An exploration of project management office features & project performance


The advantages of project management have been well documented, but project failure rates still remain high. This suggests continued exploration of new process models and organization structures to nurture strong project performance. One important candidate for improvement in this ongoing journey is the project management office (PMO). This paper is based on a two-year empirical study that investigated the establishment and use of PMOs and the environmental conditions in which they operated. It also identified and assessed an array of PMO functions and services and their influence on reported project performance. The core results were generally favorable toward the utilization of such features, with project standards and methods showing the highest correlation to performance in each of the two distinct populations.

1. Introduction

Projects have become important instruments for change and development in organizations [1]. Gareis [2] and Lundin [3] suggest that the broader utilization of projects requires a new orientation in project management (PM) and a new model for more effective operations in project-driven organizations. Munns and Bjeirmi [4] showed that more effective PM offers great potential for improving overall organizational performance by enhancing the prospects for project performance and minimizing the likelihood of failure. In spite of the advantages of using the project approach, however, Jessen [5] suggests that there is also a significant problem. Because of the one-time nature of projects, an organization may often derive little benefit from previous successes and failures due to a lack of effective knowledge transfer. The study reported in this paper examined the question of what measures organizations have taken to enhance transferability of lessons learned from previous projects, and how these measures have influenced project outcomes.

There are many dimensions for evaluating project performance [6,7], while it appears to be easier to develop consensus on determining project failure. The documentary record is replete with reports of high rates of project failures across all industries, government agencies and national boundaries [8–10].

One approach to studying project performance has been through the investigation of critical success factors (CSFs) as predictors of performance. For example, Pintos [11] identified 10 CSFs, ranging from project mission, top management support, project schedule/ plan, client consultation, technical tasks, communication to personnel recruitment/selection and training. A natural next-step would be to determine how organizations could  systematically foster CSFs on an ongoing basis.

Another conceptual guideline was provided by Might and Fischer [12], who examined how project organizational structure interrelated with outcomes. They observed structures ranging from functional design at one extreme to a dedicated project team at the other, with a matrix format somewhere in between. Their results suggest that while neither extreme showed any notable degree of association with performance, the intermediate forms did possess some positive relationship to performance. Their study pointed to the potential of a broader inquiry into alternatives at the organizational
level of analysis.

2. Background

A project office (PO, also be called a program office) is an organizational entity established to manage a specific project or a related series of projects, usually headed by a project or program manager [13]. A project management office (PMO, also called a center of excellence or center of expertise) is an organizational entity established to assist project managers, teams and various management levels on strategic matters and functional entities throughout the organization in implementing PM principles, practices, methodologies, tools and techniques [13]. A PMO carries a much broader mission and was the focus of this study.

Dinsmore [14], Fleming and Koppelman [15] and Knutson [16] call for the establishment of such an office to improve PM effectiveness, particularly by enabling the acquisition of knowledge from earlier failures and successes and by providing a range of support and facilitative services not only for projects but also for various management levels and support units. A study of PM best practices in large functional organizations [17] reinforced the notion that there is value in utilizing PMOs. Block and Frame [18] suggest that an ad hoc approach to PM leads to inefficiencies and can even be
dangerous, while establishment of a PMO can foster consistency and nurture PM professionalism. They propose the following characteristics to help improve an organization’s PM effectiveness:

  • Project support to offload administrative burdens such as reporting and software operations from project managers.
  • Consulting and mentoring, whereby professional PM expertise such as proposal development and project planning is provided.
  • Development and enforcement of standards and methods to leverage best practices and to ensure members of the organization are all  ‘‘speaking the same PM language.’’
  • Training to enhance individual skills and to encourage professional certification.
  • Assistance in staffing projects with appropriate project managers.
  • Playing a high-tech project support role by enabling virtual project offices across geographical and organizational distance.

Bates [19] further adds that PMOs should also assume tasks such as providing project risk assessment, performing post-project evaluation services  and ultimately leading the organizational transition to an effective project environment.

