Abstract
The mission of project portfolio management (PPM) is seen in evaluating, prioritising, and selecting projects in line with the business strategy. Alignment of all on-going projects with the overall business strategy is generally recognised as very important for most modern organisations. Despite this recognised relevance, the available empirical evidence remains scant, and in best cases, it is represented by qualitative case studies that do not provide a basis for generalisation of results. Quantitative empirical evidence is largely non-existent. This paper aims to develop a conceptual framework embracing a number of key variables of PPM and corresponding interrelations, derived from the extant body of literature and to test it empirically. We conducted a survey among experienced portfolio managers representing a wide range of organisations possessing established PPM mechanisms. Data obtained in this tailor-made survey were tested in the framework using structural equation modelling. Our results provide support to most of the formulated hypotheses. We find that strategic alignment between projects and the organisation's business strategy has a positive effect on the PPM performance, and this alignment, in turn, is impacted by two mutually interdependent mechanisms of portfolio establishment and portfolio steering. Based on our findings, we formulate several managerial implications and directions for further research.
Keywords: portfolio management, strategy realisation, strategic
Introduction
Acting in dynamic and turbulent environments, modern organisations strive to achieve excellence and sustain competitive advantage on the market. Design of a business strategy, specification of the organisation's mission, vision and objectives, and developing policies and plans are viewed by strategic management discipline as a necessary precondition for organisations to remain competitive and fit. While this message receives virtually universal recognition, real practices are far from being perfect. Mankins and Steele (2005) found that firms realise only 63% of their strategies' potential value, and Johnson (2004) reported that 66% of corporate strategy is never implemented. As Grundy (1998) vividly stated, strategy implementation is often the graveyard of strategy. Similarly, Hrebiniak (2006) posited that it is more difficult to make strategy work than to design a strategy.
Traditionally, the company's business strategy was meant to be realised through on-going activities, or functional operations. A modern trend is proliferation of projects as an environment for business activities, as modern organisations start to adopt projects as an organisational form of conducting their business operations. In fact, most organisations seek to find a delicate balance between “running the business” (in terms of daily, functional operations) and “changing the business” (in terms of initiating projects and running project management).
As projects are used in a wide spectrum of business operations, they are becoming a vehicle of business strategy implementation, and a topical area of professional examination and application (Hauc & Kovac, 2000). Shenhar, Dvir, Levy, and Maltz (2001) emphasised that projects are “powerful strategic weapons” as they can be considered as a central building block in implementing the intended strategy.
However, if “projectification” of business is not managed properly, it may lead to “project overload,” inefficient and ineffective use of the company's resources, and in fact, distraction from the company's strategic goals. Managers are increasingly concerned about getting better results from the projects under way in their organisations in getting better cross-organisational cooperation (Englund & Graham, 1999). One of the most common complaints of project managers is that projects appear almost randomly—“the projects seem unlinked to a coherent strategy, and people are unaware of the total number and scope of projects” (p. 52).
Englund and Graham (1999) suggested that “selecting projects for their strategic emphasis…is a corner anchor in putting together the pieces of a puzzle that create an environment for successful projects” (p. 52). Project portfolio management (PPM) emerges as a mechanism to manage this puzzle. Its mission is seen in evaluating, prioritising, and selecting a project in line with the business strategy (Archer & Ghasemzadeh, 2004; Cooper, Edgett, & Kleinschmidt, 2001). Srivannaboon (2006) described the achievement of business strategy alignment in project management as a process where projects are first selected into the project portfolio to support the implementation of the business strategy. The concept of strategic fit, or strategic alignment, has been studied in the management literature. The strategic fit of the project portfolio is the degree to which the sum of all projects reflects the business strategy (Meskendahl, 2010).
Although the idea of the strategic fit is broadly understood and shared among scholars and practitioners, the literature on it is still limited (Srivannaboon & Milosevic, 2006), specifically, empirical studies are not common. A number of in-depth case studies have been published. Nonetheless, results of case studies can hardly be generalised over a wider population of organisations. Available quantitative empirical evidence is still insufficient.
