Modeling the Capabilities of High-Performing Project Management Offices in General Contracting Companies

Abstract

The contemporary literature emphasizes a need to delve into how project management offices (PMOs) can be effectively operated in construction organizations by embedding integrated project management rather than relying on stand-alone project controls. However, capabilities for running high-performing PMOs in this complex industry are still unknown to this growing body of knowledge, which is considered a barrier to the realization of their full potential. To address this gap, the current research explores a factor structure for core capabilities using a cross-validation method with survey data from 395 experts in general contracting organizations. The results revealed that the five-factor measurement model encompasses constructs of competent human resources and supportive culture, strategic alignment, delivery support, knowledge management, and leveraging organizational capabilities. This study extends the current literature by establishing a new measurement model explaining the dimensionality of PMO capabilities.

Keywords

project management general contracting capability model project management office (PMO)factor analysis

Introduction

Notwithstanding the benefits of project management offices (PMOs), which have been frequently discussed in the literature, recent research advocates that structural and functional barriers hinder their full potential (Sandhu et al., 2019). The outstanding contribution of project management oversight structures, such as PMOs, to the value chain of business, has yet to be demonstrated by addressing project management challenges. Leaving existing barriers unresolved may eventually lead to PMO failure, which means not delivering the expected outcomes set by stakeholders in the early setup stages. It is noteworthy that the term “PMO failure” is different from “transition,” which involves major reconfiguration in response to changes or business restructuring (Aubry et al., 2010b; Almansoori et al., 2021). Facts from industry surveys have revealed a 75% average failure rate, with a significant portion of practitioners (47%) considering the PMO as an overhead cost (Raharjo et al., 2018) that adds more bureaucracy while making limited contributions to the value chain of business (Pellegrinelli & Garagna, 2009). The weak PMO performance may render poor performance of projects and portfolios, failing to meet predefined targets (Mahabir & Pun, 2022).

Unsatisfactory outcomes emanate from poor functioning or inappropriate configuration of such entities due to inadequate understanding of core capabilities. Since the PMO value is hard to define and quantify, identifying the capabilities is beneficial to determining how these units can deliver real value to frontline business processes rather than only relying on maintaining organizational routines (Singh et al., 2009). In the project management literature, the term “capability” globally refers to the degree of competence required to ensure an organization remains competitive in delivering projects (Zhang et al., 2020). Referring to previous studies on PMO outcomes (Bredillet et al., 2018; Paton & Andrew, 2019; Sandhu et al., 2019), the underlying assumption of this study is that there is a pattern of core capabilities governing the functioning of PMOs. If this pattern is first conceptualized and then contextualized in a certain industry, it can be used as a guide to potentially foster successful PMO outcomes in terms of delivery and organizational process assets.

The traditional definition of capabilities for attaining success refers to domains in which satisfactory results ensure high performance (Ofori, 2013), whereas it is required to incorporate dynamic capabilities to account for today’s rapidly changing environment that calls for agile reconfigurations (Ilmudeen et al., 2020). Formulating PMO capabilities from a dynamic capability view provides a long-term planning basis for setting performance targets and improving outcomes (Wuni & Shen, 2020). The correct formulation of capabilities requires an in-depth understanding of organizational, functional, and structural features, which have been theorized in the literature (Hadi et al., 2022). For example, the PMO descriptive model suggests that both functions and structural characteristics determine the PMO value (Aubry et al., 2011b), which is also influenced by organizational variables due to their strategic position in organizational structures (Pirotti, 2021). However, existing diverse perspectives have led to a fragmented body of knowledge. The PMO literature is limited in terms of a consistent theorization of PMO capabilities, since the similarities and distinctions of previous findings across independent studies have not been analyzed from a systematic perspective. Besides, there is a paucity of empirical examination of multiproject PMO capabilities in construction subsectors, which has left these questions unanswered: What are the core capabilities and how are they correlated to represent high-performing PMO features?

The research gap can be posited as the lack of an established construct that consistently defines and integrates domains of PMO capability. According to the literature, high-performing PMOs are assumed to be equipped with capabilities required for diagnosing project delivery reliability, capturing productivity improvement opportunities, contributing to desired business outcomes, and advancing the project management maturity level (Alvarez-Dionisi, 2017; Tahri & Kaitouni, 2017; Lavoie-Tremblay et al., 2018). It is still unclear what capabilities are effective in general contracting and how they are related in order to foster high PMO performance. Apart from these shortcomings, recent trends in contemporary research assert the demand for a shift from the pure theorization of potential characteristics to their actual examination through field surveys (Sergeeva & Ali, 2020). This gap leaves a void of empirical evidence on how to deliver effective PMOs in general contracting, which leads to the inadequacy of guidance. The diversity of the construction industry underlines the need to bridge this gap since various players, such as designers, suppliers, and general contractors, tend to approach the project management practice differently (Tan et al., 2017) and should tailor PMO capabilities accordingly. Not addressing this gap thus far can be a reason why the link between theory and practice in this industry context has not yet been fully established (Wood et al., 2016). Thus, a shift toward the effective application of PMO in general contracting has yet to reach a tipping point since it is still obscure as to how to achieve maximum potential.

In the absence of an established construct defining multiproject PMO capabilities, further elaboration is required to strengthen the link between PMO theory and practice in this target context. As mentioned previously, extensive but diverse previous research has led to the articulation of several theoretical principles that need to be examined in real-world cases of a specific industry context. Thus, adopting an integrative study in light of contextual specifications would contribute to closing the research gap. Therefore, the research questions that this study seeks to answer can be phrased as follows:

RQ1. What capabilities characterize high-performing PMOs in general contracting organizations toward integrated multiproject management? RQ2. What is the measurement model of PMO capabilities to ensure maintaining high performance?

Undertaking such a study draws on domains necessary for achieving better outcomes from running PMO units in general contracting. Besides, exploring the relationships of capabilities has both managerial and empirical implications since it provides insight into underlying patterns of drivers toward better delivery of PMO services.

