University of Birmingham
Risk, uncertainty and governance in megaprojects :
a critical discussion of alternative explanations
Sanderson, Joseph
DOI:
10.1016/j.ijproman.2011.11.002
Document Version
Publisher's PDF, also known as Version of record
Citation for published version (Harvard):
Sanderson, J 2012, 'Risk, uncertainty and governance in megaprojects : a critical discussion of alternative
explanations' International Journal of Project Management, vol 30, no. 4, pp. 432-443. DOI:
10.1016/j.ijproman.2011.11.002
Link to publication on Research at Birmingham portal
General rights
When referring to this publication, please cite the published version. Copyright and associated moral rights for publications accessible in the
public portal are retained by the authors and/or other copyright owners. It is a condition of accessing this publication that users abide by the
legal requirements associated with these rights.
• You may freely distribute the URL that is used to identify this publication.
• Users may download and print one copy of the publication from the public portal for the purpose of private study or non-commercial
research.
• If a Creative Commons licence is associated with this publication, please consult the terms and conditions cited therein.
• Unless otherwise stated, you may not further distribute the material nor use it for the purposes of commercial gain.
Take down policy
If you believe that this document infringes copyright please contact [email protected] providing details and we will remove access to
the work immediately and investigate.
Download date: 28. Jul. 2017
Available online at www.sciencedirect.com
International Journal of Project Management 30 (2012) 432 – 443
www.elsevier.com/locate/ijproman
Risk, uncertainty and governance in megaprojects: A critical discussion of
alternative explanations
Joe Sanderson ⁎
Department of Management, School of Business, University of Birmingham, Birmingham, B15 2SQ, UK
Received 25 March 2011; received in revised form 16 November 2011; accepted 22 November 2011
Abstract
This article critically discusses different explanations for the performance problems exhibited by many megaprojects, and examines the proposed
governance solutions. It proposes a three-fold typology of explanations and solutions by examining authors’ epistemological assumptions about
decision-maker cognition and about decision-maker views on the nature of the future. It argues that despite important differences in their epistemological orientation, these explanations share an acceptance of the notion of actor farsightedness. It concludes that this encourages them to focus on governance in megaprojects, made forms of organization designed ex ante, and to ignore governing in megaprojects, spontaneous micro-processes of
organizing emerging ex post. Identification of this gap adds support to calls by projects-as-practice researchers for a broadening of research to encompass the actuality of projects. A new line of enquiry within this broad projects-as-practice agenda is suggested.
© 2011 Elsevier Ltd. APM and IPMA. All rights reserved.
Keywords: Megaprojects; Governance; Risk; Uncertainty; Farsightedness; Projects-as-practice
1. Introduction
Governance has become an increasingly popular theme in the
project management literature. This reflects a widening of focus
away from the purely technical and operational tasks that need
to be fulfilled to deliver project outcomes, to encompass a
much greater interest in how interactions between the multiple
actors responsible for undertaking those tasks are organized and
coordinated (see, for example, Atkinson et al., 2006; Clegg
et al., 2002; Flyvbjerg et al., 2003; Miller and Lessard, 2000;
Pitsis et al., 2003; Pryke and Smyth, 2006; van Marrewijk
et al., 2008; Williams et al., 2009; Winch, 2001, 2009).
Kloppenborg and Opfer (2002) describe this in terms of an
increased focus on stakeholder identification and management.
This reorientation in the literature has coincided with the
increasing popularity over the last 25 years of a project form
variously described as the megaproject (Flyvbjerg et al., 2003;
⁎ Tel.: +44 121 414 7489; fax: +44 121 414 3217.
E-mail address: [email protected].
0263-7863/$36.00 © 2011 Elsevier Ltd. APM and IPMA. All rights reserved.
doi:10.1016/j.ijproman.2011.11.002
van Marrewijk et al., 2008), the large engineering project (Miller
and Lessard, 2000), or the service-led project (Alderman et al.,
2005). These labels are applied to very large-scale projects with
several shared features: the project delivers a substantial piece of
physical infrastructure or a capital asset with a life expectancy measured in decades; the client is often a government or public sector
organization; the main contractor or consortium of contractors are
usually privately-owned and financed; the contractor often retains
an ownership stake in the infrastructure/asset after the construction
phase is completed – typically as a minority shareholder in a
special purpose vehicle (SPV) – and is paid by the client for the
service that flows from the asset's operation or use over a number
of years. It is this final feature, commonly associated in the literature with the notion of public-private partnership (Skelcher,
2005), that most differentiates such infrastructure projects from
those undertaken in the years before the mid-1980s.
These characteristics, associated with what Crawford and
Pollack (2004) call ‘soft’ projects, are deemed to create exceptional challenges for project managers. There are high levels of
complexity along various dimensions (Remington and Pollack,
2007), the potential for significant conflicts of interest between
the wide variety of public and private sector stakeholders
J. Sanderson / SciVerse ScienceDirect 30 (2012) 432–443
(Alderman et al., 2005; Clegg et al., 2002), and the need to make
decisions and to act under conditions of uncertainty as well as
risk (Atkinson et al., 2006; Loch et al., 2006). There is substantial
evidence that these challenges are proving somewhat intractable,
often leading to substantial cost overruns, delays in completion
and failure to deliver against the objectives used to justify projects
(see, for example, Eden et al., 2005; Flyvbjerg, 2009; Flyvbjerg
et al., 2002, 2003; Miller and Lessard, 2000; NAO, 2006, 2009;
Williams, 2005, 2009). It might seem somewhat paradoxical,
therefore, that megaprojects have become increasingly popular in
recent years (Williams, 2009). A willingness to take on these
challenges is attributed by some to what Bauman (1998) has called
the ‘Great War of Independence from Space,’ suggesting that infrastructure enhancements are playing a key role in reducing distance
and therefore friction between actors in different parts of the world
(Flyvbjerg et al., 2003).
This article discusses how a number of different writers seek
to explain the significant performance problems exhibited by
many megaprojects, and critically examines their suggested
governance solutions. The key aims are to provide a broad
categorisation of different types of explanation and associated
solutions, and to identify any significant commonalities between
explanations. This is achieved by examining each author's fundamental epistemological assumptions about decision-maker cognition and about decision-maker views on the nature of the future
(risky or uncertain). The use of epistemological foundations as
a basis for categorising different explanations of megaproject
performance is inspired by the work of the Rethinking Project
Management Network (Cicmil et al., 2006; Winter et al., 2006).
The different ways in which these epistemological assumptions might be handled are examined in the next section, in
order to elucidate some spaces within which the selected
texts can be categorised and critiqued. Three types of explanation are identified and discussed in the subsequent section.
The key insight derived from the discussion is that despite important differences in their epistemological orientation these
three types of explanation share an acceptance of the notion
of actor farsightedness, albeit differently conceived. It is concluded that this acceptance encourages these explanations to
focus on governance in megaprojects – forms of organization
designed ex ante – and to ignore governing in megaprojects
— micro-processes of organizing emerging ex post. The possibility that changes in governance might occur and the nature
of any change as a project proceeds are therefore downplayed.
A call is made for future research to include a greater focus
on project governing, conceiving it as spontaneous order rather than something that is designed or made (Chia and Holt,
2009). The argument here, though, is not that a focus on governing should replace a focus on governance, but rather that
there should be an appreciation of both levels of analysis. It
is acknowledged that governing exists in the context of governance and that the two are mutually constitutive. The paper
thus adds to calls made by other researchers for a broadening
of research to provide a richer understanding of the actuality
of projects (Cicmil et al., 2006; Hällgren and Söderholm,
2011) and suggests a way in which this broader agenda can
be taken forward.
433
2. Epistemological spaces for categorising and critiquing
alternative explanations
The argument proposed here is that a proper critical discussion
of any explanation requires attention to its underlying assumptions about what we can know about the nature of the world.
Without attention to important differences in these assumptions
it is hard to gain proper purchase on sometimes rather subtle differences between explanations in approach and emphasis. While
this might seem a somewhat obvious point, it is by no means universal practice for academic writers to be reflexive, to critically
examine and justify their epistemological assumptions. As
Williamson (1985: 44) notes, for example, ‘many economists
treat behavioural assumptions as a matter of convenience’ and
subscribe to the opinion that ‘the realism of the assumptions is
unimportant and that the fruitfulness of a theory turns on its implications.’ This paper, by contrast, adopts the position taken by
those working within the Rethinking Project Management
Network (Cicmil et al., 2006; Winter et al., 2006), who call for
greater research attention to the actuality of projects and in
doing so place their epistemological assumptions about the
lived experience of project actors explicitly in centre stage. For
these researchers, fruitful theorizing is not about making abstract,
universal knowledge claims, but about ‘integrative pragmatic
theory and the development of social knowledge and wisdom
relevant to the context of project management practice’ (Cicmil
et al., 2006: 676).
