ThesisPDF Available

Appropriate Project Complexity Assessment at Airlines: An Analysis of Systematic Approaches

Authors:
  • Federal Digital Networks of the German Government

Abstract and Figures

Across industries, project complexity has been increasing over decades evolving to be a determining factor for the success or failure in project management. In this context, many studies have demonstrated a significant correlation between an appropriate evaluation of project complexity, the decision for a well-fitting project model and its resulting economic effects for its execution and outcome. At major airlines, however, snapshot experiences of project management practitioners have indicated difficulties in achieving effective decision making for both an adequate project model and its administrative budgets based on individual evaluation of project complexity resulting into controversial debates amongst project stakeholders. Aside the natural subjectivity in perception, an ambiguity in key terminology (e.g. the meaning of complexity itself, complex vs. complicated) and politically influenced motivations were observed as fanning factors, too. Eventually, negatively affected time of delivery, exceeding demand for financial means and resources, and inherited risks on project success had been reported consequences. Therefore, this study was conducted with the primary objective to identify an appropriate approach for project complexity assessment (PCA) that shall help airlines to take more effective decisions towards an efficacious project model. The secondary objectives were to qualify the potential in the airline industry for an improved organizational performance by PCA and to clarify key terminology. The objectives of the study have been accomplished by conducting a diligent literature research, an explorative survey of 20 airlines, the utilization of the Helmsman Complexity Scale accompanied by expert interviews and the evaluation of common PCA methods (PCAM). Consequently, the identified situation in airline practice of both its project complexity and its utilization of PCAM have revealed the potential for many airlines to considerably enhance their decision processes and project economics by employing the conclusively proposed PCAM. However, limitations apply: comprehensive PCAM can´t avoid subjective components and thus can´t entirely prevent discussions caused by perception and individual experience. While this research has been subject to the common airline category of so-called Full-Service Network Carrier (FSNC) it is likely applicable for most major airlines of all categories. As an outlook for further research, further developing, testing and calibrating the outlined custom PCA model by a representative set of airlines may substantiate its applicability in practice.
Content may be subject to copyright.
DEPAR TMENT OF STRATE GY AND
INNOV ATION
Project M anagemen t Group
AO. UNIV. P ROF. DR. M ARTINA HU EMANN
T +43-1 -313 36-5530, Welt handelsp latz 1,
Building D2, 1020 Vienna, Austria
martina. huemann@w u.ac.at, wu.ac.at
Master Thesis Evaluation
Title of the Master Thesis
Appropriate Project Complexity Assessment at
Airlines: An Analysis of Systematic Approaches
Author:
Sebastian Burgemeister
Supervisor:
a.o. Univ. Prof. Dr. Martina Huemann
Objectives of the Master Thesis
The objective of this Master Thesis is to analyse the current situation of project
complexity at airlines, validate their potential of improvement by a Project
Complexity Assessment (PCA), and then recommend an appropriate PCA
approach. The author brings extensive experience in the airline business, which was
the motivator to focus on this industry.
The research questions discussed in this Master Thesis are:
a) What does complexity mean?
b) What is the difference between complex, complicated and simple?
c) How to appropriately assess or measure complexity?
The Master Thesis focuses on commonly applied PCA methods applied for
projects of airlines known as Full-Service Network Carrier.
2
Research process and methods of the Master Thesis
For the explorative research character of this study, a qualitative research
approach is applied.
The author follows a research approach structured into five steps:
1. Building a theoretical foundation by literature review and critical
discussion.
2. Developing a research framework covering working definitions and
research models.
3. Collecting empirical data by conducting expert interviews and a survey.
4. Validating the airline situation and hypothesis, and evaluating PCA
methods.
5. Proposing an appropriate approach for project complexity assessment at
airlines.
Structure and key contents of the Master Thesis
The Master Thesis is organized in 7 chapters, plus a bibliography. Chapter 1
summarizes the research background, motivation, research rational, objective of the
thesis, research question, hypothesis and research process. Also, it presents an
overview of the structure of the thesis. Chapter 2 provides a profound theoretical
background about complexity, project complexity and its assessment. Chapter 3
describes the research process in detail and elaborates a research framework. It
provided the research strategy and methods used to address the research question
and to validate the hypotheses supporting the research rational. Chapter 4 presents
the results of the author´s empirical research followed by a comprehensive
description how airlines experience, approach and assess project complexity.
Chapter 5 describes the development and execution of a PCA methods evaluation
model. This results in a description of an evaluated set of appropriate methods and
a conclusive discussion. Chapter 6 presents the development of an appropriate
approach to assess project complexity at airlines based on the author´s theoretical
3
and empirical research. In chapter 7 the Master Thesis draws conclusions and
provides recommendations for practice.
Evaluation of the Master Thesis
The author has proven that he masters the topic “Appropriate Project Complexity
Assessment at Airlines: An Analysis of Systematic Approaches” in an excellent way.
The research based on a very comprehensive and excellent literature research that
goes much beyond the expectations of a Master Thesis. The author makes a real
contribution to project management practice in the context of airlines.
Despite some minor language issues and partly quite complicated language applied,
due to its theoretically sound and practically relevant contribution, the Master Thesis
is
graded with 1 (very good)
Vienna, 13 June 2019 Prof. Dr. Martina Huemann
VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS
MASTER THESIS
Title of the Master Thesis:
Appropriate Project Complexity Assessment at Airlines: An Analysis of Systematic Approaches
Author:
Matriculation number:
Program:
Supervisor:
I, Sebastian Burgemeister, hereby declare:
1. that I have written this master thesis, Appropriate Project Complexity Assessment at Airlines: An
Analysis of Systematic Approaches of 128 pages in length, independently and without the use of
any sources other than those listed,
2. that I have not made use of unauthorized assistance of any kind,
3. that I have not submitted parts of this work or the thesis as a whole, nationally or internationally,
as a graded paper, and
4. that this master thesis corresponds to the one assessed by my supervisor.
Date
Signature
06.05.2019
2
Abstract
Across industries, project complexity has been increasing over decades evolving to be a
determining factor for the success or failure in project management. In this context, many
studies have demonstrated a significant correlation between an appropriate evaluation of project
complexity, the decision for a well-fitting project model and its resulting economic effects for
its execution and outcome. At major airlines, however, snapshot experiences of project
management practitioners have indicated difficulties in achieving effective decision making for
both an adequate project model and its administrative budgets based on individual evaluation
of project complexity resulting into controversial debates amongst project stakeholders. Aside
the natural subjectivity in perception, an ambiguity in key terminology (e.g. the meaning of
complexity itself, complex vs. complicated) and politically influenced motivations were
observed as fanning factors, too. Eventually, negatively affected time of delivery, exceeding
demand for financial means and resources, and inherited risks on project success had been
reported consequences. Therefore, this study was conducted with the primary objective to
identify an appropriate approach for project complexity assessment (PCA) that shall help
airlines to take more effective decisions towards an efficacious project model. The secondary
objectives were to qualify the potential in the airline industry for an improved organizational
performance by PCA and to clarify key terminology. The objectives of the study have been
accomplished by conducting a diligent literature research, an explorative survey of 20 airlines,
the utilization of the Helmsman Complexity Scale accompanied by expert interviews and the
evaluation of common PCA methods (PCAM). Consequently, the identified situation in airline
practice of both its project complexity and its utilization of PCAM have revealed the potential
for many airlines to considerably enhance their decision processes and project economics by
employing the conclusively proposed PCAM. However, limitations apply: comprehensive
PCAM can´t avoid subjective components and thus can´t entirely prevent discussions caused
by perception and individual experience. While this research has been subject to the common
airline category of so-called Full-Service Network Carrier (FSNC) it is likely applicable for
most major airlines of all categories. As an outlook for further research, further developing,
testing and calibrating the outlined custom PCA model by a representative set of airlines may
substantiate its applicability in practice.
3
Table of Contents
Abstract ................................................................................................................................... 2
Table of Contents .................................................................................................................... 3
Table of Figures ...................................................................................................................... 6
List of Tables ........................................................................................................................... 8
Terminology of Structure ...................................................................................................... 8
1 Introduction ........................................................................................................................ 9
1.1 Motivation ..................................................................................................................... 9
1.2 Research Rational ....................................................................................................... 10
1.3 Research Question and Hypothesis ............................................................................ 10
1.4 Objective of the Master Thesis ................................................................................... 10
1.5 Research Approach ..................................................................................................... 11
1.6 Chapter Overview ....................................................................................................... 11
2 Complexity Foundations ................................................................................................. 13
2.1 Introduction of Project Complexity, Management and Economics ............................ 13
2.2 General Definition of Complexity On what is Complexity? ................................... 17
2.3 Complexity Theory A complex Journey through Sciences ..................................... 21
2.3.1 Introduction .......................................................................................................... 21
2.3.2 History and Paradigms of Complexity Theory ..................................................... 21
2.3.3 Basic System Logic the Concept of Non-Linearity .............................................. 24
2.3.4 Emergence and related Concepts of Complexity Theory ..................................... 25
2.3.5 Complex Systems ................................................................................................. 31
2.3.6 Complex Phenomena and related Concepts of Complexity Theory ..................... 32
2.3.7 The Edge of Chaos ............................................................................................... 35
2.3.8 Complexity Definitions in Complexity Theory .................................................... 36
2.3.9 Limitations in Complexity Theory ....................................................................... 38
2.3.10 Chapter Summary & Findings .............................................................................. 39
2.4 Complexity Theory and the Management of Human Systems ................................... 41
4
2.4.1 Introduction .......................................................................................................... 41
2.4.2 The Networked Civilization ................................................................................. 42
2.4.3 Numerosity and Nature of Components ............................................................... 44
2.4.4 Complex Adaptive Systems (CAS) ...................................................................... 47
2.4.5 CAS and Management .......................................................................................... 49
2.4.6 Limitations in Complexity Theory and Management ........................................... 53
2.4.7 Chapter Summary & Findings .............................................................................. 54
2.4.8 Conclusive Comment ........................................................................................... 56
2.5 The Distinction of being Complex A Matter of Perception and Context ................ 58
2.5.1 Introduction .......................................................................................................... 58
2.5.2 Ostensive Distinctions by relevant Authors ......................................................... 59
2.5.3 The Cynefin Framework ....................................................................................... 61
2.5.4 The Stacey Model ................................................................................................. 65
2.6 Critical Summary of the Literature ............................................................................. 66
3 Research Methodology .................................................................................................... 67
3.1 Research Process ........................................................................................................ 67
3.2 Development of a Research Reference Framework ................................................... 71
3.2.1 Development of Working Definition for Simple and Related Terms .................. 71
3.2.2 Development of Working Definitions for Complicated and Complex................. 73
3.2.3 Development of a Working Definition for Project Complexity ........................... 75
3.2.4 Development Methods for Reference Situation ................................................... 76
3.2.5 Development of Reference Use Case: Project Approval Meeting ....................... 80
3.2.6 Summary of Research Reference Framework ...................................................... 85
3.3 Hypothesis, Empirical Research and Research Design .............................................. 86
4 Analysis of PCA in Airline Practice ............................................................................... 87
4.1 Project Complexity Level and Common Project Types at Airlines ........................... 87
4.1.1 Introduction .......................................................................................................... 87
4.1.2 Research Methods and Design ............................................................................. 88
5
4.1.3 Results and Analysis ............................................................................................. 91
4.1.4 Summary and Findings ......................................................................................... 96
4.2 The Context of Project Complexity and common PCA at Airlines ............................ 98
4.2.1 Introduction .......................................................................................................... 98
4.2.2 Research Methods and Design ............................................................................. 99
4.2.3 Results and Analysis ........................................................................................... 100
4.2.4 Summary and Findings ....................................................................................... 106
5 Evaluation of PCAM for Airline Practice ................................................................... 108
5.1 Introduction ............................................................................................................... 108
