administrative

sciences

Article
Redefining the Use of Sustainable Development
Goals at the Organisation and Project
Levels—A Survey of Engineers
Paul Mansell 1,2, * , Simon P. Philbin 2 and Efrosyni Konstantinou 1
1 Bartlett School of Construction and Project Management, University College London,
London WC1E 7HB, UK; efrosyni.konstantinou@ucl.ac.uk
2 Nathu Puri Institute for Engineering & Enterprise, London South Bank University, London SE1 0AA, UK;
philbins@lsbu.ac.uk
* Correspondence: paulrmansell@gmail.com




Received: 18 June 2020; Accepted: 5 August 2020; Published: 11 August 2020


Abstract: The United Nations’ (UN) Sustainable Development Goals (SDGs) aim to deliver an
improved future for people, planet and profit. However, they have not gained the required traction
at the business and project levels. This article explores how engineers rate and use the SDGs at the
organisational and project levels. It adopts the Realist Evaluation’s Context–Mechanism–Outcomes
model to critically evaluate practitioners’ views on using SDGs to measure business and project success.
The study addresses the thematic areas of sustainability and business models through the theoretical
lens of Creating Shared Value and the Triple Bottom Line. A survey of 325 engineers indicated four
primary shortfalls for measuring SDGs on infrastructure projects, namely (1) leadership, (2) tools
and methods, (3) engineers’ business skills in measuring SDG impact and (4) how project success is
too narrowly defined as outputs (such as time, cost and scope) and not outcomes (longer-term local
impacts and stakeholder value). The research study is of value to researchers developing business
models that address the SDGs and also practitioners in the construction industry who seek to link
their investment decisions to the broader outcomes of people, planet and profit through the UN SDGs.

Keywords: sustainability; governance; UNSDG; project success; business-society; business models;

Sustainable Development Goals (SDGs); sustainable development; infrastructure projects

1. Introduction
In 2015, the international community responded to the sustainable development challenge with
the Sustainable Development Goals (SDGs) for 2030 in their report ‘Transforming Our World: The 2030
Agenda For Sustainable Development’ (United Nations 2015). The SDGs are the United Nations’
blueprint and have been signed by 193 nations. They address the global challenges, such as poverty,
inequality, climate change, environmental degradation, prosperity, and peace and justice. At the
global level, the SDGs are interconnected, and the overarching ambition is to ‘leave no one behind’ in
the achievement of the 2030 targets. However, the global nature of the SDGs means that they have
a performance framework that is well developed at regional and national levels, but this has been
difficult to cascade to the sub-national level, including at both the organisational and project levels
(Patel et al. 2017; Galli et al. 2018). The problem manifests because most infrastructure investments are
made at a local level, and therefore, without having an approach that makes adequate provision for
the longer-term impacts across SDGs, there is a likelihood that practitioners will make less-informed
decisions (Adshead et al. 2019; Thacker et al. 2019). Conversely, by using an SDG lens to view
infrastructure investments, strategic infrastructure interventions can lead to significant SDG progress.

Adm. Sci. 2020, 10, 55; doi:10.3390/admsci10030055 www.mdpi.com/journal/admsci

Adm. Sci. 2020, 10, 55 2 of 39

This implies that improved decisions at local level are possible through translating global impact
down to the project level. The contribution of this article is to harness the results of a large-scale
survey on how engineers employ the SDG performance framework at the project level to examine the
‘contextual’ strengths and weaknesses of utilizing the SDG measuring ‘mechanism’. This provides
deep insights for academics and practitioners to improve their understanding of how the SDGs can
provide increased impact at the local level. It informs further research into local measurement of SDGs
with, for example, the opportunity to assess theory-led investigations that establish a link from the
local to global levels. Practitioners can also learn from these developments, seeking new ways to
link rhetoric to action (Scheyvens et al. 2016) so that businesses can fully leverage their innovation,
responsiveness, and resources to drive SDG success.
Alarmingly, after only five years, the global commitment to deliver meaningful SDG action is
falling behind on ambitions at both the local and global levels (Office of National Statistics 2019).
This is relevant for project managers because much of tomorrow’s resilience and development will
be delivered by the project management community, across all sectors, but especially infrastructure.
More specifically, the IPCC’s 2018 Report identifies that ‘directing finance towards investment in
infrastructure for mitigation and adaptation’ is key to meeting SDG targets. The estimated $97.5 trillion
USD (Global Infrastructure Hub 2019) of investment in infrastructure projects that is required globally
by 2040, is considered by McKinsey Global Management Consultancy (Bielenberg et al. 2016) to
represent a unique opportunity to stimulate economic prosperity, reduce poverty and raise standards
in health, education and gender equality. At the same time, the challenge of measuring project
outcomes against SDG goals, targets and indicators within existing project business models is not a
new phenomenon, since the difficulty of measuring sustainability outcomes is a well-researched area
(Proctor et al. 2011; Boswell et al. 2015). The use of SDGs to measure success at project level is important
for two reasons: firstly, they can help monitor progress at an international level (Constanza et al. 2016);
secondly, they can help with selecting infrastructure projects which aim to address SDGs in the design
stage/front-end of projects (Adshead et al. 2019). In turn, investment decisions can be targeted towards
the distribution of funding to infrastructure projects that can achieve broader and longer-lasting impact
(Thacker et al. 2019). Up until this point, scientists, policymakers and practitioners seem to have
captured the greatest challenges that the world is facing. The COVID-19 pandemic has amplified
more than ever before the need to find new ways to increase the pace and scale of positive change for
governance thinking and models which address the UN SDG priorities and the need for new ways to
measure and support the delivery of the UN SDGs 2030 targets (Adshead et al. 2019).
Given the problem discussed above and noting the potential benefits and rising need and tension
to link SDGs to local delivery on infrastructure projects, the research question is: How do engineers in
the construction sector rate and use global UN SDG goals for infrastructure projects at local level?
The following section provides the literature review, which includes a brief overview of the
concept of SDGs, the relevance to projects as well as a review of how these issues are impacted by the
theories of Creating Shared Value and the Theory of Change. This is followed by the methods section
and subsequently the findings and discussion sections. The final section concludes the paper with
proposals for how this research can inform the development of a new model for measurement of SDGs
and recommends areas for further studies based on the proposed research framework.

2. Literature Review
In order to address the research question, an extensive literature search of academic publications
and professional, policy and industry reports helped identify five key themes that impact the context
of the use of SDGs on infrastructure projects. The five themes are shown in Figure 1 and discussed in
the sub-sections of the literature review.
Adm. Sci. 2020, 10, 55 3 of 39
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Research Question Understanding the Context Understanding the Outcomes

2.1 Context - UN SDG: What are

the UN SDG global goals and how
do they relate to sustainability and
sustainable development issues?
2.4 Outcomes – broadening
1. How do engineers in the definition of success to
the construction sector include the Triple Bottom Line
2.2 Context - Project Level: How
rate and use UN SDG
can the SDG global goals be
global goals for
applied at the local project level?
infrastructure projects at
2.5 Outcomes – Moving from
local levels?
Corporate Social Responsibility
2.3 Context - Infrastructure to Creating Shared Value
Sector: Do the global goals have
relevance across the infrastructure
sector?

2. Is there a problem, and if

so, how can it be fixed?