2.1. Decision-maker cognition
Inspired by the ‘cognitive approach’ to project risk and uncertainty proposed by Winch and Maytorena (2011), we focus on
three alternative conceptions of decision-maker cognition:
optimizing, optimizing within limits, and satisficing. These are
presented in Table 1.
There is no suggestion that this represents an exhaustive treatment. There are also substantial bodies of work dealing with
spontaneous or unintentional rationality in the emergence of various macro-level legal and economic institutions (cf. Hayek,
1967; Kirzner, 1973; Menger, 1963), and in the evolutionary processes operating within and between firms (cf. Alchian, 1950;
Nelson and Winter, 1982). The focus here, however, is restricted
to those types of cognition that impute conscious intention to
actors, because explanations in the existing project governance
literature tend to emphasize conscious decisions about organizing
in projects.
Table 1
Assumptions about decision-maker cognition.
Cognition Category 1:
optimizing
All decision-makers have unlimited time, information and cognitive capacity, and make choices that
maximize their best interests
Cognition Category 2:
All decision-makers operate within constraints of limoptimizing within limits ited time, information and cognitive capacity, but still
maximize their best interests within those limits
Cognition Category 3:
All decision-makers operate within constraints of
satisficing
limited time, information and cognitive capacity,
and make choices that satisfy their aspiration levels
434
J. Sanderson / SciVerse ScienceDirect 30 (2012) 432–443
In its essentials, the optimizing form of cognition assumes
that decision-makers have perfect information about the range
of options that are potentially available to them, and that they
are able to fully assess these options and discriminate between
them in a way that allows a utility maximizing choice to be
made. This reduces decision-making to a cost-free, calculative
act in which the objective probability of, and utility associated
with, all future paths is known at the outset. The decisionmaker is cast as a superhuman, super-computer with unlimited
time and unlimited cognitive capabilities (Selten, 2001).
Although this view has been widely recognized as a normative
ideal (Schoemaker, 1982), particularly given the findings of
experimental research into the psychology of decision-making
which identify severe limitations on the cognitive capabilities of
real human beings (Gilovich et al., 2002; Kahneman et al.,
1982), the core idea of utility maximization still retains a strong
appeal for many economists and decision theorists. There is a pervasive or paradigmatic belief (Schoemaker, 1982; Winch and
Maytorena, 2011) that ‘the behaviour of human beings is approximately described by the theory of full rationality’ (Selten, 2001:
14). This assumption of approximate validity supports the entire
edifice of Bayesian decision theory and its many variants, all of
which posit subjective expected utility maximization (Savage,
1954; Schoemaker, 1982; von Neumann and Morgenstern,
1944).
The two alternative views of decision-maker cognition
presented in Table 1 are both associated in the literature with
the term ‘bounded rationality.’ This was first used in the seminal
work by Simon (1947). Simon's main concern was to understand
the impact of cognitive and resource limitations (time, money,
knowledge) on the decision-making process. His core proposition
was that decision-makers are ‘intendedly rational, but only limitedly so’ (Simon, 1947: 24). This is seemingly simple, but it is an
idea that has been interpreted very differently by different writers.
Economists like Sargent (1993), Stigler (1961), and
Williamson (1985) have placed greater emphasis on the ‘intendedly rational’ clause and downplayed the impact of cognitive
and other limitations. This leads to the assumption that
decision-makers still make an optimizing (maximizing or
minimizing) choice, even though they undertake a limited search
for alternatives and for information to help them choose between
those alternatives. This emphasis on optimizing within limits
produces some rather questionable arguments, however.
Williamson (1985), for example, argues that managers are fully
capable of aligning governance mechanisms with transactions
in a way that economizes on (minimizes) transaction costs,
while implicitly accepting that it is not feasible for all possible
governance mechanisms to be considered because of the limits
on search. But how is the properly minimizing choice to be
made without an exhaustive search for alternative mechanisms?
As Gigerenzer and Selten (2001: 5) observe ‘this attempt to
model limited search leads to the paradoxical result that the
models become even less psychologically plausible’ because
‘the desire for optimization is retained.’
It has been forcefully argued that the true legacy of Simon's
work is seen in models that treat decision-making as a process
of ‘satisficing’ (Selten, 2001). Such models are most closely
associated with the organisational decision-making tradition of
the Carnegie School, which characterises decision-making as a
socially constructed, iterative learning process (Cyert and
March, 1963; March and Simon, 1958). There is also some overlap here with Weick's (1979) work on sense-making in organizations. Unlike the optimizing and the optimizing within limits
views, this conception takes into account the organizational context within which decision-makers are operating, embracing the
complexities, ambiguities and conflicts of organizational life.
Simon (1955) described decision-making as a search process
guided by aspiration levels. An aspiration level is a goal (e.g. a
certain level of cost or a delivery date) that must be reached or
surpassed by a decision alternative. Decision alternatives are
not given at the outset, but must be found in a search process
limited by constraints on time, money and cognitive capacity.
The idea that decision-makers undertake a limited search process
is superficially similar to that articulated by the optimizing within
limits view, but it differs in one crucial respect. Here the search
process only continues until a satisfactory alternative is found.
The need to make an optimizing choice is abandoned in favour
of settling on the first choice that satisfies or surpasses the
decision-maker's aspiration level. Other better alternatives
might exist, but instead of seeking those out decision-makers
are assumed to apply a number of simplifying heuristics or ‘fast
and frugal rules’ (Gigerenzer and Selten, 2001: 9). There is
evidence that the simple heuristics associated with satisficing
can be as effective as the kind of complex statistical models that
optimizing implies (Gigerenzer and Selten, 2001; Martignon
and Laskey, 1999).
2.2. Decision-maker views on the nature of the future: risky or
uncertain?
The literature addressing megaprojects overlaps significantly
in its concerns with that dealing more generally with risk and
uncertainty. The defining characteristics of megaprojects combine to produce an environment in which the stakes, financial,
economic and social, are so high that there is an understandable
desire to ‘analyse and rationalise the future implications of pursuing a particular action before deciding to act’ (Broadbent et al.,
2008: 41). As Atkinson et al. (2006: 691) suggest, ‘the whole raison d'être of project management is to remove (or substantially
reduce) uncertainty about meeting specified objectives.’ Many
texts on the megaproject phenomenon therefore typically devote
significant attention to techniques and models that are designed to
identify, assess and ultimately manage the risks and uncertainties
associated with such endeavours (cf. Flyvbjerg et al., 2003; Loch
et al., 2006; Miller and Lessard, 2000; Williams et al., 2009). The
problem is that few of these texts give full consideration to the
vital prior questions of whether, and if so how, risk differs from
uncertainty. Instead, there is a tendency to conflate these terms
and to use them interchangeably, which in effect means that
uncertainty is either treated in the same way as risk or ignored
(Froud, 2003; Perminova et al., 2008). There is a real danger
therefore that a whole range of potentially very significant issues
is silenced in the decision-making process, and a tendency to
focus on operational planning and control to the detriment of
J. Sanderson / SciVerse ScienceDirect 30 (2012) 432–443
strategic issues (Atkinson et al., 2006; Broadbent, et al., 2008;
Ward and Chapman, 2003).
The discussion here provides a corrective to this tendency by
drawing out the key distinctions between risk and uncertainty.
This is based on the seminal contributions of Keynes (1937)
and Knight (1921) and subsequent clarifications and extensions
by other authors (cf. Davidson, 1991, 1995; Froud, 2003;
LeRoy and Singell, 1987; Perminova et al., 2008; Runde, 1998;
Shackle, 1955; Winch and Maytorena, 2011). The categorization
presented in Table 2 adopts the same subjectivist position
employed by Winch and Maytorena (2011), which means that
the distinctions drawn relate to states of mind – views about the
future – rather than future states of nature.
According to Knight (1921) the concept of risk falls into two
distinct categories based, as shown in Table 2, on a priori probability and statistical probability. The common feature in both
cases is that decision-makers are assumed to be able to assign
objective probabilities to a known range of future events or
outcomes. The main difference, however, is that while probability in Risk Category 1 is mathematically derived (e.g. the probability of throwing a six with a perfect die is 1/6), probability in
Risk Category 2 is calculated on the basis of empirical data
about a certain class of events in the past (e.g. the probability of
being involved in a fire).
Based on this distinction Knight (1921: 215–16) makes two
further important observations. First, that the ‘mathematical or a
priori type of probability is practically never met with in business, while the [statistical type] is extremely common;’ second,
that ‘the statistical treatment [of probability] never gives closely
accurate quantitative results.’ Knight thus accepts that in the
case of Risk Category 2 there will necessarily be a degree of judgment about whether a particular future event or outcome is likely
to be sufficiently similar to a certain class of past instances to be
grouped with those for the purposes of calculating a probability.
