Project Management Institute

Managerial complexity in project-based operations

a grounded model and its implications for practice




Projects are key processes in modern operations. Although project management is often relegated to a single discreet chapter in operations management textbooks (e.g., Slack, Chambers, Johnston, & Betts, 2005), the implications of the performance of an organization's projects stretch way beyond such limited consideration. Much operational work is carried out as projects (e.g., client projects, new product development) and organizational change initiatives. As more work is carried out through projects, it becomes increasingly necessary that the basis of the practices being employed is subject to critique. One criticism of currently accepted practices is the lack of contingency (Hayes, 2002; Maylor, 2001) and an almost Tayloristic “one-size-fits-all” approach to management. Our research problem begins with a major input to this contingency: obtaining an understanding of what makes projects complex or difficult to manage. Several attempts to define project complexity have appeared in the literature (e.g., Shenhar, 2001; Xia & Lee, 2004), but we believe these to be inconsistent with the diversity of usage of the term in organizations today and only partially helpful in understanding the associated task of managing these activities.

The aim of this paper, therefore, is to develop a grounded model of managerial complexity. Our empirical findings suggest that in contrast to the literature, which largely defines complexity in structural terms, it has a bipartite nature, having both structural (that is, relatively stable qualities) and also important dynamic qualities.

This development has implications for examining contingency, management response, and effectiveness, and thereby the opportunity to contribute to theory and practice. Practice in this instance not only refers to the work of practitioners but also to the contents of the bodies of knowledge.1


An increasing proportion of work is being undertaken as projects (Midler, 1995; Turner & Muller, 2003). Siemens recently estimated that over 50% of all their value-adding activity is carried out in projects (The Economist Group, 2005). This fact has been coupled with a burgeoning in the membership and influence of professional institutes; the Project Management Institute (PMI) is now the world's largest professional association. “Projects are cool, it seems” (Grabher, 2004, p. 1491). However, managing projects is problematic. Projects regularly fail to meet their objectives (expressed in their simplest terms)—time, cost and quality/scope (e.g., KPMG, 2002; Standish Group, 2001; Holmes, 2001)—and such failure has a significant impact for practitioners and their employers. The practice of managing projects is, therefore, of significant interest to organizations.

Projects are nominally finite activities, with low levels of repetition, carried out by temporary organizations (Lundin & Soderholm, 1995). The scope of such work is extensive. Moreover, the term “project” has broadened through projectification (Midler, 1995; Winch, 1994; Whittington et al, 1999); emergence of service delivery projects (Levene et al, 2002); organizational change (Pettigrew, 1998); and the phenomenon of programification (Maylor, Brady, Cooke-Davies, & Hodgson, 2006). Project-based organizations (for instance, prevalent in consulting, IT, and construction) deliver much of their revenue earning through nonrepetitive operations. Even where the prevalent activity in an organization is high volume, low variety work (manufacturing or service), lower volume, higher variety activities will be evident in new product development, process improvement, organizational change, and other vital processes. In some sources, projects are defined in terms of their uniqueness (e.g., Project Management Institute [PMI], 2004). While the activity may be unique, the process that is followed will often have a higher degree of commonality with what has been done before (Davies & Brady, 2000).

The management of such processes is characterized by managers having to cope with a potentially complex array of tasks and uncertainty as to their performance (e.g., duration). There are technical or content aspects of this activity (e.g., managing the technology in new product development) alongside the business process. It is gaining a better understanding of the task of the project manager that is of interest to this paper.

For management research, a number of features of this process are of interest. The current levels of failure occur despite having numerous project management methodologies and “bodies of knowledge” (e.g., PMI, 2004; Association for Project Management [APM], 2005; Office of Government Commerce [OGC], 2005). These contain what is deemed to be accepted practice and represent a strong dominant paradigm. Bodies of knowledge are not present in many other areas of organizational activity, and so their role and contribution to practice are phenomena worth investigating.