Literature Review

The Theory of Dynamic Capability

Dynamic capabilities reflect an organization’s ability to integrate, build, and reconfigure resources in response to changes in the business environment (Killen et al., 2012). Such capabilities interact with lower-order operational capabilities to shape the long-term strategy of an enterprise. In light of today’s rapidly changing business ecosystem, project-based firms seek the capacity to build strategic assets, especially project management technology, knowledge, and culture as enablers of sustainable growth. They rely on the development of dynamic capabilities to transform the resource base in support of corporate strategies. Toward this pathway, necessary transformations are required to stimulate innovations and reinforce change drivers (Sergeeva & Ali, 2020) with a continuous upgrade of dynamic capabilities to sustain competitive advantage (Wang et al., 2022).

Recent research advocates that the scope of dynamic capabilities is not limited to adapting resources to the changing business settings through organizational learning but also involves an active contribution to shaping the external business environment (Bianchi et al., 2022). The major advantage of such capabilities is that they enable firms to gain a competitive advantage and outperform competitors by strengthening key resources (Hermano et al., 2022). Generally, the theory of dynamic capability reflects the importance of systemic change to start inside the context of an organization and then be extended to the external business environment. In contrast with systems theory, which relies upon internal stability and systemic homogeneity by alignment of all organizational elements, the theory of dynamic capability advocates heterogeneity by embracing proactive reconfigurations and transformations. Dynamic capabilities help prioritize conflicting priorities in organizing internal resources and adopting optimal strategies against competitors (Teece, 2018).

The importance of emphasizing dynamic capability becomes more evident in a rapidly changing and uncertain environment where it is essential to be equipped with effective organizational arrangements capable of reinforcing internal resources, as well as scanning for external opportunities. The concept of dynamic capabilities in the project management sphere mainly refers to the adaptation of project portfolios under uncertain conditions (Steen et al., 2021). PMO entities have been introduced as agents of innovation to lead organizational changes and shape the long-term portfolio of projects. If such entities are configured and function correctly, they can link not only the portfolio strategies to operations but also update internal resources in response to external growth opportunities. Since PMO structures oversee the whole portfolio of projects, they perform as a unique hub for advancing operational and dynamic capabilities. They are also capable of addressing project management complexities and uncertainties by providing an overview of operations while keeping track of business transformations.

According to previous studies, PMO entities can lead organizational change processes and adopt best practices necessary for advancing existing routines (Bredillet et al., 2018). In this way, they can serve as predictors of business performance for capturing development opportunities and competitive advantages in light of dynamic capabilities. For example, Bredillet et al. (2017) mapped out the dynamics of the interactions between PMOs and other key elements of project management systems to demonstrate how PMOs evolve in light of portfolio management and contribute to an organization’s strategies in a volatile business environment. The concept of supporting dynamic capabilities in light of PMO performance forms the underpinning tenet of this research. This article introduces the PMO as a center of excellence that not only advances project management practices but also drives strategic initiatives by reinforcing the organization’s dynamic capabilities. The next section discusses the set of capabilities enabling PMOs to actively contribute to the dynamic capabilities of a project-based firm.

Conceptual Framework of PMO Capabilities

PMO performance as a key attribute is measured regularly throughout its life cycle to ensure meeting stakeholders’ expectations and delivering value (Arumugam et al., 2013). PMO performance has been defined as the collective measure of outcomes delivered as a result of PMO services toward achieving organizational goals (Kaul & Joslin, 2018). Thus, the underlying distinction between high-performing and low-performing organizational units is the degree of contribution to core business values (Overgård et al., 2022). High-performing PMOs capture improvement opportunities to advance the project management maturity level. Such entities find the best solutions and leverage lessons learned to enable an organization to achieve its targets.

The capabilities of high-performing entities need to be explored and contextualized to provide insight into key areas enabling PMOs to deliver high value as per stakeholders’ expectations. This study relates to a project management theory introducing PMOs as an integral part of organizational project management, according to which PMOs are designed to foster a systematic view of project management in enterprises (Aubry et al., 2007). This theory assumes that PMO performance outcomes are affected by actors in a broad context. These outcomes are governed by organizational dynamism, which needs to be captured via proper change management strategies. Previous research sheds light on areas governing performance outcomes from an organizational project management perspective. Oliveira and Martins (2018) argued that the main conceptual areas include operations, people, and strategy, whereas Stettina and Hörz (2015) posited four categories of operations, people, organization, and technology. Such theorizations revealed that (1) strategy, (2) organization, (3) people, (4) operations, and (5) technology represent potential overarching areas that govern performance outcomes in the organizational project management setting. This study translates PMO features in these areas to ensure that main performance aspects are covered and the set of critical success factors (CSF) is comprehensive.

In this study, an integrative literature review synthesis was undertaken to identify potential PMO capabilities based on a two-round retrieval approach (Qiang et al., 2015). The purpose of the first round was to collect an initial set of articles using academic databases, including Google Scholar, Scopus, Web of Science, EBSCOhost, and ProQuest, which resulted in the retrieval of 1,820 articles. A total of 391 duplicate records were eliminated followed by screening out 1,339 irrelevant records using EndNote X9 software. Eligible studies include English-language peer-reviewed articles about PMO capabilities and characteristics. As a result of applying the eligibility criteria, 85 studies were selected for full-text review. Second, an open coding method using NVivo 12 software was conducted for arranging 32 variables and grouping them into five exhaustive categories. The main capabilities in these five areas are discussed as follows.

Alignment to Strategies and Business Objectives (ASBO)

The strategy domain of PMO capabilities in the context of organizational project management theory targets better alignment of PMO and projects with an organization’s strategic direction. Strategic alignment is essential for optimal PMO functioning since misalignment with business directions and lack of vertical integration may cause setbacks in pursuing its mission (Do Valle & Soares, 2014).