Winch and Maytorena (2011: 357) make the same observation,
referring to Savage's ‘notion that the relevance of the available
dataset for the current decision situation remains a subjective
judgment.’ This suggests that the ‘objective’ probabilities associated with Risk Category 2 can be deemed broadly accurate, but
never precise in the same way as ‘throws of a perfect die or
spins of well-calibrated roulette wheels’ (Runde, 1998: 541).
Nevertheless, as McGoun (1995) has observed, this reference
class problem has been largely ignored in the orthodox economics literature for the sake of calculative convenience. It is assumed
that the future will be (more or less) like the past, or as Davidson
435
(1995: 107) puts it ‘[f]uture outcomes are conceived as merely the
statistical shadow of the past and current price signals.’ Decisionmakers are thus able to proceed on the basis that their actions will
lead to (relatively) predictable outcomes and an optimizing orientation is possible.
The first category of uncertainty in Table 2 is associated with
what Knight (1921: 225) calls ‘estimates’, which he relates to
situations in which ‘there is no valid basis of any kind for classifying instances.’ This means that although decision-makers face a
known range of possible future events or outcomes, it is impossible for them to calculate the objective probability of each because
the events are so dissimilar as to prevent grouping into sufficiently large reference classes. These are what Winch and Maytorena
(2011: 357) call ‘known unknowns.’ Uncertainty in this case is a
function, as Thiry (2002) proposes, of a lack of relevant and
reliable data. This definition of uncertainty is echoed by Keynes
(1937: 113–14) who said that the sense in which he used the
term was ‘that in which the prospect of a European war is uncertain, or the price of copper and the rate of interest twenty years
hence…About these matters there is no scientific basis on which
to form any calculable probability whatever.’
This does not mean, however, that Knight (1921) completely
denies the relevance of probability to situations of uncertainty.
Instead ‘Knight was at pains both to emphasize the distinction between objective and subjective probability and to stress that subjective probabilities apply even in situations of uncertainty’(LeRoy
and Singell, 1987: 397). Runde (1998: 541) makes the same
point when he comments that Knight seems to be thinking in
terms of ‘a continuum of probability situations’ with a priori probability at one end and estimates linked to subjective probability at
the other. As Knight (1921: 226) himself said, ‘Yet it is true…that
a judgment of probability is actually made in such cases [under
uncertainty]. The business man himself not merely forms the best
estimate he can of the outcome of his actions, be he is likely also
to estimate the probability that his estimate is correct.’
In situations of Uncertainty Category 1, then, decision-makers
are assumed to use beliefs or expectations grounded in historical
practice to estimate the likelihood (subjective probability) of various possible future events or outcomes. This approach is most
commonly associated with Bayesian decision theory, which suggests that decision-makers are able to choose consistently among
uncertain outcomes in a way that maximizes subjective expected
utility (Dow, 1995; LeRoy and Singell, 1987). It is also the perspective that is adopted by transaction cost economics (Slater
and Spencer, 2000).
Table 2
Assumptions about decision-maker views on the nature of the future.
Risk Category 1: a priori
probability
Risk Category 2: statistical
probability
Uncertainty Category 1: subjective
probability
The decision-maker's view is that they are is able to assign objective probabilities to a known range of future events on the basis of
mathematically ‘known chances’, e.g. the probability of throwing a six with a perfect die is 1 in 6.
The decision-maker's view is that they are able to assign objective probabilities to a known range of future events on the basis of
empirical/statistical data about such events in the past, e.g. the probability of being involved in a building fire.
The decision-maker's view is that they face a known range of possible future events, but lack the data necessary to assign objective
probabilities to each. Instead they use expectations grounded in historical practice to estimate the subjective probability of future
events — akin to scenario planning.
Uncertainty Category 2: socialized The decision-maker's view is that they face a situation in which the nature and range of future events is unknown, not simply hard to
understand because of a lack of relevant data. The future is inherently unknowable, because it is socially constructed and may bear
little or no relation to the past or the present.
436
J. Sanderson / SciVerse ScienceDirect 30 (2012) 432–443
There are good reasons, however, to be critical of this view of
decision-making under uncertainty. As Kelsey and Quiggin
(1992: 135) comment, for example, ‘one might expect people to
have trouble assigning subjective probabilities in unfamiliar
circumstances or when they have little evidence concerning the
relevant variables.’ One can easily imagine such unfamiliar
circumstances or such a lack of evidence in decisions associated
with a complex, one-off infrastructure project (Froud, 2003).
Atkinson et al. (2006) explicitly identify uncertainty in estimates
as a major feature of complex or novel projects. This suggests that
there might be another category of uncertainty beyond that envisaged by Knight. The problem with Uncertainty Category 1 is that
it is really just an extension of Risk Category 2, based on the
shared assumption that decision-makers face a known range of
possible future events that can be ranked probabilistically. The
only difference is in the accuracy and objectivity of the probability statements, which are a ‘matter of degree rather than a matter
of kind’ (Runde, 1998: 542).
This second category is described, as Table 2 shows, as socialized uncertainty (Froud, 2003; Slater and Spencer, 2000). In this
case, decision-makers face a situation in which the nature and
range of future events or outcomes is unknown and unknowable,
not simply hard to predict because of a lack of relevant data. As
Rotheim (1995: 65) observes, even where good information
exists in the present it ‘cannot provide a serviceable guide to future outcomes where it is unreasonable to presume an unchanging
reality.’ This links to the argument that the world is sometimes
characterised by non-ergodic processes, which suggests that the
future might in some circumstances bear little or no relation to
the past or the present (cf. Davidson, 1991; Hicks, 1979). As
Davidson (1991: 134) comments, ‘an environment of true uncertainty (that is one which is non-ergodic) occurs whenever an
individual cannot specify and/or order a complete set of prospects
regarding the future.’ This, he argues, is most likely if past events
are seen as unique, such that it becomes ‘impossible to group past
observations to form either subjective probabilities or relative
frequencies’ (Davidson, 1991: 135).
This category is broadly synonymous with the condition of
‘unknown unknowns’ identified by, for example, Loch et al.
(2006), Taleb (2007) and Winch and Maytorena (2011). The
future is assumed to be inherently unknowable in the present,
because it is socially constructed and therefore transmutable.
The future is seen as a created process shaped by the nature and
pattern of decisions made now and in the future. The complex
and ever-changing interdependencies in this pattern of decisions,
in turn, impart radical indeterminacy to future outcomes. As
Minsky (1996: 360) comments, ‘a myriad of independent agents
make decisions whose impacts are aggregated into outcomes that
emerge over a range of tomorrows.’ In this category of uncertainty, then, the core problem is not the difficulty of accessing
relevant and accurate data on which to base decisions about the
future. Rather, it is the fact that such data do not yet exist and
that they will only exist once those decisions have been taken
and their effects felt. Atkinson et al. (2006: 688) describe this in
a project context as ‘uncertainty introduced by the existence of
multiple parties.’ This socialized uncertainty does not, however,
lead people to inaction. People still make decisions; they are
‘architects of the future’ (Slater and Spencer, 2000: 77). It is
just that they have no way of knowing with any degree of precision where those decisions will take them. As Atkinson et al.
(2006: 696) observe, this conceptualisation reflects ‘the actuality
of projects as social processes requiring ongoing construction of
the appearance of certainty and clarity in the midst of complex
uncertainty and ambiguity.’
3. Alternative explanations of megaproject performance
3.1. Text selection
The process used to identify the texts discussed here was a
version of snowball sampling, where an initial set of key selections led onto further selections (Scarbrough et al., 2004). The
sampling involved an initial scan of the literature, both manually
and electronically, using a number of predefined search terms:
‘megaprojects,’ ‘large-scale projects,’ ‘major projects,’ ‘complex
projects,’ ‘governance,’ ‘risk,’ ‘uncertainty,’ and ‘performance.’
This initial scan identified three prominent strands of megaproject
governance research, illustrated in Table 3. The decision was
taken to focus on these, because there was substantial evidence
of debate and cross-referencing between them. Also, subsequent
scanning of the literature, following the logic of conceptual saturation (Guest et al., 2006), indicated that these were the best
defined strands of research.
Table 3 is organized around three substantive categories –
arguments, solutions and assumptions – which enable a direct
comparison of the main features of each type of explanation.
The aim of the discussion that follows is to uncover both key differences and similarities between the different types of explanation. One obvious difference is that each explanation focuses on
a different point in the project life-cycle. For all of the texts discussed it was relatively easy to identify the arguments and the
suggested solutions for dealing with underperformance in megaprojects. In most of the texts, however, the underpinning epistemological assumptions remain largely implicit. Consequently
the assumptions identified in Table 3, which draw on the conceptual categories discussed above and summarised in Tables 1 and
2, are based on an interpretation of what is most consistent with
the logic of the argument being put forward.