Figure1. 1.
Figure TheThe logic
logic chain
chain illustrating
illustrating the relationship
the relationship between
between the research
the research questionquestion
and the and the
contextual
contextual and outcome
and outcome thematics. thematics.

2.1.Context—United
2.1. Context—UnitedNations
Nations Sustainable
Sustainable Development
Development Goals
Goals
Thefailure
The failureofofnot
notmeeting
meeting the the 2030 targets
targets of ofthe
theUnited
UnitedNations
NationsSustainable
Sustainable Development
Development Goals
Goals
(hereafter,
(hereafter, UN UN SDGs)
SDGs) is amongst
is amongst the most
the most significant
significant globalglobal
GrandGrand Challenges
Challenges threatening
threatening our
our survival
survival
today (IPCCtoday (IPCC
2018), and2018),
there andis thethere is the potential
potential for the
for the project project management
management community community
to play atokey
play
part
a key part (Morris 2017) in making a positive impact on the 2030 targets.
(Morris 2017) in making a positive impact on the 2030 targets. Before examining how projects can help Before examining how
projectsSDG
measure can help measure
success, SDG success,
it is important it is important
to understand how to sustainable
understand development
how sustainable hasdevelopment
evolved into a
has evolved into a ‘three-legged stool’ that seeks to balance economic,
‘three-legged stool’ that seeks to balance economic, social and environmental priorities; what many social and environmental
priorities;
refer what many
to as ‘People, Planet refer
andtoProfit’
as ‘People,
(Elkington Planet1994,
and 2018;
Profit’Sosik
(Elkington
and Jung 1994, 2018; Sosik and Jung
2018).
2018).
The definition of sustainable development has emerged from Brundtland’s formative report
The definition of sustainable development has emerged from Brundtland’s formative report
(Brundtland 1987): ‘development that meets the needs of the present without compromising the
(Brundtland 1987): ‘development that meets the needs of the present without compromising the
ability of future generations to meet their own needs’. Since then, sustainable development has
ability of future generations to meet their own needs’. Since then, sustainable development has
become an increasingly central theme for nation-states and their citizens (Sachs 2012; Sachs et al. 2016).
become an increasingly central theme for nation-states and their citizens (Sachs 2012; Sachs et al.
Today, the Planetary Boundaries (Rockström et al. 2009; Steffen et al. 2015) provide a global litmus test
2016). Today, the Planetary Boundaries (Rockström et al. 2009; Steffen et al. 2015) provide a global
for how we are doing. The concept of nine planetary boundaries, within which humanity can maintain
litmus test for how we are doing. The concept of nine planetary boundaries, within which humanity
a can
positive development
maintain a positiveprofile for future
development generations,
profile for futurewas developed
generations, was in developed
2009 by environmental
in 2009 by
scientists from the Stockholm Resilience Centre, led by Johan
environmental scientists from the Stockholm Resilience Centre, led by Johan Rockström Rockström and Will Steffen.andInWill
2011,
their work (Rockström et al. 2009) informed the then UN Secretary-General
Steffen. In 2011, their work (Rockström et al. 2009) informed the then UN Secretary-General Ban Ki- Ban Ki-moon’s urgent
appeal
moon’s for global
urgent society
appeal forto ‘helpsociety
global us defendto ‘helptheus science
defendthatthe shows
science we
thatare destabilising
shows our climate
we are destabilising
and
our climate and stretching planetary boundaries to a perilous degree’. The most significant global to
stretching planetary boundaries to a perilous degree’. The most significant global response
the Planetary
response Boundary
to the Planetary challenge
Boundarywas in 2015,was
challenge when all governments
in 2015, ratified the
when all governments UN’sthe
ratified seventeen
UN’s
Sustainable Development
seventeen Sustainable Goals (United
Development Goals Nations 2015), shown
(SDGs—United in Figure
Nations 2, to beinachieved
2015), shown Figure 2,byto 2030
be
(initially
achieved with 169 targets
by 2030 (initiallyagreed
with 169 in 2015
targets and then 244
agreed indicators
in 2015 and thenagreed in 2017). agreed
244 indicators This represents
in 2017). a
major
This step-change in thestep-change
represents a major implementation in theofimplementation
the global sustainability
of the globalagenda (Sosik and
sustainability Jung(Sosik
agenda 2018).
and Jung 2018).
Adm. Sci. 2020, 10, 55 4 of 39
Adm. Sci. 2020, 10, x FOR PEER REVIEW 4 of 38

Figure
Figure 2. The
The Global
Global Goals
Goals for Sustainable
Sustainable Development
Development (United
(United Nations
Nations 2015—permission
2015—permission to
to use
use
from
from Sustainable Development Goal (SDG) logo Guidelines).

Although the
Although theSDGs
SDGsbuildbuildon onthe theearlier
earlier Millennium
Millennium Development
Development Goals
Goals (MDGs)
(MDGs) by by focusing
focusing on
on similar
similar issues,
issues, thethe SDGsSDGs differ
differ fromfrom thethe MDGs
MDGs because
because theyare
they areforforall
allcountries
countriesin inthethe world
world to to
implement—developedand
implement—developed anddeveloping
developingalike). alike).Moreover,
Moreover, unlike
unlike thethe MDGs,
MDGs, thetheSDGsSDGs areare focused
focused on
on monitoring,
monitoring, evaluation
evaluation andand accountability—across
accountability—across society,
society, not at
not just just
theatnational
the nationallevel,level,
which which
is why is
why
it it is critical
is critical that the thatlink
theislink
made is made
from from the ‘bottom-to-top’,
the ‘bottom-to-top’, meaning meaning
from from the delivery
the delivery at theatlocal
the
local project
project level through
level through to the to the impacts
impacts at the national
at the national and global
and global levels. levels.
However, However, there appears
there appears to be a
to beThe
gap. a gap.
goldenThethread
golden thread
from from thetonational
the national the projectto the project
level seemslevel
to beseems
missing.to beThismissing. This is
is key because
key because
SDGs have been SDGs have been conceptualised
conceptualised at the global level at but
the actually
global level but actually
materialise and are materialise
operationalisedand are at
operationalised at the project level (Thacker et al. 2019). This is especially
the project level (Thacker et al. 2019). This is especially true for infrastructure projects (Adshead et al. true for infrastructure
projects
2019) that(Adshead et al. 2019)
reflect large-scale that reflect large-scale
governmental investments, governmental investments,
which are delivered which are
by multiple delivered
stakeholders
by multiple
working stakeholders
across boundaries working
and where acrosstheboundaries
linking of and where the linking
global-to-local impactsof global-to-local
at the project impactslevel is
at the project level is potentially transformative for business policy and
potentially transformative for business policy and everyday citizens, as discussed in the next section.everyday citizens, as discussed
in the next section.
2.2. Context—Global SDGs at the Project Level
2.2. Context—Global SDGs at the Project Level
Recent evidence from the UK’s Infrastructure and Projects Authority (IPA 2020) suggests that
projectsRecent evidence
are the majorfrom thethrough
vehicle UK’s Infrastructure and Projects
which national-level Authority
strategic change(UK’s Infrastructure
is delivered. and
In 2018–
Projects
2019, theAuthority 2020) suggests
IPA had oversight that projects
of 133 projects in theare the major
national vehicle
portfolio, through which
representing a whole national-level
life cost of
strategic change is delivered. In 2018–2019, the IPA had oversight
GBP £442 billion and an annual project spend of GBP £27 billion (IPA 2020). This is estimated of 133 projects in the national at
portfolio,
nearly 20%representing
of the UK’s anational
whole life cost of GBP
expenditure £442 billion
(Morris and anisannual
2017), which just theproject
‘tip ofspend of GBPas
the iceberg’, £27
it
billion (UK’s Infrastructure and Projects Authority 2020). This is estimated
does not account for all change projects, programmes or portfolios. Based on the clear imperative to at nearly 20% of the UK’s
nationalmaximum
ensure expenditure (Morris
value 2017), which
is achieved from theis just the ‘tip of the
infrastructure iceberg’, as it
investments, does
this not account
sub-section for all
explores
change projects,
whether measurementprogrammes of SDG or portfolios.
impacts atBased on the clear
the project level imperative
is currently to ensure
ineffectivemaximumdespitevalue the
is achieved from the infrastructure investments, this sub-section explores
endorsement of the SDGs by all the world’s governments. It identifies a two-fold dilemma in terms whether measurement of
SDG impacts at the project level is currently
of using global targets at business and project levels. ineffective despite the endorsement of the SDGs by all the
world’s governments.
Firstly, a fundamental It identifies a two-fold dilemma
misunderstanding in terms of usingrelationship
of the interdependent global targets at business
between and
business
project levels.
and society (Elkington 1994, 2018). The failure to appreciate this dissonance frequently leads to
Firstly, a fundamental
sustainability being overlooked, misunderstanding
both as a ofstrategic
the interdependent
opportunity relationship
for creating between
and business
maintaining and
society (Elkington 1994, 2018). The failure to appreciate this dissonance
competitive advantage by firms and as a source of significant business risk (Porter and Kramer 2011).frequently leads to sustainability
being overlooked,
Porter and Kramerboth as athat
argue strategic opportunity
if businesses, and for
thecreating
projectsandthatmaintaining competitive
drive the changes advantage
needed, are to
by firms and as a source of significant business risk (Porter and Kramer
deliver on the SDGs by 2030, a new approach is required—they term this Creating Shared Value, 2011). Porter and Kramer argue
discussed further in later sections.
Secondly, the SDG framework itself has its own limitations. As described earlier, the 17 SDGs
are defined by 169 targets. This has been further defined by another layer of granularity, by the UN
Adm. Sci. 2020, 10, 55 5 of 39