Leveraging Organizational Capabilities (LOC)

The organization domain of capabilities in the context of organizational project management theory refers to leveraging enablers, such as decision-making authority, toward robust project governance. Effective governance of multiple construction projects requires setting clear baselines based on a consistent methodology that clarifies responsibilities at different levels of control (Kutsch et al., 2015).

Competent Human Resources and Supportive Culture (CHRSC)

Human-related capabilities mainly refer to (1) the development of project management competencies (Andersen et al., 2007) and (2) maintaining supportive human relations (Aubry et al., 2007; Julian, 2008). From the perspective of the former, PMO performs well when it is teamed up with professionals of high project management competence under the direction of a qualified leader (Raharjo et al., 2018).

Effective Support of Project Delivery (ESPD)

The operations domain refers to the lowest level of the organizational project management pyramid. PMOs contribute to this level by overseeing activities to ensure on-target delivery. Construction projects are resource intensive due to the variety and quantity of resources being employed (Khattak & Mustafa, 2019; Trinh & Feng, 2019).

Effective Information and Knowledge Management (EIKM)

The term technology in organizational project management settings can be translated as the state-of-the-art infrastructure necessary for managing knowledge and information across projects (Sergeeva & Ali, 2020). Effective use of knowledge-sharing tools enables PMOs to bridge knowledge flow gaps between operational and strategic management levels (Hadi et al., 2022). Table 1 summarizes the variables associated with these five capabilities.

Table 1.

PMO Capabilities Retrieved from the Literature

Capabilities	Variables	References
1. ASBO	Project portfolio management (V11)	Qi et al., 2014; Widforss & Rosqvist, 2015; Barbalho et al., 2017; Bredillet et al., 2017; Oliveira & Martins, 2018; Ko & Kim, 2019
	Agility in responding to changes (V23)	Aubry et al., 2008; Pellegrinelli & Garagna, 2009; Singh et al., 2009; Aubry et al., 2010b; Ward & Daniel, 2013; Bredillet et al., 2018; Kaul & Joslin, 2018
	Tracking project benefits (V13)	Artto et al., 2011; Ozguler & Yilmaz, 2017; Sandhu et al., 2019
	Strategic monitoring of projects (V8)	Müller et al., 2013b; Ko et al., 2015; Parchami Jalal & Matin Koosha, 2015; Barbalho et al., 2017; Philbin, 2018
	PMO alignment with business direction (V21)	Phan, 2015; Oliveira et al., 2017; Lavoie-Tremblay et al., 2018; Raharjo et al., 2018; Paton & Andrew, 2019
	Benchmarking best practices (V4)	Artto et al., 2011; Aubry & Hobbs, 2011; Do Valle & Soares, 2014
2. LOC	Continuous improvement (V7)	Walker & Christenson, 2005; Aubry et al., 2010a; Antonio Martins & Ramos Martins, 2012; Kaul & Joslin, 2018; Lacruz & Cunha, 2018
	Compliance with methodologies (V6)	Wood et al., 2016; Oliveira et al., 2017; Lavoie-Tremblay et al., 2018
	PMO authority (V24)	Hobbs & Aubry, 2008; Singh et al., 2009; Do Valle & Soares, 2014; Phan, 2015; Ershadi & Atashfaraz, 2016; Lavoie-Tremblay et al., 2018
	Top management commitment (V31)	Schibi, 2013; Spalek, 2013; Spalek, 2014; Parchami Jalal & Matin Koosha, 2015; Lavoie-Tremblay et al., 2018
	Governance structure (V10)	Kutsch et al., 2015; Parchami Jalal & Matin Koosha, 2015; Aubry & Brunet, 2016; Philbin, 2018
	Organizational communications (V29)	Aubry et al., 2010a; Aubry et al., 2011a; Unger et al., 2012; Pemsel & Wiewiora, 2013; Tsaturyan & Müller, 2015; Oliveira et al., 2017; Carden & Brace, 2022
	Methodology development (V5)	Dai & Wells, 2004; Singh et al., 2009; Aubry et al., 2010a; Phan, 2015; Philbin, 2016
	PMO visibility (V22)	Letavec, 2007; Pellegrinelli & Garagna, 2009; Aubry et al., 2011b; Spalek, 2013; Spalek, 2014; Raharjo et al., 2018
3. CHRSC	Project management culture (V30)	Hurt & Thomas, 2009; Singh et al., 2009; Aubry, 2015; Parchami Jalal & Matin Koosha, 2015; Phan, 2015
	Project management training (V1)	Hurt & Thomas, 2009; Haddad, 2014; Kiani et al., 2015; Oliveira & Martins, 2018; Philbin, 2018
	Stakeholder engagement (V32)	Aubry & Hobbs, 2011; Haddad, 2014; Wood et al., 2016; Philbin, 2018
	PMO skill set (V27)	Andersen et al., 2007; Hurt & Thomas, 2009; Singh et al., 2009; Lavoie-Tremblay et al., 2018
	PMO leadership (V28)	Hurt & Thomas, 2009; Singh et al., 2009; Müller et al., 2013a; Raharjo et al., 2018
	Project management competencies of the PMO staff (V26)	Singh et al., 2009; Zhang et al., 2011; Ward & Daniel, 2013; Ershadi & Atashfaraz, 2016
	PMO sponsorship (V25)	Andersen et al., 2007; Schibi, 2013; Spalek, 2014
4. ESPD	Project problem-solving techniques (V18)	Hurt & Thomas, 2009; Singh et al., 2009; Artto et al., 2011; Pansini & Terzieva, 2013
	Project resource allocation (V15)	Hobbs et al., 2008; Aubry et al., 2010a; Qi et al., 2014; Ko et al., 2015; Van Der Linde & Steyn, 2016; Paton & Andrew, 2019
	Project resource productivity (V16)	Aubry & Hobbs, 2011; Pansini & Terzieva, 2013; Santos & Varajão, 2015; Tales G. A. et al., 2016
	Managing interfaces with partners (V9)	Artto et al., 2011; Parchami Jalal & Matin Koosha, 2015; Braun, 2018
	Risk management (V12)	Pellegrinelli & Garagna, 2009; Aubry & Hobbs, 2011; Qi et al., 2014; Tales G. A. et al., 2016
	Mentoring project managers (V17)	Dai & Wells, 2004; Hobbs et al., 2008; Singh et al., 2009; Kiani et al., 2015; Parchami Jalal & Matin Koosha, 2015
	Postdelivery project review (V19)	Desouza & Evaristo, 2006; Artto et al., 2011; Pansini & Terzieva, 2013; Wood et al., 2016; Oliveira et al., 2017
5. EIKM	Introducing project management tools (V3)	Spalek, 2012; Müller et al., 2013b; Kutsch et al., 2015; Widforss & Rosqvist, 2015; Van Der Linde & Steyn, 2016
	Providing project archives (V2)	Dai & Wells, 2004; Desouza & Evaristo, 2006; Spalek, 2012; Kiani et al., 2015; Hadi, 2022
	Timely and verified project reporting (V14)	Spalek, 2012; Unger et al., 2012; Qi et al., 2014; Phan, 2015; Philbin, 2018
	Project knowledge management (V20)	Pellegrinelli & Garagna, 2009; Müller et al., 2013b; Schibi, 2013; Kutsch et al., 2015; de Nadae & de Carvalho, 2017; Braun, 2018; Martinez Sanz & Ortiz-Marcos, 2019; Hadi et al., 2022

