-
Complexity
Complexity and Project Management: Challenges, Opportunities,
and Future Research
Lead Guest Editor: José Ramón San Cristóbal MateoGuest Editors:
Emma Diaz Ruiz de Navamuel, Luis Carral Couce, José Ángel Fraguela
Formoso, and Gregorio Iglesias
-
Complexity and Project Management:Challenges, Opportunities, and
Future Research
-
Complexity
Complexity and Project Management:Challenges, Opportunities, and
Future Research
Lead Guest Editor: José Ramón San Cristóbal MateoGuest Editors:
Emma Diaz Ruiz de Navamuel, Luis Carral Couce,José Ángel Fraguela
Formoso, and Gregorio Iglesias
-
Copyright © 2019 Hindawi. All rights reserved.
This is a special issue published in “Complexity.” All articles
are open access articles distributed under the Creative Commons
Attribu-tion License, which permits unrestricted use, distribution,
and reproduction in any medium, provided the original work is
properly cited.
-
Editorial Board
José A. Acosta, SpainCarlos F. Aguilar-Ibáñez, MexicoMojtaba
Ahmadieh Khanesar, UKTarek Ahmed-Ali, FranceAlex Alexandridis,
GreeceBasil M. Al-Hadithi, SpainJuan A. Almendral, SpainDiego R.
Amancio, BrazilDavid Arroyo, SpainMohamed Boutayeb, FranceÁtila
Bueno, BrazilArturo Buscarino, ItalyGuido Caldarelli, ItalyEric
Campos-Canton, MexicoMohammed Chadli, FranceÉmile J. L. Chappin,
NetherlandsDiyi Chen, ChinaYu-Wang Chen, UKGiulio Cimini,
ItalyDanilo Comminiello, ItalySara Dadras, USASergey Dashkovskiy,
GermanyManlio De Domenico, ItalyPietro De Lellis, ItalyAlbert
Diaz-Guilera, SpainThach Ngoc Dinh, FranceJordi Duch, SpainMarcio
Eisencraft, BrazilJoshua Epstein, USAMondher Farza, FranceThierry
Floquet, FranceMattia Frasca, ItalyJosé Manuel Galán, SpainLucia
Valentina Gambuzza, ItalyBernhard C. Geiger, Austria
Carlos Gershenson, MexicoPeter Giesl, UKSergio Gómez,
SpainLingzhong Guo, UKXianggui Guo, ChinaSigurdur F. Hafstein,
IcelandChittaranjan Hens, IsraelGiacomo Innocenti, ItalySarangapani
Jagannathan, USAMahdi Jalili, AustraliaJeffrey H. Johnson, UKM.
Hassan Khooban, DenmarkAbbas Khosravi, AustraliaToshikazu Kuniya,
JapanVincent Labatut, FranceLucas Lacasa, UKGuang Li, UKQingdu Li,
GermanyChongyang Liu, ChinaXiaoping Liu, CanadaXinzhi Liu,
CanadaRosa M. Lopez Gutierrez, MexicoVittorio Loreto,
ItalyNoureddine Manamanni, FranceDidier Maquin, FranceEulalia
Martínez, SpainMarcelo Messias, BrazilAna Meštrović,
CroatiaLudovico Minati, JapanCh. P. Monterola, PhilippinesMarcin
Mrugalski, PolandRoberto Natella, ItalySing Kiong Nguang, New
ZealandNam-Phong Nguyen, USAB. M. Ombuki-Berman, Canada
Irene Otero-Muras, SpainYongping Pan, SingaporeDaniela Paolotti,
ItalyCornelio Posadas-Castillo, MexicoMahardhika Pratama,
SingaporeLuis M. Rocha, USAMiguel Romance, SpainAvimanyu Sahoo,
USAMatilde Santos, SpainJosep Sardanyés Cayuela, SpainRamaswamy
Savitha, SingaporeHiroki Sayama, USAMichele Scarpiniti, ItalyEnzo
Pasquale Scilingo, ItalyDan Selişteanu, RomaniaDehua Shen,
ChinaDimitrios Stamovlasis, GreeceSamuel Stanton, USARoberto
Tonelli, ItalyShahadat Uddin, AustraliaGaetano Valenza,
ItalyDimitri Volchenkov, USAChristos Volos, GreeceZidong Wang,
UKYan-Ling Wei, SingaporeHonglei Xu, AustraliaYong Xu,
ChinaXinggang Yan, UKBaris Yuce, UKMassimiliano Zanin, SpainHassan
Zargarzadeh, USARongqing Zhang, USAXianming Zhang,
AustraliaXiaopeng Zhao, USAQuanmin Zhu, UK
-
Contents
Complexity and Project Management: Challenges, Opportunities,
and Future ResearchJosé R. San Cristóbal , Emma Diaz, Luis Carral ,
José A. Fraguela, and Gregorio IglesiasEditorial (2 pages), Article
ID 6979721, Volume 2019 (2019)
Complexity and Project Management: A General OverviewJosé R. San
Cristóbal , Luis Carral , Emma Diaz, José A. Fraguela, and Gregorio
IglesiasReview Article (10 pages), Article ID 4891286, Volume 2018
(2019)
Comparing Project Complexity across Different Industry
SectorsMarian Bosch-Rekveldt , Hans Bakker, and Marcel
HertoghResearch Article (15 pages), Article ID 3246508, Volume 2018
(2019)
Exploring Project Complexity through Project Failure Factors:
Analysis of Cluster Patterns UsingSelf-Organizing MapsVicente
Rodríguez Montequín , Joaquín Villanueva Balsera, Sonia María
Cousillas Fernández,and Francisco Ortega FernándezResearch Article
(17 pages), Article ID 9496731, Volume 2018 (2019)
Measuring the Project Management Complexity:The Case of
Information Technology ProjectsRocio Poveda-Bautista , Jose-Antonio
Diego-Mas , and Diego Leon-MedinaResearch Article (19 pages),
Article ID 6058480, Volume 2018 (2019)
Strategies for Managing the Structural and Dynamic Consequences
of Project ComplexitySerghei Floricel , Sorin Piperca, and Richard
TeeResearch Article (17 pages), Article ID 3190251, Volume 2018
(2019)
http://orcid.org/0000-0003-4104-9460http://orcid.org/0000-0003-1109-1131http://orcid.org/0000-0003-4104-9460http://orcid.org/0000-0003-1109-1131http://orcid.org/0000-0001-9309-6352http://orcid.org/0000-0003-3217-8586http://orcid.org/0000-0001-8904-5421http://orcid.org/0000-0002-3698-3411http://orcid.org/0000-0003-0272-1568
-
EditorialComplexity and Project Management:
Challenges,Opportunities, and Future Research
José R. San Cristóbal ,1 Emma Diaz,2 Luis Carral ,3
José A. Fraguela,3 and Gregorio Iglesias4
1Project Management Research Group, Universidad de Cantabria,
Santander 39004, Spain2Escuela Técnica Superior de Náutica,
Universidad de Cantabria, Santander 39004, Spain3Department of
Naval and Industrial Engineering, GEM, Universidade da Coruña,
Ferrol 15403, Spain4University of Plymouth, Plymouth, UK
Correspondence should be addressed to José R. San Cristóbal;
[email protected]
Received 23 December 2018; Accepted 24 December 2018; Published
6 January 2019
Copyright © 2019 José R. San Cristóbal et al. is is an open
access article distributed under the Creative Commons
AttributionLicense, which permits unrestricted use, distribution,
and reproduction in any medium, provided the original work is
properlycited.
Owing to its societal and economic relevance, Project
Man-agement has become an important and relevant disciplineand a
key concept in modern private and public organiza-tions. Modern
Project Management appeared during WorldWar II and, since then, it
has grown up and spread aroundthe world to become what it is today,
that is, a set of practices,principles, theories, and
methodologies.
Project Management is the application of knowledge,skills,
tools, and techniques to project activities in orderto meet project
objectives. It is more applied and inter-disciplinary than other
management disciplines. Nowadays,ProjectManagement is a
well-recognized discipline practicedby almost all organizations
which has accumulated exten-sive knowledge and wide-industry based
experience. Today,project managers have gained recognition and
employmentopportunities beyond construction, aerospace, and
defense,in pharmaceuticals, information systems, and
manufactur-ing.
However, paradoxically, many projects do not meet cus-tomer
expectations, and cost and schedule overruns are quitecommon.Why
then is so much effort spent today on projectsto achieve only
moderate levels of success? What is missingin Project Management?
Project Management is a complexactivity and a risky organizational
adventure, different thanany functional activity or ongoing
operation.
Projects have become more and more complex becauseof the
increasing factors that are considered source of
complexity. A large amount of required resources, a
turbulentenvironment, working on the edge of technology, and a
largenumber and diversity of actors working and communicatingwith
each other are all factors that affect project outcome.is complex
environment influences project planning, coor-dination, and
control; it can also affect the selection of anappropriate project
organization structure and hinder theclear identification of
project goals.
When problems fundamentally dynamic are treated stat-ically,
delays and cost overruns are common. Experiencesuggests that the
interrelationships between the project’scomponents are more complex
than it is suggested by tra-ditional approaches. ese, traditional
approaches, using astatic approach, provide project managers with
unrealisticestimations that ignore the nonlinear relationships of
aproject and, thus, are inadequate to the challenge of
today’sdynamic and complex projects.