that if businesses, and the projects that drive the changes needed, are to deliver on the SDGs by 2030,
a new approach is required—they term this Creating Shared Value, discussed further in later sections.
Secondly, the SDG framework itself has its own limitations. As described earlier, the 17 SDGs are
defined by 169 targets. This has been further defined by another layer of granularity, by the UN Statistical
Commission’s Interagency and Expert Group on SDG Indicators (IAEG-SDGs), which includes
244 individual indicators to monitor the 169 targets of the 17 SDGs. This increases the complexity of
definition, which is both good and bad. Indeed, there are many (Riley 2001; Morse 2013; Hak et al. 2016)
that actually criticise the SDGs for being too broad and deep—ultimately being impenetrable except
for the deep specialist. Conversely, the advocates, such as Lim et al. (2016), with studies in the
health-related SDGs, suggest that the 17 SDGs provide powerful ‘icons’ that galvanise efforts to
measure critical indices. They also provide the communications medium for ensuring simplification,
thereby enabling the simplest messages to be kept to 17 powerful, interlinked themes. There are further
studies (Lim et al. 2016; Sachs 2012; Holden et al. 2017) that contend which targets and indicators
are needed to add credible evidence-based measurement to ensure meaningful tracking of progress
against a pre-determined baseline. For example, climate change (IPCC 2018) uses the pre-industrial
age temperature levels and related gas emission pathways as a proxy for the objective to reduce global
warming below the 1.5 ◦ C levels by 2030. Yet some authors (Morse 2013; Hak et al. 2016) challenge the
assertion that the targets and indicators are fit for purpose by suggesting that they are inconsistent
and difficult to quantify, implement, monitor and report, as well as it being difficult to learn lessons.
They also challenge the governance of the SDG oversight mechanism because the goals are non-binding,
with each nation creating their own made-to-measure national plans that aim to be specific to their
needs to have ubiquitous relevance.
Some argue (Swain 2018) that the real challenge resides with the tactical and operational issues that
project managers have to contend with. These include: (a) What are the interdependent relationship
between SDGs to prevent them from being assessed in silos? (b) How can the targets and indicators
that were designed for national and global level reporting be cascaded down to project level? These are
both addressed in subsequent sections.

2.3. Context—Global SDGs in the Construction Sector

The Global Commission on the Economy and Climate defined Infrastructure
(New Climate Economy 2016, p. 4) as ‘structures and facilities that underpin power and other
energy systems (including upstream infrastructure, such as the fuel production sector), transport,
telecommunications, water, and waste management’. This definition takes a systems perspective and
includes energy, transport, buildings, water supply and sanitation, IT and communications but does
not explicitly include natural infrastructure such as forests, deserts, wetlands and other ecosystems
(Bhattacharya et al. 2015; New Climate Economy 2016). In the infrastructure sector, recent analysis by the UK
Infrastructure Transitions Research Consortium’s (ITRC is a partnership involving seven universities and
over 50 groups from infrastructure practice and policy) has provided some confidence that the higher-level
targets do have influence at the project level. The analysis (Hall et al. 2016; Thacker and Hall 2018) indicates
that 81% of the SDG targets are influenced by infrastructure investment projects. However, despite the
positive conclusion from the analysis (Thacker et al. 2019), there is conflicting evidence that the sub-target
indicators can be applied coherently at the project level. The 244 SDG Indicators were developed with
nations under the leadership of the Interagency and Expert Group on SDG’s (IAEG-SDG) to provide
greater granularity and relevance for measurement—but there are many challenges to their use by both
developing and developed nations, because whilst detail is good for clarity, it adds complexity. For example,
the UK’s Organisation for National Statistics (ONS), which is responsible for reporting the UK’s progress
against global SDG indicator measurement, shows that in October 2018, they only had data for 64% of the
IAEG-SDG’s indicators, with 9% of statistics ‘in progress’ and 27% with no data available. The challenge of
the indicators’ utility for measuring success becomes worse below the national level, where this research
team has identified that only 12% (29) of the 244 indicators could be used meaningfully at the project
Adm. Sci. 2020, 10, 55 6 of 39

level. As Merry (2019) and others (Price Waterhouse Coopers 2018; Hall et al. 2016; Thacker and Hall 2018;
Thacker et al. 2019) have suggested, we need to find answers about how the infrastructure sector responds.
In order to do this, we need to explore the definition of infrastructure investment success—is it for the
business, the community or wider stakeholders?

