Towards a theoretical foundation for project portfolio management
Clive Nathanael Enoch
School of Computing, University of South Africa
Research Department, University of South Africa
This paper presents a theoretical foundation for Project Portfolio Management as a discipline. The doctrine of Project Portfolio management could be criticized for suffering from deficiencies in its theoretical base and it is for this reason that this paper explores the relevance of established theories, such as Modern Portfolio theory and systems theory, to Project Portfolio Management. Definitions for Project Portfolio Management are analyzed to provide a context for Project Portfolio Management, followed by a literature review of five theories to examine the relevance and relatedness of these theories to Project Portfolio Management (PfM). The theoretical foundation for each theory is discussed and the key elements that support Project Portfolio Management in terms of its definition are illustrated. The theories are then articulated into a theoretical foundation for PfM. This paper offers a fresh perspective on Project Portfolio Management from the viewpoint that multiple established theories support the concepts and principles of the Project Portfolio Management discipline.
Keywords: project portfolio management; modern portfolio theory; organizational theory; systems theory; multi-criteria utility theory; complexity theory; theoretical foundation.
Project Portfolio Management (PfM) is considered an allied discipline of project and program management (Kwak & Anbari, 2009; Stratton, 2011). PfM is a relatively young discipline when compared to project management. While the Project Management Institute (PMI) published the first edition of A Guide to the Project Management Body of Knowledge (PMBOK® Guide) in 1996, the first edition of The Standard for Portfolio Management (also published by PMI) only appeared in 2006. These standards and guidelines are developed through a voluntary consensus standards process (PMI, 2013). This approach has attracted criticism over the years. Koskela & Howell's (2002) provocative claim that there was no underlying theory of project management just over a decade ago is equally relevant to PfM today. Koskela and Howell (2002) noted “well-established professions such as law, medicine, architecture, accounting, and nursing” (p.293) are typified by the development of a body of theory and that “mastery of theory, along with mastery of practical skills of the field, is a hallmark of professionals” (p.293).
According to Koskela and Howell (2002), a theory:
- Provides an explanation of observed behavior, contributes to understanding, and provides a prediction of future behavior.
- When shared, provides a common language or framework through which the cooperation of people in collective undertakings, such as projects and organizations, is facilitated and enabled.
- Gives direction in pinpointing the sources of further progress, and as a condensed piece of knowledge, empowers novices to do the things that formerly only experts could do.
- When explicit, testing the validity of the theory in practice leads to learning.
The goal of this paper is to contribute towards a sound theoretical foundation by investigating existing theories and applying them to the practice of PfM.
The objectives of this paper include critically analyzing various definitions of PfM in order to identify its core components; identifying existing theories relevant to PfM based on the identified components; and articulating these theories into a theoretical foundation for PfM. By providing a theoretical foundation for PfM, the discipline is able to move towards professionalization, as explained above.
The remainder of this paper provides a short explanation of the research design, explores the core components of PfM, and reviews the literature on relevant theories. The PfM organizational context (PMI, 2013) is used as a starting point for the discussion, and the paper concludes with a summary and illustration of the inter-relationship of the theories with PfM.
The layers of the research onion (Saunders, Lewis, & Thornhill, 2009) (Figure 1), which describes research design considerations, from the research philosophy to the data collection and analysis stages, was used to address the objectives of this paper.
A summary of the salient points of the onion regarding each layer from Saunders et al. (2009) is as follows:
- a) Philosophies: The research philosophy (outer-most ring), or paradigm, relates to the researcher's world-view or shared understanding, and a research question does not necessarily fall into only one philosophical domain as may be suggested by the onion.
- b) Approaches: The next layer of the onion refers to research approaches. The research approach can follow either a deductive or inductive form. Deduction involves developing a theory or hypothesis and designing a strategy to test the hypothesis. Induction, on the other hand, involves collecting data and developing a theory from an analysis of that data.
- c) Strategies: While some strategies (third layer) belong to the deductive form and others to the inductive form of research, strategies should not be considered as mutually exclusive. What is of importance is choosing a strategy that is appropriate for addressing the research problem and objectives.
- d) Choices: Research choices (fourth layer) refer to the way in which the data collection techniques and data analysis procedures are done. The various choices are described in Figure 2 to ensure clarity in understanding the terminology, which shows a diagrammatic representation of the research choices.
- e) Time horizons: This fifth layer refers to whether the research will be a snapshot (crosssectional) taken at a point in time, or a sequence of events over a period of time (longitudinal).
- f) Data collection and data analysis: The final layer refers to data collection and analysis techniques and procedures. Quantitative data collection techniques (such as questionnaires) and analysis (using graphs) generate or use numerical data. Qualitative data collection techniques (such as interviews) and analysis (such as categorizing of data) generate or use non-numerical data.
Based on the research onion taxonomy of Saunders, Lewis, & Thornhill (2009), the research philosopy of this research is interpretivist and followed an inductive approach. An archival research strategy was used based on the mono method of literature review. This review was performed on both cross-sectional and longitudinal sources.