The objective of this paper is to investigate empirically the relationship between strategy alignment and the overall performance of project portfolio management, as well as between different mechanisms and processes that contribute to strategic alignment. A critical note is that we aim to study whether projects are aligned with the current business strategy, not their contribution to the overall business performance. Jacobs (2005) indicated that PPM is the most popular approach to implement the corporate strategy, yet it is not the only approach. PPM is just one of many means to achieve business success in an organisation.
The main method is quantitative study. Data are collected in a self-administered survey, among a population of portfolio managers from a range of organisations. This data are then used to test a number of hypotheses derived from academic literature.
Theoretical Background
Project Portfolio and Project Portfolio Management
Archer and Ghasemzadeh (1999, p. 208) defined project portfolio as “a group of projects that are carried out under the sponsorship and/or management of a particular organisation.”
Project Management Institute (PMI, 2006) offers a more elaborate definition in The Standard for Portfolio Management, placing the emphasis on strategy, “a collection of projects (temporary endeavours undertaken to create a unique product, service, or result) and/or programs (a group of related projects managed in a coordinated way to obtain benefits and control not available from managing them individually) and other work that are grouped together to facilitate the effective management of that work to meet strategic business objectives” (p. 4).
PPM is a systematic approach to manage project portfolios. Other terms include “multi-project management” or “multiple project management” (Dietrich & Lehtonen, 2005). Dooley, Lupton and O' Sullivan (2005, p. 468) defined the role of PPM as “… to maintain control over a varied range of specialist projects, balance often conflicting requirements with limited resources and coordinate the project portfolio to ensure the optimum organisational outcome is achieved.” Blichfeldt and Eskerod (2008, p. 358) viewed PPM as a set of the managerial activities “that relate to (1) the initial screening, selection and prioritisation of project proposals, (2) the concurrent reprioritisation of projects in the portfolio, and (3) the allocation and reallocation of resources to projects according to priority.” Similarly, LaBrosse (2010) argued that the goals of PPM is to find the best mix and timing of current and proposed projects to achieve the organisation's overall goals, while recognising constraints familiar to us all—finite resources, a changing marketplace, or a shift in business strategy.
A formal definition by PMI (2006) provided in The Standard for Portfolio Management is as follows: it is “an approach to achieve goals by selecting, prioritising, assessing, and managing projects, programs and other related work based upon their alignment and contribution to the organisation's strategies and objectives. Project portfolio management combines (a) the organisation's focus of ensuring that projects selected for investment meet the portfolio strategy with (b) the project management focus of delivering projects effectively and within their planned contribution to the portfolio” (p. 5).
Hence, PPM is meant to address two key aspects: “doing the right projects” (the portfolio strategy) and “doing the projects right” (the project management focus). In other words, PPM's mission is not only about initial selecting the right projects, but also ensuring an effective and efficient execution of projects and their alignment with the organisation's goals and objectives. Not only does PPM enable an organisation to get an oversight of all its on-going projects and get a better grip on their execution, but it also provides information for the organisation on how to stay in tune with the demands of the marketplace and emergent situations in the business (Pennypacker & Retna, 2009).
PPM is a continuous activity including a set of processes. The main activities essential to PPM is listed in Organizational Project Management Maturity Model (OPM3®): (1) translating organisational strategies into specific initiative or business cases that become the foundation for programmes and projects; (2) identifying and initiating programmes and projects; (3) providing, allocating and reallocating resources to programmes, projects, and other activities; (4) maintaining a balanced project portfolio; and (5) supporting the organisational project management environment (PMI, 2003).