3.2. Arguments
The core argument advanced by Explanation Type A (cf.
Davidson and Huot, 1989; Flyvbjerg, 2009; Flyvbjerg et al.,
2002, 2003, 2005; Wachs, 1989, 1990) is that the performance
of megaprojects is often disappointing, because non-viable projects are so regularly undertaken. It is suggested that those actors
with a vested interest in seeing projects undertaken, typically
politicians and contractors, engage in intentional and strategic
rent-seeking behaviour to get projects approved and to win associated contracts. This rent-seeking behaviour takes the form of
systematically under-estimating project costs, over-estimating
project benefits and being over-optimistic with project scheduling. These under and over-estimates are not seen as an honest
mistake or a function of poor technical skills and inadequate
437
J. Sanderson / SciVerse ScienceDirect 30 (2012) 432–443
Table 3
A comparison of alternative explanations of megaproject performance.
Explanation type A strategic
rent-seeking behaviour
Explanation type B misaligned and
underdeveloped governance
Arguments
Project promoters and contractors regularly
engage in intentional rent-seeking behaviour
(under-estimating costs, over-estimating benefits)
to get non-viable projects approved
Solutions
Legal requirement for thorough ex ante risk
analysis and management plan; limit role of
politicians to formulating and auditing public
interest objectives; various ex ante measures to
improve accountability of project decision-making
Decision-maker cognition — optimizing
Projects subject to processes of social
construction and characterized by diverse and
often competing cultures and rationalities problems result from normal day-to-day
management practice
Conscious design and creation at the front-end of Conscious design and creation at the front-end
the project of mechanisms that enhance ex post
of the project of a shared culture supported by
governability; mechanisms must be appropriate to governance mechanisms to encourage
the particular context of the project
collaborative and coordinated behaviour
Problems result from misaligned or
underdeveloped governance arrangements
incapable of handling the emergent turbulence
inevitably associated with megaprojects
Decision-maker cognition — optimizing
within limits
Decision-maker view of the future —
Decision-maker view of the future —
Risk Category 2
Uncertainty Category 1
Selected texts Davidson and Huot, 1989; Wachs, 1989, 1990;
Miller and Lessard, 2000; De Meyer et al., 2002;
Flyvbjerg et al., 2002, 2003, 2005; Flyvbjerg, 2009 Loch et al., 2006; Miller and Hobbs, 2009;
Morris, 2009; Winch, 2009 (2001, 2009)
Assumptions
Explanation type C diverse project
cultures and rationalities
data. Rather, they are attributed to straightforward ‘deception and
lying as tactics aimed at getting projects started’ (Flyvbjerg et al.,
2003: 47).
These authors argue that such rent-seeking behaviour is
encouraged because the incentives to produce over-optimistic
estimates of project viability are very strong and the disincentives
relatively weak. Given the very lengthy time-frames that apply to
megaproject development and implementation, there is a lack of
proper accountability for project promoters, typically politicians,
because they are often not in office when the actual viability of a
project can be assessed. Getting a project approved will deliver
significant political capital in the short-term, however. Similarly,
it is argued that the accountability of contractors for their behaviour is weak, because the contractual penalties for producing
over-optimistic tenders are often low compared to the potential
profits involved (Davidson and Huot, 1989; Wachs, 1990).
Turning to Explanation Type B (cf. De Meyer et al., 2002;
Loch et al., 2006; Miller and Hobbs, 2009; Miller and
Lessard, 2000; Morris, 2009; Winch, 2009), the core argument
is that the underperformance of many megaprojects is best
explained by the presence of incoherent, inappropriate or
underdeveloped governance arrangements that are incapable
of handling the risks, uncertainties and turbulence inevitably
associated with these endeavours. In contrast to Explanation
Type A, which looks primarily at the ex ante behaviour of
vested interests, this line of argument places its emphasis on
the ex post managerial challenges of coping with emergent
and unforeseen turbulence. The complexity, scope and scale,
and the long time frames of megaprojects are seen as major
reasons for the significant turbulence experienced in most
cases. Turbulence is seen to originate either from exogenous
events in the broader macroeconomic, political, social and natural environments, or from endogenous events within and
between the organizations directly involved in a project. In
the latter case, emphasis is placed on contractual disputes and
the breakdown of partnerships or alliances.
Decision-maker cognition — satisficing
Decision-maker view of the future —
Uncertainty Category 2
Alderman et al., 2005; Atkinson et al., 2006;
Clegg et al., 2002; Pitsis et al., 2003;
van Marrewijk et al., 2008
The main conclusion of authors offering this type of explanation, then, is that megaprojects perform badly when they lack
sufficiently robust and flexible governance arrangements with
which to navigate and resolve such emergent issues. As Miller
and Lessard (2000: 23) argue, for example, ‘[t]he presence of
coherent and well-developed institutional arrangements is, without a doubt, the most important determinant of project performance. Projects shaped in incomplete and shifting arrangements
have a hard time taking off.’ The discussion of the Channel tunnel
case in Loch et al (2006: 212–18) makes the same links between
governance arrangements that were inadequate for handling
severe turbulence (political, technical, contractual) and unsatisfactory project performance.
Finally, the core argument of Explanation Type C (cf. Atkinson
et al., 2006; Clegg et al., 2002, 2006; Pitsis et al., 2003; van
Marrewijk et al., 2008) is that megaprojects are typically characterized by multiple and diverse discourses, cultures and rationalities
rather than by a singular, shared rationality as is assumed by
more orthodox, technicist perspectives. This means that different
actors within a project understand inputs to and outputs from the
project in very different, incomplete and often competing ways.
For example, the contractual documents and other boundary
objects used to define and coordinate the roles and
responsibilities of the project actors are often highly ambiguous
in meaning (Alderman et al., 2005) and provide substantial scope
for ‘language games’ leading to ‘contested action in complex,
inter-organizational and professional disputes’ (van Marrewijk
et al., 2008: 592). Similarly, megaprojects are seen to exhibit a
number of ‘soft’ characteristics (Crawford and Pollack, 2004),
wherein ‘multiple world views and perspectives are recognised
and solutions are developed through negotiations and debate
between multiple stakeholders’ (Atkinson et al., 2006: 692).
In this type of argument, then, the performance problems of
many megaprojects are neither a function of ex ante strategic
and intentional rent-seeking behaviour, nor of an absence of adequate governance arrangements to deal with ex post turbulence.
438
J. Sanderson / SciVerse ScienceDirect 30 (2012) 432–443
Instead, performance problems are an almost inevitable result of
the normal day-to-day practice of managers trying to cope with
an organizational environment that is complex, ambiguous and
often highly conflictual. Much less attention is given to exogenous sources of turbulence than is the case in Explanation Type
B. The focus here is said to be contextually grounded, looking
in detail at actual practice within project organizations. As van
Marrewijk et al. (2008: 592) comment, ‘[t]his approach recognises that project environments are subject to processes of social
construction, in which participants construct a more or less stable
working environment for themselves’. It is concluded that where
project participants are able to construct a relatively stable environment, which promotes peaceful cooperation between differing
cultures, there is a greater chance of good project performance.
3.3. Solutions
For those authors representing Explanation Type A the core
problem in megaprojects is a lack of mechanisms for enforcing
proper accountability to prevent rent-seeking behaviour that
leads directly to non-viable projects being undertaken. The solution for these authors, then, is to create well defined policies,
procedures and institutional structures that strengthen the
accountability of key project actors.
Flyvbjerg et al. (2003: 107–24) provide perhaps the most
comprehensive set of recommendations. They suggest, first,
that there should be an explicit legal requirement for the relevant
organization within the civil service to carry out a full risk analysis as part of an ex ante feasibility study for every megaproject.
This risk analysis should be made freely available to all interested
parties (public, parliaments, media, investors etc.) and a detailed
risk management plan should be drawn up. Next, they suggest
that there should be a clearer, less conflicting role for public
sector actors, particularly politicians. Politicians should not act
as project promoters or provide sovereign guarantees to private
sector investors. Instead, their role should be limited to the formulation and auditing of the public interest objectives to be met by a
project. Finally, the accountability of project decision-making
should be improved by the greater involvement of stakeholder
groups and civil society to ensure proper transparency; the use
of detailed and measurable output (performance) rather than
input specifications; the designing, ex ante, of a comprehensive
regulatory and enforcement regime linked to the risk management plan; and the use of private sector risk capital as the major
source of finance.