Defining Project Portfolio Management
In this section, definitions of PfM from various sources are critically analyzed in order to identify its core components. Key phrases that provide commonality among the definitions have been italicized. A diagram, which contextualizes the core components from the definitions of PfM, is then presented at the end of this section, followed by an elaboration of the core components.
PfM is “a dynamic decision making process whereby, a business's list of active new products and projects is constantly updated and revised; new projects are evaluated, selected, and prioritized; existing projects are accelerated, terminated, or de-prioritized; and resources are allocated and re-allocated to the active projects” (Cooper, Edgett, & Kleinschmidt, 2000, p. 14). Another definition of PfM is the combination of tools and methods used to measure, control and increase the return on investments at an aggregate enterprise level (Leliveld and Jeffery, 2003). Maizlish and Handler (2005) defined PfM as “a combination of people, processes, and corresponding information and technology that sensed and responded to change by: a) reprioritizing and rebalancing investments and assets; b) cataloguing a value-based risk assessment of existing assets; c) eliminating redundancies while maximizing reuse; d) scheduling resources optimally; and e) monitoring and measuring project plans from development through to post-implementation and disposal” (p.4). Levine (2005) described project portfolio management as “the bridge between traditional operations management and project management” (p.17). He further defined project portfolio management as “the management of the project portfolio so as to maximize the contribution of projects to the overall welfare and success of the enterprise” (p.23).
More recently, the Project Management Institute (2006, 2008, 2013) defined PfM as the centralized or coordinated management of one or more portfolios, which included identifying, prioritizing, authorizing, managing, and controlling projects, programs, and other related work, to achieve organizational strategies and objectives. They recognized that “portfolio management produces valuable information to support or alter organizational strategies and investment decisions” (PMI, 2013, p.5) and added, “resources are allocated according to organizational priorities and are managed to achieve the identified benefits” (PMI, 2013, p.5). They further elaborated that “the organizational strategy and objectives are translated into a set of initiatives that are influenced by many factors such as market dynamics, customer and partner requests, shareholders, government regulations, and competitor plans and actions” (p.8) and that through the alignment of strategic planning, these portfolios of programs, projects and operations components are established to achieve or realize the organizational strategy, objectives and performance goals.
Based on the above definitions, the core components of PfM are summarized as:
- The translation of strategy and objectives (organizational objectives) into projects, programs, and operations (identification, prioritization, authorization of portfolio components).
- The allocation of resources to portfolio components according to organizational priorities.
- Maintaining the portfolio alignment requires each component being aligned to one or more organizational objectives and the extent to which the components support the achievement of the objectives (i.e., the degree of contribution) must be understood.
- The portfolio components are managed and controlled in order to achieve organizational objectives and benefits.
The following diagram is an adaptation of the organizational context for portfolio management from The Standard for Portfolio Management - Third Edition (PMI, 2013,p. 8).
In Figure 3, the arrows numbered 1 through 4 illustrate key aspects from the definition of PfM presented above. They refer to the following:
Arrow (1) refers to the translation of organizational objectives into portfolio components. This entails an evaluation of the organizational objectives with the intention of identifying, prioritizing, and authorizing portfolio components that will contribute to the achievement of the organizational objectives.
Arrow (2) refers to the allocation of resources to prioritized components. Once a prioritized list of components has been determined, resources can be allocated to these components as a priority.
Arrow (3) refers to the evaluation of portfolio components in terms of their individual and cumulative contribution to organizational objectives. An understanding of the individual and cumulative contribution of portfolio components to organizational objectives will ensure that the right decisions are made about which components to accelerate, suspend, or terminate.
Arrow (4) refers to tracking the achievement of benefits. This is a key aspect of PfM, as it confirms the return on the investment made in executing the selected portfolio components.
Now that the core components have been identified and contextualized, the following sections examine the relevance of various theories based on these components that relate to PfM.
Theories Relating to Core Components
Koskela & Howell (2002) concluded, “theory and practice have to be developed concurrently, similarly to other science-based fields, where theory is explicated, tested and refined in a continuous dialog between the scientific and practitioner communities” (p.298). This paper contributes to the dialog by presenting existing theories that are related to the practice of PfM. Reviewing these theories enables us to have a better understanding of PfM and outline a framework which can be used to further develop the discipline of PfM. The theories presented here were chosen due to the many-to-many relationship with the components described in the definition of PfM. This relationship is explored here:
- a) Modern portfolio theory provides the financial investment management metaphor upon which PfM has been derived. It provides a way of looking at how investments are chosen based on objectives, the application of limited resources to these investment choices, and assessing the realization of benefits,
- b) Multi-criteria utility theory offers a means for evaluating portfolio components using multiple criteria. This informs the selection, categorization, and prioritization processes which are essential in PfM,
- c) Organizational theory refers to the whole organization and is relevant for PfM as it is practiced within the context of the organization. Understanding organization design, structures, relationships, and behavior of managers is necessary when designing solutions for problems that affect the organization,
- d) Systems theory is applied in understanding dynamic processes and is suitable for PfM, which is a dynamic management approach that considers the total organization and its multiple disciplines,
- e) Organizations are complex entities operating in complex business environments. Complexity theory helps us understand complex settings and enables us to successfully manage project portfolios and their components.