Another feature is the lack of contingency that exists within this dominant paradigm. In particular, little consideration is given to the management context and, in particular, to the complexity of the management task. It is clear that there is a need to better understand this context (Grabher, 2004; Shenhar, 2001). The opportunity exists to examine the nature of the knowledge provided to practitioners (e.g., the content of the bodies of knowledge) and to understand what we will argue is a key driver for contingency: the complexity of the task of managing the project.

Project management is an emerging discipline in business and management (by the criteria of Fabian, 2000). However, as a subject it is problematic: as described previously, much of the literature would best be defined as accepted practice rather than best practice; it is highly prescriptive and frequently ignores context (Maylor, 2001); there is a lack of theory building research (Turner, 1993); it is inconsistent methodologically with recognized business and management disciplines (Meredith, 2002); and what theory is claimed to exist, is obsolete (Koskela and Howell, 2002). Further, the project management literature is highly normative (e.g., Sydow, Lindkvist, & DeFillippi, 2004). Indeed, there is a prevalence of a highly Tayloristic one best way approach. This approach is inconsistent with the contextual diversity that managers face. Indeed, there appears to be much greater value in considering projects as complex adaptive systems (e.g., Harkema, 2003).

Complexity in Projects

The Random House Webster's College Dictionary says that complex means “composed of many interconnecting parts” and “complicated means composed of many elaborate interconnecting parts” (p.). The subtle distinction between complex and complicated is the nature of the relationships between the parts. A wristwatch has many interconnected parts and is indeed a complicated artefact but it is not complex. Luoma (2006) notes that complex is derived from the Latin plexus, meaning braided together. In a complex system, the different elements interact and produce outcomes that are nonlinear and unpredictable. It may be possible to recognize qualitative patterns of behavior, but complex systems are not amenable to treatment by traditional systems analysis where regularity, separability of elements, and clear cause and effect relationships are assumed. Further, a complex system has path dependence and is highly sensitive to initial conditions. Thus, any project takes place in an historical context and its starting conditions (e.g., the state of existing relationships between stakeholders, the trust between project team members) cannot be calibrated precisely to be able to make reliable predictions.

Table 1: Complexity in project management literature

Authors Key aspects of complexity
Baccarini (1996) The number of physical elements of the project and their interdependency
Williams (1999) Project complexity is characterized as structural uncertainty (number of elements and their interdependence, including multiple objectives and a multiplicity of stakeholders) and uncertainty (of goals and methods)
Shenhar (2001) Projects can be classified on a single, static dimension, as either assembly projects, system projects or arrays/programs.
Maylor (2003) Complexity is the product of organizational, technical/novelty, and scale/resource complexity.
Jaafari (2003) Creative-reflective project model for high environmental and high project complexity situations. Project managers have to recognize that their models are observer-dependent, time-dependent, and problem-dependent.
Xia & Lee (2004) Two dimensional: organizational complexity and technological complexity, and structural and dynamic elements of these two.
Cicmil & Marshall (2005) Projects involve complex communicative and power relations among actors, ambiguity, and equivocality of performance criteria, and change over time.

In Table 1, definitions of project complexity adopted in the project management literature are presented. Maylor (2003), in an attempt to produce a working model, synthesized complexity as comprising three factors: organizational complexity (the number of people, departments, organizations, locations, nationalities, languages, and time zones involved, level of organizational buy-in, authority structure), resource complexity (the scale of the project often indicated by the size of the budget), and technical complexity (the level of novelty of any technology, system, or interface, and uncertainty about the process or the requirements). Although this model has proved useful in establishing the concept in practice, in use it was evident that organizations were finding other factors that increased the complexity of the projects they carried out. Typical among these other factors (albeit observed anecdotally) was the effect of uncertainty (known unknowns and unknown unknowns). This is consistent with the findings of Turner and Cochrane (1993) and Williams (1999). Specifically, uncertainty may be in the form of ill-defined goals or methods or emergent uncertainty in the project, for instance due to changes in customer requirements or changes in personnel. This is similarly reflected in the information systems (IS) literature (e.g., Schmidt, Lyytinen, Keil, & Cule, 2001). Complexity, it seems, is not a static entity.