Features of Successful PPM
Success is a broad concept that in a most straightforward sense simply means meeting or exceeding expectations and goals (Dietrich & Lehtonen, 2005). In the project context, success is often conceptualised through a variety of success criteria and success factors. While success criteria refer shortly to the measures by which success or failure of a project or business will be judged, and success factors are defined as inputs to the management system leading directly or indirectly to the success of the project. The management approaches in a multi-project environment generally distinguish between (1) management efforts directed to single projects and (2) management activities that focus on groups of projects (McDonough & Spital, 2003). The latter is the focus of PPM. The salient feature of a successful PPM is that this collective synergetic mechanism provides opportunities for reaping benefits that would not be available if projects were managed individually (LaBrosse, 2010).
Based on formal definitions of PPM, Pennypacker and Retna (2009, p. 5) formulated five questions that a successful PPM should answer positively.
1. “Are we investing in the right things?” Since capital is a limited resource, organisations must figure out a way to invest in the right things. This is a balancing act between the desire to fulfill the business strategies, the limited available money to invest, and knowing the right time to start a project or terminate an unsuccessful one, and consequently allocate recovered capital to other projects.
2. “Are we optimising out capacity?” Capacity optimization can also be called portfolio resource optimisation with two key principles: (1) balance the demand for resources with the supply; and (2) create an open dialogue, based on factual analysis, between the portfolio management office and the business project sponsors (the decision makers). Resource optimisation is achieved through a balanced management of resources by understanding, managing, and balancing the demand side and the supply side.
3. “How well are we executing?” PPM enables the company management to receive necessary information on the status of all on-going projects; it also provides information to stay in tune with the demands of the marketplace and emergent situation in the business. It is important to know how well PPM is performing, e.g. maturity, efficiency and effectiveness of PPM practices.
4. “Can we absorb all the changes?” Given the dynamism of contemporary economic, political, technological and social environments, a modern organisation should be able to adjust to these changes and absorb them. PPM is not a static mechanism and project portfolio is not fixed either. There are different types of change that need to be considered when looking at whole portfolio as well as individual projects—change that impacts technology, change that impacts physical assets, and change that impacts people.
5. “Are we realising the promised benefits?” Effective PPM enables us to know what benefits to expect from a project and to track the realisation of those benefits as the project progresses. To realise benefits in practice, (1) staff need to be trained to use the system and exploit its capabilities; (2) business processes need to be reengineered; and (3) resources need to be redeployed.
Furthermore, an essential precondition for a successful PPM is the quality of information supplied to the decision maker, meaning an up-to-date data on the status of projects in the portfolio (Matheson & Menke, 1999; Dietrich & Lehtonen, 2005).
Dooley and O' Sullivan (2003) highlighted a number of common problems associated with portfolio management, or rather, developments that may take place if PPM is not carried out professionally. They are (a) poor leadership and direction; (b) poor alignment between goals and projects; (c) poor monitoring of holistic process results; and (d) poor planning and control of action implementation.
Hypothesis Development
The section consists of two subsections. The first one looks at the project portfolio management success and factors influencing it, particularly, strategic alignment. The second one focuses on the strategic alignment itself.
Project Portfolio Management Performance
Performance of PPM can be measured through four dimensions: (1) the average single project success of the portfolio regarding the fulfilment of time, budget, quality, and customer satisfaction objectives; (2) the use of synergies between projects within the portfolio, which covers the interdependencies between projects; (3) the portfolio's overall fit with the firm's business strategy; and (4) the portfolio's balance (Cooper, Edgett, & Kleinschmidt, 2002).
As we have elaborated in the literature review, a key factor in PPM performance is the basics, or foundations of project management. It is more commonly known as “doing the projects right.” These foundations of project management include all the tasks, functions and activities aimed at professionalisation of project management. It assures that projects are planned and executed professionally according to clear guidelines, principles, and procedures. The focus is on management at the level of individual projects. For example, good project management foundations contribute to the average single project success. We therefore propose the following hypothesis:
Hypothesis 1: The better the project management foundations, the better the project portfolio management performance.