Each of the theories is discussed in further detail and each section concludes with a description of how the theory relates to PfM as illustrated in Figure 3 above.
Modern Portfolio Theory (MPT)
In the early 1950s, Harry Markowitz began developing his modern portfolio theory (MPT). In applying the concepts of variance and co-variance, Markowitz showed that a diversified portfolio of financial assets could be optimized to deliver the maximum return for a given level of risk (Teach & Goff, 2003). In 1990, Markowitz was awarded the Nobel Prize in economics for his work in portfolio theory and he is now referred to as the “father of modern portfolio theory (MPT).”
Markowitz (1952) distinguished between efficient and inefficient portfolios. He proposed that means, variances, and co-variances of securities be estimated by a combination of statistical analysis and security analyst judgment. From these estimates, the set of efficient mean- variance combinations could be derived and presented to the investor for choice of the desired risk-return combination. He used geometrical analyses to illustrate properties of efficient sets, assuming nonnegative investments subject to a budget constraint.
Markowitz (1999,p. 5) gives credit to A.D. Roy for his contribution to MPT: “Roy also proposed making choices on the basis of mean and variance of the portfolio as a whole. He proposed choosing the portfolio that maximized a portfolio (E – d)/ o, where d is a fixed disastrous return and o is standard deviation of return. Roy's formula for the variance of the portfolio included the co-variances of returns among securities” (Markowitz, 1999, p.5). The main differences between Roy's analysis and Markowitz' analysis are that Markowitz required nonnegative investments whereas Roy's allowed the amount invested in any security to be positive or negative. Markowitz also proposed allowing the investor to choose a desired portfolio from the efficient mean-variance combinations whereas Roy recommended choice of a specific portfolio (Markowitz, 1999).
In essence, the work by Markowitz provided the concepts and foundation for subsequent studies—even in non-financial fields. For example, in 1981, the Harvard Business Review published an article by Warren McFarlan, which argued that the fundamentals of modern portfolio theory could be applied to corporate technology assets. He identified deficiencies with Information Systems (IS) projects from personal experience in the 10 years prior to his article. These he summarized as having to do with a failure to assess individual project risk and the failure to consider the aggregate risk of the portfolio of projects. He pointed out that the systematic analysis of risks at the portfolio level reduces the number of failures and helps in communication between IS managers and senior executives toward reaching agreement on risks to be taken in line with corporate goals.
Further, McFarlan (1981) suggested that the selection of projects based on the risk profile of the portfolio could reduce the risk exposure to the organization. However, McFarlan does not go into any detail regarding the portfolio management methodology, approach, or definition but merely introduces the concept of portfolio management from a perspective of risk management. Nevertheless, the application of portfolio theory in a new field, specifically IT, has resulted in further study towards developing methods and standards for applying portfolio theory to PfM.
Verhoef (2002) suggested that MPT does not work for IT. According to Verhoef, IT investments are illiquid, that is they cannot be readily converted into cash. Liquidity is a necessary assumption for applying MPT. Nevertheless, trade articles such as those written by Berinato (2001) and Ross (2005) recognized that the process of managing IT projects using a financial investment portfolio metaphor has attracted much interest from CIOs (Chief Information Officers) in Fortune 1000 companies. Teach and Goff (2003) referred to a Meta Group survey done that year, which found that more than half of the 219 IT professionals surveyed had either implemented or planned to implement some aspect of portfolio theory by the end of 2004.
Kersten and Ozdemir (2004) subsequently presented results of the application of Markowitz's modern portfolio theory (MPT) on a product portfolio of an IT company. They concluded that with the mean variance theory constructed by Markowitz, the management of a product portfolio could be improved. The results showed a considerable decrease in risk, while maintaining the same return. Even with constraints applied on the portfolio and its products, the optimal portfolios performed far better. They added that the mean variance theory has proved its worthiness for an IT-product portfolio. By evaluating returns achieved in the past, portfolio selection is possible; however, returns from the past do not guarantee the same results in future. The model cannot foresee any event that could occur in the future. It only diversifies the portfolio by looking at the results of the past. They added that the results gave some insight to the executive board of their case study about which direction to adjust the portfolio. They concluded that the application of MPT to domains other than for which it was originally developed yielded interesting results. Their study introduced a quantitative approach to product portfolios and IT portfolios.
Modern portfolio theory (MPT) is relevant for PfM as it provides a financial investment metaphor that can be applied to PfM. Projects, programs, and operational initiatives can be viewed as investments that must be aligned to organizational goals and objectives. The project portfolio mix should be balanced in terms of risk exposure and investment returns. To understand the full impact of decisions regarding individual portfolio components, the aggregate must be considered, as opposed to the singular, projects, programs, and operational initiatives.
The next section discusses the Multi-Criteria Utility Theory (MCUT) and how it is used to evaluate projects for the purpose of selection.
