Sage Journals: Discover world-class research

Abstract

Scale is commonly deployed as a descriptor in the extant project management literature without an associated discussion of what this means. Following a literature review and synthesis of 172 papers we identify three findings. First, most papers addressed the foundational concept of scale obliquely, suggesting a conceptual gap. Second, where scale is mentioned, it is mostly in association with large-scale projects and megaprojects. There are few papers discussing small- to medium-scale projects. Third, the limited categorization of scale is linked to limited discussion of its managerial implications. We then synthesize this research to establish a project scale framework and position future research avenues.

Keywords

scale scaling large-scale projects megaprojects literature review research agenda

Introduction

The scale of a project is frequently used as a meaningful descriptor (Söderlund, 2010; Maier & Branzei, 2014; Davies, 2017). Projects are characterized as large (Toor & Ogunlana, 2010; Lewis et al., 2023), small (Sohail et al., 2002), or—increasingly—mega (Flyvbjerg, 2014; Denicol et al., 2020), with the apparent implication that such designations are both important and reflect an accepted set of constituent properties. Despite this, there appears to be limited work in the project management literature offering clarity regarding definitions, or scales, for project scale (Hetemi et al. 2021; Ansar et al., 2017; Lycett et al., 2004). There have been some attempts at precision; Flyvbjerg (2014, p. 6), for example, suggests that megaprojects are “large-scale” and “complex” ventures, and this is defined as costing “more than US$1 billion, taking many years to develop and build, involving multiple stakeholders, is transformational and will impact millions of people.” Some scholars have offered (broader) reflections on the scale of projects as part of their study. Jørgensen et al. (2017, p. 1576), for example, qualify their findings by stating: “the small size of projects in this study [may] make the results not relevant for larger projects.” Söderlund (2010) reports the success of large-scale transformation projects to be contingent on high-quality project management/knowledge integration practices. Other work, such as that by Artto et al. (2009, p. 1), asks related questions such as “are programs just scale-ups of projects, or do they represent something unique?” but the most common approach in the extant project management literature is to invoke scale by implication (e.g., Maier & Branzei, 2014; Aranda-Mena et al., 2009). Consequently, the literature largely fails to consider scale as a focal concern.

In this article, we argue this is an important conceptual and pragmatic limitation. Conceptually, for example, what are the dimensions of project scale? When academics or practitioners describe a project as large, does this refer to physical size, financial commitment, or something else? How do these dimensions interact? What are the meaningful boundaries among small-, medium-, and large-scale projects? Pragmatically, all project management practices are contingent on an accurate diagnosis of current and potential scale (de Waard et al., 2014). Lacking a meaningful measure could therefore lead to scale–practice misalignment (e.g., inappropriate or insufficient modularization) with significant implications for the management of project delivery and performance (Artto et al., 2009). Given this context, our article presents, to the best of our knowledge, the first systematic review and synthesis (Chbaly & Brunet, 2023) of academic literature on: (1) scale (and its dimensions) and (2) scale-related management practices in the project management field. We analyzed 172 papers from the leading project management journals that explicitly mentioned scale with a view to igniting a debate around what we believe to be an important project construct (Zerjav et al., 2023).

Our contributions to prior studies are threefold: First, the review confirms that a significant majority of papers (90%) address scale by implication or assumption and, consequently, there is a definitional and conceptual gap. We address this gap through the presentation of a guiding framework to classify and evaluate projects of differing scales. Second, most mentions of scale (65%) are associated with “large”-ness, with very few papers (10%) describing small(er) or midsize projects. We consider the scholarly and practical implications of this finding. Third, an inconsistent and underdeveloped basis for categorizing scale raises important questions for related managerial practices. The article concludes with future research opportunities centered on the themes of: (1) scale and value; (2) scale and scaling (up and down); (3) scale and time/temporality; and (4) scale and other project constructs.

Methodology

Literature reviews are a vital research tool to establish key themes and relationships among concepts, thus driving and enabling more structured future research efforts (Söderlund, 2011; Derakhshan et al., 2018; Denicol et al., 2020; Alzoubi et al., 2023). We adopted a systematic literature review (SLR) and synthesis approach to examine the published literature on project scale (Denyer & Tranfield, 2009), because of its ability to cope with substantial amounts of information, and support and derive meaning in a transparent and reproducible fashion (Tranfield et al., 2003; Crossan & Apaydin, 2010; Suhonen & Paasivaara, 2011; Lappi et al., 2018; Wang et al., 2022). Following Denyer and Tranfield (2009) as well as Denicol et al. (2020), we separated the literature review into distinct stages to address researcher bias, ensure procedural transparency, and ensure outcome replicability (Klein & Müller, 2020).

Selecting the Sample Population

The first activity involved identifying and reviewing the core project management journals and, following prior SLRs in project management (e.g., Derakhshan et al., 2018), we selected: (1) Project Management Journal® (PMJ); (2) International Journal of Project Management (IJPM); (3) International Journal of Managing Projects in Business (IJMPB); and (4) Project Leadership and Society (PLS). While we fully appreciate that project management scholarship does not reside solely in project management journals, but also in fields such as operations management (e.g., Ramasesh & Browning, 2014), construction management and economics (e.g., Lahdenperä, 2012), and innovation/general management (e.g., Koch & Schermuly, 2021), we wanted in the first instance to understand how scale has been addressed in core project management journals. We excluded project management books (and book chapters) from this SLR (following recent reviews such as those from Denicol et al., 2020 and Papadonikolaki et al., 2022) as they are often categorized into gray literature (Adams et al., 2017), not systematically included in the databases commonly used for SLRs (Denicol et al., 2020), and often not subject to the same detailed peer-review processes as journal publications (Papadonikolaki et al., 2022). Conference proceedings/papers were also excluded (Cantarelli & Genovese, 2021) for similar concerns regarding variable consistency and quality of peer-review processes (Gunasekaran et al., 2015).

In narrowing our sample population, we were able to include all journal issues in all the key project management journals (from 1982 to 2022). This ensured that any subsequent argumentation and analysis built on the entirety of the core project management literature. Both the Web of Science (WoS) and Scopus databases, the world’s largest and leading scientific citation search platforms (Li et al., 2018), were used (following other recent SLRs in the project management field, e.g., Denicol et al., 2020; Papadonikolaki et al., 2022).

Search Processes

The search stage involved agreeing on key search terms (and synonyms thereof). “Scal*(e)” was selected as the primary search stem. This stem therefore included words such as “scaling” and “scaled” too. Although the differences between the terms are subtle, they do possess differing connotations (Delisle & Olson, 2004) with the expectation that “scaling” might elicit some greater discussion of temporal and processual dynamics. We chose not to use any additional search terms as common synonyms/related terms for “scale” as this could have biased our sample.

We searched for the key term(s) in the title and abstract of all journal articles. In executing the search, we found a significant number of papers (n = 172) in which scale was a focal construct (e.g., Lycett et al., 2004) or contextual entity (e.g., Hetemi et al., 2021). No papers were subsequently excluded from our sample. Web of Science and Scopus returned identical outputs. The following results, by journal, were returned: IJPM (n = 121), IJMPB (n = 32), and PMJ (n = 19). PLS returned no papers that met our search criteria.

Analysis

The analysis stage followed a three-step, iterative analytic process. First, we used a structured coding framework utilizing accepted best practice guidance (e.g., Gioia et al., 2012) to clarify the meaning of scale (Pittaway et al., 2004). More specifically, we used codes such as “large,” “small,” and “mega” to assess how scale has been defined and discussed. We found a heavy skew toward large projects. During the coding process, we examined each paper in its entirety and extracted several key pieces of information (such as industry type and geographic data) to assist the subsequent analysis. All coding was performed by the lead researcher to ensure consistency (Strauss & Corbin, 1998). Results were refined in discussion with the wider authoring team to mitigate any potential single-coder bias (Tangpong, 2011).

In step two, we analyzed the precise nature of scale definition (e.g., Dille & Söderlund, 2013; Patanakul et al., 2016). We evaluated how different papers justified their categorical labels (e.g., small, large, mega) for scale. Here, we carefully examined each paper (in full) and extracted any precise metric, which was used as justification for a project being classified as a certain scale.