5.2 Research Methods and Design .................................................................................. 108
5.3 Results and Analysis ................................................................................................. 111
5.4 Summary and Findings ............................................................................................. 119
6 A custom PCA Model for Airlines (Conceptual Blueprint) ....................................... 119
6.1 Introduction ............................................................................................................... 119
6.2 Step 1.0: Simple or Not Simple ................................................................................ 120
6.3 Step 1.1: Domain Simple Simply Complicated vs. Simply Complex ................... 122
6.4 Step 2: Domain Complex Complicated or Complex ............................................. 125
6.5 Visualized custom PCA Model for Airlines ............................................................. 126
7 Discussion and Conclusion ............................................................................................ 127
7.1 Discussion ................................................................................................................. 127
7.2 Conclusion ................................................................................................................ 128
Bibliography ........................................................................................................................ 130
Appendix ............................................................................................................................. 138
6
Table of Figures
Figure 2-1: Project life cycle and project management process (Cicmil et al. 2009, p.3)........ 14
Figure 2-2: Project management performance, functions of project model complexity (Vidal et
al. 2007, p.2) ............................................................................................................................. 15
Figure 2-3: Process of Literature Review and for Complexity Theory .................................... 21
Figure 2-4: The many Foundations of Complexity Science (NWO, 2014, p.10) ..................... 22
Figure 2-5: 2018 Map of the Complexity Sciences (Castellani, 2018) .................................... 22
Figure 2-6: Basic System Logic: Linear and Cyclic Relationships of Nuclear System Elements
.................................................................................................................................................. 24
Figure 2-7: System Types with Boundaries and Surroundings ................................................ 28
Figure 2-8: Nested Systems in Ecosystem ............................................................................... 29
Figure 2-9: Review process for Complexity Theory and Human Systems .............................. 41
Figure 2-10: A schematic history of human civilization (Bar-Yam, 2002, p.18) ..................... 42
Figure 2-11: The concept of communications path complexity (peer-to-peer communication)
(Trip, 2011) ............................................................................................................................... 44
Figure 2-12: Perception of Project Complexity attributed by Attributes of Complex Adaptive
System A Theoretical Framework (Rakhman & Zhang, 2008, p.10).................................... 48
Figure 2-13: Chapter Process The Distinction of being Complex A Dictionary View ............ 59
Figure 2-14: The Cynefin Framework (Snowden & Boon, 2007) ......................................... 61
Figure 2-15: The Cynefin Framework (Snowden, 2010) ....................................................... 61
Figure 2-16: Adopted Excerpt from “A Leader’s Framework for Decision Making ............. 62
Figure 2-17: Cynefin Framework (Kiddy & Partners, 2016) ................................................... 63
Figure 2-18: The Stacey Model ................................................................................................ 66
Figure 3-1: Chapter structure of Research Methodology ......................................................... 67
Figure 3-2: Research Process Stage 1 ...................................................................................... 67
Figure 3-3: Research Process Stage 2 ...................................................................................... 68
Figure 3-4: Research Process Stage 3 ...................................................................................... 68
Figure 3-5: Research Process Stage 4 ...................................................................................... 69
7
Figure 3-6: Research Process Stage 5 ...................................................................................... 70
Figure 3-7: Ontological Taxonomy of Simplicity .................................................................... 73
Figure 3-8: UML Use Case Diagram Example (uml-diagrams.org, 2018) .............................. 78
Figure 3-9: Use Case Example SITA (SITA, 2018) ................................................................. 79
Figure 3-10: Perceptions of Project Complexity during a Project Approval Meeting ............. 85
Figure 4-1: Helmsman Complexity Scale (ICCPM, 2017) ...................................................... 88
Figure 4-2: Goals-and-Methods Matrix (Turner & Cochrane, 1993) ....................................... 88
Figure 4-3: Characteristics of Sample (Burgemeister, 2019, p.4) .......................................... 101
Figure 5-1: PCSM Evaluation Result ..................................................................................... 118
Figure 6-1: Example screenshots from criteria development in Microsoft Excel .................. 120
Figure 6-2: Process Visualization for Step 1.0: Simple or Not Simple .................................. 122
Figure 6-3: Step 1.0: Simple or Not Simple Domains ............................................................ 122
Figure 6-4: Process Visualization for Domain Simple Simply Complicated vs. Simply
Complex ................................................................................................................................. 124
Figure 6-5: Step 1.0: Simply Complicated vs. Simply Complex Domains ............................ 124
Figure 6-6: Process Visualization for Domain Complex Complicated or Complex ........... 125
Figure 6-7: Step 2.0: Domain Complex Complicated or Complex ..................................... 125
Figure 6-8: Custom PCA Model for Airlines ......................................................................... 126
8
List of Tables
Table 2-1: General Complexity Definitions ............................................................................. 18
Table 2-2: The “knowable” schema (Perez, 2017) ................................................................... 64
Table 2-3: The Current State of Knowledge (Perez, 2017) ...................................................... 64
Table 3-1: Definitions and synthesized Derivatives of the Word Simple and related Terms .. 72
Table 4-1: List of Participants for the Telephone Interviews of Airline Experts ..................... 90
Table 4-2: Methodically Evaluated List of Projects (Categories) at Airlines .......................... 93
Table 4-3: Distribution of Type 4 Project Categories per Range of Complexity Level ........... 95
Table 4-4: Distribution of Business Areas per Range of Complexity Level ............................ 96
Table 4-5: Definitions of Project Complexity by Survey Feedback (Burgemeister, 2019, p.7)
................................................................................................................................................ 102
Table 4-6: Project Complexity increasing Criteria (Burgemeister, 2019, p.10) ..................... 104
Table 4-7: Project Complexity decreasing Criteria (Burgemeister, 2019, p.10) .................... 105
Table 5-1: Multi Criteria Analysis of CIFTER ...................................................................... 110
Table 5-2: Pre-Evaluated Results of Desktop Research for PCAM ....................................... 115
Table 5-3: Synthesized PCSM ................................................................................................ 117
Table 5-4: Synthesized PCSM for Airline Practice ................................................................ 117
Terminology of Structure
The thesis is structured by the following elements and indications:
Parts by a headline with integer prefix. For example: 1 Top Level
Chapters by headline and decimal prefix: For example: 1.1 Second Level
Sections by headline and a decimal prefix plus one outliner: 1.1.1 Third Level
Subsectionby headline and a decimal prefix plus two outliners: 1.1.1.1 Fourth Level
9
1 Introduction
1.1 Motivation
Like many companies today, major airlines are large and complex business organizations that
have been facing the ubiquitously increasing complexity of the modern, continuously changing
corporate environment in a more and more globalizing world. Logically, this complexity and
rapid change is impacting their conduct of project management having exhibited the need to
evaluate project complexity in favor of good decision making. Pinpointing for this study, project
complexity assessment (PCA) is usually aiming on a management decision for both an
appropriate project model (e.g. traditional, agile or a suitable mix of both) and the respectively
required resources (e.g. people, means, time) for its execution. The expected benefit of accurate
PCA is spanning three dimensions by addressing a considerable increase in project performance
(faster implementation), project efficiency (less effort and means) and project efficacy (higher
likeliness for project success). Reflecting this economically, one could assume best practice
application of PCA would exist across industries and therefore at airline organizations as well.
During the last years of our professional conduct at airline practice, however, project managers
and senior decision makers from a couple of major airlines have reported difficult debates in
project approval meetings arising by the evaluation of project complexity based on personal
experience and perception. In such cases, the discussion often evolved to fundamental debates
addressing the basic questions of a) “What does complexity mean?”, b) “What is the difference
between complex, complicated and simple?” and c) “How to appropriately assess or measure
complexity?. Due to diverting motivations of decision makers accompanied by an unsystematic
PCA without an acceptably objectified reference and involving ambiguous key terminology,
quite often, decisions for rather questionable types and dimensions of project models were taken.
This holistically was thought to negatively have impacted both the project performance and its
efficiency, and was seen as posing significant risks on the project success.
While such subjective evaluation of project complexity has been a matter of profound research
by scientists and practitioners and thus has led to a variety of systematic approaches to better
objectify the evaluation process, the deployment of those at airline practice couldn’t be neither
observed by the reporting managers nor by ourselves.
For our study ambitions consequently, it has been interesting to research two focus areas: How
do airlines experience PCA in their project management affairs in general and which PCA
approaches would be recommendable for them. For this ambition, we also considered a deep
dive into the academic cosmos of complexity itself and its related concepts and terminology.
10
1.2 Research Rational
After the successful completion of our research, the added-value of the endeavors will be
threefold with a practical, a theoretical and a personal component. By the recommendation of an
appropriate PCA approach for airlines, those companies affected by the lack of such approach
may economically improve in these three vital dimensions: their project performance, their
project efficiency and their project efficacy. Beyond this, our analysis may serve as an industry
benchmark that can help airlines to compare their PCA situation (e.g. approach, requirements,
maturity). Also, the currently unknown room for the corresponding organizational improvement
at the airline industry will be qualified and by this the results will contribute to the body of
knowledge for further research in related fields. Eventually, on a personal level, the insights out
of this study will have enriched both our academic acumen in terms of complexity science and
our professional competencies to deliver prosperous project management in practice.
1.3 Research Question and Hypothesis
Based on both the literature background and experiences anecdotally reported from airline
practice, we have conceived the research question. Q1) Which systematic approach is
appropriate to assess project complexity at airlines? is the pivotal question to be answered in
this study. Q2) What is complexity?, Q3) How is the word complex different to simple,
complicated, easy and difficult?, Q4) Which methods are available to assess project
complexity and Q5) What is the current situation of project complexity and its assessment in
airline practice? are also important for investigation to support the focus of the research. To
qualify the economic reasonability of the study, we assume the following hypotheses to be true:
Many airlines have the potential to improve their project performance by the
deployment of an appropriate PCA approach.
1.4 Objective of the Master Thesis
By addressing the research question, the objective of our thesis is to set up a research framework
to first discover the current situation of project complexity at airlines, validate their potential of
improvement by PCA, and then recommend an appropriate PCA approach. This includes
obtaining the required theoretical foundation to conduct the study, defining ambiguous key
terminology and a reference use case for project approval meetings at airlines, investigating
commonly available methods, and researching specific requirements on evaluation methods for
airline practice. The scope of this research is limited to commonly available PCA methods and
mainly to the prevalent airline category known as Full-Service Network Carrier (FSNC). To
direct the research towards the objective a research process was developed as follows.
11
1.5 Research Approach
We propose the following research approach structured by 5 stages:
1. Building a theoretical foundation by literature review and critical discussion.
2. Developing a research framework covering working definitions and research models.
3. Collecting empirical data by conducting expert interviews and a survey.
4. Validating airline situation and hypothesis, and evaluating PCA methods
5. Proposing an appropriate approach for project complexity assessment at airlines.
These stages will be will described in more detail in Chapter 3 Research Methodology.
1.6 Chapter Overview
The thesis is organized into 7 chapters structured and explained as follows:
Chapter 1 Introduction: This chapter summarizes the research background, motivation,
research rational, objective of the thesis, research question, hypothesis and research process.
Also, it presents an overview of the structure of the thesis including its vital details.
Chapter 2 Complexity Foundations: This chapter provides a profound theoretical
background about complexity, project complexity and its assessment. Relevant notions and
key terminology will be introduced by employing literature from complexity science to
complex project management. Thereby, basic relations to businesses, economical
reasonability, management approaches will be highlighted as well as common project
complexity assessment methods. Gaps in the available body of knowledge are outlined and
by this addressed as subject for our continued research.
Chapter 3 Research Methodology: This chapter describes the research process in detail,
elaborates a research framework and reference system, and the strategy and methods used
to a) address the research question and b) to validate the hypothesis supporting the research
rational.
Chapter 4 Analysis of PCA in Airline Practice: This chapter will present the results of
our empirical research followed by a conclusive description how airlines experience and
approach project complexity and its assessment in current practice. While this will qualify
the potential of airlines to improve their project performance by an appropriate PCA
approach in general, our findings will serve as an important input helping to identify suitable
PCA methods in Chapter 6 and proposing an appropriate PCA approach in Chapter 7.
12
Chapter 5 Evaluation of PCAM for Airline Practice: This chapter will describe the
development and execution of our PCA methods evaluation model to identify fitting
PCAM for airlines. It will cover determining factors from literature and those empirically
researched being applied on commonly available PCAM. This will result in a description
of an evaluated set of appropriate methods and a conclusive discussion.
Chapter 6 A custom PCA Model for Airlines (Conceptual Blueprint): This chapter
will present the development of an appropriate approach to assess project complexity at
airlines employing the results and findings realized in our theoretical and empirical research
followed by a summary and reflection of the model.