2.4. Outcomes—Broadening the Definition of Success to Include the Triple Bottom Line
At the project level, the Association of Project Management’s Body of Knowledge
(Association for Project Management 2019) defines sustainability as ‘an environmental, social and
economically integrated approach to development that meets present needs without compromising
the environment for future generations’. The APM’s definition has been based on the modern concept
of sustainable development as derived from the Brundtland Report (Brundtland 1987), which suggests
that efforts to create improvements in the short-term should be without a negative impact in the
longer-term. It also recognises that project strategies need to consider success against the triple bottom
line (or otherwise noted as TBL or 3BL) of social, environmental (or ecological) and financial aspects.
However, the over-emphasis on the last of the TBL criteria, namely finance, brings us to the
root of the problem of measuring projects’ SDG impact. This is because the crux of the sustainability
reporting problem lies with the dominance of accounting tools, which has been the pre-eminent
business method of reporting for over 500 years since Luca Paccioli first published his papers on
double-entry bookkeeping (Yamey 1949). This has largely remained unchanged. In other words,
there has been a proliferation of mechanisms and economic models to track different elements of the
TBL, including: ESG (environmental, social and governance) (Elkington 1994, 2018) that includes
the three core areas used in the business investments measurement of ethical and sustainability
impacts of a company; Social Return on Investment (SROI) (Emerson et al. 2000; Millar and Hall 2013);
Net Positive (Forum for the Future 2018; Rainey et al. 2015); Double and Quadruple Bottom Lines
(Sawaf and Gabrielle 2014); a myriad of capital (human, social, manufactured, financial, natural)
analysis models; Environmental Full Cost Accounting (Schaltegger and Burritt 2000); Boston Consulting
Group’s Total Societal Impact framework; Integrated Reporting (Eccles and Krzus 2010); Blended and
Shared Value (Bonini and Emerson 2005); and Impact Investment (Bugg-Levine and Emerson 2011).
Recently this has been extended to new frameworks that focus on specific issues such as Sharing and
Circular Economies (Preston 2012), Carbon Productivity (Malhi et al. 2009; Suess 1980) and Biomimicry
(Elkington 2018). The contention of this research study is that the proliferation of financially driven
sustainability measurement theories, tools and concepts causes confusion and often leads to sub-optimal
governance because of the short-term focus on profit instead of wider TBL outcomes.
Current analysis suggests that the TBL framework is in need of ‘rethinking’ (Elkington 2018).
Indeed, Elkington’s contention is that his definition has not been implemented according to its true
meaning, and he insists that businesses should now monitor and report economic (not just financial),
social, and environmental value added—or they will become negatively impacted. Many contend
(Hubbard 2009; Elkington 1994, 2018; Joyce and Paquin 2016) that private sector success is still overly
influenced by financial perspectives. This is often restrictively linked to share price value and viewed
as an inherent weakness of the system that drives short-termism in governance and decision-making
(Elkington 2018). As a result, and relevant to the assessment of how project managers can measure
projects’ SDG impacts, there has been a growing demand for knowledge on how sustainability reporting
can be broadened.
As a result of the increased knowledge and tempo of the uptake of sustainability language, it has
become more mainstream with many academics (Tilt 2009) and practitioners (Perrini and Tencati 2006)
seeking to further develop the current accounting-centric method towards a broader, or holistic,
approach, such as the Balanced Scorecard (Kaplan and Norton 1996). However, the proliferation of
sustainability accounting terminology (sustainability accounting is often referred as social accounting,
corporate social reporting corporate social responsibility reporting, social and environmental accounting,
Adm. Sci. 2020, 10, 55 7 of 39

and non-financial reporting) also negatively impacts the ability to have a single consistent view, and this
contributes to the project world being mired in confusion.

2.5. Outcomes—Moving from Corporate Social Responsibility to Creating Shared Value

The debate on businesses’ responsibility to ‘give back’ to society and the environment have
evolved from earlier notions associated with corporate social responsibility (CSR) agenda, and these in
turn have been influenced by the notion of creating shared value (CSV), which seeks to change the way
in which business creates value for its stakeholders (Porter and Kramer 2011), i.e., through placing
socially aware priorities, such as the SDGs, at the heart of core business’ thinking and strategies.
In Porter’s paper (Porter and Kramer 2011), he states that ‘the concept of shared value can be defined as
policies and operating practices that enhance the competitiveness of a company while simultaneously
advancing the economic and social conditions in the communities in which it operates.’ CSR is clearly
a key part of this idea of ‘give back to society’ and is of importance to investors (Hayward et al. 2013).
Hayward goes further by suggesting that the major problem is with businesses, especially the ones
that have remained caught in outdated approaches to value creation, that they view too narrowly.
In effect, they are seeking shareholder-driven short-term profits before longer-term broader outcome
success. Most of the current sustainability efforts have focused on the identification of harms to society
in general and the creation of corporate responses to meet those harms as described in general. As a
result, many sustainability efforts have been largely divorced from the specific business model of
each organisation. In reality, sustainability activities have often functioned as ‘surplus’, ‘add-on’,
‘nice-to-have’ actions for the purpose of deflecting stakeholder criticism, conducted regardless of their
actual relevance and impact on the business’ strategy, capabilities, suppliers or customers. The net effect
is to leave core business activities and risks unchanged. Moreover, sustainability cannot be delivered
through CSR because it is both inefficient and ineffective (Porter and Kramer 2011). Porter argues
that CSR is inefficient because it can create irrelevant ‘add-on’ activities that add to the costs of doing
business without either adding to the real value created for any of the business’ stakeholders or
removing real business risks. Additionally, CSR is ineffective because it continues to pit society and
business as opposing forces rather than recognising the opportunities dormant in their interdependence
(Porter and Kramer 2011). However, counter to Porter’s view of CSV–CSR differentiation, there is an
alternative perspective that CSR has rapidly evolved in recent years and closed the CSV-CSR gap.
In a recent paper (Albuquerque et al. 2019), they explore how CSR affects systemic risk and impacts
the value proposition (Bénabou and Tirole 2010). This provides useful indicators that suggest that
CSR is closing a gap with CSV, especially in its social value insights. More importantly for this paper,
it illustrates that both CSR and CSV have an important role in linking organisational and project success
to SDG impacts.
The CSV business model, first developed by leading business strategist Michael Porter of Harvard
Business School (Porter and Kramer 2011), enables the potential to provide a link from local projects’
objectives to global SDGs by rethinking projects’ definition of success by demonstrating impact across
the TBL (Elkington 1994), as discussed in the previous section. It can do this because it: (a) recognises
the interdependence between society and business; (b) moves society and business away from zero-sum
competition to positive-sum competition; (c) enables new ways for the business to create competitive
advantage that are more resilient against sustainability risks and mimicry by other firms; and (d)
combines traditional CSR and business operations into new, integrated and company-specific, strategies
for creating shared value. Since business and society are interdependent, the best outcomes for each
will be obtained when businesses develop strategies that integrate social needs with real commercial
opportunities and vice versa.
Having identified that the theories of CSV and TBL could enable a broader understanding of how
to measure business success at the sub-national level, this research project was established to test its
practical implications. It achieved this by developing a methodology that could engage engineers
in the construction sector, to gauge their perceived value of measuring SDG impacts at project level,
Adm. Sci. 2020, 10, 55 8 of 39

and what contextual issues affected the success of this approach. The questions were framed using the
Realist Evaluation approach, Context–Mechanism–Outcomes, which is discussed in the next section.
The intent was to identify ways to improve the outcomes of using SDG measurement (the mechanism)
by better understanding the contextual issues that affect likely success.