For those authors representing Explanation Type B the core
reason for underperformance in megaprojects is a lack of robust
and coherent governance capacity or ‘governability’ (Miller and
Floricel, 2000: 137). This is the capacity of project participants
to cope with any turbulence that emerges over the life of the project. The principal set of solutions focuses, then, on the ex ante
design of governance mechanisms. This is described as ‘planning
for the journey rather than planning the journey’ (Miller and
Lessard, 2000: 203), in contrast to the much more detailed risk
analysis, risk allocation and planning proposed by exponents of
Explanation Type A.
Broadly speaking, these governance mechanisms are intended
to build stronger, more cooperative and more flexible relationships
between project participants. Examples might include an alliance
ownership structure, combining balanced equity positions with a
strong leader; financial guarantees from government to support
project financiers; rendezvous clauses which make it possible to
revisit parts of a contract by officially deferring to a later date discussion of matters on which agreement cannot be reached at the
outset; integrated project teams with financial incentives to stimulate innovation; and multiple sources of finance to diversify dependencies (Miller and Floricel, 2000). There is also an explicit
recognition, however, that the governance mechanisms selected
and designed must be appropriate to the particular context and
characteristics of a project (Miller and Floricel, 2000; Winch,
2009).
Those texts representing Explanation Type C are much less
instrumental than either Types A or B in their approach to the notion of ‘solutions’ to performance problems. Specific recommendations for generalizable best or even better practice are not
typically offered, but potential solutions are still suggested by
the linkages drawn between certain observed actions and
outcomes. So, given that for these authors performance problems
are a function of normal day-to-day complexity, ambiguity and
conflict between the different cultures and rationalities represented within projects, solutions are about taming that complexity
and ambiguity and channelling that conflict in more productive
directions. This encompasses ‘generic management processes
associated with building trust, sense-making, organisation learning, and building an appropriate organizational culture’
(Atkinson et al., 2006: 688).
One such solution is identified in a series of articles reporting
research on a project to build a 20 kilometre long tunnel under the
area north of Sydney Harbour in the run up to the Olympic
Games in 2000 (Clegg et al., 2002; Pitsis et al., 2003; van
Marrewijk et al., 2008). These authors link the broadly successful
delivery of this project – on time and only slightly over budget –
to the decision at the beginning of the process to create ‘a project
culture that was explicitly designed and crafted to encourage
shared behaviours, decision-making, and values’ (Pitsis et al.,
2003: 576). A number of governance mechanisms were used to
underpin this collaborative project culture, including a formal
statement of key values as a basis for resolving disputes internally
and a risk/reward regime based on monetized key performance
indictors (KPIs). The institutional element of project governance
– the Project Alliance Leadership Team (PALT) – was explicitly
created to be legally and spatially separate from the four parent
organizations. The intention was ‘to produce a designer culture
for the project rather than have it as an arena in which the various
project organizations’ cultures fought for dominance (van
Marrewijk et al., 2008: 595).
Interestingly, there seems little to differentiate this potential
solution from many of the consciously designed governance
mechanisms suggested by Explanation Type B. There is one
notable difference, however. Alongside this discussion of a
designer culture there is a much more explicit and detailed focusing on the day-to-day practices of people within the project. Most
significantly, based on observations from meetings of the PALT
J. Sanderson / SciVerse ScienceDirect 30 (2012) 432–443
the researchers arrived at the idea that the project was being
managed through what they call a ‘future perfect strategy.’ It is
suggested that managers dealt with the pressure to deliver an
innovative project outcome in circumstances of extreme complexity, ambiguity and uncertainty by combining a forward looking projection of desired ends with a visualization of the means to
achieve that projected future. This is differentiated from scenario
planning on the basis that it is emergent and subject to constant
revision rather than being explicitly scripted and grounded in
past expectations (Pitsis et al., 2003: 574–75).
While this is an important insight into practices on the ground,
it seems reasonable to suggest that the real crux of this explanation still lies in the notion of a consciously designed project
culture. The formal statement of collaborative values and monetized KPIs enshrined in the culture acted as powerful incentives,
driving participants in the project to ‘think creatively and laterally
to come up with solutions considered best for the project rather
than merely to implement second-best solutions already known
from previous projects’ (Pitsis et al., 2003: 577). It might be
argued, then, that future perfect thinking was really a result of
the context and incentives created by the designer culture rather
than an independent explanatory factor.
3.4. Assumptions
Each of the explanation types discussed here is based on a
particular world-view within which a number of important epistemological assumptions are embedded. This discussion draws on
the assumptions categorized in Tables 1 and 2.
Explanation Type A is built on a view of world in which some
actors are able to calculate the likely future path of a project,
based on data from previous similar projects, and therefore
could, if they chose to, produce an appropriate and comprehensive plan of action and associated governance structures and
contractual documents to ensure a successful outcome. The core
argument of this type of explanation is that these key actors
consciously choose not to use this ability and instead opportunistically exploit information asymmetries by misrepresenting the
future path of the project, in terms of costs, benefits, and schedule, to serve their own interests. In summary then, Explanation
Type A sees actors as being prone to opportunistic behaviour;
as being able to make choices that fully optimize that their own
position (Cognition Category 1); and as having the view that
project outcomes are calculable using statistical probability
(Risk Category 2).
The solutions suggested by Explanation Type A are, unsurprisingly, premised upon the same world view. The manifesto
put forward by Flyvbjerg et al. (2003) is in essence an attempt
to address politician and contractor opportunism by designing
comprehensive ex ante governance arrangements containing
institutional and legal mechanisms to enable more effective ex
post monitoring and enforcement. Explanation Type A thus
draws on the notion of what Williamson (1985: 67) calls ‘comprehensive contracting,’ wherein detailed analysis, planning and
mechanism design in the pre-project stage are assumed to deliver
better control in the project delivery stage. Future actions and
their outcomes are assumed to be knowable in sufficient detail
439
in the present for them to be encompassed within unambiguous
and legally enforceable (complete) contracts.
Explanation Type B occupies the same conceptual landscape
as transaction cost economics (cf. Williamson, 1985, 1996), and
indeed Winch (2001, 2009) explicitly acknowledges his association with this school of thought. The world view here is similar to
that advanced by Explanation Type A in that actors are assumed
to be opportunistic, exploiting information asymmetries to
achieve an advantage. As Miller and Lessard (2000: 22–3) note
in their discussion of factors that might cause turbulence in a
project, ‘[p]arties know that opportunistic actions pay off; agreements, community of interests, and reputation are then pushed
aside.’ Unlike Explanation Type A, however, decision-makers
are assumed to have cognitive and other resource limits on their
ability to optimize and to operate in an environment in which
project outcomes are often seen to be uncertain.
The core argument is that projects experience difficulties
primarily ‘because sponsors cannot rise to the managerial
challenges of coping with unforeseen turbulence’ (Miller and
Lessard, 2000: 22). This does not mean that the future is regarded
as unforeseeable or unknowable, however, in the way that it
would be under a socialized conception of uncertainty. Instead,
the future is seen to be characterised by known unknowns
(Uncertainty Category 1). Decision-makers find it difficult to assign objective probabilities to future project events, because they
lack sufficient, reliable reference class data. Actors can though
look into the future and discern possible events, including sources
of turbulence, and assign a subjective probability to these events
based on past experience. This assumption is intrinsic to the core
solution offered by Explanation Type B, the ex ante design of appropriate and robust governance arrangements to enhance project
governability. The idea that actors are able to consciously design
and build governance arrangements that are appropriate to a project's particular context also assumes an optimizing orientation
(Cognition Category 2), an echo of the discriminating alignment
argument in transaction cost economics (Williamson, 1996). This
alignment is explicitly associated, though, with decision-making
as a limited search process. Governance options have to be
discovered using ‘experience and judgement’ rather than being
given at the outset (Miller and Floricel, 2000: 149).
Finally, Explanation Type C inhabits a world-view in which
project actors satisfice in their decision-making, because they
have limited time, cognitive capacity and other resources (Cognition Category 3). Moreover, the future path of any project is seen
to a significant extent as unknown and unknowable, because the
environment of each project is socially constructed by a group of
actors interacting as though for the first time (Uncertainty
Category 2). Project performance problems are not attributed to
actor opportunism in this case, but to simple self-interest seeking
in the context of many different and competing cultures and rationalities, and to imperfect decision-making as a result of bounded
rationality (incomplete knowledge).
One of the governance solutions suggested in this category of
explanation – a consciously designed project culture to encourage trusting and collaborative behaviour – reveals some interesting inconsistencies and tensions with the underlying world-view.
On one hand we have actors who are assumed to satisfice under
440
J. Sanderson / SciVerse ScienceDirect 30 (2012) 432–443
conditions of bounded rationality and who face a project future
that is seen as largely unknowable. On the other we have the
idea that the success of a project is intimately linked to the
creation of a shared culture underpinned by a formal governance
institution (the PALT) and explicit incentives to collaborate
(monetized KPIs). The crucial point here is that the project culture and its supporting mechanisms are designed ex ante to deal
with future events that are assumed to be unknowable in the
present. The idea of a penalty/reward scheme based on measurable KPIs is perhaps the most obvious locus of this tension
between conscious ex ante design and an unknowable future.