Methods

Sample

This empirical study was conducted in New South Wales (NSW), Australia, and the research design is based on methods adopted by prior studies in the construction project management discipline (Kock & Gemünden, 2021; Naji et al., 2022). The steps for developing the measurement model include: (1) pretesting the conceptual framework, (2) exploratory factor analysis, and (3) confirmatory factor analysis. Thus, sampling in this study was conducted in three steps. The first step of sampling involves the selection of 20 experts to review the conceptual model, validate the content, and suggest any important factors missing from the list of factors. The selection criteria at the pretest stage include (1) project management practitioners with (2) at least 3 years of experience in PMOs of general contracting organizations. These experts were sourced from a list of members of an Australian professional body (Infrastructure Partnerships Australia) and were selected using purposive sampling. Twenty practitioners participated in the pretest process, including 12 PMO directors and 8 PMO analysts. Forty percent of these participants had more than 15 years of experience, 40% with 11 to 15 years, and 20% with 5 to 10 years of job tenure. They confirmed the validity of the 32 variables and enriched the content of the questionnaire by suggesting six more context-specific variables to be included in the questionnaire.

The survey study involves an exploratory phase followed by a confirmatory phase (Hair et al., 2013). For this purpose, first, a list of 56 general contracting organizations in NSW was sourced from the database of Infrastructure Partnerships Australia. These companies represent large construction organizations with branches and offices across the NSW state that run projects in a variety of construction subsectors. Invitations were sent to these organizations and a total of 39 out of 56 firms (70%) accepted to participate. Initially, a list of eligible participants in these 39 organizations was developed. Since this study adopted a cross-validation approach, this list was partitioned into two independent samples and the link of the questionnaire was shared with the eligible staff in each group; the first sample (10 organizations, 590 eligible staff) for the exploratory pilot survey and the second sample for the main confirmatory survey (29 organizations, 1,350 eligible staff).

To minimize the risk of bias and ensure that suitable participants are getting involved, this study followed a two-step recruitment process. The target cohort of this study includes experts who meet two selection criteria: (1) construction project management practitioners who preferably hold international project management certifications; and (2) individuals who possess the experience of working with a firm-level PMO in the general contracting sector (for more than three years). Therefore, in the participating organizations, eligible participants who meet these two selection criteria were selected and the whole sample was divided into two subsections for undertaking the cross-validation survey method. The smaller part of the sample was selected for the pilot study and the larger part was adopted for the main survey.

The profiles of respondents in the survey study revealed their significant industry experience and qualifications. Altogether, 66% of respondents’ companies (39 general contractors) are owned by the private sector and the remaining 34% by the public sector. Among the selected companies, the most frequent construction business sectors respectively include building (commercial, institutional, industrial, and residential), highways and bridges, railway and subway, mine infrastructure, energy infrastructure, and dams/canals (Table 2).

Table 2.

Profiles of Survey Respondents

Characteristics	Exploratory Survey			Confirmatory Survey
Organizational position	Executive	9	8%	23	8%
	PMO manager	20	19%	56	20%
	Project manager	38	35%	96	33%
	Project planner	25	23%	43	15%
	PMO analyst	11	10%	30	10%
	Other	5	5%	39	14%
Construction project management experience	More than 15 years	33	30%	123	43%
	11–15 years	30	28%	71	25%
	6–10 years	32	30%	54	19%
	3–5 years	13	12%	39	13%
Qualifications	Doctorate	3	3%	7	2%
	Master’s degree	37	34%	120	42%
	Bachelor’s degree	68	63%	160	56%
Name of PMO entity in the respondent’s organization	Project management office	52	48%	138	48%
	Delivery management office	26	24%	76	26%
	Planning department	20	19%	22	8%
	Other	10	9%	51	18%

Measurement Model

The three steps of measurement model development have been explained as follows.

Step 1. Pretest the Conceptual Framework and Develop a Questionnaire

This step involves obtaining the viewpoints of a group of industry experts on the conceptual framework before the main survey. This step helps to eliminate errors and ensure the content validity of the framework by obtaining the initial viewpoints required for formulating a valid questionnaire. As a result of the pretest, a questionnaire was designed based on the 32 synthesized variables in the conceptual framework (V1 to V32), as well as the complementary factors proposed by industry experts (V33 to V38). The main research instrument is this self-administered questionnaire, the content of which was validated to ensure the clarity and completeness of all questions (Ornstein, 2013).