Complex projects demand an exceptional level of man-agement and
the application of the traditional tools andtechniques developed
for ordinary projects have been foundto be inappropriate for
complex projects. Complex ProjectManagement is a specialist
profession that requires a specificset of competencies and a deep
understanding of the projectand its environment. If project
managers want to executea project successfully, in a context of
increased complexity,it is not only necessary that they attend the
demands ofan increasingly complex environment or they develop
the
HindawiComplexityVolume 2019, Article ID 6979721, 2
pageshttps://doi.org/10.1155/2019/6979721
http://orcid.org/0000-0003-4104-9460http://orcid.org/0000-0003-1109-1131https://creativecommons.org/licenses/by/4.0/https://creativecommons.org/licenses/by/4.0/https://doi.org/10.1155/2019/6979721
-
2 Complexity
right strategies to address the new challenges, a willingnessto
change leadership style will also be required. Projectmanagers must
be able to make decisions in the dynamicand unstable environments
that are continuously changingand evolving in a random fashion and
are hard to predict.To achieve this objective, more integrated
approaches formanaging projects andnewmethods of planning,
scheduling,executing, and controlling projects must be
investigated.
e aimof this special issue is to publish selective
ongoingresearch contributions that contribute and stimulate
thedebate in the topic.
Conflicts of Interest
Regarding this special issue, the lead guest editor and
theothers guest editors do not have any possible conflicts
ofinterest or private agreements with companies.
José R. San CristóbalEmma DiazLuis Carral
José A. FraguelaGregorio Iglesias
-
Review ArticleComplexity and Project Management: A General
Overview
José R. San Cristóbal ,1 Luis Carral ,2 Emma Diaz,3 José A.
Fraguela,2
and Gregorio Iglesias4
1Project Management Research Group, Universidad de Cantabria,
Santander 39004, Spain2Department of Naval and Industrial
Engineering, GEM, Universidade da Coruña, Ferrol 15403,
Spain3Escuela Técnica Superior de Náutica, Universidad de
Cantabria, Santander 39004, Spain4University of Plymouth, Plymouth,
UK
Correspondence should be addressed to José R. San Cristóbal;
[email protected]
Received 25 April 2018; Accepted 25 July 2018; Published 10
October 2018
Academic Editor: Roberto Natella
Copyright © 2018 José R. San Cristóbal et al. This is an open
access article distributed under the Creative Commons
AttributionLicense, which permits unrestricted use, distribution,
and reproduction in any medium, provided the original work is
properly cited.
As projects have become more and more complex, there has been an
increasing concern about the concept of project complexity.An
understanding of project complexity and how it might be managed is
of significant importance for project managers because ofthe
differences associated with decision-making and goal attainment
that are related to complexity. Complexity influences
projectplanning and control; it can hinder the clear identification
of goals and objectives, it can affect the selection of an
appropriate projectorganization form, or it can even affect project
outcomes. Identifying the different concepts associated to project
complexity, itsmain factors and characteristics, the different
types of project complexity, and the main project complexity
models, can be ofgreat support in assisting the global project
management community. In this paper, we give a general overview of
howcomplexity has been investigated by the project management
community and propose several ideas to address this topic inthe
future.
1. Introduction
The origins of complexity theory applied to project man-agement
can be traced back to the works by Morris [1, 2],Bennet and Fine
[3], Bubshait and Selen [4], Bennet andCropper [5], Gidado [6],
Wozniak [7], and Baccarini [8].All these works highlight the
importance of complexity inproject contexts in general and in
particular its effects onproject goals and objectives, project
organization form andarrangement, and in the experience
requirements for themanagement personnel.
The importance of complexity to the project manage-ment process
is widely acknowledged for several reasons[1–8]: (i) it influences
project planning, coordination, andcontrol; (ii) it hinders the
clear identification of goals andobjectives of major projects;
(iii) it can affect the selectionof an appropriate project
organization form and experiencerequirements of management
personnel; (iv) it can be usedas criteria in the selection of a
suitable project management
arrangement; and (v) it can affect different project
outcomes(time, cost, quality, safety, etc.).
An understanding of project complexity and how itmight be
managed is of significant importance for projectmanagers because of
the differences associated withdecision-making and goal attainment
that appear to berelated to complexity [8, 9]. As projects have
become moreand more complex, there has been an increasing
concernabout the concept of project complexity and the
applicationof traditional tools and techniques developed for
simpleprojects has been found to be inappropriate for
complexprojects [1, 8]. According to Parsons-Hann and Liu [10],it
is evident that complexity contributes to project failurein
organizations; what is not clear is to what degree thisstatement
holds true. Identifying and characterizing differentaspects of
project complexity in order to understand moreefficiently the
stakes of project management complexitycan be of great support in
assisting the global projectmanagement community.
HindawiComplexityVolume 2018, Article ID 4891286, 10
pageshttps://doi.org/10.1155/2018/4891286
http://orcid.org/0000-0003-4104-9460http://orcid.org/0000-0003-1109-1131https://creativecommons.org/licenses/by/4.0/https://creativecommons.org/licenses/by/4.0/https://doi.org/10.1155/2018/4891286
-
Complexity can have both a negative and a positiveinfluence on
projects. The negative influence, in terms ofdifficulty to be
understood and controlled, is because of theemergence of new
properties that none of the elements ofthe system owns. The
positive influence is due to the appari-tion of phenomena that
could not be predicted due to the soleknowing, even complete, of
the behaviour and interactionsof the elements of the system. In
order to properly man-age complexity, project managers must know
how to seizethe opportunities emerging from complexity and to
knowhow to avoid or at least diminish the negative effects
ofcomplexity [11].
In this paper, we give a general overview of how complex-ity,
which is the main purpose of this special issue, has beenaddressed
to date in the project management literature. Webegin by discussing
the different definitions of complexityin project contexts. Next, a
summary of the project complex-ity factors and characteristics is
presented. Then, the differenttypes of project complexity and the
main project complexitymodels are presented. Finally, the current
and the futuremanagement approaches to address this topic in the
futureare proposed.
2. Definitions of Project Complexity
In project contexts, there is a lack of consensus on what
com-plexity really is [12–20]. There does not even seem to be
asingle definition of project complexity that can capture thewhole
concept [11, 20–24]. Within the Luhmannian systemtheory, complexity
is the sum of the following components[25]: differentiation of
functions between project partici-pants, dependencies between
systems and subsystems, andthe consequential impact of a decision
field. Project complex-ity can also be interpreted and
operationalized in terms ofdifferentiation (number of elements in a
project) and inter-dependencies and connectivity (degree of
interrelatednessbetween these elements), which are managed by
integra-tion, that is, by coordination, communication, and
control[1, 8, 26–29]. Custovic [30] defines complexity as
thatproperty of a system which makes it difficult to formulateits
overall behaviour in a given language, even when givenreasonable
complete information about its atomic compo-nents and their
interrelations. In a similar context, Vidaland Marle [11] define
project complexity as that propertyof a project which makes it
difficult to understand, foresee,and keep under control its overall
behaviour. Tatikondaand Rosenthal [31] view complexity as
consisting of interde-pendencies among the product and process
technologies andnovelty and difficulty of goals. Pich et al. [32]
define complex-ity as information inadequacy when too many
variablesinteract. Ward and Chapman [33] view the number
ofinfluencing factors and their interdependencies as constitu-ents
of complexity.
Some authors associate complex or complicated projectswith the
number of elements and with the concept of linear-ity. Girmscheid
and Brockmann [34] argue that any differ-ence between a complicated
project and a complex projecthas to do with the number of elements
as opposed to therelationships between the elements (complex).
Richardson
[35] associates linearity with complicated projects
andnonlinearity with complex projects, which implies
thatnonlinearity makes the relationship between inputs andoutputs
unpredictable. Remington et al. [9] defines a com-plex project as
one that demonstrates a number of charac-teristics to a degree or
level of severity that makes itextremely difficult to predict
project outcomes, to controlor manage the project. Girmscheid and
Brockmann [34]define project complexity as a set of problems that
consistsof many parts with a multitude of possible
interrelations,most of them being of high consequence in the
decision-making process that brings about the final result.
3. Project Complexity Factorsand Characteristics
Experience suggests that the interrelationships between
theproject’s components are more complex than is suggestedby the
traditional work breakdown structure of projectnetwork. Identifying
the sources and factors that contributeor increase project
complexity is paramount for projectmanagers. Gidado [36] determines
four different sources ofcomplexity: employed resources,
environment, level ofscientific and technological knowledge
required, and numberof different parts in the workflow. Thus, a
large amount ofrequired resources, a turbulent environment, working
onthe edge of technology, and innumerable possible interac-tions
are certainly identifiable factors for complex projects.
Since there has been a lack of consensus and difficulty
indefining complexity, some authors have focused on identify-ing
the factors that contribute or increase project
complexity.Remington et al. [9] suggest to differentiate between
dimen-sions, characteristics, or sources of complexity, and
severityfactors, those factors that increase or decrease the
severityof complexity. Vidal and Marle [11] consider the
followingfactors as necessary but nonsufficient conditions for
pro-ject complexity: size, variety, interdependences and
interre-lations within the project system, and context
dependence.Remington et al. [9] group a number of factors that
seemto contribute to the perception of project complexity underthe
following headings: goals, stakeholders, interfaces
andinterdependencies, technology, management process,
workpractices, and time. Table 1 shows the main factors that
areconsidered in the literature as drivers of project
complexity.
3.1. Size. Size has traditionally been considered the
primarycause of complexity in organizations [37–40]. However,
toconsider size an indication of complexity, the
organizationalstructure of a system should be over a minimum
critical sizeand their elements need to be interrelated [41].
Substantialrelationships have been found in both cross-sectorial
andlongitudinal studies in many different samples of organiza-tions
between size and various components of complexitysuch as personal
specialization, division of labor, andstructural differentiation
[38]. A large number of studies havefound that size is related to
structural differentiation, but therelationship between size and
complexity is less clear [37, 40,42] . According to a study
performed by Beyer and Trice [38]on several departments of the US
governments, size is a more
2 Complexity
-
important predictor of complexity while in a similar studyfrom
state employment agencies, Blau and Schoenherr [37]found that
division of labor is a more important predictorof complexity.