Multi-Criteria Utility Theory (MCUT)
According to Stewart and Mohamed (2002), many organizations appear to approach the whole management of technology in an unstructured manner throughout the system's life cycle, thus making it rather difficult for comparisons between information technology (IT) or information systems (IS) projects of different size or organizational impact. In addition, they stated that organizations adopting limited selection criteria lack assurance that their IT/IS projects will meet organizational goals and objectives.
MCUT takes into consideration the decision-maker's preferences in the form of utility function, which is defined over a set of criteria (Goicoechea, Hansen, & Duckstein, 1982). Utility is a measure of desirability or satisfaction and provides a uniform scale to compare tangible and intangible criteria (Ang & Tang, 1984). A utility function quantifies the preferences of a decision maker by assigning a numerical index to varying levels of satisfaction of a criterion (Mustafa & Ryan, 1990).
All decisions involve choosing one or a few alternatives from a list of several. Each alternative is assessed for desirability on a number of scored criteria. The utility function connects the criteria scores with desirability. According to Stewart and Mohamed (2002), the most common formulation of a multi-criteria utility function was the additive model by Keeney and Raiffa (1993). To determine the overall utility function for any alternative, a decision-maker needs to determine the total number of criteria one-dimensional utility functions for that alternative. MCUT generally combines the main advantages of simple scoring techniques and optimization models.
According to Stewart and Mohamed (2002), business unit managers typically proposed projects they wished to implement in the upcoming financial year. These projects were supported by business cases in which costs were detailed. As cost is only one criterion related to project selection, other criteria would be based on business value, risk, and organization needs that the project proposes to meet, and also other benefits to the organization like product longevity and the likelihood of delivering the product. Each criterion is made up of a number of factors that contribute to the measurement of that criterion. For example, to determine the value that a PfM investment delivers, organizations need to go beyond the traditional Net Present Value (NPV) and Return on Investment (ROI) analysis methods. Value can be defined as the contribution of technology to enable the success of the business unit. Parker, Benson, and Trainor (1988) suggest the assessment of two domains— business and technology—as they state that these determine value and should include:
Business Domain Factors:
- Return on investment (ROI): the cost benefit analysis plus the benefit created by the investment on other parts of the organization.
- Strategic match: the degree to which a proposed IT project supports the strategic aims of the organization.
- Competitive advantage: the degree to which IT projects create new business opportunity or facilitate business transformation.
- Organizational risk: the degree to which a proposed IT project depends on new untested corporate skill, management capabilities, and experience.
Technology Domain Factors:
- Strategic architecture alignment: the degree to which the proposed IT project fits into the overall organization structure.
- Definition uncertainty risk: the degree to which the users' requirements are known.
- Technical uncertainty risk: the readiness of the technical domain to embrace the IT project.
- Technology infrastructure risk: the degree to which extra investment (outside the project) may be necessary to undertake the project.
The business and technology domain factors, as suggested above, are factors that could be considered by an organization as those that contribute towards the Value criterion being measured. An organization may choose different factors to represent Value. Other criteria, such as Longevity or the Likelihood of Delivering a product can also be used to evaluate portfolio components.
Stewart and Mohamed (2002) discussed IT investment management process, project selection process and framework, IT investment evaluation, and multiple criteria decision-making. This is relevant to PfM as the evaluation of multiple criteria is required when assessing the contribution of portfolio components to organizational objectives.
The next section discusses organization theory and its applicability to PfM.
Organization theory has been defined as the “study of organizational designs and organizational structures, relationship of organizations with their external environment, and the behavior of managers and technocrats within organizations. It suggests ways in which an organization can cope with rapid change” (BusinessDictionary.com, 2014).
Organization theory has been developed over many decades with many authors contributing to the body of knowledge on organization theory. Researchers (Champoux, 2006; Daft, Murphy & Willmott, 2010; Dessler, 1980) attribute the foundation of organization theory to key individuals such as: Frederick W. Taylor – 1911 (Scientific Management); Henri Fayol – 1919 (Theory of Administration); Max Weber – 1922 (Bureaucracy); Mary Parker Follett – 1925 (Organizations and Management); Chester I. Barnard – 1938 (Functions of the Executive); The Hawthorne Studies – 1939; Douglas McGregor – 1960 (Theory X and Theory Y); and Peter F. Drucker – 1995 (Management). Current ideas in organization theory focus on organizational challenges such as competitive global market or globalization, demographic changes, social responsibility, diversity, and technological developments. Organizations are complex and varied and apply processes, structure, and decision-making differently from each other.
Crowther and Green (2004) stated that “the earliest approach to organization theory was based on the assumption that there was a single best way of organizing the factors of production, and was brought about by the increasing size and complexity of organizations. Initially it was based upon the organization of jobs within the organization but later changed to organizing functions either within the organization or within the wider environment in which the organization operates” (p. 16). In their research they have described various approaches that have been applied in organization theory over time. These include, Critical Approach, Postmodern Approach, Social Constructionism, and Environmentalism. They observed that organizations are an integral part of society and concluded that the problems of organizing have not been solved despite the extensive development of theory, as each theory only contains a partial solution.