Little systematic empirical research had been done to test the growing body of anecdotal evidence, however. The major objective of this study was to enhance the strength of the empirical research base that complements these findings, examining the particular question of what correlations might exist between the presence of PMO features and project performance.

To clarify the differences and often interchangeable use among the names of PMO, PO and other possible forms (e.g., SPO – systems program office), this study used the notion of PMO presence which focused on the functions and services an organization performs and provides, rather than which name was used. This approach permitted the inclusion of ‘‘intermediate’’organizations which had no formally established PMO entity but did have resources providing PMO functions and  services to project managers and teams.

3. PMO presence

Although a standard set of PMO presence features has yet to be agreed upon in theory or practice, the literature review led to the identification of the categories enumerated below.

3.1. Developing and maintaining PM standards and methods

A PMO can develop and maintain a set of standards and methods, becoming a steward of documented PM expertise within the organization. These standard procedures should be detailed enough to provide guidance but not so excessively detailed as to inhibit creativity. The following list includes representative areas, each of which was reflected in the survey instrument: proposal development, change management, risk assessment, documentation standards and project closeout.

3.2. Developing and maintaining project historical archives

The PMO can provide a centralized archive to systematically collect and store project knowledge such as lessons learned and templates.   Representative areas include records of project performance such as status re-ports, variance analysis and changes to the baseline plan, risk lists and other risk management documents, information on prior successful and unsuccessful projects and a database of lessons learned.

3.3. Providing project administrative support

As project numbers and scale grow large, the associated administrative requirements also expand. Administrative work often is not reflected directly in project deliverables and thus can represent a distraction to the core project team. Representative work areas in this category include maintenance of a project binder or web site, assistance in generating standardized reports, provision of a ‘‘war room’’ for reviews and meetings and standardization and assistance with PM software.

3.4. Providing human resource/staffing assistance

As more organizations carry out their activities through projects, the demand for qualified project managers has grown. Assistance can be provided in identifying the proper person to manage a project and the proper skill requirements for the project team, in gathering data to conduct performance evaluations, in recruiting project staff outside the organization and in granting awards or other types of extraordinary recognition.

3.5. Providing PM consulting and mentoring

As organizations become more sophisticated in PM, the need to move from an ad hoc to a more strategic PM approach increases. A PMO can contribute by providing the following areas of consulting and mentoring: assistance in employing PM methodologies and responding to risk events, mentoring on unique measures that must sometimes be taken to foster project success (and sharing those same ideas with upper management) and
group sharing sessions for project managers.

3.6. Providing or arranging PM training

As organizations devote more resources to conducting business on a project basis, the need for PM training grows.APMOcan take a leadership role in working with a human resource department in the areas of skill set identification, training on PM and related software, financial support to conduct training and one-on-one coaching.

4. Research methodology

4.1. Measurement instrument

The six functions and services above were embodied in a questionnaire research instrument to characterize the PMO presence associated with a  project outcome. Selected PM and PMO experts were consulted to help fine-tune the instrument. Each questionnaire item used a 7-point Likert scale to indicate the level of agreement with a statement regarding the associated PMO feature. In treating Not Applicable (N/A) responses, a list-wise
deletion method was used. The respondents were asked to form their responses based on a recently completed project on which they had participated. The presence of each feature was then calculated as the mean of the associated item scores.

Following Pinto and Slevin [6], multiple measures were used to form the dependent variable, reported project performance. These included  dimensions of the triple constraint as well as other indicators of whether the delivered asset was judged to contribute to broader organizational performance. The survey instrument was loaded into a web application hosted at The George Washington University.

4.2. Samples for data gathering

The Project Management Institute (PMI) year 2000 membership list served as one population. One thousand members in North America were randomly selected from a pool of 35,880 members who had not chosen to exclude their names from the list. A total of 234 (23.4%) responses were received. This will be referred to as the ‘‘random’’population sample.