In the last step, we focused on the challenges and management practices associated with project scale. Given the studies split into a large group of large designated projects (e.g., Vo et al., 2021) and a much smaller group of small designated projects (e.g., Thierie & De Moor, 2017), we separated our results by these two themes. We then applied thematic codes, such as ‘benefits’ and ‘governance,’ and observed differences in the (albeit imbalanced) small- and large-scale project groupings.

Descriptive Analysis

An overview of scale within the project management literature highlights the lack of precision in application. For example, it frequently functions as a noun, denoting the size of various inputs, outputs, processes, and (sometimes simultaneously) as a verb, relating to the action of scaling, to increase or adapt project scope or reach. We also collated all metrics that were used as justification for a project being labeled as a certain scale (Table 1). The modal descriptor for scale was the total whole life cost of the project. All figures were standardized into U.S. dollars (inflation adjusted; year: 2022) to account for variable currencies and inflation (using the U.S. Bureau for Labor Statistics inflation calculator—https://www.bls.gov/data/inflation_calculator.htm). Table 1 illustrates the imprecision that exists. To give a specific example, while Saunders et al. (2016a, 2016b) defined large-scale as US$52 million, Chan and Yu (2005)—10 years earlier—defined large-scale as US$12 billion.

Table 1.

Scale in Extant Project Management Studies

Label	Measure	Indicative Value(s)
Small	Cost	N/A
	Teams	1 team (Hobbs & Petit, 2017)
	People	≤49 employees (Müller et al., 2016)
Medium	Cost	N/A
	Teams	2–9 teams (Hobbs & Petit, 2017)
	People	50–249 employees (Müller et al., 2016)
Large	Cost	US$52 million–US$21 billion (Saunders et al., 2016a), US$110 million–US$1.6 billion (Kolltveit & Grønhaug, 2004), US$113 million (Halman & Burger, 2002), US$130 million (Patanakul et al., 2016), US$166 million–US$677 million (Carpintero & Petersen, 2015), US$200 million (Dingsøyr et al., 2018), US$274 million (Tam, 2000), US$364 million–US$855 million (Aerts et al., 2017), US$409 million (Walker & Johannes, 2003), US$526 million (Dille & Söderlund, 2013), US$570 million (Dawes, 1988), US$1.1 billion (Ling & Lau, 2002), US$12 billion (Chan & Yu, 2005),US$17 billion (Mangioni, 2018)
	Teams	10 + teams (Hobbs & Petit, 2017)
	People	≥250 = employees (Müller et al., 2016), 496 employees (Halman & Burger, 2002)
	Physical (length)	1.07–6 km (Aerts et al., 2017), 8.2 km–22.4 km (Carpintero & Petersen, 2015), 20 km (van den Ende & van Marrewijk, 2019), 21 km–34 km (Mangioni, 2018), 506 km (Hetemi et al., 2020, 2021), 2,500 km (Albonetti & Manganelli, 1984)
	Physical (area)	47–500 hectares (Harris, 2017), 125 hectares (Shen et al., 2006)
	Impact	“will impact 26 million people” (Yu & Kittler, 2012), “national impact reach” (Dille et al., 2018), “global [impact] reach” (Drechsler & Breth, 2019)
	Duration	>3 years but <12 years (Saunders et al., 2016a)
Mega	Cost	>US$1.2 billion (Flyvbjerg, 2014)
	Impact	“millions of people” (Flyvbjerg, 2014)
	Duration	“many years” (Flyvbjerg, 2014)

In addition to financial and/or physical size of a project (e.g., Toor & Ogunlana, 2010; Liu & Wilkinson, 2014; Mok et al., 2015; Aerts et al., 2017), there were also several other less common measures such as: (1) number of teams employed on a project (Hobbs & Petit, 2017); (2) number of employees being part of a project (Müller et al., 2016); and (3) number of people the project will impact (Halman & Burger, 2002). There was a strong emphasis on large project descriptors in the sample (e.g., Yeo, 1995; Floricel & Miller, 2001; Frederiksen & Davies, 2008; Ross, 2009; Pitsis et al., 2018). Another descriptive finding was that when authors referenced scale, they were mostly referring to the scale of the project—there was very limited mention of the scale of the program (Artto et al., 2009; Lycett et al., 2004) or scale of processes and tasks within a project (de Waard et al., 2014).

The specific question of how scale was managed featured in less than 15% of the articles surveyed and, where it was (Table 2) modularity was the most frequently, formally, discussed management practice (e.g., de Waard et al., 2014). We first open coded all papers within our dataset, focusing on papers that made a reference to any scale management tools/practices (thereby obtaining first-order codes). We then categorized the codes into second-order themes before arriving at an aggregate dimension. This helped to ensure transparency and assisted us to accurately represent discussions of scale management that have occurred in the literature sample.

Table 2.

Project Scale and Its Management

	How is Scale Managed?
Aggregate dimension	• Modularity
Second-order themes	• Modularity • Time • Scale and planning/control (or lack of) • Uncertainty mitigation • Complexity reduction
First-order codes (and exemplar representative studies)	• Scale and modularity (de Waard et al., 2014) • Management of scale through time (Dille & Söderlund, 2013; Hetemi et al., 2020) • Scale as consequence (Pitsis et al., 2018) • Naturally occurring and omnipresent scale (Van der Merwe, 2002; Aerts et al., 2017) • Scale and complexity/uncertainty (Santana, 1990; Charoenngam & Yeh, 1999; Vidal et al., 2011; Lu et al. 2015) • Scale as evolution(ary) throughout project life cycle (Meier, 2010)

In the last part of the analysis, we looked at the distinct challenges aligned with projects of specific scales and, given the split of the dataset, this was mostly large and some small projects (Table 3). For example, large-scale projects consider perceptions of success to a greater extent (Turner & Zolin, 2012) as they are often used as vehicles for political communication (Harris, 2017).

Table 3.

Management Challenges Across Small- and Large-Scale Projects

Challenges	Small-Scale Project	Large-Scale Project
Benefits	• No economies of scale for benefits realization (Kleiss & Imura, 2006)	• Greater understanding of the sociocultural benefits context needed (Dyer, 2017) • Harder to record and evaluate benefits (especially emergent ones) (Vo et al., 2021)
Governance	• Lack of institutional structure (for governance) (Thierie & De Moor, 2017) • Participative negotiation, speedier implementation (Sohail et al., 2002)	• Governance and organizational values alignment required (Müller et al., 2016) • Governance of risk needed (Cuppen et al., 2016) • Partnering in governance required (Liu and Wilkinson, 2014) • “Enhanced” governance required (Vo et al., 2021)
Human capital	• Variable quality concerns present for project management staff/contractors but can be overcome because of the ability to closer observe/provide regular feedback (Tam, 2000)	• “The right mix of people required” (in relation to the critical importance of specialist expertise) (Marabelli et al., 2013, p. 310) • Project managers faced with regular ethical dilemmas (Loo, 2002, 2003) • Problem-solving capability for project professionals needs to be strong (Cavaleri et al., 2012)
Project requirements	• Lower transaction costs because processes and requirements are simpler (Thierie & De Moor, 2017)	• Greater conflicts over timing of project activities (and interventions) (Dille & Söderlund, 2013) • Project has far more requirements than merely successful delivery (e.g., non-financial benefits, political consentability, part of a rigid and inflexible project management process) (Patanakul et al., 2016)
Stakeholders	• Less predictability (and more uncertainty) around stakeholder behaviors (Kolltveit & Grønhaug, 2004)	• Greater stakeholder conflicts (because of differing stakeholder wishes/intentions) (Ross, 2009; Meier, 2010) • Value cocreation by stakeholders (Dick et al., 2015) • “Perceptions” of success for stakeholders need to be considered (Turner & Zolin, 2012) • More communication and cooperation with external environment needed (to realize project ‘success’) (Hannevik et al., 2014)

Synthesis and Emerging Research Themes

This section presents the four key themes arising from the descriptive analysis: (1) conceptual gap and definitional framework; (2) why is there a bias toward large scale?; (3) modularity and project scale; and (4) time, temporality, and project scale.