Chapter 7 Discussion and Conclusion: This chapter will discuss and summarize the
results of our research and outline the achievements in context of its utilization in practice
and contribution in research. Beyond this, we will also cover the limitations of the study
and recommend areas for further research that have been enabled by our work.
13
2 Complexity Foundations
2.1 Introduction of Project Complexity, Management and Economics
As a mutual context, we all would agree with the impression of living in a complex and dynamic
world. Nobody would deny the perception, that this complexity and dynamism have not only
been increasing but have rather accelerated in such rate of change over the last century. It’s not
far to surmise, that the progression of this isn´t just a perception of individuals but can be
observed in all aspects of modern life including the global economy, the business community
and the organizational landscape. Thus, handling the phenomenon of rising complexity and
dynamism in the corporate ecosystem, externally and internally, has become a vital requirement
for continued survival in today´s competitive markets. This ubiquitous trend has been
continuing to constantly influence industries in general and thus is mattering for every
profession. Consequently, the growth of complexity has been gaining attention especially in the
field of project management since, in accordance to the trend, projects and its management
undoubtedly have become more complex during the past decades, too. Out of this development,
the deployment of adequate project management methodology has been an organizational
response at many companies to target both: handling the evolving project complexity
effectively and reducing complexity related risks. To meet the associated demand for formal
methodologies across industries, several standards and compendiums have been arising and
continuing in their development. Today, these methodologies offer a rich set of managerial
methods tailorable to the specific requirements of an organization or a particular project.
Depending on the industry and geographical region, PMI/PMBoK®, IPMA/ICB, Prince2®,
APM, or Happy Projects! are commonly known examples to mention.
While these project management frameworks have been proven to be successful in providing
prescriptive methodology for structurally complex projects implicating a stricter rigor in the
application of an operationalized project management model, projects exhibiting complexity
due to sociological aspects or product innovation have shown to be less effective manageable
by a merely control driven, prescriptive approach. Such type of complexity in projects poses a
challenge to the managerial endeavors because it affects the predictability of the project
behavior and therefore hinders a rigid and reliable planning. This is logical, since the need for
creativity in innovation and the flexibility to react on unpredictable situations are by nature less
effective to meet by control and prescription but require adaption and exploration during the
project conduct. To handle such type of project complexity, new project management
frameworks have been developed. These commonly are used complementary or alternatively
14
to the prescriptive models and have been coined with the term agile project management. Most
project managers will probably have heard of Scrum developed by Jeff Sutherland (and John
Scumniotales, and Jeff McKenna) in the mid-90’s as one of the most popular agile frameworks
in this field.1
However, the execution of any formal project management model (or in short, project model)
usually creates notable and therefore costly administrative effort in addition to those productive
tasks which directly contribute to the outcome of a project. Supplementary, such administrative
effort requires time in its conduct and therefore may impact the duration of a project. While an
under-dimensioned amount of administrative effort would result in disorganization and thus
would negatively impact the cost, time (duration) and specification (quality) of a project. These
are known as the three common dimensions and main indicators of project performance also
referred to as the Golden Triangle (adaptable by negotiation amongst project stakeholders
during the concept phase of the project life cycle) or the Iron Triangle (fixed constrains at the
handover/closure phase) as illustrated in Figure 2-1. The same undesired effect in project
performance would occur through an over-dimensioning of methodical procedures.
Figure 2-1: Project life cycle and project management process (Cicmil et al. 2009, p.3)
In project management research, the appropriate dimensioning of management methodology is
not only discussed in context of the correlation between the level of project complexity and the
corresponding administrative effort. Indeed, there is project complexity to consider and beyond
this, there is complexity in the management of the same. It is argued, that project management
performance is related to the project model complexity, stating that the project model should
1 there are others who vouch for Hirotaka Takeuchi and Ikujiro Nonaka as inventing Scrum in 1986. (Krishnamurthy, 2012)
15
reach a lower level of complexity without reaching an upper level of complexity so that
unnecessary complexity resulting in high risks and costly administration gets avoided. This is
in order to create more value by effective tasks since project management performance,
functions of project model complexity, is strongly likely to have an optimum as shown in Figure
2-2. Thus, the effort to perform redundant tasks emerging through over-dimensioning of the
project model and its resulting increased complexity in management would affect the project
performance and may even jeopardize the project success. (Vidal et al. 2007).
Figure 2-2: Project management performance, functions of project model complexity
(Vidal et al. 2007, p.2)
The impact of project complexity on project performance, and consequently on a successful
outcome, is well underlined in a more recent journal article by Luo et al. (2017) who state:
Researchers have achieved consensus that project complexity has a negative effect on project
performance and thus have focused their efforts in the areas of risk management, management
style, and adaptive capacity.’ (Luo et al. 2017, p.9)
For the efficient management of complex projects, therefore, it is a natural requirement to
decide for a) the most effective balance between prescriptive and adaptive project management
methodology based on the type of complexity encountered, and b) the optimally tailored set of
methodologically available tools and techniques used for the respective project model suitable
to manage the specific level of project complexity. (PMI, 2017)
Usually, the decision for an appropriate type and dimension of the project model are taken at
the initiation phase of a project by the project stakeholders in steering function whereby
situational adjustments of the model may be decided at later stages. Reasonably, good decisions
16
for an appropriate project model require an assessment of both the predominant type of project
complexity as well as the level of project complexity. (Cooke-Davies, 2011; Cicmil et al. 2009)
While project complexity is known as a matter of perception involving the individual
experiences of assessors and the context of the assessment, decisions based on project
complexity can be affected by political motivations of the decision makers. Such political
motivations, for example, can originate from a limited budget available during a financial
period to fund the project. This available budget may be exceeded due to the required resources
(administrative cost) for an appropriate project model. Consequently, the budget owner may
see the cost of the proposed project model as a target for cost savings leading to potentially
inappropriately smaller dimensioned models and thus to inappropriate decisions. So-called
hidden agendas of the project stakeholders involving different expectation on the project
conduct and outcome would add another aspect. Due to these subjective influences, different
viewpoints may arise about the encountered type and level of project complexity during
decision processes. Thereby, different opinions may emerge about the appropriate type and
dimension of the project model and its related impact on the golden triangle which together are
difficult to discuss without an objectified reference including an aligned understanding of key
terminology. Thus, to support a better decision making, systematic approaches of project
complexity assessment (PCA) have been developed in form of formal assessment methods by
both project management researchers and practitioners. (Cooke-Davies, 2011; Remington &
Pollack, 2007).
In summary, PCA methods can support a more transparent and objectified assessment of project
complexity during project decision processes potentially resulting in more appropriate
decisions for the design of the project model. Therefore, the utilization of suitable PCA methods
in decision processes have the potential to improve the project performance and eventually may
increase the likeliness for project success. However, it has to be regarded according to
PMBOK® Guide: It is possible for a project to be successful from a scope/schedule/budget
viewpoint, and to be unsuccessful from a business viewpoint. This can occur when there is a
change in the business needs or the market environment before the project is completed.(PMI,
2017, p.35)
Because a deeper understanding of complexity as a concept and its common terminology has
been essential to research project complexity and its measurement, we will provide the
fundamentals as synthesized from literature in the next chapters.
17
2.2 General Definition of Complexity On what is Complexity?
If you're not confused, you're not paying attention.
Tom Peters
Scores of books and articles have been written on complexity, and various definitions for this
have been suggested in different fields of science. The use of the word complexity itself is a
widespread, common usage with an individual understanding of what the term means. The
variety of meanings becomes apparent by 31 definitions of complexity listed at the
Massachusetts Institute of Technology (MIT) in cooperation with the Santa Fe Institute (SFI)
according to Overman (2001). Seth Lloyd a professor at the MIT known for his research area
of the interplay of information with complex systems, especially quantum systems offered 32
definitions in his book ‘Programming the Universe’ (Lloyd, 2006) while more than 30 versions
have been reported by Bac Phuong Dao in his more recent doctor thesis “Exploring and
Measuring Project Complexity” (Dao, 2016).
Beyond this, there seems only limited hope reasonable for discovering a meaningful single-
universal definition of complexity in future since Peter Corning a complex system scientist at
the Institute for the Study of Complex Systems (ISCS) comments to the endeavor ‘if someone
does develop a grand, unifying definition-description of complexity, I predict that it will add
very little to the tree of knowledge (pardon the pun). But that shouldn’t deter us from trying;
the very effort to do so will surely enrich our understanding (Corning, 1998, para 10).
Almost two decades later, Steve Burbeck in 2015 ascertains There has been serious theoretical
work by heavyweights such as Andrey Kolmogorov, Gregory Chaitin, and Stephen Wolfram to
define complexity in a general, rigorous, and formal way. while relativizing straightly But
there is no general definition or theory of complexity. Nor have any of those attempts given rise
to a theory of complexity suitable for characterizing the way the human brain models the world
(Burbeck, 2015, para 2).
However, following Corning’s encouragement of achieving a more enriched understanding, we
have provided a prominent set of relatively general definitions together with a brief review of
these. This includes additional viewpoints of recognized scientists and key authors. Four of
those definitions described as being most cited according to Dao’s (2016) diligent screening
process and two others sourced from dictionaries are exemplarily listed in Table 2-1.
18
Authors
Year
Complexity Definitions
Perrow
1965
‘The complexity of a task is the degree of difficulty and the amount
of thinking time and knowledge required to perform the task.’
Edmonds
1999
‘Complexity is that property of a model, which makes it difficult to
formulate its overall behavior.’
Brockmann
&
Girmscheid
2007
‘The complexity is the degree of manifoldness, interrelatedness, and
consequential impact of a decision field.’
Hass
2008
‘Complexity is characterized by a complicated or involved
arrangement of many inter-connected elements that it is hard to
understand or deal with.’
Merriam-
Webster
Dictionary
2018
Complexity: ‘The quality or state of being complex’
Complex: (1) ‘a whole made up of complicated or interrelated parts’
(2) ‘a group of obviously related units of which the degree and nature
of the relationship is imperfectly known’
Oxford
Dictionaries
2018
Complexity: ‘The state or quality of being intricate or complicated.’
Table 2-1: General Complexity Definitions
As a fair commonality to discover in this selection, complexity has been revealed as the property
or degree of something that is both compound and difficult to understand. However, while some
authors refer that something to structural entities such as ‘arrangements, a whole made-up’ of
parts, units or elements, others think of a decision field, a task or a model. This may
appear confusing for the broader audience, because those named entities usually relate to quite
different concepts in practice. In addition, grasping a consistent conception of complexity from
those definitions seems somehow hindered by its variety and ambiguity in semantics. For
example, comparing the Merriam-Webster (2018) dictionary definitions Complexity is the
quality or state of being complex with complex meaning ‘a whole made up of complicated or
interrelated parts poses the question of how the words intricate and complicated are
different to complex. Also, the authors assign attributes with quantitative characteristics such
as manyor manifoldness, but what does it mean in absolute numbers? In this context, another
aspect becomes apparent by reading syntax like difficult to formulate, hard to understand’,
obviously related or imperfectly known. Undoubtedly, these characteristics live in the eye of
the beholder implicating the individual ability to comprehend and communicate those.
19
For Cicmil et al. (2009, p.19) who is an excellent and often cited reference for the exploration
of complexity of projects the encountered linguistic sensation would be no surprise since they
believe any discussion on a concept as broad as complexity or complex projects is bound to
encounter risks inherent in the use of language’. Supplementary, as Cooke-Davies et al. (2011,
p.33) demonstrate in their book Aspects of Complexity: Managing Projects In a Complex
World” a compendium with contributing editors such as the distinguished expert Terry M.
Williams, PhD, known for his essential publications in modelling complex projects the
definition of complexity is billed as highly problematic because it is influenced by perception
and context. In this course, their editor in chief premier awarded by the Association for Project
Management (APM) for his outstanding contribution and a PhD in Project Management
shares his finding (Cooke-Davies et al. 2011, p.2):
Complexity, then, is relative to an individual’s understanding.
In a web article, the Stellenbosch Institute for Advanced Study (SIFAS, n.d., para 5) concludes,
that complexity is a slippery notion; the term is used in a myriad of contexts. […] as a matter
of fact, we do not really have a language which can deal with general complexity. As the
theoretical biologist Robert Rosen[2] said, we can only approximate an understanding of such
complexity by employing a plurality of descriptions or models.