Cicmil and Marshall (2005) further describe this evolutionary nature of complexity in a construction process, with the processes of social interaction interfacing with the persisting ambiguity in the context of flux and change. Projects are socially constructed (Lundin & Soderholm, 1995) and definitions of complexity should reflect this, by including social and technical dynamics.

A consistent theme from the project management complexity literature is that the various definitions lack grounding; indeed, Xia and Lee (2004) state that “there are no well-defined frameworks in the literature that can be used to systematically describe the key dimensions and characteristics of [project] complexity” (p.71). We further argue that this is only one element of managerial complexity - hence our research question: what makes projects complex to manage?

This lack of conceptualization provides an opportunity for theory building, with a view to understanding what might constitute an appropriate organizational response to managerial complexity. With this understanding comes the possibility of influencing practice. For instance, for a given project, could the complexity be addressed in an effort to manage business risk, or be used to determine the level of systematization that is required? These are not currently subjects within the bodies of knowledge identified. However, in order to determine or assess the response to a level of complexity, a good understanding of what is being treated as the independent variable is required before the implications for the dependent variable can be established.


The objective of this study was to answer the research question: What makes a project complex to manage? The research methodology seeks to surface the subjective views of project managers, to explore how they perceive and construct their notions of complexity around their tasks of ‘managing’. The research design involved two stages: first, exploratory workshops were held with in-company small groups to establish project complexity themes and to test the data collection method; second, a large scale workshop was conducted at a regional meeting attended by more than 100 project managers. There was no overlap in the membership of these groups.

The first-stage workshops were carried out with small groups of project managers (four to seven at a time in single organizations). The objectives were threefold: (1) to develop and test a data collection method that would generate a comprehensive set of concepts of complexity; (2) to provide a process of sensitisation to managers’ perceptions of the effects of such complexity; and (3) to determine if this was indeed an investigation that was likely to yield any useful theoretical insight. Three exploratory workshops were carried out at three different organizations, a purposive sample, consistent with an exploratory phase of research, and providing a sample that offered diversity on the projects undertaken (Dobbert, 1982). The organizations were drawn from the telecommunications sector (major global corporation), the defense sector (major provider of scientific services), and a regional transport infrastructure provider. For each organization, a short written brief of the purpose of the workshop was provided, and personal contacts of the authors were used to secure participation and involvement of individuals. Individual attendees were selected on the basis that they were or had been in the role of project manager.

Within the workshop, the process began with an introduction. Participants were then provided with pens and Post-it® Notes and asked to write down what made managing a project complex in their experience. No further grounding in the terminology was provided. The participants initially worked in silence, then once the personal lists were exhausted, they worked in groups, initially describing and then grouping concepts with common themes, organized hierarchically (this is consistent with other workshop methodologies, such as Bossert, (1990). The concepts and the categories provided were both recorded.

In the first workshop, 39 concepts were generated, which were classified into 8 categories. In the second workshop, a further 19 concepts were identified, and in the last of these initial explorations, a further 13 were generated. At this stage it appeared that there was more to uncover. Recordings of the workshops (audio and/or extensive notations taken during the workshops) were used to check the meaning during the process of transferring the workshop data into research notes. It was desired to maintain an open theoretical perspective on the main issue, and anything that was heard or seen that might even be remotely relevant was captured. At this stage the argument for retaining any concept did not have to be made; it simply had to be recorded (consistent with Huxham & Vangen, 2000).

With 71 concepts at this stage, the method for running the workshops was now sufficiently robust and the research moved to stage two to allow a greater number of responses to be included. A group of 107 project managers at a regional meeting of a professional project management association provided the responses. The results were gathered in the same way, but with groups of 8 to 10 preparing their concepts and classifications. Each group presented their findings in plenary, thus allowing the researchers to carry out a plausibility check (Whyte, 1978) of any terms that had not been included previously.