Theme 1: A Conceptual Gap and Definitional Framework

Scale as a synonym for size was found in more than 80% of the articles (e.g., Tam, 2000; Ling & Lau, 2002; Walker & Lloyd-Walker, 2016). The precision in the use of the term was limited, however. For example, while some authors described projects costing US$110 million as “large-scale” (Kolltveit & Grønhaug, 2004), others considered projects costing as much as US$17 billion “large-scale” (Mangioni, 2018). We consequently argue that greater clarity is needed for future studies and therefore propose a scale framework (Table 4). To develop Table 4, we used cost as a metric. This is because cost is the modal descriptor of scale and the only shared (precise and quantitative) characteristic present across all types of projects (Ruiz Diaz, 2020). Cost as a measure, for all its limitations, allows for the ready comparison and translation of ideas, thinking, and solutions between domains and across different scales (Nordhaus, 2017).

Table 4.

Preliminary Framework to Classify Project Scale (Based on Total Net Whole Life Project Cost)

Project Scale	Small	Medium	Large	Very Large	Mega	Tera
Justification	Smaller investment, faster delivery, less regulatory oversight, generates change quicker	Elusive middle ground where very few projects are studied (with little discussion of this “scale”)	Smallest of “large-scale”. Authors do not (regularly) study less than US$100 million but study in this range often	Delivery of significant output, significant investment, and inclusion of multiple project teams	Common accepted definition present and used by multiple key project management works (“political/ media projects”)	Definition initiated (and continued) by key works/ reviews in the project management field
Cost (in US$; 2022)	<US$10 million	≥US$10 million and < US$100 million	≥US$100 million and < US$500 million	≥US$500 million and < US$1 billion	≥US$1 billion and < US$1 trillion	≥US$1 trillion
Exemplar references	Kleiss and Imura, 2006; Hobbs and Petit, 2017	Currie, 2003	Bachy and Hameri, 1997; Patanakul et al. 2016	Müller et al., 2016; Aerts et al., 2017	Flyvbjerg, 2014; Denicol et al., 2020	Flyvbjerg, 2014; Pitsis et al., 2018

We developed a six-point classification for scale (Table 4). While acknowledging the limitations of existing studies, we propose a framework using existing definitional work such as Merrow et al. (1988) and Flyvbjerg’s (2014) characterization of a megaproject. Our framework is also broadly aligned with some public agencies’ articulation of scale in projects (Institute for Government, 2017; HM Treasury, 2022). We added “tera” to our framework as Flyvbjerg (2014, p. 7) highlighted that “there is no indication that the relentless drive to scale is abating in megaproject development. Quite the opposite—scale seems to be accelerating.”

One of the unavoidable challenges in creating a scale for project scale is the inherent relativity of projects (Lycett et al., 2004); some firms commission and execute projects that cost more than the entire gross domestic product (GDP) of countries (Flyvbjerg, 2014). For example, California High-Speed Rail (CHSR) is projected to cost up to US$100 billion, greater than the GDP of more than half the countries in the world (California High Speed Rail Authority, 2022). Relativity also applies to the type of organization delivering the project. A US$10 million project for a small- and medium-sized enterprise will have different implications/challenges to a US$10 million project executed by a large multinational corporation (MNC). Similarly, other measures of scale (e.g., physical size, people/teams, duration, organization type, type of project) can offer valuable additional insights but will face the challenge of comparability across different types of projects. One intriguing future option could be to develop multiple “scales-for-scale” potentially connecting to some form of project contingency, for example, separation by: (1) infrastructure and construction; (2) government transformation and service delivery; (3) military capability; and (4) information and communications technology (ICT) project categories (cf. Infrastructure and Projects Authority, 2023).

Theme 2: Why Is There a Bias Toward Large-Scale?

Acknowledging some variability in use of the terms large and small, the skew toward large-scale descriptors was pervasive, leading us to conduct further analysis of the meaning of large-scale. Figure 1 illustrates the differential and divergent use of small- and large-scale descriptors in project scholarship over time. Small-scale projects have been neglected in project management research (with some notable exceptions outlined in Table 3 (e.g., Sohail et al., 2002; Thierie & De Moor, 2017). Large-scale projects have received the majority of scholarly attention (e.g., Yu & Kittler, 2012; Flyvbjerg, 2014); in fact, one could argue that large-scale project management is essentially the field?

Figure 1.

Growth of publications mentioning “scale,” and especially small- and large-scale projects, in core project management journals (authors’ own creation, based on the SLR sample).

Why is this? Is the skew because large-scale projects are more difficult and need more research? Or could it be because of the power (Yeo, 1995), seductiveness (Flyvbjerg, 2014), and political nature of large(r) scale projects (Samset & Volden, 2016; Harris, 2017)? Multiple authors have argued that, in practice, larger projects get commissioned (even if smaller might be more appropriate) because of the power that something large can have in terms of election cycles and political announcements (e.g., Flyvbjerg, 2014; Denicol et al., 2020). Politicians, the public, and media all have opinions about large projects (Williams & Samset, 2010; Samset & Volden, 2016), whereas small-scale projects can be “very different in [substance and] nature” and are often less interesting (Cooke-Davies & Arzymanow, 2003, p. 472). Whatever the underlying explanation, as large-scale projects are the focus of an ever-rising number of studies (Carpintero & Petersen, 2015; Aerts et al., 2017; Mangioni, 2018; Roehrich et al., 2024), this at the very least reinforces the need for more extended debate around what scale (large-small) is.

Some research (n = 16) has investigated small-scale projects. For example, Jørgensen et al. (2017) and Thierie and De Moor (2017) detail how to manage small-scale software development and public–private partnership (PPP) projects. Despite the differing topics, both studies evidenced common challenges for small projects such as variable quality concerns (see Table 3). Here, there seems to be another skew in the sample. If the work is small in scale it tends to skew toward more complex activities (e.g., Currie, 2003). For example, all studies in this part of the sample have focused on new or emerging technologies/tools, industries, or processes. Conversely, studies on large-scale projects have received an even broader coverage of a myriad of topics (e.g., Söderlund, 2010; Marabelli et al., 2013). Perhaps this reflects a bias for scholars to only seek out empirical subjects of sufficient richness but one could argue this does create a potential blind spot in the project management field.

We also observed the absence of scholarship investigating medium-scale projects (Nogeste, 2010; Bakker et al., 2011). Where there have been any mentions of mid-scale, it has been in the definitional context of small- and medium-sized enterprises/projects; upon closer examination, however, such scholarship almost entirely focused on small-scale projects (e.g., Mok et al., 2015). Moreover, from the limited number of studies investigating small-scale projects, we found that there is a common assumption that small is a simplification of large (Marcelino-Sádaba et al., 2014). In other words, large-scale project activities are replicated across all project scales, simply contingent upon project aims, objectives, and funding. As detailed in Table 3, however, small-scale projects (to the extent they have been studied) have fundamental and distinct challenges, including governance (Sohail et al., 2002), stakeholder management (Mok et al., 2015), and benefits management (Vo et al., 2021).

When synthesizing these findings, we argue that there is significant unrealized practical and scholarly potential to be found in studying small- and medium-scale projects. Subsequent research should better consider how project challenges differ among different scales (even if relative). In Table 3, we start to identify some key differences apparent in extant literature. For example, some authors reported that larger-scale projects have more prohibitive and restrictive governance, and detailed ways to overcome this such as scope and boundary setting, clear mechanisms for conflict resolution, and a larger need for coordination (Vo et al., 2021; Liu & Wilkinson, 2014; Roehrich et al., 2023). However, limited reflection, for example, has been made on how restrictive governance differs among a large project, a very large project, and a megaproject. Given that all three of these types of projects are fundamentally different in terms of actors, client/parent organizations, and institutional norms, this might be an important omission.

Theme 3: Modularity and Project Scale

Modularity, the reduction of the whole into—or construction from—smaller components/parts (Phillips et al., 1999; de Waard et al., 2014), is widely cited as a way of managing large-scale projects (Davies & Mackenzie, 2014; Floricel et al., 2016). The main focus of prior studies has been using modularity to (verb) scale down and reduce complexity and the cost of a project (de Waard et al., 2014; Whyte & Davies, 2021). Flyvbjerg (2021) presents a case example involving Tesla’s Gigafactory 1 project in Reno, Nevada. From the outset, Tesla defined a minimum viable production facility and then, as the project needed to scale, additional modular blocks could be added or removed to support further expansion or contraction based on project needs (Tesla, 2022). Ironically, for all the discussion of modularity in the scale literature, this was one of the first empirical examples of how projects could be managed via modularity to scale up in a systematic and structured way.