George J. Gumerman and Murray Gell-Mann who is a Nobel laureate in physics, both
distinguished fellows at the SFI, which is known as “The world headquarters for complexity
science” also think complexity isn’t easy to define. For them, it is apparent that in most
scientific usage and in much ordinary discourse what is meant by the complexity of a system
being observed is, more or less, the length of the description given by the observer. (Gumerman
& Gell-Mann, 1994)
Reflecting on these authors viewpoints, the SIFAS (n.d.) confirms its difficulties in finding the
right language to describe complexity. This is in line with Cicmil et al. (2009) who have
addressed language as a potential risk for such attempt. The impression, that context poses a
major factor for general definitions is agreed by both SIFAS (n.d.) and Cooke-Davies et al.
(2011). The latter see perception as a critical influence and this goes well along with
Gumermann & Gell-Mann (1994) who have highlighted the dependence on the observer as a
clearly subjective condition. All authors have experienced defining complexity as at least
ambitious if not impossible for universal validity. However, from a semantic perspective, as
2 Robert Rosen (1934-1998) was a theoretical biologist who strived to answer the question the Nobel physicist
Erwin Schrödinger posed in 1943: “What is Life?”’ (Gwinn, 2019)
20
taken in this chapter, most authors would agree with the current director of the Institute for the
Study of Complex Systems (Corning, 1998, para 6):
[The word complexity]is used in many different ways
and encompasses a great variety of phenomena.
Jorge Taborga (2012) currently serving as Executive Vice President, Research &
Development at Omnicell introduces his insightful post on a web blog of the Saybrook
University, Oakland, California, by asking So what is complexity?’ and concludes seemingly
laconically but, for those who have researched the subject, rather meaningfully:
‘The definition of complexity is… complex.’ (Taborga, 2012, para 2)
In summary, we have introduced to common definitions of complexity, examined their key
semantics and learned how other authors view the cause. It turned out that lexical definitions
lead to a merry-go-round of tautological expressions. These offer a vague notion of complexity
as a “contrasted attribute of an entity comprising many interrelated components with an overall
behavior that is difficult to comprehend”. The state or degree of complexity is described by
highly interpretable vocabulary of qualitative and quantitative nature which signifies its
potential for measurement on one side. On the other side, there is a strong indication that
complexity is a matter of differing perception and dependency on context. This usually doesn’t
help the affairs of both objective measurement and achieving a collective understanding as the
natural goals of commonly set for academic work. However, the authors have offered
perspectives to better conceive complexity which is a) by employing a plurality of descriptions
models (Robert Rosen in SIFAS, n.d.) and b) to think of complexity as an attribute to a system
(Gumerman & Gell-Mann, 1994).
To account for these perspectives, the next chapters will cover complexity theory as like
Complexity theory itself comprises a broad group of ideas, models, and predictive descriptions
about how complex systems behave.’ (Cooke-Davies et al. 2011, p. 31).
21
2.3 Complexity Theory A complex Journey through Sciences
The complexity of things - the things within things - just seems to be endless.
I mean nothing is easy, nothing is simple.
Alice Munro
2.3.1 Introduction
In this chapter we will introduce to complexity theory and its underlying concepts to a) found
an academic understanding of complexity, b) provide an appropriate research paradigm. This
includes the origins of complexity theory, the perspectives of system theory and implicated
fields of sciences with a focus on the important concept of emergence. The introduction is
accompanied by an encompassing background about complex systems and the illustration of
their distinct phenomena and characteristics. To substantiate clarity in terminology, common
definitions within the scope of complexity theory will be presented. Compromising the idea of
achieving clarity in definition and understanding by this chapter, we will briefly present insights
on the philosophical aspects of complexity and emergence indicating fundamental problems in
terms of defining and measuring both notions. The process of the concomitant literature review
relates to the structure of the chapter as illustrated in Figure 2-3.
Figure 2-3: Process of Literature Review and for Complexity Theory
2.3.2 History and Paradigms of Complexity Theory
Complexity theory, also commonly known as complexity science or in business referred to
alternatively as complexity management (Financial Times Lexicon, 2018), stems from
Cartesian philosophy, a Newtonian understanding of the nature of reality, and an Enlightenment
epistemology constituting the traditional, mechanistic world view as often described by the
allegory of a ‘clockwork masterpiece’ (Cicmil at al. 2009, p.22). It has been discussed in the
scientific community since the 1800s and progressively unfolded from chaos theory during the
1960s during an upcoming convergence of fundamental fields of science that previously had
been researched in a rather isolated way. These fields include by others: natural science,
mathematics, economics, sociology, chaos theory, system thinking, and management. Those
have been exemplarily illustrated by the Netherlands Organisation for Scientific Research
(NWO) in Figure 2-4.
22
Figure 2-4: The many Foundations of Complexity Science (NWO, 2014, p.10)
In addition, Brian Castellani (Castellani, 2018) provides a more holistically mapping of
complexity science as shown in Figure 2-5 which highlights its development over time and the
specific fields of application showcasing to the reader its very own complexity in research.
Figure 2-5: 2018 Map of the Complexity Sciences (Castellani, 2018)
At the addressed time of convergence in academic fields during the 1960s, scientists from a
wide range of disciplines started to question the basic assumptions of rational causality based
on determinism and linear reductionism as the so far predominant basis for substantial parts of
science.
23
The underlying philosophies are useful to know for the discourse and commonly defined as:
Determinism: ‘The theory holds that the universe is utterly rational because complete
knowledge of any given situation assures that unerring knowledge of its future is also
possible’ (Encyclopedia Britannica, 2018). It implies ‘that all events, including human
action, are ultimately determined by causes’ Oxford Dictionaries (2018).
Reductionism: ‘1: […] a theory or doctrine that complete reductionism is possible
Oxford Dictionaries (2018); ‘a view that asserts that entities […] are […] combinations
of entities of a simpler […] kind’ (Encyclopedia Britannica, 2018).
For reductionism theory, two viewpoints should be recognized: a) linear reductionism and b)
organismic reductionism. Linear reductionism suggests that entities are characterized by their
internal qualities and the whole is explainable through they study of its elements and causal
law. In contrast, organismic reductionism is the view where an entity can be reductively studied
but only in the context of the whole entity from which it obtains its elementariness. (Slife, 1993,
p. 248) In this context and important for our study Cicmil et al. (2009, p.21) note that the
deterministic, linear-reductionistic notion is derived from the Cartesian/ Newtonian/
Enlightenment paradigm from which the practice of project management has emerged.
The traditional paradigm was increasingly replaced by system thinking. System thinking origins
from system theory and studies the question how structures and orders emerge by the interaction
of many interconnected elements within complex systems. By this it has departed the traditional
view of linear reductionism and incorporates organismic reductionism as a more holistic
approach to understand the reciprocity between the whole and its parts. Remington Kaye who
is senior experienced in the further development of project management concepts and course
director of the Master of Project Management program at the University of Technology Sydney
and Julien Pollack an associate professor at the University of Sidney holding an Action
Research Ph.D. practicing project management research confirm this development and
complement:
Thinking of Systems is something we do naturally (Remington & Pollack, 2007, p.3)
Complexity theory accompanied by system thinking heralded its era of universal acceptance at
the SFI during the 1980s. Out of the illustrated, ongoing convergence of scientific fields, it
today presents a kaleidoscopic set of integrated concepts and so-called deep themes that seek
to explain phenomena not explainable by traditional theories. The significance of this
development for our study is excellently highlighted by Cicmil et al. (2009, p.21) who state:
24
The deep themes that are emerging from complexity theory can be said to amount to
nothing less than the expansion and enrichment of the Cartesian/Newtonian/
Enlightenment paradigm whereby the nature of the world we live in will be ultimately
comprehensible through empirical research.
2.3.3 Basic System Logic the Concept of Non-Linearity
Understanding complexity theory relies on the interplay of elements and their relationships in
systems which we will illustrate in this section. Thereby, we refer to system as a set of two or
more interrelated elements which by each have a) an effect on the functioning on the whole and
b) are affected by at least one other element of the system. These both properties should apply
to all possible subgroups of elements, too. (Ackoff, 1981, p. 15-16 in Laszlo & Krippner, 1998)
In literature with focus on system theory, systems are commonly categorizable into either trivial
or non-trivial systems. Trivial systems are constituted by both rational causality and linear
relationships at their atomic level which explains why these systems are recursively
determinable, predictable and entirely comprehensible through a reductionistic approach.
Analogically, nudging a line of dominoes initiates a chain of events where cause and effect are
fully knowable at any time. In contrast, non-trivial systems are complex because they comprise
non-linear relationships implicating feedback loops and circularity causality (Kirilyuk, 1997, p.
386). This reasons why the actual state of such systems is on one side rather unequivocally
ascertainable, but its past or future state only can be anticipated with uncertainty as this
compares well to the ancient The chicken or the egg causality dilemma. In complexity
science, this is addressed by the concept of non-linearity constituting a key concept. We have
illustrated exemplarily the two types of systems to highlight their linearity and non-linearity in
cause and effect in Figure 2-3 where from a linear view (1) a cause A makes an effect B happen
while in non-linear causality (2) B is also a cause of itself and modulates or perpetuates A. The
logic becomes circular when A causes B causes A and so on. In fields of science where the
exchange of information or energy is emphasized, this is also known as a closed feedback loop.
This is illustrated with a cause x1 initiated by A with effect on B is followed by a feedback
cause x2 from B with effect on A. (Simon, 2013; Richter & Rost, 2002)
Figure 2-6: Basic System Logic: Linear and Cyclic Relationships of Nuclear System Elements
25
For the purpose of our study, we define the interplay of elements on an atomic system level as
basic system logic.
In summary, we have shown that systems can exhibit different characteristics depending on the
logic their elements interact. The key finding is that systems can be either completely
understandable and predictable when cause and effect in their basic system logic are
unequivocally (trivial system), in contrast to systems which are ambiguous in that logic and
therefore the analysis of its current state doesn’t help to predict and its future behavior with
certainty (non-trivial systems).
While this has created a basic understanding of systems at their micro-level and introduced to
the Concept of Non-Linearity, the next section will cover systems from a holistic view because
‘Increasing the number of elements and the number of interactions can create unexpected,
emergent phenomena’ (Remington & Zolin in Cooke-Davies, 2011, p.126).
2.3.4 Emergence and related Concepts of Complexity Theory
Emergence is one of the central concepts to know for building a more comprehensive
understanding of complexity. Thus, we will present the general notion of emergence and a more
detailed view from system theory by references of commonly cited authors. By this we will
introduce to further concepts of complexity science and related theories and highlight their
relevance for the complexity of systems starting with a common paradox that is often addressed
to demonstrate nature of emergence but also the limitations of its idea.
2.3.4.1 The Whole, The Sum of its Parts and the Limitations of both Views
To explain emergence in context of complexity theory, we think of systems delimited by
boundaries. When numerous elements are interconnecting based on basic system logic they
constitute larger formations exhibiting new characteristics at their global boundaries that
haven’t existed at the lower level from which they have emerged. For example, our human brain
constitutes a system with an obvious biologically determined boundary. While its basic system
logic presents cells and electricity from a metabolic view, the analysis of this logic won’t reveal
our ability of cognition and feeling. Exhibiting a consciousness out of a neuronal network is an
emergent phenomenon, too. In other words, a more radical reductionistic view on the atomic
level with electrons, neutrons and protons won´t reveal further relevant insights how our brains
and sensors recognize something, how we feel about it or how we have developed our human
ability for consciousness.
26
These emergent phenomena holistically are described by the concept of emergence in
complexity science that explains, why something can be more than the sum of its parts.