The result of the stage 2 workshop confirmed the 71 initial concepts and when analyzed provided further concepts and clarification of the multiple dimensions of those concepts. The process of capturing these concepts ensured that the voice of the respondents was not obscured, and where alternative meanings were possible, these were explored. Following the recording of the raw concepts, the classifications provided in the workshops were examined to determine their ability to reduce the data effectively. The classifications presented here provided a best-fit with the overall data set (consistent with Kolb, Rubin, & MacIntyre, 1984) and were generated by active experimentation as part of the research process, involving both researchers in much iteration. The process of sense-making here is consistent with Strauss & Corbin (1999), the process stopping after open coding and classification had taken place.

The results generated 160 concepts. These confirmed and elaborated the original concepts identified in stage one and provided 89 new ones. A total of 128 responses were incorporated into the analysis, and while not precluding the discovery of further elements (e.g., generated by the specific conditions found in one industry or sector), an initial model of managerial complexity for projects, which will be carried forward for further testing and refinement, was yielded.


The first finding emerging from the data is that basic or structural complexity is multifaceted and comprises concepts as shown in Figure 1. These have been represented as a tree structure. Structural complexity gives a static, or snapshot, view of the project and its environment, comprising five dimensions: mission, organization, delivery stakeholders and team. In Figures 2-6, these categories are expanded and show the detailed concepts that emerged from the study. To avoid making a judgement about the project complexity factors, e.g., this factor is good, this one is bad, and to provide consistent presentation of the data, the factors are phrased as questions.

Dimensions of perceived managerial complexity

Figure 1: Dimensions of perceived managerial complexity

Mission concepts

Figure 2: Mission concepts

Key: * concept appears 3-4 times in data ** concept appears 5-8 times in data

*** concept appears more that 9 times in data

Organization concepts

Figure 3: Organization concepts

Key: * concept appears 3-4 times in data ** concept appears 5-8 times in data

*** concept appears more that 9 times in data

Delivery concepts

Figure 4: Delivery concepts

Key: * concept appears 3-4 times in data ** concept appears 5-8 times in data

*** concept appears more that 9 times in data

Stakeholder concepts

Figure 5: Stakeholder concepts

Key: * concept appears 3-4 times in data ** concept appears 5-8 times in data

*** concept appears more that 9 times in data

Team concepts

Figure 6: Team concepts

Key: * concept appears 3-4 times in data ** concept appears 5-8 times in data

*** concept appears more that 9 times in data

In coding the concepts, it was noted that a concept often has a profile of its impact on the complexity and outcomes of the project. Many of the responses contained expressions of extremes of what can be regarded as a continuum for each variable, for instance, “lack of senior management support” was considered to be a problem factor for projects as was “interference by senior management.” Thus, as a first approximation, this profile can be stated as “the right amount of a factor is beneficial but too much or too little increases the level of complexity that project managers experience.” This is represented by a U curve, as shown in Figure 7.

MODeST provides a grounded structural model of managerial complexity. However, a consistent and repeated theme from practitioners was the interaction between the concepts that make up this model. It was described as ‘a multiplicative effect’ of many concepts on the overall picture for practitioners. Thus, the structural elements of the project complexity model should be thought of as interdependent and it is the interconnections that give rise to complexity beyond the individual structural dimensions. A second part of complexity revealed by the data, therefore, is the interaction effects between the elements of structural complexity. For instance, it was clear from the data that external stakeholders had an impact on the way that a project was managed, expressed in terms used here as interaction effects between elements of the level of stakeholder involvement and the project management methodology (PMM) used. Even within dimensions, there is interaction complexity, for instance in interdependencies and relationships between suppliers and other external stakeholders in a project.

Suggested general impact of individual concepts on project complexity

Figure 7: Suggested general impact of individual concepts on project complexity

The language used by the respondents indicated that the elements of structural complexity are either an initial condition or an element with at best some temporary stability. Many respondents described a further set of elements that were identical in nature to the structural set, but that involved change. For instance “organizational structure” was a clear influence on complexity, however many respondents noted that “organizational changes during project” also provided another element. Further analysis showed that for every structural element there is a corresponding dynamic element.

Table 2 shows the MODeST model including examples of both structural and dynamic elements of managerial complexity.

