons) and PhD Dr. Paul Ammann, M. Sc. / EMBA-HSG / Dr. oec HSG

Acknowledgements

Ms Honey Yam, BSc (Drake) Mathematics, from Malaysia, for translating the researched variables into algorithms and equations as well as being supportive with the multivariate data analyses.

Mr Yaroslav Bykov, Ph.D. (Russian Academy of Sciences) from Russia, for programming the same algorithms and equations into an online software application which was used for the online survey on www.The2ndCeo.com.

Name of Grant / Sponsor

Amount of funding

Yves Clerc

Roughly 1’000 AUD

Name of HREC Charles Sturt University Ethics Human Research Committee

HREC Protocol Number 200/2014/04

Expected benefits for the wider community are:

1) An approach to calculate the allocation of decision power among Top Management Team Members 2) An approach to encircle the potential influencers by analysing organizational structures and team roles.

Table 3.8 Administrative details of this thesis

3.7.3 Profile of Participants

In the context of this survey the author has addressed through social media the

professional workforce which is in general 18 to 65 years old. Cultural and ethnic

background as well as gender or any personal or political orientation has been ignored.

The geographical clusters would be Southeast Asia Region due to the researcher’s

actual place of working as well as Switzerland, the place of origin of the researcher.

Because the link to our online application was also advertised on discussion forums,

which were mainly related to management or Human Resources issues, it is possible

mailto:[email protected]

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that people from other regions have participated. Initially, it was intended to approach at

least 10 volunteers for each echelon: CEO, Top Management Team, and Staff. Because

of the option of participating anonymously, however, it is no longer possible for the

researcher to identify the individual participants and even if identification was possible

since the link for the online application was advertised on open forums it is no longer

possible to know all participants in order to categorize them.

3.7.4 LOTE Subjects

LOTE Subjects: It is possible that anyone who has access to a computer might have

coincidentally participated in this survey. There is no physical or psychological harm to

anyone who clicks through it. In the worst case the result might just make no sense.

From the researcher’s point of view, these results will not have an influence on the total

results, as the coincidental participation of any population as defined as LOTE Subjects

is possible but is unlikely or has no consequence due to the fact that the addressed

participant should be actively involved in a profit oriented organization.

3.7.5 Unequal Relationship

There is a chance that some of the participants are working in the same organization as

the author, due to the fact that the holding structure includes more than 40 companies.

Those participants working in the same group of companies, however, have been

addressed to test run the application in regards to their previous working place so that

there should be no overlapping with the environment of the author. Although the

organization would be known and all involved people are working there, since the

whole application is based on giving points without writing anything it is the sole

discretion of a participant to submit the data anonymously. Furthermore, participants are

free to decide if they want to participate in general and free to decide if the outcome

shall be saved or not until the complete end when the results are already visible on the

participants’ screen.

3.8 Conclusion to Chapter 3: Methodology

In the introduction this chapter has defined and explored the justification for the

methodology employed in this research, the role of the researcher during this

dissertation as well as the clarification of details about the procedures, the instruments

and also the limitations of this research. Then the author has described how the

collection of data will be conducted for all of the key parameters of the four

perspectives evaluated in the Literature Review (Chapter 2) which are; Typology,

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Constellation of the Top Management, Managers’ Characteristics and Span of Control.

In the final section, the ethics approval for the survey including its consent process are

described and details about the on-line survey group of participants are provided.

Chapter 4 on the Data Collection then follows and details the setup and functionality of

the online survey which gathers the primary data for the thesis. Firstly, it enumerates all

the questions that the participant will be using during the survey after giving consent.

Whereas for some questions (such as Typology, department, education, size and

complexity of each department, age and tenure) a selection is given, the questions about

the Characteristics have to be answered on Likert-scales and only the name (real or

fictitious) are keyed in by the participants. Chapter 4 describes each step in detail

supporting it with screenshots from the actual online survey. In the second part a range

of selected submissions are discussed for each team size to highlight the findings and

observations that occurred through the data collection. The most interesting samples

were collected among the team sizes of three to five Top Management Team members

(excluding the CEO) because the findings from six team members and upwards were

insignificant.

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4 Chapter four: Data Collection

4.1 Introduction

This chapter discusses the method and type of data that was collected for this thesis

through the online survey which was specifically designed for this research. The

participant was guided through a number of questions that are explained in the

following pages. All the questions had a selection of answers from which the participant

had to choose one answer except for a few questions where it was necessary to key in

the name, age or tenure of each Top Management Team member. The participant was

asked to share information about the following topics:

Ten questions to define the Typology of the Company

The size of the company which is one parameter of Span of Control

Information about the CEO’s age and tenure; to improve the quality of the

calculation regarding the Top Management Team’s constellation*

Information about the TMT-Member’s department; in order to group the Top

Management Team by Porter’s Value Chain and the Miles & Snow Typology

The size in manpower and complexity of the TMT-Members’ Department so as

to define together with No 2 the maximum Span of Control

Age, Tenure and Education Level to calculate the fit into the Team Constellation

The TMT-Members’ Characteristics, to rank the ideal managerial Personality

Judgement of the Participant concerning the influence of the TMT-Members in

order to receive, although this is subjective, a measurable benchmark.

*Note (on the third point): Initially there was no question about the CEO’s age and tenure

because the focus was purely on the remaining Top Management Team members. After

nine submissions the author realized that in cases where the size of the Top Management

Team was at its minimum (CEO + 2 TMT-Members) conducting the calculation based only

on the two Top Management Team Members would not result in a meaningful prediction to

describe the fit of one into a team constellation. With only two members each of them

would fit or not equally fit since each is representing 50% of the group. Consequently, the

survey was adjusted to obtain also the age and tenure from the CEO as well. Together with

the CEO who is a member of the Top Management Team by default, although not analysed

any further in the survey, there is always the data set of a minimal group of three which

most likely results in a situation where two members could be more similar than the

remaining one, which will help assist in predicting a pattern.

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4.1.1 Online Survey

The participants have to start up their web browser and open the web-link

www.The2ndCeo.com to begin the test run of the survey. Test run because the survey

has been already furnished with the algorithms and comes out with a result at the end.

The collection of data submissions by participants is necessary to analyse the reliability

of the algorithms. The framework of the survey was created in an earlier stage of the

work for this thesis (June 2014) under another domain for testing purposes but was re-

designed several times and did not result in any calculation at that time. This is due to

the fact, that several of the parameters were found through the research and

comprehensive literature review and also the formulas connecting the parameters had to

be created first.

Screenshot 4.1: URL of the online survey

Right after keying in the domain name, a window will pop up with the Information

about who and what the research covers together with the Consent form. Participants

have to confirm twice to demonstrate that they understand the information and are

giving their consent to use the resulting data in the conceptual framework of the

research. The participants are also informed that they can quit the procedure at any time

and that the data only will be stored when they press the submit button after all

questions are answered. Also at this final stage it is still possible to submit

anonymously.

4.1.2 Description of the Online Application

After the participants are requested to give consent for the use of the data for research

purposes a pop up window appears, explaining the basic principle of the survey. Then

on the left side, first the questions in regards to the Typology are answered, and on the

right side information about the Team Constellation is collected.

http://www.the2ndceo.com/

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Screenshot 4.2: Instructions to start the online survey

The survey about the Typology follows the order of the Miles & Snow (1978) scheme;

however, the sequence of the questions does not have an influence on the final result.

Each question adds to the balance of the weighting of the different Typologies existing

in the organisation examined. Each Typology stands for a specific Dominant Coalition.

Screenshot 4.3: Typology perspective I

All questions regarding the Typology are by default set to ‘Not selected’ and the

software is conditioned in such a way that it is not possible to skip any of the questions.

Participants have to select the most suitable among four given answers which are coded

to be representative for an organisation acting as a Defender, Prospector, Analyser or

Reactor. This is to describe the typology of the company for which they would like to

undertake the survey.

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Screenshot 4.4 Typology perspective II

Since there are ten questions in total; the answer for each of the questions does not have

the amplitude to change the overall result. The computation is done in such a way that

for each question the dominant coalition is calculated where the dominant departments

always will receive a higher percentage than the subordinate departments. As explained

in the previous chapter, there may be cases where there are fewer departments in the

secondary group than in the dominant group. Then the answers of all ten questions are

adding up to a total score per department. At this stage of the application the score is

expressed in a percentage as part of 100% among the Top Management Team members.

Only at the end, the percentages of all parts of the survey are consolidated and then

calculated without the share of the CEO, which is described in this dissertation as 34%

so as to possess a blocking minority.

Screenshot 4.5: Size of the company

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The survey continues with the collection of the parameters about the company size. The

answer to this question is subjective and relative dependent on the participant’s point of

view. That is why it is explained in this question that the size is indicated in relation to

the industry the company is in which should provide a benchmark for the participant.

The size of the company is amongst the questions which are not related to the

percentage calculation of Typology, Team Constellation or Managers’ Characteristics

because this answer is used later to give one dimension of the calculation if a Manager

has exceeded their Span of Control.

Screenshot 4.6: Size of the Top Management Team

Also the participant is asked about the age and tenure of the CEO. Although the CEO is

not evaluated in the research for this thesis, the collected data is helpful to ensure that

the Constellation of the Top Management Team can be calculated even in cases where

there are only two Top Management Team members or for larger teams because it helps

to produce smoother results. After this, the participants are requested to evaluate each

member of the top management team in regard to:

the title of the Department (for which the selection of the seven departments

defined by Porter’s Value Chain is given) and this helps to allocate managers by

the Typology of the company into the Dominant Coalition,

the complexity of work in this department as well as the number of staff in this

department (which are together the other two dimensions to evaluate the Span of

Control of each of the departments) and

questions in regard to the age, tenure and education of each manager (which are

defining the fit into the Team Constellation of a Manager into the Top

Management Team).

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Screenshot 4.7: CEO’s age and tenure

The questions about each member of the Top Management Team are then divided into

six Characteristics which were selected earlier in this thesis to describe the ideal profile

of a manager. When those Characteristics were identified it was discussed that some

Characteristics of managers are required in both directions; horizontal (manager to

manager) as well as vertical (manager to subordinates) which are in this thesis

Reliability, Archetype (Role Model), Communication and Competence.

Screenshot 4.8: Details about each member of the Top Management Team

Additionally, there are two more Characteristics; Social Competence for the relationship

of manager to subordinate and Network for the relationship of manager to manager. All

six Characteristics had to be keyed in for each member of the top management team

except for the CEO because the CEO’s power and influence is calculated separately.

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Screenshot 4.9: Details of every Manager’s Characteristics

At the end of the survey, the participants were required to judge which manager is

believed to have the most influence on strategic decisions in the analysed company. The

participants had to rank the top three managers which they keyed in previously.

Screenshot 4.10: Ranking of the influence of the Top Management Team members by assumption

This final question is crucial in respect to receiving a benchmark to validate the

outcome of the calculated survey. Although the ranking of the managers by the

assumptions of the participant is subjective so are the majority of the answers of the

whole survey. It is assumed for most participants, however, that they do apply a

constant high or low rating throughout the survey. Exceptions are coming from the

participants’ fatigue or whenever a misunderstanding appears.

The calculated percentage at the end becomes less important than the ranking itself,

depending on the number of Top Management Team members. This is due to the given

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limitation occurring when one is fragmenting a capped value among an increasing

number of people. In this thesis a maximum of 66% of the decision power is allocated

among the Top Management Team. As the CEO has a blocking minority of 34% any

value larger than 16% allocated to one member of the Top Management Team would

cumulate in a majority when teaming up with the CEO. Although it is hypothetically

possible that one manager is outstanding in their performance and all others are

performing poorly, it is more likely to have a balanced team where no one person would

collect the vast majority of the points. This would mean that results showing potential

candidates receiving around 16% share on dominance are most likely in teams of up to

five managers (plus CEO). In larger teams the application should still detect more

powerful managers but may not result anymore in a dominating figure. The core value

of the developed application is finally the ability to distinguish people by factors which

are non-metric in order to express something in a value. This is the central outcome of

this thesis and where future research could be expanded.

4.1.3 Sample Survey-Report

When the participants have finished the data entry, the choice is given between

submitting the data showing the participants’ details or doing so anonymously.

Regardless of which option is chosen the participants are rewarded for their work with

an instant evaluation of the submitted data. The report below is constructed to

demonstrate a potential outcome.

Screenshot 4.11: Sample survey-report organizational chart with percentage values and indication of potential influencers

The Survey-Report starts with a visualization of an organizational chart. In the example

above a team with four managers; finance, production, marketing and sales is analysed.

Beside the name of the manager and the indication of the department they are heading,

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there is also the result of the calculated dominance in the percentage shown. In this

example, Sarah, the head of Production achieved the highest value (21%), Annabelle

heading up Marketing, ends up second (18%) where both could form a dominant

coalition with the CEO (34%). Peter, the head of Finance, reaches 15% and Marlene the

head of Sales attains only 10%. Attached to the organisational chart is one more field

below Sarah, Production, which contains the word ‘Influencer’. This reveals that Sarah

has due to the headcount supervised by her in relation to the complexity of work in her

department and also the size of the company, exceeded the maximum Span of Control.

Therefore it is appropriate to assume in her department that there is at least one

subordinate officially or unofficially influencing Sarah. Since Sarah is representing the

strongest department, this influencer indirectly may lead the company.

After that the report shows a ranking of the manager’s dominance assumed by the

participant compared with the ranking calculated by the online tool. Throughout the

whole report, next to each topic, there is a short explanation describing in brief what the

data is about or what the reader should be expecting. There is also an icon at the bottom

of each text linked to a more detailed explanation of the research.

Screenshot 4.12: Sample survey-report comparing ranking by calculation (computer) and assumption (participant)

The table Typology is showing all aspects (market, technology etc.) and the implication

of the selected answer on the dominant coalition according to Miles & Snow (1978).

Based on Typology the example company is mostly dominated by financial issues

which has given Peter a clear advantage over the other managers.

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Screenshot 4.13: Sample survey-report perspective Typology

When analysing the Constellation table, however, it becomes clearer why Peter has lost

his pole position. Compared to the rest of the managers, Peter loses some points because

his age in proportion to the rest of the team is a bit far from the ideal, not because he is

the oldest, but because his positioning is a deviation from the group. The lowest score

was reached by Marianne who is the youngest one and even further away in the

deviation. Marianne’s tenure fits more to the group than Peter’s tenure because he has

stayed too long with the company which could negatively influence his openness to

dynamic change. Sarah who was behind in the Typology chart is gaining points with the

best fit into the Top Management team by having the right age, a good tenure and the

highest education. After Typology and Constellation, Annabelle and Sarah are still head

to head having collected 55.8% and 60.2% respectively.

Screenshot 4.14: Sample survey-report perspective Constellation

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The virtual competition in the example is only decided after reviewing the last

perspective; Managers’ Characteristics. Sarah is dominating clearly because of earning

high scores across the six Characteristics whereas Peter and Annabelle are positioned

more in the middle and Marlene is receiving the lowest points as shown in the

screenshot below.

The sample report which was described on the previous pages was designed to

demonstrate that each of the perspectives (Typology, Team Constellation

or Managers’ Characteristics) is not enough to predetermine a domination favouring one

Screenshot 4.15: Sample survey-report perspective Managers’ Characteristics

department. Although the Typology of the example company was clearly in favour of

the Finance department, the Constellation and Characteristics perspectives have

influenced the total result in such a way that the constellation of the dominant coalition

has changed and Production and Marketing have taken over the lead.

Throughout the following pages, the valid submissions of the online survey are analysed

in the same format as just demonstrated, to summarize in the next chapter if it was

possible through this research to verify the hypotheses of this thesis or parts thereof.

The review is separated into an overall review in statistical form in which all

submissions in their entirety are reflected. This leads to the second section where

selected submissions are discussed to illuminate specific outcomes.

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4.2 Collection of Data

4.2.1 Synopsis of 114 Submissions

To motivate more than 100 professionals from different ethnic and demographic

backgrounds was the goal when participants were sourced to test run the developed

application on the web domain www.The2ndCeo.com which was set up specifically for

this thesis. In a period of roughly one month, 114 submitted data forms were filled out

by professionals from 37 large, 46 medium and 31 small organizations. Unfortunately, 9

out of the 114 submissions were not filled out completely so that in some of the

evaluations only 105 are mentioned as the total participating companies. This chapter

begins with the synopsis of the collected data where not all count towards the results in

regards to evaluating the most dominant manager; however, they do contribute to the

body of knowledge in order to answer the hypotheses.

Starting with the average age of the CEOs’ which from all the submissions was 48years

and 11months, with spans between 22 and 62 years old (the most common ages

mentioned are 8x50, 7x40, 7x52, 7x57 and 6x45 out of a total of 114). There is a very

broad band of CEOs’ tenure ranging from 1 up to 40 years which is probably less

meaningful than the average tenure which is 11.5 years gained from the submissions

collected.

In the selection of Typology where the participant chooses from among the four

possible Typologies to describe the overall company there were only 14 out of 114

companies which did not select at least once each possible Typology (Defender,

Analyser, Prospector or Reactor). This is leading to the interpretation that there is

almost never something like a uniform Typology among organizations because the vast

majority of the companies are organised as a hybrid form of Typologies. The table

below shows how many companies have selected any specific Typology more than

once. For example, there is only one company which has selected nine times the same

Typology; almost a homogeneous selection. Whereas for example, 22 companies have a

dominant Typology that was selected five times.

No of Companies 104 84 65 22 21 7 3 1

…selected x time the same Typology (maximum possible is 10)

2 3 4 5 6 7 8 9

Table 4.1: Weighting of Typology by number of participating companies

Overall Defender was the most often selected Typology as it was selected 412 times

followed by Prospector 309 times, Analyser 220 times and Reactor 185 times.

http://www.the2ndceo.com/

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In total the data from 428 Managers working for 114 companies was collected. Without

the CEO, the management team counted 28x2, 31x3, 19x4, 24x5, 5x6, 5x7, 2x9

managers which results in an average of 3.75 Managers in the Top Management Teams

(not counting the CEO). The table below is showing the number of times each of the

departments was selected. Sales and Finance are ranking on top of the list whereas

Logistics and Infrastructure are forming the bottom of the table. In terms of the

algorithm in the online application two things have to be mentioned. These are that

Procurement is treated in terms of the calculation similarly to Finance whenever the

Typology ‘Analyser’ is chosen because the supportive activities according to Porter’s

Value Chain (1980) are grouped together.

Department …times

mentioned


Sales 89 X X

Finance 81 X X X

Production 55 X X

Service 50 X

Marketing 37 X X

HR 36 X X

R & D 32 X X X

Logistics 20 X

Infrastructure 14 X X

Table 4.2: Frequency of departments in the survey and the potential Dominant Coalition they belong to, according to

Miles & Snow (1978)

The largest amount of data was collected in regards to the Top Manager Team

Members. For each member of the Top Management Team six Characteristics have

been evaluated through Likert-Scales. The first table below shows the number of data

sets collected and the average values which were given from the participants.

Table 4.3 Characteristics by frequency and departments

In a second step the values in dependence of each Characteristic over all the

departments are sorted out to ensure that a comparison could be undertaken. Each of the

Characteristics was analysed for the highest and the smallest values and then the

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complete span of values was divided into deciles. After that, each department was

grouped into the decile by Characteristics or if a value was in between the deciles the

result was rounded up to allocate scores between 1and 10. With this system for each of

the Characteristics the weakest and the strongest department could be visualized. For

example, in terms of the Characteristic ‘Network’ the head of department from Sales

will obtain the best rating with a 10 whereas the head of department from Finance will

gain the lowest rating with 1.

Table 4.4: Balanced weighting of Managers’ Characteristics

The question about the Complexity of Departments did result overall in a medium to

high perception of complexity that the departments have to manage.

Table 4.5: Complexity of departments with the core area indication in yellow

The table above shows for example that Marketing, Human Resources and Procurement

are departments which are perceived to be challenged by an average level of complexity

whereas the Research & Development department was judged from almost all

participants to handle tasks of high complexity. The results of the above table are not

Department …times mentioned High Complexity Medium Complexity Low Complexity

Sales 89 42% 49% 9%

Finance 81 46% 41% 13%

Production 55 49% 44% 7%

Service 50 66% 28% 6%

Marketing 37 35% 54% 11%

HR 36 22% 50% 28%

R & D 32 94% 6%

Logistics 20 40% 45% 15%

Procurement 14 29% 50% 21%

Infrastructure 14 50% 21% 29%

131

really helpful in judging the department Infrastructure because the participants have

rated this department in all levels of complexity.

The team size of departments was mostly spread over the whole spectrum from 1 to 42

for each of the departments. The limitation of 42 was given as an upper limit in the

online survey because everything above 42 is exceeding the Span of Control

measurement in this thesis. So it is assumed that there were some larger departments.

Such team sizes, however, have appeared seldom in the data collection for this research.

Certainly, such team sizes exist, without regard for the averages of team sizes calculated

for each specific department; however, some of the answers collected for this research

containing the maximum team size may also be the result of fatigue, with participants

just selecting sometimes the smallest and sometimes the biggest option available to

work their way through the survey. This assumption is based on some responses

collected which are not impossible per se but at least did not give a logical impression

when analysed.

Team sizes that were analysed: Production (average 19.2), Service (average 14.6), Sales

(average 13), Infrastructure (average 11.2), Finance (average 8.8), Procurement

(average 8.8), R&D (average 8.5), Logistics (average 8.5), Marketing (average 8.1),

H&R (average 7.8).

The average Age among all 428 Managers of the Top Management Team for which data

was collected is 44y10m, ranking almost exactly in the middle of the range from 22 –

65 years which was the given span of choice in the survey. Participants were also

requested to share information about the Education Level in the section of the

Constellation analysis. The choice was given between no Tertiary education, or with a

Bachelor’s, Master’s or Doctoral degree.

Table 4.6: Educational level of the managers by departments in nominations

Count of competence

Without Tertiary Bachelor's Master's Doctoral Grand Total

Finance 4 42 31 4 81

HR 4 21 10 1 36

Infrastructure 2 2 9 1 14

Logistics 2 12 6 20

Marketing 3 18 16 37

Procurement 1 9 2 2 14

Production 12 18 22 3 55

R & D 3 13 11 5 32

Sales 26 42 20 1 89

Service 6 24 19 1 50

Grand Total 63 201 146 18 428

132

Since the number of managers analysed is different for each department, the same

information was described in the second table below by expressing the results in

percentages. About 15% of all Managers are not holding any degree whereas almost

47% of Managers have a Bachelor’s degree and 34% of Managers obtained a Master’s

degree. The percentage of Doctorates is below 5% when looking at the Top

Management Teams overall. It is important to remember that these evaluations are done

without the CEO. Data on the CEO was restricted in its collection to age and tenure due

to the fact that those two variables have to be put in relation to the remaining team

members to calculate the Team Constellation. Whereas education is a standalone factor

in Team Constellation with the limitation that more education is more relevant than the

type of education itself, however, this is not related to other team members.

Table 4.7: Educational level of the managers by department as a percentage

By evaluating the outcome of the calculation to determine which manager has more

influence, it becomes apparent which of the three perspectives Typology, Constellation

or Characteristics has more influence on the computed outcome. The following table

shows how many of the No 1 ranking managers in each perspective have finally been

ranked No 1 overall. Furthermore, the table is split by the number of Top Management

Team members per company. For example, in participating companies with a Top

Management Team of 3 Managers (without CEO) in 17 out of 27 companies the

Number 1 ranked department in Typology was the Number 1 ranking department

overall. Out of 105 companies which submitted sufficient data to compile this table (9

out of 114 submissions had to be taken out) there are 71 departments calculated in the

perspective Typology with the highest percentage which finally was also ranked

Number 1 overall. In the perspective Constellation still 64 out of 105 matched and in

Characteristics 52 out of 105. Although with one exception Typology does have the

highest weighting. It is interesting to interpret from the table that from an overall

Count of competence in percentages

Without Tertiary Bachelor's Master's Doctoral

Finance 5% 52% 38% 5%

HR 11% 58% 28% 3%

Infrastructure 14% 14% 64% 7%

Logistics 10% 60% 30% 0%

Marketing 8% 49% 43% 0%

Procurement 7% 64% 14% 14%

Production 22% 33% 40% 5%

R & D 9% 41% 34% 16%

Sales 29% 47% 22% 1%

Service 12% 48% 38% 2%

Master's

133

judgement the importance of Constellation and Managers’ Characteristics is

proportionally higher in smaller teams whereas the importance of these perspectives is

lessened with increasing team size.

Table 4.8: Occurrence of first ranking department vs perspective, split by team size

The next two tables are showing the rankings of the head of Departments by calculation

of the online application (first table) and then by assumption of the participant (second

table). Both tables are set up the same way to allow an in-depth comparison. Vertically

there are four main sections showing the accumulated results when the department was

ranked first, second and third, whereas for each rank there is an additional, separate

section afterwards. In the horizontal axis the tables are split in the team sizes of the Top

Management.

Overall it is apparent that the intuitive feelings of the participants have yielded a very

high consensus with the calculated algorithm of this thesis. Finance / Sales / Production

were calculated as ranked most often among the top three with

67 – 67 – 40 compared to the assumption table with 67 – 75 – 36. Whereas the

calculation as predicted on the first rank Finance / Sales / Marketing with

36 – 29 – 13 nominations versus the assumption of 26 – 33 – 14. In both, calculation

and assumption, Production is third among the most often named departments but does

not appear among the top three on the 1st Rank section. Analysing the

2nd

Rank section finds Finance / Sales / Production nominated in total

19 – 25 – 19 times versus the assumed nominations of 25 – 24 – 17. In both the first

ranking departments as well as the second ranking departments the number of

nominations are dropping significantly. The pattern seems to change in the 3rd

rank

where the calculation sees Service / Production / Sales to be strongest whereas the

assumption chart is still dominated by Sales and Finance. The differences in the results,

however, are becoming less obvious and vaguer.

Overall 1st department vs 1st in perspective Number of Managers in the TMT without CEO

2 Mngr 3 Mngr 4 Mngr 5 Mngr 6 Mngr 7 Mngr 8 Mngr 9 Mngr Total

Typology 17 17 10 17 4 4 0 2 71

Constellation 22 17 10 10 1 4 0 0 64

Characteristics 20 13 8 8 2 1 0 0 52

Total number of companies 27 27 18 22 4 5 0 2 105

134

Table 4.9: Departments by team size and rank according to the computer calculation

Based on the frequency with which a department is named compared to their ranking a

similar result which could be termed ‘the winner takes it all’ was the outcome and the

performance of the remaining departments is dropping abruptly. From the 428

departments keyed in throughout the entire survey, there are most entries for 83 Sales,

76 Finance and 51 Production (followed by 45 Service, 36 Marketing and 35 HR).

Whereas Sales on average ((calculation + assumption) / 2) gets Ranked 1st by 85%, but

Finance with fewer nominations achieved a 88% chance of being Ranked 1st.