However, Niklas Luhmann an eminent thinker in systems theory, who is increasingly
recognized as one of the most important social theorists of the 20th century (Albert, 2016)
argues that the question how something could be more than the sum of its parts is wrongly
asked. The interest should be how something could be less than the sum of its parts (Greve &
Schnable, 2011, p. 14). A third interesting perspective was given by Dr. Phillip W. Anderson
who won a Nobel Prize in physics in 1977 when describing complex systems More is
different.. (Czerwinski, 1998, p.253). In this context, it is worth to remember the notion of
organismic reduction which on one side promotes a reductive, micro-deterministic investigation
as meaningful but on the other side only in the context of the whole entity. While other analogies
can be observed in nature for example by swarms of birds or fishes, the SFI during the early
1980s simulated on a computer model how ants with rather frantic behavior at a microlevel are
contrasted to the more rhythmic behavior of the colony as whole. This example we will employ
to demonstrate complex phenomena later on. (Greve & Schnabel, 2011; Cicmil et al. 2009;
Hass, 2009; Christen & Franklin, 2004; Slife, 1993)
The irreducibility of emergent entities is not only relevant in natural science in context of
epistemology but also in human science in context of ontology as we can highlight under the
aspect of ontological identities. For example, a “group” is the abstract idea or identity of an
entity we mentally assign when we recognize individuals with established relations. Reducing
a group to its core identities the individuals is possible but the mentally given identity and
distinct qualities of a group will disappear. Thus, thinking of a group MEANS more in
ontological qualitative terms but it COUNTS less regarding the physical number of identities
we distinguish. From a practical view, emergence theorist would probably agree with seeing all
of these entities emerging by a group of each the project members to the project group, the
encompassing entity project to a group of projects which if interdepend may form a
program. All of these entities as a whole may constitute the project and program portfolio of a
company. Each of these entities can’t be reduced without losing qualities that only exist on their
aggregated level. Complexity as an attribute of such emergent entities is one of those qualities.
Related to the perceptual perspective of irreducibility and emergent entities, Cicmil et al.
introduce the concept of objects and ideas (Hacking, 2000, in Cicmil et al. 2009, p.20) as a key
concept for complexity thinking to understand project complexity pointing to the idea of
complexity in projects and the elements and characteristics that constitute a complex project.
Our rather simplified interpretation in short: ideas (relating to identities as addressed) mean
27
conceptions about particular aspects of reality while objects (relating to entities as addressed)
exist without an observer as parts of the reality. This context is relevant because it warrants for
a short note on the limitations in the concept of emergence: The ontological perspective and the
resulting matter of subjectivity may contradict the concept of emergence because according to
Peter Corning (2002, para 3), some theorists seem to take the position that emergence does not
exist if it is not perceived; it must be apparent to an observer. As similar as with the definition
of complexity, Corning (2002, para 7) also concludes There is no universally acknowledged
definition of emergence, nor even a consensus about such obvious examples as water. And he
rhetorically wonders And if emergence cannot be defined in concrete termsso that you will
know it when you see ithow can it be measured or explained?.
This poses questions about the notion of reality itself and therefore highly philosophical aspects.
For example, Cicmil et al. addresses extreme relativism when ideas and objects are seen as
totally separate while they relate to extreme realism when both constitute the same thing.
Because these philosophical matters seem to be inherited by the notion of complexity as our
literature has revealed, we will cover the affairs of reality, objectivity, subjectivity and how
these play a pivotal role for measuring complexity in later chapters.
However, the meaning of emergence as initially rendered in this section conforms with the
common view among many complexity theorists and helps us to better understand their subject
and related concepts. A summary of the most relevant of those will be given in the next
subsection.
2.3.4.2 The Concept of Communication
In complexity theory with the focus on system theory, emergent systems are often described as
open systems and therefore exchange information, energy or elements across their boundary as
illustrated in Figure 2-7. However, not all systems that exchange information are open systems
as the following definitions as sourced from Simon (2013) and Richter & Rost (2002) show:
Open Systems: Open systems have defined boundaries to their surroundings and can
exchange both elements, energy and information with their surroundings.
Closed Systems: In closed systems, no transfer of elements is possible across the
boundaries of the system, but energy and information may transfer.
Isolated Systems: Isolated systems don’t allow any transfer across their boundaries.
28
Figure 2-7: System Types with Boundaries and Surroundings
While physicists usually more often elaborate how mass and energy transmit in and out of
systems, other scientists discuss the affair as interchanging information in scope of information
theory, social science or management (for this study in particular the management of projects,
programs and portfolios) and describe this process by the concept of communication
(Remington & Pollack, 2007). Complexity theory acknowledges that humans by nature when
working together are open systems (Curlee & Gordan, 2011, p.4 with reference to Byrne, 1998;
Hass, 2009).
2.3.4.3 The Concept of Hierarchy
A specific type of emergent systems is called emergent nested systems. Those have been
exhaustively described by Christian Walloth (2016) beyond the scope of our study. However,
the relevance for our discourse is that systems can enclose other systems and those are
simultaneously enclosed by even other systems. This is illustrated in Figure 2-8 where the
systems A1 A3 are subsystems of B1, and B1 is a subsystem of the all-encompassing eco-
system. AB is a system that is extern to B1, yet it interchanges with one subsystem in B1. It
shows that the relationship between nested systems can connect multiple layers of the hierarchy.
Hass (2009) is addressing the issue more generally with the term nested systems and stating
Most systems are nested within other systems, and many systems are made up of smaller
systems’. Other authors describe nested systems as system-of-systems (Gideon et al. 2005) or
sometimes supra-systems embedded in an eco-system.
29
Figure 2-8: Nested Systems in Ecosystem
This holistically constitutes networked eco-systems where containing systems on each level
represent an elementary identity by its own being able to communicate across the hierarchy and
the entire environment. While in metaphysics claims this is discussed as hierarchical realism,
complexity scientists investigate such formations by the concept of hierarchy. Christen &
Franklin (2004, p.2), who have delineated the multi-level perspective in more detail in their
paper “The Concept of Emergence in Complexity Science recommend the following:
Note that the decomposition of a level into parts, as well as the whole system into levels,
are not unproblematic tasks and may depend on the objectives of a particular
investigation. Hierarchical realism claims only that the system is partitionable into levels,
not that those levels must always be independent of the pragmatics of investigation.
They also point out that in system thinking, the entities of the upper level must be consistent
with the entities and theories at the lower level. Management theorists will broadly agree that
this way of thinking directly relates to a project and program portfolio as the macro system
living within a company and its environment as the networked eco-system implicating
programs, projects and people on their lower levels.
It is a logical derivative that such system and component constellations won´t exist in a static
state but evolve over time leading us the role of dynamism and how this phenomenon takes us
to the notion of complex systems.
2.3.4.4 From Dynamic Emergent Systems to the Science of Complex Systems
Dynamism emerges by the multi-responsive communication across system hierarchies in
process by implicating both inter-systemic change and corresponding with the environment
30
over time constituting dynamic emergent systems (Cohen, 2000). It has revealed the
conceptional source for both system dynamism in hierarchies (Gideon et al. 2005) and its
definition relating to complexity specifically as like paraphrasing Cicmil et al. (2009, p.29): if
a system has the capacity within itself to respond to its environment in more than one way, it is
defined as a complex dynamical system (CDS). The encompassing aspect of complex system
dynamics and related phenomena is also addressed by dynamic system theory (DST) that stems
from mathematics. According to De Bot et al. (2007, p.8), DST is originally about very simple
systems such as the two coupled variables in a double pendulum. Even though such a system
has only two interacting variables or degrees of freedom, the trajectory of the system is
complex.
2.3.4.5 Summary of Section
In summary, the concept of emergence has shown that systems with permeable boundaries can
interconnect and thereby holistically constitute by each the elements of a larger systems that
exhibit irreducible characteristics of its own different to those from which they have emerged.
This has explained the notion that something can be more or less than the sum of its parts,
depending on the perspective whether its natural (referring to an object) or ontological
(referring to an idea), and therefore can’t be reduced without abandoning its inherited and
distinct nature. However, due to the implication of observers and perception, the concept of
emergence has shown some considerable criticisms. Beyond the philosophical dispute and
being relevant for both emergence and complexity science in general, the exchange of
information between systems has been introduced as the concept of communication, while the
notion of generated system levels has been addressed with the concept of hierarchy.
The consequently arising responsiveness and dynamism in emergent systems has been found
as the central source to warrant for the definition of complex dynamic systems. This has led us
to dynamic system theory (DST) which in more detail explained would exceed the scope of our
study. However, referencing DST has briefly showcased how complexity occurs even from
systems with simple structures and few elements (e.g. a pendula). DST also has been mentioned
due its prominent place in complexity theory as like: When applied to a system that is by
definition complex, such as a society or a human being, where innumerable variables may have
degrees of freedom, DST becomes the science of complex systems’ (De Bot et al. 2007, p.8).
This raises the question what is meant by saying a system is by definition complex and thus
we will provide common viewpoints in literature on the definition of a complex system. The
human aspect will be covered in the subsequent Chapter 2.4.
31
2.3.5 Complex Systems
This section will briefly illustrate the current state of science in the endeavor to define a
complex system in general. By this, we will provide a selection of common definitions to be
considered for this study.
Ladyman et al. (2012) who have dedicated more than 35 pages including various
investigations of common literature to answer the question What is a Complex System?
found out, that there is no concise definition of a complex system. This has been discovered
as well by David Geelan a senior lecturer in science education at the University of Queensland
who pragmatically concludes in his specific field: In many ways the field of complexity
science seems to me to define itself by ostension: not creating a fixed definition, but pointing to
particular examples neural networks, cellular automata, genetic algorithms and so on and
saying ‘this is a complex system’. For my purposes in this paper, […] complex systems will be
defined as those which are not amenable to reductive analysis. (Geelan, 2006, p. 9)
However, Ladyman et al. suggest a set of characteristics for complex systems to be
acknowledged and list non-linearity’, feedback, spontaneous order, robustness and lack
of central control, emergence, hierarchical organization and numerosity as the key
attributes being discussed in their study. In addition, they propose a tentative definition of
complexity (Ladyman et al. 2012, p.28) which they consider as a physical account of
complexity:
A complex system is an ensemble of many elements which are interacting
in a disordered way, resulting in robust organisation and memory.
Another definition, as representing the nature of many commonly suggested in literature, has
been attempted by the Department of Computer Science of the Cornell University in New York,
Ithaca, US defining two types of complex systems whereby Type (I) is characterized by its
numerosity of intricate interconnections, and Type (II) by the occurrence of complex
phenomena. The author supplements, if the components of system Type (I) are identical and
their interconnection is regular, then they holistically can appear quite simple (e.g. computer
memory chips) while systems of Type (II) can have concise descriptions but also may exhibit
chaotic behavior (e.g. weather). (Cornell CIS, 1997)
Padmakar Oak (2013, p.26-30) who conducted a PhD thesis about an approach to managing
the complexity of knowledge intensive business processes in his PhD thesis offers a well
elaborated and more detailed summary about defining complex systems, relevant authors and
their views. For example, he mentions different forms of complex systems like complex
32
adaptive systems and complex deterministic systems (Roos & Oliver, 1997)and echoes further
sets of characteristics that constitute complex systems as given by prominent authors, e.g.
Holland (1995), Simon (1996), Cilliers (1998), Plsek & Greenhalgh (2001), (Bar-Yam, 2003),
Casti (2003), Levin (2003). Based upon his review, he identified characteristics of complex
systems that would be relevant to business processes as a related topic to project management.
Reflecting on the authors research, there is no single universal definition of complex system
agreed, and the reasons may relate back to the difficulties to attempt the same with complexity
itself. However, there seem patterns and characteristics identifiable that describe a complex
system well. According to our review, the most relevant ones to build a knowledge foundation
for our discourse and to approach the attributes of project complexity are those characteristics
concluded by Ladyman et al. (2012). These we have already partly highlighted in the last section
as the concept of hierarchy, emergence, feedback and non-linearity, and the remaining others
we will address during the further discourse. Irreducibility and therefore not being fully
comprehensible by reductive analysis (Geelan, 2006) is a pivotal characteristic of complex
systems as well and will be a distinct matter to discuss for appropriate management tactics later
on. Looking at further definitions, the authors broadly agree about the two characteristics of
many elements involved being interconnected in a not-obvious way (numerosity) and the
occurrence of complex phenomena (such as organization or memory). However, while Cornell
CIS (1997) indicates both a) numerosity and b) complex phenomena distinctively to justify two
types of complex systems, Ladyman et al. (2012) see complex phenomena as the result of
numerosity and thereby providing the two of those characteristics (Type I and Type II) in one
single definition. Why both views of the authors are applicable to describe complex systems
and complexity itself but also why they are distinct in their characteristic, will be better
understood during the next chapters. This, as well, will support the understanding definition of
complex projects and its measurement. However, the basic characteristics that define complex
systems are the main points to take away from this section. They are leading us to the need of
learning more about the notion of complex phenomena and related concepts of complexity
theory as described in the remaining part of this subchapter. The important matters of
numerosity will be addressed thereafter in context of human systems in Chapter 2.4.