Table 2: Examples of MODeST Structural and Dynamic Complexity Elements

Structural Dimension Dynamic Dimension
Mission Are the requirements clear? How frequently do the requirements change?
Organization Is there a mismatch between matrix structure of project and department structure of organization? Is there ongoing organizational restructuring that impacts the project?
Delivery How well do the project team understand the project management methodology? Is a new project management methodology being introduced?
Stakeholders How many stakeholders are there? Are the stakeholders changing?
Team Are the team members motivated? Is the level of motivation of team members changing?

A further aspect of complexity was seen to arise from the dynamics of individual structural elements being compounded by interactions with other structural and dynamic elements. For instance, changes in the organization in which a project was being conducted through re-structuring provided a complexity in itself, but had a dynamic interaction with external stakeholders, resources, and decision making/governance.

Lastly, there was a category of data which has not been covered so far. In answer to the question What makes a project complex to manage? a number of workshop participants recognized their own roles in this process, for instance stating ‘not breaking down a project sufficiently’ or ‘failure to recognise activity interdependencies.’ Indeed, regarding the individual concepts shown in Figures 2-6, the project manager has an impact on many of these by their considerations and actions, for instance, with the issue of stakeholder analysis shown in Figure 5, the question is raised Are there any unidentified stakeholders? Given that project management has some (if not the main) responsibility for stakeholder management, the project manager is a key actor and is embedded within the conceptualization of the complexity of their projects rather than an external observer of the project. This finding is not surprising, and indeed suggests that many of the elements of managerial complexity, can themselves be managed.


The point of departure for this paper was the identification of the ubiquity of the project as an organizational form. Projects are important both for revenue-generating activities and organizational change. There are many well-established methodologies for managing projects (bodies of knowledge, PRINCE2). These are poor at dealing with variations in the context in which they are applied. Complexity was identified as a key contextual element where the definitions used in its discussion appeared inconsistent with the actuality described by practitioners. While some organizations do have their own methods for assessing project complexity (along the lines of Maylor, 2003), these were noted to be deficient by not having considered the . Specifically, the data has shown that managerial complexity is a far more extensive construct than previously described. As a result, it does question how appropriate the bodies of knowledge and accepted practice are to deal with this extended model of complexity, in particular the dynamic aspects..

In structural complexity terms, the data identified stakeholders as a major dimension contributing to project complexity. Adapting Freeman (1984, p. 46) and Mitroff and Linstone (1993, p. 141) we have used the term stakeholder to cover “any individual, group, organization, or institution that can affect or is affected by the achievement of the project's process or objectives.” Indeed, from an organizational theory perspective, a project can be seen as being constituted from the entire set of relationships it has with itself and with its stakeholders (Mitroff & Linstone, 1993). The data shows that project managers are also concerned with the relationships between stakeholders (interaction effects) and (consistent with Rowley, 1997) that the project is embedded in a network of stakeholder relationships which an impact on the complexity of managing the project.

The literature shows that project managers need to understand the salience (e.g., power, legitimacy, urgency) of different stakeholder groups (Mitchell, Agle, & Wood, 1997) and develop strategies for stakeholder management (Savage, Nix, Carlton, & Blair, 1991) as well as practical techniques for identifying and communicating with stakeholders (McManus, 2004). However, the data shows that any consideration of stakeholder management is incomplete if it begins from the standpoint of stakeholders being static, independent entities.

A further practical implication of being able to describe (if not actually measure) complexity is that not all individuals are suited to dealing with all aspects of complexity. For instance, a virtual team has different organizational, stakeholder, and team complexities than a colocated team. Specifically, a major issue for virtual teams is the establishment of trust, which can be particularly problematic when team members have not worked together before (Jarvenpaa & Leidner, 1999). Kayworth and Leidner (2000) identify problems of management that are unique to virtual teams: “delayed communication, misunderstanding arising from lack of response, lack of a shared context in which to interpret messages, and an inability to monitor team members” (p. 192). All of these aspects have been captured in the dimensions of complexity shown. Similarly, projects with high levels of dynamic complexity will require managers who are able to deal with such dynamics. Staff selection and development, therefore, needs to consider how individuals cope with the challenges presented by all the relevant dimensions of managerial complexity for that project.