A second population consisted of a targeted group of organizations that had been identified in advance as having some version of a PMO. Some candidates were drawn from those on the Official Attendee List of the PMI Annual Symposium 2000 who identified themselves as being a member of a PMO or a similar entity. Others were contacts affiliated with the Master of Science in Project Management Degree Program at George Washington University. The target organizations were not restricted to North America, but only a small number were from elsewhere. Ninety-six PMO representatives participated, with 52 being partnered with a project manager from the same organizations. The criterion for selecting the partner was that the individual had recently completed managing a project for the organization. PMO representatives were instructed not to be concerned about the completed project’s outcome in selecting a potential partner to participate. This will be referred to as the ‘‘targeted’’population. The choice of
the two distinct populations led to an enriched set of findings and comparisons.

4.3. Data conditioning

Confirmatory factor analysis was used to assess the construct validity of the factors comprising PMO presence. Three of the 30 questionnaire items weresubsequently loaded with factors that matched more closely. Cronbach a scores were then used to assess measure reliabilities. Six a values substantially exceeded the minimum threshold [20] of 0.70, ranging from 0.81 to 0.92. An independent samples t-test between early and late respondents (those who did not respond until after the second mailing) was conducted [21], with results indicating no response bias.

5. Research findings and discussion

The random and targeted samples presented similar work patterns. Respondents had, on average and across many industry and governmental areas, 20 years of work experience with about 12 years in PM. For the random group, 72% had served as a project manager; the proportion for the targeted group was 92%.

5.1. Profile of the establishment and use of PMOs

Of 234 responses in the random sample, 113 indicated having a PMO or an entity similar to a PMO. Of these, the overwhelming majority of PMOs were established in the mid-1990s to 2000 (Fig. 1).

The majority of respondents reported multiple motivations for initiating a PMO. The most frequently reported factors are listed in Table 1. Both the random and the targeted samples indicate a significant number of information technology firms or information systems departments were prominent in the movement toward PMO establishment.

An overwhelming proportion of PMOs was approved at a top/upper management level (Table 2), and the number that reported to senior management possessed a large margin over those that did not.

Growth in PMO establishment

Fig. 1. Growth in PMO establishment over time (random sample).

A significantly higher rate of mission statement use was reported by the targeted respondents compared to the random respondents, 72% compared with 23%. Many respondents of the random survey did not seem to have the same degree of access to such documents as did the targeted respondents who were PMO representatives/ managers. A wide range of mission statement purposes was revealed. Some focused more broadly on organizational goals, while others concentrated more specifically on improving PM skills within the organization. While there were many variations in the wording
of the mission statements, several themes appeared prominently, including:

  • Advocate and support the implementation of best-inclass PM practices, processes and principles across the organization.
  • Structure and promote an environment in which processes, methods and tools for system development, change management and PM are optimally employed and continuously improved in the business for achieving strategic goals.
  • Standardize PM skills and disciplines organizationwide, while creating an integrated delivery process so reliable, effective and responsive that our customers identify us as giving them a competitive advantage.

A point of inquiry in the survey dealt with what organization policy documents had been issued on the establishment and use of PMOs, with the following representative elements:

  • PMO charter
  • PM policy/strategy
  • PM methodology guidelines
  • Various standard operating procedures
  • Business justification document
  • Policies on key areas (e.g., training, project tracking, planning and configuration management)
  • Project metrics and standards
  • Reporting mechanisms
  • PMO website or corporate website
  • IT governance policy
  • Best practices database
  • Capital project cost reduction initiatives
  • Quality assurance policy, risk management implementation

A high proportion (over 93%) of full-time PMO staffing was reported in both surveys (Table 3). Respondents in the targeted group also reported various types of support the PMO provided to managers and project teams (Table 4). Of special significance was that developing and maintaining PM standards and methods was being attempted in all 96 PMO entities, providing evidence that PMOs are being used as facilitative units rather than as another line of directive management. This finding may help defuse concerns that a PMO might contribute another layer of bureaucracy that would interfere with a project manager’s authority.

With regard to PMO funding, 56 out of 113 organizations in the random sample provided information, as did 69 out of 96 organizations in the targeted sample.