3.2. Interdependence and Interrelations. It creates a link
orinfluence of different types between entities in such away that
an event in an interconnected structure can causetotally unknown
effects on another entity inside the struc-ture [43]. The number of
systems and subsystems thatintegrate the project, the different
methodological andphilosophical assumptions across these systems,
the cross-organizational and schedule interdependencies
betweenactivities, the upgrading and retrofitting works, and the
sheersize and entanglement in the project are all key
factorsinfluencing complexity.
3.3. Goals and Objectives. Goals and objectives must
beadequately and properly defined, both at a strategic and atan
operational level. In addition, all project participantsincluding
owners, managers, contractors, and consultantsmust be clear about
these goals and objectives.
3.4. Stakeholders. The number of project participants andhow the
information flows between them are a key factoraffecting project
complexity. If the project is politicallysensitive and of high
visibility, project complexity can con-siderably be increased.
Managing conflicting agendas ofvarious stakeholder management
strategies and processes,which is linked to structural complexity,
can also amplifythe complexity of a project.
3.5. Management Practices. Organizational and
interactivemanagement is one of the riskiest parts of a
project.Contractor relationships and ethics, supplier
monopolies,overlapping of processes and activities, methodologies,
andtechniques based on either hard or soft approaches that
canaffect the degree of definition of project goals and
objectivesare all factors that can influence project
complexity.
3.6. Division of Labor. Dividing labor into distinct tasks
andcoordinating these tasks define the structure of an
organiza-tion [44]. Adding project organizational structure
bydividing labor into smaller and more specialized tasks, theway
for personnel selection, and the level of pressure on thepersonnel
to achieve project objectives are all factors thatcan increase
project complexity.
3.7. Technology. Broadly speaking, technology can be definedas
the transformation process which converts inputs intooutputs using
materials, means, techniques, knowledge, andskills [8, 26]. The
most critical dimension of technology isthe variety of tasks that
need to be accomplished, what issometimes called task scope and is
proposed as a determinantof horizontal differentiation [42]. It
explains why there is aneed for a variety of technologies and a
given level special-ization in each of them. Baccarini [8] proposes
to definetechnological complexity in terms of differentiation
andinterdependencies. Technological complexity by differenti-ation
refers to the variety and diversity of some aspectsof a task such
as number and diversity of inputs/outputs,number and diversity of
tasks to undertake, and numberof specialities and contractors,
involved in the project.
Table 1: Main factors affecting project complexity.
Factor
SizeTo consider it an indication of complexity, the
organizational structure of the project should be over
a minimum critical size and their elements need to be
interrelated.
Interdependence and interrelationsAn event in an interconnected
structure can cause totally unknown effects on another entity
inside
the structure.
Goals and objectives They must be adequately and properly
defined both at a strategic and at an operational level.
StakeholdersThe number of project participants and how the
information flows between them are a key factor
affecting project complexity.
Management practicesRelationships between project participants,
suppliers, overlapping of activities, methods, and
techniques are factors that affect project complexity.
Division of laborAdding project organizational structure by
dividing labor, the way for personnel selection, andthe level of
pressure on this personnel to achieve project objectives are
factors that increase
project complexity.
TechnologyTask scope or the variety of tasks that need to be
accomplished is the most critical dimension of
technology. It explains why there is a need for a variety of
technologies and a given levelspecialization in each of them.
Concurrent engineeringIt breaks down functional and departmental
barriers by integrating team members with different
discipline backgrounds often known as cross-functional
teams.
Globalization and context dependenceGlobalization boots
complexity by the erosion of boundaries, higher mobility,
heterarchy, and
higher dynamics. It can be an essential feature of
complexity.
Diversity A higher number of elements and a higher variety
across elements increase complexity.
Ambiguity It expresses uncertainty of meaning in which multiple
interpretations are plausible.
FluxFlux is affected by external and internal influences. It
also implies constant change and adaptation to
changing conditions.
3Complexity
-
Technological complexity by interdependency
encompassesinterdependencies between tasks, within a network of
tasks,between teams, between different technologies, and
betweeninputs (technological interdependence can be one of
threetypes, pooled, sequential, and reciprocal, with
reciprocalinterdependency the prevalent type in construction
projects).
3.8. Concurrent Engineering. The ever increasing pressureto
execute projects more rapidly has led many companiesto deploy
project organizations comprised of distributedand often outsourced
teams and in many cases to executeconcurrently many activities
[45]. Concurrent Engineeringbreaks down functional and departmental
barriers by inte-grating team members with different discipline
back-grounds often known as cross-functional teams [46].
Thisprocess requires changes in the organizational structureand a
more vigorous communication, coordination, andcollaboration
[47].
3.9. Globalization and Context Dependence. Globalizationboots
complexity by the erosion of boundaries, higher mobil-ity,
heterarchy, and higher dynamics [46]. The context andenvironment
under which the project is undertaken can bean essential feature of
complexity. In fact, the methods andpractices applicable to a
project may not be directly transfer-able to other projects with
different institutional, language,and cultural configurations.
3.10. Diversity. Diversity is defined as the plurality
ofelements. It encompasses two components, the number ofelements
(multiplicity) and their dissimilarity (variety). Ahigher number of
elements and a higher variety acrosselements increase
complexity.
3.11. Ambiguity. Ambiguity can be defined as too muchinformation
with less and less clarity on how to interpretand apply findings
[43]. Ambiguity expresses uncertainty ofmeaning in which multiple
interpretations are plausiblewhich leads to the existence of
multiple, often conflictingsituations, goals, and processes
[46].
3.12. Flux. Flux implies constant change and adaptation
tochanging conditions making temporary solutions
regardinginterdependence, diversity, and ambiguity outdated fromone
day to another [48]. Flux is affected by external andinternal
influences. External influences can either be politicalor
market-related changes, while internal influences comefrom changes
in strategy, in individual behaviour, etc.
4. Types of Project Complexity
Bosch-Rekveldt et al. [16] conducted an online survey usingthe
TOE framework (technical, organizational, and environ-mental) and
came to determine the position of therespondents about the nature
of the complexity of theorganization in engineering projects. They
concluded thatproject managers were more concerned with
organizationalcomplexity than technical or environmental
complexities.Vidal and Marle [11] argued that approximately 70% of
thecomplexity factors of the project are organizational. This
seems to be in line with Baccarini’s [8] opinion on
organiza-tional complexity which, according to him, is influenced
bydifferentiation and operative interdependencies.
According to Vidal and Marle [11], there are historicallytwo
main approaches of complexity. The one, usually knownas the field
of descriptive complexity, considers complexity asan intrinsic
property of a system, a vision which invitedresearchers to try to
quantify or measure complexity. Theother one, usually known as the
field of perceived complexity,considers complexity as subjective,
since the complexity of asystem is improperly understood through
the perception ofan observer. For all practical purposes, a project
managerdeals with perceived complexity as he cannot understandand
deal with the whole reality and complexity of the project.According
to this perceived complexity, project managersmake the
corresponding decisions and take the correspond-ing actions to
influence the project evolution and reach thedesired project state
[11, 49].
How complexity is perceived and interpreted by projectmanagers
may result in different types of project complexity.Baccarini [8]
considers technological and organizationalcomplexities as the core
components of project complexity.According to [25, 34], four
different types of project com-plexity, overall, task, social, and
cultural, help to best under-stand and prevent projects from
failure. Task complexityrefers to the density of the units, causal
links, and conse-quences within a temporal and spatial frame.
Social complex-ity describes the number of members communicating
andworking with each other and the differentiation of their
tasks,while cultural complexity encompasses the number of
differ-ent historical experiences and sense-making processes
thatconfront each other in a project. Cultural complexity
com-presses the history, experience, and sense-making processesof
different groups that joint the effort in a project. Overalland
task complexity can be managed by a functional orga-nization with
decentralized decision-making and socialcomplexity by trust and
commitment, whereas culturalcomplexity by sense-making
processes.
Pollack and Remington and Pollack [50, 51] emphasizethat a clear
distinction on the type of complexity helps inselecting the
appropriate model to manage a project. Basedon the source of
complexity, the authors suggest four typesof project complexity:
structural, technical, directional, andtemporal complexity.
Structural complexity stems fromlarge-scale projects which are
typically broken down intosmall tasks and separate contracts.
Projects in the engineer-ing, construction, IT, and defence sectors
where the complex-ity stems from the difficulty in managing and
keeping track ofthe high number of interconnected tasks and
activities arelikely to have this type of complexity [51].
Technical com-plexity is found in architectural, industrial design,
and R&Dprojects which have design characteristics or
technicalaspects that are unknown or untried and where
complexityarises because of the uncertainty regarding the outcome
formany independent design solutions [51]. Baccarini [8]
cate-gorizes technological complexity in terms of
differentiationand interdependence, which is further categorized
into threetypes given in an ascending order of complexity: (i)
pooled,in which each element gives a discrete contribution to
the
4 Complexity
-
project; (ii) sequential, where one element’s output
becomesanother’s input; and (iii) reciprocal, where each
element’soutput becomes inputs for other elements [51, 52].
Direc-tional complexity is often found in change projects wherethe
direction of the project is not understood and when it isclear that
something must be done to improve a problematicsituation [51].
Temporal complexity results in projects wheredue to unexpected
legislative changes of rapid changes intechnology, there is a high
level of uncertainty regardingfuture constraints that could
destabilize the project. Opera-tive complexity, i.e., the degree to
which organizations ofthe project are independent when defining
their operationsto achieve given goals, and cognitive complexity
whichidentifies the degree to which self-reflection,
sense-makingprocesses, the emergence of an identity, or even an
organi-zational culture is possible, are also different types
ofcomplexity identified in the literature [36].