Production on the other hand, although the third most often named department, is only

achieving a 35% chance of Ranking 2nd

which is about the same for Marketing,

obtaining a 37% chance of being Ranked 1st. The highest result in a proportional chance

for Service is only at 3rd

Rank with 27% and finally for HR the opportunity to Rank 2nd

or 3rd

is only 20% and 19% respectively.

CALCULATION of Ranking Number of Managers in the TMT without CEO


Frequency a department ranked 1st, 2nd or 3rd

Finance 13 16 14 16 4 3 0 1 67

Production 8 12 5 13 1 1 0 0 40

Marketing 10 5 3 8 1 0 0 0 27

R & D 4 4 3 2 2 2 0 0 17

Sales 12 22 15 12 2 4 0 0 67

HR 2 6 6 4 2 1 0 2 23

Service 5 10 6 3 0 2 0 2 28

Infrastructure 0 0 1 2 0 0 0 0 3

Logistics 0 3 0 3 0 1 0 0 7

Procurement 0 3 1 3 0 1 0 1 9

Frequency a department ranked 1st

Finance 4 7 8 11 3 3 0 0 36

Production 3 1 0 3 0 0 0 0 7

Marketing 6 4 1 2 0 0 0 0 13

R & D 3 0 1 1 1 1 0 0 7

Sales 8 10 6 4 0 1 0 0 29

HR 1 2 1 0 0 0 0 1 5

Service 2 1 0 0 0 0 0 0 3


Logistics 0 1 0 0 0 0 0 0 1

Procurement 0 1 0 1 0 0 0 1 3

Frequency a department ranked 2nd

Finance 9 4 5 1 0 0 0 0 19

Production 5 6 1 6 0 1 0 0 19

Marketing 4 0 1 3 1 0 0 0 9

R & D 1 0 2 0 1 1 0 0 5

Sales 4 6 7 5 1 2 0 0 25

HR 1 4 0 1 1 0 0 1 8

Service 3 5 1 0 0 0 0 1 10


Logistics 0 0 0 3 0 1 0 0 4

Procurement 0 2 1 2 0 0 0 0 5

Frequency a department ranked 3rd

Finance 0 5 1 4 1 0 0 1 12

Production 0 5 4 4 1 0 0 0 14

Marketing 0 1 1 3 0 0 0 0 5

R & D 0 4 0 1 0 0 0 0 5

Sales 0 6 2 3 1 1 0 0 13

HR 0 0 5 3 1 1 0 0 10

Service 0 4 5 3 0 2 0 1 15


Logistics 0 2 0 0 0 0 0 0 2

Procurement 0 0 0 0 0 1 0 0 1

Total number of companies 27 27 18 22 4 5 0 2

135

Table 4.10: Departments by team size and rank according to participants’ assumptions

Comparing the accuracy in which the online application has matched the assumption of

the below table demonstrates impressively that in all company sizes the developed

system achieved a higher accuracy than by allocating the percentages by chance. As an

example in a company of three Managers in the Top Management Team there would be

a chance of 33.3% to identify the leader by random guessing, however, the

system reached an accuracy of 55.5% on the first rank and 51.8% on the second

rank. Bearing in mind that the system is based on the same algorithm and is compared

with the unpredictable feelings of 114 individuals who have keyed in the data.

Table 4.11: Matching of the survey’s calculation and assumption’s results

ASSUMPTION of Ranking Number of Managers in the TMT without CEO


Frequency a department ranked 1st, 2nd or 3rd

Finance 12 16 14 15 4 5 0 1 67

Production 8 12 4 10 0 1 0 1 36

Marketing 10 5 4 6 0 1 0 0 26

R & D 4 4 3 5 2 1 0 0 19

Sales 13 22 15 16 4 4 0 1 75

HR 2 6 3 1 0 0 0 0 12

Service 5 10 5 4 0 2 0 3 29


Logistics 0 3 3 4 1 1 0 0 12

Procurement 0 3 1 2 0 0 0 0 6

Frequency a department ranked 1st

Finance 5 7 3 7 2 2 0 0 26

Production 3 1 1 5 0 0 0 0 10

Marketing 6 2 3 3 0 0 0 0 14

R & D 1 1 1 0 0 0 0 0 3

Sales 9 9 5 6 1 2 0 1 33

HR 0 2 1 0 0 0 0 0 3

Service 3 4 3 0 0 1 0 1 12


Logistics 0 0 0 0 1 0 0 0 1

Procurement 0 1 0 1 0 0 0 0 2

Frequency a department ranked 2nd

Finance 7 3 6 5 1 3 0 0 25

Production 5 7 1 3 0 0 0 1 17

Marketing 4 2 1 1 0 0 0 0 8

R & D 3 1 1 2 1 1 0 0 9

Sales 4 7 6 5 2 0 0 0 24

HR 2 2 1 1 0 0 0 0 6

Service 2 3 0 2 0 0 0 1 8


Logistics 0 1 0 1 0 1 0 0 3

Procurement 0 1 1 1 0 0 0 0 3

Frequency a department ranked 3rd

Finance 0 6 5 3 1 0 0 1 16

Production 0 4 2 2 0 1 0 0 9

Marketing 0 1 0 2 0 1 0 0 4

R & D 0 2 1 3 1 0 0 0 7

Sales 0 6 4 5 1 2 0 0 18

HR 0 2 1 0 0 0 0 0 3

Service 0 3 2 2 0 1 0 1 9


Logistics 0 2 3 3 0 0 0 0 8

Procurement 0 1 0 0 0 0 0 0 1

Total number of companies 27 27 18 22 4 5 0 2

Accuracy of calculation vs assumption Number of Managers in the TMT without CEO


1st rank match calculation vs assumption 16 15 6 6 2 1 0 0 46

2nd rank match calculation vs assumption 15 14 6 6 1 2 0 1 45

Total number of companies 27 27 18 22 4 5 0 2 105

136

Examining the 1st and 2

nd Ranks together the performance of the developed algorithm

can be summarized as promising. Taking for example, the case of 22 companies with a

team size of five, means there are 110 managers of which one would have to hit two

matches being blindfolded resulting in a chance result of 1.8% whereas the system has

shown an accuracy of 10.9% in the same case. Those matching accuracies ignore

‘nearby’ results and the chances of the unreliability of the participants where some of

the participants were too tired or unmotivated to answer the final question (which is the

basis for the above comparison) or have keyed in a wrong result. The ranking by

assumption is reflecting, as far as when filled in honestly, a cumulative objective

impression or an assumption that this could be what the designer of the survey is

expecting. Whereas the system is computing the results based on a set of value grids

without having preferences for any result as shown in the example at the beginning of

this chapter where the ranking has kept on changing after each perspective.

Finally, in the discussion is the revelation of the existence of Influencers, split by team

size and department and based on the phenomena of exceeding the Span of Control. The

bottom line of the table below shows the percentage of departments overall which have

lost the Span of Control and therefore are most likely to have at least one second

important person per department who may act as an influencer. Overall more than one

third (36%) of all head of Departments are depending on internal support. Looking at it

separated by departments the results are drifting apart. The lowest risk of losing control

is a factor for the heads of Departments of Human Resources with only 17% when there

is as much as 58% of all heads of Production for that condition. Second lowest is

Finance with 24% followed by Procurement (a department located due to its scope of

work nearby Finance) with 25%. Service and Logistics on the other side are ranked

second and third from the top with 49% and 45% respectively.

Table 4.12: Number of departments having an influencer separated by the sizes of the Top Management Teams

Share of influencers by department and by TMT size

TMT size w/o CEO 2 3 4 5 6 7 8 9

Total

Influencers

Total

Deptmtnts

share of

influencer by

department

Finance 1 3 2 9 2 1 0 0 18 76 23.7%

Production 5 8 5 9 2 1 0 0 30 51 58.8%

Marketing 3 1 2 4 0 0 0 0 10 36 27.8%

R & D 2 0 1 2 2 1 0 0 8 24 33.3%

Sales 3 8 4 11 3 2 0 0 31 83 37.3%

HR 1 1 0 4 0 0 0 0 6 35 17.1%

Service 2 4 3 4 0 1 0 8 22 45 48.9%

Infrastructure 0 0 2 3 0 0 0 0 5 12 41.7%

Logistics 0 0 1 5 2 1 0 0 9 20 45.0%

Procurement 0 1 0 2 0 0 0 0 3 12 25.0%

Total Influencers 17 26 20 53 11 7 0 8 142

Total deptmts in this team size 54 81 72 110 24 35 0 18 394

share of influencer by TMT size 31.5% 32.1% 27.8% 48.2% 45.8% 20.0% 44.4% 36.0%

137

4.2.2 Partial Review of Survey-Reports

In the next few pages a selection of some of the 114 submitted forms is discussed which

were found to be special or different from the others. As some of the submitted forms

are anonymous and some not, it was decided to name them by

‘Team_Size_Point_No_of_Submission’ for example in the report 2.7 it means the Top

Management Team consists of two managers (without the CEO) and was the 7th

submission in its category.

4.2.3 Top Management Teams with 2 Managers plus CEO

Screenshot 4.16: Survey-Report 2.7

Survey-Report number 2.7 was chosen to show here how sensitive the created software

is. The submitted example could not be simpler with the smallest team size of only three

(including the CEO) and the starting position for the two managers is as close as it can

occur. Based on Typology there would be no advantage for either, Marketing or Finance

when they are just 0.2% apart. Even when it comes to Constellation; age, tenure and

education are the same and do not result in an advantage of one over the other.

Managers’ Characteristics finally ends up with the same total number of points

collected. The amplitude for the software to allocate 1% more to Marketing than to

Finance is that the Manager responsible for Marketing, who is gaining fewer points in

Reliability and Archetype and pointing similarly in Social Competence and Competence

Name: field was not filled by user Submitted: 04:52:40 16-01-2017

E-mail: field was not filled by user IP: 103.47.135.45

Ranking by calculation using your answers provided during survey

Rank Manager Influencer Department %

1 Rudy - Marketing 33.09

2 Andes - Finance 32.91

CEO

34%

Rudy Andes

Ranking by your assumption Marketing Finance

Rank Manager 33.09% 32.91%

1 Rudy

2 Andes

Typology

Market Success Observartion Growth Technology Tech II Tech III Planning Structure Control

Rudy 1 1 0 0 0 0 1 1 1 0 4.99 49.9

Andes 0 1 1 1 1 1 0 0 0 1 5.01 50.1

Constellation

Age Tenure (y) Education Age Tenure (Years)Education Level TOTAL

Rudy 25 2 Without Tertiary Marketing 3 2 1 6 50

Andes 27 4 Without Tertiary Finance 3 2 1 6 50

Characteristics

Manager Reliability Archetype Comm. Social Comp. Competence Network TOTAL Characteristics %

Rudy 4 5 8 5 8 6 6 50.5

Andes 7 7 5 5 8 3 6 49.5

Dominant TagManager TOTAL Typology %

Developed by AvantLab, www.avantlab.com

Constellation Input ScoresManager Department Const. %

138

but is allocated higher on the criteria Communication which is weighted more than the

others. Intuitively it could be assumed that Marketing might be stronger in

Communication than Finance, this fact was proven in the overall statistics in the

previous chapter, and so it is considered positive that the computer selected the same

Manager to be more influential as the participant has already indicated.


Survey-Report 2.13 is another sample of a small top management team and it is selected

to illustrate an example where Finance is in an insignificant position due to the

Typology of the company (which is not automatically given through the fact of having

only two departments observed). Except for Growth where Sales and Finance are

earning points, all other Typology factors chosen are only rewarding one of them each

so the result could be quite extreme. What is evident in this example is that the Manager

of Finance has a disadvantage in the team constellation which is quite significant with

5% in such a small team size. Although the Manager of Finance is holding a degree

compared with the Sales Manager, however, the latter fits just perfectly into the Top

Management Team in terms of age and tenure. Yet the Finance Manager is outmatching

the Sales Manager in five out of six Characteristics. The accumulated points although

more segregated than in the previous example are enough for the system to rank the

Managers in the right order.

Name: Mohammad Aidil Alladin Submitted: 04:39:36 18-01-2017

E-mail: [email protected] IP: 113.210.125.96



1 Suhairul - Finance 33.42

2 Rosnani - Sales 32.58

CEO

34%

Suhairul Rosnani

Ranking by your assumption Finance Sales


1 Suhairul

2 Rosnani

Typology


Suhairul 0 1 1 1 1 1 1 0 1 1 5.05 50.5

Rosnani 1 0 0 1 0 0 0 1 0 0 4.95 49.5

Constellation


Suhairul 37 7 Bachelor's Finance 2 4 2 8 47.1

Rosnani 40 7 Without Tertiary Sales 4 4 1 9 52.9

Characteristics


Suhairul 8 7 7 8 7 7 7 54.3

Rosnani 8 6 5 6 6 6 6 45.7




139

In contrast to Survey-Reports 2.7 and 2.13 where the perspective Managers’

Characteristics gave the final amplitude in the total scoring it can be demonstrated that

this is not a pattern overall. Survey-Report 2.15 demonstrates that someone who is

scoring lower in Characteristics and has a non-significant advantage through the

perspective Typology, (where the participant has selected three times a Typology which

is not rewarding either one) can win the contest in the perspective Constellation if age

and tenure turns out favourably.


This is evident in Constellation where the heads of the departments of Sales and Service

both have tenure with the company for 10 years and each are holding a bachelor’s

degree. One would not assume that the five year age difference would impact in such an

overall balanced picture. Checking the database, however, reveals that the CEO’s age is

41 and is closer to the Sales Manager than to the Service Manager. Therefore, the main

(only) amplitude was given by the Age, which in this case is advantageous for one of

the managers but of course also could lead to a disadvantage. Although ranking lower in

the Characteristics only the computed data of Constellation has made the final

difference – and this has matched the ranking assumed by the participant.

The same counts occur for the last sample out of the category with only two Managers,

Survey-Report 2.25. Where the Service Manager had a disadvantage from the

perspectives of Typology and Characteristics but scored significantly better than the

Marketing Manager in the Constellation. By the logic of the applied algorithm which

Name: user voted anonymously Submitted: 08:29:42 16-01-2017

E-mail: user voted anonymously IP: 115.134.150.216



1 Jolene - Sales 33.71

2 MK - Service 32.29

CEO

34%

Jolene MK

Ranking by your assumption Sales Service


1 Jolene

2 MK

Typology


Jolene 0 1 0 1 1 1 1 0 1 1 5.04 50.4

MK 0 0 0 0 1 1 1 0 0 0 4.96 49.6

Constellation


Jolene 40 10 Bachelor's Sales 3 4 2 9 52.9

MK 35 10 Bachelor's Service 2 4 2 8 47.1

Characteristics


Jolene 8 6 7 10 6 10 8 49.9

MK 10 7 7 8 7 8 8 50.1




140

accumulates percentages there is no other possible outcome to rank the Service Manager

higher, but surprisingly, although the type of business of the company and even the

Characteristics of the Service Manager should result in a lower ranking s/he has made it

to the top.

In the reviewed example depicted below the age and tenure are making the difference.

The sample is also selected here due to the situation that the leading Manager obviously

has exceeded their Span of Control (the complexity of the task was selected to be high

with a team size of 7 in a small company). This example is exactly such a case where

the most dominant member of the Top Management Team is depending on a lower

ranking staff member which could result in a bottom up leading of the entire company.


4.2.4 Top Management Teams with 3, 4 and 5 Managers plus CEO

The average size of Top Management Teams (without CEOs) in the undertaken survey

is 3.75 with a 65% polarisation among teams with three, four and five Top Management

Team Members (without CEOs). Whereas the perspective Constellation was unable to

compute any preference throughout all the samples which were collected for the teams

of only two managers (as each of the Top Management Team members would be

similarly off the average ideal) an ideal Constellation can be identified easily now when

analysing larger team sizes, even starting with only three managers. Furthermore, from

the computation of the developed algorithm the allocation of points in Constellation is





1 ju +Influencer Service 34.9

2 je - Marketing 31.1

CEO

34%

ju je

Ranking by your assumption Service Marketing


1 ju

2 je

Typology


ju 0 0 0 0 0 1 0 0 0 0 4.97 49.7

je 0 1 0 1 0 1 0 0 1 0 5.03 50.3

Constellation


ju 58 25 Bachelor's Service 3 4 2 9 60

je 46 10 Bachelor's Marketing 2 2 2 6 40

Characteristics


ju 4 5 4 4 8 5 5 48.9

je 4 6 5 5 6 5 5 51.1




141

becoming smoother due to the fact that standard deviation is limited in force of

expression for smaller teams but draws a smoother bell curve when taking parameters

from a larger sample group.


Survey-Report 3.5 shows a perfect match of the calculated ranking versus the

participant’s assumption of the same ranking where all three Managers are in the same

order. The Survey-Report is even more interesting in respect of a not obvious outcome

since each of the Managers is dominating in one of the perspectives. For Typology,

Manager 1 (Finance) would take over the lead, whereas in Constellation the odds are

preferable for Manager 3 (Marketing) and in terms of Characteristics, Manager 2

(Production) seems the one with the most ideal profile. As an external consultant,

without knowing the Managers and the company personally, it would not be possible

any longer to make a prediction of the influence among the Members of the Top

Management Team. Marketing, the department which is the strongest member of the

Management Team in the final ranking is surprisingly the lowest scoring when it comes

to Typology where one would assume the basis of preference for different departments

is found. Finance on the other hand which is according to Typology predicted to be the

strongest one loses its advantage with low scores in an average Constellation and with a

poor profile in Managers’ Characteristics.

Name: Mario Miranda Submitted: 11:28:46 13-01-2017




1 Manager 3 - Marketing 24.95

2 Manager 2 +Influencer Production 22.22

3 Manager 1 - Finance 18.83

CEO

34%

Manager 3 Manager 2 Manager 1

Ranking by your assumption Marketing Production Finance

Rank Manager 24.95% 22.22% 18.83%

1 Manager 3

2 Manager 2

3 Manager 1

Typology


Manager 1 1 1 0 1 1 1 0 1 1 1 3.54 35.4

Manager 2 0 1 0 1 1 1 0 0 1 1 3.01 30.1

Manager 3 0 1 1 1 0 1 1 0 0 0 3.45 34.5

Constellation

Age Tenure (y) Education Age Tenure (y) Education TOTAL

Manager 1 41 11 Bachelor's Finance 2 3 2 7 30.4

Manager 2 55 4 Masters Production 2 2 3 7 30.4

Manager 3 50 12 Bachelor's Marketing 4 3 2 9 39.1

Characteristics

Manager Reliability Archetype CommunicationSocial competenceCompetence Network TOTAL Characteristics %

Manager 1 5 4 4 2 7 2 4 19.8

Manager 2 9 7 8 8 9 8 8 40.5

Manager 3 9 5 8 9 8 9 8 39.8




142


The Survey-Report 3.9 above is selected to demonstrate under what circumstances the

developed algorithm is still able to detect the most dominant manager in a report where

the participant became either distracted or bored during the process of the data

collection. This participant has given all three managers in each Characteristic the

maximum score of 10, where it can be assumed that it is unlikely for any person to have

a perfect personality and even more unlikely for this to be the case for each member of

the Top Management Team. The mathematical result highlighted in this case is that due

to the above circumstances, there will be no advantage or disadvantage for any team

members in Characteristics so that the other perspectives of Constellation and Typology

are solely responsible for the final result. In this sample, Typology is very favourable to

Finance, where 8 out of 10 criteria are rewarding Finance, so that even ranking last in

Constellation (which has resulted due to being the youngest Manager in the team) does

not disrupt the overall results from ranking Finance in first place which is congruent

with the participant’s assumption. The second calculated rank then goes, against the

assumption of the participant, to Manager 3 due to the fact that s/he is the only one

holding a doctoral degree paired with the right age.

On a separate note we would like to discuss the finding that in the category of 3

Managers + CEO the developed algorithm has yielded 37% accuracy to predict all ranks





1 Person 1 - Finance 24.19

2 Person 3 - Service 21.36

3 Person 2 +Influencer Service 20.45

CEO

34%

Person 1 Person 3 Person 2

Ranking by your assumption Finance Service Service

Rank Manager 24.19% 21.36% 20.45%

1 Person 1

2 Person 2

3 Person 3

Typology


Person 1 1 1 1 1 1 0 1 1 0 1 4.75 47.5

Person 2 0 0 0 0 0 0 0 0 0 0 2.63 26.3

Person 3 0 0 0 0 0 0 0 0 0 0 2.63 26.3

Constellation


Person 1 49 8 Masters Finance 2 2 3 7 29.2

Person 2 58 20 Masters Service 2 3 3 8 33.3

Person 3 56 22 Doctoral Service 3 2 4 9 37.5

Characteristics


Person 1 10 10 10 10 10 10 10 33.3

Person 2 10 10 10 10 10 10 10 33.3

Person 3 10 10 10 10 10 10 10 33.3




143

1, 2 and 3 correctly which happened in a total of 10 out of 27 Survey-Reports (3.05,

3.08, 3.11, 3.13, 3.14, 3.16, 3.18, 3.21, 3.24, 3.25 all printed in the Appendix). There are

four (15%) of the Survey-Reports, however, with a completely opposite result and no

matches in comparison with the assumption made by the participants (3.03, 3.06, 3.12,

3.19 available in the Appendix). There is always the possibility of a participant not

understanding the task or just having filled out nonsense. It was noticeable in the review

that 3 out of those 4 Survey-Reports without any matches were following the sizes of

the departments in terms of staff when ranking them. Similarly, there were 3 out of

those 4 Survey-Reports where the last position was allocated to the oldest Manager who

of all of them had the smallest team to supervise.

The following sample collection of four Managers plus CEO is interesting because the

allocated value of the 66% dominant power (100 in total minus 34% allocated for the

blocking minority of the CEO) is just marginally enough to build a majority with only

one Manager in combination with the CEO (34 + 16.5 = 50.5) in a case where the total

value is distributed equally. This was not the case in teams with only two Managers in

which case each of them in a balanced situation would receive 33% or with three

managers where each Top Management Team Member would receive 22% and could

easily team up with the CEO. From combinations of 5 managers onwards it is necessary

for one manager to evolve significantly more influence than the others (66 / 5 = 13) in

order to describe her influence as really dominant.

Chapter 5 explores to what extent the percentage figures can be taken as a valid

representation of a manager’s influence and opportunity to guide the business in

cooperation with the CEO or if the percentage figure is primarily a help to rank and/or

compare Managers. One finding in the selection of 4 Managers + CEO is that there are

10 out of 18 (56%) cases where Finance and Sales are ranking first and second or vice

versa whereas the same set of departments (Finance + Sales) only have been ranking 6

out of 27 cases (22%) on the first two places in teams of 3 Managers + CEO, or in 6 out

of 22 cases (27%) in teams of 5 Managers + CEO. In teams of 6 Managers + CEO, the

same consideration comes to a frequency of 25% and in teams of 7 Managers + CEO to

40%, however, it has to be noted that the last two mentioned categories are only

represented with four and five Survey-Reports each. It seems that Finance and Sales

Managers can assume a positive environment supporting a strong position when

working in constellations of Top Management Teams of 4 Managers + CEO.

144


Continuing the findings in regard to the dominance of Finance and Sales in teams of 4

Managers + CEO it became obvious that Typology selections have reached more

extreme levels in some Survey-Reports than in others. This is apparent in Survey-

Report 4.4 above where Finance receives 41% of the score (2.4 times more than the

weakest two team members). Although the same Finance manager in this report is

scoring second lowest both in the perspectives of Managers’ Characteristics and

Constellation, which could explain why the participant has set Finance as rank three

only, however, this has resulted in the first rank overall in the computed result. Because

of this biased result the Survey-Reports have been separately reviewed with an

additional filter and it was found that 10 out of 18 Survey-Reports where Finance and

Sales have ranked first and second or vice versa, Finance has been ranked seven times

highest in Typology but only twice as the highest in Characteristics. Whereas Sales has

ranked six times highest in Characteristics but only twice as the highest in Typology (in

the perspective of Constellation both have ranked three times as the highest). This

Survey-Report was submitted anonymously so it was unfortunately impossible to

investigate any further why the participant has rated the Manager of Service highest

although this manager has not ranked highest in any perspective and is even lowest in

Typology and Constellation.

As an example of a very balanced Management Team it is worthwhile showing Survey-

Report 4.16. None of the perspectives of Typology, Constellation or Managers’





1 Loo SH - Finance 18.79

2 Michael +Influencer Sales 18.73

3 Christina - HR 15.16

4 KS Liew +Influencer Service 13.33

CEO

34%

Loo SH Michael Christina KS Liew

Ranking by your assumption Finance Sales HR Service

Rank Manager 18.79% 18.73% 15.16% 13.33%

1 KS Liew

2 Michael

3 Loo SH

Typology


KS Liew 0 0 0 0 0 0 0 0 0 0 1.71 17.1

Michael 0 0 0 0 0 0 0 1 0 1 2.4 24

Christina 0 0 0 0 0 1 0 0 0 0 1.73 17.3

Loo SH 1 1 1 1 1 1 1 0 1 0 4.16 41.6

Constellation


KS Liew 47 15 Bachelor's Service 1 2 2 5 16.7

Michael 40 10 Bachelor's Sales 4 4 2 10 33.3

Christina 41 13 Bachelor's HR 4 3 2 9 30

Loo SH 38 6 Bachelor's Finance 2 2 2 6 20

Characteristics


KS Liew 8 8 8 7 9 6 8 26.8

Michael 7 8 8 8 8 9 8 27.8

Christina 6 6 7 6 7 5 6 21.6

Loo SH 6 7 8 6 8 6 7 23.8




145

Characteristics in this sample favours any department nor does it seem that the

participant has made any shortcut (through boredom or misunderstanding) which would

result in an unreliable outcome. The biggest peak out of the norm is the age of the

Finance Manager, however, this is balanced against his/her good ratings in

Characteristics. The results of the four managers are very close together with less than

1.1% difference from the highest to the lowest value with three of them being in a range

of less than 0.2% variance. Without anticipating the last chapter of this thesis it is

unlikely that anyone in this top management team is exercising a dominating role or

viewing it from the antipode each of the managers has an equal opportunity to table his

or her request to the Top Management Team with a valid chance it being accepted. The

only additional variable is the +influencer remark for the Marketing Manager who

obtains this due to the selection of high complexity with a team size of 10 staff to

supervise which means that besides the four managers and the CEO there is most likely

at least one more influential person hidden in the Marketing team.