2.3.6 Complex Phenomena and related Concepts of Complexity Theory
The notion of complex phenomena one can consider as a science by itself. This becomes
apparent by Friedrich Hayek’s study of The Theory of Complex Phenomena which is,
according to Hayek a precocious and far-sighted attempt to illuminate a topic that has loomed
increasingly important in the 53 years since the printing of the paper: the epistemology of
33
complexity(Hayek, 1967). While a comprehensive delineation would break the mold for this
section, yet, a set of complex phenomena as commonly described in literature has demonstrated
by analogies in nature how complexity can be observed and understood from the angle of
complexity science. Hence, by prevailingly employing the essence of the views of our main
authors for project complexity related literature, we will present the key aspects as follows.
Thereby, we will highlight further concepts of complexity theory that matter to the cause.
2.3.6.1 The Concept of Sensitivity to Initial Conditions
Complex phenomena are derivatives out of turbulent formations with innumerable elements
exhibiting recurring patterns that spontaneously arise from small events those holistically being
part of global systems in a state of balance. For this, common allegories from nature exist in
literature and often serve to illustrate central concepts of complexity science. One of the most
popular known is the Butterfly Effect originating from Edward Lorenz who is recognized for
his pioneering contribution in chaos theory during the 1960s and showcased how a butterfly
wing flap at one part of the world can drastically change the weather conditions in another one.
While this created the understanding that all forces are connected as a significant landmark for
complexity science, it is the same that explains the cause by the concept of sensitivity to initial
conditions. (Cicmil et al. 2009; Curlee & Gordon; and others)
2.3.6.2 The Concept of Self-Organization
Involving the work from Lorenz, other scientist at that time started to investigate recurring
patterns which they called strange attractors in seemingly chaotic environments. These
recurring patterns relate to a specific type of CDS which Ilya Prigogine a Nobel laureate in
the 1970s for researching thermodynamics called dissipative structures because they are
continually both receiving and transmitting energy. While it is common to refer to the butterfly
effect causing hurricanes as the corresponding phenomenon within the global weather system,
Prigogine addressed by his research the issues of irreversible change and quasi-predictability.
These are explained by the mathematical concept of bifurcations which allows for an
anticipation what may happen based on the knowledge of the initial condition and the long-
term observation of recurring patterns. However, in non-linear causality it cannot answer
questions with certainty about the precise frequency, durations, magnitude or other
characteristics that determine a specific recurring pattern, neither is the impact on the global
system entirely foreseeable. Paradoxically, the weather system is also known as a globally
rather stable system yet locally unstable. This paradox addresses a series of complex
phenomena to which complexity theory offers a deeper understanding by the concept of self-
organization.
34
In this context, the scientists at the SFI discovered a state of balance occurring between order
and chaos during their observation of living and simulated ant colonies as briefly addressed
before in context of emergence. Cicmil et al. (2009, p.25) describe this phenomenon nicely:
Ants as the individuals exhibit chaotic tendencies, continually switching between frantic
activities and static inactivity. The colony, however, exhibits a pattern of behavior that is both
rhythmic and orderly. Stuart Kauffmann, one of the foremost biologists and commonly cited
in this context, compares the phenomenon to the different states of water. Water can exist in
three aggregate levels where ice represents the completely ordered one, steam the chaotic. He
points out, that it is the intermediate state (liquid) which offers the best opportunities to develop
complex activities.
2.3.6.3 The Concepts of Control
Out of the previously described research, a natural tendency of coherence between system
elements can be observed and thereby fosters both a) stability of the current system state and b)
resistance against external change. This is underlined by Remington and Pollack (2007, p.5)
who reveal Control is what holds the system together. Accordingly, this phenomenon can be
found as the concept of control in complexity science.
2.3.6.4 The Concepts of Fitness Landscape, Adaptiveness and Phase Transition
Complexity theorists use self-organization as well to explain, why systems develop the ability
to respond and adapt and thus better fit or survive during altering conditions and thereby
thinking of the concept of fitness landscapes. The phenomenon of adaptation itself is treated by
the concept of adaptiveness while the processes of morphing from one form to another one is
described by the concept of phase transition. How this is not only relevant to different states of
water or ant colonies but eventually involves mankind by its genetic core is highlighted by
Cicmil et al. (2009, p.25) accordingly: The fact that these simple molecules can organize into
three such qualitatively different structures is itself significant, but it is from Kauffmann’s
primary area of study (evolutionary biology) that the concept of “fitness landscapes” has
emerged, offering explanations for the success or failure of different species as their
environment changes. (Curlee & Gordon, 2011; Cicmil at al. 2009; Hass, 2009; Remington &
Pollack, 2007).
35
2.3.6.5 Summary of Section
While this brief journey through complex phenomena should have given an idea how
complexity evolves and looks like in nature thereby the concepts of sensitivity to initial
conditions, self-organization, control, fitness landscapes, adaptiveness and phase transition
have been introduced, its ontological perspective requires attention, too. Paraphrasing Hayek
(1967, p3): Complex phenomena are a matter of recognition of some regularity (or recurring
pattern, or order) of some similar feature in otherwise different circumstances since our minds
are so made that when we notice such regularity in diversity we suspect the presence of the
same occurrence and become curious to detect it. As useful to realize for any study, they
emphasize:
To such curiosity we owe the beginning of science
2.3.7 The Edge of Chaos
In context of complex phenomena, one important aspect is commonly highlighted addressing
that systems exist on a spectrum ranging from equilibrium (leads to paralysis or death) to chaos
(results in an inability to function) as Hass (2009, p.22) well points out. She is relating to Peter
Fryer a former Chief Executive of the Humberside Training and Enterprise Council (TEC) in
the UK and pioneer in introducing the principles of complexity and self-managed teams to the
organization (Thompson, 2005) when revealing:
The most productive state to be in is at the edge of chaos,
where maximum diversity and creativity lead to new possibilities.
A more detailed description of the edge if chaos is provided in an internet blog by Greg Fisher
(2012) in which he emphasis the difference between chaos as a deterministic system and
complexity as a non-deterministic, emergent system. This thought is followed by Ryan
Burnham (n.d.) who in his paper “An Overview of Complexity Theory for Project Management
has pointed out the complementary character of both the chaos and the complexity notion in
organizational context: It represents a point that sits along a spectrum with determinism at the
one end and randomness (not chaos) at the other. The edge of chaos is where you have enough
structure / patterns in the system that it is not random but also enough fluidity and emergent
creativity that it’s not deterministic. (Fisher, 2012, para 14) Thus, chaos theory focuses on
how simple systems give rise to complicated and unpredictable behavior whereas complexity
theory focuses on how systems consisting of many elements lead to well organized and
predictable behavior. Due to this, chaos and complexity should be viewed as complementary
ideas in order to fully influence different thought processes as to how organizations and systems
behave.(Burnham, n.d., p.3).
36
The edge of chaos is also important to note as ‘to be a locus for maximum complexity and the
dynamics driving evolution’ (Complexity Labs, 2016). However, Ralph D. Stacey (1997, pp.14-
15) a British organizational theorist known as one of the pioneers of addressing complexity
theory for understanding human organizations and their management argues in context of
organizational behavior, that the creative process at the edge of disintegration, as he seems to
use this term synonymously for the edge of chaos, is inherently destructive, messy, and
paradoxical. While he looks at that mess as a very raw material of creative activity, his
view logically implies in addition that systems may shift to disintegration and potentially
dissolve into their surroundings. This may be caused by extremely disruptive conditions as, for
example, there have been extinctions of species being the result of asteroid impacts reported in
Earth’s history.
The theoretical relevance of the edge of chaos for our research subject in terms of measuring
project complexity becomes apparent by knowing complexity and creativity to culminate at this
point and occurring, for example, in any project at one time (Remington & Pollack, 2007).
While there seem to be differing viewpoints of the authors in regard to the edge of chaos as the
point of separating order from disorder and their understanding of disorder as chaotic but fully
deterministic states against complete randomness depending on the way they distinguish these
concepts (also philosophically), the fact of systems moving close to chaos, randomness and
disintegration as a possible consequence should certainly be considered as a risk on system
stability in general and for projects in specific.
This also explains well, why in literature in general and specifically for organizational and
project management complexity often is called a friend and/or a foe or even neither a
friend nor a foe, but simply a feature of the environment within which the art and science of
project management are practiced.’ (Cooke-Davies, 2011, p. 13).
2.3.8 Complexity Definitions in Complexity Theory
To take into account the various presented concepts reasoning and describing complexity in
complexity theory, we have dedicated a section to reflect the definition of complexity from the
view of these concepts.
In scope of our literature reviewed, most authors avoid saying: This is the definition of
complexity in complexity science. They rather refer to a vaguer notion how complexity can be
understood based on describing complex phenomena and using typical jargon provided by
the presented concepts of complexity theory and their foundations. Revisiting chapter 2.1 and
reflecting on chapter 2.2 so far, George Johnson (in Czerwinski, 1998, p. 253) presented an
excellent summary that well suits our holistic impression:
37
Whether cells interact to form a [sic] organism or organisms to form an ecosystem, or gas
molecules interact to form a weather front, the result is what people intuitively consider
complex. For all of the efforts to understand these phenomena, scientists are still puzzling over
a very basic question: What precisely is meant by complexity? People think they know it when
they see it. It is orderly, but not too orderly; surprising, but not completely random. A brain
seems more complex than a kidney; a cell more complex than a crystal; a symphony more
complex than a song. But how can the essence of complexity be captured and quantified in a
precise definition that scientists can use?
“Complexity is still almost a theological concept,”
said Dr. Dan Stein, chairman of the physics department at the University of Arizona and an
associate of the Santa Fe Institute […]. “Everybody talks about it. But nobody knows what it
really is. In the absence of a good definition, complexity is pretty much in the eye of the
beholder.”
However, attempting to define general definitions and reviewing those should enrich our
understanding from a scientific perspective as noted in chapter 2.1. Thus, we have outlined
exemplarily how relevant authors who focus on organizational and project complexity suggest
explaining general complexity concisely by their way of having applied the body of knowledge
offered by complexity science.
Cicmil et al. (2009, p.68) for example, propose to understand complexity as a particular
dynamic or “movement” in time that is simultaneously stable and unstable, predictable and
unpredictable, known and unknown, certain and uncertain. Remington & Pollack (2007)
understand complexity, very generally, as a result of interrelationships and feedback between
increasing numbers of areas of uncertainty or ambiguity. Other authors define complexity in
context of organizational management as correlating to the space for novelty or creativity
(Stacey 1996, p.95). To Stacey (2003, p.242), it seems that there are at least four important
matters taking different positions in interpreting complexity: (1) the significance of self-
organization, (2) the nature of emergence, (3) the importance of unpredictability, and (4) the
implications for the scientific method. From the angle of project management, Cicmil et al.
(2009, pp.29-30) offer their understanding as like looking at the complexity of projects (or
establishing whether a project is best described as complex or not […] may mean focusing on
the level of non-linearity, evolution, emergence and radical unpredictability in the interaction
among, and behavior of, project participants (considering both human actors and non-human
actants such as technology) and their implications for management of a project.
38
Reflecting on the authorsattempts to define complexity in a more concise way than outlined
in chapter 2.1, it seems that they prefer encircling the notion of complexity through description
implicating what we have demonstrated as basic system logic, numerosity, the concepts of
complexity theory, complex phenomena and behavior, including peculiar characteristics such
as predictability, uncertainty, knowability and ontological aspects. Despite our increased
multi-perspective understanding of complexity by the definition-like explanations in this
section, we also tend to agree with Seth Lloyd (Johnson in Czerwinski, 1998, p. 259) who liked
to ask his colleagues what they mean by complexity and usually having received the answer:
“I can’t define it for you, but I know it when I see it.”
“That,” he said, “remains the tried and true definition.”