Other authors (Daft, Murphy & Willmott, 2010) added that numerous challenges, such as “globalization, diversity, ethical concerns, rapid advances in technology, the rise of e-business, a shift to knowledge and information as organizations' most important form of capital and the growing expectations of workers for meaningful work and opportunities for personal and professional growth” (p. 29), require new responses or approaches to the problems faced by organizations.
Given this explication, it can be established that organization theory (understanding organization design, structures, relationships, and behavior of managers and technocrats within the organization) is necessary when designing solutions for problems that affect the organization. It is relevant to PfM as PfM assists organizations in executing business plans and realizing business goals. PfM is a dynamic decision-making process whereby, a) an organization's list of active components are constantly updated and revised; b) new components are evaluated, selected, and prioritized; c) existing components are accelerated, terminated, or de-prioritized; and resources are allocated and re-allocated to the active components. PfM combines people, processes, information, and technology to respond to organization change and maximize the contribution of portfolio components to the overall welfare and success of the organization. It can be concluded from this discussion that there is a cohesive relationship between organization theory and PfM.
The next section discusses systems theory and its applicability to PfM.
A system was defined by Skyttner (1996) as “a set of interacting units or elements that form an integrated whole intended to perform some function ... [exhibiting] order, pattern and purpose” (p.16–17). He further added that “a system is distinguished from its parts by its organization” (p.17). According to Vidal and Marle (2008), “a system is an object, which, in a given environment, aims at reaching some objectives by doing an activity while its internal structure evolves through time without losing its own identity” (p. 1095). They concluded that projects should be considered as systems as they exist within a specific environment and aim to achieve objectives.
Systems theory (or General Systems Theory—GST) has developed over a number of decades. In 1951, Ludwig von Bertalanffy described open systems using an analogy to anatomy (muscles, skeleton, circulatory system, and so on). From this was laid the foundation for systems thinking in project and portfolio management.
Skyttner (1996) sums up the contributions of various authors to systems theory by describing the properties that make up GST as follows:
- Interrelationship and interdependence of objects and their attributes: Unrelated and independent elements can never constitute a system.
- Holism: Holistic properties impossible to detect by analysis should be possible to define in the system.
- Goal seeking: Systemic interaction must result in some goal or final state to be reached or some equilibrium point being approached.
- Transformation process: All systems, if they are to attain their goal, must transform inputs into outputs. In living systems this transformation is mainly of a cyclical nature.
- Inputs and outputs: In a closed system the inputs are determined once and for all; in an open system additional inputs are admitted from its environment.
- Entropy: This is the amount of disorder or randomness present in any system. All nonliving systems tend towards disorder; left alone they will eventually lose all motion and degenerate into an inert mass. When this permanent stage is reached and no events occur, maximum entropy is attained. A living system can, for a finite time, avert this unalterable process by importing energy from its environment. It is then said to create negentropy, something which is characteristic of all kinds of life.
- Regulation: The interrelated objects constituting the system must be regulated in some fashion so that its goals can be realized. Regulation implies that necessary deviations will be detected and corrected. Feedback is therefore a requisite of effective control.
- Hierarchy: Systems are generally complex wholes made up of smaller subsystems. This nesting of systems within other systems is what hierarchy implies.
- Differentiation: In complex systems, specialized units perform specialized functions. This is a characteristic of all complex systems and may also be called specialization or division of labor.
- Equifinality and multifinality: Open systems have equally valid alternative ways of attaining the same objectives (divergence) or, from a given initial state, obtain different, and mutually exclusive, objectives (convergence).
Earlier, Hendrickson (1992) presented a dynamic system model to describe the fact that organizations are constantly changing due to internal and external factors. They act as open systems adapting to the broader environment, and the managers within organizations can anticipate and prepare for issues faced by their organizations. This is opposed to the traditional theory, which viewed organizations as closed systems that did not take into account environmental influences impacting the efficiency of organizations. Katz and Kahn (1978) as cited in Hendrickson (1992) expressed the view that “traditional organization theories overemphasize internal functioning while failing to understand the adaptation process. In open systems theory, the system receives inputs from the environment, transforms these inputs into outputs, and then exchanges the outputs for new inputs. This input-throughput-output cycle is the process by which the firm counteracts entropy and therefore assures its survival” (p.20).
As described above, Ludwig von Bertalanffy and others have contributed to the development of general systems theory over the past few decades. The development of the theory has guided research in several disciplines over this period. This has led to an understanding of systems that have evolved to the point where we incorporate the concepts in everyday language.
Cusins (1994) stated that in systems theory, a system is merely a way of understanding any dynamic process, whether it is riding a bicycle, a biological process, an organization, a machine, or any other entity which involves a dynamic process. Systems theory was therefore applied broadly across numerous disciplines.
Kerzner (2013) classified systems theory as “a management approach that attempts to integrate and unify scientific information across many fields of knowledge” (p. 48). He further stated that systems theory looks at the total picture when solving problems and that “it implies the creation of a management technique that is able to cut across many organizational disciplines” (p.48). He suggested that system thinking is vital for the success of a project, and by extension, the success of a program and portfolio.