3. Methodology
Adm. Sci. 2020, 10, x FOR PEER REVIEW 8 of 38
Given the issues identified in the literature review, the methodology needed to be suitable to
UN SDG
address thegoals for stated
earlier businesses and projects
research question: at How
local do
level? An online
engineers in the survey is used
construction as rate
sector the and
firstuse
part of a
global
mixed methods approach that provides a triangulation (Creswell and Cresswell 2017)
UN SDG goals for businesses and projects at local level? An online survey is used as the first part of a mixed of data (i.e.,
through approach
methods literature that
review, surveya and
provides interviews)
triangulation to inform
(Creswell andtheCreswell
development
2017) of the
dataprototype SDG
(i.e., through
Measurement
literature review,Model
surveyas andshown in Figure
interviews) 3. Inthe
to inform this way, in what
development Creswell
of the prototype (2017)
SDG describes
Measurement as a
Sequential Explanatory Design, the literature review informs the survey questions
Model as shown in Figure 3. In this way, in what Creswell and Creswell (2017) describes as a Sequential and analysis, that
sequentiallyDesign,
Explanatory informsthe theliterature
structurereview
and questions
informs theof the interview
survey stage.
questions Creswell
and analysis,(2017)
that suggests that
sequentially
this sequential
informs approach
the structure has the benefit
and questions of theof being the
interview mostCreswell
stage. straightforward in its design
and Creswell because there
(2017) suggests that
are sequential
this discrete stages that are
approach haseasy to describe
the benefit and the
of being to report. The main difficulty
most straightforward in itsisdesign
the length
becauseof time
therein
thediscrete
are data collection
stages thatphase. For this
are easy study, the
to describe andtime required
to report. Thefor eachdifficulty
main step of the research
is the lengthdesign
of timewasin
allocated in full. The interviews are not included in this article but are central
the data collection phase. For this study, the time required for each step of the research design was to the follow-on
research.in full. The interviews are not included in this article but are central to the follow-on research.
allocated

Sequential Explanatory Design

QUAN qual

QUAN QUAN qual qual Interpretation

Data Data Data Data of Entire
Collection Analysis Collection Analysis Analysis
(Creswell, 2017)

Mixed method approach – quant & qual data

Survey of Engineers Interviews of

Literature Review
(n = 325) Organisations (n = 40)

Case Study Theory-led Triangulation of

Investigation Prototype analysis

Conclusions & Recommendations for adoption

of the new prototype to measure global SDG at
local level

Figure3.3. The
Figure The research
researchapproach
approachofofmixed-method
mixed-methodSequential
SequentialExplanatory
ExplanatoryDesign,
Design,adapted
adaptedfrom
from
Creswelland
Creswell (2017).
Creswell (2017).

3.1.
3.1.Using
Usingthe
theRealist
RealistEvaluation
EvaluationMethodology
MethodologytotoStructure
Structurethe
theSurvey
Survey
The
Theresearch
researchstudy
study adopts thethe
adopts Critical Realism
Critical perspective
Realism of philosophers
perspective such assuch
of philosophers Bhaskar (2013)
as Bhaskar
to(2013)
inform the choice of the Realist Evaluation approach, primarily because of its practical utility
to inform the choice of the Realist Evaluation approach, primarily because of its practical utility and
its
andwidespread use in
its widespread usesocial science
in social research
science intointo
research thethe
impacts
impacts of of
programmes
programmes(Linsley
(Linsleyetetal.
al.2015).
2015).
ItItalso
also provides a way to develop theory-led investigations, which is what this research seeksdo
provides a way to develop theory-led investigations, which is what this research seeks to toondo
SDG
on SDGmeasurement.
measurement. The adoption of theofRealist
The adoption Evaluation’s
the Realist Context–Mechanism–Outcome
Evaluation’s Context–Mechanism–Outcome (C–M–O) (C–
configuration
M–O) configuration(Pawson and Tilley
(Pawson and 1997)
Tilleyis1997)
widely used across
is widely clinicalclinical
used across research (Pawson
research et al. 2005)
(Pawson et al.
and increasingly also across the social sciences (Linsley et al. 2015). Pawson and
2005) and increasingly also across the social sciences (Linsley et al. 2015). Pawson and Tilley Tilley specifically
recommend
specifically the C–M–O strategy
recommend so that strategy
the C–M–O ‘programme theories
so that can be tested
‘programme for thecan
theories purposes of refining
be tested for the
purposes of refining them’ (2005, p. 2). In this regard, the investigation is not about what works but
asks instead, ‘what works for whom in what circumstances and in what respects, how?’ (2005, p. 2).
Therefore, this approach gives a multi-layered methodological framework for analysing engineers’
perception of the context of SDG measurement as well as its potential outcome on redefining
investment decisions to achieve broader SDG impacts. For the purposes of this paper, the definitions
Adm. Sci. 2020, 10, 55 9 of 39

them’ (2005, p. 2). In this regard, the investigation is not about what works but asks instead, ‘what works
for whom in what circumstances and in what respects, how?’ (2005, p. 2). Therefore, this approach
gives a multi-layered methodological framework for analysing engineers’ perception of the context
of SDG measurement as well as its potential outcome on redefining investment decisions to achieve
broader SDG impacts. For the purposes of this paper, the definitions of C–M–O are:

• Context: The conditions in a context of action encompass ‘material resources and social structures,
including the conventions, rules and systems of meaning in terms of which reasons are formulated’
Adm. Sci. 2020, 10, x FOR PEER REVIEW 9 of 38
(Sayer 1992, p. 112; in Easton 2010).
• Mechanism:
Mechanism: The Theunderlying
underlying entities, processes,
entities, or structures
processes, which which
or structures operateoperate
in particular contexts
in particular
to generate
contexts to outcomes of interestof(Astbury
generate outcomes interest and Leeuw
(Astbury 2010,
and p. 386).
Leeuw 2010, p. 386).
• Outcome:
Outcome: The Thepractical
practicaleffects produced
effects producedby causal mechanisms
by causal being being
mechanisms triggered in a given
triggered in acontext
given
(Tilley
context2016, p. 145).
(Tilley 2016, p. 145).
Based on
Based on these
these definitions,
definitions, the
the survey
survey captures
captures engineers’
engineers’ perceptions
perceptions of of SDG
SDG goals
goals for
for the
the
measurement of
measurement of project
project success,
success, using
using aa questionnaire
questionnaire based
based onon aa C–M–O
C–M–O structure,
structure, as as shown
shown in in
Figure
Figure 4. Taking Bhaskar’s view (Bhaskar 2013), critical realism assumes that certain
Taking Bhaskar’s view (Bhaskar 2013), critical realism assumes that certain events exist, events exist, and
thenthen
and people apply
people applydifferent
differentperspectives
perspectives and meaning
and meaningtototheir
theirinterpretation
interpretationof of the truth. The survey
survey
aimedto
aimed to capture
capturethe the first-level
first-level‘empirical’
‘empirical’observed
observedand andexperienced
experiencedviewsviewsof ofengineers’
engineers’use useofof SDG
SDG
measurement by seeking to quantify their perspectives on three areas: (a) the
measurement by seeking to quantify their perspectives on three areas: (a) the value and importance value and importance
they placed
they placed in in defining
defining SDG SDG outcomes
outcomes asas aa measure
measure of of project
project success,
success, (b)(b) their
their insights
insights into
into the
the use
use
of the mechanism (the ‘trigger’ being the tools, process, structures and strategy for measurement of aa
of the mechanism (the ‘trigger’ being the tools, process, structures and strategy for measurement of
prioritisedlist
prioritised listof
ofSDG
SDGgoalsgoalsand
andtargets
targetsat
atthe
the project
projectlevel)
level)and
and(c)(c) the
the context
context ofof issues
issues that
that affect
affectthe
the
likely success
likely successof ofachieving
achievingthe theoutcomes
outcomesfrom fromthetheuse
useofofthe
themechanism.
mechanism.