Another key factor in PPM performance is the alignment of project portfolio with the company's business strategy. By contrast to foundations of project management, the focus here is on “doing the right projects.” This strategic fit of the project portfolio is the degree to which the sum of all projects reflects the business strategy (Meskendahl, 2010). In a broad sense, strategic alignment involves all the tasks, functions, and activities aimed at bringing the project portfolio in tight integration with the business strategy. In other words, this mechanism should assure that only the projects that serve (contribute to) the business strategy are added in the portfolio, and otherwise, prevent projects that do not serve the company strategic goals and priorities from inclusion into the portfolio. This leads to the following hypothesis:
Hypothesis 2: The better the strategic alignment, the better the project portfolio management performance
Strategic Alignment
In this section, strategic alignment is analysed. Strategic alignment can be achieved by two (interrelated) mechanisms: (1) the initial establishment of project portfolio; and (2) the on-going portfolio steering, calibrating and adjusting of project portfolio (Jonas, 2010). While portfolio establishment takes place cyclically in fixed moments of time, for example, four times a year, but can be different, portfolio steering is a continuous process throughout the whole year. Portfolio steering receives input from portfolio establishment, which in its turn provides feedback back to portfolio establishment. This is visualised in Figure 1. These two processes play an important part in strategic alignment.
Initial portfolio establishment groups all the tasks, functions, and activities aimed at initial identification, screening and actual selection of projects, and their prioritisation in accordance with predefined strategic targets and objectives. This initial process is considered as recurring, as it repeats in certain pre-defined periods (e.g., once in a year, every quarter of a year, etc.), and describes the firm's ability to integrate PPM into its existing strategic processes. Jonas (2010) identified four tasks that are initially undertaken to set up a target portfolio derived from the business strategy of an organisation: strategic portfolio planning, definition of long-term target portfolio, evaluation of project proposals, and selection of projects. Consequently, the following hypothesis is suggested:
Hypothesis 3: The better the initial portfolio establishment, the greater the strategic alignment
The second mechanism is the on-going portfolio steering, calibrating and adjusting of the previously established project portfolio. Dooley, Lupton, and O' Sullivan (2005) pointed out that decisions concerning which project proposal should join the portfolio may be influenced by issues such as the success of existing projects within the portfolio. Thus, only a mechanism for evaluating prospective projects is not enough to effectively manage multiple projects. There should be also continuously reviewing on-going projects relative to their suitability to the current environment and also relative to the other projects in the portfolio.
Portfolio steering includes all the continuous tasks that are necessary for a permanent coordination of the portfolio (Müller, Martinsuo, & Blomquist, 2008), such as continuously monitoring, screening and adjusting projects in the current portfolio. This screening aims to ensure that all initially selected and launched projects still contribute to the business strategy and they still fit the portfolio. It seeks to enhance synergies between these individual projects. The tasks of portfolio steering include: (1) monitor and evaluate the current portfolio status in terms of strategic alignment and capacity utilisation; (2) development of corrective measures in case of deviations from the target portfolio; (3) coordination of projects across organisational units to identify synergies between comparable projects; and (4) identify and abort obsolete projects (Jonas, 2010). This leads to the following hypothesis:
Hypothesis 4: The better the continuous portfolio steering, the greater the strategic alignment
Further, these two mechanisms—portfolio establishment and portfolio steering— contribute to a stronger strategic alignment are interrelated, as explained previously, and shown in Figure 1. Portfolio establishment provides necessary input for portfolio steering, and then receives certain feedback. These two processes are two sides of the same coin, mutually reinforcing each other. Organisations are expected to have a similar degree of maturity in both processes. Hence, the following hypothesis is proposed:
Hypothesis 5: Initial portfolio establishment and continuous portfolio steering are mutually positively related.