Those Survey-Reports with 5 Managers + CEO and above seem to invite departments

other than Finance and Sales to be among the Dominant Coalition which could derive

from the increased complexity of the business models possible for such Top

Management Team sizes. Finance outperforms all other departments by far in reaching

Name: isabell erlenmaier Submitted: 22:39:06 01-02-2017




1 david - Sales 16.84

2 Hans Pete - Production 16.73

3 rico +Influencer Marketing 16.66

4 werner - Finance 15.77

CEO

34%

david Hans Pete rico werner

Ranking by your assumption Sales Production Marketing Finance

Rank Manager 16.84% 16.73% 16.66% 15.77%

1 rico

2 david

3 werner

Typology


rico 0 0 0 0 1 1 0 1 1 1 2.41 24.1

david 0 0 0 0 1 1 0 1 1 1 2.41 24.1

werner 1 1 1 1 1 0 1 0 0 0 2.76 27.6

Hans Pete 1 0 1 1 1 0 1 0 0 0 2.41 24.1

Constellation


rico 47 15 Masters Marketing 3 4 3 10 28.6

david 51 20 Masters Sales 4 3 3 10 28.6

werner 65 22 Masters Finance 1 2 3 6 17.1

Hans Pete 51 9 Masters Production 4 2 3 9 25.7

Characteristics


rico 6 5 3 5 7 6 5 23

david 5 5 6 4 7 6 5 23.8

werner 8 6 5 5 9 4 6 27

Hans Pete 6 5 7 5 6 7 6 26.2




146

11 out of 22 times (50%) the 1st rank. In larger configurations of team size where Sales

obtains four times the first placing, just one more than Production, however, other

departments like Marketing, R&D and Procurement can step into the spotlight. As

mentioned earlier, the percentage value per manager does become flatter with increasing

numbers of Managers, however, there are still a significant number of 10 out of 22

Survey-Reports (45%) in the team size of 5 Managers + CEO, where one manager is

dominating in a way which could be described as ‘the winner takes it all’ (examples are:

5.2, 5.3, 5.8, 5.9, 5.11, 5.14, 5.18, 5.19, 5.20 and 5.21). To demonstrate this disparity,

Survey-Report 5.11 is highlighted although it is not the one with the biggest gap

between the highest and lowest ranking manager; but it has the most extreme lacuna

between the dominant leader and the average of the others. Surprisingly, the

constellation is clearly against the Sales Manager due to his/her age and education,

however, the Typology and Characteristics score is segregating him/her towards

the others into a separate caste. The sales manager has accumulated more than 17%

of power which allows a dominant coalition with the CEO and is rare in a team size

of 5 + CEO.






1 BL - Sales 17.55

2 JN - Logistics 13.38

3 VP - HR 12.47

4 MM - Procurement 12.11

5 Tee - Finance 10.5

CEO

34%

BL JN VP MM Tee

Ranking by your assumption Sales Logistics HR Procurement Finance

Rank Manager 17.55% 13.38% 12.47% 12.11% 10.50%

1 Tee

2 JN

3 BL

Typology


Tee 0 0 1 0 1 0 1 0 0 1 1.61 16.1

BL 1 1 0 1 1 1 0 1 1 0 3.79 37.9

VP 0 0 0 0 1 0 1 0 0 1 1.52 15.2

JN 0 0 0 0 1 0 0 0 0 0 1.47 14.7

MM 0 0 1 0 1 0 1 0 0 1 1.61 16.1

Constellation


Tee 55 37 Bachelor's Finance 1 2 2 5 13.9

BL 34 10 Without Tertiary Sales 2 3 1 6 16.7

VP 46 9 Bachelor's HR 4 3 2 9 25

JN 40 30 Bachelor's Logistics 4 3 2 9 25

MM 36 2 Bachelor's Procurement 3 2 2 7 19.4

Characteristics


Tee 9 5 5 5 8 3 6 17.7

BL 9 5 9 9 9 9 8 25.2

VP 6 6 5 5 5 6 5 16.5

JN 8 3 7 8 8 8 7 21.1

MM 6 8 6 7 5 7 6 19.5




147

4.2.5 Teams of six and above Managers

Firstly, there were not many larger Top Management Teams submitted overall. Just four

Survey-Reports with six Managers, five Survey-Reports with seven Managers and two

Survey-Reports with nine Managers were collected. Whereas, the sets with nine

Managers (although interesting) are questionable due to one of them having included

some managers from the second line. The other Survey-Report is showing one HR and

eight Service Departments which is not impossible but is not representative of a widely

spread type of business model. It is also apparent that Finance expands its dominant

position in six out of nine (66%) Survey-Reports (ignoring the two questionable sets

with nine Managers). The Sales Department is reaching the top position just once but it

is interesting to see R&D appearing twice. It is apparent that not only companies

classified as large have big management teams, which is a logical assumption, but seven

out of nine (78%) declare themselves as being medium sized. Regarding larger entities

it is worth mentioning that two Managers who were asked to participate in this research

informed us that their company structure was too large and or too complex in order to

be captured accurately in the developed software. One company was Siemens and the

other Swiss Post.

From a diagrammatical point of view there was nothing exceptional discovered in the

team sizes of six and upwards which is why any further discussion on large sized

Management Teams is waived. The interested reader is referred to the Appendix where

all submitted Survey-Reports are available.

4.3 Summary

This chapter has depicted the functionality and design of the online survey through

which 114 valid data sets were collected. With the exception of the perspective

Constellation, where the calculation of the age and tenure was computed without the

same information for the CEO for the first 9 data sets, the remaining 105 submissions

were complete, valid and useful for statistical analysis.

The average tenure of the CEO’s of the participating companies is 11.5years. The

majority of the companies analysed selected themselves to represent the Typology of a

Defender. The average size of all 114 Top Management Teams was 3.75 mangers plus

the CEO. The most named departments were Sales and Finance followed by Production.

Not surprisingly, it was Production with the largest average team size of 19.2, whereas

the smallest teams were found in HR departments with 7.8. Data was collected from

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428 Top Management Team members who were 44years and 10 months old on average.

In terms of qualifications, 15% of them have no degree, and almost every second

manager has a bachelor’s degree (47%) whereas 34% gained a master’s and 5% possess

a doctoral degree. The Finance department, at least in the context of this research, is the

most educated department with 95% of all Finance managers holding a degree.

In the evaluation of which department would be most influential in regards to

contributing to strategic decisions it was also Finance which ranked in 88% of the cases

in the 1st Rank, followed by Sales, which ranked first in 85% of the cases proportional

to the existence of those departments. The departments HR and Services were ranked as

least influential in regards to the decision-making process.

The evaluation of the fourth perspective, Span of Control, revealed that 36% of all 428

evaluated Top Management Team members are categorized as having lost control over

their departments and therefore as most likely to being more receptive to the input of

influencers. In conclusion, of all the perspectives analysed to understand the allocation

of the Top Management Team members it was shown that the Typology of the company

is a precursor to the power allocation among its Top Management Team members.

Whereas the perspectives Managers’ Characteristics and Team Constellation where

more important among smaller teams. Those two perspectives, however, are losing

importance with the increasing size of the Top Management Teams.

This chapter has profiled in this last section selected submissions in each team size from

two up to six Top Management Team members (excluding the CEO) to elaborate some

of the observations in more detail. There were a few submissions with seven, eight and

nine Top Management Team members but the value of information gained from those

submissions is negligible.

The final chapter will discuss the findings and their interpretation of the seven

hypotheses put forward at the beginning of this thesis. Some of these answers are based

on statistical values received through the survey. Consequently, the question of whether

the Top Management Team members can be profiled through the developed algorithm

is answered by using a confusion matrix through which the accuracy of the results was

measured. To evaluate which of the perspectives (Typology, Team Constellation,

Characteristics) is having most influence on the final ranking a regression analysis was

applied. A summary of all collected data is presented and its meaning in regards to the

initial question of ‘who is leading the company?’ is resolved. This question also

149

incorporates the answer as to how and where to find the hidden influencers in an

organisational chart. The final chapter concludes with a declaration of the limitations of

the research and explores why the ranking described in the previous chapter could prove

more meaningful than the allocation of a numeric value. It also outlines the potential for

future research in examining the Dominant Coalition within an organisation.

151

5 Chapter five: Conclusions and implications

5.1 Introduction

In this fifth and final chapter answers are provided to the questions raised at the

beginning of this thesis. During the search for potential parameters through literature

reviews in chapter two it was possible to develop an online survey to profile managers

according to their presumed dominance in the Top Management Team. This was

described in Chapter 3 where more than 100 professionals were encouraged to

participate in the online survey presented in the collected data in Chapter 4. This final

chapter will answer all seven Hypotheses before it concludes with a discussion about

the implications of the findings, limitations and potential further research related to the

research topic.

5.2 Conclusions from each research question and hypotheses

Hypothesis 1: The impossibility to allocate numerical values to relationships of

decision-makers in organizational structures as Gulick et al. have written in 1937 can

be refuted.

Accepted: This thesis has demonstrated that with the knowledge developed after 1937

in business research today that it is possible to select specific criteria and charge these

criteria with different weights in order to profile the members of the Top Management

Team and then to rank them accordingly. In this thesis, research was undertaken to

analyse the influence of different perspectives in terms of a system (the company) and

sub-system (the Top Management Team) and a single key element of the sub-system

(the manager). To facilitate this a line was drawn from the perspective of the Typology,

describing a business in its entirety, down to the perspective Constellation, describing

the highest echelon of staff in the Top Management Team, and lastly the perspective of

Managers’ Characteristics which profiles the individual member of the Top

Management Team. The fourth perspective Span of Control is not directly connected to

the other three but gives an indication of potential influence from people not evaluated

in the main three perspectives. The research has provided a detailed overview of the

academic work which was published over time for all of the perspectives:

Span of Control is probably the oldest of the perspectives in this thesis and has

been discussed in the past but then again in academic literature research from the

early 20th

century with Hamilton (1921) applying the term in the military context

152

and also Graicunas (1933), Gulick et al. (1937), Urwick (1956), Entwisle and

Walton (1961) and Van Fleet and Bedeian, (1977) have contributed to the

concept in its fundamentals. Later research was more focused on Span of

Control’s limitations and relevance as for example work by Schroeder,

Lombardo and Strollo (2000), Gittel (2001), Yassine, Goldberg and Yu (2005),

McManus (2007) and Nickols (2011),.

Characteristics is also a perennial topic with its modern format deriving from

C.G. Jung’s Psychological Types (1921/1923) and The Archetypes and The

Collective Unconscious (1934–1954) which was the basis for the Myers-Briggs

Type Indicator (1944/1956). Today MBTI is still one of the fundamental

instruments for team building and management development and its usage is

increasing according to Gardner & Martinko (1996, p.46). There are concerns,

however, about MBTI’s psychometric properties (1996, p.77) which

demonstrates again, that this topic is being continuously researched.

Whereas Team Constellation is a perspective which academic research only

developed after Characteristics gave a blueprint for the analysis of personality

and interaction among the different team members. The Constellation

perspective is important today and is repeatedly discussed for example in

relation to the effectiveness of homogenous versus heterogeneous team

constellations, gender related topics or its contribution to company performance

(as for example by Hambrick, Cho & Chen, 1996; Child, 1997; Knight et al.,

1999; Lankau, 2007; Richard & Shelor, 2011; or Agnihotri, 2014).

The fourth perspective on the nature of business or Typology is derived from the

Miles and Snow model which is the youngest of the perspectives analysed in this

thesis and our discussion here is based on one or a combination of the three basic

models of Miles & Snow, Mintzberg or Porter. All of these were developed

within a seven year time frame and even today they are unchanged in their

basics. The Miles & Snow Typology (1978) offered a classification through 10

parameters which also determined the Dominant Coalition that was very

important for the development of our research. Mintzberg’s model of the five

structural configurations (1980) is based on an assumption of five core parts of

an organisation and provides the design of organizational structures and

potential contingency factors. Porter’s model of generic competitive strategies

(1985) is restricted to a focus on companies attempting to perform better than

their competition.

153

When Gulick et al. (1937) discuss a group’s teams and configuration they explore the

numbers of relationships among every group member and this is described as

encouraging direct single relationships, cross relationships and direct group

relationships. The complete statement about how to compare such relationships is given

here:

[s]ince it is not possible to assign comparable weights to these different varieties

of relationship, it is probably safest to accept the most inclusive assumption as

the standard by which to judge the relative complexity of supervision imposed

by varying numbers of subordinates. (Gulick et al. 1937, p.185)

The authors at that time were convinced that the complexity of work for each supervisor

could not be weighted and therefore the number of subordinates and the emerging

quantity of relationships was used as a scale for comparison.

The tools for comparing the perspectives of the entire company, the Top Management

Team and the individual Manager seem obvious today but were not available to the

authors in 1937. Span of Control and Characteristics were relatively new to academic

research, whereas the Team Constellation has only expanded after more awareness was

gained about Characteristics early and in the mid-twentieth century. The concept of

Typology which may be the most important key for weighting the departments, was

developed around forty years after Gulick et al.’s article. In 2017 with this research it is

documented and proven that it is possible to compare and weight the relationships of

managers in horizontal positions, towards other managers, and in vertical relationships

towards subordinates. Finally, the above statement by Gulick et al. (1937) can be

revised in respect of the knowledge from a different century where the complexity of

supervision is influenced by more important factors than purely the number of

subordinates.

Hypothesis 2: Members of the top management team with an identical hierarchical

level have differing amounts of influence on strategic decisions.

Accepted: Although the organizational chart shows all members of the Top

Management Team on the same echelon each of the perspectives analysed in this thesis

do confirm that every Manager enjoys a different amount of influence. Starting with the

perspective Typology, where depending on 10 criteria the company’s nature is

described as Defender, Prospector, Analyser or Reactor. Each of these Typologies has a

154

predetermined constellation of a Dominant Coalition; which results in these

Departments having more influence than the remaining ones.


Finance X X X

Production X X

Marketing X X

Sales X X

R & D X X X

HR X X

Infrastructure X X

Logistics X

Service X

Some other parameters are directly or indirectly influenced by the Manager themselves.

For example, the findings of the literature review have resulted in three main parameters

defining the Constellation of a Top Management Team; Age, Education and Tenure

with the company. These three parameters are to some extent independent, however, it

is clear that a certain age is required to gain for example education. But the same could

be said about the qualifications and experience necessary to contribute as a Member of

the Top Management Team. Finally, this may differ from company to company

depending also on specific industries but actually only gains its importance when

compared with other members of the same team. For example, nobody is too old or too

young, if it matches the rest of the team’s pattern and is homogeneous (however it also

can be a heterogeneous match if there is no pattern apparent in age among all team

members). There is only no match if there is a pattern but the candidate does not comply

with it.

Through the literature review and the first extensive online opinion poll with 525 valid

submissions, 21 of the most important Managers’ Characteristics were evaluated. As it

would have rough-handled time constraints for the participants of the online survey to

key in 21 Characteristics of each of the Top Management Team members; the 21

Characteristics which scored highest in the opinion poll were reduced to the six most

important ones. Four of them have been named to be important for horizontal and

vertical relationships (Reliability, Archetype, Communication and Competence) plus

one Characteristic which is only named for vertical relationships (Social Competence)

and one which is only selected for horizontal relationships (Network). Those six

Characteristics were used as the relevant set for a profiling of each of the Top

Management Team members to compare them with each other.

Table 5.1: Constellation of Dominant Coalition based on the Miles & Snow Typology (1978), by the author (repeated Table 3.5)

155

These three perspectives analysed either alone or together will result in an advantage or

disadvantage for the different departments which confers an inequality of dominance in

the Top Management Team. Therefore, members of the Top Management Team will

wield differing amounts of dominance although they belong to the same echelon.

Hypothesis 3: An algorithm can be used to evaluate the members of the Dominant

Coalition.

Accepted: Despite the question of whether interdependence of Top Management Team

members in regards to Company and Team can be measured it is important to

understand to which degree this process is achieved without human interference and

attained just by feeding a system with a minimal set of data. If this is possible such an

evaluation can be done without having personal knowledge of either the company or the

Top Management Team members.

The aim of the online survey was to evaluate the dominance of Top Management Team

members based on the evaluation of collected data on the perspectives of Typology,

Team Constellation and Characteristics. In order to evaluate the accuracy of the survey

approach we have chosen a Confusion Matrix (Kohavi and Provost, 1998) to measure

the accuracy of actual (assumed by the participant) versus predicted rankings (the

computation of the online survey). For the application of the confusion matrix it has to

be assumed that the answers of the participants are the actual (true) ones since there is

no possibility to prove whether wrong information is given. The data matrix below is

used to calculate with a confusion matrix the accuracy of the applied algorithm of the

online survey. For example, in the first field, top left with the entry ‘43’ how many

managers the survey predicted (calculated) to rank first in comparison with how many

of those managers the participants have assumed (actual) to be in the first place.

Whereas, the field next to it with the entry ‘30’ shows how many of the managers were

calculated to rank second but have been assumed to be on the first rank by the

participants.

Table 5.2: Confusion matrix showing calculated ranking vs assumed ranking

1 2 3 4 TOTAL

1 43 30 20 13 106

2 39 41 7 16 103

3 9 20 29 21 79

4 14 14 22 56 106

TOTAL 105 105 78 106 394

Actual RankCalculated

156

From the confusion matrix the information was sourced for the accuracy-tables below

(confusion matrices) for the rankings 1st to 3

rd whereas 4

th is representing all others and

the last one is the summary or average of all accuracy tables. The indication ‘+’ stands

for correct/positive whereas ‘-’ means incorrect/negative. As an example for the 1st

accuracy table below; 43 managers have been calculated 1st and assumed 1

st whereas we

calculated 62 to be 1st which were not assumed to be 1

st. The remaining 226 managers

who were neither calculated nor assumed to be 1st are indicated at the bottom right of

the 1st accuracy table. The accuracy is derived from the addition of true positive + true

negative and then set in relation to the total number of managers (there were 114

submission with a total of 428 managers, however, due to some submissions being not

complete this evaluation is based on 105 submissions with 394 managers in total).

Table 5.3: Confusion matrix split by ranks into accuracy tables

Whereas, the prediction of the survey has achieved an average accuracy of more than

71% the system has shown as stable with 68.3% and 68% for the first two ranks and

even a higher percentage for the third or the remaining ranks with 74.9% and 74.6%

respectively. As the accuracy tables are showing the accuracy of the prediction of the

exact ranking combined with the accuracy of which manager would not be on that rank

it can be said in the argumentum e contrario that only 28.6% of the participants’

assumptions could not be matched from which some assumptions could have been

Accuracy

Rank 1 Calc + Calc -

Actual + 43 63

68.3% Actual - 62 226


Actual + 41 62

68.0% Actual - 64 227


Actual + 29 50

74.9% Actual - 49 266


Actual + 56 50

74.6% Actual - 50 238

Avg Calc + Calc -

Actual + 42 56

71.4% Actual - 56 239

157

wrong by a false answer or wrong by misinterpretation. In conclusion, the computation

of the confusion matrices confirm that with the selected factors and the resulting

algorithm, key managers qualifying for a potential Dominant Coalition can be predicted

with accuracy of 68% and above. Future research could improve the rate of accuracy

even further and it is clear that different ways of aiming for the same goal could be

considered as well.

Proposition 3a: Which of the analysed perspectives, Typology, Team Constellation or

Managers’ Characteristics, has most influence on the constitution of the Dominant

Coalition?

Research outcome: Having selected three perspectives as important in regards to the

constitution of the Dominant Constellation it is evaluated if one of those perspectives is

contributing more towards the result than the others.

Table 5.4: Comparison of the impact of the perspectives Typology, Team Constellation and Managers’

Characteristics on the final result

To investigate the performance of each perspective, Typology, Team Constellation and

Managers’ Characteristics we have conducted a Least-Squares Estimation Regression

(Gauss-Markov theorem) where the adjusted R-squared represents the percentage our

perspectives have on the outcome and 75% are coming from whatever we have not

considered for our online survey (inheritance of power as an example). The Regression

Residuals:

Min 1Q Median 3Q Max

-2.62786 -0.6962 0.06263 0.83474 2.55699

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 4.52692 0.34886 12.976 < 2e-16 ***

ConTOT -0.01265 0.03071 -0.412 0.68054

ChaTOT -0.09551 0.02934 -3.255 0.00123 **

TypeTOT -0.49121 0.04281 -11.475 < 2e-16 ***

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9999 on 390 degrees of freedom

Multiple R-squared: 0.2532, Adjusted R-squared: 0.2475

F-statistic: 44.08 on 3 and 390 DF, p-value: < 2.2e-16

Residual Std. Error 22.1%

Residual Standard Error

158

is based on the rankings to be independent from the variety of scorings for Typology,

Team Constellations and Characteristics.

In the table of the Coefficients TypoTOT stands for Typology Total in the field

‘PR(>|t|)’ which represents the P value, therefore Typology has most influence on the

Constellation of the Dominant Coalition (which was defined in this case to be among

the top 3 ranked Managers). As already mentioned in Chapter 1, it has to be

remembered that the perspective Typology is literally pre-set from the nature of the

company and is not influenced by the individual manager. Typology is not ranking Top

Management Team members; it is splitting the team into two groups where one

constitutes the Dominant Coalition. This explains why this perspective has the most

influence in the online survey.

Proposition 3b: If the Dominant Coalition is predetermined through the Typology what

influence does Team Constellation have on the final setting?

Research outcome: As a sub-question to the importance of the three perspectives

analysed it is interesting to know if the right person in a compatible company might fail

if the constellation of the Top Management Team is unsuitable for them. It is impossible

to answer this based on this thesis. What can be said is that according to the outcome of

the Regression analysis the perspective Team Constellation does not have much

influence in recognizing members of the Dominant Coalition. As indicated in

Hypothesis 4 Team Constellation helps together with Managers’ Characteristics to rank

the Top Management Team members only. One interesting aspect which was

discovered was the aspect that Team Constellation calculated with Standard Deviation

is permanently transforming. If Age and Tenure are evaluated with a bell curve the

results are moving like a wave year by year and though the limitations of professional

life (assuming 20 to 65 years and accordingly also the Tenure is limited) or by natural

changes such as resignation or dismissal at some point the Team Constellation will

change and might be reset with new conditions for existing managers. At some point a

long tenure could be an advantage whereas a few years later the remaining Manger/s

with long tenure may not fit into a team which was undergoing some renewal.

Proposition 3c: If the Dominant Coalition is predetermined through Typology what is

the influence of Managers’ Characteristics on the final setting?

Research outcome: Typology and Team Constellation cannot be influenced by an

individual Manager, whereas the Characteristic profile can be developed by everyone.

159

From the Regression Analysis it can be seen that P is significantly higher than in Team

Constellation but lower in Typology. Whereas, Typology is pre-setting the Dominant

Coalition members therefore Managers’ Characteristics is steered to a certain extent by

each Manager to influence their participation in the Dominant Coalition. Looking at the

Questions 4, 4a and 4b one could argue the reverse way that if a Manager wants to

become a member of the Dominant Coalition, they should first identify the nature of

business of a potential employer to ensure the Typology suits them and then study the

Top Management Team to find the right time slot, or a more suitable Top Management

Team of a comparable business to join under more favourable circumstances.

Hypothesis 4: The head of Finance is predominantly the hidden deputy (number two) in

the Top Management Team

Accepted: It is reasonable to assume that the Chief Financial Officer has an important

role especially when companies declare themselves as profit oriented entities. This

research also investigated to what extend the head of Finance is the shadow of the CEO.

The perspective Typology sees Finance and R&D in comparison to all other

departments, in three out of the four Typologies within the Dominant Coalition.

Although as an example, Finance is important for a small laboratory developing a

treatment against cancer; however, it seems sensible to assume that the role of the head

of R&D of the same company is of utmost importance because their results directly

impact on the laboratory’s future existence. Subsequently, 76 companies reported

having a Finance department, whereas only 32 companies stated they had an R&D

department. The sales department was the most often mentioned with 83. Among the

top three ranks our online application has calculated Finance and Sales 67 times and

Production 40 times as the three most ranked departments. Whereas, the participants

also listed the departments of Finance, Sales and Production most often with 67-75-36

votes respectively. The rankings after the surveys took place appear as follows:

1st Rank

By online calculation: Finance / Sales / Marketing with 36 – 29 – 13

Assumption of the participants Finance / Sales / Marketing with 26 – 33 – 14

2nd

Rank

By online calculation: Finance / Sales / Production 19 – 25 – 19

Assumption of the participants Finance / Sales / Production 25 – 24 – 17

160

By both calculation and assumption, Production is third amongst the most named

departments but does not show up among the top three on the 1st Rank section. When

looking at the results for the 2nd

Rank section one will find Finance / Sales / Production.

From the first to the second rank, the first ranked departments as well as the second

ranked departments the counts are dropping significantly. The pattern seems to change

on the 3rd

rank where the calculation sees Service / Production / Sales to be strongest

whereas the assumption chart is still dominated by Sales and Finance.

Considering the frequency with which a department is named compared to their ranking

a similar result is obtained which could be termed ‘the winner takes it all’ and the

remaining results are dropping rapidly. From the 428 departments keyed in throughout

the entire survey there were most entries for 83 Sales, 76 Finance and 51 Production

followed by 45 Service, 36 Marketing and 35 HR. Whereas, Sales on average

((calculation + assumption) / 2) is Ranked 1st by 85%, however, Finance with less

counts is achieving 88% chances of being Ranked 1st. Production on the other hand,

although being the third most often named department is only obtaining a 35% chance

of Ranking 2nd

which is about the same for Marketing, having a 37% chance of being

Ranked 1st. The highest result in a proportional chance for Service is only at 3

rd Rank

with 27% and for HR finally the chances to Rank 2nd

or 3rd

are only at 20% and 19%

respectively.

So the question as to whether Finance is the grey eminence in a company can be

confirmed with a chance of 88%, however, it is equal to Sales with 85%. Yet this only

counts if those departments exist. From 114 participants, 22% did not report a Sales

department and 29% did not have a Finance department in their organisation.

Hypothesis 5: Dominance in a Management Team is related to Education and the

Complexity of Tasks of the dominant team members.