Critical Realism The survey seeks to collect empirical data on engineers’ perceptions of
the C-M-O of using SDG goals for the measurement of project success

Realist Evaluation
The empirical: events Mechanism
that are actually
Trigger

observed and
experienced

The actual: events (and non- Action

events) that are generated by
the mechanism Input Events Outcome

The real: mechanism and structures Context

with enduring properties (often cannot
be observed) that cause events
Source: adapted from Bhaskar’s Critical Realism’s three levels (1975, 2002, 2008)

Figure 4.4. The

Figure The Realist
Realist Evaluation
Evaluation (Context–Mechanism–Outcome
(Context–Mechanism–Outcome (C–M–O))
(C–M–O)) model model mapped
mapped to
to the
the
CriticalRealism
Critical Realismtheory
theoryofofreal-actual-empirical
real-actual-empiricaladapted
adaptedfrom
fromZachariadis
Zachariadisetetal.
al.(2013).
(2013).

As
As shown
shown in in Figure
Figure 3,3, the
thedevelopment
development of of aaprototype
prototype SDG
SDGmeasurement
measurement model model based
based onon the
the
TBL-CSV theories was to be based on the triangulation of learning from the literature
TBL-CSV theories was to be based on the triangulation of learning from the literature review, the review, the survey
of 325 engineers
survey and theand
of 325 engineers subsequent interviews
the subsequent of 40 of
interviews senior executives.
40 senior executives.Only the the
Only survey
surveystage is
stage
shared in this paper. The survey procedure, discussed below, is structured using
is shared in this paper. The survey procedure, discussed below, is structured using the C–M–O the C–M–O approach,
and this enables
approach, discreet
and this parts
enables of the causal
discreet parts ofchain
the to be examined—the
causal context, the SDG
chain to be examined—the measurement
context, the SDG
approach
measurement (the approach
mechanism), (theand its outcomes
mechanism), and(the wider definition
its outcomes (the widerof success).
definitionInofthe engineering
success). In the
research field, the C–M–O configuration is not as widely used as it is in the clinical field;
engineering research field, the C–M–O configuration is not as widely used as it is in the clinical field; however,
illustrative examples on
however, illustrative the use on
examples of this approach
the use of thisare provided
approach areinprovided
Table 1. in Table 1.

Table 1. Examples of the use of Critical Realism and Realist Evaluation C–M–O configuration that
informs similar use on evaluation of phenomena on engineering projects.

How Might the Insights Inform

# C–M–O Configuration Reference Engineering Project SDG Measurement
Adm. Sci. 2020, 10, 55 10 of 39

Table 1. Examples of the use of Critical Realism and Realist Evaluation C–M–O configuration that informs similar use on evaluation of phenomena on
engineering projects.

# C–M–O Configuration Reference How Might the Insights Inform Engineering Project SDG Measurement Analysis?
Through understanding the origins of the C–M–O approach and its terminology, research
Terminology. Pawson and Tilley’s Realistic Evaluation (Pawson
into engineering projects can build on established protocols and use their approach to
1 and Tilley 1997) is widely held as the originators of the Realist
widen our understanding of its employability outside the clinical and educational sectors,
Evaluation CMO configuration.
where they are most frequently used.
Pawson and Tilley suggest that the value of the C–M–O strategy is that it enables the
researcher to better analyse the nature of programmes and projects and, more
importantly, how they work. Thus, the core element of the realist approach is to provide a
Projects application. Pawson and Tilley’s Realistic Evaluation
2 new perspective on how intervention using a mechanism brings about outcomes that
(Pawson and Tilley 1997)
represent change. Engineering-based research can thus adopt Pawson and Tilley’s
approach to better understand and explore the mechanism of change in order to evaluate
a project or programme.
As a proponent of realism, Tilley argues for a pragmatic approach in engineering to be
adopted for its evaluations. The research adapts the C–M–O model to the EMMIEI
Engineering application. Tilley (2016) developed the C–M–O approach that includes Effects, Mechanism, Moderator (or context), Implementation and
3 model to assess how it can be used by engineers to improve their Economic Impact. Importantly, the differences between engineering physical worlds and
decision making for policy and project decisions. the social world are recognised, but it is suggested that both benefit from a pragmatic
research strategy. This is helpful for the measurement of SDGs because it gives
confidence of relevance to the engineering domain of using the C–M–O configuration.
The project evaluation of ‘e-government for You’ used the theory-driven evaluation
Engineering application. Horrocks and Budd (2015) used the
approach based on the C–M–O model to enhance the focus and granularity for their
C–M–O structure to evaluate a European e-services systems
4 study. This supports the usage of the C–M–O model for SDG measurement because it
engineering project to establish the outcomes and understand the
allows for the mixed-method approach and structures the analysis framework in a readily
why, for whom, and how?
understood causal chain.
Construction project application. Peters et al. (2013) examined The origins of the C–M–O model are from the Critical Realism traditions, and therefore,
Critical Realism evaluation models to study business networks. the Peters et al. (2013) article provides a useful insight into the approach that a strongly
5 They used a UK construction project to explore the managerial theoretical lens can use when applied to the ground level of a local construction project.
phenomenon, specifically the practice of novation in temporary This helps shape the SDG approach by giving confidence that the ‘realistic learning’ from
organisational networks. the Peters study can be replicated for SDGs.
Adm. Sci. 2020, 10, 55 11 of 39

Table 1. Cont.