“Doing the projects right” and “doing the right projects” are two key aspects of PPM in any organisation. As postulated in Hypotheses 1 and 2, they are both equally important and professionalism in both of them is considered vital for PPM performance. It is reasonable to assume then that these two variables are positively related to each other and mutually reinforcing. However, this is not necessarily the case. Organisations doing the right projects may not necessarily do them in a right way, and by contrast, organisations doing the wrong projects in a right way. Artto and Dietrich (2004) presented these two dimensions as a trade-off “for the successful management of multiple projects, it is important to distinguish whether the projects are established for effectiveness or for efficiency. Effectiveness refers to doing the right thing, and efficiency refers to doing the thing right. Effectiveness often means creating something new; efficiency means perfecting something that is already known” (p. 18). It entails that effectiveness may be achieved at the cost of efficiency and vice versa.
While the interplay between “doing the projects right” and “doing the right projects” remain controversial, we suggest that it is the relationship between “doing the projects right” and the process of continuous, on-going portfolio steering that should be examined. Expertise in on-going portfolio steering as an act of a day-to-day management may be complementary to the foundations of project management, which involves routinely processes of management of projects as well. In line with this reasoning, the following hypothesis is proposed:
Hypothesis 6: Continuous portfolio steering and foundations of project management are mutually positively related.
Analytical Framework
Five variables can be defined on the basis of the six hypotheses developed previously, namely, (1) PPM performance PMP, (2) strategic alignment SA, (3) foundations of project management PM, (4) initial portfolio establishment PE, and (5) on-going portfolio steering PS. All six hypotheses and corresponding five variables are visualised in Figure 2. Boxes represent five variables. Single-headed arrows represent causal relationships between variables, while double-headed arrows visualise co-variations. References to respective hypotheses are placed above the arrows.
Data and Methodology
This section presents introduces our data collection method (a self-administered survey), description of the obtained sample and the methodology—structural equation modelling (SEM)—to be used in further analysis
The Survey Instrument
The data were collected in a self-administered survey tailored to the research objective and developed hypotheses and variables. A questionnaire was designed to collect data. Its content was decided with reference to the objectives of the project and theoretically anchored in the project management and strategic management literature. More specifically, several publications were consulted (e.g., Jonas, 2010), questionnaires developed by professional consultancy organisations (Dutch subsidiary of Nolan, Norton & Co), and other.
Before the questionnaire was administered, it was qualitatively pretested in pilot interviews with projects and portfolio managers, scholars and business strategy consultants. As a result, minor changes were made to eliminate or alter ambiguous questions and phrasings and to remove indicators not capturing the constructs for which they were designed. This procedure increased face validity of our measures.
Data Collection
The data collection was a two-step strategy. The first step was a traditional face-to-face data collection, executed at the event of the Dutch branch of the International Project Management Association (IPMA), “Project Management Parade” in Nieuwegein (The Netherlands) in April 2011. This was a professional meeting of project and portfolio managers from a variety of organisations. Visitors were kindly asked to contribute to this research and fill in the questionnaire. Approximately half of the sample was collected at this venue. Because the survey was held in The Netherlands, a version of the questionnaire in Dutch was developed. As it was more convenient for respondents to read and answer the questions. Before the survey took place, both versions of the questionnaire were crosschecked to avoid any misinterpretation in translation.
The second step is an online web-based survey. Professional social network LinkedIn was used to invite respondents. An announcement was posted in a group of portfolio management professionals with an invitation to proceed to a website on which an electronic questionnaire was located. A web-based survey has several advantages, which were pointed out by Dillman (2000): (1) Cost saving due to no use of paper, postage, mail out and data entry costs. Once the electronic data collection system is developed, cost of surveying additional respondents is much lower. Reminders and follow-up on non-respondents are relatively easy. (2) Time saving, as the time required for implementation of survey can be considerably reduced. (3) Labour saving, since data from the web-based survey are instantly available and can be easily imported into data analysis software.