Accepted with conditions: In this research 114 companies were evaluated resulting in

the data for 428 individual managers from which data was collected in respect of their

education and the complexity of their role in the whole company. For the statistics

below the data from 394 Managers were sufficient for a comparison. In the first part of

the table below it is listed for each department how many times education appeared. For

example, there was one Manager in the Sales department holding a Doctoral degree.

Then points were allocated for each Educational level (1 for Without Tertiary, 2 for

Bachelor’s, 3 for Master’s and 4 for Doctoral) which were multiplied by the number of

161

Managers in this department (for example 83 in Sales). By multiplying this, a sum was

obtained which again was divided by the number of Sales Managers to receive an

average educational indication which was then ranked among all departments. Sales

managers, in this research, obtained the lowest level of education (on Average) and

R&D was the highest. The second part of the table below illustrates how many of the

Managers per department are amongst the top 3 ranked Managers, split by Education.

For example, 74 (second part) out of the 83 (first part) of Sales Managers have secured

their place among the most dominant Managers overall. This is expressed as a

percentage with the title ‘% Ranked Top 3’.

Table 5.5: Education level of dominant Top Management Team members

In the last section of the above table an overview of the Education distribution per

Department is given. For the hypothesis it was observed in the table with the title ‘%

Ranked Top 3’ that higher Education does not necessarily mean a higher percentage of

opportunity to be among the top three Managers. In the total column on the complete

right in the part of the table (Title: % Ranked Top 3) holders of a Doctoral degree have

an overall 80% chance to rank among the Top 3 most influential Managers, holders of a

Master’s degree 74% and Bachelor’s 70% which so far would be a logical gradation. It

seems counterintuitive that 79% of all Managers without a tertiary degree ranked in the

Top 3, the second highest result. One explanation for this phenomena is found in the

tenure aspect where Managers without a tertiary degree stay on average longest within

Education Sales Finance Prod Service Mktng HR R & D Logistics Infra Procure Total

Doctoral 1 2 3 1 1 4 1 2 15

Master's 20 31 20 16 15 10 9 6 7 2 136

Bachelor's 39 39 17 21 18 20 10 13 2 7 186

Without Tertiary 23 4 11 6 3 4 1 2 2 1 57

Grand Total 83 76 51 44 36 35 24 21 12 12 394

Total 165 183 117 100 84 78 64 46 31 29 897

Avg 1.99 2.41 2.29 2.27 2.33 2.23 2.67 2.19 2.58 2.42 2.28

Complexity 1 7 5 4 6 3 10 2 9 8

Count of Managers by Ranking


Top 3

Doctoral 1 2 3 1 3 1 1 12

Master's 19 26 13 9 12 3 7 5 4 2 100

Bachelor's 32 35 12 15 13 7 8 7 2 131

Without Tertiary 22 4 8 4 1 2 1 1 1 1 45

Grand Total 74 67 36 29 26 12 19 13 6 6 288

% Ranked Top 3


Doctoral 100% 100% 100% 100% 0% 75% 100% 50% 80%

Master's 95% 84% 65% 56% 80% 30% 78% 83% 57% 100% 74%

Bachelor's 82% 90% 71% 71% 72% 35% 80% 54% 0% 29% 70%

Without Tertiary 96% 100% 73% 67% 33% 50% 100% 50% 50% 100% 79%

Grand Total 89% 88% 71% 66% 72% 34% 79% 62% 50% 50% 73%

% Education by Department


Doctoral 1% 3% 6% 2% 3% 17% 8% 17% 4%

Master's 24% 41% 39% 36% 42% 29% 38% 29% 58% 17% 35%

Bachelor's 47% 51% 33% 48% 50% 57% 42% 62% 17% 58% 47%

Without Tertiary 28% 5% 22% 14% 8% 11% 4% 10% 17% 8% 14%

Grand Total 83 76 51 44 36 35 24 21 12 12

162

the same organisation (14.9 years) compared to managers with Doctoral qualifications

(13.9 years), Master’s (10.3 years) and Bachelor’s (10.7 years). This could imply that if

someone does not have a degree they can demonstrate organisational stamina to

compensate for a lack of qualifications. Therefore, Education can be a fast-track to

break into a Top Management Team but it is not the only way. Throughout the research

undertaken it was found that except in HR, R&D and Procurement, 100% of the

Managers holding a Doctoral degree were ranked among the top 3 most influential

Managers, however, the small number of Doctoral degree holders (15) is not making

this statement very representative.

Proposition 5a: So who leads the company? Where are the influencers hidden?

Research outcome: The hypothesis is that if the number of subordinates exceeds the

Span of Control of a manager, there is a requirement for a hidden number two in that

team or department. Because the manager is overloaded with tasks, this number two

will take over partially and make proposals to the manger. In this situation the manager

will or has to trust these proposals and include them into his or her contribution to the

overall top management team. This means that the contribution to the top management

team is not only coming from the manager them self but also indirectly from their

influencers which is making them by default the real leaders.

The table below is showing the existence of Influencers among the participant-portfolio

of 105 submissions. The matrix is arranged by type of department in the vertical

arrangement and size of the Top Management Team in the horizontal direction. The

numbers in the cells are indicating how many momentums of losing control were

counted whereas the percentage indication at the bottom is showing the proportion of

influencers in that category of team size. Percentage indication on the right side

describes the proportion of cases where influencer/s are existing split by departments in

respect to the overall existence of this specific department.

Overall more than one third (36%) of all head of departments are depending on internal

support. Separated by departments, however, the results are drifting apart. The lowest

risk of losing control is at 17% for Human Resources, Finance 24% and Procurement (a

department linked closely to Finance) with 25%. Exposed to the highest risk of losing

control are the heads of Production with 58%, with Service and Logistics ranking

second and third from the top with 49% and 45% respectively. It must be stated that the

high number of influencers for the Service department is strongly influenced by one

163

single submission from the online survey – excluding that result would give Service a

37% risk of losing control. Surprisingly, the results are not only a reflection of a

department with large team sizes that is suffering from this problem. For example, R&D

was shown as handling the most complex tasks which would make the head of this

Department at risk of losing control with a very low number of staff. A head of R&D,

however, is only at a 33% risk of losing control which is pretty much in the middle of

all the departments’ risk.

Table 5.6: Number of departments having an influencer separated by Top Management Team sizes

5.3 Conclusions about the research problem

This thesis although limited by its time and financial funding has resulted in a feasible

approach to the research questions evaluating ‘who is leading the company’ through the

analysis of organisational structures. Based on the perspectives of Typology, Team

Constellation, Managers’ Characteristics and Span of Control it was possible to analyse

sample cases which were submitted by more than 114 participants and through this

collect a reasonable quantity of 428 data sets of different managers. It is apparent that

there are different ways to conduct a similar research project or to select other

parameters to focus on but in the aim of classifying a Top Management Team within a

reasonable set of questions it can be concluded that our approach has resulted in

measurable results unveiling new facts and adding to the body of knowledge. Whereas,

the perspective Typology has delivered half of the answer to who is leading the

company by preselecting the Dominant Coalition; the perspectives of Team

Constellation and Managers’ Characteristics helped in ranking the Top Management

Team members and also demonstrated that Managers’ Characteristics has performed as

a better indicator than Team Constellation. The sub criteria of Span of Control assisted

in indicating which head of Department might have reached the momentum of losing

Influencers per department, separated by TMT size

164

control which then increases the chance that there are one or more influencers in this

department.

Who is leading the company is dependent on the nature (Typology) of the company and

also on the Constellation and Managers’ Characteristics. The chances are at 88% and

85% respectively that the head of Finance or Sales are the most dominant members of

the Top Management Teams in companies that have one or both of those departments.

The chances of having a potentially hidden influencer in the departments lies highest in

Production, Service and Logistics at around 58%, 49% and 45% respectively.

Therefore, perceptions of dominant Top Management Team members may in reality not

be quite as dominant as assumed when compared with others because it is the

combination of all perspectives which may strengthen or weaken the position of

someone. Finally, it does matter under what circumstances which of the different Top

Management Team members combine together to facilitate championing a proposal. It

is the coalition of Top Management Team members which makes them convincing.

Assuming that Finance or Sales is the most influential in an enterprise leaves us with the

question of whether there is a combination of the remaining heads of Department who

could build up a strong position or if there are key departments besides Finance and

Sales who could form a coalition with either one of them. Another consideration is that

although superficially one would assume that Finance and Sales exist in each company

actually from 114 participants, 22% have not reported having a Sales department and

29% lacked a Finance department. This can leave a lot of room for different Dominant

Coalitions in Top Management Teams.

5.4 Implications

As indicated in the first Chapter, there is potential for the private (and public) sector to

benefit from the application of this research. Further development of this empirical

approach would allow different interest groups to focus their activities, reducing costs

and time. Also, Sales teams could ensure the right Top Management Team members or

even the influencers of a targeted client would join their presentations. Marketing teams

could evaluate not only individual companies, but also analyse market segments in

order to channel their flow of information in the right context. Mergers and Acquisition

firms could ensure they bring the relevant stakeholders into their negotiations or Human

Resources departments, or even individuals who are connecting with new colleagues or

teams, could use such a tool for a better prediction if the new Team Constellation is

most likely to be productive to select a few possibilities. Without excluding the chances

165

that there are other ways to analyse all the above situations, this research has narrowed

down the different perspectives to a minimal set of questions which ensures fast results

with a high level of accuracy.

Considering the theory and research it is apparent that assumptions made in the first half

of the twentieth century were overwritten with further research conducted in the same or

related fields. As cited earlier Gulick et al. have argued that the complexity of work of

each supervisor could not be weighted and therefore the number of subordinates and the

emerging quantity of relationships could be used as a scale for comparison when saying

that “it is probably safest to accept the most inclusive assumption as the standard by

which to judge the relative complexity of supervision imposed by varying numbers of

subordinates.” (1937, p.185). Yet this research has collected data from Typology, Team

Constellation and Managers’ Characteristics to undertake what was defined as

unquantifiable. The analysis of the number of subordinates was important in terms of

defining the momentum of losing control when it comes to Span of Control. This has

not been documented in such a comprehensive form before it was mapped for this thesis

and brings together the whole topic of Span of Control in a nine-field-matrix applicable

for further research. Also the findings in Team Constellation with just three and

Managers’ Characteristics with six parameters are comprehensive to focus on the most

important ones considering that more parameters impinge on the time and effort of

collecting data in proportion to a more diluted result. It is clear, however, that specific

other studies may be case sensitive.

5.5 Limitations

It was decided at the beginning of the research to separate the CEO with 34% from the

TMT and this was done to symbolize a blocking minority with which a CEO is enforced

to steer a company’s direction to be able to say ‘no’ to a proposal. Although this still

seems sensible after having undertaken this research it became obvious that the

remaining 66% of decision power allocated among the remaining TMT members results

in some limitations in terms of allocating a meaningful figure to each of them. In total

the data from 428 Managers working for 114 companies was collected. The

management team counted 28x2, 31x3, 19x4, 24x5, 5x6, 5x7, 2x9 managers which

results in an average of 3.75 Managers in the TMT (without counting the CEO).

Whereas, the 64% distributed equally to three, four or five managers (nearest logical

team sizes) would result in 21%, 16% and 13% allocation for each manager if equally

dominant which means any comparison is becoming flatter and less meaningful with

increasing TMT size. Consequently, in larger teams the application can still detect more

166

influential managers but the percentage indication is becoming less meaningful. In such

cases, the ranking is probably becoming more important, and not the fact if a Manager

could team up with the CEO for a majority dominance based on the percentage

calculation.

Another limitation which is worthwhile mentioning is that the developed approach is

solely created in such a way to compare each of the individual Top Management Team

members but there was no research undertaken to measure the personal relationships

among them (i.e. having sympathies or not for each other). This approach is ranking all

members of the TMT and also assumes the eventuality that depending on the nature of a

business proposal each of the Managers could team up with like-minded colleagues to

achieve a dominant position. There may be cases, where the mathematical combination

would make sense, but non-measured personal sympathies will hinder any cooperation

by them. This would be the responsibility of the observer to judge real or opportunistic

collaborations when comparing possible clusters in the Top Management Team. The

developed approach is ignoring underlying and overt animosities and each relationship

between the Top Management Team members is hypothetically set to be equal.

5.6 Further research

During the research and in terms of the algorithm of the online survey to equip the Top

Management Team members with a value representing their dominance in the team

each of the perspectives of Typology, Team Constellation and Managers’

Characteristics were allocated with the same importance. At the conclusion of this thesis

in retrospect from a meta point of view it can be seen that Typology is predetermining a

disadvantage for those departments which are not among the Dominant Coalition as per

Miles & Snow (1978). There is also the consideration that Typology is formed by the

nature of the business and only can be influenced by the managers themselves through

active organisational change management. Whereas, Team Constellation is a permanent

changing value in regards to the increasing age and tenure of the team members and

Managers’ Characteristics can be directly influenced by each Manager.

Although the system is designed this way and there were observations of such special

cases, where Managers dominating from each perspective are able to influence the final

results to their advantage, it is seen that Typology is somehow building one half and

Constellation and Characteristic the other half of the shape of a team. Further research

could analyse the impacts if looking at the whole concept either without Typology and

judging just Constellation and Characteristics to rank team members or the other way

167

around where based on the Typology only those Top Management Team members are

further evaluated who are falling into the Dominant Coalition team. The second

approach would mean that less data is collected and that the percentage allocation

among the remaining Managers should be more meaningful. In this context it is also

worthwhile to consider if one of the perspectives is disturbing the accuracy of the

overall result or if another perspective which was not considered would improve it.

The reliability of this approach is based on the comparison between the online survey

calculations versus the actual assumptions of the participants. No research was

undertaken as to the reliability of the participants. It is most likely that the indication for

Typology, Constellation and Characteristics are easier to judge than the overall ranking

of the Top Management Team members amongst each other.

5.7 Conclusion

As cited on the first page of this thesis: “[i]dentifying which factors affect firms’

performance is a critical issue in strategic management research” according to Escribá-

Esteve, Sánchez-Peinado and Sánchez-Peinado (2009, p.581). The significant number

of research papers that were written during the last few decades concentrating on the

Constellation of Top Management Teams and the impact of Top Management Teams on

company’s performance seem to confirm this statement (for example research by

Hambrick and Mason, 1984; Wagner, Pfeffer and O’Reilly, 1984; Finkelstein and

Hambrick, 1990; Jackson and Withney, 1995; Hambrick et al., 1996; Hambrick, Cho

and Chen, 1996; Child, 1997; Richard and Shelor, 2011; Lankau, 2007; or Agnihotri,

2014). This thesis has analyzed potential factors influencing the Dominant Coalition of

Top Management Teams with the aim to rank managers by their share of dominance

considering the perspectives of Typology, Team Constellation and Managers’

Characteristics equally.

Through the literature reviews the most widely discussed factors of each perspective

were isolated followed by the experiment to connect them all in an equation which was

tested in an online survey and verified to have a high level of accuracy. The CEO as

such has been excluded as a fixed value from this equation. The result is an opportunity

to explore the topics of TMT Constellation and the impact of Top Management Teams

on company’s performance from a different angle. This research is a comprehensive

study of the Dominant Coalition offering a method to profile TMT members and it

encourages other researchers to build upon it or explore further research in this field.

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7 Appendices

7.1 Survey Reports

Name: Annie Eng Submitted: 03:37:11 11-01-2017




1 Florence Sales 34.05

2 Helen Finance 31.95

CEO

34%

Florence Helen

Ranking by your assumption Sales Finance


1 Florence

2 Florence

Typology


Helen 1 1 1 1 1 1 1 1 1 1 5.1 51

Florence 0 0 0 0 0 0 0 0 0 0 4.9 49

Constellation


Helen 53 26 Bachelor's Finance 3 3 2 8 50

Florence 38 7 Bachelor's Sales 3 3 2 8 50

Characteristics

Manager Reliability Archetype Comm. Social Comp. Competence Network TOTAL Character %

