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; jose.sancristobal@unican.es

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; jose.sancristobal@unican.es

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.

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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; m.g.c.bosch-rekveldt@tudelft.nl

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