5. Project Complexity Models
Trying to find the most appropriate model for managing aproject
can be a difficult task. If the model is too simple, itis not
enough close to reality. On the contrary, if it is toocomplex, it
can be useless to project managers. Next, someof the most relevant
complexity models in the projectmanagement literature will be
revised.
5.1. Goals and Methods Matrix. Based on how well-definedare the
goals and methods of achieving these goals in a pro-ject, Turner
and Cochrane [53] developed the goals andmethods matrix shown in
Figure 1 where four types of pro-jects can be found: (i) type 1
projects are projects in whichgoals and methods are well-defined
and understood. In thiscase, the role of the project manager is
that of a conductor;(ii) type 2 projects are projects with
well-defined goals butpoorly defined methods. In this case, the
role of the projectmanager is that of a coach; (iii) type 3
projects are projectsplanned in life-cycle stages with poorly
defined goals butwell-defined methods; and (iv) type 4 projects are
projectswith no defined goals and no defined methods.
Typically,engineering and construction projects fall within the
cate-gory of type 1 projects. Product development projects belongto
type 2, while application software development and R&Dand
organizational change projects belong to type 3 and type4 projects,
respectively.
5.2. Stacey’s Agreement and Certainty Matrix. Stacey
[54]analysed complexity on two dimensions, the degree of cer-tainty
and the level of agreement and, based on these twodimensions,
developed the matrix shown in Figure 2 withthe following zones: (i)
close to agreement, close to certainty:in this zone, we can find
simple projects where traditionalproject management techniques work
well and the goal isto identify the right process to maximize
efficiency andeffectives; (ii) far from agreement, close to
certainty: in thiscase, coalitions, compromise, and negotiation are
used tosolve this type of situations; (iii) close to agreement, far
fromcertainty: in this case, traditional project management
tech-niques may not work and leadership approaches must be
used to solve this type of situations; and (iv) far from
agree-ment far from certainty: this is the zone of anarchy with
ahigh level of uncertainty and where traditional
managementtechniques will not work.
5.3. William’s Model. Williams and Hillson [55]
extendBaccarini’s model by one additional dimension. In additionto
the two components of complexity suggested by Baccarini,i.e., the
number of elements and the interdependency of theseelements, the
authors introduce uncertainty and attributesthe increasing
complexity in projects to two compoundingcauses, the relationship
between product complexity andproject complexity and the length of
projects. The resultingmodel is shown in Figure 3 where, as can be
seen, projectcomplexity is characterized by two dimensions,
structuralcomplexity and uncertainty, each of one having two
subdi-mensions, number and interdependency of elements,
anduncertainty in goals and methods, respectively.
5.4. Kahane’s Approach. Kahane’s [56] approach to complex-ity is
deeply rooted in a social environment. He introducesthe U-process
as a methodology for addressing complexchallenges and distinguishes
complexity in three ways:(i) dynamic complexity: the cause and
effect of complexityare far apart and it is hard to grasp from
first-hand experi-ence; (ii) generative complexity: a situation
where the solu-tion cannot be calculated in advance based only on
whathas worked in the past; and (iii) social complexity: the
peopleinvolved who have different perspectives and interests
mustparticipate in creating and implementing the solution.
Whenusing the U-process developed by Kahane [56], projectmanagers
undertake three activities: (i) sensing the currentreality of the
project; (ii) reflecting about what is going onand what they have
to do; and (iii) realizing and actingquickly to bring forth a new
reality.
5.5. Cynefin Decision-Making Framework. Snowden andBoone [57]
developed the Cynefin framework, which allowsexecutives to see new
things from new viewpoints, assimilatecomplex concepts, and address
real-world problems andopportunities. The framework sorts it into
five domains,simple, complicated, complex, chaotic, and disorder,
each
No Type 2 projectsProduct developmentType 4 projects
R & D andorganizational change
Type 1 projectsEngineering &construction
Type 3 projectsApplication so�ware
development
Methods well-defined
Yes
NoYesGoals well-defined
Figure 1: Goals and methods matrix [53].
5Complexity
-
of one requires different actions based on cause and effect.The
simple and complicated domains are characterized bycause and effect
relationships, and right answers can be deter-mined based on facts.
The complex and chaotic domains donot have a clear cause and effect
relationship, and decisionsmust be made based on incomplete data.
The last domain,disorder, is applied when it is unclear which of
the four isdominant and is tackled by breaking it down into
smallercomponents and then assigning them to the other fourdomains.
Table 2 shows the characteristics of each context,the leader’s job,
the danger signals, and the response to thesedanger signals
[57].
5.6. The UCP Model. The UCP model classifies projectsaccording
to uncertainty, complexity, and pace. Further-more, uncertainty has
been broken down into four levelsof technological uncertainty
(low-, medium-, high-, andsuper high-technology projects).
Complexity into threelevels of system scope is based on a hierarchy
of systemsand subsystems (assembly, system, and array) and paceinto
three levels (regular, fast-competitive, and
critical-blitzprojects) [58–60]:
(a) The technological dimension
(i) Low-Technology Projects. Projects based onexisting and
well-established technologies
(ii) Medium-Technology Projects. Projects basedmainly on
existing technologies but incorporat-ing a single new technology or
feature
(iii) High-Technology Projects. Projects that inte-grate a
collection of newbut existing technologies
(iv) Super High-Technology Projects. Projects basedon non-yet
existing technologies in which,although the project goal is clear,
no technologyis known to achieve the final product
(b) The system scope dimension (complexity)
(i) Scope 1: Assembly. A collection of componentsin a single
unit, performing a well-definedlimited function
(ii) Scope 2: System. A complex collection of inter-active units
jointly performing a wide rangeof functions
(iii) Scope 3: Array. A large collection of systems func-tioning
together to achieve a common purpose
(c) The pace dimension
(i) Regular Projects. Projects that, although con-fined to a
limited time-frame, still can achievetheir objectives
(ii) Fast-Competitive Projects. Projects conceived tocreate
strategic positions, address market oppor-tunities, etc. In this
type of projects, since time tomarket is directly associated with
competitive-ness, missing the deadline might not be fatalbut it
could hurt competitive positions
(iii) Critical-blitz projects are the most urgent andmost
time-critical projects in which meetingschedule is critical to
success and project delaymeans project failure.
6. Current and Future Approaches toManage Complexity
Understanding how project managers deal with the differenttypes
of complexity and how they reply to these differenttypes can help
to prevent projects from failure. Stacey [54],Kahane [56], and
Snowden and Boone [57] focus on howcomplexity, particularly messy
or ill-structured problems,might influence leadership style and
decision-making inperiods of organizational change. Clift and
Vandenbosch[61], in a survey conducted with project manager leaders
ofnew product development teams, found that long-cyclecomplex
projects were run by autocratic leaders, adhered toa well-defined
standardized, serial processing approach. Incontrast, short-cycle
complex projects were run by projectmanagers who used a more
participative management stylewith many external sources of
information.
Project complexity has been addressed by researchersfrom
different perspectives and approaches. Early methodsfrom the
general management literature include Declerckand Eymery’s [62]
method for analysing ill-structuredproblems and Turner and
Cochrane’s goals and methodsmatrix [53]. Part of the literature has
focused on uncertainty[32, 63]. Williams [64] views the number of
elements andtheir interrelationships as constituents of structural
uncer-tainty which is proposed as an element of complexity.Shenhar
[65] regards complexity and uncertainty as orthog-onal to each
other. Atkinson et al. [66] considers complexityas an element of
uncertainty while Geraldi et al. and Mülleret al. [17, 67] support
uncertainty as an element of complex-ity. Perminova et al. [68]
equate complexity to systematicuncertainty. Pich et al. [32]
associate categories of uncer-tainty with variations, foreseen
uncertainty, unforeseenuncertainty, and chaos. Sommer and Loch [12]
treat com-plexity and unforeseeable uncertainty as separate
constructs.
Far f
rom
agre
emen
t
Complicated
Complicated
Complex
Anarchy
Simple
Close to certainty Far from certainty
Clos
e to
agre
emen
t
Figure 2: Agreement and certainty matrix [54].
6 Complexity
-
Williams [69] defines two additional types of
uncertainty,aleatoric uncertainty relating to the reliability of
calculationsand existence uncertainty stemming from lack of
knowledgeand leading to project complexity.
Other approaches used to deal with complexity in
projectmanagement contexts include systems theory to help
under-stand how different aspects affect the project as a system[8,
51, 55]. Payne [70] takes a perspective which combinesdifficulty
and systems thinking, associating complexitywith the multiple
interfaces between individual projects,
the organization, and the parties concerned. Laufer et al.[71]
explore the evolution of management styles associatedwith the
organizational complicacy of simple and complexprojects. Tatikonda
and Rosenthal [31] and Pundir et al.[72] relate technological
novelty to technological maturityof the organization; immaturity
leads to task uncertainty.
The increasingly fast-paced systems of today’s businessand
social environment, characterized by discontinuity andchange, force
organizations to make decisions and take thecorresponding actions
based on multiple unknown variables.
Structuralcomplexity
Projectcomplexity
Uncertainty
Size, number ofelements Interactions in
complex ways,total is more than
sum of parts
Structuralcomplexity
compounded byuncertainty
Interdependence ofelements
Uncertainty ingoals
Uncertainty inmethods
Figure 3: William’s model [55].
Table 2: Context’s characteristics, leader’s job, danger
signals, and response to danger signals.