Table 1 Frequently reported motivations for PMO establishment

Table 1 Frequently reported motivations for PMO establishment

Tabe 2 Environmental factors related to the establishment of PMOs

Table 2 Environmental factors related to the establishment of PMOs

The reported data indicated that the proportion of budget devoted to a PMO was estimated at less than 2% of the organization’s operational budget for 59% in the random sample and 62% in the targeted sample.

table 3 Summary of PMO staffing type
Table 3 Summary of PMO staffing type

5.2. Profile of reported projects

Data on reported industry are shown in Table 5. Results show that while only 26% of the random respondents and 31% of the targeted respondents reported their organizations as being part of the software development or computer/data processing/IT industries, about 53% and 62%, random and targeted, respectively, of the reported projects were associated with these areas (see Table 6).

table 4 Summary of PMO functions and services from 96 targeted organizations

Table 4 Summary of PMO functions and services from 96 targeted organizations

table 5 Frequency distribution on industry

Table 5 Frequency distribution on industry

table 6 Frequency distribution on end product

Table 6 Frequency distribution on end product

This result reflects the growing importance of electronic project deliverables in comparison with physical products. With regard to project size, 63% and 69% of reported projects, random and targeted, respectively, fell into one of the two categories: $100,000–$1 million or $1 million–
$10 million.

5.3. Results of statistical modeling

A comparative summary of simple linear regressions for the random and the targeted samples is presented in Table 7. Abbreviations used for the six PMO presence categories and the dependent variable are as follows:

  • ST – PM Standards and Methods
  • AR – Project Historical Archives
  • AD – Project Administration Support
  • HR – Human Resources and Staffing Assistance
  • TR – PM Training
  • CN – PM Consulting and Mentoring
  • DV – Reported Project Performance

All correlations of the DV with the PMO presence variables were positive, with significant slope coefficient estimates. The standards and methods variable (ST) explained the most variation (R2) in reported project performance from both populations. The Fortune 500 PM Benchmarking Study [17] reported a similar finding regarding

table 7 Summary of simple regression against indicated variables

Table 7 Summary of simple regression against indicated variables

the importance of standardized methodologies as a ‘‘key success factor and core best practice’’. It seems that as a practical matter, organizations that take steps – either via a PMO or otherwise – to standardize their PM practices across the organization are more likely to have stronger project performance.

The variable with the next highest proportion of variation explained for the targeted population was AR (18%), but for the random population it was TR (4.4%). It seems that the targeted population placed much more emphasis on maintaining and using lessons learned from previous projects than the random population. This view regarding the value of organizational learning was compatible with that of Jessen [5] and the Fortune 500 Benchmarking Study [17].

A rather surprising finding from the targeted population was that PM consulting and mentoring (CN) had the lowest R2 (<5%) and failed to generate a statistically significant slope coefficient estimate – notwithstanding that this service was acclaimed among PM authorities and PMO practitioners. Perhaps consulting and mentoring are often provided for projects that are already at risk for other reasons, thus diluting their perceived efficacy.

Similar weak performance resulted for project historical archives (AR) in the random population. This was particularly surprising given the stronger performance of this variable for the targeted population and high status accorded this feature by the Fortune 500 Benchmarking Study [17]. This may reflect that either archives have not yet been established in many organizations or the established archives need to improve their service and support to project managers and teams.

In general, data from the targeted population yielded higher R2 values, indicating that correlations between PMO functions and services and project performance are stronger here than in the random population. One of the reasons could be that those project managers from the targeted group who were selected by their PMO representatives/managers were inherently more aware of a PMO presence, which highlighted the PMO contribution
to project performance. On the other hand, it seems that in many organizations there are factors in addition to PMO presence that are influencing project outcomes.

The diagnostic tests for several variables in the random population called for weighted least square transformation to satisfy regression assumptions, but the resulting model estimates ended up being very close to the original estimates. Details of all regression models, including diagnostic tests on the residuals, are described in [22].