Helen 5 5 5 5 6 5 5 44.2

Florence 7 6 7 5 7 7 6 55.8

Survey-Report 2.1




Name: Huwiler Submitted: 13:20:06 13-01-2017




1 Mangler +Influencer R & D 33.54

2 Werner +Influencer Marketing 32.46

CEO

34%

Mangler Werner

Ranking by your assumption R & D Marketing


1 Werner

2 Mangler

Typology


Werner 0 0 0 1 0 0 0 0 0 0 4.97 49.7

Mangler 1 1 0 1 0 0 0 0 1 0 5.03 50.3

Constellation


Werner 38 15 Masters Marketing 3 3 3 9 50

Mangler 56 20 Masters R & D 3 3 3 9 50

Characteristics


Werner 5 5 3 1 7 7 5 47.9

Mangler 6 5 5 1 7 6 5 52.1

Survey-Report 2.2








1 Person b +Influencer Sales 35.37

2 CFO +Influencer Finance 30.63

CEO

34%

Person b CFO

Ranking by your assumption Sales Finance


1 Person b

2 CFO

Typology


CFO 1 1 1 1 0 1 1 1 0 0 5.02 50.2

Person b 0 1 0 1 1 0 0 0 1 1 4.98 49.8

Constellation


CFO 45 4 Masters Finance 3 3 3 9 50

Person b 55 7 Masters Sales 3 3 3 9 50

Characteristics


CFO 0 3 1 2 7 4 3 39

Person b 4 3 5 3 5 5 4 61

Survey-Report 2.3








1 Datuk Naz - HR 37.26

2 Amir Azli - Service 28.74

CEO

34%

Datuk Naz Amir Azli

Ranking by your assumption HR Service


1 Amir Azli

2 Datuk Naz

Typology


Amir Azli 1 0 0 0 0 0 0 0 0 0 4.97 49.7

Datuk Naz 1 1 0 1 0 1 0 0 0 0 5.03 50.3

Constellation


Amir Azli 50 10 Masters Service 3 3 3 9 52.9

Datuk Naz 57 8 Bachelor's HR 3 3 2 8 47.1

Characteristics


Amir Azli 0 0 0 0 0 7 1 28

Datuk Naz 2 3 4 1 5 1 3 72

Survey-Report 2.4








1 test2 +Influencer Marketing 33.59

2 test1 - Finance 32.41

CEO

34%

test2 test1



1 test1

2 test2

Typology


test1 1 1 0 1 1 1 0 1 1 1 5.06 50.6

test2 0 0 1 0 0 0 1 0 0 0 4.94 49.4

Constellation


test1 32 17 Bachelor's Finance 4 2 2 8 50

test2 59 10 Bachelor's Marketing 2 4 2 8 50

Characteristics


test1 4 4 3 5 4 6 4 46.7

test2 5 5 6 5 5 3 5 53.3

Survey-Report 2.5








1 Manager 2 +Influencer Sales 36.86

2 x +Influencer Production 29.14

CEO

34%

Manager 2 x

Ranking by your assumption Sales Production


1 Manager 2

2 x

Typology


x 1 0 1 0 0 1 0 1 0 1 4.99 49.9

Manager 2 0 1 1 1 1 0 1 0 1 0 5.01 50.1

Constellation


x 50 25 Masters Production 3 2 3 8 47.1

Manager 2 62 19 Masters Sales 2 4 3 9 52.9

Characteristics


x 5 6 4 3 7 5 5 35.5

Manager 2 8 8 8 10 10 10 9 64.5

Survey-Report 2.6








1 Rudy - Marketing 33.09

2 Andes - Finance 32.91

CEO

34%

Rudy Andes



1 Rudy

2 Andes

Typology


Rudy 1 1 0 0 0 0 1 1 1 0 4.99 49.9

Andes 0 1 1 1 1 1 0 0 0 1 5.01 50.1

Constellation


Rudy 25 2 Without Tertiary Marketing 3 2 1 6 50

Andes 27 4 Without Tertiary Finance 3 2 1 6 50

Characteristics


Rudy 4 5 8 5 8 6 6 50.5

Andes 7 7 5 5 8 3 6 49.5

Survey-Report 2.7








1 Eric Kogu - Marketing 33.58

2 Melvin Ta - Marketing 32.42

CEO

34%

Eric Kogu Melvin Ta

Ranking by your assumption Marketing Marketing


1 Eric Kogu

2 Melvin Ta

Typology


Melvin Ta 1 0 0 0 0 0 0 1 1 1 5 50

Eric Kogu 1 0 0 0 0 0 0 1 1 1 5 50

Constellation


Melvin Ta 40 5 Bachelor's Marketing 4 3 2 9 47.4

Eric Kogu 40 5 Masters Marketing 4 3 3 10 52.6

Characteristics


Melvin Ta 10 10 10 10 10 10 10 50

Eric Kogu 10 10 10 10 10 10 10 50

Survey-Report 2.8




Name: Muff Submitted: 14:03:48 17-01-2017




1 Matsubara - Sales 41.33

2 Aoki - Sales 24.67

CEO

34%

Matsubara Aoki

Ranking by your assumption Sales Sales


1 Aoki

2 Matsubara

Typology


Matsubara 1 0 0 0 1 1 1 1 1 0 5 50

Aoki 1 0 0 0 1 1 1 1 1 0 5 50

Constellation


Matsubara 45 20 Without Tertiary Sales 4 4 1 9 56.3

Aoki 40 20 Without Tertiary Sales 2 4 1 7 43.8

Characteristics


Matsubara 2 4 2 7 4 4 4 81.6

Aoki 2 0 1 0 1 1 1 18.4

Survey-Report 2.9








1 Joe +Influencer Service 34.66

2 Martin - Service 31.34

CEO

34%

Joe Martin

Ranking by your assumption Service Service


1 Joe

2 Martin

Typology


Joe 0 1 0 0 0 0 0 0 0 0 5 50

Martin 0 1 0 0 0 0 0 0 0 0 5 50

Constellation


Joe 46 4 Masters Service 3 3 3 9 56.3

Martin 53 5 Masters Service 2 2 3 7 43.8

Characteristics


Joe 10 2 5 2 3 10 5 51.3

Martin 9 4 2 7 5 3 5 48.7

Survey-Report 2.10









2 Manager 1 - R & D 32.1

CEO

34%

Manager 2 Manager 1

Ranking by your assumption Finance R & D


1 Manager 1

2 Manager 2

Typology


Manager 1 1 1 1 0 1 0 1 1 0 0 5 50

Manager 2 0 1 1 1 0 1 0 0 1 1 5 50

Constellation


Manager 1 41 10 Bachelor's R & D 3 2 2 7 46.7

Manager 2 50 4 Masters Finance 2 3 3 8 53.3

Characteristics


Manager 1 10 10 5 1 9 1 6 49.3

Manager 2 6 5 7 7 8 4 6 50.7

Survey-Report 2.11




Name: Andi Lapon Submitted: 03:53:41 18-01-2017




1 GC - R & D 33.04

2 RVH - Finance 32.96

CEO

34%

GC RVH

Ranking by your assumption R & D Finance


1 RVH

2 GC

Typology


RVH 0 0 1 1 1 1 1 1 1 1 5.04 50.4

GC 1 1 0 1 1 0 0 0 0 0 4.96 49.6

Constellation


RVH 41 5 Masters Finance 2 3 3 8 53.3

GC 45 3 Masters R & D 2 2 3 7 46.7

Characteristics


RVH 8 0 8 4 7 8 6 46.1

GC 8 0 8 8 9 8 7 53.9

Survey-Report 2.12




Name: Mohammad Aidil Alladin Submitted: 04:39:36 18-01-2017




1 Suhairul - Finance 33.42

2 Rosnani - Sales 32.58

CEO

34%

Suhairul Rosnani

Ranking by your assumption Finance Sales


1 Suhairul

2 Rosnani

Typology


Suhairul 0 1 1 1 1 1 1 0 1 1 5.05 50.5

Rosnani 1 0 0 1 0 0 0 1 0 0 4.95 49.5

Constellation


Suhairul 37 7 Bachelor's Finance 2 4 2 8 47.1

Rosnani 40 7 Without Tertiary Sales 4 4 1 9 52.9

Characteristics


Suhairul 8 7 7 8 7 7 7 54.3

Rosnani 8 6 5 6 6 6 6 45.7

Survey-Report 2.13








1 LKW +Influencer Production 33.8

2 TCM +Influencer Marketing 32.2

CEO

34%

LKW TCM

Ranking by your assumption Production Marketing


1 TCM

2 LKW

Typology


TCM 0 0 0 1 1 1 0 1 0 0 4.98 49.8

LKW 0 1 1 0 1 1 1 0 1 0 5.02 50.2

Constellation


TCM 55 25 Bachelor's Marketing 3 3 2 8 50

LKW 42 19 Bachelor's Production 3 3 2 8 50

Characteristics


TCM 7 2 5 6 7 9 6 46.6

LKW 8 5 6 7 9 6 7 53.4

Survey-Report 2.14








1 Jolene - Sales 33.71

2 MK - Service 32.29

CEO

34%

Jolene MK

Ranking by your assumption Sales Service


1 Jolene

2 MK

Typology


Jolene 0 1 0 1 1 1 1 0 1 1 5.04 50.4

MK 0 0 0 0 1 1 1 0 0 0 4.96 49.6

Constellation


Jolene 40 10 Bachelor's Sales 3 4 2 9 52.9

MK 35 10 Bachelor's Service 2 4 2 8 47.1

Characteristics


Jolene 8 6 7 10 6 10 8 49.9

MK 10 7 7 8 7 8 8 50.1

Survey-Report 2.15




Name: Michael Heinzer Submitted: 17:14:32 16-01-2017




1 Roman - Finance 35.46

2 Michael +Influencer Production 30.54

CEO

34%

Roman Michael

Ranking by your assumption Finance Production


1 Michael

2 Roman

Typology


Michael 0 0 1 0 1 0 0 0 1 1 4.97 49.7

Roman 1 1 1 1 1 0 0 0 1 1 5.03 50.3

Constellation


Michael 42 13 Without Tertiary Production 2 2 1 5 35.7

Roman 51 21 Without Tertiary Finance 4 4 1 9 64.3

Characteristics


Michael 10 8 4 7 9 7 7 53.4

Roman 5 4 7 9 8 6 7 46.6

Survey-Report 2.16




Name: Urs Wohlgemuth Submitted: 17:39:51 26-01-2017




1 Christian - Production 36.92

2 Marcel - Finance 29.08

CEO

34%

Christian Marcel

Ranking by your assumption Production Finance


1 Marcel

2 Christian

Typology


Marcel 1 1 1 1 0 0 0 1 1 1 5.01 50.1

Christian 1 1 0 1 0 0 0 1 1 1 4.99 49.9

Constellation


Marcel 62 26 Bachelor's Finance 2 2 2 6 42.9

Christian 38 9 Bachelor's Production 2 4 2 8 57.1

Characteristics


Marcel 2 4 5 3 8 2 4 39.2

Christian 5 6 5 7 9 5 6 60.8

Survey-Report 2.17




Name: Syeikh Submitted: 03:04:15 27-01-2017




1 Manager 1 - Sales 40.36

2 Manager 2 - Production 25.64

CEO

34%

Manager 1 Manager 2



1 Manager 1

2 Manager 2

Typology


Manager 1 1 0 0 0 0 0 1 1 0 1 5.01 50.1

Manager 2 0 1 0 0 1 1 0 0 0 0 4.99 49.9

Constellation


Manager 1 63 18 Bachelor's Sales 3 4 2 9 60

Manager 2 36 3 Bachelor's Production 2 2 2 6 40

Characteristics


Manager 1 5 3 5 5 7 6 5 73.4

Manager 2 2 4 2 2 0 1 2 26.6

Survey-Report 2.18










CEO

34%

Manager 1 Manager 2



1 Manager 1

2 Manager 2

Typology


Manager 1 1 0 0 0 0 0 1 1 0 1 5.01 50.1

Manager 2 0 1 0 0 1 1 0 0 0 0 4.99 49.9

Constellation




Characteristics


Manager 1 5 3 5 5 7 6 5 73.4

Manager 2 2 4 2 2 0 1 2 26.6

Survey-Report 2.19










CEO

34%

Manager 2 Manager 1



1 Manager 1

2 Manager 2

Typology


Manager 1 0 1 1 0 1 0 1 0 1 1 5.01 50.1

Manager 2 1 1 0 1 0 0 1 1 0 0 4.99 49.9

Constellation


Manager 1 47 11 Without Tertiary Production 2 2 1 5 45.5

Manager 2 60 14 Without Tertiary Sales 2 3 1 6 54.5

Characteristics


Manager 1 6 0 5 5 9 0 4 45.5

Manager 2 7 10 3 2 4 5 5 54.5

Survey-Report 2.20








1 Binzegger +Influencer Production 34.28

2 Gulich - Finance 31.72

CEO

34%

Binzegger Gulich

Ranking by your assumption Production Finance


1 Binzegger

2 Gulich

Typology


Binzegger 1 1 0 1 0 1 0 0 0 1 4.99 49.9

Gulich 1 1 1 1 0 1 0 0 0 1 5.01 50.1

Constellation


Binzegger 42 16 Masters Production 4 2 3 9 56.3

Gulich 51 3 Masters Finance 2 2 3 7 43.8

Characteristics


Binzegger 2 2 6 6 6 6 5 49.7

Gulich 6 5 4 4 8 1 5 50.3

Survey-Report 2.21








1 Figols - Marketing 39.4

2 Aoki - Sales 26.6

CEO

34%

Figols Aoki

Ranking by your assumption Marketing Sales


1 Aoki

2 Figols

Typology


Aoki 1 0 1 1 0 0 1 1 1 1 5 50

Figols 1 0 1 1 0 0 1 1 1 1 5 50

Constellation


Aoki 36 15 Without Tertiary Sales 3 4 1 8 53.3

Figols 38 9 Bachelor's Marketing 3 2 2 7 46.7

Characteristics


Aoki 2 2 1 1 1 1 1 17.6

Figols 5 3 8 6 8 8 6 82.4

Survey-Report 2.22









2 Manager 2 +Influencer HR 31.17

CEO

34%

Manager 1 Manager 2

Ranking by your assumption Finance HR


1 Manager 1

2 Manager 2

Typology


Manager 1 1 1 1 1 1 1 1 1 0 1 5.05 50.5

Manager 2 0 0 1 0 1 1 0 1 0 0 4.95 49.5

Constellation



Manager 2 38 5 Masters HR 2 2 3 7 38.9

Characteristics


Manager 1 8 5 4 5 7 7 6 46.7

Manager 2 8 5 8 9 6 4 7 53.3

Survey-Report 2.23










CEO

34%

Manager 2 Manager 1



1 Manager 2

2 Manager 1

Typology


Manager 1 1 1 1 1 1 1 0 1 1 1 5.04 50.4

Manager 2 0 1 1 1 0 1 1 0 0 0 4.96 49.6

Constellation




Characteristics


Manager 1 10 2 4 6 10 2 6 39.5

Manager 2 7 6 10 10 10 10 9 60.5

Survey-Report 2.24








1 ju +Influencer Service 34.9

2 je - Marketing 31.1

CEO

34%

ju je

Ranking by your assumption Service Marketing


1 ju

2 je

Typology


ju 0 0 0 0 0 1 0 0 0 0 4.97 49.7

je 0 1 0 1 0 1 0 0 1 0 5.03 50.3

Constellation


ju 58 25 Bachelor's Service 3 4 2 9 60

je 46 10 Bachelor's Marketing 2 2 2 6 40

Characteristics


ju 4 5 4 4 8 5 5 48.9

je 4 6 5 5 6 5 5 51.1

Survey-Report 2.25








1 Thuan +Influencer R & D 33.77

2 Vinh - Sales 32.23

CEO

34%

Thuan Vinh

Ranking by your assumption R & D Sales


1 Vinh

2 Thuan

Typology


Thuan 0 1 1 0 1 0 1 1 1 1 5.02 50.2

Vinh 0 0 1 0 1 0 1 0 1 1 4.98 49.8

Constellation


Thuan 46 13 Bachelor's R & D 3 2 2 7 53.8

Vinh 41 3 Bachelor's Sales 2 2 2 6 46.2

Characteristics


Thuan 6 5 8 8 7 8 7 49.4

Vinh 6 6 8 8 7 8 7 50.6

Survey-Report 2.26




Name: Nguyen Thi Thu Huong Submitted: 10:22:44 27-01-2017




1 Mr. Tung - Marketing 33.81

2 Ms. Hieu - Finance 32.19

CEO

34%

Mr. Tung Ms. Hieu



1 Mr. Tung

2 Ms. Hieu

Typology


Mr. Tung 1 1 1 0 1 1 0 1 1 1 5.04 50.4

Ms. Hieu 0 0 1 1 1 0 1 0 0 0 4.96 49.6

Constellation


Mr. Tung 35 3 Masters Marketing 4 4 3 11 55

Ms. Hieu 30 3 Masters Finance 2 4 3 9 45

Characteristics


Mr. Tung 9 6 10 10 8 8 9 48.3

Ms. Hieu 9 8 10 10 9 9 9 51.7

Survey-Report 2.27




Name: Yves Submitted: 04:11:43 13-01-2017




1 Yves - Sales 25.86

2 Michael +Influencer Production 22.73

3 Alain - Finance 17.42

CEO

34%

Yves Michael Alain

Ranking by your assumption Sales Production Finance

Rank Manager 25.86% 22.73% 17.42%

1 Alain

2 Yves

3 Michael

Typology


Yves 0 1 1 1 1 1 1 1 1 1 3.84 38.4

Michael 1 1 1 1 1 1 0 0 0 1 3.08 30.8

Alain 1 1 1 1 1 1 0 0 0 1 3.08 30.8

Constellation


Yves 28 5 Bachelor's Sales 3 4 2 9 34.6

Michael 28 5 Without Tertiary Production 3 4 1 8 30.8

Alain 34 5 Masters Finance 2 4 3 9 34.6

Characteristics


Yves 3 5 8 9 8 5 6 44.5

Michael 8 4 4 4 10 6 6 41.8

Alain 1 1 2 2 4 2 2 13.8

Survey-Report 3.1








1 Iris C - HR 22.25

2 Naresh K - HR 22.12

3 CK Lai - Service 21.62

CEO

34%

Iris C Naresh K CK Lai

Ranking by your assumption HR HR Service

Rank Manager 22.25% 22.12% 21.62%

1 CK Lai

2 Naresh K

3 Iris C

Typology


CK Lai 0 1 1 0 0 1 0 0 0 0 3.31 33.1

Naresh K 0 1 1 0 0 1 0 0 1 0 3.34 33.4

Iris C 0 1 1 0 0 1 0 0 1 0 3.34 33.4

Constellation


CK Lai 40 5 Bachelor's Service 2 3 2 7 31.8

Naresh K 45 5 Bachelor's HR 3 3 2 8 36.4

Iris C 45 4 Bachelor's HR 3 2 2 7 31.8

Characteristics


CK Lai 9 7 8 7 9 6 8 33.3

Naresh K 7 7 6 7 8 8 7 30.8

Iris C 8 7 7 10 8 10 8 35.9

Survey-Report 3.2








1 Bill - Procurement 22.67

2 Tom - Finance 21.71

3 Jo - R & D 21.62

CEO

34%

Bill Tom Jo

Ranking by your assumption Procurement Finance R & D

Rank Manager 22.67% 21.71% 21.62%

1 Jo

2 Bill

3 Tom

Typology


Jo 0 1 1 1 1 1 0 0 0 1 3.25 32.5

Bill 1 1 1 1 1 1 1 1 1 1 3.37 33.7

Tom 1 1 1 1 1 1 1 1 1 1 3.37 33.7

Constellation


Jo 32 5 Without Tertiary R & D 4 3 1 8 34.8

Bill 22 4 Bachelor's Procurement 2 4 2 8 34.8

Tom 35 1 Bachelor's Finance 3 2 2 7 30.4

Characteristics


Jo 9 9 8 9 9 10 9 31

Bill 10 10 10 10 10 10 10 34.5

Tom 10 10 10 10 10 10 10 34.5

Survey-Report 3.3




Name: Alex Lim Submitted: 05:58:43 13-01-2017




1 Kim - HR 23.12

2 Melinda +Influencer Service 23.04

3 KY +Influencer Finance 19.84

CEO

34%

Kim Melinda KY

Ranking by your assumption HR Service Finance

Rank Manager 23.12% 23.04% 19.84%

1 KY

2 Melinda

3 Kim

Typology


KY 0 1 1 1 1 1 0 0 1 1 4.05 40.5

Melinda 0 1 0 0 0 1 0 0 0 0 2.96 29.6

Kim 0 1 0 1 0 1 0 0 0 0 2.99 29.9

Constellation


KY 30 10 Bachelor's Finance 2 2 2 6 27.3

Melinda 50 15 Bachelor's Service 4 3 2 9 40.9

Kim 60 15 Bachelor's HR 2 3 2 7 31.8

Characteristics


KY 1 1 3 5 2 6 3 22.4

Melinda 9 1 7 2 5 2 5 34.2

Kim 5 1 10 10 5 2 6 43.4

Survey-Report 3.4




Name: Mario Miranda Submitted: 11:28:46 13-01-2017







CEO

34%



Rank Manager 24.95% 22.22% 18.83%

1 Manager 3

2 Manager 2

3 Manager 1

Typology


Manager 1 1 1 0 1 1 1 0 1 1 1 3.54 35.4

Manager 2 0 1 0 1 1 1 0 0 1 1 3.01 30.1

Manager 3 0 1 1 1 0 1 1 0 0 0 3.45 34.5

Constellation





Characteristics


Manager 1 5 4 4 2 7 2 4 19.8

Manager 2 9 7 8 8 9 8 8 40.5

Manager 3 9 5 8 9 8 9 8 39.8

Survey-Report 3.5




Name: johnny Poh Submitted: 07:25:46 16-01-2017




1 Mike Lim - Marketing 22.89

2 John Wong - Service 22.47

3 Alex Ooi - Sales 20.64

CEO

34%

Mike Lim John Wong Alex Ooi

Ranking by your assumption Marketing Service Sales

Rank Manager 22.89% 22.47% 20.64%

1 Alex Ooi

2 Mike Lim

3 John Wong

Typology


Alex Ooi 0 0 1 1 1 1 0 0 0 0 3.35 33.5

Mike Lim 0 0 1 1 1 1 0 0 0 0 3.35 33.5

John Wong 0 0 0 1 1 0 0 0 0 0 3.29 32.9

Constellation


Alex Ooi 30 5 Without Tertiary Sales 2 3 1 6 27.3

Mike Lim 36 3 Bachelor's Marketing 4 1 2 7 31.8

John Wong 35 4 Without Tertiary Service 4 4 1 9 40.9

Characteristics


Alex Ooi 5 5 7 7 5 6 6 33

Mike Lim 7 6 8 7 7 6 7 38.7

John Wong 7 5 5 3 6 4 5 28.3

Survey-Report 3.6




Name: NGUYEN TRUNG QUAN Submitted: 08:56:51 16-01-2017





2 Manager 3 +Influencer Procurement 20.6


CEO

34%


Ranking by your assumption Sales Procurement Production

Rank Manager 25.44% 20.60% 19.96%

1 Manager 1

2 Manager 2

3 Manager 3

Typology


Manager 1 1 1 0 1 1 1 1 1 1 0 4.46 44.6

Manager 2 0 0 1 0 1 0 0 0 0 0 2.64 26.4

Manager 3 0 0 1 0 1 0 0 0 0 1 2.9 29

Constellation




Manager 3 35 3 Masters Procurement 3 4 3 10 35.7

Characteristics


Manager 1 7 5 7 8 7 9 7 35.3

Manager 2 9 7 7 8 7 5 7 35.8

Manager 3 7 5 6 5 6 6 6 28.9

Survey-Report 3.7




Name: Michael Degiampietro Submitted: 14:27:34 16-01-2017




1 Manager 1 - Service 23.09



CEO

34%


Ranking by your assumption Service Service Marketing

Rank Manager 23.09% 21.89% 21.02%

1 Manager 1

2 Manager 2

3 Manager 3

Typology


Manager 1 0 0 0 1 0 0 0 0 0 0 2.89 28.9

Manager 2 0 0 0 1 0 0 0 0 0 0 2.89 28.9

Manager 3 1 1 0 1 1 1 0 0 1 0 4.22 42.2

Constellation


Manager 1 50 15 Masters Service 3 3 3 9 42.9

Manager 2 50 15 Bachelor's Service 3 3 2 8 38.1

Manager 3 35 2 Bachelor's Marketing 1 1 2 4 19

Characteristics


Manager 1 7 7 7 8 10 10 8 33.2

Manager 2 7 5 10 10 7 8 8 32.5

Manager 3 10 6 8 8 8 10 8 34.3

Survey-Report 3.8








1 Person 1 - Finance 24.19

2 Person 3 - Service 21.36

3 Person 2 +Influencer Service 20.45

CEO

34%

Person 1 Person 3 Person 2

Ranking by your assumption Finance Service Service

Rank Manager 24.19% 21.36% 20.45%

1 Person 1

2 Person 2

3 Person 3

Typology


Person 1 1 1 1 1 1 0 1 1 0 1 4.75 47.5

Person 2 0 0 0 0 0 0 0 0 0 0 2.63 26.3

Person 3 0 0 0 0 0 0 0 0 0 0 2.63 26.3

Constellation


Person 1 49 8 Masters Finance 2 2 3 7 29.2

Person 2 58 20 Masters Service 2 3 3 8 33.3

Person 3 56 22 Doctoral Service 3 2 4 9 37.5

Characteristics


Person 1 10 10 10 10 10 10 10 33.3

Person 2 10 10 10 10 10 10 10 33.3

Person 3 10 10 10 10 10 10 10 33.3

Survey-Report 3.9




Name: Hinrich Submitted: 20:51:44 17-01-2017




1 Jörg - Finance 23.77

2 Sven - Production 23.09

3 Dörthe - R & D 19.14

CEO

34%

Jörg Sven Dörthe

Ranking by your assumption Finance Production R & D

Rank Manager 23.77% 23.09% 19.14%

1 Jörg

2 Dörthe

3 Sven

Typology


Dörthe 0 0 0 0 0 0 1 0 0 0 3.33 33.3

Sven 1 1 1 1 1 1 0 1 1 1 3.34 33.4

Jörg 1 1 1 1 1 1 0 1 1 1 3.34 33.4

Constellation


Dörthe 60 5 Masters R & D 2 1 3 6 24

Sven 50 2 Doctoral Production 3 2 4 9 36

Jörg 50 3 Masters Finance 3 4 3 10 40

Characteristics


Dörthe 7 5 2 7 2 5 5 29.7

Sven 6 5 5 6 6 6 6 35.6

Jörg 6 5 5 6 6 5 6 34.7

Survey-Report 3.10








1 CFO - Finance 22.82

2 Head of M +Influencer Production 21.97

3 Head of E - R & D 21.21

CEO

34%

CFO Head of M Head of E

Ranking by your assumption Finance Production R & D

Rank Manager 22.82% 21.97% 21.21%

1 CFO

2 Head of M

3 Head of E

Typology


CFO 0 1 1 0 1 1 0 0 1 1 3.03 30.3

Head of M 0 1 1 0 1 1 0 0 0 1 3 30

Head of E 1 0 0 1 1 0 1 1 1 0 3.97 39.7

Constellation


CFO 43 5 Bachelor's Finance 3 3 2 8 34.8

Head of M 40 2 Bachelor's Production 4 3 2 9 39.1

Head of E 35 2 Bachelor's R & D 1 3 2 6 26.1

Characteristics


CFO 8 6 8 8 9 5 7 38.6

Head of M 7 5 5 7 7 4 6 30.7

Head of E 8 6 4 6 7 4 6 30.6

Survey-Report 3.11




Name: Shammi Submitted: 03:18:07 18-01-2017

E-mail: Krishnan IP: 175.144.54.70



1 Mr. C - Logistics 23.03

2 Mr. B - Sales 21.79

3 Mr. A +Influencer Service 21.19

CEO

34%

Mr. C Mr. B Mr. A

Ranking by your assumption Logistics Sales Service

Rank Manager 23.03% 21.79% 21.19%

1 Mr. A

2 Mr. C

3 Mr. B

Typology


Mr. A 0 0 0 0 0 0 0 0 0 0 2.89 28.9

Mr. B 1 1 0 1 0 1 0 0 0 1 4.22 42.2

Mr. C 0 0 0 0 0 0 0 0 0 0 2.89 28.9

Constellation


Mr. A 40 18 Bachelor's Service 3 2 2 7 33.3

Mr. B 54 12 Without Tertiary Sales 1 4 1 6 28.6

Mr. C 42 1 Masters Logistics 3 2 3 8 38.1

Characteristics


Mr. A 8 6 8 8 9 5 7 34

Mr. B 6 5 6 6 8 6 6 28.3

Mr. C 8 7 9 8 9 8 8 37.7

Survey-Report 3.12




Name: Sunil Submitted: 15:47:46 18-01-2017






3 Manager 3 - R & D 18.89

CEO

34%


Ranking by your assumption Finance Sales R & D

Rank Manager 25.40% 21.71% 18.89%

1 Manager 1

2 Manager 2

3 Manager 3

Typology


Manager 1 1 1 1 1 1 1 1 1 0 0 4.37 43.7

Manager 2 0 0 0 0 0 0 0 0 1 1 2.78 27.8

Manager 3 0 0 0 0 1 0 1 0 1 1 2.84 28.4

Constellation


Manager 1 35 1 Masters Finance 4 3 3 10 40

Manager 2 36 1 Masters Sales 3 3 3 9 36

Manager 3 31 2 Bachelor's R & D 1 3 2 6 24

Characteristics


Manager 1 5 5 5 5 5 5 5 31.7

Manager 2 5 5 10 0 9 4 5 34.9

Manager 3 9 5 4 5 8 0 5 33.4

Survey-Report 3.13








1 Fred +Influencer Sales 23.5

2 Jim +Influencer Production 22.3

3 Joe +Influencer Finance 20.2

CEO

34%

Fred Jim Joe

Ranking by your assumption Sales Production Finance

Rank Manager 23.50% 22.30% 20.20%

1 Fred

2 Jim

3 Joe

Typology


Joe 0 0 1 0 1 1 0 0 0 0 2.74 27.4

Jim 0 0 1 0 1 1 0 0 0 0 2.74 27.4

Fred 1 1 0 1 0 1 1 1 1 1 4.53 45.3

Constellation


Joe 64 10 Masters Finance 2 3 3 8 34.8

Jim 53 11 Masters Production 2 4 3 9 39.1

Fred 54 22 Bachelor's Sales 3 1 2 6 26.1

Characteristics


Joe 7 5 4 5 8 7 6 29.7

Jim 8 5 6 7 7 9 7 34.9

Fred 6 7 7 6 8 9 7 35.4

Survey-Report 3.14









2 Manager 1 - HR 23.28


CEO

34%


Ranking by your assumption Production HR Production

Rank Manager 24.04% 23.28% 18.69%

1 Manager 1

2 Manager 3

3 Manager 2

Typology


Manager 1 0 0 0 0 0 0 0 0 0 1 3.39 33.9

Manager 2 1 1 0 0 1 1 1 1 0 0 3.31 33.1

Manager 3 1 1 0 0 1 1 1 1 0 0 3.31 33.1

Constellation


Manager 1 34 5 Bachelor's HR 3 4 2 9 39.1



Characteristics


Manager 1 7 8 8 8 9 4 7 32.8

Manager 2 6 7 6 7 8 7 7 30.1

Manager 3 7 9 10 8 9 7 8 37.1

Survey-Report 3.15




Name: Knut HAENJES Submitted: 16:05:53 26-01-2017




1 Cathro - Finance 26.29

2 Emanu +Influencer Sales 21.76

3 Nils +Influencer Sales 17.95

CEO

34%

Cathro Emanu Nils

Ranking by your assumption Finance Sales Sales

Rank Manager 26.29% 21.76% 17.95%

1 Cathro

2 Emanu

3 Nils

Typology


Emanu 0 0 0 0 1 0 1 0 0 0 2.64 26.4

Nils 0 0 0 0 1 0 1 0 0 0 2.64 26.4

Cathro 1 1 1 1 1 1 0 1 1 1 4.73 47.3

Constellation


Emanu 45 10 Without Tertiary Sales 3 4 1 8 38.1

Nils 31 3 Without Tertiary Sales 1 2 1 4 19

Cathro 43 8 Without Tertiary Finance 4 4 1 9 42.9

Characteristics


Emanu 8 8 8 7 6 3 7 34.4

Nils 8 6 9 8 8 3 7 36.2