Context’s characteristics Leader’s job Danger signals Response
to danger signals
Simple
Repeating patternsClear cause-and-effect
relationshipsKnown knowns
Fact-based management
Sense, categorize, respond,and delegate
Use best practicesCommunicate in clear and
direct ways
Complacency and comfortMake simple problems
complexEntrained thinking
Create communication channelsDo not assume things are
simpleRecognize both the value andlimitations of best practice
Complicated
Expert diagnosis requiredCause-and-effectrelationships not
immediately apparentKnown unknowns
Fact-based management
Sense, analyse, and respondCreate panels of expertsListen to
conflicting
objectives
Experts overconfident intheir own solutionsAnalysis
paralysis
Viewpoints of nonexpertsexcluded
Encourage external and internalstakeholders to challenge
expert opinionsUse experiments and games
to force people to think outsidethe familiar
Complex
Flux and unpredictabilityNo right answers
Unknown unknownsMany competing ideasA need for creative
andinnovative approachesPattern-based leadership
Probe, sense, and respondCreate environments andexperiments that
allowpatterns to emerge
Increase levels of interactionand communicationUse methods that
can
generate ideas
Temptation to fall backinto habitual, command-
and-control modelDesire for acceleratedresolution of
problems
Allow time for reflectionUse approaches that encourage
interaction so patternscan emerge
Chaotic
High turbulenceNo clear cause-and-effect
relationshipsUnknowablesHigh tension
Many decisions to make andno time to think
Pattern-based leadership
Act, sense, and respondLook for what works insteadof seeking
right answersTake immediate action to
reestablish orderProvide clear and direct
communication
Applying a command-and-control approachlonger than needed
Missed opportunity forinnovation
Chaos unabated
Set up mechanisms to takeadvantage of the opportunities
afforded by a chaoticenvironment
Work to shift the context tochaotic to complex
7Complexity
-
According to Pundir et al. [72], since projects exhibit
thecharacteristics of complex systems, the method to managethem
cannot be predicted in advance, it will emerge fromthe interactions
between the project elements and theenvironment. Richardson [35]
explores the implications ofcomplexity from the management of
organizations and how“thinking complexity” may affect the way in
which projectmanagers do their jobs. According to the author, if
thereare limits to what we can know about our organization,
thereare limits to what we can achieve in a predetermined
andplanned way. H. Singh and A. Singh [73] argue that it is atthe
edge of chaos, where linear systems begin to fail andnonlinear
systems begin to dominate and where projectmanagers must begin to
pay greater attention to the non-linear and subtle influences in
their planning and manage-ment styles.
7. Conclusions
When problems fundamentally dynamic are treated stati-cally,
delays and cost overruns are common. Traditionalproject management
tools and techniques, based on theassumptions that a set of tasks
can be discrete, with well-defined information about time, cost,
and resources, andwith extensive preplanning and control, are often
foundinadequate. These traditional approaches that utilize astatic
approach provide project managers with unrealisticestimations
ignoring multiple feedback processes and non-linear relationships
of the project. The interrelationshipsbetween the components of a
project are more complexthat is suggested by traditional
techniques, which makesthem inadequate to the challenges of today’s
dynamic pro-ject environment.
The new complex and dynamic environments requireproject managers
to rethink the traditional definition of aproject and the ways to
manage it. Project managers mustbe able to make decisions in these
dynamic yet unstable sys-tems that are continuously changing and
evolving in a ran-dom fashion and are hard to predict, very
different fromthe linear, predictable systems traditionally
studied. Toachieve this objective, more integrated approaches for
man-aging projects in complex environments and new methodsof
planning, scheduling, executing, and controlling projectsmust be
investigated.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
References
[1] P. W. G. Morris, The Management of Projects, ThomasTelford,
London, 1997.
[2] P. W. G. Morris, “Science, objective knowledge and the
theoryof project management,” Civil Engineering, vol. 150, no.
2,pp. 82–90, 2002.
[3] J. Bennett and B. Fine, “Measurement of complexity in
con-struction projects,” in Department of Construction Manage-ment,
University of Reading, 1980.
[4] K. A. Bubshait and W. J. Selen, Project Characteristics
thatInfluence the Implementation of Project Management Tech-niques:
a Survey, Project Management Institute, 1992.
[5] P. Bennett and S. Cropper, “Uncertainty and conflict:
combin-ing conflict analysis and strategic choice,” Journal of
Behav-ioral Decision Making, vol. 3, no. 1, pp. 29–45, 1990.
[6] K. Gidado, Numerical Index of Complexity in
BuildingConstruction to Its Effect on Production Time, University
ofBrighton, UK, 1993.
[7] T. M. Wozniak, Significance vs. Capability:“Fit for Use”
ProjectControls, AACE International Transactions, 1993.
[8] D. Baccarini, “The concept of project complexity—a
review,”International Journal of Project Management, vol. 14, no.
4,pp. 201–204, 1996.
[9] K. Remington, R. Zolin, and R. Turner, “A model of
projectcomplexity: distinguishing dimensions of complexity
fromseverity,” in Proceedings of the 9th International
ResearchNetwork of Project Management Conference, Berlin,
IRNOP,2009.
[10] H. Parsons-Hann and K. Liu, “Measuring
requirementscomplexity to increase the probability of project
success,” inProceedings of the Seventh International Conference on
Enter-prise Information Systems - Volume 3: ICEIS, pp.
434–438,Miami, USA, 2005.
[11] L.-A. Vidal and F. Marle, “Understanding project
complexity:implications on project management,” Kybernetes, vol.
37,no. 8, pp. 1094–1110, 2008.
[12] S. C. Sommer and C. H. Loch, “Selectionism and learning
inprojects with complexity and unforeseeable
uncertainty,”Management Science, vol. 50, no. 10, pp. 1334–1347,
2004.
[13] H. Maylor, R. Vidgen, and S. Carver, “Managerial
complexityin project-based operations: a groundedmodel and its
implica-tions for practice,” Project Management Journal, vol.
39,1_supplement, pp. S15–S26, 2008.
[14] T. Cooke-Davies, S. Cicmil, L. Crawford, and K.
Richardson,“We’re not in Kansas anymore, Toto: mapping the
strangelandscape of complexity theory, and its relationship to
projectmanagement,” Project Management Journal, vol. 38, no. 2,pp.
50–61, 2007.
[15] D. McLain, “Quantifying project characteristics related
touncertainty,” Project Management Journal, vol. 40, no. 4,pp.
60–73, 2009.
[16] M. Bosch-Rekveldt, Y. Jongkind, H. Mooi, H. Bakker, andA.
Verbraeck, “Grasping project complexity in large engi-neering
projects: the TOE (technical, organizational andenvironmental)
framework,” International Journal of ProjectManagement, vol. 29,
no. 6, pp. 728–739, 2011.
[17] J. Geraldi, H. Maylor, and T. Williams, “Now, let’s make
itreally complex (complicated) a systematic review of
thecomplexities of projects,” International Journal of
Operations& Production Management, vol. 31, no. 9, pp. 966–990,
2011.
[18] L.-A. Vidal, F. Marle, and J.-C. Bocquet, “Measuring
projectcomplexity using the analytic hierarchy process,”
InternationalJournal of Project Management, vol. 29, no. 6, pp.
718–727, 2011.
[19] T. Brady and A. Davies, “Managing structural and
dynamiccomplexity: a tale of two projects,” Project
ManagementJournal, vol. 45, no. 4, pp. 21–38, 2014.
[20] M. Padalkar and S. Gopinath, “Are complexity anduncertainty
distinct concepts in project management? Ataxonomical examination
from literature,” International
8 Complexity
-
Journal of Project Management, vol. 34, no. 4, pp.
688–700,2016.
[21] R. V. Ramasesh and T. R. Browning, “A conceptual
frameworkfor tackling knowable unknown unknowns in project
manage-ment,” Journal of Operations Management, vol. 32, no. 4,pp.
190–204, 2014.
[22] S. M. Qureshi and C. Kang, “Analysing the
organizationalfactors of project complexity using structural
equation model-ling,” International Journal of Project Management,
vol. 33,no. 1, pp. 165–176, 2015.
[23] F. C. Saunders, A. W. Gale, and A. H. Sherry,
“Conceptualisinguncertainty in safety-critical projects: a
practitioner perspec-tive,” International Journal of Project
Management, vol. 33,no. 2, pp. 467–478, 2015.
[24] S. Sinha, A. I. Thomson, and B. Kumar, “A complexity
indexfor the design process,” WDK Publications, vol. 1, pp.
157–163, 2001.
[25] C. Brockmann and G. Girmscheid, Complexity of
Megaproj-ects, in: CIB World Building Congress: Construction
forDevelopment: 14–17 May 2007, Cape Town InternationalConvention
Centre, South Africa, CIB, 2007.
[26] H. Mintzberg, Mintzberg on Management: Inside our
StrangeWorld of Organizations, Simon and Schuster, 1989.
[27] A. D. Hall, A Methodology for Systems Engineering,
1962.
[28] J. S. Russell, E. J. Jaselskis, and S. P. Lawrence,
“Continuousassessment of project performance,” Journal of
ConstructionEngineering andManagement., vol. 123, no. 1, pp. 64–71,
1997.
[29] P. R. Lawrence and J. W. Lorsch, “Differentiation
andintegration in complex organizations,” Administrative
ScienceQuarterly, vol. 12, no. 1, p. 1, 1967.
[30] E. Custovic, “Engineering management: old story,
newdemands,” IEEE Engineering Management Review, vol. 43,no. 2, pp.
21–23, 2015.
[31] M. V. Tatikonda and S. R. Rosenthal, “Technology
novelty,project complexity, and product development project
execu-tion success: a deeper look at task uncertainty in product
inno-vation,” IEEE Transactions on Engineering Management,vol. 47,
no. 1, pp. 74–87, 2000.