How can meaningful targets, linked to a scale of financial penalties and rewards, be set for such performance indicators without
appropriate benchmarks? Performance benchmarks are normally
created by looking at historical data, but if it is assumed that the
future is unlikely to reflect the past then this association breaks
down. It is suggested that this tension was resolved in the Sydney
Harbour tunnel case by using ‘future perfect thinking’ to imagine
what various performance levels, from poor to outstanding,
would look like so that actual performance could be subject to
audit (Pitsis et al., 2003). It would appear, then, that to make
sense of how the project was managed an assumption that the
future is in many ways unknowable has been relaxed in favour
of an acceptance of actor farsightedness.
4. Conclusion: from farsighted governance to
spontaneous governing
This paper has identified three distinct types of explanation for
the performance problems that plague many megaprojects. The
strategic rent-seeking explanation argues that project underperformance is a function of pre-planned opportunistic behaviour
by key vested interests leading to the regular approval of nonviable projects (cf. Davidson and Huot, 1989; Flyvbjerg, 2009;
Flyvbjerg et al., 2002, 2003, 2005; Wachs, 1989, 1990). The explanation that focuses on governance arrangements argues that
performance problems are a result of misaligned or underdeveloped governance mechanisms, which in turn mean that project
actors are unable to provide a sufficiently flexible and robust
response to inevitable turbulence (cf. De Meyer et al., 2002;
Loch et al., 2006; Miller and Hobbs, 2009; Miller and Lessard,
2000; Morris, 2009; Winch, 2001, 2009). Finally, the explanation
that focuses on diverse and competing project cultures and
rationalities argues that performance problems are an almost
inevitable result of the organisational complexity, ambiguity
and conflict facing project actors on a day-to-day basis (cf.
Atkinson et al., 2006; Clegg et al., 2002, 2006; Pitsis et al.,
2003; van Marrewijk et al., 2008).
Despite their differences in argument, suggested solutions and
underlying assumptions, however, these explanations do share
one significant feature — an acceptance of the notion of actor
farsightedness. In a broad sense, this means that all three explanations accept that actors can and should prepare for the future
before it has happened. Having said that, interpretations of what
it means to be farsighted do differ between explanations. This is
unsurprising given their different assumptions about decisionmaker views on the nature of the future.
So, Explanation Type A, based on an assumption that
decision-makers see future events and project outcomes as risky
and therefore susceptible to detailed analysis and contingency
planning, adheres to the most extreme version of farsightedness.
Actors can and should undertake comprehensive ex ante contracting and thereby control the future. Explanation Type B presents a
less extreme position. It assumes that decision-makers see project
outcomes as uncertain, but only in the sense that they lack the
necessary reference class data to undertake a calculation of statistical probability. Experience and judgement still allow actors to
be farsighted enough to know the range of possible future events,
to rank them based on subjective probability, and therefore to prepare appropriate governance arrangements ex ante to manage
those events ex post. This reflects the position taken in transaction
cost economics (cf. Williamson, 1985, 1996). Explanation Type
C presents an apparent paradox. On one hand it assumes that
decision-makers see projects as taking place in an environment
characterized, at least in part, by socialized uncertainty — future
outcomes are socially constructed and therefore largely unknowable in the present. Farsightedness thus appears to be substantially ruled out. On the other it argues that ex post problems can be
more effectively addressed if a collaborative project culture is
consciously designed ex ante. This is only possible if actors are
assumed to be sufficiently farsighted to design a culture that
will effectively handle likely events in a project. One reading of
farsightedness in this case is in terms of the notion of future
perfect thinking (Pitsis et al., 2003) being used to generate ‘a coherent story about the future’ (Winch and Maytorena, 2011: 360).
The key gap in all of these explanations relates therefore to
their emphasis on project governance as a form of organization
that can be consciously designed ex ante. As a result of their acceptance of actor farsightedness, none gives sufficient attention to
project governing manifested as spontaneous micro-processes of
organizing as a project unfolds ex post. The focus is on what Chia
and Holt (2009) call made order. The various pre-designed forms
of governance – contracts, agreements, alliances, KPIs – are
imbued with a sense of concreteness and permanence, which constrains, shapes and directs interactions between the actors
involved in a project. There is, of course, recognition that actors
might consciously choose to renegotiate the terms of governance
during a project, but this simply represents the replacement of one
made order with another. The possibility that agency might spontaneously (re)create structure, that the activities of governing
might (re)create the forms of governance, is downplayed or ignored. A key reason why such spontaneous recreation might
take place, governance mechanism incompleteness, is also either
discounted by definition – farsightedness means it does not exist
– or interpreted as a consciously identified opportunity for
‘continuous improvement in search of excellence’ (Clegg et al.,
2002: 333).
This paper agrees with the general argument that research on
projects ‘should spend less time looking at strategic planning
and more time researching everyday organizational life’ (Pitsis
et al., 2003: 588), and supports similar calls for a greater focus
on the ‘actuality of project based working and management’
(Cicmil et al., 2006: 675) to stimulate a more reflexive and developmental approach to understanding project performance. The
J. Sanderson / SciVerse ScienceDirect 30 (2012) 432–443
Explanation Type C texts discussed here have opened up this line
of enquiry. They provide some vital insights into the importance
for project performance of building organisational infrastructure,
capabilities and culture, what might broadly be called governance
capacity, to facilitate trusting and collaborative behaviours in the
face of uncertainty (Atkinson et al., 2006; Clegg et al., 2002;
Pitsis et al., 2003). They remain largely focused, however, on
governance as a consciously designed form of organization.
The core argument being made here, then, is that such explanations do not go far enough and that future research must give greater attention to spontaneous governing within projects if we are to
develop a richer understanding of why they develop as they do.
This is not to say that a focus on project governing should replace
a focus on project governance, but rather that there should be an
appreciation of both levels of analysis. One possible source of
inspiration for this research agenda is the so-called ‘projects-aspractice’ approach (Bechky, 2006; Blomquist et al., 2010;
Hällgren and Söderholm, 2011; Hällgren and Wilson, 2007,
2008), which draws heavily on the ‘strategy-as-practice’ approach
(Jarzabkowski, 2004; Jarzabkowski et al., 2007; Samra-Fredericks,
2003; Whittington, 1996, 2003, 2006).
There are a number of related reasons why the projects-aspractice approach might be a useful springboard for this greater
focus on spontaneous governing in projects. First, researchers following this approach adopt a primarily micro-analytic focus upon
the day-to-day activities of management practitioners and their
meaning in a specific social setting. A link is made between
what managers do to get the job done – praxis – and the socially
defined norms, values, rules and policies – collectively termed
practices – that they draw upon and in turn (re)construct when
acting (Jarzabkowski et al., 2007; Whittington, 2006). It is
noted that practices exist on a number of levels: micro (suborganizational), meso (organizational) and macro (extra-organizational) (Hällgren and Söderholm, 2011; Whittington, 2006).
Consequently, this approach is not entirely focused upon the
micro level of analysis, but it does take seriously ‘the nitty gritty
work of practitioners’ (Whittington, 1996: 732) as a micro-basis
for understanding organizational phenomena such as projects.
As Hällgren and Söderholm (2011: 502) put it, a ‘practice
approach treats the project as the constantly renegotiated sum of
the activities of the individuals involved’.
Second, the approach draws attention to the wide range of
actors involved, both formally and informally, in creating a project, in making it happen. Orthodox research has typically
focused on those with a direct and formal role and responsibility
for managing the project. This is because the project is given a
priori status as an object, a property of the organization or organizations carrying it out, with pre-specified content (objectives,
designs, and methods). It is therefore a thing to be managed by
a discrete group of actors, called project managers, who are
deemed to have the necessary experience and expertise. The
projects-as-practice approach challenges this relationship
between a pre-defined object and a narrow group of actors and
suggests instead that researchers should be interested in ‘anyone
involved in the making, shaping, and execution of the project’
(Hällgren and Söderholm, 2011: 507). Projects, according to
this logic, are continuously constituted and reconstituted through
441
the socially situated activities of all of the practitioners involved,
however tangentially. Applying this to our proposed research
agenda, we can suggest that governing in projects should not be
studied solely on the basis of an a priori assumption that there
is a discrete set of organizational artefacts and actors formally associated with governance. Research should recognise that project
governing, like the project itself, only exists after it has been constituted by praxis drawing upon certain practices.
Third, the projects-as-practice approach emphasizes the
relevance and importance of emergent, non-programmed, in
other words spontaneous, work activities for an understanding
of how a project develops. Consequently, it provides the
analytical scope to encompass spontaneous project governing.