The targeted population data yielded a multiple linear regression model on ST and AR with an F-statistic of 8.455 (Table 8) and an adjusted R2 of 22.62%, a small but significant improvement over the simple regression on ST alone. The addition of the second predictor AR was significant at a 90% confidence level, given that ST was already in the model. The targeted population consistently emphasized saving and applying lessons learned from previous projects vis-a-vis the random population.

table 8 Regression model with independent variables ST and AR

Table 7 Summary of simple regression against indicated variables

The simple regression against ST was the best linear model for the random population. No variable offered any statistically significant predictive power once the ST variable was included. 5.4. Project performance in organizations with PMOs versus those without PMOs Data were also gathered from the random population on whether project performance varied between organizations that had established PMO entities versus those
that did not. The organizations compared were: (1) those having a fully established PMO, (2) those having no PMO and (3) those having something in-between, where somebody was tasked with working on PMO functions and services, but no formal entity had been established. The corresponding research hypothesis was:

Ha: The mean scores of reported project performance differ between organizations having a PMO, having no PMO and having something in-between.

In addition, we examined whether the use of PMO functions and services differed significantly among organizations. It was intended to determine whether the establishment of a PMO entity contributed to the use of PM standards and methods, historical archives, administrative support, human resources/staffing assistance, training, and mentoring and consultancy. The second research hypothesis was:

Ha: The mean scores for the PMO fun tions and services variables (ST, AR, AD, HR, TR and CN) differ between organizations having a PMO, having no PMO, and having something in-between.

Sheffe’s a posteriori test was conducted to investigate variable status, with groupings presented in Table 9. When the populations share identical codes (such as three As), the means among three groups are not statistically different; but when they have different codes (such as A, B and AB), the means of the three groups are statistically different. For example, for the PM training variable TR, between populations of PMOs and No-PMOs, there is
statistical evidence (at a 95% confidence level) to conclude that the mean scores differ. Likewise, between populations of PMOs and In-Betweens,  here is statistical evidence to conclude that the mean scores differ.

table 9 Scheffe’s a posteriori test for PMOs, No-PMOs and in-betweens for the random sample

Table 9 Scheffe’s a posteriori test for PMOs, No-PMOs and in-betweens for the random sample

The results in the final row of Table 9 show that reported project performance is higher in organizations that have a PMO in comparison with organizations that do not (5.66 vs. 5.51), but not high enough to merit statistical significance. On the other hand, organizations that have a PMO have clearly done more than those that do not have a PMO in promoting PM standards and methods, historical archives, training, and consulting and mentoring.

6. Discussion of implications

The results from this research support several conclusions in terms of the stated research question. They also point to a number of practical guidelines for organizations already operating a PMO and for those considering a move in this direction. Among the specific ways in which the results might be practically applied are the following:

  • Many organizations are moving in the direction of establishing PMOs or have already done so, demonstrating a high level of management confidence in the utility of this innovation.
  • There is strong evidence that PM standards and methods are most highly correlated with project performance. This PMO feature should take priority over the others studied, whether a formal PMO entity is being established or not.
  • The use of project historical archives also showed a significant correlation with project performance.
  • Pioneers in establishing PMOs are providing information and advice on essential policies and documents that should accompany the establishment and use of a PMO (see [22] for examples of such documents currently in use).

Several areas where additional research in the future might be worthwhile include:

  • There were indications that PMOs have been adopted more rapidly in the newer technological areas (e.g., IT) than in the older, more mature industries. This phenomenon requires further inquiry to determine its validity and whether the source is simply an artifact of the research design or the nature of the survey populations.
  • PMOs in early stages of their use may not be fully representative of what will finally unfold in later years. It may be necessary to design a research protocol that is based on the examination of PMOs after they have been in operation for some considerable period of time and accumulated a significant data base. Moreover, the use of detailed case studies would complement the broader survey approach used in this study.
  • A number of disparities emerged in the findings when comparing the different populations. The major onewas with respect to the value of a PMO in contributing to project performance as seen by organizations that have PMOs and those that do not. Additional researchmay help illuminate further correlates with these reported performance differences.


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Management Science Department, School of Business and Public Management, The George Washington University

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