[32] M. T. Pich, C. H. Loch, and A. D. Meyer, “On
uncertainty,ambiguity, and complexity in project management,”
Manage-ment Science, vol. 48, no. 8, pp. 1008–1023, 2002.
[33] S. Ward and C. Chapman, “Transforming project
riskmanagement into project uncertainty management,” Interna-tional
Journal of Project Management, vol. 21, no. 2, pp. 97–105,
2003.
[34] G. Girmscheid and C. Brockmann, “The inherent complexityof
large scale engineering projects,” Project Perspectives,vol. 29,
pp. 22–26, 2008.
[35] K. A. Richardson, “Managing complex organizations:
com-plexity thinking and the science and art of
management,”Emergence: Complexity and Organization, vol. 10, p. 13,
2008.
[36] K. I. Gidado, “Project complexity: the focal point of
construc-tion production planning,” Construction Management
andEconomics, vol. 14, no. 3, pp. 213–225, 1996.
[37] P. M. Blau and R. A. Schoenherr, The Structure of
Organiza-tions, Basic Books (AZ), 1971.
[38] J. M. Beyer and H. M. Trice, “A reexamination of the
relationsbetween size and various components of
organizationalcomplexity,” Administrative Science Quarterly, vol.
24, no. 1,pp. 48–64, 1979.
[39] P. De Meyer, F. Maes, and A. Volckaert, “Emissions
frominternational shipping in the Belgian part of the North Seaand
the Belgian seaports,” Atmospheric Environment, vol. 42,no. 1, pp.
196–206, 2008.
[40] A. R. Meyer and L. J. Stockmeyer, “The equivalence
problemfor regular expressions with squaring requires
exponentialspace,” in 13th Annual Symposium on Switching and
Autom-ata Theory (swat 1972), pp. 125–129, USA, October 1972.
[41] L. M. Corbett, J. Brockelsby, and C. Campbell-Hunt,
TacklingIndustrial Complexity, Tackling Industrial Complexity,
Insti-tute for Manufacturing, Cambridge, 2002.
[42] R. Dewar and J. Hage, “Size, technology, complexity,
andstructural differentiation: toward a theoretical
synthesis,”Administrative Science Quarterly, vol. 23, no. 1, pp.
111–136, 1978.
[43] U. Steger, W. Amann, and M. L. Maznevski, Managing
Com-plexity in Global Organizations, John Wiley & Sons,
2007.
[44] H. Mintzberg, The Structuring of Organisations: a Synthesis
ofthe Research, University of Illinois, Champaign, IL, USA,
1979.
[45] R. E. Levitt, J. Thomsen, T. R. Christiansen, J. C. Kunz,
Y. Jin,and C. Nass, “Simulating project work processes and
organiza-tions: toward a micro-contingency theory of
organizationaldesign,” Management Science, vol. 45, no. 11, pp.
1479–1495, 1999.
[46] E. C. Conforto, E. Rebentisch, and D. Amaral, “Learning
theart of business improvisation,” MIT Sloan ManagementReview, vol.
57, p. 8, 2016.
[47] J. Priest and J. Sanchez, Product Development and Design
forManufacturing: a Collaborative Approach to Producibilityand
Reliability, CRC Press, 2012.
[48] D. Woodward and H. M. College, Understanding Complexity:a
Critique and Synthesis, Henley Management College, 1992.
[49] A. Jaafari, “Project management in the age of complexity
andchange,” in Project Management Journal, vol. 34, no. 4,pp.
47–57, 2003.
[50] J. Pollack, “The changing paradigms of project
management,”International Journal of Project Management, vol. 25,
no. 3,pp. 266–274, 2007.
[51] K. Remington and J. Pollack, Tools for Complex
Projects,Routledge, 2016.
[52] J. D. Thompson, Organizations in Action: Social Science
Basesof Administration, McGraw-Hill, New York City, NY,
USA,1967.
[53] J. R. Turner and R. A. Cochrane, “Goals-and-methods
matrix:coping with projects with ill defined goals and/or methods
ofachieving them,” International Journal of Project Manage-ment,
vol. 11, no. 2, pp. 93–102, 1993.
[54] R. D. Stacey, Complexity and Creativity in
Organizations,Berrett-Koehler Publishers, 1996.
[55] T. Williams and D. Hillson, “Editorial–PMI Europe
2001,”International Journal of Project Management, vol. 20, no.
3,pp. 183-184, 2002.
[56] A. Kahane, Solving Tough Problems: an Open Way of
Talking,Listening, and Creating New Realities,
Berrett-KoehlerPublishers, 2004.
[57] D. J. Snowden and M. E. Boone, “A leader’s frameworkfor
decision making,” Harvard Business Review, vol. 85,p. 68, 2007.
[58] A. J. Shenhar, Integrating Product Development and
ProjectManagement, in: 28th Annual Symposium of PMI,
ProjectManagement Institute, Chicago, IL, 1997.
9Complexity
-
[59] A. J. Shenhar, “Strategic project management: the new
frame-work,” in PICMET '99: Portland International Conference
onManagement of Engineering and Technology. ProceedingsVol-1: Book
of Summaries (IEEE Cat. No.99CH36310),pp. 382–386, Portland, OR,
USA, July 1999.
[60] A. J. Shenhar and D. Dvir, “Toward a typological theoryof
project management,” Research Policy, vol. 25, no. 4,pp. 607–632,
1996.
[61] T. B. Clift and M. B. Vandenbosch, “Project complexity
andefforts to reduce product development cycle time,” Journal
ofBusiness Research, vol. 45, no. 2, pp. 187–198, 1999.
[62] P. Declerck and R. P. et Eymery, Le management et
l’ana-lyse des projets, Editions Hommes et Techniques,
Paris,France, 1976.
[63] T. Little, “Context-adaptive agility: managing complexity
anduncertainty,” IEEE Software, vol. 22, no. 3, pp. 28–35,
2005.
[64] T. M. Williams, “The need for new paradigms for
complexprojects,” International Journal of Project Management,vol.
17, no. 5, pp. 269–273, 1999.
[65] A. J. Shenhar, “One size does not fit all projects:
exploringclassical contingency domains,” Management Science, vol.
47,no. 3, pp. 394–414, 2001.
[66] R. Atkinson, L. Crawford, and S. Ward, “Fundamental
uncer-tainties in projects and the scope of project
management,”International Journal of Project Management, vol. 24,
no. 8,pp. 687–698, 2006.
[67] R. Müller, J. G. Geraldi, and J. R. Turner, “Linking
complexityand leadership competences of project managers,” in
Proceed-ings of IRNOP VIII (International Research Network
forOrganizing by Projects) Conference, Brighton, UK,
CD-ROM.Universal Publishers, 2007.
[68] O. Perminova, M. Gustafsson, and K. Wikström,
“Defininguncertainty in projects–a new perspective,” International
Jour-nal of Project Management, vol. 26, no. 1, pp. 73–79,
2008.
[69] T. Williams, “Assessing and moving on from the
dominantproject management discourse in the light of project
over-runs,” IEEE Transactions on Engineering Management,vol. 52,
no. 4, pp. 497–508, 2005.
[70] J. H. Payne, “Management of multiple simultaneous
projects:a state-of-the-art review,” International Journal of
ProjectManagement, vol. 13, no. 3, pp. 163–168, 1995.
[71] A. Laufer, G. R. Denker, and A. J. Shenhar,
“Simultaneousmanagement: the key to excellence in capital
projects,” Inter-national Journal of Project Management, vol. 14,
no. 4,pp. 189–199, 1996.
[72] A. K. Pundir, L. Ganapathy, and N. Sambandam, “Towards
acomplexity framework for managing projects,” Emergence:Complexity
and Organization, vol. 9, p. 17, 2007.
[73] H. Singh and A. Singh, “Principles of complexity and
chaostheory in project execution: a new approach to
management,”Cost Engineering, vol. 44, p. 23, 2002.
10 Complexity
-
Research ArticleComparing Project Complexity across Different
Industry Sectors
Marian Bosch-Rekveldt , Hans Bakker, and Marcel Hertogh
Faculty of Civil Engineering and Geosciences, Delft University
of Technology, Stevinweg 1, 2628 CN Delft, Netherlands
Correspondence should be addressed to Marian Bosch-Rekveldt;
[email protected]
Received 26 October 2017; Accepted 9 May 2018; Published 24 June
2018
Academic Editor: Emma Diaz Ruiz de Navamuel
Copyright © 2018 Marian Bosch-Rekveldt et al. This is an open
access article distributed under the Creative Commons
AttributionLicense, which permits unrestricted use, distribution,
and reproduction in anymedium, provided the original work is
properly cited.
Increasing complexity of projects is mentioned as one of the
reasons for project failure—still. This paper presents a
comparativeresearch to investigate how project complexity was
perceived by project practitioners in different industry sectors.
Five sectorswere included: process industry, construction industry,
ICT, high-tech product development, and food processing industry.
Intotal, more than 140 projects were included in the research,
hence providing a broad view on Dutch project practice. From
thecomplexity assessments, it is concluded that only one complexity
element was present in the top complexity elements of
projectsacross the five sectors: the high project schedule drive.
The variety of external stakeholders’ perspectives, a lack of
resources andskills availability, and interference with existing
site were found in the top lists of three sectors. It was concluded
that aframework to grasp project complexity could support the
management of complex projects by creating awareness for
the(expected) complexities. Further research could be focused on
the subjective character of complexity as well as on theapplication
of cross-sector learning, since this research does show
similarities between large technical projects in different
sectors.