According to the practice approach ‘project work is seldom just
about tools; rather it is constant small changes in the activities
that keep naturally unstable projects stable’ (Hällgren and
Söderholm, 2011: 506). For example, Hällgren and Wilson's
(2007, 2008) research into the management of deviations in
projects showed how project managers assembled ad hoc
response teams. The focus of these teams was on getting the
job done rather than following a set of pre-determined assumptions about how the work should be done. Similarly,
Söderholm (2008) showed how managers took spontaneous,
non-programmed action to deal with unexpected events in a
project, using either innovative activity, detachment from
other project activities, intensive meetings, or negotiation of
project conditions.
The projects-as-practice approach has obvious potential, therefore, as a platform for the research agenda suggested here, because
it is focused upon ‘organizing rather than organization, on becoming rather than being’ (Hällgren and Söderholm, 2011: 514). There
are undoubtedly significant epistemological and methodological
challenges associated with this approach, in particular the pattern
and relevance challenges discussed by Smyth and Morris (2007)
and Hällgren and Söderholm (2011). These force us to ask how
far the practice approach can provide insights applicable to and
useful for project management as a general knowledge domain.
A discussion of how these challenges might be addressed is beyond the scope of this paper, but the argument made here is that
they must be tackled if we are to gain a fuller understanding of
both governance and governing in megaprojects. If we admit the
view that the future unfolds in unknowable ways through myriad
decisions and interactions between autonomous actors, then we
must give proper attention to the ways in which project governing
happens in a situated, relational sense, rather than focusing solely
on governance as a set of pre-designed objects. To borrow a phrase
from Shackle (1968: 36) we need to get beyond these ‘imagined biographies of the future’ to look at how project governing is happening in the here-and-now.
References
Alchian, A., 1950. Uncertainty, evolution and economic theory. Journal of Political
Economy 58, 211–221 (June).
Alderman, N., Ivory, C., McLoughlin, I., Vaughan, R., 2005. Sense-making as a
process within complex service-led projects. International Journal of Project
Management 23 (5), 380–385.
442
J. Sanderson / SciVerse ScienceDirect 30 (2012) 432–443
Atkinson, R., Crawford, L., Ward, S., 2006. Fundamental uncertainties in projects
and the scope of project management. International Journal of Project Management 24 (8), 687–698.
Bauman, Z., 1998. Globalization: The Human Consequences. Polity Press,
Cambridge.
Bechky, B.A., 2006. Gaffers, gofers, and grips: role-based coordination in temporary organizations. Organization Science 17 (1), 3–21.
Blomquist, T., Hällgren, M., Nilsson, A., Söderholm, A., 2010. Project-as-practice:
in search of project management research that matters. Project Management
Journal 41 (1), 5–16.
Broadbent, J., Gill, J., Laughlin, R., 2008. Identifying and controlling risk: the
problem of uncertainty in the Private Finance Initiative in the UK's National
Health Service. Critical Perspectives on Accounting 19, 40–78.
Chia, R.C.H., Holt, R., 2009. Strategy without Design: The Silent Efficacy of
Indirect Action. Cambridge University Press, Cambridge.
Cicmil, S., Williams, T., Thomas, J., Hodgson, D., 2006. Rethinking project
management: researching the actuality of projects. International Journal of
Project Management 24 (8), 675–686.
Clegg, S.R., Pitsis, T.S., Rura-Polley, T., Marosszeky, M., 2002. Governmentality
matters: designing an alliance culture of inter-organizational collaboration for
managing projects. Organization Studies 23 (3), 317–337.
Clegg, S.R., Pitsis, T.S., Marosszeky, M., Rura-Polley, T., 2006. Making the future
perfect: constructing the Olympic dream. In: Hodgson, D., Cicmil, S. (Eds.),
Making Projects Critical. Palgrave Macmillan, Basingstoke, pp. 265–293.
Crawford, L., Pollack, J., 2004. Hard and soft projects: a framework for analysis.
International Journal of Project Management 22 (8), 645–653.
Cyert, R.M., March, J.G., 1963. A Behavioural Theory of the Firm. Prentice
Hall, Englewood Cliffs, NJ.
Davidson, P., 1991. Is probability theory relevant for uncertainty? A post
Keynesian perspective. Journal of Economic Perspectives 5 (1), 129–143.
Davidson, P., 1995. Uncertainty in economics. In: Dow, S., Hillard, J. (Eds.),
Keynes, Knowledge and Uncertainty. Edward Elgar, Aldershot.
Davidson, F.P., Huot, J.-C., 1989. Management trends for major projects. Project
Appraisal 4 (3), 133–142.
De Meyer, A., Loch, C.H., Pich, M.T., 2002. Managing project uncertainty:
from variation to chaos. Sloan Management Review 60–67 Winter.
Dow, S., 1995. Uncertainty about uncertainty. In: Dow, S., Hillard, J. (Eds.),
Keynes, Knowledge and Uncertainty. Edward Elgar, Aldershot.
Eden, C., Ackerman, F., Williams, T., 2005. The amoebic growth of project
costs. Project Management Journal 36 (2), 15–27.
Flyvbjerg, B., 2009. Optimism and misrepresentation in early project development. In: Williams, T.M., Samset, K., Sunnevåg, K.J. (Eds.), Making Essential
Choices with Scant Information: Front-End Decision Making in Major
Projects. Palgrave Macmillan, Basingstoke, pp. 147–168.
Flyvbjerg, B., Skramis Holm, M.K., Buhl, S.L., 2002. Underestimating costs in
public works projects: error or lie? Journal of the American Planning Association
68 (3), 279–295.
Flyvbjerg, B., Bruzelius, N., Rothengatter, W., 2003. Megaprojects and Risk:
An Anatomy of Ambition. Cambridge University Press, Cambridge.
Flyvbjerg, B., et al., 2005. How (in)accurate are demand forecasts in public
works projects? the case of transportation. Journal of the American Planning
Association 71 (2), 131–146.
Froud, J., 2003. The Private Finance Initiative: risk, uncertainty and the state.
Accounting, Organizations and Society 28, 567–589.
Gigerenzer, G., Selten, R., 2001. Rethinking rationality. In: Gigerenzer, G.,
Selten, R. (Eds.), Bounded Rationality: The Adaptive Toolbox. MIT
Press, Cambridge MA, pp. 1–12.
Gilovich, T., Griffin, D., Kahneman, D. (Eds.), 2002. Heuristics and Biases:
The Psychology of Intuitive Judgment. Cambridge University Press,
Cambridge.
Guest, G., Bunce, A., Johnson, L., 2006. How many interviews are enough? an
experiment with data saturation and variability. Field Methods 18, 59–82.
Hällgren, M., Söderholm, A., 2011. Projects-as-practice: new approach, new
insights. In: Morris, P.W.G., Pinto, J.K., Söderlund, J. (Eds.), The Oxford
Handbook of Project Management. Oxford University Press, Oxford, pp.
500–518.
Hällgren, M., Wilson, T., 2007. Mini muddling: learning from project plan deviations. Journal of Workplace Learning 19 (2), 92–107.
Hällgren, M., Wilson, T., 2008. The nature and management of crises in construction projects: projects-as-practice observations. International Journal of Project
Management 26 (8), 830–838.
Hayek, F., 1967. Studies in Philosophy, Politics and Economics. Routledge &
Kegan Paul, London.
Hicks, J.R., 1979. Causality in Economics. Basic Books, New York.
Jarzabkowski, P., 2004. Strategy as practice: recursiveness, adaptation, and
practices-in-use. Organization Studies 25 (4), 529–560.
Jarzabkowski, P., Balogun, J., Seidl, D., 2007. Strategizing: the challenges of a
practice perspective. Human Relations 60 (1), 5–27.
Kahneman, D., Slovic, P., Tversky, A. (Eds.), 1982. Judgment under Uncertainty:
Heuristics and Biases. Cambridge University Press, Cambridge.
Kelsey, D., Quiggin, J., 1992. Theories of choice under ignorance and uncertainty.
Journal of Economic Surveys 6 (2), 133–153.
Keynes, J.M., 1937. The general theory of employment. Quarterly Journal of
Economics 51, 209–223.
Kirzner, I.M., 1973. Competition and Entrepreneurship. University of Chicago
Press, Chicago.
Kloppenborg, T.J., Opfer, W.A., 2002. The current state of project management
research: trends, interpretations, and predictions. Project Management Journal
33 (2), 5–18.
Knight, F.H., 1921. Risk, Uncertainty and Profit. Beard Books, Washington DC.
LeRoy, S.F., Singell, L.D., 1987. Knight on risk and uncertainty. Journal of
Political Economy 95 (2), 394–406.