In applying modularity to the management of scale we identify three functions. First is modularization to address the dysfunction of scale in terms of complexity and uncertainty (de Waard et al., 2014). A frequently used example is the Crossrail underground rail line in central London in the United Kingdom (e.g., Schünmann, 2021; Whyte & Davies, 2021). Second, as illustrated by the Tesla vignette, is modularity to help scale up a project. Here, additional modules can be added to the existing project structure to increase the scale of project operations, thereby increasing the benefits the project will likely generate (cf., project-based economies of scale). Further advantages for modular scale include increased speed of delivery (e.g., Madrid Metro) and reduced likelihood of cost overrun (Flyvbjerg, 2021). Third is modularity to decompose project operations and removing parts of a project to appropriate some benefits while reducing the scope (and cost) of what the project seeks to achieve (Fu et al., 2022).

It is also important to consider these distinct modularity functions as they apply to projects of differing scales (noun). Both examples referenced (Crossrail and Tesla’s Gigafactory) are megaprojects (cost exceeding US$1 billion) and it is essential to better understand the differential benefits that a small project (i.e., costing less than US$10 million) is likely to receive from modularizing compared to a megaproject (Flyvbjerg, 2014).

Theme 4: Time, Temporality, and Project Scale

Time and temporality clearly affect scale and scaling in projects (e.g., Habison, 1985; Lycett et al., 2004; Dille & Söderlund, 2013; Dille et al., 2018). A linear temporality places scale as an unrealized outcome in the ex-ante/front-end project phase (Samset & Volden, 2016; Hetemi et al., 2021). Then, when capital spend begins, or the construction phase starts, scale starts becoming physically enacted (in the in-progress project phase; Meier, 2010). The final project phase (ex-post/back-end) is where the intended scale is realized (e.g., through project completion; Zerjav et al., 2018). This linear logic has significant consequences for the management of project scale with the expectation that a project will be a specific scale at specific points in time. This then leads to the incidental and implicit management of scale through the imposition of controls to check project progress. This reinforces the assumption that scale in projects is cumulative and additive: Scale starts small and continually grows, unless complexity/uncertainty becomes so problematic a reducing intervention, such as modularization, is required. This logic could cause professionals to overlook options to improve project outcomes (Turner et al., 2009) via subtractive changes (i.e., scaling down).

Future Research Agenda

This future research section builds on our systematic literature review and synthesis. Each of the themes has been crafted to further our understanding of scale in projects based on gaps identified within our review. We center possible future research avenues in project management (Table 5) on four key themes: (1) scale and value; (2) scale and scaling (up and down); (3) scale and time/temporality; and (4) scale and other project constructs.

Table 5.

Future Research Questions for Scale in Project Management

	Scale and Value	Scale and Scaling	Scale and Time/ Temporality	Scale and Other Project Constructs
Potential research questions	• How can scale generate (economic, social) value for projects? • What types of value are generated in moving/altering the scale of a project? • Would a range of projects at different scales (e.g., more midscale projects) generate extra (social and economic) value for organizations/ governments?	• What is the role of scale/scaling in creating a project ecosystem comprising of a diverse range of projects? • What are the skills, knowledge, and capabilities (e.g., systems integration) required for scaling a project (up and down)? • Can midscale projects achieve better (relative) outcomes than large- and small-scale projects?	• How do different conceptions of time inform project scale (and scaling) decisions? • What is the role of project/ temporal environment fit in scaling decisions? (e.g., why do organizations prefer to start projects of differing scales at differing times?) • How are the concepts of speed and scale linked in projects?	• Does the scale of complexity/ technology/pace/ novelty, dictate the overall scale of a project? Or, conversely, does the scale of the project impact the scale of these constructs? • Does any interdependency/linkage exist between the scale and scope constructs in a project management context? • Does increasing scale require distinct organizational forms and capabilities (e.g., program management)?

We strongly encourage researchers to consider scale as more than merely a depiction of size (as measured by the cost of the project). In this study, we have started to tease out several consequences, qualities, and clusters of practice associated with scale. Merely considering scale as size (cost), as has been done in most existing studies, fails to explore the full richness of this important construct.

The first research opportunity we highlight is to consider how scholarship can address the full range of project scales (noun). How to better understand (relatively) small- and midscale projects is a significant limitation that could be addressed under such a theme (Dittrich, 2022). One specific suggestion is to link this exploration into conversations in the wider management discourse around noneconomic and social value creation (e.g., Caldwell et al., 2017; Gil & Fu, 2022; Roehrich & Kivleniece, 2022). It could be that particular midsized projects are more appropriate to create certain outcomes and values. These projects may also circumvent the potentially prohibitive impediments of large-scale projects, such as stakeholder conflict (Ross, 2009) and enhanced (restrictive) governance (Vo et al., 2021), while also avoiding some of the equally limiting characteristics of small-scale projects (e.g., higher prices due to less economies of scale; Kleiss & Imura, 2006).

The second avenue for future research is understanding the process of scaling projects (verb). When we started the task of crafting the protocol for the SLR, we carefully ensured that keywords included results not just for scale but also terms associated with scaling (Sweetman & Conboy, 2018). Despite an explicit effort to include all relevant terms, very few papers have directly explored the notion of project scaling. In other fields, such as information systems or innovation management, scaling has been shown to have significant positive benefits (while often described as more difficult to execute than initially expected; Sahay & Walsham, 2006; Klingebiel & Joseph, 2015). Here a particularly interesting application might therefore be to explore scaling projects as vehicles for addressing key economic and social challenges (Dittrich, 2022; Ika & Munro, 2022)—if a project is delivering such value, is there scope to increase its scale? In addition, following from the modularity discussions, we know modularity may reduce the reliance on the systems integrator functions (Davies, 2017; Whyte & Davies, 2021), but little is known about how scaling dynamics affect the requirements for systems integration in large projects (Tee et al., 2019).

The third promising research avenue is to deepen our understanding of time (Söderlund, 2010; Söderlund & Pemsel, 2022) and temporality in the context of project scale, an underexplored area in extant studies. Discussions of time that have featured within our sample have adopted a linear processual view of time (Boonstra, 2013) and scale across different project phases. There are alternative perspectives. For example, scale and speed are closely linked in the innovation literature (e.g., Wonglimpiyarat, 2005; Klingebiel & Joseph, 2015) but have received very limited attention from project management scholars. Both scale and speed are important because they are primary drivers that enable or prevent the realization of benefits (Mahmoud-Jouini et al., 2004). To address the problem a project was commissioned to solve, it must be solved at an appropriate scale (e.g., to cover the target population) and at an appropriate speed (e.g., to realize benefits for the target population; Kock et al., 2016). They are also interrelated. For example, if a project is not executed at an appropriate speed, scale (costs) will increase. Additionally, we urge future research efforts to focus on institutional interpretations of time (Lundin & Söderholm, 1995; Geraldi et al., 2020). For example, do certain organizations prefer projects of differing scales at differing times (and are there differences between public and private organizations)? Based on existing research, we already have some tentative insights into the scale of projects initiated according to government election cycles (Samset & Volden, 2016).

As the fourth future research opportunity, scholars could also evaluate the impact of project scale across different project contingencies (van Marrewijk et al., 2008; Winch et al., 2022). We have already flagged the need for scales of scale but, for example, using Shenhar and Dvir’s (2007) Diamond Model, does the scale of complexity/technology/pace/novelty dictate the overall scale of a project? Or, conversely, does the scale of the project impact the scale of these dimensions? Is the complexity dimension merely a synonym for scale, as the most complex projects are nearly always “large in scale” (Shenhar & Dvir, 2007, p. 105)? In a practical sense, projects with significant complexity are likely to be larger (Pitsis et al., 2018), but is this largeness driven by a deliberate and concerted attempt to deliver a larger project or does complexity, for example, create more (emergent) scale? Empirical scholarship seems to suggest this could be the case with multiple papers studying projects, such as the delivery of various Olympic Games, suggesting that complexity can drive the overall scale of a project, requiring new forms of organization and program management capabilities (e.g., Davies & Mackenzie, 2014; Fernandes et al., 2018).