1. Introduction
There has been a lot of attention for assessing project
com-plexity in literature in the previous years [1–4]. Several
stud-ies show the potential for, and opportunities of,
projectcomplexity [5, 6] in an attempt to exploit certain
complexi-ties and/or explicitly choose to increase complexity, but
moreoften the potential negative consequences of project
com-plexity are emphasized [7]. Since complexity is
potentiallyhindering project performance, better managing
projectcomplexity is considered an important research topic,
still.
In the tradition of project management, where the dom-inant
paradigm is shifting from a one-size-fits-all approachin the 1950s
towards a more contingent approach, moreand more it is realized
that projects are unique and shouldbe treated as such, explicitly
taking into account contextualinfluences [8–10]. The project
management approach shouldbe chosen to best accommodate specific
project circum-stances and context.
Despite such a supposed “fit-for-purpose” approach, itis felt
that there could be similarities in projects in differentsectors
and these very different projects possibly couldlearn from each
other. This raised the question how project
complexities in one sector would compare to project
com-plexities in other sectors. Are similar problems being
faced?Whereas literature did report on theoretical insights
anddebates in the field of complexity [11, 12], insight in real
pro-ject practice was lacking. Therefore, comparative researchwas
performed to investigate how project complexity wasperceived by
project practitioners in different industry sec-tors. Also, it was
investigated how a framework to grasp pro-ject complexity could
support the management of complexprojects. The following research
questions were formulated:
(1) How do practitioners in different industry sectorsperceive
project complexity in large technicalprojects?
(2) How could a framework to grasp project complexitybe used to
improve project performance?
For this study, the earlier published TOE framework tograsp
project complexity, with TOE referring to technical,organizational,
and external, was selected [1]. This frame-work, based on extensive
literature study as well as empiricaldata, provided a broad base
and enabled a rich view on thepotential aspects causing complexity
in very different projects.
HindawiComplexityVolume 2018, Article ID 3246508, 15
pageshttps://doi.org/10.1155/2018/3246508
http://orcid.org/0000-0001-9309-6352https://doi.org/10.1155/2018/3246508
-
Empirical research was performed in different sectors:process
industry, construction industry, ICT industry,high-tech product
development industry, and food process-ing industry. These sectors
have in common that engineer-ing tasks constitute a main part of
all projects, albeitdifferent types of engineering, like software
engineering,industrial engineering, mechanical engineering, and
civilengineering. These sectors also have in common that
projectperformance is disappointing [13, 14]. Given the
relationbetween project complexity and project performance
[7],exploring similarities and differences in the complexitiesfaced
in projects in these sectors might provide opportuni-ties for
cross-sectoral learning.
Using both case studies and broader surveys, data wasgathered
from several respondents in these five industries.In the different
researches, slightly different approaches wereused because of the
different specific scopes, but the com-mon factor in all researches
was the use of the TOE frame-work. More details on the data
gathering are provided inthe next section.
The relevance of the research is considered from a scien-tific
point of view and a social point of view. To start with thelatter:
the research is aimed at contributing to the improve-ment of
business practice by a better understanding of pro-ject complexity
in different industries, thereby improvingproject performance.
Given the high failure rate of projects,social benefits seem
evident. From the scientific point of view,this research embraces
some of the project management“schools of thought” as distinguished
in literature [15]: partic-ularly the factor school and the
contingency school, henceillustrating a pluralistic approach in
project managementresearch. Also, it contributes to improving the
understandingof the notion of complexity in projects.
This paper is structured as follows. In Section 2, theapplied
methods are discussed, starting with presentingthe further
developed TOE framework to grasp projectcomplexity which is used as
the main source supportingthe data gathering. Next, the set-up of
the data gatheringin the five industry sectors is described.
Section 3 presentsthe results of the five studies with regard to
the complexityassessments. This includes a cross-sector comparison
of theresults, highlighting similarities and differences between
thedifferent sectors. In Section 4, the potential value of theTOE
framework for practice is discussed. This leads tothe implications
for managing complex projects and learn-ing across projects as
discussed in Section 5. This paper isconcluded with the conclusions
and recommendations inSection 6.
2. Methods
This paper has the character of a meta-study; the results offive
separate researches, which have in common the appli-cation of the
TOE framework to grasp project complexity,are compared thoroughly.
Bringing together these resultsis expected to contribute to the
understanding of practi-tioners’ perspectives on elements causing
complexity intheir projects.
2.1. Further Developments of the TOE Framework. Theframework as
published earlier [1] was slightly modified asa result of
subsequent research [7]. To modify the frame-work, a mixed-methods
approach was used, combining qual-itative and quantitative methods
[16, 17]. As a result, someelements of the framework were adapted
or reformulated,some switched category, and the majority of the
elementswere simply confirmed. In the version of the framework
aspresented in Figure 1, the T-elements represent the
potentialcomplexity causes in the project related to the project
scopeor content of the project. The O-elements represent
thepotential complexity causes in the project related to the
pro-ject internal organization. The E-elements represent all
thepotential external complexity causes in the project, relatedto
external issues or external organizational complexities.
The intended use of the TOE framework consists of pro-viding the
project team a means to create a complexity foot-print of the
project at hand. By sharing the (expectedly)different views in the
project team and with other stake-holders involved, discussion is
facilitated and awareness iscreated for the expected complexities
in the project. It is rec-ognized that this should not be a one-off
exercise as the pro-ject complexities are expected to evolve during
the differentphases of the project.
2.2. Data Gathering and Methods per Substudy. This
sectiondescribes the data gathering in the different industry
sectorsincluding an elaboration on the specific methods used
persubstudy. Table 1 shows when the data was gathered andsummarizes
the number of respondents in each of the stud-ies. Given the fact
that in four studies, the data was gatheredin 2011/2012, one could
argue that the value of this study islimited. The data for the
fifth sector was gathered morerecently. How time could have
influenced the overall picturewill be discussed after providing the
results.
Large differences are observed in Table 1 regarding thenumber of
projects/respondents involved in the differentstudies. The nature
of the research—evaluating complexityin different industries using
an existing framework—domi-nantly asks for a quantitative approach
[18]. We are inter-ested in general information on relative large
numbers ofprojects, as opposed to detailed in-depth information
onsmall numbers of projects, and therefore, a survey is a suit-able
research tool [19, 20]. Indeed a web-based survey wasapplied in
studies A and B. However, in the researches car-ried out in IT,
high-tech industry, and food processing indus-try, only part of the
research was related to measuring projectcomplexity, and those
researches were organized around in-depth case studies, hence
explaining the relative limitedamount of data from sectors C, D,
and E. Details perresearch, in terms of methods used and data
gathered, areprovided below.
2.2.1. Sector A: Process Industry. The study described in
thissection draws heavily upon Chapter 9 of a dissertation [7].In
the survey, respondents were asked to indicate their com-pany’s
role (owner/contractor/other) and their experiencelevel (as a
project manager and working for their company).Next, the
respondents had to score each of the 47 elements of
2 Complexity
-
the TOE framework on their potential contribution to
thecomplexity of a project (not–little–some–substantial–verymuch).
They were not explicitly asked for one specific pro-ject; they
could answer the question for any project in mind.
Subsequently, the elements that were scored “substantial”or
“very much” by the respondent were listed on the screenand the
respondent had to select those three elements thatin their opinion
contribute most to a project’s complexity.Next, the elements that
were scored “none” or “little” bythe respondent were listed on the
screen, and the respondenthad to select which three of these would
contribute least to
project complexity. Also, questions were posted
regardingtreating project complexity, but these are outside the
scopeof the current paper. Finally, the respondents were askedfor
their opinion about the potential use of the TOE com-plexity
framework by means of open questions:
(1) How would you apply the TOE project complexityframework in
your daily practice?
(2) What would be the added value of using the TOEframework in
your projects, if any?
Technical complexity(17 elements)
External complexity(13 elements)
High number of project goalsNonalignment of project goals
Unclarity of project goalsUncertainties in scope
Strict quality requirementsProject duration
Size in CAPEXNumber of locations
Newness of technology (worldwide)Lack of experience with
technology
High number of tasksHigh variety of tasks
Dependencies between tasksUncertainty in methods
Involvement of different technical disciplinesConflicting norms
and standards
Technical risks
External risksNumber of external stakeholders
Variety of external stakeholders’ perspectivesDependencies on
external stakeholders
Political influenceLack of company internal support
Required local contentInterference with exiting site
Remoteness of locationLack of experience in the country
Company internal strategic pressureInstability of project
environment
Level of competition
Organizational complexity(17 elements)
High project schedule driveLack of resource & skills
availability
Lack of exprience with parties involvedLack of HSSE
awareness
Interfaces between different disciplinesNumber of financial
sources
Number of contractsType of contract
Number of different nationalitiesNumber of different
languages
Presence of JV partnerInvolvement of different time zones
Size of project teamIncompatibility between different pm
method/tools
Lack of trust in project teamLack of trust in contractor
Organizational risks
Figure 1: TOE model as used in this study [7].
Table 1: Summary of data gathered.
Study ID Industry Data gathered in Number of projects Number of
respondents
A Process 2011 64 64
B Construction and infra 2012 35 164
C IT 2011 8 8
D High-tech product development 2011 16 16
E Food 2015 21 25
3Complexity
-
For the latter part of the survey, open questions were usedas
they do not restrict the respondent in their answers. Themixture of
open questions and closed questions in one surveyperfectly fits a
mixed-methods approach [21].
In the development and design of the survey, severalmeasures
were taken to ensure the internal validity. Beforethe survey was
published on the internet, several experts wereasked to test
concept versions of the survey. Based on theirfeedback, questions
were reformulated and terminology wasclarified. The external
validity of this study was positivelyinfluenced by the fact that
the survey was distributedamongst four totally different companies,
all activelyinvolved in the NAP network, the competence network
ofthe Dutch process industry.