Cathro 10 6 2 3 9 5 6 29.4

Survey-Report 3.16









2 Manager 3 +Influencer Service 22.41

3 Manager 2 - Logistics 19.65

CEO

34%


Ranking by your assumption Sales Service Logistics

Rank Manager 23.94% 22.41% 19.65%

1 Manager 3

2 Manager 1

3 Manager 2

Typology


Manager 1 1 1 1 0 1 0 0 1 1 1 4.39 43.9

Manager 2 0 0 1 0 0 0 0 0 0 0 2.8 28

Manager 3 0 0 1 0 0 0 0 0 0 0 2.8 28

Constellation



Manager 2 38 1 Bachelor's Logistics 2 4 2 8 29.6


Characteristics


Manager 1 3 4 5 4 4 4 4 24.1

Manager 2 6 5 6 6 5 3 5 31.7

Manager 3 7 7 7 8 7 8 7 44.2

Survey-Report 3.17








1 NH - Sales 22.85

2 wpv - Sales 21.67

3 SL - Sales 21.48

CEO

34%

NH wpv SL

Ranking by your assumption Sales Sales Sales

Rank Manager 22.85% 21.67% 21.48%

1 NH

2 wpv

3 SL

Typology


NH 0 0 0 1 1 1 0 1 1 0 3.33 33.3

wpv 0 0 0 1 1 1 0 1 1 0 3.33 33.3

SL 0 0 0 1 1 1 0 1 1 0 3.33 33.3

Constellation


NH 25 3 Bachelor's Sales 3 3 2 8 32

wpv 26 5 Bachelor's Sales 4 3 2 9 36

SL 25 3 Bachelor's Sales 3 3 2 8 32

Characteristics


NH 9 8 8 7 10 7 8 38.5

wpv 5 6 8 6 7 5 6 29.2

SL 7 6 7 7 7 7 7 32.3

Survey-Report 3.18








1 Vogel - Sales 23.74

2 Röscher - Finance 23.29

3 Rüttimann +Influencer Production 18.97

CEO

34%

Vogel Röscher Rüttimann

Ranking by your assumption Sales Finance Production

Rank Manager 23.74% 23.29% 18.97%

1 Rüttimann

2 Vogel

3 Röscher

Typology


Rüttimann 1 0 0 1 1 1 1 0 0 1 3.02 30.2

Vogel 0 0 0 1 0 1 0 1 1 0 3.43 34.3

Röscher 1 1 1 1 1 1 1 0 0 1 3.55 35.5

Constellation


Rüttimann 51 20 Without Tertiary Production 3 1 1 5 22.7

Vogel 46 10 Bachelor's Sales 4 3 2 9 40.9

Röscher 53 8 Bachelor's Finance 3 3 2 8 36.4

Characteristics


Rüttimann 10 7 3 6 5 10 7 33.3

Vogel 10 5 5 5 5 10 7 32.7

Röscher 10 6 8 5 7 5 7 34

Survey-Report 3.19








1 CLYW - Sales 23.28

2 JYA - Sales 21.55

3 IYAL - Sales 21.18

CEO

34%

CLYW JYA IYAL

Ranking by your assumption Sales Sales Sales

Rank Manager 23.28% 21.55% 21.18%

1 IYAL

2 JYA

3 CLYW

Typology


JYA 0 0 1 0 1 0 0 0 1 1 3.33 33.3

IYAL 0 0 1 0 1 0 0 0 1 1 3.33 33.3

CLYW 0 0 1 0 1 0 0 0 1 1 3.33 33.3

Constellation


JYA 36 3 Bachelor's Sales 3 3 2 8 33.3

IYAL 36 10 Bachelor's Sales 3 2 2 7 29.2

CLYW 40 8 Bachelor's Sales 4 3 2 9 37.5

Characteristics


JYA 7 5 10 10 10 10 9 31.3

IYAL 10 7 9 10 10 10 9 33.8

CLYW 10 8 10 10 10 10 10 35

Survey-Report 3.20




Name: ha.lee Submitted: 04:24:06 03-02-2017




1 Nam - Sales 26.61

2 Hieu - Finance 21.28

3 Huong - Logistics 18.11

CEO

34%

Nam Hieu Huong

Ranking by your assumption Sales Finance Logistics

Rank Manager 26.61% 21.28% 18.11%

1 Nam

2 Hieu

3 Huong

Typology


Hieu 0 0 1 1 1 0 1 0 0 0 3.16 31.6

Nam 1 1 0 1 1 1 0 1 1 1 4.22 42.2

Huong 0 0 0 1 1 0 0 0 0 0 2.63 26.3

Constellation


Hieu 40 5 Bachelor's Finance 3 2 2 7 31.8

Nam 34 4 Bachelor's Sales 4 4 2 10 45.5

Huong 28 2 Bachelor's Logistics 2 1 2 5 22.7

Characteristics


Hieu 8 8 8 8 8 8 8 33.3

Nam 8 8 8 8 8 8 8 33.3

Huong 8 8 8 8 8 8 8 33.3

Survey-Report 3.21








1 Tom - Sales 22.71

2 Philip - Finance 21.92

3 David - Production 21.37

CEO

34%

Tom Philip David

Ranking by your assumption Sales Finance Production

Rank Manager 22.71% 21.92% 21.37%

1 Tom

2 David

3 Philip

Typology


Tom 1 0 1 1 1 1 1 0 1 1 3.86 38.6

David 0 0 1 0 1 1 1 0 0 0 2.8 28

Philip 0 1 1 0 1 1 1 1 0 0 3.33 33.3

Constellation


Tom 30 3 Bachelor's Sales 3 3 2 8 32

David 35 5 Bachelor's Production 4 3 2 9 36

Philip 29 3 Bachelor's Finance 3 3 2 8 32

Characteristics


Tom 8 6 10 5 8 8 8 32.6

David 8 5 7 8 10 8 8 33.1

Philip 9 8 8 9 8 5 8 34.3

Survey-Report 3.22








1 Kettelhut +Influencer Finance 24.98

2 Naier +Influencer Sales 21.66

3 Brocker - Production 19.36

CEO

34%

Kettelhut Naier Brocker

Ranking by your assumption Finance Sales Production

Rank Manager 24.98% 21.66% 19.36%

1 Naier

2 Kettelhut

3 Brocker

Typology


Kettelhut 1 1 1 1 1 0 1 1 1 1 3.48 34.8

Naier 0 0 0 0 1 1 1 0 0 0 3.3 33

Brocker 1 1 1 1 1 0 1 1 0 1 3.22 32.2

Constellation


Kettelhut 47 3 Masters Finance 2 3 3 8 40

Naier 48 5 Without Tertiary Sales 3 3 1 7 35

Brocker 61 36 Bachelor's Production 2 1 2 5 25

Characteristics


Kettelhut 7 7 8 9 9 7 8 38.7

Naier 5 6 8 5 8 5 6 30.5

Brocker 6 7 3 7 8 7 6 30.8

Survey-Report 3.23








1 T. Abegg +Influencer Sales 23.68

2 N. Iseli - HR 23.01

3 P.Zangger +Influencer Sales 19.31

CEO

34%

T. Abegg N. Iseli P.Zangger

Ranking by your assumption Sales HR Sales

Rank Manager 23.68% 23.01% 19.31%

1 T. Abegg

2 N. Iseli

3 P.Zangger

Typology


P.Zangger 0 0 1 1 1 1 1 0 0 0 3.26 32.6

N. Iseli 0 1 1 1 1 1 0 0 0 0 3.49 34.9

T. Abegg 0 0 1 1 1 1 1 0 0 0 3.26 32.6

Constellation


P.Zangger 40 18 Without Tertiary Sales 4 1 1 6 28.6

N. Iseli 36 6 Without Tertiary HR 2 4 1 7 33.3

T. Abegg 40 4 Without Tertiary Sales 4 3 1 8 38.1

Characteristics


P.Zangger 8 7 6 6 6 5 6 26.7

N. Iseli 9 8 9 9 10 7 9 36.3

T. Abegg 9 9 10 8 9 8 9 37

Survey-Report 3.24








1 CMO +Influencer Marketing 26.22

2 Prod. +Influencer Production 21.59

3 CFO - Finance 18.19

CEO

34%

CMO Prod. CFO


Rank Manager 26.22% 21.59% 18.19%

1 CMO

2 Prod.

3 CFO

Typology


CFO 1 1 1 1 1 1 0 1 1 0 3.56 35.6

CMO 0 0 0 1 0 0 1 0 0 1 3.41 34.1

Prod. 1 1 1 1 1 0 0 0 1 0 3.03 30.3

Constellation


CFO 64 40 Bachelor's Finance 1 1 2 4 19

CMO 39 8 Masters Marketing 3 3 3 9 42.9

Prod. 35 4 Bachelor's Production 3 3 2 8 38.1

Characteristics


CFO 6 5 3 7 8 1 5 28.1

CMO 9 8 9 6 7 6 8 42.2

Prod. 4 5 7 5 7 4 5 29.7

Survey-Report 3.25











CEO

34%


Ranking by your assumption Marketing HR Sales

Rank Manager 23.15% 22.11% 20.74%

1 Manager 1

2 Manager 3

3 Manager 2

Typology


Manager 1 1 0 1 1 0 1 0 0 0 1 3.47 34.7

Manager 2 1 1 0 1 0 1 0 0 1 1 3.27 32.7

Manager 3 1 1 0 1 0 1 0 0 1 1 3.27 32.7

Constellation


Manager 1 60 30 Without Tertiary HR 3 3 1 7 31.8

Manager 2 33 10 Bachelor's Sales 2 3 2 7 31.8

Manager 3 40 4 Masters Marketing 3 2 3 8 36.4

Characteristics


Manager 1 10 7 8 10 10 7 9 34

Manager 2 8 7 9 5 7 10 8 29.8

Manager 3 8 9 9 10 10 10 9 36.2

Survey-Report 3.26








1 hoc - Finance 23.49

2 mep - Procurement 23.07

3 meg - Service 19.43

CEO

34%

hoc mep meg

Ranking by your assumption Finance Procurement Service

Rank Manager 23.49% 23.07% 19.43%

1 mep

2 hoc

3 meg

Typology


mep 0 1 1 1 1 1 1 0 0 0 3.37 33.7

hoc 0 1 1 1 1 1 1 0 0 0 3.37 33.7

meg 0 0 1 0 1 0 0 0 0 0 3.25 32.5

Constellation


mep 37 10 Without TertiaryProcurement 3 4 1 8 42.1

hoc 38 4 Bachelor's Finance 3 3 2 8 42.1

meg 65 33 Without Tertiary Service 1 1 1 3 15.8

Characteristics


mep 8 1 3 3 7 6 5 29

hoc 8 2 5 6 6 2 5 31

meg 8 10 3 5 5 8 6 40

Survey-Report 3.27




Name: Yves Submitted: 02:34:10 11-01-2017




1 Pee Finance 21.02

2 Brandon Sales 18.5

3 Shamsul HR 14

4 Melvin Logistics 12.47

CEO

34%

Pee Brandon Shamsul Melvin

Ranking by your assumption Finance Sales HR Logistics

Rank Manager 21.02% 18.50% 14.00% 12.47%

1 Pee

2 Shamsul

3 Brandon

Typology


Shamsul 0 0 1 1 1 0 0 0 0 0 1.91 19.1

Pee 1 0 1 1 1 1 1 1 0 0 3.3 33

Brandon 0 1 1 0 0 0 0 0 1 1 2.91 29.1

Melvin 0 0 1 0 0 0 0 0 0 0 1.87 18.7

Constellation


Shamsul 46 5 Bachelor's HR 2 1 2 5 16.1

Pee 41 1 Masters Finance 4 3 3 10 32.3

Brandon 37 2 Without Tertiary Sales 2 4 1 7 22.6

Melvin 41 1 Bachelor's Logistics 4 3 2 9 29

Characteristics


Shamsul 10 1 5 10 5 8 7 28.4

Pee 10 9 7 4 10 2 7 30.3

Brandon 8 8 8 8 6 7 8 32.4

Melvin 1 1 0 3 7 1 2 8.9

Survey-Report 4.1








1 FN - Finance 19.38

2 S - Sales 16.71

3 SV - Service 14.96

4 LG - Logistics 14.95

CEO

34%

FN S SV LG

Ranking by your assumption Finance Sales Service Logistics

Rank Manager 19.38% 16.71% 14.96% 14.95%

1 S

2 FN

3 LG

Typology


FN 0 1 1 0 1 0 1 1 1 1 3.8 38

S 1 0 0 1 1 1 0 0 0 0 2.76 27.6

LG 0 0 0 0 1 0 0 0 0 0 1.72 17.2

SV 0 0 0 0 1 0 0 0 0 0 1.72 17.2

Constellation


FN 30 1 Bachelor's Finance 4 3 2 9 26.5

S 30 2 Bachelor's Sales 4 1 2 7 20.6

LG 30 1 Bachelor's Logistics 4 3 2 9 26.5

SV 30 1 Bachelor's Service 4 3 2 9 26.5

Characteristics


FN 9 6 10 8 8 7 8 23.6

S 9 9 10 10 9 10 10 27.8

LG 8 8 8 9 8 9 8 24.3

SV 8 8 9 8 9 8 8 24.3

Survey-Report 4.2








1 Pearl - Finance 18.55

2 Ashikeen - Sales 17.08

3 Shaiful - HR 16.27

4 Bruno - Sales 14.09

CEO

34%

Pearl Ashikeen Shaiful Bruno

Ranking by your assumption Finance Sales HR Sales

Rank Manager 18.55% 17.08% 16.27% 14.09%

1 Pearl

2 Ashikeen

3 Bruno

Typology


Ashikeen 1 0 0 1 0 1 1 1 1 1 2.45 24.5

Bruno 1 0 0 1 0 1 1 1 1 1 2.45 24.5

Shaiful 0 1 1 0 0 0 0 0 0 1 2.37 23.7

Pearl 0 1 1 0 1 0 0 0 0 1 2.72 27.2

Constellation


Ashikeen 35 6 Bachelor's Sales 2 3 2 7 23.3

Bruno 36 1 Bachelor's Sales 3 3 2 8 26.7

Shaiful 46 6 Bachelor's HR 3 3 2 8 26.7

Pearl 48 1 Bachelor's Finance 2 3 2 7 23.3

Characteristics


Ashikeen 10 3 5 5 8 8 7 29.8

Bruno 2 2 4 2 2 5 3 12.9

Shaiful 1 5 9 6 1 9 5 23.6

Pearl 9 8 7 7 9 4 7 33.8

Survey-Report 4.3








1 Loo SH - Finance 18.79




CEO

34%

Loo SH Michael Christina KS Liew


Rank Manager 18.79% 18.73% 15.16% 13.33%

1 KS Liew

2 Michael

3 Loo SH

Typology


KS Liew 0 0 0 0 0 0 0 0 0 0 1.71 17.1

Michael 0 0 0 0 0 0 0 1 0 1 2.4 24

Christina 0 0 0 0 0 1 0 0 0 0 1.73 17.3

Loo SH 1 1 1 1 1 1 1 0 1 0 4.16 41.6

Constellation





Loo SH 38 6 Bachelor's Finance 2 2 2 6 20

Characteristics


KS Liew 8 8 8 7 9 6 8 26.8

Michael 7 8 8 8 8 9 8 27.8

Christina 6 6 7 6 7 5 6 21.6

Loo SH 6 7 8 6 8 6 7 23.8

Survey-Report 4.4












CEO

34%

Manager 1 Manager 2 Manager 4 Manager 3

Ranking by your assumption Sales Finance Production Production

Rank Manager 18.71% 18.35% 14.60% 14.34%

1 Manager 1

2 Manager 2

3 Manager 3

Typology


Manager 1 1 0 0 0 1 1 1 1 1 0 3.31 33.1

Manager 2 0 1 1 1 1 1 0 0 0 1 2.69 26.9

Manager 3 0 0 1 0 1 1 0 0 0 1 2 20

Manager 4 0 0 1 0 1 1 0 0 0 1 2 20

Constellation



Manager 2 54 30 Bachelor's Finance 4 3 2 9 31



Characteristics


Manager 1 7 0 8 7 9 7 6 31.3

Manager 2 6 0 7 4 7 7 5 25.4

Manager 3 6 0 5 5 7 7 5 24.5

Manager 4 5 0 3 4 7 4 4 18.8

Survey-Report 4.5




Name: Azreen Bashah Submitted: 07:36:30 13-01-2017





2 Siew - Finance 17.66



CEO

34%

Michael Siew KS Liew Christina

Ranking by your assumption Sales Finance Service HR

Rank Manager 18.04% 17.66% 15.34% 14.96%

1 KS Liew

2 Michael

3 Siew

Typology


KS Liew 0 0 1 0 1 0 0 0 0 0 1.88 18.8

Christina 0 0 1 0 1 0 1 0 0 0 1.9 19

Michael 1 0 1 0 1 0 0 1 0 1 2.92 29.2

Siew 0 1 1 1 1 1 1 0 1 0 3.29 32.9

Constellation





Siew 43 8 Bachelor's Finance 4 2 2 8 25.8

Characteristics


KS Liew 7 7 7 7 8 7 7 28.3

Christina 5 5 6 5 5 4 5 19.9

Michael 7 8 7 8 9 7 8 30.2

Siew 5 5 5 5 8 5 5 21.6

Survey-Report 4.6








1 Manager 3 - R & D 18.56




CEO

34%


Ranking by your assumption R & D Marketing Sales Production

Rank Manager 18.56% 17.22% 15.76% 14.47%

1 Manager 1

2 Manager 4

3 Manager 2

Typology


Manager 1 1 0 0 0 1 1 1 0 0 1 2.1 21

Manager 2 1 0 0 0 1 1 1 0 0 1 2.1 21

Manager 3 1 0 1 0 1 1 1 0 0 1 2.44 24.4

Manager 4 0 1 0 1 1 0 0 1 1 1 3.36 33.6

Constellation




Manager 3 46 20 Masters R & D 4 3 3 10 30.3

Manager 4 38 10 Bachelor's Production 1 1 2 4 12.1

Characteristics


Manager 1 4 5 6 6 8 6 6 27

Manager 2 3 4 4 4 6 10 5 23.4

Manager 3 3 6 6 6 9 9 6 29.6

Manager 4 3 3 4 5 6 5 4 20

Survey-Report 4.7




Name: Teerapatch Tachasirodom Submitted: 08:11:35 20-01-2017




1 Yodchai S +Influencer Sales 18.63

2 Nuanwan B +Influencer Finance 18.22

3 Piya Y. +Influencer Service 16.1

4 Sanjai B. +Influencer Logistics 13.05

CEO

34%

Yodchai S Nuanwan B Piya Y. Sanjai B.

Ranking by your assumption Sales Finance Service Logistics

Rank Manager 18.63% 18.22% 16.10% 13.05%

1 Piya Y.

2 Yodchai S

3 Sanjai B.

Typology


Nuanwan B 1 1 1 1 0 1 0 1 1 1 3.63 36.3

Piya Y. 0 0 0 1 0 0 0 1 0 1 1.89 18.9

Yodchai S 0 0 0 1 1 0 1 1 0 1 2.59 25.9

Sanjai B. 0 0 0 1 0 0 0 1 0 1 1.89 18.9

Constellation


Nuanwan B 53 4 Masters Finance 3 1 3 7 23.3

Piya Y. 54 10 Bachelor's Service 3 3 2 8 26.7

Yodchai S 54 10 Masters Sales 3 3 3 9 30

Sanjai B. 65 10 Bachelor's Logistics 1 3 2 6 20

Characteristics


Nuanwan B 7 7 7 7 6 7 7 23.2

Piya Y. 9 8 7 8 7 10 8 27.6

Yodchai S 10 10 8 7 9 7 9 28.8

Sanjai B. 7 5 5 7 5 7 6 20.4

Survey-Report 4.8




Name: Scherer Claudia Submitted: 11:54:55 24-01-2017




1 Okle - Infrastructure 18.89

2 Käslin - Service 16.04

3 Klauz - Service 16.02

4 Ivoli - R & D 15.05

CEO

34%

Okle Käslin Klauz Ivoli

Ranking by your assumption Infrastructure Service Service R & D

Rank Manager 18.89% 16.04% 16.02% 15.05%

1 Okle

2 Ivoli

3 Klauz

Typology


Käslin 0 0 0 0 1 1 0 0 0 0 2.48 24.8

Okle 1 0 0 1 1 1 0 0 0 0 2.52 25.2

Ivoli 1 0 0 1 1 1 0 0 0 0 2.52 25.2

Klauz 0 0 0 0 1 1 0 0 0 0 2.48 24.8

Constellation


Käslin 58 26 Masters Service 3 2 3 8 24.2

Okle 48 15 Masters Infrastructure 4 2 3 9 27.3

Ivoli 35 19 Bachelor's R & D 1 4 2 7 21.2

Klauz 59 22 Bachelor's Service 3 4 2 9 27.3

Characteristics


Käslin 10 5 5 4 8 9 7 23.9

Okle 10 8 10 9 10 10 10 33.4

Ivoli 10 5 6 5 6 5 6 22

Klauz 5 5 5 6 5 10 6 20.8

Survey-Report 4.9








1 Sales - Sales 19.23

2 Finance - Finance 16.98

3 HR - HR 16.13

4 Operation - Logistics 13.67

CEO

34%

Sales Finance HR Operation

Ranking by your assumption Sales Finance HR Logistics

Rank Manager 19.23% 16.98% 16.13% 13.67%

1 Sales

2 Finance

3 HR

Typology


Sales 1 0 0 1 1 0 1 0 0 0 2.91 29.1

HR 0 0 0 0 1 1 0 1 0 0 1.91 19.1

Finance 0 1 1 0 1 1 0 1 1 1 3.3 33

Operation 0 0 0 0 1 0 0 0 0 0 1.87 18.7

Constellation


Sales 40 3 Bachelor's Sales 4 4 2 10 32.3

HR 48 5 Masters HR 2 3 3 8 25.8

Finance 46 1 Bachelor's Finance 3 1 2 6 19.4

Operation 35 5 Bachelor's Logistics 2 3 2 7 22.6

Characteristics


Sales 7 4 8 8 7 4 6 26

HR 7 6 8 7 8 6 7 28.4

Finance 6 6 6 6 7 6 6 24.8

Operation 5 6 5 5 6 4 5 20.8

Survey-Report 4.10









2 Manager 4 - Procurement 16.96



CEO

34%


Ranking by your assumption Sales Procurement Finance Service

Rank Manager 17.79% 16.96% 15.99% 15.26%

1 Manager 1

2 Manager 4

3 Manager 3

Typology


Manager 1 1 1 0 1 1 0 1 1 1 1 2.41 24.1

Manager 2 0 0 1 0 0 1 0 0 0 0 2.94 29.4

Manager 3 0 0 0 0 0 0 0 0 0 0 2.25 22.5

Manager 4 1 1 0 1 1 0 1 1 1 1 2.41 24.1

Constellation





Manager 4 52 17 Bachelor's Procurement 3 4 2 9 26.5

Characteristics


Manager 1 10 7 7 6 9 6 8 25.1

Manager 2 7 7 8 8 7 8 8 25

Manager 3 9 7 6 6 8 6 7 23.4

Manager 4 8 8 8 7 8 9 8 26.5

Survey-Report 4.11








1 FC - Finance 17.41

2 SM - Sales 16.69

3 HR - HR 16.57

4 OM - Service 15.33

CEO

34%

FC SM HR OM


Rank Manager 17.41% 16.69% 16.57% 15.33%

1 HR

2 FC

3 SM

Typology


FC 1 0 1 1 1 1 1 1 1 0 2.63 26.3

HR 1 0 1 1 1 1 1 1 0 0 2.28 22.8

OM 0 0 1 1 1 0 0 0 0 0 2.2 22

SM 0 1 1 1 1 0 0 0 0 1 2.89 28.9

Constellation


FC 50 2 Bachelor's Finance 3 1 2 6 20.7

HR 50 5 Masters HR 3 3 3 9 31

OM 51 5 Bachelor's Service 1 3 2 6 20.7

SM 50 5 Bachelor's Sales 3 3 2 8 27.6

Characteristics


FC 10 5 8 7 10 10 8 32.2

HR 8 5 5 5 5 5 6 21.5

OM 8 5 8 10 5 5 7 27

SM 5 5 5 5 5 5 5 19.3

Survey-Report 4.12








1 cfo - Finance 18.15

2 verkauf - Sales 17.42

3 verkauf - Sales 16.11

4 logistik - Logistics 14.32

CEO

34%

cfo verkauf verkauf logistik

Ranking by your assumption Finance Sales Sales Logistics

Rank Manager 18.15% 17.42% 16.11% 14.32%

1 verkauf

2 verkauf

3 logistik

Typology


cfo 1 1 1 1 0 1 0 0 1 1 3.77 37.7

logistik 0 0 1 0 0 1 0 0 0 0 2.04 20.4

verkauf 0 0 1 0 1 1 1 1 0 0 2.1 21

verkauf 0 0 1 0 1 1 1 1 0 0 2.1 21

Constellation


cfo 35 4 Bachelor's Finance 2 3 2 7 24.1

logistik 48 19 Without Tertiary Logistics 4 1 1 6 20.7

verkauf 41 3 Masters Sales 3 3 3 9 31

verkauf 58 10 Without Tertiary Sales 2 4 1 7 24.1

Characteristics


cfo 9 9 6 5 7 0 6 20.6

logistik 5 9 9 6 4 10 7 24

verkauf 9 6 8 10 5 10 8 27.2

verkauf 10 5 7 9 9 10 8 28.1

Survey-Report 4.13




Name: Matthias Gantner Submitted: 08:29:54 31-01-2017




1 Gantner - Finance 18.41

2 Curdin - R & D 16.31

3 Studer +Influencer Production 15.91

4 Strahm +Influencer Infrastructure 15.38

CEO

34%

Gantner Curdin Studer Strahm

Ranking by your assumption Finance R & D Production Infrastructure

Rank Manager 18.41% 16.31% 15.91% 15.38%

1 Studer

2 Gantner

3 Curdin

Typology


Strahm 1 1 1 1 1 1 0 1 0 0 2.41 24.1

Studer 1 0 1 1 1 1 0 0 1 1 2.37 23.7

Curdin 1 1 1 1 1 1 1 1 0 0 2.76 27.6

Gantner 1 1 1 1 1 1 0 1 1 1 2.45 24.5

Constellation


Strahm 47 2 Bachelor's Infrastructure 3 3 2 8 24.2

Studer 47 15 Bachelor's Production 3 1 2 6 18.2

Curdin 45 8 Masters R & D 1 4 3 8 24.2

Gantner 46 3 Doctoral Finance 4 3 4 11 33.3

Characteristics


Strahm 6 4 4 5 9 5 5 21.5

Studer 9 6 7 8 9 7 8 30.4

Curdin 7 5 4 5 8 5 6 22.3

Gantner 9 5 6 5 9 5 7 25.8

Survey-Report 4.14




Name: Milan Pumpalovic Submitted: 11:22:02 31-01-2017




1 Mr. G - HR 18.17

2 Mr. T +Influencer Sales 17.57

3 Mr. S - Production 15.55

4 Mr. H +Influencer Finance 14.71

CEO

34%

Mr. G Mr. T Mr. S Mr. H

Ranking by your assumption HR Sales Production Finance

Rank Manager 18.17% 17.57% 15.55% 14.71%

1 Mr. T

2 Mr. H

3 Mr. S

Typology


Mr. T 1 0 0 0 0 0 0 0 1 0 2.94 29.4

Mr. S 0 0 1 0 1 1 1 1 0 1 2.37 23.7

Mr. H 0 1 1 1 1 1 1 1 0 1 2.41 24.1

Mr. G 0 1 0 1 0 0 0 0 0 0 2.29 22.9

Constellation


Mr. T 56 15 Masters Sales 2 4 3 9 26.5

Mr. S 52 20 Masters Production 4 1 3 8 23.5

Mr. H 42 13 Masters Finance 1 2 3 6 17.6

Mr. G 51 16 Masters HR 4 4 3 11 32.4

Characteristics


Mr. T 9 9 7 5 8 7 7 24

Mr. S 8 9 7 6 9 5 7 23.5

Mr. H 9 6 8 7 10 7 8 25.2

Mr. G 8 7 10 9 9 8 9 27.4

Survey-Report 4.15




Name: isabell erlenmaier Submitted: 22:39:06 01-02-2017




1 david - Sales 16.84

2 Hans Pete - Production 16.73

3 rico +Influencer Marketing 16.66

4 werner - Finance 15.77

CEO

34%

david Hans Pete rico werner

Ranking by your assumption Sales Production Marketing Finance

Rank Manager 16.84% 16.73% 16.66% 15.77%

1 rico

2 david

3 werner

Typology


rico 0 0 0 0 1 1 0 1 1 1 2.41 24.1

david 0 0 0 0 1 1 0 1 1 1 2.41 24.1

werner 1 1 1 1 1 0 1 0 0 0 2.76 27.6

Hans Pete 1 0 1 1 1 0 1 0 0 0 2.41 24.1

Constellation


rico 47 15 Masters Marketing 3 4 3 10 28.6

david 51 20 Masters Sales 4 3 3 10 28.6

werner 65 22 Masters Finance 1 2 3 6 17.1

Hans Pete 51 9 Masters Production 4 2 3 9 25.7

Characteristics


rico 6 5 3 5 7 6 5 23

david 5 5 6 4 7 6 5 23.8

werner 8 6 5 5 9 4 6 27

Hans Pete 6 5 7 5 6 7 6 26.2

Survey-Report 4.16








1 Kohler +Influencer Marketing 20.13

2 Gerber - Finance 16.85

3 Lang +Influencer Production 15.12

4 Burger +Influencer Infrastructure 13.9

CEO

34%

Kohler Gerber Lang Burger

Ranking by your assumption Marketing Finance Production Infrastructure

Rank Manager 20.13% 16.85% 15.12% 13.90%

1 Kohler

2 Burger

3 Gerber

Typology


Kohler 1 1 0 0 0 1 0 1 0 1 3.75 37.5

Burger 0 0 0 1 0 0 0 0 0 0 2.04 20.4

Gerber 0 0 1 1 1 0 1 0 1 0 2.12 21.2

Lang 0 0 1 0 1 0 1 0 1 0 2.1 21

Constellation


Kohler 43 4 Bachelor's Marketing 4 4 2 10 28.6

Burger 52 12 Masters Infrastructure 2 1 3 6 17.1

Gerber 45 3 Masters Finance 4 4 3 11 31.4

Lang 38 1 Masters Production 2 3 3 8 22.9

Characteristics


Kohler 10 8 8 10 8 10 9 25.4

Burger 10 9 6 10 10 10 9 25.7

Gerber 10 8 6 10 10 7 9 24

Lang 10 8 7 10 8 10 9 24.9

Survey-Report 4.17




Name: Michael Flückiger Submitted: 18:13:39 28-01-2017




1 Christoph - Finance 17.74

2 Tino +Influencer R & D 17.71

3 Sabine - Service 15.82

4 Michael - Marketing 14.74

CEO

34%

Christoph Tino Sabine Michael

Ranking by your assumption Finance R & D Service Marketing

Rank Manager 17.74% 17.71% 15.82% 14.74%

1 Tino

2 Michael

3 Christoph

Typology


Tino 1 1 1 0 0 1 1 1 0 0 2.19 21.9

Christoph 1 1 1 1 1 1 1 0 1 1 3.56 35.6

Michael 0 0 1 0 0 1 0 1 0 0 2.13 21.3

Sabine 0 0 1 0 0 1 0 0 0 0 2.11 21.1

Constellation


Tino 49 3 Doctoral R & D 4 4 4 12 34.3

Christoph 53 7 Masters Finance 3 1 3 7 20

Michael 43 1 Masters Marketing 1 3 3 7 20

Sabine 53 1 Masters Service 3 3 3 9 25.7

Characteristics


Tino 9 8 7 7 10 10 8 24.3

Christoph 10 8 8 9 9 8 9 25

Michael 9 8 10 10 9 7 9 25.6

Sabine 10 8 8 9 9 8 9 25

Survey-Report 4.18








1 C - Marketing 13.84

2 E - Logistics 13.71

3 D - HR 13.35

4 B +Influencer Sales 12.63

5 A - Finance 12.47

CEO

34%

C E D B A

Ranking by your assumption Marketing Logistics HR Sales Finance

Rank Manager 13.84% 13.71% 13.35% 12.63% 12.47%

1 A

2 B

3 C

Typology


A 0 1 1 0 1 0 1 0 0 1 1.98 19.8

B 1 0 0 1 1 1 0 1 1 1 2.17 21.7

C 1 0 0 1 1 1 0 1 1 1 2.17 21.7

D 0 1 1 0 1 0 1 0 0 1 1.98 19.8

E 0 0 0 0 1 0 0 0 0 1 1.71 17.1

Constellation


A 35 10 Bachelor's Finance 3 3 2 8 20

B 45 9 Bachelor's Sales 1 3 2 6 15

C 35 4 Masters Marketing 3 2 3 8 20

D 41 3 Masters HR 3 2 3 8 20

E 38 10 Masters Logistics 4 3 3 10 25

Characteristics


A 5 5 5 5 5 5 5 16.9

B 6 6 6 6 6 7 6 20.8

C 6 6 6 6 8 6 6 21.3

D 6 6 6 7 6 6 6 20.8

E 6 6 6 6 6 6 6 20.3

Survey-Report 5.1







1 CPO +Influencer Procurement 15.22

2 CTO +Influencer Infrastructure 14.6

3 CDO +Influencer Production 12.73


5 CSDO +Influencer Infrastructure 11.65

CEO

34%

CPO CTO CDO CFO CSDO

Ranking by your assumption Procurement Infrastructure Production Finance Infrastructure