Sample Description
Thirty-five observations formed the sample. The respondents are professional and experienced portfolio managers from a variety of organisations. The collected sample varies from companies that were founded hundreds of years ago to companies founded in the past decade. In terms of their organisational forms and sectors, they ranged from NGOs and governmental organisations to financial institutions and high-tech companies. Seventy-four percent of companies were Dutch (but not necessarily acting only on national market), 17% were of other origin, and 9% did not specify it. Forty percent of the samples are organisations where projects serve as a primary business. Forty-three percent of the samples are organisations that practice PPM because projects are considered as a secondary business supporting the core business. For example, the core business of a bank is providing financial services to customers, while its IT department practices PPM because they need projects to innovate on their IT system to secure and support their financial services. The remaining 17% are organisations where projects function as primary and secondary businesses.
Measures
The operationalization of our variables is shown in Table 1. Most items in our scales were purposely developed for the project, building on previous research and theory. They can be considered to be reliable and valid measures. The questionnaire includes 28 closed multiple-choice questions. All indicators were measured using five-item Likert scale. The Likert scale allows respondents to express the degree of agreement with the formulated questions.
Project Portfolio Management Performance. This measure is operationalised as the extent of maturity, or professionalism in conducting PPM. Cooper et al. (2002) suggested that the success of PPM can be measured through four dimensions: (1) the average single project success of the portfolio regarding the fulfilment of time, budget, quality, and customer satisfaction objectives; (2) the use of synergies between projects within the portfolio, which covers the interdependencies between projects; (3) the portfolio's overall fit with the firm's business strategy; and (4) the portfolio's balance. We broadly followed these dimensions, and we asked respondents to what extent various features of a mature PPM system are present (project success, overview of projects), and specifically, about the balance in the project portfolio (between high and low risk projects, synergy between projects).
Foundations of Project Management is operationalised as the extent of professionalism of project front-end development and project execution. We asked respondents about the use of professional project management tool and methodologies (e.g., PRINCE2, PMBOK Guide®) and administrative process, sufficient capacity, and expertise to execute projects, and so on.
Strategic Alignment. This construct captures the extent to which the alignment between the projects and the business strategy is achieved. We asked respondents about strategic themes and strategic projects, consistency between objectives of single projects and the overall business objective, and respondent's ability to assess contribution of the projects to the business strategy, etc.
Initial Portfolio Establishment. This measure is operationalised as the extent of maturity of all processes related to the initial portfolio establishment. We asked respondents about standardised methodologies and procedures for formulating an initial business case, definition, selection and approval of projects to be included in the portfolio.
On-going Portfolio Steering. This measure reflects the company's ability to assess consistently and continuously whether the projects in the portfolio are still contributing the business objective (Müller et al., 2008). We asked respondents whether contribution of on-going projects to the business strategy is explicitly traced, whether there is a parallel coordination between various projects, whether a methodology is present that checks for deviations from original business cases.
Data Analysis Technique
We analysed the data using a structural equation modelling (SEM) technique. Considering our analytical framework (Figure 1), involving numerous regressions and interdependencies, SEM is regarded as the most appropriate statistical technique for estimating it in a single model. SEM is a statistical technique for testing and estimating causal relations using a combination of statistical data and qualitative causal assumptions. SEM involves series of multiple regression equations—all equations are fitted simultaneously. Recently, SEM became increasingly popular among researchers in social sciences, as it allows to model complex social systems with multiple variables and interrelations between these variables.
Structural equation modelling is a flexible and powerful extension of the general linear model. Like any statistical method, it features a number of assumptions. One of them is a reasonable sample size. A good rule of thumb is 15 cases per predictor in a standard ordinary least squares multiple regression analysis. Because SEM is closely related to multiple regressions, 15 cases per measured variable in SEM seem reasonable. Bentler and Chou (1987) noted that researchers may go as low as five cases per parameter estimate in SEM analyses, but only if the data are perfectly well-behaved. In total, a rule of thumb is that the sample should consist of at least 100 observations and 200 as a goal. The general recommendation is thus to obtain more data whenever possible. Models for which there is an upper limit on population (e.g., countries), 200 might be an unrealistic standard. Practical limitations are often the chief determinant of the sample size. Moreover, as it emerges from the meta-study of Westland (2010), 80% of research articles using SEM methodology drew conclusions from insufficient samples. Lower sample sizes are generally accepted for simpler models, models with no latent variables, models where all loadings are fixed, etc. These are indeed characteristics of our explorative model. Every single effort was made to collect a large sample; however, we have managed to obtain only 35 observations. Considering the argumentation above, in this explorative state of research, a total sample of 35 respondents is still sufficient to create an overview of the current practice in organisations.