In the validated questionnaire, participants were asked to rate the importance of these 38 variables from a general contracting perspective based on a Likert scale using five choices: not important (1), slightly important (2), moderately important (3), important (4), and very important (5). An exploratory pilot study should be conducted to test whether the suggested conceptual factor structure fits into the data-driven categorization of the correlated variables (Abdullahi et al., 2015). The questionnaire includes three sections: (1) introduction and definition of key terms (PMO, capabilities, construction contracting), (2) questions about the importance of each of the 38 variables based on a Likert scale, and (3) demographic questions.

Step 2. Exploratory Factor Analysis

The exploratory factor analysis (EFA) aims to explore a preliminary structure out of survey data so the potential constructs can be identified. In the cross-validation method, it is recommended that an exploratory data collection for EFA be carried out on a smaller sample of at least 100 individuals (Hair et al., 2013) followed by the main data collection from another independent sample for running the confirmatory factor analysis (CFA) (Abdullahi et al., 2015). First, an exploratory survey was conducted based on the first version of the questionnaire (38 variables) by obtaining responses from respondents in the first part of the sample. In this survey, a total of 112 responses were obtained indicating a response rate of 19% among the eligible staff who received the invitation email. After screening the dataset, 108 completed responses underwent EFA analysis using IBM’s SPSS Statistics 25 software to purify items and identify a factor structure.

To interpret the EFA outcomes and adjust the initial list of variables in the conceptual model, 10 interviews were conducted and experts’ feedback was obtained. The selection criteria at this stage include project management practitioners with at least three years of experience in PMOs of general contracting organizations. These interviewees were also sourced from the previously mentioned list of members of an Australian professional body that was used in the pretest process using purposive sampling to select the most experienced practitioners. This step resulted in confirming the EFA outcomes to retain 35 variables, remove three uncorrelated variables, and rearrange two variables in the five-factor structure.

Step 3. Confirmatory Factor Analysis

In this step, confirmatory factor analysis was conducted to test the measurement model. The hypothesized constructs extracted via exploratory factor analysis are to be adjusted and established using the larger part of the sample. A total of 297 responses were obtained from the staff who received the invitation email indicating a response rate of 22% at this stage. After screening the dataset, 287 completed responses were selected for confirmatory data analysis. Regarding the adequate sample size and the normal distribution of data, the covariance-based structural equation modeling (CB-SEM) using IBM’s SPSS AMOS 25 software was conducted to test the hypothesized model. This software uses the maximum likelihood method of estimation as a robust method for measuring the structural path coefficients. The main data collection was followed by applying the CB-SEM to confirm the measurement model using confirmatory factor analysis (CFA) (Lin et al., 2019).

Results

The Result of the Pretest

The empirical stages of the research begin with pretesting the conceptual model followed by a pilot survey and the main survey. Although this study followed an integrative approach to synthesize all of the PMO capabilities discussed in the scholarly literature, the pretest step can further enrich the conceptual framework by including any important capability variable that has not been discussed in previous studies. The experts engaged in the pretest phase are highly experienced professionals (a majority of participants had over 10 years of professional project management experience in the construction industry) who can provide insight into any variables that are not included in the literature review and research. The conceptual framework was reviewed by these experts so that they can validate the applicability of these variables in the context of the construction industry and suggest any missing variables to be added to the framework. Experts reviewed the list of variables and suggested new variables to be included. Therefore, the content validity of V1 to V32 was confirmed and six new variables were added to the five-factor structure (V33 to V38 in Figure 1) as follows:

V33. Integrated team performance evaluation: Establishing coherent mechanisms for evaluating the project team’s performance was suggested by three experts. It was suggested that an effective PMO in the target context needs to be capable of evaluating team performance.

V34. Manage project interdependencies: Managing dependencies among projects to support on-target delivery was suggested by two experts. It was asserted that projects’ dependencies should be one of the concerns of PMOs in general contracting organizations.

V35. Preproject evaluations: Participating in the evaluation and selection of projects was asserted by two experts. Although senior managers are responsible for selecting projects, an effective PMO must contribute its experience to the evaluation of potential projects.

V36. Project execution strategy: Formulating effective project execution strategies in collaboration with project leaders is key to an effective PMO. An expert proposed that a PMO is a repository of best practices and should offer the best project delivery strategies.

V37. Integrated management of project information: Integrating mechanisms of collecting, analyzing, and reporting project information was also asserted. Participants advised that effective PMOs in this context need to integrate information obtained from multiple involved project parties and consolidate the process of performance analysis.

V38. Domain knowledge: One expert suggested that domain knowledge of the PMO team can be a variable referring to the adequate knowledge of PMO staff in relevant business areas. PMO team members should not limit themselves to competence in project management knowledge areas.

Figure 1.

Adjustments in the hypothesized arrangement of observable variables (phase II).

The Result of the EFA Analysis

Following the model pretest, an exploratory data collection was conducted to test the correlations among variables and purify them to minimize possible errors in the main survey (Hair et al., 2013). Principal axis factoring and oblique rotation method were applied as the extraction and rotation method due to a significant correlation between factors in the factor correlation matrix (Gaskin & Happell, 2014), respectively. Regarding Kaiser’s criterion (1960), five retained factors had an eigenvalue of higher than one, accounting for 57.92% of the variance altogether. The explained variance after extraction for factors 1 through 5 is 23.19%, 11.93%, 9.96%, 8.27%, and 4.57%, respectively. However, the percentage variance explained by each factor after rotation cannot be reported as the oblique rotation is adopted. Eigenvalues after extraction and rotation are provided in Table 3.

Table 3.