Rank Manager 15.22% 14.60% 12.73% 11.80% 11.65%

1 CDO

2 CPO

3 CTO

Typology


CDO 0 0 0 1 1 1 0 0 0 1 1.83 18.3

CFO 1 1 1 1 1 1 0 1 1 1 2.08 20.8

CSDO 1 1 1 0 1 0 0 1 1 0 2.01 20.1

CTO 1 1 1 0 1 0 0 1 1 0 2.01 20.1

CPO 1 1 1 1 1 1 0 1 1 1 2.08 20.8

Constellation


CDO 50 20 Masters Production 3 2 3 8 18.2

CFO 35 2 Masters Finance 1 2 3 6 13.6

CSDO 50 2 Masters Infrastructure 3 2 3 8 18.2

CTO 45 15 Doctoral Infrastructure 4 3 4 11 25

CPO 45 10 Masters Procurement 4 4 3 11 25

Characteristics


CDO 8 7 5 5 10 10 7 21.4

CFO 8 7 5 5 10 5 7 19.2

CSDO 3 7 4 5 10 2 5 14.7

CTO 5 7 6 7 10 10 7 21.3

CPO 8 7 10 8 10 5 8 23.4

Survey-Report 5.2



Name: honey Submitted: 10:20:03 13-01-2017




1 Tiger +Influencer Sales 15.94

2 Sid +Influencer Production 14.45

3 Fan - Finance 12.23

4 DipDip +Influencer HR 11.9

5 Ops +Influencer Logistics 11.49

CEO

34%

Tiger Sid Fan DipDip Ops

Ranking by your assumption Sales Production Finance HR Logistics

Rank Manager 15.94% 14.45% 12.23% 11.90% 11.49%

1 Sid

2 Tiger

3 Fan

Typology


Sid 0 0 0 0 0 1 0 1 1 0 1.58 15.8

Tiger 1 0 0 1 0 1 1 1 1 1 3.13 31.3

DipDip 0 1 1 0 1 1 0 1 1 0 1.86 18.6

Ops 0 0 0 0 0 1 0 1 1 0 1.58 15.8

Fan 0 1 1 0 1 1 0 1 1 0 1.86 18.6

Constellation


Sid 45 1 Masters Production 3 2 3 8 21.1

Tiger 48 1 Masters Sales 3 2 3 8 21.1

DipDip 46 3 Bachelor's HR 3 3 2 8 21.1

Ops 25 3 Bachelor's Logistics 1 3 2 6 15.8

Fan 33 3 Bachelor's Finance 3 3 2 8 21.1

Characteristics


Sid 10 10 10 10 10 10 10 28.8

Tiger 6 4 8 7 9 8 7 20.1

DipDip 4 5 6 6 5 4 5 14.5

Ops 7 8 7 7 7 7 7 20.6

Fan 6 7 7 4 5 4 6 16

Survey-Report 5.3







1 Winston - Finance 14.6

2 Melvin - Sales 14.18

3 Andrea - Marketing 13.36

4 Margerat - HR 12.32

5 Kevin +Influencer Sales 11.55

CEO

34%

Winston Melvin Andrea Margerat Kevin

Ranking by your assumption Finance Sales Marketing HR Sales

Rank Manager 14.60% 14.18% 13.36% 12.32% 11.55%

1 Winston

2 Margerat

3 Kevin

Typology


Winston 1 1 1 1 1 1 0 0 0 1 2.74 27.4

Kevin 0 0 1 0 1 0 1 1 1 0 1.77 17.7

Margerat 0 1 1 1 1 1 0 0 0 0 1.97 19.7

Melvin 0 0 1 0 1 0 1 1 1 0 1.77 17.7

Andrea 0 0 1 0 1 0 1 1 1 0 1.77 17.7

Constellation


Winston 46 3 Bachelor's Finance 2 4 2 8 21.1

Kevin 42 1 Bachelor's Sales 2 3 2 7 18.4

Margerat 45 44 Bachelor's HR 3 1 2 6 15.8

Melvin 45 2 Bachelor's Sales 3 4 2 9 23.7

Andrea 43 1 Bachelor's Marketing 3 3 2 8 21.1

Characteristics


Winston 7 3 3 3 7 3 4 17.9

Kevin 4 3 2 6 5 4 4 16.4

Margerat 5 3 5 5 5 7 5 20.5

Melvin 3 6 7 5 7 6 6 23.1

Andrea 4 4 6 7 5 6 5 22

Survey-Report 5.4







1 Manager 1 +Influencer Finance 14.64


3 Manager 5 +Influencer Infrastructure 13.25

4 Manager 3 +Influencer R & D 12.67


CEO

34%

Manager 1 Manager 2 Manager 5 Manager 3 Manager 4

Ranking by your assumption Finance Production Infrastructure R & D HR

Rank Manager 14.64% 13.65% 13.25% 12.67% 11.78%

1 Manager 2

2 Manager 1

3 Manager 3

Typology


Manager 1 1 1 1 1 1 1 1 1 1 1 2.23 22.3

Manager 2 0 1 1 0 1 0 0 0 1 0 1.93 19.3

Manager 3 1 1 0 1 0 1 1 1 0 1 1.95 19.5

Manager 4 1 1 0 1 0 1 1 1 0 1 1.95 19.5

Manager 5 1 1 0 1 0 1 1 1 0 1 1.95 19.5

Constellation






Manager 5 48 5 Bachelor's Infrastructure 4 3 2 9 21.4

Characteristics


Manager 1 8 6 6 8 10 8 8 18.1

Manager 2 7 6 9 8 9 9 8 19

Manager 3 10 8 8 10 8 10 9 21.4

Manager 4 8 8 10 10 10 10 9 22.2

Manager 5 8 8 7 8 9 9 8 19.3

Survey-Report 5.5



Name: MOHD AZMI BIN MAHZAN @ MOHD ZIN Submitted: 16:07:41 18-01-2017





2 Manager 1 +Influencer Marketing 13.45



5 Manager 4 +Influencer Logistics 12.37

CEO

34%


Ranking by your assumption Production Marketing R & D Finance Logistics

Rank Manager 13.51% 13.45% 13.39% 13.28% 12.37%

1 Manager 3

2 Manager 2

3 Manager 1

Typology


Manager 1 1 0 0 1 0 0 0 1 1 1 2.09 20.9

Manager 2 0 1 1 0 0 1 1 0 0 0 2 20

Manager 3 0 1 1 0 1 1 1 0 0 0 2.09 20.9

Manager 4 0 0 0 0 0 0 0 0 0 0 1.63 16.3

Manager 5 1 0 0 1 1 0 0 1 1 1 2.18 21.8

Constellation




Manager 3 33 33 Bachelor's Finance 4 4 2 10 20

Manager 4 33 33 Bachelor's Logistics 4 4 2 10 20

Manager 5 33 33 Bachelor's R & D 4 4 2 10 20

Characteristics


Manager 1 8 8 8 9 9 8 8 20.2

Manager 2 8 9 9 9 9 9 9 21.4

Manager 3 8 8 8 8 8 8 8 19.4

Manager 4 8 8 9 8 8 8 8 19.9

Manager 5 8 7 8 8 8 8 8 19.1

Survey-Report 5.6







1 Bleisch - Sales 14.8

2 Stalder - Production 14.08

3 Meier - Finance 12.48

4 Fretz - Marketing 12.35

5 Mathis - Infrastructure 12.29

CEO

34%

Bleisch Stalder Meier Fretz Mathis

Ranking by your assumption Sales Production Finance Marketing Infrastructure

Rank Manager 14.80% 14.08% 12.48% 12.35% 12.29%

1 Meier

2 Bleisch

3 Stalder

Typology


Meier 0 0 0 0 1 0 1 1 0 1 1.95 19.5

Bleisch 1 1 1 1 1 1 0 0 1 0 2.22 22.2

Fretz 1 1 1 1 1 1 0 0 1 0 2.22 22.2

Stalder 0 0 0 0 1 0 0 1 0 1 1.85 18.5

Mathis 0 0 0 0 1 0 1 0 0 0 1.76 17.6

Constellation


Meier 55 22 Masters Finance 2 1 3 6 14

Bleisch 46 12 Bachelor's Sales 4 4 2 10 23.3

Fretz 33 5 Masters Marketing 1 2 3 6 14

Stalder 48 9 Masters Production 4 4 3 11 25.6

Mathis 42 7 Masters Infrastructure 4 3 3 10 23.3

Characteristics


Meier 10 6 10 10 10 10 9 23.3

Bleisch 10 5 10 10 7 10 9 21.8

Fretz 10 5 10 5 8 10 8 20

Stalder 5 5 10 10 10 8 8 19.9

Mathis 6 5 7 6 5 7 6 15

Survey-Report 5.7



Name: zulfadhli Submitted: 08:12:05 26-01-2017




1 roslee - Production 16.6

2 rizal - HR 13.01

3 chan +Influencer Marketing 12.54

4 lim +Influencer Logistics 12.21

5 rosli - Infrastructure 11.63

CEO

34%

roslee rizal chan lim rosli

Ranking by your assumption Production HR Marketing Logistics Infrastructure

Rank Manager 16.60% 13.01% 12.54% 12.21% 11.63%

1 chan

2 rosli

3 lim

Typology


rosli 0 1 1 1 1 1 0 0 0 0 1.72 17.2

chan 1 1 0 1 1 0 0 0 0 0 1.93 19.3

rizal 0 1 1 1 1 1 0 0 0 0 1.72 17.2

lim 0 1 0 1 1 0 0 0 0 0 1.54 15.4

roslee 0 1 0 1 1 0 1 1 1 1 3.09 30.9

Constellation


rosli 37 15 Masters Infrastructure 1 3 3 7 17.1

chan 40 17 Masters Marketing 3 2 3 8 19.5

rizal 40 10 Masters HR 3 4 3 10 24.4

lim 40 3 Masters Logistics 3 2 3 8 19.5

roslee 40 4 Masters Production 3 2 3 8 19.5

Characteristics


rosli 10 3 8 7 5 6 7 18.6

chan 6 5 10 5 6 7 7 18.2

rizal 7 6 4 7 7 7 6 17.5

lim 10 5 5 10 6 8 7 20.6

roslee 9 9 8 10 10 8 9 25.1

Survey-Report 5.8



Name: VALLIRAJAH RAJAMONEY Submitted: 08:30:31 26-01-2017




1 SUKRI - Finance 16.03

2 RAUNNY - Sales 14.99

3 RITA - HR 13.27

4 LOGAN - Service 10.86

5 SALLEH +Influencer Logistics 10.84

CEO

34%

SUKRI RAUNNY RITA LOGAN SALLEH

Ranking by your assumption Finance Sales HR Service Logistics

Rank Manager 16.03% 14.99% 13.27% 10.86% 10.84%

1 RAUNNY

2 SUKRI

3 SALLEH

Typology


SALLEH 1 1 0 0 1 1 0 0 0 0 1.58 15.8

RITA 1 1 0 0 1 1 1 0 0 0 1.67 16.7

LOGAN 1 1 0 0 1 1 0 0 0 0 1.58 15.8

SUKRI 1 1 0 1 1 1 1 1 1 0 2.83 28.3

RAUNNY 1 1 1 0 1 1 0 0 0 1 2.35 23.5

Constellation


SALLEH 53 15 Bachelor's Logistics 2 3 2 7 17.5

RITA 45 7 Bachelor's HR 4 3 2 9 22.5

LOGAN 38 4 Bachelor's Service 1 2 2 5 12.5

SUKRI 47 12 Masters Finance 4 4 3 11 27.5

RAUNNY 50 16 Masters Sales 3 2 3 8 20

Characteristics


SALLEH 5 5 5 5 5 7 5 16

RITA 7 7 7 7 7 7 7 21.2

LOGAN 6 6 7 8 7 8 7 21.1

SUKRI 6 6 6 4 6 6 6 17.1

RAUNNY 8 8 8 8 8 9 8 24.6

Survey-Report 5.9



Name: Benny Lee Submitted: 08:48:01 26-01-2017




1 MOK +Influencer Marketing 15.35

2 Shirley - Procurement 14.45

3 Khor - Sales 14.39

4 GEOK LAN - Finance 11.54

5 Mahmood - HR 10.27

CEO

34%

MOK Shirley Khor GEOK LAN Mahmood

Ranking by your assumption Marketing Procurement Sales Finance HR

Rank Manager 15.35% 14.45% 14.39% 11.54% 10.27%

1 MOK

2 GEOK LAN

3 Khor

Typology


Khor 0 0 0 0 0 0 0 1 1 1 2 20

Mahmood 1 0 1 1 0 0 1 0 0 0 1.82 18.2

GEOK LAN 1 1 1 1 1 1 1 0 0 0 2.1 21

Shirley 1 1 1 1 1 1 1 0 0 0 2.1 21

MOK 0 0 0 0 0 0 0 1 1 1 2 20

Constellation


Khor 55 30 Without Tertiary Sales 3 3 1 7 19.4

Mahmood 40 2 Bachelor's HR 3 1 2 6 16.7

GEOK LAN 37 15 Bachelor's Finance 2 4 2 8 22.2

Shirley 40 20 Bachelor's Procurement 3 4 2 9 25

MOK 60 37 Bachelor's Marketing 2 2 2 6 16.7

Characteristics


Khor 4 6 6 8 6 9 6 26

Mahmood 0 0 4 4 4 6 3 11.8

GEOK LAN 0 2 5 0 7 0 2 9.3

Shirley 5 5 5 6 5 3 5 19.8

MOK 8 5 10 8 9 9 8 33.2

Survey-Report 5.10







1 BL - Sales 17.55

2 JN - Logistics 13.38

3 VP - HR 12.47

4 MM - Procurement 12.11

5 Tee - Finance 10.5

CEO

34%

BL JN VP MM Tee

Ranking by your assumption Sales Logistics HR Procurement Finance

Rank Manager 17.55% 13.38% 12.47% 12.11% 10.50%

1 Tee

2 JN

3 BL

Typology


Tee 0 0 1 0 1 0 1 0 0 1 1.61 16.1

BL 1 1 0 1 1 1 0 1 1 0 3.79 37.9

VP 0 0 0 0 1 0 1 0 0 1 1.52 15.2

JN 0 0 0 0 1 0 0 0 0 0 1.47 14.7

MM 0 0 1 0 1 0 1 0 0 1 1.61 16.1

Constellation


Tee 55 37 Bachelor's Finance 1 2 2 5 13.9

BL 34 10 Without Tertiary Sales 2 3 1 6 16.7

VP 46 9 Bachelor's HR 4 3 2 9 25

JN 40 30 Bachelor's Logistics 4 3 2 9 25

MM 36 2 Bachelor's Procurement 3 2 2 7 19.4

Characteristics


Tee 9 5 5 5 8 3 6 17.7

BL 9 5 9 9 9 9 8 25.2

VP 6 6 5 5 5 6 5 16.5

JN 8 3 7 8 8 8 7 21.1

MM 6 8 6 7 5 7 6 19.5

Survey-Report 5.11








2 Manager 5 - Logistics 13.09

3 Preecha +Influencer Sales 12.99


5 Pinyawee - Marketing 12.33

CEO

34%

Manager 3 Manager 5 Preecha Manager 4 Pinyawee

Ranking by your assumption Finance Logistics Sales Service Marketing

Rank Manager 14.91% 13.09% 12.99% 12.68% 12.33%

1 Preecha

2 Manager 4

3 Manager 5

Typology


Preecha 1 0 1 0 0 1 1 1 1 1 2.1 21

Pinyawee 1 0 1 0 0 1 1 1 1 1 2.1 21

Manager 3 0 1 0 1 1 0 1 0 0 0 2.71 27.1

Manager 4 0 0 0 0 0 0 1 0 0 0 1.55 15.5

Manager 5 0 0 0 0 0 0 1 0 0 0 1.55 15.5

Constellation


Preecha 45 13 Bachelor's Sales 4 1 2 7 18.4

Pinyawee 35 3 Bachelor's Marketing 1 3 2 6 15.8




Characteristics


Preecha 9 10 9 9 9 9 9 19.6

Pinyawee 10 9 8 9 9 9 9 19.3

Manager 3 9 8 9 9 10 10 9 19.6

Manager 4 10 10 10 10 9 10 10 21.1

Manager 5 9 10 10 9 9 10 9 20.3

Survey-Report 5.12







1 L - Finance 14.98

2 R +Influencer Sales 13.49

3 G +Influencer Service 13.29

4 D - R & D 12.22

5 T +Influencer Production 12.03

CEO

34%

L R G D T

Ranking by your assumption Finance Sales Service R & D Production

Rank Manager 14.98% 13.49% 13.29% 12.22% 12.03%

1 R

2 D

3 G

Typology


D 1 1 1 1 1 1 1 1 0 0 2.26 22.6

R 1 1 1 1 1 0 0 0 0 0 1.98 19.8

T 0 0 1 0 1 0 0 0 1 1 1.89 18.9

G 0 0 1 0 1 0 0 0 0 0 1.71 17.1

L 0 0 1 0 1 1 1 1 1 1 2.17 21.7

Constellation


D 48 20 Bachelor's R & D 1 1 2 4 10.8

R 58 10 Without Tertiary Sales 4 3 1 8 21.6

T 52 9 Bachelor's Production 3 3 2 8 21.6

G 60 13 Without Tertiary Service 3 4 1 8 21.6

L 60 12 Bachelor's Finance 3 4 2 9 24.3

Characteristics


D 8 5 9 5 7 5 7 22.2

R 5 3 5 6 9 8 6 19.9

T 4 5 7 2 5 2 4 14.2

G 9 6 9 4 5 5 6 21.7

L 9 7 7 5 9 2 7 22.1

Survey-Report 5.13












CEO

34%


Ranking by your assumption Finance Sales Service HR Logistics

Rank Manager 16.72% 14.24% 12.96% 12.60% 9.48%

1 Manager 1

2 Manager 5

3 Manager 2

Typology


Manager 1 1 0 1 1 1 0 1 1 1 1 3 30

Manager 2 0 1 0 1 0 1 0 0 0 0 2.24 22.4

Manager 3 1 0 0 1 1 0 1 0 0 1 1.83 18.3

Manager 4 0 0 0 1 0 0 0 0 0 0 1.47 14.7

Manager 5 0 0 0 1 0 0 0 0 0 0 1.47 14.7

Constellation







Characteristics


Manager 1 10 6 8 7 10 5 8 22.4

Manager 2 8 8 6 9 6 7 7 21.3

Manager 3 10 8 8 5 7 4 7 20.5

Manager 4 8 7 5 6 5 6 6 17.9

Manager 5 8 7 7 4 6 5 6 18

Survey-Report 5.14



Name: Alexander Melikyan Submitted: 07:16:55 27-01-2017




1 FI - Finance 15.11

2 MP +Influencer Procurement 14.15

3 JK +Influencer Production 13.77

4 HS - Marketing 11.57

5 MR +Influencer Sales 11.4

CEO

34%

FI MP JK HS MR

Ranking by your assumption Finance Procurement Production Marketing Sales

Rank Manager 15.11% 14.15% 13.77% 11.57% 11.40%

1 MP

2 MR

3 FI

Typology


MP 0 1 1 1 1 1 0 0 1 1 1.98 19.8

HS 1 0 1 1 1 0 1 1 0 0 2.08 20.8

MR 1 0 1 1 1 0 1 1 0 0 2.08 20.8

JK 0 0 1 1 1 1 0 0 1 1 1.88 18.8

FI 0 1 1 1 1 1 0 0 1 1 1.98 19.8

Constellation


MP 58 8 Doctoral Procurement 2 2 4 8 19.5

HS 44 8 Masters Marketing 2 2 3 7 17.1

MR 54 35 Without Tertiary Sales 3 1 1 5 12.2

JK 50 20 Masters Production 4 4 3 11 26.8

FI 45 19 Masters Finance 3 4 3 10 24.4

Characteristics


MP 8 9 7 10 7 10 8 25

HS 1 2 10 10 1 5 5 14.7

MR 10 6 2 2 10 9 6 18.8

JK 7 4 3 10 7 3 6 16.9

FI 10 6 7 7 10 10 8 24.5

Survey-Report 5.15



Name: Marc Nideröst Submitted: 09:04:51 27-01-2017




1 JLE - Production 14.76

2 PSC - Production 13.46

3 WHU - Production 13.28

4 FMA +Influencer Production 13.01

5 ADU - Production 11.49

CEO

34%

JLE PSC WHU FMA ADU

Ranking by your assumption Production Production Production Production Production