PfM draws from systems theory, as it is a dynamic management approach that considers the total organization and cuts across many organizational disciplines. The PfM process itself follows a systems approach as it a) considers inputs (e.g. strategy definition), b) translates those inputs into outputs (e.g., products consumed by the organization or its customers) using various techniques or mechanisms (e.g. projects and programs), and c) provides a feedback in terms of achievement of the strategy through performance measurement (benefit tracking).
The next section discusses complexity theory and its applicability to PfM.
Complexity theory has become a broad area of investigation. Although developed in the natural sciences, it has much to offer the social sciences. Complexity theory can be defined as “the study of how order, structure, pattern and novelty arise from extremely complicated, apparently chaotic systems, and conversely, how complex behavior and structure emerge from simple underlying rules” (Cooke-Davies, Cicmil, Crawford, & Richardson, 2007, p. 52).
Earlier, Baccarini, 1996) proposed that “project complexity be defined as consisting of many varied interrelated parts and can be operationalized in terms of differentiation and interdependency” (p. 202). He considers types of complexity as being organizational (vertical and horizontal differentiation as well as the degree of operational interdependencies) and technological (the transformation processes which convert inputs into outputs). He regards these as the core components of complexity. He suggests that “this definition can be applied to any project dimension relevant to the project management process, such as organization, technology, environment, information, decision-making, and systems” (Baccarini, 1996, p. 202).
According to Manson(2001), complexity theory research can be divided into three categories: (1) Algorithmic complexity, (2) Deterministic complexity, and (3) Aggregate complexity. Aggregate complexity is relevant for this research and relates to how individual components of a system work together to create complex behavior. Manson (2001) outlined the set of interrelated concepts that define a complex system. These included: a) relationships between entities, b) internal structure and surrounding environment, c) learning and emergent behavior, and d) the different means by which complex systems change and grow.
The behavior of complex systems, according to Solow and Szmerekovsky (2006) is affected greatly by the central organization, which exerts control over the agents of the system. The amount of this control toward achieving optimal performance must be determined as this has implications for the system. They added that leadership in an organization must be aware of how the actions and decisions in one functional area affect the performance of other functional areas. This included decisions regarding projects, programs, and operations that have a crossfunctional dependency. In other words, the performance of a project portfolio as a complex system was impacted by the leadership or management decisions regarding the components of the project portfolio.
According to Vidal, Marle, and Bocquet (2010), project complexity can be characterized by factors classified into four families. They suggested that all are necessary but are not sufficient conditions for project complexity. The first family encompasses project size factors. The second gathers factors of project variety. The third gathers those that are relative to the interdependencies and inter-relations within the project system. The fourth deals with project complexity and are context-dependent.
In many organizations today, a multitude of projects, programs, and operational activities (portfolio components) are initiated, some having a direct inter-dependency while others have an indirect inter-dependency. This implies that in one way or another, changes in projects within an organization have an impact on other projects within the same organization as a result of various types on inter-dependencies between projects. It is crucial, then, that the right decisions are made when managing the portfolio. A portfolio makes up a complex system. Leadership and management decision-making in a portfolio requires understanding of how to lead and manage complexity and complex systems.
The next section articulates the aforementioned theories into a theoretical foundation for PfM.
Theoretical Foundation for Project Portfolio Management
In summary, Figure 4 illustrates the key elements from each theory relevant to the core components of PfM.
The key elements from each theory relevant to the core components of PfM are described below:
- Modern Portfolio Theory – provides the investment management metaphor applied in PfM. From Figure 4, the identification of portfolio components (1); the allocation of organizational resources (2); and the realization of benefits (4) in the diagram are aligned to the MPT philosophy.
- Multi-Criteria Utility Theory – offers a way to evaluate portfolio components using multiple criteria. MCUT contributes to the understanding of using multiple criteria when determining the contribution of portfolio components to organizational objectives and is aligned with the arrow labeled (3) in the diagram.
- Organization Theory – refers to the organization designs, structures, relationship of organizations with their external environment, and the behavior of managers and technocrats within organizations. Organization theory applies to the whole organization. PfM is a capability within the organization that enables the execution of business plans and the realization of organizational objectives. For PfM to be effective, it must operate within the framework of organization design, structure, relationships, and behavior or culture of its people. This will ensure better alignment between the organization and how PfM is practiced within that specific context.
- Systems Theory – A systems approach is used in the PfM process as it considers inputs (e.g. strategy and organizational objectives), converts those inputs into outputs (e.g. products consumed by the organization or its customers) using project, program, and operational techniques, and provides feedback in terms of achievement of the strategy through performance measurement.
- Complexity Theory – The inter-dependent relationships among portfolio components and the relationships between portfolio components and organizational objectives results in a complex portfolio management system. The performance of a project portfolio as a complex system is impacted by the leadership or management decisions regarding the components of the project portfolio. Understanding the characteristics of complexity theory contributes to the understanding of PfM as a complex system.