# C–M–O Configuration Reference How Might the Insights Inform Engineering Project SDG Measurement Analysis?
Construction project application. Poirier et al. (2016) evaluated There are strong parallels of the study into the AEC’s value derived from collaboration in
the use of a Critical Realism lens to assess the delivery of project delivery. The relevance to the C–M–O approach is that they both derive from the
building projects in the architecture, engineering and critical realism tradition and seek to understand causal patterns and assess what are the
6
construction (AEC) sector. They evaluated how collaboration can outcomes of employing a specific mechanism within a given context. In particular, the
improve performance and value across five core entities, namely study highlights how the learnings can be structured in a way that is most readily
process, structures, agents, artefacts and context. understood by practitioners in an area of great complexity.
Multi-sector application. Although Bergeron and Gaboury
There are a number of methodological challenges that are identified and should be noted
(2020) come from a clinical care perspective, their recent study
by engineering researchers using this approach. Solutions to the analytic difficulties are
highlights some of the challenges of using the C–M–O
7 shared that can help the identification of patterns and assist the researcher to maintain
framework that has relevance to all disciplines and sectors that
transparency in the analytical process, thus strengthening the ability to make
use its causal framework. Further challenges were also identified
generalisations.
in the article described below, by Crosthwaite.
While not focused on an engineering project, this article highlights the use of the model in
Education application. Crosthwaite et al. (2012) used the the education sector to evaluate the causal impact based on the identification of specific
8 C–M–O approach to understand the educational impacts of the issues within the C–M–O framework. In regard to the SDG approach, there is value in
‘Engineers without Borders’ in Australia and New Zealand. combining the qualitative and quantitative approaches to improve the understand the
observed outcomes.
Health system application. Greenhalgh et al. (2009) used the
The broad nature of evaluating organisational systemic transformation has similarities of
C–M–O method to evaluate a health system. This wide-ranging
complexity with research into the measurement of SDG impacts at project and
9 analysis of a systems transformation environment sought to
organisational level. The simpler C–M–O approach offers a means to help explain causal
understand the reasons for how and why the outcomes were
effects more simply, and this has benefits for both research and practitioners.
achieved.
Projects application. Berge (2017) used a realist evaluation Realist evaluation is used to scrutinize what it is about the telecare system that works for
10 approach on a Norwegian telecare project. While the study has a whom, why, how, and in which circumstances. The study provides a more nuanced
clinical orientation, its project approach is instructive. approach.
Adm. Sci. 2020, 10, 55 12 of 39

3.2. The Survey

The survey approach to data gathering was selected because it is recognised (Lenth 2001) as one
of the most important methods in applied social science. This approach, shown in Table 2, enabled a
rapid and cost-effective way to assess a statistically significant group of engineering project managers’
perceptions of the relative importance and current capability for measuring SDG impacts on their
projects. The participants were all members of the UK’s leading civil engineering professional society,
the Institution of Civil Engineers (ICE) who sanctioned the survey and provided access to their
members’ contact details. A benefit of using this style of survey approach is that it delivered both
qualitative research outputs, by using open-ended questions that captured text-based commentary
and quantitative research outputs, by using a Likert scoring mechanism aligned to the questions.
The eight-step model used to structure the survey is described below in Table 2.
The questions (shown in Figure 5 and Appendix A) were structured to measure attitudes in relation to the
research question. The questions focused on three areas: the perceived value and importance of measuring
SDGs on their projects (i.e., the outcomes); their current approach and capability (i.e., the mechanism), such as
skills, tools, processes, structures and methods (Astbury and Leeuw 2010, p. 386); and their identification of
the challenges and opportunities (i.e., the context) of measuring projects’ SDG impact. SurveyMonkey © was
chosen as the web-enabled survey tool because it was highly customisable and provided a comprehensive
set of back-end capabilities, such as data analysis and visual representation tools, that helped present the
dataSci.
Adm. in 2020,
a concise andPEER
10, x FOR informative
REVIEW manner. 13 of 38

Page 1
Welcome page Type of response
• Purpose • Engineering 10 mins survey:
• Time to complete Organisation • 20 Qs for an engineering
(10 mins) • A member of the organisations
• Anonymous except engineering • 13 Qs for an individual
where agreed POC community

Research Question: How do

Page 2a and 2b
engineers in the construction
sector rate and use global UN Engineering Member of the
SDG goals for infrastructure Organisation engineering
Questions community Factual Questions
investment decisions at local
General Questions General Questions
level? (Sect 1a: 9*Qs) (Sect 1b: 6*Qs)
Adopting the Realist Evaluation
Method of C-M-O:
(Pawson and Tilley, 2004; Zachariadis et al., 2013)
SDG Outcome
Outcomes (O) Page 3 Questions (Sect 2: Attitude Questions (Likert
4*Qs)
scaled & open ended)

SDG Mechanism
Mechanism (M) Page 4 Questions (Sect 3: Page 6
4*Qs)
Thanks
• For time and effort
SDG Measurement • POC of research
Context (C) Page 5 Context: Challenges lead for further
& Opportunities questions
(Sect 4: 3*Qs)

Figure
Figure 5. Survey
SurveyQuestions
Questions Sequence;
Sequence; adopting
adopting RealistRealist Evaluation
Evaluation methodology
methodology of Context-
of Context-Mechanism
Mechanism-Outcomes (Pawson
-Outcomes (Pawson et al. et al. 2005; Zachariadis
2005; Zachariadis et al.Appendix
et al. 2013). (See 2013). (See
A Appendix
for the fullAtable
for the full table
of questions).
of questions).

The tool also provided guidance on bias-elimination and sample-selection best practice. This
enabled a structured approach for presenting the questions, which were designed to capture the
required data, which at its core, sought to establish whether this research area was of perceived
importance to practising professionals, and if it was of high importance, was there a gap between the
import of measuring SDG impact versus their capability to do so? It achieved this by using both open-
ended and closed questions within a clear structure that explored firstly broader and secondly, more
specific areas and concepts within the research areas. The survey was sent to participants by the UK’s
leading civil engineering institution, the ICE, thereby providing reassurance to the participants since
they would recognise the institution’s name and logo, which would be likely to increase the response
rate. Data protection methods fulfilled ethical and legal data management requirements, including
Adm. Sci. 2020, 10, 55 13 of 39

Table 2. Analysis method adopted (adapted from: Creswell and Creswell 2017; Nardi 2015).
Survey Design and Analysis Methods
1 Problem specification and Research Question As captured in the introduction paragraph of this paper; formulation of problem and objectives.
With support from the Institution of Civil Engineers (ICE); seeking a representative sample from
2 Population Definition
across the engineering community.
Use of Survey Monkey © software tool—for design and running of survey data collection. It also
3 Selection of Mode
provided statistical analysis, supported by SPSS.
Identify types of questions aligned to the Realist Evaluation C–M–O approach; draft of the
4 Design Instrument expected results to assess whether the design would achieve the outcomes; draft the questions on
Likert scale; use closed and open questions selectively; test the approach with experts.
5 Specify and Test Procedure Build the logic framework in the tool and run a pilot to test the success.
ICE distributes the survey to 1500 of its members; 325 complete the survey, ca. 20% response rate,
6 Data Collection
providing representative sample.
Analysis completed in four stages of diagnostic analysis:
Stage 1: Download all data (quantitative and qualitative) in MS ExcelTM ; remove erroneous and
false data, e.g., delete test data from the pilot. Structure data for analysis—e.g., charts and graphs
to visualize data.
Stage 2: Use software tool on survey monkey and SPSS to analyse the data’s statistical
significance; identify patterns and gaps/overlaps against research question’s objectives.
7 Analysis Stage 3: Analyse data touch points (using C–M–O coding) and correlate findings to the original
research question and the C–M–O model. Complete initial write-up for review.
Stage 4: Share data findings with expert panel (of 12 qualified engineers) organised by the ICE;
test the findings; keep integrity of the data but use expert panel to assess the implications and
possible next steps. For example, the panel suggested that the low level of organisational
responses could be addressed in the interview stage of the research. (Note: The three separate
workshops were recorded, but they have not been included in this article.)
Step 1: Build the data charts that illustrate the findings.
Step 2: Write up the findings: test and adjust to ensure recommendations and conclusions are
8 Reporting
consistent with original research question; identify lessons and insights that inform the next
stage of research—the 40 interviews.
Adm. Sci. 2020, 10, 55 14 of 39

The tool also provided guidance on bias-elimination and sample-selection best practice.
This enabled a structured approach for presenting the questions, which were designed to capture
the required data, which at its core, sought to establish whether this research area was of perceived
importance to practising professionals, and if it was of high importance, was there a gap between
the import of measuring SDG impact versus their capability to do so? It achieved this by using both
open-ended and closed questions within a clear structure that explored firstly broader and secondly,
more specific areas and concepts within the research areas. The survey was sent to participants by the
UK’s leading civil engineering institution, the ICE, thereby providing reassurance to the participants
since they would recognise the institution’s name and logo, which would be likely to increase the
response rate. Data protection methods fulfilled ethical and legal data management requirements,
including GDPR (general data protection regulation). For example, by sending the survey from the
engineering institution to their members, the approach conformed with the members’ original opt-in
agreement to receive similar knowledge-sharing initiatives.