2.3.9 Limitations in Complexity Theory
During our review, complexity theory has offered fundamental insights to explain natural and
organizational phenomena related to complexity and its manifestations. However, authors have
indicated their reservation about the complexity perspective. Also, the uncovering of some
essential paradoxes and ambiguity about the reality addressing theological and philosophical
views including the matter of subjectivity and observers should be mentioned as a critical
context, too. As related opinions have been presented by the reflection of the definition of
complexity, some more perspectives will continue and conclude on the discourse in this section.
Padmaker Oak provides an excellent summary of critical viewpoints by relevant authors:
Rosen (1985; 1991) and Cariani (1989) express doubts about the concept of emergence. Rosen
(1991) demonstrates that complexity science must effectively satisfy two contradictory
conditions: (1) the models of analytic science are (ideally) complete with respect to causation,
and (2) the models of analytic science are clearly not complete with respect to causation, since
there are further things to explain, which equates to further causes being needed. (Oak, 2013,
p.24). The situation of emergence potentially not being definable in concrete terms (Corning,
2002) doesn´t help the cause. In addition, the review of other authors revealed further
fundamental theories on which complexity is fundamentally built on as not being conclusively
researched, e.g. system theory (Mainzer, 2008).
Sardar and Ravetz (1994) also wonder if complexity science is only a fad. Richardson, Cilliers,
and Lissack (2000) express concern over the hype around 25 complexity science, and suggest
that complexity science has “some affinity with sceptical postmodernism” in that it tends to
undermine all attempts to fully characterize the world, including its own attempts. Hiett (2001)
suggests that it is therefore a “grey” rather than a “black and white” science. In Cohen’s
39
(1999) view, we do not yet have a unified, theoretically coherent account of complexity, only a
rapidly growing collection of results, models, and methods. Horgan (1995) describes this state
of affairs as perplexity in another guise. (Oak, 2013, p.24-25).
Eventually, there doesn´t seems to be THE ONE Complexity theory since the term is
becoming widely applied to research in many domains, each of which with distinct
characteristics distinguishing it to some degree from the others (Cicmil et al. 2009).
While the one could assume complexity theory becoming less fuzzy for more circumscribed
fields of research such as project management and getting more doubtlessly practicable, the
today´s situation reveals even conceptual hurdles associated with applying complexity theory
to this domain (Remington & Zolin in Cookie-Davies, 2011, p. 123).
Being aware of the author´s raised imperfection in complexity being researched by a fully
conclusive and consistent theory, it presents an angle by which the measurement of project
complexity and its development can be better understood.
2.3.10 Chapter Summary & Findings
We have introduced to complexity theory as a reasonable research paradigm implicating
scientific ideas, concepts and academic terminology to support the understanding, description
and discussion of complexity for our study. System thinking and system theory have explained
how both numerous elements and intricate interconnections based on basic system logic
develop non-linear, dynamic behavior that emerges to complex systems with new
characteristics which then become part of supra-systems exhibiting complex phenomena and
corresponding with their environment.
During the course we have adopted Russell L. Ackoff’s general definition of a system and the
definition of complex system from the Cornell University for the discourse. The latter has
created an understanding of complexity in systems by two types where Type I has been
addressing the numerosity of intricately interconnected elements and Type II was setting the
focus on the occurrence of complex phenomena. Complex phenomena we have exemplarily
illustrated by popular analogies from nature given by well recognized scientists in their fields
such as in chaos theory, thermodynamics or mathematics, these holistically leading to common
concepts of complexity theory that support to explain and describe complex system behavior.
This basic knowledge has created the fundamentals about complexity theory for our study and
by this has been related to basic aspects around companies and project management. Beyond
an initial understanding for the reader, this will help us for further discussing projects as both
systems and complex systems.
40
Also relevant for studies in general is Hayek’s finding, who relates complex phenomena to the
recognition of regularity as a matter of perception and mind thereby activating human’s
curiosity and reasoning the beginning of science. This is in accordance to Lloyd’s tried and
true definition, which as well indicated a strong correlation with individual perception. This
has been in-line with what we have learned about the concept of emergence and the observer’s
perspective implicating the notion of reality and opposing philosophical views. Thus,
perception of complexity is a pivotal aspect to consider when contrasting complexity in terms
of its magnitude, levels and categories as the logical fundament of its measurement and
assessment.
Furthermore, the edge of chaos as the culminating point of complexity in self-organizing
systems has been identified as a central point of opportunity for new possibilities and creativity
while at the same time it may pose risks on systems stability. It has showcased, how complexity
can arise as either a foe or friend for any organized system that has been established for survival
and productive reasons. This applies to both natural and economic organizations particularly
projects in regard to our study.
In addition, the review has been covering definitions of complexity from the perspective of
complexity theory resulting into ostensive descriptions incorporating the concepts and language
of complexity science. This has been significantly contributing to an enriched understanding of
what complexity constitutes in general and how author´s views are both congruent and different.
However, as expected in context of the findings from Chapter 1, complexity science still owes
us a universal and commonly agreed definition of complexity as such and also of its
underlying domains as we have outlined with the concept of emergence and the definitions of
the same. Consequently, definitions and terminology of complexity specific to the respective
field of research seems a requirement which we will attempt to meet for project complexity and
its measurement throughout the thesis.
Since projects are commonly known as social systems, we will introduce the affairs of
complexity related to human systems and further pivotal characteristic relevant to its
measurement in the next chapter.
41
2.4 Complexity Theory and the Management of Human Systems
The complexity of the human life becomes double each year.
Ramesh Kumar Jha
2.4.1 Introduction
As a logical next step of investigating complexity theory in context of our study, the human
aspect has been dedicated a closer view. The significance of utilizing complexity theory for
human systems and for a better general understanding of the same becomes apparent by Hass
who by partly citing Waldrop (1992) remarks:
Researcher ranging from graduate students to Nobel laureates formulated the theory
that the application of ideas like complexity, adaption, and turmoil at the edge of chaos
can begin to explain “…the spontaneous, self-organizing dynamics of the world in a way
that no one ever has before with potential for immense impact on the conduct of
economics, business, and even politics.” (Hass, 2009, p.21)
The chapter is subdivided into five sections as shown in Figure 2-9. The first section addresses
how complexity has been evolving within the development of our civilization and thereby
covers the important background of hierarchical control systems versus network systems. It
also explains, why human systems are considered as the most complex systems and which
forces and circumstances have led to this state. In this regard, the particular topic of numerosity
and nature of system components has been highlighted revealing fundamental knowledge about
the property of complexity and something being complex by explaining why this isn´t
necessarily the same. The second focus of the review was set on Complex Adaptive Systems
(CAS) as the central system form identified in complexity theory related to projects and their
complexity. The meaning of complexity theory and CAS in terms of management directions
has been addressed in a separate section followed by its limitation in scope of organizational
and project management and our findings.
Figure 2-9: Review process for Complexity Theory and Human Systems
42
2.4.2 The Networked Civilization
The process of evolution in terms of both humankind and complexity by each and in
combination is excellently illustrated by Bar-Yam (2002) in Figure 2-10. He shows how our
civilization has been emerging from small, scattered and partly isolated savage tribes with a
more orderly control structure via early civilizations building nations with predominately
hierarchical control structures to the globally converging, interconnected mankind of today.
This constitutes the notion of a networked civilization. In parallel, he illustrates how by this
development the individual complexity (CIndividual = describes the complexity of influence one
human being can have on another) as a reference point has been exceeded by the collective
complexity of our civilization once we moved from hierarchical control systems to expanding
network structures being intricately interconnected across the underlying hierarchies and the
ecologic environment. As displayed, this led to a significant increase of diversity which
consequently has been extending our human potential to new possibilities and economic
productivity.
Figure 2-10: A schematic history of human civilization (Bar-Yam, 2002, p.18)
43
From the view of complexity theory, this current state of collective human being compares to
the notion of a complex system where the concepts of the same apply well. Bar-Yam compares
our civilization to an organism capable of behaviors that are of greater complexity than those
of an individual human being and therefore the concept of emergence is evident. Weaver (2017)
supplements that basically all human relationships are non-linear which univocally relates to
the concept of non-linearity. Numerosity and intricate interconnections as delineated in the
previous chapter as the characteristic of complex systems Typ (I) are obviously given from a
basic system view. We the billions of human beings present by each the dynamic core-
actors of interconnected social systems such as families, circle of friends, crowds, political and
economic organizations including business projects from local to global scale. These again
interact with natural systems by holistically forming an interrelated ecology of nested systems
and therefore addressing the concept of hierarchy, including the aspect of network-like
structures. In this construct we humans directly and indirectly communicate based on analogue
and digital infrastructure by transmitting multifarious information and herby revisiting the
concept of communication. It is notable that In a mere 200 years, the speed of information
transmission has increased some 100 billion times (Heylighen, 2002, p.3) and many scientists
predict no slow-down of this development for the foreseeable future. This is often described as
an effect out of the general rise of multiplicity of variables in the organizational environment
(Majta, 2012) and the human ecosystem (Hass, 2009). Globalization, technology innovation
(Lewin & Regine, 1999) and information explosion (Heylighen, 1994) are commonly seen as
the predominant drivers of this development.
Today, even completely new types of systems have emerged through networking technology
like mobile communication and the omni-present World Wide Web building large and dynamic
socio-technical systems3 such as Facebook, WhatsApp or LinkedIn. The storage, exchange and
processing of information, its entropy4 and dynamism all together affect the degree and
structure of complexity (Mainzer, 2008) and thereby underlining Biggiero’s (2001) view
covered by his research so far: building on all typical complexity characteristics implicated, it
appears that human systems are the most complex systems. He argues Human systems are
characterized by the presence of all sources of complexity, and therefore are the most complex
systems we face with.(Biggiero, 2001, p.4).
3 A Socio-technical system often describes an interlinked, systems-based mixture of people, technology and their
environment (Walker et al. 2008)
4 ‘Refers to Shannon entropy: The expected value (average) of the information contained in each a variable
(Vajapeyam, 2014)
44
2.4.3 Numerosity and Nature of Components
Sources of complexity are an essential background to discuss its assessment and measurement.
This section highlights two vital aspects that often have been addressed in literature and also
have been emphasized in our review so far: Numerosity as a synonym for the quantitative
aspects in regard to the components of complex systems and the nature of such components as
the qualitative aspects.
2.4.3.1 Numerosity: Components Quantity Aspects of Complexity
Reflecting on the previous section, numerosity of system elements and their intricate
interconnections have revealed to be an important quantitative aspect that can constitute
complexity. Accordingly, the significance of quantity for complexity has been well highlighted
by Mainzer (2008), Biggiero’s (2001) and Bar-Yam (2002) in context of the human civilization
and its increasing number of elements (e.g. humans, information, technology) and by
Remington & Pollack (2007) pointing to the relationship between increasing numbers of areas
of uncertainty and ambiguity as these by each and all together affect the degree and structure
of complexity. According to Luhman, the relation between the elements can grow
exponentially when they are multiplied, and the system, consequently, grows. But in fact, the
capacity of combining elements has limits, something that, even in a small number of
combinations, forces a selective combination of elements (Neves & Neves, 2006, chapter 2,
para 19).
As a supporting prominent example for the understanding of growing complexity in context of
communication in project management by increasing the amount of interacting people and
interfaces, John Trip (Trip, 2011) provides the reader with an common illustration showing the
concept of communications path complexity in peer-to-peer communication (Figure 2-11). By
referring to Fred Brooks (‘The Mythical Man Month’) he states, adding people to a project
creates more theoretical communications paths, and therefore more potential communications
complexity (Trip, 2011, para 3).
Figure 2-11: The concept of communications path complexity (peer-to-peer communication)
(Trip, 2011)
45
Remington & Pollack (2007, p. 24) well summarize the aspect of quantity in terms of the
structure and state of all interconnected elements:
The greater the number of interconnected elements, the more complex the project is likely to
appear, as it becomes increasingly difficult to keep track of the state of all elements,
and as the number of possible outcomes rapidly increases.