The theoretical foundation provided by the aforementioned theories for PfM is valuable, as according to Koskela and Howell (2002), they:
- Contribute to our understanding of PfM and provide a prediction of future behavior. Applying the investment management principles of MPT and using multiple criteria for selecting and evaluating portfolio components can achieve better planning. Predictability regarding the achievement of strategy is informed by knowledge of the organization and the environment within which it operates, as well as by the behavior of leaders and managers within the organization.
- Facilitate the achievement of strategy through collaboration and cooperation among people in cross-functional project teams. The contribution that the aforementioned theories make here is that they provide a common language or framework within which people in a local or global setting can collaborate due to their familiarity with concepts and methods outlined in existing theory.
- Empower novices to quickly become productive in PfM as they can draw from the related aspects of existing theories. By drawing on existing theories, practitioners of PfM do not need to redefine and debate aspects that have already been proven, such as the efficient frontier of MPT, or the evaluation of components using multiple criteria.
- Lead to learning. As these theories have been in existence for some time, their application to a new discipline like PfM makes it possible for people to grasp the related concepts and draw from experience of and enhancements to these theories.
The purpose of this paper was to provide a theoretical foundation for PfM in terms of established theories that support its core components. To achieve this, various definitions for PfM were analyzed, followed by a presentation of five theories that relate to PfM.
The established theories that were considered were chosen based on their relevance to the PfM definition and processes outlined in literature. Justification for the theories ranged from the financial investment management concepts borrowed from Modern Portfolio Theory, to a means for evaluating components using Multi-Criteria Utility Theory, as well as organizational theory, and systems and complexity theories.
For each of the theories, a description of the theory was presented, indicating key contributors to the respective theory. The respective theory section was then concluded, outlining the relatedness between the theory and reality. Through the process of investigating these theories and applying them to the practice of PfM (see Figure 4), the goal of contributing towards a sound theoretical foundation for PfM has been achieved. As with established professions, the development and mastery of PfM theory and practice helps us move the discipline closer towards professionalization.
Future research is required to look at other theories that may make a contribution to PfM. One suggestion is the Theory of Constraints, for example. Here a researcher would consider key resource or key-man dependency and the associated risk of overloading resources. Other theories may include, but are not limited to, Human Relation theories (Mayo, 2003), Neo-human relations theories (Herzberg, 1964; Maslow, 1943; McGregor, 1966), and Bureaucracy theory (Weber, 1946).
Ang, A. H. S., & Tang, W. H. (1984). Probability concepts in engineering planning and design, volume II: Decision, risk and reliability. New York: John Wiley.
Baccarini, D. (1996). The concept of project complexity—a review. International Journal of Project Management, 14(4), 201–204. doi:10.1016/0263–7863(95)00093-3
Berinato, S. (2001). Do the math. Retrieved from www.cio.com
BussinessDictionary.com. (2014). What is organization theory? definition and meaning. Retrieved March 28, 2013, from http://www.businessdictionary.com/definition/organization- theory.html
Champoux, J. E. (2006). Organizational behavior: Integrating individuals, groups and organizations. (3rd ed.). International: Thompson South-Western.
Cooke-Davies, T., Cicmil, S., Crawford, L., & Richardson, K. (2007). We're not in Kansas anymore, Toto: Mapping the strange landscape of complexity theory, and its relationship to project management. Project Management Journal, 38(2), 50–61.
Cooper, R. G., Edgett, S. J., & Kleinschmidt, E. J. (2000). New problems, new solutions: making portfolio management more effective. Research-Technology Management, 43(2), 18–33. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-0034155948&partnerID=40&md5=13f5ed8d17c5dc9e7b1f5b2e51f1e953
Crowther, D., & Green, M. (2004). Organisational Theory. London: The Chartered Institute of Personnel and Development.
Cusins, P. (1994). Understanding quality through systems thinking. The TQM Magazine, 6(5), 19.
Daft, R. L., Murphy, J., & Willmott, H. (2010). Organization theory and design. Cengage Learning EMEA. Retrieved from http://books.google.co.za/books?id=s6MAkpcuaZQC&printsec=frontcover&dq=organization+theory+and+design&hl=en&sa=X&ei=uyLkU0vtJ8bMhAfBkIHoBQ&ved=0CDQQ6AEwAA#v=onepage&q=organizationtheoryanddesign&f=false
Denzin, N. K., & Lincoln, Y. S. (2011). The SAGE handbook of qualitative research. (4th ed.). Thousand Oakes, CA: SAGE Publications.
Dessler, G. (1980). Organization theory: Integrating structure and behavior. New Jersey: Prentice-Hall.
Goicoechea, A., Hansen, D., & Duckstein, L. (1982). Multiobjective decision analysis with engineering and business applications. New York, NY: Wiley.
Hendrickson, L. U. (1992). Bridging the gap between organization theory and the practice of managing growth: The dynamic systems planning model. Journal of Organizational Change Management, 5(3), 18.
Katz, D., & Kahn, R. L. (1978). The social psychology of organizations. (2nd ed.). New York, NY: John Wiley & Sons.