Coefficients and Statistics of the EFA Analysis (SPSS Statistics 25 Software Outputs)

Item	M ± SD	h^2*	Pattern Coefficient		Structure Coefficient		Eigenvalues			α***
Item	M ± SD	h^2*	Factor**	Value	Factor**	Value	Initial	Extraction	Rotation	α***
V35	3.73 ± 1.12	0.625	F1	0.852	F1	0.750	9.218	8.814	6.408	0.914
V11	4.03 ± 1.04	0.702	F1	0.843	F1	0.832
V23	3.82 ± 0.99	0.582	F1	0.801	F1	0.748
V13	4.08 ± 1.04	0.595	F1	0.787	F1	0.765
V8	3.99 ± 0.99	0.584	F1	0.767	F1	0.756
V21	4.10 ± 0.99	0.658	F1	0.726	F1	0.798
V4	3.98 ± 0.98	0.549	F1	0.666	F1	0.714
V12	4.09 ± 1.04	0.537	F1	0.639	F1	0.698
V7	3.95 ± 0.91	0.657	F2	0.847	F2	0.802	4.965	4.535	5.895	0.899
V6	3.89 ± 1.03	0.507	F2	0.785	F2	0.668
V24	3.96 ± 0.97	0.623	F2	0.778	F2	0.786
V31	4.02 ± 0.97	0.651	F2	0.732	F2	0.795
V10	3.94 ± 0.95	0.525	F2	0.700	F2	0.688
V33	3.95 ± 0.99	0.457	F2	0.699	F2	0.656
V29	4.11 ± 0.96	0.613	F2	0.689	F2	0.763
V5	3.93 ± 0.96	0.450	F2	0.680	F2	0.657
V22	3.54 ± 1.08	0.377	-	-	-	-
V27	3.45 ± 1.15	0.362	-	-	-	-
V30	4.02 ± 1.07	0.732	F3	0.868	F3	0.851	4.179	3.783	6.040	0.923
V1	3.91 ± 1.06	0.752	F3	0.864	F3	0.862
V32	4.06 ± 1.03	0.745	F3	0.830	F3	0.857
V38	3.69 ± 1.01	0.569	F3	0.815	F3	0.718
V28	3.87 ± 1.00	0.652	F3	0.797	F3	0.800
V26	3.85 ± 1.03	0.649	F3	0.786	F3	0.798
V25	3.71 ± 1.12	0.409	-	-	-	-
V36	3.90 ± 0.95	0.659	F4	0.768	F4	0.797	3.508	3.141	4.519	0.784
V18	3.92 ± 1.09	0.587	F4	0.768	F4	0.757
V15	4.00 ± 0.92	0.563	F4	0.760	F4	0.735
V16	4.18 ± 0.88	0.527	F4	0.694	F4	0.696
V34	4.20 ± 0.98	0.612	F4	0.690	F4	0.736
V9	3.94 ± 0.94	0.547	F4	0.657	F4	0.719
V17	4.07 ± 0.88	0.383	F4	0.628	F4	0.609
V3	4.06 ± 0.86	0.776	F5	0.901	F5	0.876	2.155	1.735	5.380	0.885
V2	3.70 ± 0.99	0.623	F5	0.776	F5	0.753
V37	4.04 ± 0.95	0.609	F5	0.753	F5	0.777
V14	4.12 ± 0.91	0.577	F5	0.747	F5	0.738
V20	4.05 ± 0.94	0.601	F5	0.716	F5	0.770
V19	3.97 ± 0.95	0.381	F5	0.586	F5	0.609

Note: * Communality, ** Factor having the maximum correlation with an item, *** Cronbach’s alpha

The literature advocates that the minimum acceptable percentage of extraction variance depends on the study context (Adeyemi & Aigbavboa, 2022). In social sciences and management research, the explained extraction variance is commonly as low as 50% to 60% due to the interdependent nature of study variables (Pett et al., 2003; Williams et al., 2010), and the findings of prior project management studies confirm this principle. Bowen et al. (2014) reported an extraction variance of 59.4% and extracted factors explaining 58.3% of the variance in the dataset. In such cases, to ensure that the right number of factors have been extracted, it is suggested that the results be cross-checked with another factor extraction criterion (Bowen et al., 2014), since applying multiple criteria helps to avoid underextraction and overextraction of factors when reducing variables into a fewer number of dimensions (Watson, 2017). The result of applying Catell’s scree test revealed that five factors can be retained to represent the scales (Cattell, 1966). The factor loadings in the pattern and structure matrices also showed distinguishable constructs.

Pattern and structure matrices were both considered for a better interpretation of factors representing observable variables (Watson, 2017). The minimum acceptable factor loading for the correlation of variables and factors depends on the sample size. Considering the 108 responses collected in the exploratory survey, a loading cutoff of 0.5 was applied (Hair et al., 2013). According to the results, 35 out of 38 variables (92%) are appropriate indicators of their corresponding scale due to high component loading and negligible cross-loading (Tabachnick et al., 2007). The reliability of the measurement model was tested using Cronbach’s alpha scores, indicating that the overall reliability of the whole factor structure and its scales are higher than 0.7 as an acceptable level of internal consistency (Yalegama et al., 2016).

The EFA results yielded a five-factor structure almost similar to the hypothesized categorization with some modification in observable variables. To interpret and use EFA outcomes for adjusting the initial conceptual model, 10 interviews were conducted with experts, and the following changes were confirmed to be made to the hypothesized model before the main survey:

Two observable variables were loaded on different factors. Risk management (V12) and postdelivery project review (V19) were both initially categorized under effective support of project delivery. However, according to EFA results and interviewees’ interpretation, V12 is more associated with the capability ASBO since multiproject PMOs focus on managing risks from a strategic perspective and integrating project-, program-, and portfolio-level risks. Besides, V19 is associated with the capability EIKM due to the importance of this variable in the final documentation and validation of the lessons learned.

Three uncorrelated variables that were not significantly loaded on a factor were removed from the model. These cross-loaded variables include V22 (PMO Visibility), V25 (PMO Sponsorship), and V27 (PMO Skillset) which demonstrated poor factor loadings (Table 3).

Regarding the suggestion of interviewees, all seven hypotheses were confirmed to be included in the final hypothesized model that should be established in the main survey (Figure 1).