While the logic how authors explain the correlation of growing complexity and the increase of
parts, people, interfaces and interactions accompanied by their upscaling dependencies
ambiguity and uncertainty appears similar in literature and with a general consensus to us, the
authors avoid quantifying complexity by precise numbers. Most authors prefer to describe such
quantity by phrases like: large numbers, innumerable, countless, many and so on. Remington
& Pollack (2007, p. 20) indicate an explanation for this hesitation to commit numeric thresholds
by stating ‘Below a certain threshold, the complexity in a situation is easily understood by the
human mind. Past the threshold, the variety of potential links between areas of ambiguity and
the consequences of these different links become too much to hold at once. They believe, when
adding more ambiguous elements to a project, it passes from being merely difficult to being
complex’. Obviously, becoming too much to hold at once depends on the individuum and
thus precise numbers would maybe apply for one person or certain groups of people with very
similar cognitive ability but won´t be appropriately definable for all humans in general. Such
reasoning, of course, is acceptable in a named scope of complexity that concerns observers
for example when defined as a matter of perception or as subjective complexity. Objective
complexity or complexity in scope of defined paradigm with objectifiable references may offer
room for precise, numeral quantification of complexity and thresholds. This eventually comes
close to the endeavor of classifying and measuring complexity in specific context such as
project complexity.
It has to be remarked that just increasing size does not necessarily affects complexity as there
is a general acceptance that complex means something more than simply big” paraphrasing
Williams (2002, p.49) in context of complex projects. It is […the] ambiguity between different
interconnected aspects of a project which creates the perception of complexity’ (Remington &
Pollack, 2007, p. 24).
2.4.3.2 Nature of Components: Components Quality Aspects of Complexity
In the context of quantifying components, many authors have been considering the specific
natures of involved components as a qualifying aspect, too. For example, that the increase in
complexity is not only proportional to the level of numerosity but becomes amplified by the
structural nature of its interconnection has been addressed by Bar-Yam (2001) when having
46
illustrated hierarchical control structures developing to intricate network structures as an effect
of globalization. Also, ambiguity between interconnected parts is an important quality to
emphasize. Given the previous example of sociotechnical systems, technological aspects have
been identified to constitute a significant catalyst for the increasing complexity in socio-
technological-economical systems, too. Information technology has been presented as
amplifying both the magnitude and velocity of complexity driving factors such as the change
of conditions, numerosity of exchanging information in communication, hierarchical control
structures transforming into intricate networks, and so on. This exemplarily shows how certain
sources of complexity (e.g. information technology aspects) constitute a quality of components
(e.g. circuit components) driving the quantity of related complexity sources (e.g. amount of
information and communication interfaces) leading to new qualities (network behavior) and so
forth. More specific to our subject, projects, at some point, then may go through phase transition
and start exhibiting emergent properties and non-linear behavior as Remington & Pollack
(2007, p.20) point out. They add ‘This change in quality is a function of the number of different
elements in the project and their interconnectedness’. Consequently, out of the relevance given
to the nature and characteristics of system components and their connections in conjunction
with what is called their numerosity, the authors pay attention to different sources of complexity
and relate those to various types of complexity. Classifying complexity by nature, source and
type is a pivotal topic for our study which we will outline in more detail for project complexity
in Chapter Error! Reference source not found. because it constitutes the foundation for its a
ssessment and measurement.
2.4.3.3 Conclusive comment
Our basic finding out of the reflection of numerosity and nature of components is that
complexity seems to exist as a property of a system independently from the numbers of
elements and interconnections, and the nature of both. According to our understanding, this
implicates, that a system always provides a state of complexity even when this complexity is
just constituted by two interrelated elements that affect each other. This state may be
indefinitely low and in specific context such as general relativity and quantum cosmology, it
may amount to zero as Herrera (2018, p.1) states ‘We start by assuming that the homogeneous
(in the energy density) fluid, with isotropic pressure is endowed with minimal complexity. For
this kind of fluid distribution, the value of complexity factor is zero.’ by referring to ‘zero
complexity systems’ in his complexity definition paper for static and spherically symmetric
self-gravitating systems. While we haven´t discovered a “zero complexity” definition for
systems like projects so far, it seems reasonable to believe that a system can appear simple and
understandable when exhibiting only little complexity and therefore maybe called “not
47
complex” or “non-complex” in layman terms. But by increasing elements, interconnections and
linked fields of ambiguity by each or all simultaneously, it seems a fact for all fields of practical
application, that the degree of complexity increases, too. Then, from an observer´s perspective,
it first becomes harder to be understood by a human mind and therefore appears more and more
difficult and then gets perceived as being more complex when a full understanding can´t be
achieved anymore by reductive analysis. This due to too many interconnected areas of
ambiguity and emergent behavior exhibiting complex phenomena, and thus also becoming
more and more unpredictable. This is an important realization to know for the understanding
how the terms simple, difficult, complicated and complex are different as delineated in Chapter
XX. It will also explain, why those terms can cause problems in management discussions when
determining whether a project is complex or not and how to measure this issue with consensus.
However, revisiting the theoretical context that addresses the issue of subjectivity and
observation which we will discuss later on, we always have to keep in mind a common
hypothesis ‘without the observer there is no complexity’ as Luhmann points out (Neves, 2006).
2.4.4 Complex Adaptive Systems (CAS)
Businesses and specifically projects are human systems and therefore complex systems with
the potential to exhibit highly developed forms and magnitudes of complexity as illustrated in
the previous sections. Such complex systems are often poised on the edge of chaos, but
simultaneously handling that chaos successfully by their ability to self-organize and adapt. In
complexity science, this type of system commonly is introduced as complex adaptive systems
(CAS) as the SFI coined the term.
2.4.4.1 Definition of CAS
Hass (2009) explains CAS as being a) complex in that they are diverse and compromise multiple
interconnected elements while they are b) adaptive in that they have the capacity to change and
learn from experience. Stacey (1996, p.10) suggests in this context when we come together as
a group we constitute a complex adaptive system in both a biological and a mental sense.
Accordingly, Peter Fryer in his case study about Being a Learning Organisation (Fryer in
Thompson, 2005, chapter 3, para 2) adds: All organisations are complex adaptive systems
whether we want them to be or not. No matter how much command and control is exerted on
the system, underneath the organisation also operates as a complex adaptive system, therefore
it makes sense to view them as one.
In literature related to project complexity, however, both terms complex systems and CAS are
discussed from different viewpoints. For example, while some authors use the terms
synonymously, others distinguish both by their differing sets of co-properties in addition to
48
their predominant and common feature of adaptiveness. Cooke-Davis et al. (2011, p.117)
describe CAS as being also known for dynamic systems and beyond this note that not all
systems or subsystems within a CAS exemplify all characteristics of the CAS itself.
2.4.4.2 Attributes of CAS
Beside the heterogeneity in definitions, Rakhman & Zhang (2008) who were supervised in
their master thesis by one of our key references for project complexity Kaye Remington have
chosen an interesting approach that considers the common properties of general systems and
CAS within one definition framework. By this, they have assigned hierarchy, communication
and control as attributes (as they prefer to use this term instead of properties) of all systems.
For CAS specifically, they have attributed phase transition, adaptiveness, emergence, non-
linearity and sensitivity on initial conditions as illustrated in Figure 2-12. While they have
thereby adopted Remington & Pollack’s (2007) understanding for a specific set of concepts of
complexity that constitute a CAS, the illustration reveals as well that the corresponding
attributes holistically equal the authors’ understanding of the perception of project complexity.
Figure 2-12: Perception of Project Complexity attributed by Attributes of
Complex Adaptive System A Theoretical Framework (Rakhman & Zhang, 2008, p.10)
This perception is strongly underpinned by the view which Remington & Pollack (2007, p.3)
share during their discussion of complex projects where they importantly point out: ‘A complex
project is a complex adaptive system. Remington & Zolin in Cooke-Davies et al. (2011, p.117)
conclude nicely in line with the discourse: The particular branch of complexity theory that
draws upon systems thinking, and which has attracted the most recent attention by project
management theorists, is complex adaptive systems.
2.4.4.3 Conclusive comment
The significance of CAS for researching project management and project complexity is beyond
dispute for most authors. However, the different views and definitions of CAS and the
interpretable way how those are fully applicable to real world projects makes it difficult for us
49
to take derivatives with total conviction. Yet, following the logic and commonalties of the
authors being pre-eminent in their fields related to our subject, we suggest acknowledging the
following finding for this study:
All projects are complex adaptive systems comprising numerous, interconnected elements
implicating fields of ambiguity and dynamism, and therefore can exhibit typical complex
phenomena based on the underlying concepts of complexity theory which holistically and in
their variety of peculiarity constitute the perception of both project complexity and complex
projects while they are limited in predictability and distinct in their capacity to change and
learn from experience. Yet, this doesn’t mean that all projects are complex projects because
the threshold that demarcates a non-complex project from a complex project appears to entail
a cognitive process implicating ontological aspects depending on the observer in general terms
and therefore requires an objectified reference system to be specifiable. Consequently, both
non-complex projects and complex projects may be based on complex adaptive systems, but
contrasting non-complex projects to complex projects tendentially relates to the subject of
systematic assessment, measurement and scale development and thus will have to be defined in
such context.(Freyer in Thompson, 2005; Remington & Pollack, 2007; Rakhman & Zhang,
2008; Cicmil et al. 2009; Hass, 2009; Remington & Zolin in Cooke-Davies 2011; and others).
For our study, highlighting the distinct CAS characteristic of projects being capable to learn
from experience is important because it leads to vital management strategies suggested to
respond to the complexity of projects with a higher degree of the same.
2.4.5 CAS and Management
While CAS have been identified as the most adequate type of complex systems to study
complex projects and project complexity in theory, it provides an important body of knowledge
in economics constituting a valuable source of explanations and strategies for a more effective
management of complex organizations in general. Within this background, management and its
purpose we generally understand as the administrative efforts holistically required to achieve a
defined goal. More precisely this is defined in dictionaries where the following description
represents a common view:
1. The organization and coordination of the activities of a business in order to achieve defined
objectives. […] Management consists of the interlocking functions of creating corporate policy
and organizing, planning, controlling, and directing an organization's resources in order to
achieve the objectives of that policy.’ (BusinessDirectory, 2018)
50
However, it is fundamental for this study to understand, why complexity theory may support
the achievement of goals and how this influences the decision for suitable management
strategies. In the next paragraphs, we will reflect essential elements of complexity theory in
context of organizations with a focus on both businesses and projects and will highlight the
author’s views in terms of appropriate management approaches.
Just like CAS in general are tending to maintain stability, learn from experience and developing
new characteristics to warrant for their survival, groups of people in general and business as
well as projects in particular behave similarly. If the environment changes dramatically like at
times of economic crisis and moving into turbulence, businesses require to realign their goals
and the way how to achieve those. As Hass (2009) points out, a business may operate at chaos,
when old ways of doing need to be abandoned and new ways need to be found. At this stage,
which closely compares to the edge of chaos in complexity theory, experiments and creativity
together are the more effective response to adapt to changing conditions. From a managerial
perspective, traditional deterministic-reductionistic strategies would focus on the maintenance
of the status and the decided approach as-is by forcing current control aiming on productive
stability in operation and conduct. However, Curlee & Gordan (2011, p.54) think that the need
to control and maintain onerous processes and procedures encourages the as is on a project,
which research suggests does not work on complex projects by referring to Cooke-Davies,
Cicmil, Crawford, & Richardson (2007); Singh and Singh (2002) and Overman & Loraine
(1994). This seems logical based on Stacey’s (1996, p.71) finding because ‘human systems are
not deterministic, hence we can´t expect solely deterministic-reductionistic approaches to work
most effectively. As a consequence, at least some aspects of their [ref. systems] behaviour,
quite possibly including many of the interesting ones, can be worked out only through explicit
simulation or observation. Many asymptotic questions about their infinite time behaviour thus
cannot be answered by any finite computations, and are thus formally undecidable. as Stephen
Wolfram (2018) a recognized businessman and known for his research in complex systems
and cellular automata adds in a similar context.
Beyond this, businesses that allow for experiments to learn and adapt, and novelty in control
may develop a more innovative approach. By this, they would not only support the survival
during exceptional states such as the turbulences of economic crises but consequently increase
their competitive advantage within their markets. Yet, as presented, operating at the edge of
chaos can mean operating at the edge of disintegration also in terms of businesses. In other
words, allowing for diversity of thought and novelty in approach inherits essential risks (e.g.
diluting cash-flow due to failed experiments or inefficiently assigned resources) on that control
51
naturally meant to warrant for stability. However, businesses are C