Four companies, key players of the NAP network, wereselected for
participation: two owner companies and twocontractor companies. All
four approached companies werewilling to participate, and the
department heads distributedthe link to the web-based survey
amongst their project man-agers. In total, 111 survey requests were
sent and the surveywas started by 68 respondents. Of these
respondents, 64indeed completed and submitted the completed
results,hence obtaining a high overall response rate of 58%. Forthe
contractor group (smaller in size), the response rate wasa little
higher than for the owner group. An overview of theresponse rate,
overall as well as per group (owner/contrac-tor), is given in Table
2.
While completing the survey, the progress was saved onthe
participant’s computer. Measures were taken to preventdouble
submissions from one participant. Apart from theirtypical company
role and their work experience, no specificinformation about the
respondents was included in the dataanalysis. The survey was
developed and executed in theweb-based application NetQ. The
majority of the respon-dents needed 30 minutes to complete the
survey. Data wasstored in SPSS compatible format. All data was
gatheredbetween February 25th 2011 and March 21st 2011.
2.2.2. Sector B: Construction and Infra. The study describedin
this section was performed in collaboration with KING(a network
consisting of project management teams oflarge-scale
infrastructural projects) and the RijksProjectAca-demie (an academy
for project managers of public construc-tion projects).
The survey contained similar questions as the surveydescribed in
Section 2.2.1, without the part on applicationof the TOE framework
and dealing with project complexi-ties. Both the survey and the TOE
framework had to betranslated to Dutch because of the dominant use
of this lan-guage in the Dutch construction sector. Again, an
internetsurvey was used.
After consultation with several experts in the field of
con-struction projects, some elements were added to the
TOEframework of Figure 1 while translating it to Dutch in orderto
increase its applicability to construction industry (seeTable 3).
As will be shown in Section 3, only 2 of them actu-ally proved
relevant for describing project complexity.
In total, 454 project practitioners from 35 projects wereinvited
to participate in the research. For all projects, one
or more responses were obtained providing 164 completedsurveys
in total (response rate of 36% overall).
Again, the web-based application NetQ was used (pro-gram name in
the meantime changed to Collector). Datawas stored in SPSS
compatible format. All data was gatheredbetween July 1st 2012 and
September 21st 2012.
2.2.3. Sector C: ICT. A MSc study was undertaken in 2011
toinvestigate which (combination of) factors determine
thecomplexity of IT projects and how to manage these complex-ities
[22]. Case studies were done in which eight IT projectsin the
financial services were analyzed. Cases were selectedto represent a
broad portfolio of projects in the IT sector(infrastructure,
application, middleware, and other) and withdifferent performance
scores. The case analysis was based onsemistructured interviews,
held with the project managers ofthe IT service provider, and
detailed project documentation.In the interviews, project managers
were asked about theirprojects: their challenges in projects, their
view on the com-plexity of projects, and how project complexity was
actuallymanaged. To identify their view on what factors
contributedto the complexity of their projects, the TOE framework
asprovided in Figure 1 was used.
In the interviews, respondents were asked to indicate towhat
extent the different elements of the TOE frameworkcontributed to
the complexity of the project (not (1)–little–some–substantial–very
much (5)). Data was stored in writteninterview transcripts and
Excel files for storage of the TOEscores. Data was gathered in the
Summer of 2011.
2.2.4. Sector D: High-Tech Product Development. AnotherMSc study
was undertaken in 2011 to investigate the benefitsof applying the
TOE framework in a company developinghigh-tech products [23, 24].
Case studies were performed inwhich 16 high-tech projects were
investigated. The amountof 16 cases was considered to provide a
good balance betweenobtaining a broad overview of the projects and
in-depth datagathering. Cases were selected from different business
lines ofthe company involved. Selection criteria also included
theproject nature (product development or process develop-ment
oriented) and the product design characteristics (new/old
technology). Project managers of the 16 projects wereinterviewed,
with the assumption that the project managerhas the most extensive
knowledge about the project.
In the interviews, project managers were asked generalquestions
about the project, about the project’s complexity,and about its
management. Respondents were asked to iden-tify and scale
complexities from the TOE framework in rela-tion to their projects
(not applicable–very much applicable).Data was gathered in the
Summer of 2011.
Table 2: Overview of responses in study A.
Group Number of requests Respondents Response rate
Total 111 64 58%
Contractor 35 24 69%
Owner 76 40 53%
4 Complexity
-
Table 3: Comparison TOE elements.
A: Process industry B: Construction
T
High number of project goals Aantal projectdoelstellingen
Nonalignment of project goals Incongruentie van
projectdoelstellingen
Unclarity of project goals Onduidelijkheid over
projectdoelstellingen
Uncertainties in scope Onzekerheid over de scope
Strict quality requirements Niveau van kwaliteitseisen
Project duration Projectduur
Size in CAPEX Investeringskosten
Number of locations Aantal locaties
Newness of technology (worldwide) Gebruik nieuwe technologie
Lack of experience with technology Ervaring met toegepaste
technieken
High number of tasks Aantal deelprojecten
High variety of tasks Diversiteit van deelprojecten
Dependencies between tasks Afhankelijkheid tussen
deelprojecten
Uncertainty in methods Onzekerheid over technische methoden
Involvement of different technical disciplines Diversiteit van
technische disciplines
Conflicting norms and standards -
Technical risks Technische risico’s
O
High project schedule drive Druk op de tijdsplanning
Lack of resource and skills availability Beschikbaarheid van
capaciteit en vaardigheden
Beschikbaarheid van middelen
Discontinuïteit in bemensing
Lack of experience with parties involved Ervaring met
projectpartijen
Lack of HSSE awareness VGM-bewustzijn
Interfaces between different disciplines Interfaces tussen
verschillende disciplines
Number of financial sources Aantal financieringsbronnen
Number of contracts Aantal uitvoeringscontracten en interfaces
daartussen
Type of contract Contractvorm
Kwaliteit van het hoofdcontract
Number of different nationalities Aantal verschillende
nationaliteiten
Number of different languages Aantal verschillende talen
Presence of JV partner Samenwerking tussen aannemers
Aantal opdrachtgevers
Involvement of different time zones Werktijden
Bereikbaarheid en bouwlogistiek
Size of project team Aantal projectmedewerkers
Incompatibility different PM methods/tools Aansluiting tussen
gebruikte PM tools & technieken
Lack of trust in project team Vertrouwen tussen projectteam en
opdrachtgever
Lack of trust in contractor Vertrouwen tussen projectteam en
aannemer(s)
Cultuurverschillen
Organizational risks Organisatorische risico’s
E
External risks Externe risico’s
Number of external stakeholders Aantal externe stakeholders
Variety of external stakeholders’ perspectives Diversiteit in
belangen van externe stakeholders
Dependencies on external stakeholders Afhankelijkheid van
externe stakeholders
Political influence Politieke invloed
Lack of company internal support Management support vanuit de
eigen organisatie
Required local content -
Interference with existing site Interfaces met andere
projecten
5Complexity
-
2.2.5. Sector E: Food Processing Industry. In the course of2015,
a study was performed in the food processing industryin one
specific company. In total, 21 projects were selected torepresent
all four business sectors for the project manage-ment community in
company E. In total, 25 project man-agers participated in the
research.
Semistructured interviews were held including questionsabout the
application of project management processes andthe importance of
project success criteria. As part of theinterview session, the
interviewees completed a writtenTOE complexity assessment for their
project (scoring the ele-ments on a 1 (not contributing) to a 5
(most contributing).
Data was stored on answering sheets. Complexity scoreswere
analyzed using Excel.
3. Results: Complexity Assessments
Although the various researches provide a rich set of empir-ical
data, this paper will dominantly focus on the resultsrelated to the
complexity assessments. For each sector, thefollowing findings are
discussed: background of the respon-dents, highest scoring
complexity elements in T-, O-, andE-categories, and overall
impression of complexity scores.
3.1. Sector A: Process Industry. Respondents were asked fortheir
project management experience. As can be seen inFigure 2, the vast
majority of the survey respondents did haveconsiderable project
management experience (only 10 out of64 had less than 5 years of
project management experience),thereby increasing the value of this
study.
The respondents indicated to what extent the elements ofthe TOE
framework (potentially) contributed to the project’scomplexity
(table with results added in Figure 3). Amongstthe highest-scoring
elements were the elements related toproject goals and scope
(unclarity of goals, nonalignment ofgoals, and uncertainties in
scope), boundary conditions forthe project (lack of resource and
skills scarcity), and softer fac-tors (a lack of trust in the
project team, a lack of trust in thecontractor). The vast majority
of the elements were scoredbetween “some” and “substantial,”
indicating the perceivedrelevance of these elements in their
contribution to projectcomplexity. Subsequently, respondents
indicated their top-3s of most contributing elements (see Table
4).
Comparing the results in Figure 3 and Table 4,
allhighest-scoring elements of Figure 3 appear in the top-3,except
for the element lack of trust in the contractor.Table 4 shows that
the top-3 of most often mentioned
Table 3: Continued.
A: Process industry B: Construction
Remoteness of location Aard van de omgeving
Lack of experience in the country -
Company internal strategic pressure Invloed van stakeholders van
binnen de organisatie
Instability of project environment Discontinuïteit bemensing
stakeholders
Economische omstandigheden
Level of competition Marktomstandigheden
BLVC bewustzijn
Ervaring van omgevingspartijen met grote projecten
Media invloed
Sociale impact
Conflicterende wet- en regelgeving
Planologisch / juridische procedures
Less than 5 years
Between 5 and 10 years
Between 10 and 15 years
Between 15 and 20 years
Between 20 and 25 years
More than 25 years
Number of respondents0 2 4 6 8 10 12 14 16 18
Figure 2: Project man