Likewise, what about economies of scale, the cost advantages obtained with increased size and volume of production (Chandler, 1990; Söderlund & Tell, 2011). Scale is often used in this way to distinguish between stages of production (comparing product and process dynamics) in many well-known schemas of industrial organization (e.g., Woodward, 1965; Hayes & Wheelwright, 1984; Wheelwright & Clark, 1992). In such schemas, projects are the low-volume unit stage of unique/highly customized product production processes and at the opposite end of a spectrum from the high-volume production of standardized products (Davies & Hobday, 2005; Maylor et al., 2018). What is less well known is how the project stage (internally) might also vary considerably according to the scale of production, and research could develop a typology using scale as one of the dimensions to distinguish among projects. Project management research might also explore and unpack the close association between project scale and scope constructs (Chandler, 1990; Brusoni et al., 2001; Macher & Boerner, 2006; Freeman et al., 2021).

Conclusions and Methodological Limitations

Guldi (2012, p. 31) reported how at the start of the “infrastructure revolution” in the 1750s, British roadbuilders made it “possible to conceive of [an] entire project of making a way across mountains by combining smaller projects of limited scope,” highlighting that from the development of contemporary project management discourse, consideration of scale has been a focal concern. Despite such an explicit statement, examination of the scale construct in recent project management scholarship has been lacking, forming the motivation of our research study. Based on our comprehensive analysis of extant project management studies mentioning scale, we draw out three key insights to inform future project management scholarship. First, the analysis confirmed that most project management papers (90%) address scale by implication or assumption; consequently, there is a definitional and conceptual gap, which we addressed through the formulation of a classification of project scale. Second, most mentions of scale (65%) are associated with large-ness with very few papers describing small- or midscale projects (10%). We argue that this has important implications for theory and practice in terms of current (and future) project practice and scholarship possessing an implicit large bias. Third, the inconsistent and underdeveloped basis for categorizing scale raises important questions for the clusters of practice associated with the management of project scale. Most existing discussions surround the management of scale by focusing on modularity.

This study also has some limitations. From a methodological perspective, the Web of Science and Scopus databases were used as they offer extensive coverage of peer-reviewed and published academic research and have been deployed in several recent literature reviews within the field of project management (e.g., Denicol et al., 2020) and beyond (e.g., Olsen & Tomlin, 2020). These databases, however, are only as good as the decisions made by the researchers regarding inclusion and exclusion criteria (Bouazzaoui et al., 2020). While every effort has been made to ensure that all relevant articles have been obtained, there is always the risk of some articles being missed (Roehrich et al., 2014).

While we bounded our study on core project management journals, future studies should expand the sample to formally incorporate literature from other fields, such as operations and supply chain management, innovation and general management journals, and field-level journals (e.g., energy sector), as these will inevitably also address (albeit likely in a relative and/or implicit way) project scale. There is also the considerable practitioner or gray literature (e.g., Svejvig & Andersen, 2015; Adams et al., 2017). Such work has not been surveyed in this article, and therefore represents an additional fruitful future research opportunity.

In summary, we believe that scale is a significant (and underexplored) construct which, if developed and explored more formally, has the potential to add significant value to future project management scholarly discourse and practice. We hope the insights and research themes discussed in this article will encourage future research efforts.

Footnotes

Acknowledgments

The authors would like to thank attendees of the 2023 Academy of Management (AoM) conference and the 2023 European Operations Management Association (EurOMA) conference for their generous and constructive feedback, which has provided significant assistance in helping us to greatly improve this manuscript. We would also like to thank Professor Graham Winch (University of Manchester) and Dr. Juliano Denicol (University College London) for their detailed guiding comments on earlier versions of the manuscript.

Declaration of Conflicting Interests

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

Funding

The authors disclosed receipt of the following financial support for the research and authorship of this article: This work was supported by the Economic and Social Research Council (ESRC) in the United Kingdom via a South West Doctoral Training Partnership (SWDTP) Funding Award [Grant Number: #2229577].

ORCID iDs

Joseph W. Harrison

Jens K. Roehrich

Andrew Davies

Author Biographies

Joseph W. Harrison is a Doctoral Researcher at the University of Bath, School of Management, United Kingdom. His research focuses on improving the management of large and complex infrastructure projects. Joseph is a recipient of the Economic and Social Research Council (ESRC) doctoral scholarship (administered by the South West Doctoral Training Partnership). He can be contacted at jwh65@bath.ac.uk

Michael A. Lewis is Professor of Operations and Supply Management at the University of Bath, School of Management, United Kingdom. His wide-ranging interests include operations/technology strategy, governance, productivity, and major projects. He also has a long-standing interest in sustainability and healthcare. He is author and coauthor of numerous journal articles as well as several books, including a widely adopted and translated book on operations strategy. He can be contacted at M.A.Lewis@bath.ac.uk

Jens K. Roehrich is Professor of Supply Chain Innovation at the University of Bath, School of Management, United Kingdom. He has carried out research, executive education, and competence development activities with a wide range of public and private organizations in large projects. His research focuses on long-term relationships across public and private organizations with a particular emphasis on the role of contractual and relational governance mechanisms in large projects, as well as the dark side of relationships such as coordination failures, trust breaches, and conflicts. His research has been published in journals, including Journal of Operations Management, Journal of Management Studies, International Journal of Operations & Production Management, British Journal of Management, Social Science & Medicine, California Management Review, and International Journal of Project Management, among others. He can be contacted at J.Roehrich@bath.ac.uk

Andrew Davies is RM Phillips Freeman Chair and Professor of Innovation Management at Science Policy Research Unit, University of Sussex, UK. His research focuses on the management of innovation in large, complex, and high-risk infrastructure projects. He is author of Projects: A Very Short Introduction (Oxford University Press, 2017) and has published in a range of leading management journals such as Research Policy, Organization Studies, Industrial Marketing Management, Industrial and Corporate Change, International Journal of Operations & Production Management, International Journal of Project Management, California Management Review, and MIT Sloan Management Review. He can be contacted at Andrew.Davies@sussex.ac.uk

References

Adams

Smart

Huff

. (2017). Shades of grey: Guidelines for working with the grey literature in systematic reviews for management and organizational studies. International Journal of Management Reviews, 19(4), 432–454.

Aerts

Dooms

Haezendock

. (2017). Knowledge transfers and project-based learning in large scale infrastructure development projects: An exploratory and comparative ex-post analysis. International Journal of Project Management, 35(3), 224–240.

Albonetti

Manganelli

. (1984). The Trans-Mediterranean natural gas pipeline. International Journal of Project Management, 2(2), 94–96.

Alzoubi

Locatelli

Sainati

. (2023). Modern slavery in projects: A systematic literature review and research agenda. Project Management Journal, 54(3), 235–252.

Ansar

Flyvbjerg

Budzier

Lunn

. (2017). Big is fragile: An attempt at theorizing scale. In Flyvbjerg

(Ed.), The Oxford handbook of megaproject management (pp. 60–95). Oxford University Press.

Aranda-Mena

Crawford

Chevez

Froese

. (2009). Building information modelling demystified: Does it make sense to adopt BIM? International Journal of Managing Projects in Business, 2(3), 419–434.

Artto

Martinsuo

Gemünden

Murtoaro

. (2009). Foundations of program management: A bibliometric view. International Journal of Project Management, 27(1), 1–18.

Bachy

Hameri

. (1997). What to be implemented at the early stage of a large-scale project. International Journal of Project Management, 15(4), 211–218.

Bakker

Knoben

de Vries

Oerlemans

(2011). The nature and prevalence of inter-organizational project ventures: Evidence from a large-scale field study in the Netherlands 2006-2009. International Journal of Project Management, 29(6), 781–794.

10.

Boonstra

. (2013). How do top managers support strategic information system projects and why do they sometimes withhold this support? International Journal of Project Management, 31(4), 498–512.

11.