Keeney, R. L., & Raiffa, H. (1993). Decisions with multiple objectives: Preferences and value tradeoffs. Cambridge: Cambridge University Press.
Kersten, H. M. P., & Ozdemir, S. (2004). Optimising a product portfolio of an IT-company. Free University Amsterdam.
Kerzner, H. (2013). Project management: A systems approach to planning, scheduling, and controlling. (11th ed.). Hoboken, New Jersey: John Wiley & Sons. Retrieved from http://www.amazon.com/Project-Management-Approach-Scheduling-Controlling/dp/1118022270/ref=sr_1_1?ie=UTF8&qid=1371581960&sr=8-1&keywords=Project+Management%3A+A+Systems+Approach+to+Planning%2C+Scheduling%2C+and+Controlling.
Koskela, L., & Howell, G. (2002). The Underlying Theory of Project Management is Obsolete. In PMI Conference (pp. 293–302). Seattle.
Kwak, Y. H., & Anbari, F. T. (2009). Analyzing project management research: Perspectives from top management journals. International Journal of Project Management, 27, 435–446. doi:doi:10.1016/j.ijproman.2008.08.004
Leliveld, I., & Jeffery, M. (2003). IT portfolio management. Research, Kellogg School of Management. Retrieved from www.kellogg.northwestern.edu
Levine, H. A. (2005). Project portfolio management: A song without words. Saratoga Springs, NY & San Diego, CA: The Project Knowledge Group.
Maizlish, B., & Handler, R. (2005). IT portfolio management: Step-by-step. Unlocking the business value of technology. Hoboken, NJ: John Wiley & Sons.
Manson, S. M. (2001). Simplifying complexity: A review of complexity theory. Geoforum, 32, 405–414. Retrieved from http://www1.fee.uva.nl/cendef/upload/76/2001_Manson_Simplifying_Complexity.pdf
Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1), 77–91.
Markowitz, H. M. (1999). The early history of portfolio theory: 1600–1960. Financial Analysis Journal, 55(4), 5–16.
McFarlan, F. W. (1981). Portfolio approach to information systems. Harvard Business Review, 59(5), 142–150. doi:10.1225/81510
Mustafa, M. A., & Ryan, T. C. (1990). Decision support for bid evaluation. International Journal of Project Management, 8(4), 230–235.
Parker, M. M., Benson, R. J., & Trainor, H. E. (1988). Information economics: Linking business performance to information technology. (E. Cliffs, Ed.). Journal of Information Technology , 5, 287. Prentice Hall. doi:10.1057/jit. 1990.13
Project Management Institute. (1996). A guide to the project management body of knowledge – (PMBOK® guide) – First edition. Newtown Square, PA: Author.
Project Management Institute. (2006). The standard for portfolio management – First edition). Newtown Square, PA: Author. Retrieved from www.pmi.org
Project Management Institute. (2008). The standard for portfolio management – Second edition). Newtown Square, PA: Author. Retrieved from www.pmi.org
Project Management Institute. (2013). Standard for portfolio management – Third edition. Newtown Square, PA: PMI. Retrieved from www.pmi.org
Ross, M. (2005). A holistic approach to IT department wellness – systematic portfolio management planning. Retrieved from www.galarath.com
Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students. (5th ed.). Edinburgh Gate, Harlow, Essex, England: Pearson Education Limited. Retrieved from www.pearsoned.co.uk
Skyttner, L. (1996). General systems theory: Origin and hallmarks. Kybernetes Academic Research Library, 25(6), 16.
Solow, D., & Szmerekovsky, J. G. (2006). The role of leadership: What management science can give back to the study of complex systems. E:CO, 8(4), 52–60.
Stewart, R., & Mohamed, S. (2002). IT/IS projects selection using multi-criteria utility theory. Logistics Information Management, 15(4), 254–270. doi:10.1108/09576050210436101
Stratton, M. J. (2011). Portfolio management: Perceptions of the project manager (Doctoral Dissertation). Retrieved from Dissertations and Theses Database (UMI No. 3460932)..
Teach, E., & Goff, J. (2003). The fundamentals of portfolio management are being applied to corporate technology assets. CFO Magazine. Retrieved from www.cfo.com
Verhoef, C. (2002). Quantitative IT portfolio management. Science of Computer Programming, 45, 1–96.
Vidal, L.A., & Marle, F. (2008). Understanding project complexity: Implications on project management. Kybernetes, 37(8), 1094–1110. doi:10.1108/03684920810884928
Vidal, L.A., Marle, F., & Bocquet, J.C. (2010). Measuring project complexity using the Analytic Hierarchy Process. International Journal of Project Management. doi:10 1016/j.
Dr. Clive Enoch currently resides in Johannesburg, South Africa. He completed his PhD in computer science at the University of South Africa (UNISA). His thesis resulted in a model for decision making in project portfolio management. Clive is passionate about project portfolio management and has contributed to the PMI Standard for Portfolio Management – Third Edition as a core committee member.
Clive has presented papers at various conferences including the 2010 PMI Research Conference in Washington D.C. and the 2012 PMI Research Conference in Ireland.
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