We use IBM SPSS AMOS software package to estimate our model. AMOS enables SEM to build models with more accuracy than with standard multivariate statistics techniques.
Results
First, we present descriptive statistics and internal consistency analysis of the variables used in our analysis. Further, we discuss the model fit. Lastly, we report regression estimates and covariances (hypothesis testing).
Internal Consistency Analysis
Collected data allow us to construct five variables for the SEM model (Table 2). Each of the variables consists of five to six items (shown in Table 1). All Cronbach's α values are above 0.7 indicating a very good internal consistency and meaning that specific questionnaire items essentially represent the same thing and can be grouped into respective variables. It allows us to calculate variables as mean values of respective items, where individual items have the same weight. All variables are on a five-item Likert scale.
Model Fit
Fit refers to the ability of a model to reproduce the data. Assessment of fit is a basic task in SEM modelling. A good fitting model is one that is reasonably consistent with the data and so does not require re-specification. The output of SEM programmes includes matrices of the estimated relationships between variables in the model. Assessment of fit essentially calculates how similar the predicted data are to matrices containing the relationships in the actual data.
Formal statistical tests and fit indices have been developed for these purposes. Individual parameters of the model can also be examined within the estimated model in order to see how well the proposed model fits the driving theory.
AMOS reports that the minimum was achieved with no errors or warnings. The fit output contains a large array of model fit statistics. All are designed to test or describe overall model fit. Each researcher has his or her favourite collection of fit statistics to report.
Saturated and independence models refer to two baseline or comparison models automatically fitted by AMOS as part of every analysis. The saturated model contains as many parameter estimates as there are available degrees of freedom or inputs into the analysis. The saturated model is thus the least restricted model possible that can be fit by AMOS. By contrast, the independence model is one of the most restrictive models that can be fit: it contains estimates of the variances of the observed variables only. The independence model is in fact the null model in AMOS terminology.
Table 3 presents an overall model fit. The chi-square test is reported, along with its degrees of freedom and probability value.
In our model: the number of distinct sample moments is 15, the number of distinct parameters to be estimated is 11, and df = 4. Chi-square is equal to 22.223. All the reported values lie closer to the saturated model than to the independence one. They are deemed as acceptable.
Other commonly reported measures are the comparative fit index (CFI), the Bentler-Bonett index or normed fit index (NFI), or the incremental fit index (IFI). These indices compare the absolute fit of the specified model to the absolute fit of the Independence model. The greater the discrepancy between the overall fit of the two models, the larger the values of these descriptive statistics. Next, it is Akaike information criterion (AIC), a test of relative model fit. As a rough rule of thumb, models having their AIC within 1-2 of the minimum have substantial support and should receive consideration in making inferences. Table 4 reports these indices for the specified model. CFI, NFI, and IFI are all above 0.7 indicating a good fit. Similarly, AIC is within 2 of the minimum.
Another popular measure of model fit that is now reported in most papers is root mean square error of approximation (RMSEA), an absolute measure of fit is based on the noncentrality parameter. However, the RMSEA can be misleading when the df are small and sample size is not large; this is exactly the case in our model. For this reason, Kenny, Kaniskan, and McCoach (2011) argued to not even compute the RMSEA for such models.
To sum up, we have obtained a model that fits reasonably well (considering the limitations in the sample size) and, what is more, is theoretically consistent. Therefore, the next step is to interpret the parameter estimates and individual tests of significance of each parameter estimate.