Rank Manager 14.76% 13.46% 13.28% 13.01% 11.49%

1 FMA

2 PSC

3 WHU

Typology


ADU 0 1 1 1 1 0 1 0 0 0 2 20

WHU 0 1 1 1 1 0 1 0 0 0 2 20

PSC 0 1 1 1 1 0 1 0 0 0 2 20

JLE 0 1 1 1 1 0 1 0 0 0 2 20

FMA 0 1 1 1 1 0 1 0 0 0 2 20

Constellation


ADU 45 10 Masters Production 3 2 3 8 18.6

WHU 60 35 Doctoral Production 3 2 4 9 20.9

PSC 41 20 Without Tertiary Production 2 4 1 7 16.3

JLE 45 20 Masters Production 3 4 3 10 23.3

FMA 65 30 Doctoral Production 2 3 4 9 20.9

Characteristics


ADU 6 5 3 2 7 3 4 13.6

WHU 9 5 4 4 7 8 6 19.4

PSC 9 8 7 8 9 6 8 24.9

JLE 8 8 7 8 9 5 8 23.8

FMA 7 5 4 4 5 10 6 18.2

Survey-Report 5.16







1 Manager 2 - R & D 14.08





CEO

34%


Ranking by your assumption R & D Marketing Finance Production Sales

Rank Manager 14.08% 13.51% 13.21% 13.09% 12.12%

1 Manager 3

2 Manager 2

3 Manager 1

Typology


Manager 1 1 1 1 1 1 0 0 1 0 0 2.22 22.2

Manager 2 0 1 0 0 1 1 1 1 1 1 2.04 20.4

Manager 3 0 0 0 0 1 1 1 0 1 1 1.86 18.6

Manager 4 1 0 1 1 1 0 0 0 0 0 2.03 20.3

Manager 5 0 0 0 0 1 1 1 0 1 1 1.86 18.6

Constellation




Manager 3 55 22 Doctoral Sales 1 1 4 6 14.6



Characteristics


Manager 1 8 8 8 7 8 8 8 20.8

Manager 2 8 7 8 7 8 5 7 19.2

Manager 3 6 9 8 8 9 10 8 21.9

Manager 4 6 7 6 7 7 6 6 17.2

Manager 5 8 7 9 7 8 8 8 20.9

Survey-Report 5.17










4 Manager 4 - R & D 12.63


CEO

34%


Ranking by your assumption Finance Production Sales R & D HR

Rank Manager 16.01% 14.40% 12.76% 12.63% 10.19%

1 Manager 1

2 Manager 5

3 Manager 4

Typology


Manager 1 0 1 1 1 1 1 1 0 1 1 2.39 23.9

Manager 2 0 0 0 0 0 0 0 1 0 0 1.68 16.8

Manager 3 1 0 0 0 0 0 0 0 0 0 1.75 17.5

Manager 4 1 0 0 0 0 0 0 1 0 0 1.77 17.7

Manager 5 0 1 1 1 1 1 1 1 1 1 2.41 24.1

Constellation






Manager 5 46 17 Doctoral Finance 4 4 4 12 27.9

Characteristics


Manager 1 7 9 9 7 8 9 8 20.7

Manager 2 7 9 7 9 8 8 8 20.2

Manager 3 7 8 6 6 6 8 7 17.3

Manager 4 8 9 8 8 8 9 8 21.1

Manager 5 9 8 8 7 9 8 8 20.7

Survey-Report 5.18







1 Hong - Finance 15.44

2 Craig - Production 13.26

3 Kong +Influencer Finance 12.79

4 Paul - Infrastructure 12.37

5 Wong - Procurement 12.14

CEO

34%

Hong Craig Kong Paul Wong

Ranking by your assumption Finance Production Finance Infrastructure Procurement

Rank Manager 15.44% 13.26% 12.79% 12.37% 12.14%

1 Hong

2 Craig

3 Paul

Typology


Paul 1 1 1 1 0 0 0 0 0 0 1.9 19

Hong 1 1 1 1 1 0 0 1 1 0 2.05 20.5

Craig 0 1 1 0 1 0 0 1 1 0 1.95 19.5

Kong 1 1 1 1 1 0 0 1 1 0 2.05 20.5

Wong 1 1 1 1 1 0 0 1 1 0 2.05 20.5

Constellation


Paul 48 20 Without TertiaryInfrastructure 3 2 1 6 16.2

Hong 45 20 Bachelor's Finance 4 2 2 8 21.6

Craig 41 5 Bachelor's Production 3 3 2 8 21.6

Kong 50 4 Masters Finance 2 2 3 7 18.9

Wong 38 9 Bachelor's Procurement 2 4 2 8 21.6

Characteristics


Paul 5 3 9 10 3 5 6 21

Hong 10 8 4 10 10 6 8 28

Craig 9 9 2 2 10 1 5 19.2

Kong 10 8 2 2 8 2 5 18.7

Wong 6 3 2 5 3 3 4 13.1

Survey-Report 5.19



Name: Stephan Wilk Submitted: 11:53:57 27-01-2017






3 Manager 1 +Influencer Marketing 12.87



CEO

34%


Ranking by your assumption Finance Sales Marketing Sales Sales

Rank Manager 15.99% 13.21% 12.87% 12.07% 11.85%

1 Manager 1

2 Manager 2

3 Manager 4

Typology


Manager 1 1 0 0 1 1 0 1 0 1 0 1.66 16.6

Manager 2 1 0 0 1 1 0 1 0 1 0 1.66 16.6

Manager 3 0 1 1 0 0 1 0 1 0 1 3.35 33.5

Manager 4 1 0 0 1 1 0 1 0 1 0 1.66 16.6

Manager 5 1 0 0 1 1 0 1 0 1 0 1.66 16.6

Constellation



Manager 2 59 8 Without Tertiary Sales 2 3 1 6 15




Characteristics


Manager 1 7 8 5 8 8 4 7 19.4

Manager 2 7 8 8 8 5 10 8 22.2

Manager 3 9 4 5 5 9 2 6 16.7

Manager 4 8 6 8 9 7 10 8 23.3

Manager 5 7 6 7 5 6 7 6 18.4

Survey-Report 5.20







1 CEO +Influencer Sales 16.02


3 CTO +Influencer Service 12.41

4 CTO - Logistics 12.33

5 HR +Influencer HR 11.87

CEO

34%

CEO CFO CTO CTO HR

Ranking by your assumption Sales Finance Service Logistics HR

Rank Manager 16.02% 13.36% 12.41% 12.33% 11.87%

1 CEO

2 CFO

3 CTO

Typology


CEO 1 0 1 1 1 1 1 1 1 1 3.16 31.6

CTO 1 0 1 1 1 0 1 0 0 0 1.61 16.1

CTO 1 0 1 1 1 0 1 0 0 0 1.61 16.1

CFO 1 1 1 1 1 0 1 0 0 0 2 20

HR 1 0 1 1 1 0 1 0 0 0 1.61 16.1

Constellation


CEO 38 6 Masters Sales 3 3 3 9 22.5

CTO 45 10 Masters Service 3 2 3 8 20

CTO 49 8 Masters Logistics 1 4 3 8 20

CFO 42 5 Bachelor's Finance 4 2 2 8 20

HR 37 10 Masters HR 2 2 3 7 17.5

Characteristics


CEO 9 7 7 8 7 10 8 18.7

CTO 10 8 8 8 10 8 9 20.3

CTO 9 9 8 9 8 8 9 19.9

CFO 8 9 10 9 9 8 9 20.7

HR 9 8 9 9 9 8 9 20.4

Survey-Report 5.21







1 philipp - Finance 14.14

2 mario - Marketing 14.01

3 jürg +Influencer Production 13.84

4 corinna - R & D 13.73

5 Bruno - Sales 10.28

CEO

34%

philipp mario jürg corinna Bruno

Ranking by your assumption Finance Marketing Production R & D Sales

Rank Manager 14.14% 14.01% 13.84% 13.73% 10.28%

1 Bruno

2 mario

3 corinna

Typology


Bruno 1 1 0 1 0 1 0 0 1 0 1.87 18.7

mario 1 1 0 1 0 1 0 0 1 0 1.87 18.7

philipp 0 0 1 0 1 0 1 1 0 1 2.2 22

jürg 0 0 1 0 1 0 0 1 0 1 2.11 21.1

corinna 1 1 0 1 0 1 1 0 1 0 1.96 19.6

Constellation


Bruno 55 15 Without Tertiary Sales 1 1 1 3 7.5

mario 51 6 Masters Marketing 3 3 3 9 22.5

philipp 43 6 Masters Finance 3 3 3 9 22.5

jürg 45 10 Without Tertiary Production 4 4 1 9 22.5

corinna 40 8 Doctoral R & D 2 4 4 10 25

Characteristics


Bruno 8 5 8 8 6 10 8 20.6

mario 9 5 9 9 9 8 8 22.5

philipp 9 5 6 9 9 5 7 19.8

jürg 9 5 6 8 9 5 7 19.3

corinna 8 5 5 7 9 5 7 17.8

Survey-Report 5.22










4 Manager 6 - R & D 10.51



CEO

34%

Manager 1 Manager 5 Manager 3 Manager 6 Manager 2 Manager 4

Ranking by your assumption Finance Marketing Sales R & D Sales HR

Rank Manager 12.49% 11.62% 10.63% 10.51% 10.41% 10.34%

1 Manager 1

2 Manager 3

3 Manager 6

Typology


Manager 1 0 0 1 0 1 0 1 1 1 1 2.64 26.4

Manager 2 1 1 0 1 0 1 1 0 0 0 1.48 14.8

Manager 3 1 1 0 1 0 1 1 0 0 0 1.48 14.8

Manager 4 0 0 0 0 1 0 1 1 0 0 1.4 14

Manager 5 1 1 0 1 0 1 1 0 0 0 1.48 14.8

Manager 6 1 1 0 1 1 1 1 1 0 0 1.52 15.2

Constellation





Manager 4 40 6 Masters HR 2 3 3 8 18.2



Characteristics


Manager 1 9 6 6 6 9 7 7 16.8

Manager 2 7 7 9 8 6 5 7 16.6

Manager 3 9 9 8 5 9 5 8 17.6

Manager 4 5 4 7 8 5 9 6 14.8

Manager 5 8 7 8 6 9 7 7 17.5

Manager 6 9 6 5 6 9 8 7 16.7

Survey-Report 6.1












5 Manager 3 - Infrastructure 9.17


CEO

34%


Ranking by your assumption Finance Sales HR Logistics Infrastructure Service

Rank Manager 15.35% 11.89% 11.45% 9.66% 9.17% 8.48%

1 Manager 2

2 Manager 1

3 Manager 3

Typology


Manager 1 1 0 1 1 0 1 1 1 1 1 3.27 32.7

Manager 2 0 1 0 1 1 0 0 0 0 0 1.99 19.9

Manager 3 0 0 0 1 0 0 1 0 0 1 1.21 12.1

Manager 4 0 0 0 1 0 0 1 0 0 1 1.21 12.1

Manager 5 0 0 0 1 0 0 0 0 0 0 1.17 11.7

Manager 6 0 0 0 1 0 0 0 0 0 0 1.17 11.7

Constellation




Manager 3 38 11 Masters Infrastructure 2 2 3 7 14.3

Manager 4 42 8 Doctoral HR 3 4 4 11 22.4



Characteristics


Manager 1 7 5 5 5 9 7 6 14.7

Manager 2 7 5 7 8 9 10 8 17.8

Manager 3 5 8 6 5 8 8 7 15.3

Manager 4 8 7 7 7 9 7 7 17.5

Manager 5 9 7 7 7 9 7 8 18

Manager 6 6 8 8 6 8 7 7 16.7

Survey-Report 6.2














CEO

34%


Ranking by your assumption Finance R & D Production Marketing Sales Sales

Rank Manager 13.47% 11.94% 11.51% 10.78% 9.94% 8.36%

1 Manager 1

2 Manager 3

3 Manager 4

Typology


Manager 1 1 1 1 1 1 0 1 1 1 1 2.44 24.4

Manager 2 1 0 1 1 1 0 1 1 1 0 2.18 21.8

Manager 3 0 1 0 0 0 1 0 0 0 1 1.54 15.4

Manager 4 0 0 0 0 0 1 0 0 0 0 1.28 12.8

Manager 5 0 0 0 0 0 1 0 0 0 0 1.28 12.8

Manager 6 0 0 0 0 0 1 0 0 0 0 1.28 12.8

Constellation


Manager 1 45 2 Masters Finance 3 3 3 9 18

Manager 2 55 2 Masters Production 2 3 3 8 16

Manager 3 54 15 Doctoral R & D 3 3 4 10 20


Manager 5 43 6 Masters Marketing 2 4 3 9 18


Characteristics


Manager 1 10 10 10 10 10 10 10 18.8

Manager 2 8 8 5 8 8 10 8 14.6

Manager 3 10 10 10 10 10 10 10 18.8

Manager 4 7 5 7 10 7 10 8 14.4

Manager 5 10 8 10 10 10 10 10 18.2

Manager 6 7 6 6 10 10 10 8 15.2

Survey-Report 6.3




Name: Frank Wagner Submitted: 00:43:19 29-01-2017




1 R1 +Influencer R & D 13.38

2 H1 - HR 11.14

3 F1 +Influencer Finance 11.01

4 L1 +Influencer Logistics 10.8

5 S1 +Influencer Sales 10.75

6 P1 +Influencer Production 8.92

CEO

34%

R1 H1 F1 L1 S1 P1

Ranking by your assumption R & D HR Finance Logistics Sales Production

Rank Manager 13.38% 11.14% 11.01% 10.80% 10.75% 8.92%

1 L1

2 S1

3 F1

Typology


F1 0 0 1 1 1 0 0 1 0 1 1.63 16.3

P1 0 0 1 1 1 0 0 0 0 1 1.61 16.1

L1 0 0 0 1 1 0 0 0 0 0 1.35 13.5

S1 1 1 0 1 1 1 1 0 1 0 2.01 20.1

R1 1 1 0 1 1 1 1 1 1 0 2.03 20.3

H1 0 0 0 1 1 0 0 1 0 0 1.37 13.7

Constellation


F1 48 10 Bachelor's Finance 2 4 2 8 16

P1 50 20 Bachelor's Production 3 1 2 6 12

L1 58 10 Masters Logistics 1 4 3 8 16

S1 56 5 Masters Sales 2 2 3 7 14

R1 48 11 Doctoral R & D 2 4 4 10 20

H1 52 12 Masters HR 4 4 3 11 22

Characteristics


F1 8 6 7 8 8 7 7 17.7

P1 5 5 5 5 7 4 5 12.4

L1 8 8 8 8 9 8 8 19.6

S1 6 7 5 6 8 5 6 14.8

R1 8 8 9 9 9 8 9 20.5

H1 6 5 8 6 7 5 6 15

Survey-Report 6.4














7 Manager 6 - Marketing 8.26 CEO

34%

Manager 1 Manager 4 Manager 5 Manager 7 Manager 3 Manager 2 Manager 6

Ranking by your assumption Finance Sales Service Logistics Sales Production Marketing

Rank Manager 11.28% 10.23% 9.39% 9.38% 9.01% 8.46% 8.26%

1 Manager 3

2 Manager 1

3 Manager 5

Typology


Manager 1 1 0 1 1 1 1 1 1 1 1 2.73 27.3

Manager 2 1 0 1 0 1 1 1 0 0 1 1.45 14.5

Manager 3 1 1 0 0 1 1 1 0 0 0 1.18 11.8

Manager 4 1 1 0 0 1 1 1 0 0 0 1.18 11.8

Manager 5 1 0 0 0 1 1 1 0 0 0 1.13 11.3

Manager 6 1 1 0 0 1 1 1 0 0 0 1.18 11.8

Manager 7 1 0 0 0 1 1 1 0 0 0 1.13 11.3

Constellation




Manager 3 61 12 Without Tertiary Sales 3 3 1 7 13.2


Manager 5 58 21 Without Tertiary Service 4 4 1 9 17

Manager 6 58 37 Without Tertiary Marketing 4 2 1 7 13.2

Manager 7 54 20 Without Tertiary Logistics 4 4 1 9 17

Characteristics


Manager 1 9 10 6 6 10 8 8 14.5

Manager 2 9 6 7 7 7 6 7 12.7

Manager 3 9 9 9 10 8 8 9 15.9

Manager 4 8 9 9 9 9 9 9 15.8

Manager 5 9 8 7 8 8 8 8 14.3

Manager 6 5 6 7 9 7 8 7 12.5

Manager 7 9 8 7 7 9 8 8 14.3

Survey-Report 7.1








1 A - Sales 11.66

2 F - Logistics 10.82

3 E - Procurement 10.04

4 V - Production 9.76

5 J - HR 8.88

6 r - Marketing 8.5

7 C - Finance 6.35 CEO

34%

A F E V J r C

Ranking by your assumption Sales Logistics Procurement Production HR Marketing Finance

Rank Manager 11.66% 10.82% 10.04% 9.76% 8.88% 8.50% 6.35%

1 C

2 F

3 A

Typology


C 1 1 1 1 1 1 1 1 1 1 1.65 16.5

J 0 1 0 0 1 1 1 0 0 0 1.36 13.6

E 1 1 1 1 1 1 1 1 1 1 1.65 16.5

A 0 0 0 0 1 1 0 0 0 0 1.27 12.7

F 0 0 0 0 1 1 0 0 0 0 1.27 12.7

V 1 0 1 1 1 1 0 1 1 1 1.55 15.5

r 0 0 0 0 1 1 0 0 0 0 1.27 12.7

Constellation


C 55 10 Bachelor's Finance 1 4 2 7 11.9

J 33 14 Without Tertiary HR 2 3 1 6 10.2

E 45 2 Doctoral Procurement 3 1 4 8 13.6

A 40 10 Masters Sales 4 4 3 11 18.6

F 38 9 Masters Logistics 4 4 3 11 18.6

V 42 15 Bachelor's Production 4 3 2 9 15.3

r 36 16 Bachelor's Marketing 3 2 2 7 11.9

Characteristics


C 0 0 0 0 0 1 0 0.5

J 6 5 2 7 6 2 5 16.6

E 3 7 5 2 5 5 4 15.6

A 7 7 5 5 8 5 6 21.7

F 6 5 4 7 6 2 5 17.9

V 4 4 3 5 4 3 4 13.6

r 2 6 4 6 3 3 4 14.1

Survey-Report 7.2








1 KW - R & D 11.97

2 EL - Production 10.06

3 CD - Service 9.58

4 L - HR 9.28

5 AW - Finance 8.54

6 HC - Service 8.51

7 PW +Influencer Service 8.05 CEO

34%

KW EL CD L AW HC PW

Ranking by your assumption R & D Production Service HR Finance Service Service

Rank Manager 11.97% 10.06% 9.58% 9.28% 8.54% 8.51% 8.05%

1 PW

2 AW

3 EL

Typology


PW 1 0 0 1 0 1 0 0 0 1 1.15 11.5

AW 1 1 1 1 1 1 0 0 1 1 1.67 16.7

KW 1 1 0 1 0 1 1 1 0 1 2.06 20.6

EL 1 0 1 1 1 1 0 0 1 1 1.62 16.2

CD 1 0 0 1 0 1 0 0 0 1 1.15 11.5

HC 1 0 0 1 0 1 0 0 0 1 1.15 11.5

L 1 1 0 1 0 1 0 0 0 1 1.2 12

Constellation


PW 51 20 Bachelor's Service 2 1 2 5 9.4

AW 50 10 Without Tertiary Finance 3 1 1 5 9.4

KW 48 15 Bachelor's R & D 4 4 2 10 18.9

EL 40 15 Masters Production 1 4 3 8 15.1

CD 45 15 Bachelor's Service 4 4 2 10 18.9

HC 42 15 Without Tertiary Service 2 4 1 7 13.2

L 50 12 Bachelor's HR 3 3 2 8 15.1

Characteristics


PW 7 4 6 7 9 9 7 15.6

AW 6 5 5 5 6 7 6 12.7

KW 6 6 7 7 7 7 7 15

EL 7 6 7 7 6 5 6 14.4

CD 6 5 6 6 6 6 6 13.2

HC 6 5 8 6 5 7 6 14

L 7 7 7 7 7 5 7 15.1

Survey-Report 7.3




Name: Andi Lapon Submitted: 09:43:12 18-01-2017




1 RVH - Finance 10.58

2 GC - R & D 10.21

3 PN - Sales 9.69

4 SKM +Influencer Production 9.66

5 ALH - Procurement 9.25

6 JKD - HR 8.5

7 JNG - Sales 8.09 CEO

34%

RVH GC PN SKM ALH JKD JNG

Ranking by your assumption Finance R & D Sales Production Procurement HR Sales

Rank Manager 10.58% 10.21% 9.69% 9.66% 9.25% 8.50% 8.09%

1 RVH

2 GC

3 PN

Typology


RVH 0 0 1 1 1 1 1 1 1 1 1.54 15.4

GC 1 1 0 1 1 1 0 1 0 0 1.44 14.4

JKD 0 0 0 1 1 1 0 1 0 0 1.35 13.5

SKM 0 0 1 0 0 0 1 0 1 1 1.44 14.4

ALH 0 0 1 1 1 1 1 1 1 1 1.54 15.4

PN 1 1 0 0 0 0 0 0 0 0 1.35 13.5

JNG 1 1 0 0 0 0 0 0 0 0 1.35 13.5

Constellation


RVH 41 5 Masters Finance 4 4 3 11 18.3

GC 45 2 Masters R & D 4 3 3 10 16.7

JKD 56 4 Bachelor's HR 1 4 2 7 11.7

SKM 39 4 Bachelor's Production 4 4 2 10 16.7

ALH 36 2 Bachelor's Procurement 3 3 2 8 13.3

PN 43 5 Without Tertiary Sales 4 4 1 9 15

JNG 35 12 Bachelor's Sales 2 1 2 5 8.3

Characteristics


RVH 8 5 8 8 8 8 8 14.4

GC 8 6 8 9 8 9 8 15.3

JKD 8 7 7 8 7 5 7 13.5

SKM 9 8 6 5 7 5 7 12.8

ALH 8 7 6 6 9 6 7 13.4

PN 7 8 8 9 8 9 8 15.6

JNG 8 8 7 8 8 8 8 15

Survey-Report 7.4




Name: Andreas Volkart Submitted: 17:12:21 26-01-2017




1 Philip - Finance 12.28

2 Fabienne +Influencer Sales 9.97

3 Beat - HR 9.22

4 Lukas - Infrastructure 8.96

5 Stefan - Marketing 8.56

6 Thomas +Influencer R & D 8.52

7 Andreas - Service 8.49 CEO

34%

Philip Fabienne Beat Lukas Stefan Thomas Andreas

Ranking by your assumption Finance Sales HR Infrastructure Marketing R & D Service

Rank Manager 12.28% 9.97% 9.22% 8.96% 8.56% 8.52% 8.49%

1 Fabienne

2 Philip

3 Stefan

Typology


Philip 0 1 1 1 1 0 1 1 1 1 2.86 28.6

Stefan 1 1 0 1 1 1 1 0 0 0 1.24 12.4

Fabienne 1 1 0 1 1 1 1 0 0 0 1.24 12.4

Andreas 0 1 0 1 1 0 1 0 0 0 1.14 11.4

Lukas 0 1 0 1 1 0 1 0 0 0 1.14 11.4

Beat 0 1 0 1 1 0 1 0 0 0 1.14 11.4

Thomas 1 1 0 1 1 1 1 0 0 0 1.24 12.4

Constellation


Philip 40 2 Masters Finance 4 2 3 9 15.8

Stefan 35 3 Bachelor's Marketing 1 4 2 7 12.3

Fabienne 48 3 Masters Sales 1 4 3 8 14

Andreas 39 2 Bachelor's Service 3 2 2 7 12.3

Lukas 41 4 Masters Infrastructure 4 2 3 9 15.8

Beat 44 3 Bachelor's HR 3 4 2 9 15.8

Thomas 45 4 Masters R & D 3 2 3 8 14

Characteristics


Philip 7 2 1 5 2 6 4 11.5

Stefan 2 5 6 6 4 6 5 14.3

Fabienne 6 2 8 7 6 9 6 18.9

Andreas 6 5 3 6 7 3 5 14.9

Lukas 5 4 2 3 7 7 5 13.5

Beat 2 5 6 6 3 8 5 14.7

Thomas 5 4 3 3 8 2 4 12.3

Survey-Report 7.5








1 Mimi - HR 9.14

2 Li Lin +Influencer Service 7.43

3 Aizura +Influencer Service 7.43

4 Chan +Influencer Service 7.37

5 Craig +Influencer Service 7.22

6 Nazli +Influencer Service 7.16

7 Quek +Influencer Service 7.11 CEO

8 Murali +Influencer Service 6.79

9 Mark +Influencer Service 6.37 34%

Mimi Li Lin Aizura Chan Craig Nazli Quek Murali Mark

Ranking by your assumption HR Service Service Service Service Service Service Service Service

Rank Manager 9.14% 7.43% 7.43% 7.37% 7.22% 7.16% 7.11% 6.79% 6.37%

1 Craig

2 Li Lin

3 Murali

Typology


Craig 1 0 0 0 1 1 0 0 0 0 1.01 10.1

Li Lin 1 0 0 0 1 1 0 0 0 0 1.01 10.1

Mimi 1 1 0 1 1 1 0 0 0 0 1.91 19.1

Mark 1 0 0 0 1 1 0 0 0 0 1.01 10.1

Nazli 1 0 0 0 1 1 0 0 0 0 1.01 10.1

Quek 1 0 0 0 1 1 0 0 0 0 1.01 10.1

Aizura 1 0 0 0 1 1 0 0 0 0 1.01 10.1

Murali 1 0 0 0 1 1 0 0 0 0 1.01 10.1

Chan 1 0 0 0 1 1 0 0 0 0 1.01 10.1

Constellation


Craig 50 2 Masters Service 3 2 3 8 9.6

Li Lin 44 20 Masters Service 3 4 3 10 12

Mimi 45 5 Masters HR 4 2 3 9 10.8

Mark 57 2 Masters Service 1 2 3 6 7.2

Nazli 44 25 Masters Service 3 3 3 9 10.8

Quek 45 25 Masters Service 4 3 3 10 12

Aizura 45 25 Masters Service 4 3 3 10 12

Murali 46 25 Masters Service 4 3 3 10 12

Chan 45 14 Masters Service 4 4 3 11 13.3

Characteristics


Craig 9 9 9 9 9 9 9 13

Li Lin 8 8 8 8 8 8 8 11.6

Mimi 8 8 8 8 8 8 8 11.6

Mark 8 8 8 8 8 8 8 11.6

Nazli 8 8 8 8 8 8 8 11.6

Quek 7 7 7 7 7 7 7 10.1

Aizura 8 8 8 8 8 8 8 11.6

Murali 6 6 6 6 6 6 6 8.7

Chan 7 7 7 7 7 7 7 10.1

Survey-Report 9.1








1 Down - Procurement 8

2 Sally - HR 7.92

3 Over - Finance 7.68

4 Dide - Production 7.42

5 Reich - Marketing 7.35

6 Mass - Sales 7.21

7 McCaverty - Infrastructure 6.95 CEO

8 Thomas - Service 6.8

9 Dover - Sales 6.67 34%

Down Sally Over Dide Reich Mass McCaverty Thomas Dover

Ranking by your assumption Procurement HR Finance Production Marketing Sales Infrastructure Service Sales

Rank Manager 8.00% 7.92% 7.68% 7.42% 7.35% 7.21% 6.95% 6.80% 6.67%

1 Dover

2 Dide

3 Over

Typology


Down 1 0 1 1 0 1 1 1 1 1 1.29 12.9

Dover 0 1 0 1 1 0 0 0 0 0 1.09 10.9

McCaverty 1 0 0 1 0 1 1 1 0 0 1.03 10.3

Over 1 0 1 1 0 1 1 1 1 1 1.29 12.9

Mass 0 1 0 1 1 0 0 0 0 0 1.09 10.9

Dide 0 0 1 1 0 0 0 0 1 1 1.18 11.8

Sally 1 0 0 1 0 1 1 1 0 0 1.03 10.3

Reich 0 1 0 1 1 0 0 0 0 0 1.09 10.9

Thomas 0 0 0 1 0 0 0 0 0 0 0.91 9.1

Constellation


Down 45 23 Bachelor's Procurement 3 3 2 8 13.1

Dover 58 10 w/o Tertiary Sales 1 3 1 5 8.2

McCaverty 55 21 w/o Tertiary Infrastructure 2 4 1 7 11.5

Over 38 16 Bachelor's Finance 1 4 2 7 11.5

Mass 51 35 w/o Tertiary Sales 4 1 1 6 9.8

Dide 43 6 w/o Tertiary Production 3 2 1 6 9.8

Sally 50 15 w/o Tertiary HR 4 4 1 9 14.8

Reich 48 7 w/o Tertiary Marketing 4 2 1 7 11.5

Thomas 45 28 w/o Tertiary Service 3 2 1 6 9.8

Characteristics


Down 9 10 9 7 10 4 8 10.3

Dover 10 9 8 8 10 9 9 11.2

McCaverty 9 10 7 6 10 5 8 9.8

Over 10 10 7 8 10 5 8 10.5

Mass 10 10 9 9 10 10 10 12.1

Dide 10 10 10 9 10 9 10 12.1

Sally 10 10 9 9 10 4 9 11

Reich 9 9 9 8 10 8 9 11.1

Thomas 10 10 10 10 10 7 10 12

Survey-Report 9.2




7.2 Approval of the Business Human Research Ethics Committee

Who leads the Company? - CSU Research Output

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