Finally, a recurring theme in the data is the dynamics of complexity. Although change is an inevitable part of project work, the data shows that scale of change and frequency of change are important factors in what makes a project complex to manage. Traditionally, the dynamic elements of projects are managed through processes such as ongoing risk management, configuration management, and change control (e.g., PMI, 2004; OGC, 2005). However, our data shows that the nature of change considered by the existing approaches is limited and that such programmatic responses may be inappropriate. The data suggests that this consideration is limited and that the interactions of such change with other changes and other structural elements cause further complexity. Such a challenge to existing practice is also demonstrated by Eden et al (2000). They showed that changes may elicit managerial responses that although consistent with accepted practice, exacerbated issues in a form of a ‘positive feedback’ loop.

Such dissatisfaction with traditional requirements engineering and command and control project management strategies has led to an interest in agile project management approaches. Agility has been defined as “the ability to create and respond to change” (Highsmith & Cockburn, 2001), a definition which reflects the idea of a project as agent for change rather than passive servant of the organization (and supports the idea of embedded action). Software development projects have been at the forefront of the agile movement and reflected in methods such as extreme programming (XP) and Scrum (Highsmith, 2002). In agile projects the focus is on delivering working functionality rather than delivering against project plan tasks. Rather than plan the entire project, a vision is set and planning is conducted in intense microcycles, often as short as a week in duration but of no more than 30 days. This adaptive process allows the project team to explore alternative paths and for the requirements to emerge from a collaboration of developer and user. Particularly in turbulent business environments, the adaptive approach to software project management is gaining in popularity and has been applied more generally to project management (Highsmith, 2004). This implicit acceptance of the dynamic and interdependent character of elements of complexity, is consistent with the expanded model of managerial complexity proposed here.

Summary and Conclusions

The data presented in this paper has challenged the current limitations of one key aspect of project processes, what makes projects complex or difficult to manage. Managerial complexity is seen to have a wide and diverse set of meanings. This has demonstrated that the existing interpretations of complexity in the project environment have been partial at best.

The MODeST model shows what makes a project complex to manage and both dynamic and interactive elements of complexity have been identified.

From a practical perspective, the findings provide a framework to challenge the existing dominant paradigms— accepted practices and the bodies of knowledge. They are shown to be partial in their ability to deal with complexity, focusing on one element only of this construct. In particular, the conceptualization of stakeholder management, manager and team selection, and dynamism are inadequate. Agile and related approaches to managing projects appear to be consistent with the expanded model of complexity proposed here.

There are considerable implications for teaching here. The notion of one-size-fits-all has been rejected in various competency models, and we see that there is considerable potential for more targeted educational efforts if the difficulty of the managerial task can be more precisely defined. The potential exists for the frameworks to be developed to allow this to happen.

Areas for further research include:

  • To refine the concepts in Figures 2-6 and highlighting which are the key drivers in complexity as perceived by project managers;
  • To determine whether a quantification of complexity is feasible;
  • To explore the nature of the key concepts further to determine the range of influences on overall complexity and whether the U curve of Figure 6 is generalizable;
  • To explore the dynamic relationships between the three dimensions of complexity (project, organization, stakeholders), possibly using coevolutionary theory (Kauffman, 1993);
  • To explore the possibility of managing complexity;
  • To reframe the role of the project manager as embedded participant and consider management strategies in a complex environment;
  • To define project success and failure in a complex environment (going beyond time, cost, functionality to a broader notion of project as intervention in a complex environment);
  • To investigate how the bodies of knowledge are made intelligently contingent using complexity as a key variable for determining the nature of that contingency.


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1 Descriptors of accepted practice used as certification baselines by the major project management professional associations including the Project Management Institute (PMI) and the Association of Project Management (APM) in the U.K.

This material has been reproduced with the permission of the copyright owner. Unauthorized reproduction of this material is strictly prohibited. For permission to reproduce this material, please contact PMI or any listed author.

© 2008 Project Management Institute



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