Bouazzaoui

Roehrich

J. K.

Squire

Roath

. (2020). Justice in inter-organizational relationships: A literature review and future research agenda. Industrial Marketing Management, 87(1), 128–137.

12.

Brusoni

Prencipe

Pavitt

. (2001). Knowledge specialization, organizational coupling, and the boundaries of the firm: Why do firms know more than they make? Administrative Science Quarterly, 46(4), 597–621.

13.

Caldwell

Roehrich

J. K.

George

. (2017). Social value creation and relational coordination in public-private collaborations. Journal of Management Studies, 54(6), 906–928.

14.

California High Speed Rail Authority. (2022). Capital costs and funding. https://hsr.ca.gov/about/capital-costs-funding/

15.

Cantarelli

Genovese

. (2021). Innovation potential of megaprojects: A systematic literature review. Production Planning and Control, 34(14), 1350–1370.

16.

Carpintero

Petersen

. (2015). Bundling and unbundling in public–private partnerships: Implications for risk sharing in urban transport projects. Project Management Journal, 46(4), 35–46.

17.

Cavaleri

Firestone

Reed

. (2012). Managing project problem-solving patterns. International Journal of Managing Projects in Business, 5(1), 125–145.

18.

Chan

. (2005). Contract strategy for design management in the design and build system. International Journal of Project Management, 23(8), 630–639.

19.

Chandler

. (1990). Scale and scope: Dynamics of industrial capitalism. Harvard University Press.

20.

Charoenngam

Yeh

. (1999). Contractual risk and liability sharing in hydropower construction. International Journal of Project Management, 17(1), 29–37.

21.

Chbaly

Brunet

. (2023). Empowering users as a principle of construction project management. Project Management Journal, 54(6), 665–682.

22.

Cooke-Davies

Arzymanow

. (2003). The maturity of project management in different industries: An investigation into variations between project management models. International Journal of Project Management, 21(6), 471–478.

23.

Crossan

Apaydin

. (2010). A multi-dimensional framework of organizational innovation: A systematic review of the literature. Journal of Management Studies, 47(6), 1154–1191.

24.

Cuppen

Bosch-Rekveldt

Pikaar

Mehos

. (2016). Stakeholder engagement in large-scale energy infrastructure projects: Revealing perspectives using Q methodology. International Journal of Project Management, 34(7), 1347–1359.

25.

Currie

. (2003). A knowledge-based risk assessment framework for evaluating web-enabled application outsourcing projects. International Journal of Project Management, 21(3), 207–217.

26.

Davies

. (2017). Projects: A very short introduction. Oxford University Press.

27.

Davies

Hobday

. (2005). The business of projects: Managing innovation in complex products and systems. Cambridge University Press.

28.

Davies

Mackenzie

. (2014). Project complexity and systems integration: Constructing the London 2012 Olympics and Paralympics games. International Journal of Project Management, 32(5), 773–790.

29.

Dawes

. (1988). The greater Cairo wastewater project. International Journal of Project Management, 6(2), 107–115.

30.

Delisle

Olson

. (2004). Would the real project management language please stand up? International Journal of Project Management, 22(4), 327–337.

31.

Denicol

Davies

Krystallis

. (2020). What are the causes and cures of poor megaproject performance? A systematic literature review and research agenda. Project Management Journal, 51(3), 328–345.

32.

Denyer

Tranfield

. (2009). Producing a systematic review. In Buchanan

Bryman

(Eds.), The Sage handbook of organizational research methods (pp. 671–689). Sage Publications.

33.

Derakhshan

Turner

Mancini

. (2018). Project governance and stakeholders: A literature review. International Journal of Project Management, 37(1), 98–116.

34.

de Waard

Volberda

Soeters

(2014). Decentralization and decomposability: Determinants of responsive crisis deployment. International Journal of Managing Projects in Business, 7(3), 380–404.

35.

Dick

Sankaran

Shaw

Kelly

Soar

Davies

Banbury

. (2015). Value co-creation with stakeholders using action research as a meta-methodology in a funded research project. Project Management Journal, 46(2), 36–46.

36.

Dille

Söderlund

. (2013). Managing temporal misfits in institutional environments: A study of critical incidents in a complex public project. International Journal of Managing Projects in Business, 6(3), 552–575.

37.

Dille

Söderlund

Clegg

. (2018). Temporal conditioning and the dynamics of inter-institutional projects. International Journal of Project Management, 36(5), 673–686.

38.

Dingsøyr

Moe

Seim

. (2018). Coordinating knowledge work in multiteam programs: Findings from a large-scale agile development program. Project Management Journal, 49(6), 64–77.

39.

Dittrich

. (2022). Scale in research on grand challenges. In Gümüsay

Marti

Trittin-Ulbrich

Wickert

(Eds), Research in the sociology of organizations: Organizing for societal grand challenges (pp. 187–203). Emerald Publishing.

40.

Drechsler

Breth

. (2019). How to go global: A transformative process model for the transition towards globally distributed software development projects. International Journal of Project Management, 37(8), 941–955.

41.

Dyer

. (2017). Cultural sense-making integration into risk mitigation strategies towards megaproject success. International Journal of Project Management, 35(7), 1338–1349.

42.

Fernandes

Spring

Tarafdar

. (2018). Coordination in temporary organizations: Formal and informal mechanisms at the 2016 Olympics. International Journal of Operations and Production Management, 38(6), 1340–1367.

43.

Floricel

Michela

Piperca

. (2016). Complexity, uncertainty-reduction strategies, and project performance. International Journal of Project Management, 34(7), 1360–1383.

44.

Floricel

Miller

. (2001). Strategizing for anticipated risks and turbulence in large-scale engineering projects. International Journal of Project Management, 19(8), 445–455.

45.

Flyvbjerg

. (2014). What you should know about megaprojects and why: An overview. Project Management Journal, 45(2), 6–19.

46.

Flyvbjerg

. (2021). Make megaprojects more modular. Harvard Business Review, 99(6), 50–54.

47.

Frederiksen

Davies

. (2008). Vanguards and ventures: Projects as vehicles for corporate entrepreneurship. International Journal of Project Management, 26(5), 487–496.

48.

Freeman

Savva

Scholtes

. (2021). Economies of scale and scope in hospitals: An empirical study of volume spillovers. Management Science, 67(2), 673–697.

49.

Zhang

Chen

. (2022). Coping with institutional complexity and voids: An organization design perspective for transnational interorganizational projects. Project Management Journal, 53(1), 49–66.

50.

Geraldi

Stjerne

Oehmen

. (2020). Acting in time: Temporal work enacting tensions at the interface between temporary and permanent organizations. In Braun

Lampel

(Eds), Tensions and paradoxes in temporary organizing (pp. 81–103). Emerald Publishing.

51.

Gil

. (2022). Megaproject performance, value creation, and value distribution: An organizational governance perspective. Academy of Management Discoveries, 8(2), 224–251.

52.

Gioia

Corley

Hamilton

. (2012). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational Research Methods, 16(1), 15–31.

53.

Government

Institute for

. (2017). Big vs small infrastructure projects: Does size matter? https://www.instituteforgovernment.org.uk/explainer/big-vs-small-infrastructure-projects

54.

Guldi

. (2012). Roads to power: Britain invents the infrastructure state. Harvard University Press.

55.

Gunasekaran

Irani

Choy

Filippi

Papadopoulos

. (2015). Performance measures and metrics in outsourcing decisions: A review for research and applications. International Journal of Production Economics, 161(1), 153–166.

56.

Habison

. (1985). Time and cost securing in complex large-scale projects. International Journal of Project Management, 3(3), 178–181.

57.

Halman

Burger

. (2002). Evaluating effectiveness of project start-ups: An exploratory study. International Journal of Project Management, 20(1), 81–89.

58.

Hannevik

Lone

Bjørklund

Bjørkli

Hoff

. (2014). Organizational climate in large-scale projects in the oil and gas industry: A competing values perspective. International Journal of Project Management, 32(4), 687–697.

59.

Harris

. (2017). Competitive precinct projects: The five consistent criticisms of “global” mixed-use megaprojects. Project Management Journal, 48(6), 76–92.

60.

Hayes

Wheelwright

. (1984). Restoring our competitive edge: Competing through manufacturing. Wiley Publishing.