Confirmatory Factor Analysis

Results of the CFA analysis revealed that the minimum factor loading of 0.60 for all variables was achieved (Hair et al., 2013). The fitness indexes indicate how fit the observable variables are in measuring their relevant scale in terms of absolute, incremental, and parsimonious fit. The absolute fit of the model was examined using the root mean square error of approximation (RMSEA), standardized root mean squared residual (SRMR), goodness of fit index (GFI), and AGFI indices. The value of RMSEA for the model is 0.025, which meets the threshold of 0.05 (Fabrigar et al., 1999). The value of SRMR is 0.042, which lies in an acceptable range (less than 0.05). The GFI and adjusted GFI in the model are 0.881 and 0.864, respectively, which are above the limit of 0.85 (Schermelleh-Engel et al., 2003; Maruish, 2004). The incremental fit of the model was achieved according to the comparative fit index (CFI = 0.983) and Tucker-Lewis index (TLI = 0.981), which should exceed 0.9. The parsimonious fit of the model is acceptable according to chi-square/degrees of freedom (CMINDF = 1.174) with an acceptable value of less than 2 (Meade et al., 2008), parsimonious goodness-of-fit index (PGFI = 0.769), parsimonious normed fit index (PNFI = 0.827), and parsimonious comparative fit index (PCFI = 0.908) with an acceptable value of more than 0.5 (Mulaik et al., 1989).

The reliability of a measurement model can be assessed using the composite reliability (CR) considering a value above 0.7. Internal consistency was also measured using Cronbach’s alpha coefficient, which is higher than 0.7. The convergent validity was achieved, as the average variance extracted (AVE) for constructs is higher than 0.5 (Hair et al., 2013). The discriminant validity was achieved since the correlations between constructs are less than 0.85, with no correlation larger than the square root of the AVEs (Liu et al., 2017). Calculations in AMOS revealed that the regression p-values for all scales are less than 0.001 (two-tailed) and, thus, highly significant (Table 4). The result of the correlation analysis indicated that all five factors are closely interrelated, implying that all five domains need to be taken into account for higher performance (Figure 2).

Figure 2.

The standardized measurement model of PMOSF (AMOS 25 software output — CFA).

Table 4.

Analysis of the Measurement Model of PMO Capabilities (AMOS 25 Software Outputs)

Factors	Standardized Correlations					Items	AVE	CR**	α***	Variance	Standard error	Critical ratio
Factors	1	2	3	4	5	Items	AVE	CR**	α***	Variance	Standard error	Critical ratio
1. ASBO	(0.731)					8	0.535	0.902	0.901	0.670*	0.091	7.326
2. LOC	0.680*	(0.714)				8	0.510	0.892	0.888	0.536*	0.095	5.618
3. CHRSC	0.706*	0.643*	(0.751)			6	0.564	0.886	0.885	0.634*	0.082	7.720
4. ESPD	0.577*	0.623*	0.589*	(0.735)		7	0.541	0.891	0.890	0.461*	0.082	5.608
5. EIKM	0.601*	0.662*	0.693*	0.542*	(0.785)	6	0.616	0.906	0.905	0.620*	0.084	7.406
Overall						35	0.518	0.974	0.955

Note: Values in parentheses: the square root of AVE, *significance at p-value less than 0.001, ** Composite Reliability, *** Cronbach’s alpha for each construct

Discussion

The establishment of complex engineered systems in construction contracting organizations depends upon robust project-based monitoring and control systems (Maier-Speredelozzi & Still, 2021). This study revealed that PMOs as a central hub for project monitoring and control can benefit such organizations if they are equipped with essential capabilities. Examination of such PMO capabilities revealed that high performance can be achieved by maintaining the coherency of all five capabilities. An analysis of the correlations between constructs revealed that the value of a high-performing multiproject PMO is (1) created in the light of human competence and support (CHRSC), (2) reinforced by leveraging organizational capabilities (LOC), and (3) delivered in the form of effective project support (ESPD), strategic alignment (ASBO), and information and knowledge management (EIKM). Figure 3 highlights the intersection of PMO capability areas and the five main dimensions of organizational project management. PMOs play a central role in fulfilling the targets of organizational project management via capturing improvement opportunities and taking initiatives supported by proper change management plans.

Figure 3.

Translation of PMO capabilities in the context of organizational project management.

Rethinking PMO Capabilities for Higher Impacts

The new knowledge presented in this article enriches the organizational project management theory in three ways. First, this study encourages a paradigm shift in multiproject PMO practice by extending their scope of influence from people, technology, and operations, which have been overemphasized in previous studies (Spalek, 2013; Ward & Daniel, 2013) to domains of strategy and organization. Although people, technology, and operations are key domains for performance outcomes, the high performance of PMOs cannot be guaranteed without active involvement in corporate decision-making and reflection of operational feedback into business policies. Integration of all five of these domains is key to enhancing PMOs’ contributions to frontline business outcomes.

Second, the results support the theory of dynamic PMO behavior in the context of project-based organizations (Bredillet et al., 2018). However, to account for a rapidly changing environment, high-performing multiproject PMOs need to continuously upgrade their resources and act agile in response to changes. This study found three variables, including agility (V23), benchmarking (V4), and alignment (V21) constitute pillars for reflecting dynamic aspects of the PMO capability

Third, since general contractors simultaneously run multiple projects of different sizes with variations that impose changes to the configuration of higher-level PMOs, emerging challenges, such as competition of projects over limited resources, escalated risks, and complex project interdependencies, need to be addressed (Oliveira et al., 2017). This study confirmed that three variables, including resource allocation (V15), strategic risk management (V12), and cross-project coordination (V34), act together toward addressing the challenges of general contracting enterprises in multiproject management