Loch, C.H., De Meyer, A., Pich, M.T., 2006. Managing the Unknown: A New
Approach to Managing High Uncertainty and Risk in Projects. John Wiley
& Sons, Hoboken NJ.
March, J.G., Simon, H.A., 1958. Organizations. Wiley, New York.
Martignon, L., Laskey, K.B., 1999. Bayesian benchmarks for fast and frugal
heuristics. In: Gigerenzer, G., Todd, P.M. (Eds.), Simple Heuristics that
Make Us Smart. Oxford University Press, New York, pp. 169–188.
McGoun, E., 1995. The history of risk measurement. Critical Perspectives on
Accounting 6, 511–532.
Menger, K., 1963. Problems in Economics and Sociology. University of Illinois
Press, Urbana IL.
Miller, R., Floricel, S., 2000. Building governability into project structures. In:
Miller, R., Lessard, D.R. (Eds.), The Strategic Management of Large
Engineering Projects: Shaping Institutions, Risks and Governance. MIT
Press, Cambridge MA, pp. 131–150.
Miller, R., Hobbs, B., 2009. The complexity of decision-making in large projects
with multiple partners: be prepared to change. In: Williams, T.M., Samset, K.,
Sunnevåg, K.J. (Eds.), Making Essential Choices with Scant Information:
Front-End Decision Making in Major Projects. Palgrave Macmillan, Basingstoke, pp. 375–389.
Miller, R., Lessard, D.R., 2000. Public goods and private strategies: making sense
of project performance. In: Miller, R., Lessard, D.R. (Eds.), The Strategic
Management of Large Engineering Projects: Shaping Institutions, Risks and
Governance. MIT Press, Cambridge MA, pp. 19–49.
Minsky, H.P., 1996. Uncertainty and the institutional structure of capitalist
economies. Journal of Economic Issues 30 (3), 357–368.
Morris, P.W.G., 2009. Implementing strategy through project management: the
importance of managing the project front-end. In: Williams, T.M., Samset,
K., Sunnevåg, K.J. (Eds.), Making Essential Choices with Scant Information:
Front-End Decision Making in Major Projects. Palgrave Macmillan,
Basingstoke, pp. 39–67.
NAO, 2006. Ministry of Defence: Major Projects Report. HMSO, London.
NAO, 2009. Commercial Skills for Complex Government Projects. HMSO,
London.
Nelson, R.R., Winter, S.G., 1982. An Evolutionary Theory of Economic
Change. Harvard University Press, Cambridge MA.
Perminova, O., Gustafsson, M., Wikström, K., 2008. Defining uncertainty in
projects: a new perspective. International Journal of Project Management 28
(1), 73–79.
Pitsis, T.S., Clegg, S.R., Marosszeky, M., Rura-Polley, T., 2003. Constructing
the Olympic dream: a future perfect strategy of project management. Organization Science 14 (5), 574–590.
Pryke, S., Smyth, H. (Eds.), 2006. The Management of Complex Projects: A
Relationship Approach. Blackwell Publishing, Oxford.
J. Sanderson / SciVerse ScienceDirect 30 (2012) 432–443
Remington, K., Pollack, J., 2007. Tools for Complex Projects. Gower,
Aldershot.
Rotheim, R., 1995. Keynes on uncertainty and individual behaviour within a
theory of effective demand. In: Dow, S., Hillard, J. (Eds.), Keynes, Knowledge
and Uncertainty. Edward Elgar, Aldershot.
Runde, J., 1998. Clarifying Frank Knight's discussion of the meaning of risk
and uncertainty. Cambridge Journal of Economics 22, 539–546.
Samra-Fredericks, D., 2003. Strategizing as lived experience and strategists
everyday efforts to shape strategic discourse. Journal of Management Studies
40 (1), 141–174.
Sargent, T.J., 1993. Bounded Rationality in Macroeconomics. Oxford University
Press, Oxford.
Savage, L.J., 1954. The Foundation of Statistics. Wiley, New York.
Scarbrough, H., Swan, J., Laurent, S., Bresnen, M., Edelman, L., Newell, S., 2004.
Project-based learning and the role of learning boundaries. Organization
Studies 25 (9), 1579–1600.
Schoemaker, P.J.H., 1982. The expected utility model: its variants, purposes,
evidence and limitations. Journal of Economic Literature 20, 529–563 (June).
Selten, R., 2001. What is bounded rationality? In: Gigerenzer, G., Selten, R.
(Eds.), Bounded Rationality: The Adaptive Toolbox. MIT Press, Cambridge
MA, pp. 13–36.
Shackle, G., 1955. Uncertainty in Economics and Other Reflections. Cambridge
University Press, Cambridge.
Shackle, G., 1968. Policy, poetry and success. In: Shackle, G. (Ed.), On the Nature
of Business Success. Liverpool University Press, Liverpool, pp. 2–38.
Simon, H.A., 1947. Administrative Behaviour. Macmillan, New York.
Simon, H.A., 1955. A behavioural model of rational choice. Quarterly Journal
of Economics 69, 99–117.
Skelcher, C., 2005. Public-private partnerships and hybridity. In: Ferlie, E.,
Lynn, L., Pollitt, C. (Eds.), The Oxford Handbook of Public Management.
Oxford University Press, Oxford, pp. 347–370.
Slater, G., Spencer, D.A., 2000. The uncertain foundations of transaction cost
economics. Journal of Economic Issues 34 (1), 61–86.
Smyth, H.J., Morris, P.W.G., 2007. An epistemological evaluation of research
into projects and their management: methodological issues. International
Journal of Project Management 25 (4), 423–436.
Söderholm, A., 2008. Project management of unexpected events. International
Journal of Project Management 26 (1), 80–86.
Stigler, G.J., 1961. The economics of information. Journal of Political Economy
69, 213–225.
Taleb, N.N., 2007. The Black Swan: The Impact of the Highly Improbable.
Penguin Books, London.
Thiry, M., 2002. Combining value and project management into an effective
programme management model. International Journal of Project Management
20 (3), 221–227.
443
van Marrewijk, A., Clegg, S.R., Pitsis, T.S., Veenswijk, M., 2008. Managing
public-private megaprojects: paradoxes, complexity and project design. International Journal of Project Management 26, 591–600.
von Neumann, J., Morgenstern, O., 1944. Theory of Games and Economic
Behaviour. Princeton University Press, Princeton NJ.
Wachs, M., 1989. When planners lie with numbers. Journal of the American
Planning Association 55 (4), 476–479.
Wachs, M., 1990. Ethics and advocacy in forecasting for public policy. Business
and Professional Ethics Journal 9 (1/2), 141–157.
Ward, S., Chapman, C., 2003. Transforming project risk management into project
uncertainty management. International Journal of Project Management 21 (2),
97–105.
Weick, K.L., 1979. The Social Psychology of Organizing, 2nd Edition. AddisonWesley, Reading MA.
Whittington, R., 1996. Strategy as practice. Long Range Planning 29 (5), 731–735.
Whittington, R., 2003. The work of strategizing and organizing: for a practice
perspective. Strategic Organization 1 (1), 119–127.
Whittington, R., 2006. Completing the practice turn in strategy research. Organization Studies 27 (5), 613–634.
Williams, T.M., 2005. Assessing and building on project management theory in
the light of badly over-run projects. IEEE Transactions on Engineering
Management 52 (4), 497–508.
Williams, T.M., 2009. Decisions made on scant information: overview. In:
Williams, T.M., Samset, K., Sunnevåg, K.J. (Eds.), Making Essential Choices
with Scant Information: Front-End Decision Making in Major Projects.
Palgrave Macmillan, Basingstoke, pp. 3–17.
Williams, T.M., Samset, K., Sunnevåg, K.J. (Eds.), 2009. Making Essential
Choices with Scant Information: Front-End Decision Making in Major Projects. Palgrave Macmillan, Basingstoke.
Williamson, O.E., 1985. The Economic Institutions of Capitalism. The Free
Press, New York.
Williamson, O.E., 1996. The Mechanisms of Governance. Oxford University
Press, Oxford.
Winch, G.M., 2001. Governing the project process: a conceptual framework.
Construction Management and Economics 19, 799–808.
Winch, G.M., 2009. Managing Construction Projects: An Information Processing
Approach, 2nd Edition. Wiley-Blackwell, Oxford.
Winch, G.M., Maytorena, E., 2011. Managing risk and uncertainty on projects:
a cognitive approach. In: Morris, P.W.G., Pinto, J.K., Söderlund, J. (Eds.),
The Oxford Handbook of Project Management. Oxford University Press,
Oxford, pp. 345–364.
Winter, M., Smith, C., Morris, P.W.G., Cicmil, S., 2006. Directions for future
research in project management: the main findings of a UK governmentfunded research network. International Journal of Project Management 24
(8), 638–649.