61.

Hetemi

Jerbrant

Mere

. (2020). Exploring the emergence of lock-in in large-scale projects: A process view. International Journal of Project Management, 38(1), 47–63.

62.

Hetemi

van Marrewijk

Jerbrant

Bosch-Rekveldt

(2021). The recursive interaction of institutional fields and managerial legitimation in large-scale projects. International Journal of Project Management, 39(3), 295–307.

63.

Hobbs

Petit

. (2017). Agile methods on large projects in large organizations. Project Management Journal, 48(3), 3–19.

64.

Ika

Munro

. (2022). Tackling grand challenges with projects: Five insights and a research agenda for project management theory and practice. International Journal of Project Management, 40(6), 601–607.

65.

Infrastructure and Projects Authority. (2023). Annual report on major projects 2022-23. Cabinet Office.

66.

Jørgensen

Mohagheghi

Grimstad

. (2017). Direct and indirect connections between type of contract and software project outcome. International Journal of Project Management, 35(8), 1573–1586.

67.

Klein

Müller

. (2020). Literature review expectations of Project Management Journal®. Project Management Journal, 51(3), 239–241.

68.

Kleiss

Imura

. (2006). The Japanese private finance initiative and its application in the municipal solid waste management sector. International Journal of Project Management, 24(7), 614–621.

69.

Klingebiel

Joseph

. (2015). Entry timing and innovation strategy in feature phones. Strategic Management Journal, 37(6), 1002–1020.

70.

Koch

Schermuly

. (2021). Managing the crisis: How COVID-19 demands interact with agile project management in predicting employee exhaustion. British Journal of Management, 32(4), 1265–1283.

71.

Kock

Heising

Gemünden

. (2016). A contingency approach on the impact of front-end success on project portfolio success. Project Management Journal, 47(2), 115–129.

72.

Kolltveit

Grønhaug

. (2004). The importance of the early phase: The case of construction and building projects. International Journal of Project Management, 22(7), 545–551.

73.

Lahdenperä

. (2012). Making sense of the multi-party contractual arrangements of project partnering, project alliancing and integrated project delivery. Construction Management and Economics, 30(1), 57–79.

74.

Lappi

Karvonen

Lwakatare

Aaltonen

Kuvaja

. (2018). Toward an improved understanding of agile project governance: A systematic literature review. Project Management Journal, 49(6), 39–63.

75.

Lewis

Harrison

Roehrich

J. K

. (2023). It’s all a bit fuzzy? The front-end in project and innovation management. In Davies

Lenfle

Loch

Midler

(Eds.), Handbook on innovation and project management (pp. 101–124). Edward Elgar Publishing.

76.

Rollins

Yan

. (2018). Web of Science use in published research and review papers 1997–2017: A selective, dynamic, cross-domain, content-based analysis. Scientometrics, 115(1), 1–20.

77.

Ling

Lau

. (2002). A case study on the management of the development of a large-scale power plant project in east Asia based on design-build arrangement. International Journal of Project Management, 20(6), 413–423.

78.

Liu

Wilkinson

. (2014). Large-scale public venue development and the application of public-private partnerships. International Journal of Project Management, 32(1), 88–100.

79.

Loo

. (2002). Tacking ethical dilemmas in project management using vignettes. International Journal of Project Management, 20(7), 489–495.

80.

Loo

. (2003). Assessing “team climate” in project teams. International Journal of Project Management, 21(7), 511–517.

81.

Luo

Wang

Shi

. (2015). Measurement model of project complexity for large-scale projects from task and organization perspective. International Journal of Project Management, 33(3), 610–622.

82.

Lundin

Söderholm

. (1995). A theory of the temporary organization. Scandinavian Journal of Management, 11(4), 437–455.

83.

Lycett

Rassau

Danson

. (2004). Programme management: A critical review. International Journal of Project Management, 22(4), 289–299.

84.

Macher

Boerner

. (2006). Experience and scale and scope economies: Trade-offs and performance in development. Strategic Management Journal, 27(9), 845–865.

85.

Mahmoud-Jouini

Midler

Garel

. (2004). Time-to-market vs. time-to-delivery: Managing speed in engineering, procurement, and construction projects. International Journal of Project Management, 22(5), 359–367.

86.

Maier

Branzei

. (2014). On time and on budget: Harnessing creativity in large scale projects. International Journal of Project Management, 32(7), 1123–1133.

87.

Mangioni

. (2018). Evaluating the impact of the land acquisition phase on property owners in megaprojects. International Journal of Managing Projects in Business, 11(1), 158–173.

88.

Marabelli

Rajola

Frigerio

Newell

. (2013). Managing knowledge in large-scale virtual projects: A community-based approach. International Journal of Managing Projects in Business, 6(2), 310–331.

89.

Marcelino-Sádaba

Pérez-Ezcurdia

Lazcano

Villanueva

. (2014). Project risk management methodology for small firms. International Journal of Project Management, 32(2), 327–340.

90.

Maylor

Meredith

Söderlund

Browning

. (2018). Old theories, new contexts: Extending operations management theories to projects. International Journal of Operations and Production Management, 38(6), 1274–1288.

91.

Meier

. (2010). Causal inferences on the cost overruns and schedule delays of large-scale U.S. federal defense and intelligence acquisition programs. Project Management Journal, 41(1), 28–39.

92.

Merrow

McDonnell

Argüden

. (1988). Understanding the outcomes of megaprojects: A quantitative analysis of very large civilian projects. RAND Corporation.

93.

Mok

Shen

Yang

. (2015). Stakeholder management studies in mega construction projects: A review and future directions. International Journal of Project Management, 33(2), 446–457.

94.

Müller

Zhai

Wang

Shao

. (2016). A framework for governance of projects: Governmentality, governance structure and projectification. International Journal of Project Management, 34(6), 957–969.

95.

Nogeste

. (2010). Understanding mergers and acquisitions (M&As) from a program management perspective. International Journal of Managing Projects in Business, 3(1), 111–138.

96.

Nordhaus

. (2017). Revisiting the social cost of carbon. Proceedings of the National Academy of Sciences, 114(7), 1518–1523.

97.

Olsen

Tomlin

. (2020). Industry 4.0: Opportunities and challenges for operations management. Manufacturing and Service Operations Management, 22(1), 113–122.

98.

Papadonikolaki

Krystallis

Morgan

. (2022). Digital technologies in built environment projects: Review and future directions. Project Management Journal, 53(5), 501–519.

99.

Patanakul

Kwak

Zwikael

Liu

. (2016). What impacts the performance of large-scale government projects? International Journal of Project Management, 34(3), 452–466.

100.

Phillips

. (1999). Management of modular projects: A templating approach. Project Management Journal, 30(4), 33–41.

101.

Pitsis

Clegg

Freeder

Sankaran

Burdon

. (2018). Megaprojects redefined—Complexity vs cost and social imperatives. International Journal of Managing Projects in Business, 11(1), 7–34.

102.

Pittaway

Robertson

Munir

Denyer

Neely

. (2004). Networking and innovation: A systematic review of the evidence. International Journal of Management Reviews, 5(3), 137–168.

103.

Ramasesh

Browning

. (2014). A conceptual framework for tackling knowable unknown unknowns in project management. Journal of Operations Management, 32(4), 190–204.

104.

Roehrich

J. K.

Davies

Tyler

Mishra

Bendoly

. (2024). Large interorganizational projects (LIPs): Toward an integrative perspective and research agenda on interorganizational governance. Journal of Operations Management, 70(1), 4–21.

105.

Roehrich

J. K.

Kalra

Squire

Davies

. (2023). Network orchestration in a large inter-organizational project. Journal of Operations Management, 69(7), 1078–1099.

106.

Roehrich

J. K.

Kivleniece

. (2022). Creating and distributing sustainable value through public-private collaborative projects. In George

Hass

Joshi

McGahan

Tracey

, Handbook on the business of sustainability: The organization, implementation, and practice of sustainable growth (pp. 473–499). Edward Elgar Publishing.

107.

Roehrich

J. K.

Lewis

George

. (2014). Are public-private partnerships a healthy option? A systematic literature review. Social Science and Medicine, 113(1), 110–119.