Implications of the PMO Capability Model

While prior studies theorized generic features of PMO as a broad range of possible theoretical options to be chosen for various contexts, this study took one step forward by testing PMO capabilities and establishing a coherent industry-specific capability model for general contractors. The unique theoretical implications of this model can be elucidated from the perspective of five dimensions. From the perspective of strategic alignment (ASBO), the findings modified this preconceived belief that PMO merely focuses on project-level risk assessment (Dai & Wells, 2004), since a strategic perspective in risk management should be adopted to ensure integrated analysis of both project-level and portfolio-level risks and systematically address their root causes. The second capability, LOC, further elaborates on this tenet that the function of PMO is more than a passive means of transferring best practices (Walker & Christenson, 2005). The findings challenge this view that PMOs may undertake a passive role by relying on creating a project management repository without enforcement authority (Desouza & Evaristo, 2006) because they would not guarantee high-performance outcomes. The third capability, CHRSC, acts as an exogenous enabler for PMO functioning. Prior studies asserted that the project management expertise of staff constitutes a basis for offering high-quality services (Oliveira & Martins, 2018). However, this study asserted that project management expertise can be effective when it is coupled with adequate domain knowledge on the construction business sector. The fourth capability, ESPD, reveals this fact that apart from the supporting functions, such as mentoring and problem-solving (Dai & Wells, 2004, Desta et al., 2006), high-performing PMOs also address cross-project interdependence. The fifth capability, EIKM, reconciles two PMO facets, including knowledge management and information management (Martinez Sanz & Ortiz-Marcos, 2019). These facets were studied separately in prior studies, whereas the results revealed that their coherency should be maintained due to the intense information exchange among many subcontractors and the new knowledge that is being generated in such interactions. Besides, this study extends the existing theories that PMOs leverage new information management technologies to fulfill their mission as generators of knowledge and innovation owing to their central position in the project management environment (Hadi, 2022).

Limitations and Future Research

Adopting survey questionnaires as research instruments for data collection from individuals can imply the possibility of self-reporting bias. The set of elicited variables underwent a pretest and pilot survey to minimize bias and ambiguity. This study focuses only on general contracting, whereas future research is encouraged to replicate this study for other construction subsectors. There are still gaps in this area of the project management literature in terms of PMO capabilities in other construction subsectors, such as engineering consultants and suppliers, which should be addressed by further elaborations in this area to allow a cross-sector comparison of the specifications characterizing high-performing PMOs.

Another limitation related to the development of the conceptual framework elicited from the literature review. The data and findings for some of the articles reviewed for the development of this framework pertain to a specific country or region. The inclusion of findings from different countries enriched the conceptual framework to make sure different, possible perspectives and conditions have been incorporated into the framework. However, the generalizability of the research findings needs to be achieved. The extensive two-phase exploratory and confirmatory survey from a broad range of 395 participants helped to address this limitation concerning the generalization of the findings.

Although this study was undertaken in Australia, the construct of PMO capabilities refers to a universal concept potentially applicable to other regions. Meanwhile, future research is encouraged to further examine the proposed construct in other contexts and regions and compare the outcomes. Regarding the insight gained into the specification of construction PMOs in this empirical study, other avenues for future research are recommended to (1) rethink PMO features concerning its evolution in light of the dynamism of the construction business using system dynamics modeling, (2) examine the role of PMOs in the adoption of new technologies, such as building information modeling, and (3) evaluate the application of agile PMO principles in the construction industry.

Conclusion

This study explored the dimensionality of the PMO concept and identified capabilities describing how to deliver high-performing multiproject oversight entities in general contracting. Toward making a more useful contribution, the scope of the study was narrowed down in terms of both the PMO type and context by focusing on multiproject PMOs in general contracting. The results of the CB-SEM revealed that PMO capabilities can be translated from the perspective of five broad domains of strategy, organization, people, operations, and technology that govern organizational project management performance. The variables associated with the people domain constitute the most underpinning enablers for running high-performing PMOs under the mediation of organization-related variables. The capabilities related to technology, operations, and strategy are considered underpinning variables, since the quality of PMO services in these triple areas significantly hinges upon the level of human competence and relations, as well as access to adequate organizational resources. A new explanation of how organizational capabilities can be leveraged to transform a PMO from a passive project management library into an active entity with enforcement authority was offered.

It is concluded that a high-performing PMO integrates intense horizontal and vertical communications among involved parties in the multiproject environment and considers interfaces with other disciplines as well as the interests of key stakeholders before making decisions. Such a unit integrates reporting mechanisms rather than creating multiple stand-alone reporting systems that lead to confusion for involved parties. This unit draws on know-how obtained from cooperation with various subcontractors to make better decisions and adopt more effective delivery strategies. This study contributed to filling the gap in understanding how structural, functional, and organizational features can be conductive to higher performance of PMO.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Mahmoud Ershadi

Author Biographies

Mahmoud Ershadi, PhD, is Researcher in Construction Management at the School of Architecture and Built Environment, University of Newcastle, Australia. He is a Project Management Professional (PMP)^® and PMI Risk Management Professional (PMI-RMP)^® certification holder with over 10 years of project planning experience in a variety of industries. He has published in peer-reviewed journals and his research interests include organizational project management, construction management, and infrastructure delivery. He can be contacted at Mahmoud.ershadi@uon.edu.au

Marcus Jefferies, PhD, is Head of the Construction Management Discipline at the School of Architecture and Built Environment, University of Newcastle, Australia. He has successfully supervised several postgraduate students and published articles on public–private partnerships (PPPs), risk assessment, contract administration, and construction project management in leading international journals. He can be contacted at Marcus.jefferies@newcastle.edu.au

Peter Davis, PhD, is Professor of Construction Management at the School of Architecture and Built Environment, University of Newcastle, Australia. He has equal industry and academic experience in the construction management discipline and has published in academic publications. He has directed several research projects in the construction and infrastructure sector. He can be contacted at Peter.davis@newcastle.edu.au

Mohammad Mojtahedi, PhD, is Senior Lecturer in Construction Management at the Faculty of Built Environment, University of New South Wales, Australia. He has established his research in the areas of risk management, building construction management and project planning, optimization, and decision-making. He is a Project Management Professional (PMP)^® certification holder and an active member of several professional bodies. He was awarded two research excellence awards from Engineers Australia in 2012 and 2013. He can be contacted at M.mojtahedi@unsw.edu.au

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