Estimates
This model has several features. First, it contains manifest (observed) variables; second, it contains both causal relationships among latent variables, represented by single-headed arrows, and correlational or bi-directional relationships among several of the residuals.
AMOS reports the unstandardised estimate, its standard error, critical ratio and p-values. Standardised estimates allow evaluation of the relative contributions of each predictor variable to each outcome variable. The standardised estimates for the fitted model appear in Table 5.
The standard measure of a critical ratio greater than 1.96 creates significance—values for all estimates are higher than that, except for the last one (PE↔PM). Likewise, p-values of estimates 1 to 5 were smaller than .05 (or .01), indicating statistical significance.
To sum up, we have obtained a model that fits well and that is theoretically consistent and it provides statistically significant parameter estimates, now we shall interpret it in the light of the objective of this paper.
Table 6 presents these results in relation to the hypotheses developed previously in the paper. Our results indicated that hypotheses 1 to 5 are supported; the estimates are both positive and significant. The estimate 6 is positive, in line with the respective hypothesis, yet, it is not statistically significant. Therefore, hypothesis 6 is not supported.
Discussion and Conclusions
Strategic alignment has emerged as key topic in project portfolio management literature, and more globally, in the whole discipline of project management.
We find empirical support to the majority of our hypotheses. Our empirical evidence supports the claim that PPM performance is directly influenced by enhancing the foundations of project management (“doing the project right”) and by strengthening the strategic alignment between projects and business strategy (“doing the right projects”).
In order to achieve a higher degree of strategic alignment, two mechanisms should be designed and deployed in an organisation: initial portfolio establishment and continuous portfolio steering. These two mechanisms are found to be both contributing positively to strategic alignment. Moreover, they are mutually complementing, meaning that expertise in one mechanism reinforces that in the other.
We do not find any significant relations between the mechanism of on-going portfolio steering and foundations of project management, meaning that capabilities and expertise in these two fields are unrelated.
Managerial Implications
Perhaps the main implication is that organisations should recognise the value of PPM in achieving their strategic goals. In order to achieve it, it is reasonable to start with the development and improvement of portfolio establishment processes. It is recommended to create an integrated system or procedure involving screening, selection, and prioritising of project proposals. This also gives the portfolio manager an overview of all the project proposals and how to prioritise them according to the strategic intention, available resources, or financial benefits. Another set of procedures and methodologies should be designed and implemented for continuous portfolio steering.
Project/portfolio managers and top executives should both recognise the importance of strategy realisation through PPM in their organisation. Portfolio managers should then be empowered by sufficient authority and autonomy for this on-going steering, however it does not mean complete autonomy. Staying aligned with upper management level is key. Clear rules of the game and the right frequency of communication should be developed.
Finally, strategic alignment is only one factor influencing PPM performance, while foundations and professionalism of project management is another. It should be enhanced too in order to achieve a better PPM performance.
Academic Contribution and Future Research
Our study aimed to contribute to further development of theory. We developed a conceptual framework combining several variables purporting to explain performance of PPM in organisations, with specific focus on strategic alignment. Next, we generated a data set tailored to this framework and tested it.
Future research may proceed in two directions: elaboration of the framework and data collections. As for the framework, more variables may be added to provide a more precise explanation of the strategic alignment and inherent mechanisms and procedures. In terms of data, a larger sample should be collected that would allow to discern differences between sub-samples in terms of sectoral effects, age, size, etc. A critical aspect is measurement; as it is commonly done, we have relied on perceptions of individual portfolio managers in assessing the current state of PPM in their respective organisations. In order to minimise potential psychological bias, responses of several managers of the same organisation would need to be collected. They are to be complemented by more objective data like internal company documents. However, such more precise data may come at the cost of the sample size. These are potential directions worth further exploration, and we are confident that this topic will remain a promising avenue of research.