108.

Ross

. (2009). The use of partnering as a conflict prevention method in large-scale urban projects in Canada. International Journal of Managing Projects in Business, 2(3), 401–418.

109.

Ruiz Diaz

(2020). What drives the failure of private participation in infrastructure projects? International Journal of Managing Projects in Business, 13(6), 1167–1185.

110.

Sahay

Walsham

. (2006). Scaling of health information systems in India: Challenges and approaches. Information Technology for Development, 12(3), 185–200.

111.

Samset

Volden

. (2016). Front-end definition of projects: Ten paradoxes and some reflections regarding project management and project governance. International Journal of Project Management, 34(2), 297–313.

112.

Santana

. (1990). Classification of construction projects by scales of complexity. International Journal of Project Management, 8(2), 102–104.

113.

Saunders

Gale

Sherry

. (2016a). Responding to project uncertainty: Evidence for high reliability practices in large-scale safety–critical projects. International Journal of Project Management, 34(7), 1252–1265.

114.

Saunders

Gale

Sherry

. (2016b). Mapping the multi-faceted: Determinants of uncertainty in safety-critical projects. International Journal of Project Management, 34(6), 1057–1070.

115.

Schünmann

(2021). Offsite and modular building: An evolutionary tale. https://barbour-abi.com/offsite-and-modular-building-an-evolutionary-tale/

116.

Shen

Platten

Deng

. (2006). Role of public private partnerships to manage risks in public sector projects in Hong Kong. International Journal of Project Management, 24(7), 587–594.

117.

Shenhar

Dvir

. (2007). Reinventing project management: The diamond approach to successful growth and innovation. Harvard University Press.

118.

Söderlund

. (2010). Knowledge entrainment and project management: The case of large-scale transformation projects. International Journal of Project Management, 28(2), 130–141.

119.

Söderlund

. (2011). Pluralism in project management: Navigating the crossroads of specialization and fragmentation. International Journal of Management Reviews, 13(2), 153–176.

120.

Söderlund

Pemsel

. (2022). Changing times for digitalization: The multiple roles of temporal shifts in enabling organizational change. Human Relations, 75(5), 871–902.

121.

Söderlund

Tell

. (2011). The p-form corporation: Contingencies, characteristics, and challenges. In Morris

Pinto

Söderlund

, J (Eds.), The Oxford handbook of project management (pp. 201–223). Oxford University Press.

122.

Sohail

Miles

Cotton

. (2002). Developing monitoring indicators for urban micro contracts in South Asia. International Journal of Project Management, 20(8), 583–591.

123.

Strauss

Corbin

. (1998). Basics of qualitative research (2nd ed). SAGE Publications.

124.

Suhonen

Paasivaara

. (2011). Shared human capital in project management: A systematic review of the literature. Project Management Journal, 42(2), 4–16.

125.

Svejvig

Andersen

. (2015). Rethinking project management: A structured literature review with a critical look at the brave new world. International Journal of Project Management, 33(2), 278–290.

126.

Sweetman

Conboy

. (2018). Portfolios of agile projects: A complex adaptive systems’ agent perspective. Project Management Journal, 49(6), 18–38.

127.

Tam

. (2000). Design and build on a complicated redevelopment project in Hong Kong: The Happy Valley racecourse redevelopment. International Journal of Project Management, 18(2), 125–129.

128.

Tangpong

. (2011). Content analytic approach to measuring constructs in operations and supply chain management. Journal of Operations Management, 29(6), 627–638.

129.

Tee

Davies

Whyte

. (2019). Modular designs and integrating practices: Managing collaboration through coordination and cooperation. Research Policy, 48(1), 51–61.

130.

Tesla. (2022). Gigafactory: Nevada. https://www.tesla.com/en_gb/giga-nevada

131.

Thierie

De Moor

(2017). Constraints related to developing small-scale PPPs and how to reduce them. International Journal of Managing Projects in Business, 10(1), 109–120.

132.

Toor

Ogunlana

. (2010). Beyond the ‘iron triangle’: Stakeholder perception of key performance indicators (KPIs) for large-scale public sector development projects. International Journal of Project Management, 28(3), 228–236.

133.

Tranfield

Denyer

Smart

. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.

134.

Treasury

. (2022). The green book. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1063330/Green_Book_2022.pdf

135.

Turner

Ledwith

Kelly

. (2009). Project management in small to medium-sized enterprises: A comparison between firms by size and industry. International Journal of Managing Projects in Business, 2(2), 282–296.

136.

Turner

Zolin

. (2012). Forecasting success on large projects: Developing reliable scales to predict multiple perspectives by multiple stakeholders over multiple time frames. Project Management Journal. 43(5), 87–99.

137.

van den Ende

van Marrewijk

(2019). Teargas, taboo, and transformation: A neo-institutional study of community resistance and the struggle to legitimize subway projects in Amsterdam 1960–2018. International Journal of Project Management, 37(2), 331–346.

138.

Van der Merwe

(2002). Project management and business development: Integrating strategy, structure, processes, and projects. International Journal of Project Management, 20(5), 401–411.

139.

van Marrewijk

Clegg

Pitsis

Veenswijk

(2008). Managing public-private megaprojects: Paradoxes, complexity, and project design. International Journal of Project Management, 26(6), 591–600.

140.

Vidal

Marle

Bocquet

. (2011). Measuring project complexity using the analytic hierarchy process. International Journal of Project Management, 29(6), 718–727.

141.

Kirkham

Williams

Howells

Forster

Cooke-Davies

. (2021). An empirical study of assurance in the UK government major projects portfolio: From data to recommendations, to action or inaction. International Journal of Managing Projects in Business, 14(4), 865–897.

142.

Walker

Johannes

. (2003). Construction industry joint venture behavior in Hong Kong—Designed for collaborative results? International Journal of Project Management, 21(1), 39–49.

143.

Walker

Lloyd-Walker

. (2016). Understanding the motivation and context for alliancing in the Australian construction industry. International Journal of Managing Projects in Business, 9(1), 74–93.

144.

Wang

Müller

Zhu

. (2022). Network governance for interorganizational temporary organizations: A systematic literature review and research agenda. Project Management Journal, 54(1), 35–51.

145.

Wheelwright

Clark

. (1992). Revolutionizing product development—Quantum leaps in speed, efficiency and quality. The Free Press Inc.

146.

Whyte

Davies

. (2021). Reframing systems integration: A process perspective on projects. Project Management Journal, 52(3), 237–249.

147.

Williams

Samset

. (2010). Issues in front-end decision making on projects. Project Management Journal, 41(2), 38–49.

148.

Winch

Maytorena-Sanchez

Sergeeva

. (2022). Strategic project organizing. Oxford University Press.

149.

Wonglimpiyarat

. (2005). Does complexity affect the speed of innovation? Technovation, 25(8), 865–882.

150.

Woodward

. (1965). Industrial organization: Theory and practice. Oxford University Press.

151.

Yeo

. (1995). Planning and learning in major infrastructure development: Systems perspectives. International Journal of Project Management, 13(5), 287–293.

152.

Kittler

. (2012). Matching programme structure to environment: A comparative study of two IS-based change programmes. International Journal of Project Management, 30(6), 740–749.

153.

Zerjav

Edkins

Davies

. (2018). Project capabilities for operational outcomes in inter-organisational settings: The case of London Heathrow Terminal 2. International Journal of Project Management, 36(3), 444–459.

154.

Zerjav

Martinsuo

Huemann

. (2023). Developing new knowledge: A virtual collection of project management review articles. International Journal of Project Management, 41(1), 1–6.

Scale in Project Management: A Review and Research Agenda

Abstract

Keywords

Introduction

Methodology

Selecting the Sample Population

Search Processes

Analysis

Descriptive Analysis

Synthesis and Emerging Research Themes

Theme 1: A Conceptual Gap and Definitional Framework

Theme 2: Why Is There a Bias Toward Large-Scale?

Theme 3: Modularity and Project Scale

Theme 4: Time, Temporality, and Project Scale

Future Research Agenda

Conclusions and Methodological Limitations

Footnotes

Acknowledgments

Declaration of Conflicting Interests

Funding

ORCID iDs

Author Biographies

References