3.3. Access
The survey aimed to access between 200–300 qualified engineers. In actuality, the Institution of
Civil Engineers’ communications team selected a random representative distribution of its members,
aiming to achieve ca. 20% of a total number of 1500 targeted participants. Since the respondents
voluntarily opted in, this was considered a non-probability sample, which Tansey (2007) suggests
is preferable to identify what he terms ‘elite interview subjects’ in order to avoid the randomness
of generic sampling. The response rate of 325 completed surveys was relatively high by the ICE’s
previous experience of surveys, typically achieving only 5% to 30% responses, the latter higher
response rates being due to well-publicised events, such as committee elections. In this case, the ICE
only sent a single email without any follow-ups; therefore, the response rate was considered good.
The ICE also confirmed that the sub-set of the 325 respondents from the 1500 targeted participants was
representative of the wider membership population (of 6500) because it included a sample selection
across all experience levels, from student to engineers with over 20 years of experience, and this added
to the statistical validity of the sample.
The questionnaire included a question on demographics designed to distinguish between the
generations and, more specifically, capture the responses of millennials (i.e., people born between
1983 and 2000) (US Public Interest Research Group 2016; Howe and Strauss 1991) who, according
to the U.S. Bureau of Labor Statistics (Labor 2017), within the next two years, will reflect 50% of the
US workforce, growing to 75% by 2030. The millennials can be viewed as the generation who are
rapidly becoming the organisational leaders and already acting as policy shapers (Baird 2015), which is
relevant to this study as they will increasingly be owning the selection and reporting of SDG priorities
on their projects.

4. Data Analysis and Results

4.1. Descriptive Statistics

Using an anonymous data collection approach, 325 survey responses were received during June
2018, 24 of which were corporate responses, with an average completion time of 7 min. There was a
minimum of 159 answers for each survey response from individuals. From the 301 responses from
individuals, 81% (243) were from qualified engineers, of which 45% had over 20 years of experience.
When all the years of experience were added together, it provided a cumulative total of 3628 years
of professional engineering expertise, not including the non-engineers, which covered professions
ranging from lawyers and investment specialists to academics. This indicates that although the total
number of survey respondents was limited to the 325, it did include a high level of expertise that adds
to the weight and power and thus validity for the research findings (Diekhoff 1992). The descriptive
statistical data are shown in the tables and charts in Figure 6.
 When all the years of experience were added together, it provided a cumulative total of 3628 years of
professional engineering expertise, not including the non-engineers, which covered professions
ranging from lawyers and investment specialists to academics. This indicates that although the total
number of survey respondents was limited to the 325, it did include a high level of expertise that
adds
Adm. Sci.to the10,weight
2020, 55 and power and thus validity for the research findings (Diekhoff 1992).
15 ofThe
39
descriptive statistical data are shown in the tables and charts in Figure 6.

Europe 176 responses

Research Survey – Engineering SDG Impact Responses
Africa 0 responses
Middle East 33 responses
24 176
Indian Sub-continent 17 responses 43
33
Asia 43 responses
Australasia 11 responses 17
North America 24 responses 11
21

Latin America 21 responses

(a). Geographical region.

Qualified engineer 81%
Engineer working towards chartered status 4%
Student engineer 10%
Academic / educator 2%
Supply chain 0%
Related engineering business 3%
(b). Types of respondents.
1–5 years 20%
6–10 years 11%
11–20 years 10%
20+ years 45%
N/A 14%
Adm. Sci. 2020, 10, x FOR PEER REVIEW (c). Years of experience. 15 of 38

Millennials 40%
Non-millennials 60%

(d). Percentage of Millennials reporting.

Figure6.6.Statistical
Figure Statisticaldescriptive
descriptivedata
datafrom
fromsurvey.
survey.

4.2. Survey Results

4.2. Survey Results
Responses from the survey’s primary C–M–O-related questions are shown below, with summary
Responses from the survey’s primary C–M–O-related questions are shown below, with
data and supporting analysis.
summary data and supporting analysis.
4.2.1. Question 1 (Outcomes): Should Engineering Businesses Seek Ways to Measure and Report SDG Impact?
4.2.1. Question 1 (Outcomes): Should Engineering Businesses Seek Ways to Measure and Report
SDG Impact?
Data overview: The first set of results show that there was overwhelming agreement that it was
Datathat
important overview: The first
engineering set of results
businesses show
seek ways tothat thereand
measure wasreport
overwhelming agreement
SDG impact. that itper
Eighty-seven was
important that engineering businesses seek ways to measure and report SDG impact. Eighty-seven
cent of respondents either agreed or agreed strongly that this was important. Millennials rated this as
per cent
more of respondents
important either agreed
than non-millennials didor(94%
agreed
andstrongly that this was
82%, respectively) (seeimportant.
Figure 7). Millennials rated
this as more important than non-millennials did (94% and 82%, respectively) (see Figure 7).
4.2.1. Question 1 (Outcomes): Should Engineering Businesses Seek Ways to Measure and Report
SDG Impact?
Data overview: The first set of results show that there was overwhelming agreement that it was
important that engineering businesses seek ways to measure and report SDG impact. Eighty-seven
per cent
Adm. of respondents
Sci. 2020, 10, 55 either agreed or agreed strongly that this was important. Millennials16rated
of 39
this as more important than non-millennials did (94% and 82%, respectively) (see Figure 7).

Figure 7.
Figure 7. Response
Response to
to Question
Question 1:
1: Should
Should engineering
engineering businesses
businesses seek
seek ways
ways to
to measure
measure and
and report
report
SDG impact?
SDG impact?

Relationtotothe
Relation the research
research question:
question: the data
the data suggest
suggest that engineers
that engineers rate the rate
use ofthe use
SDG forofmeasuring
SDG for
measuring
impact impact isand
is important, important, and this
this provides provides
a starting a starting
point point deeper
for delving for delving deeper
into the into mechanism
context, the context,
mechanism and outcomes issues that affect
and outcomes issues that affect its application. its application.
Differencesanalysis:
Differences analysis: there
there were
were few
few differences,
differences,and
andthis
thisstrengthened
strengthenedthe thefindings.
findings.

4.2.2. Question 2 (Outcomes): What Are the Top 5 SDG Goals Most Relevant to Measuring Impact of
Your Infrastructure Projects and Programmes?
Data overview: The survey results showed that engineers have a strong focus on five priority
SDGs (shown in Figure 8), namely SDG6 (clean water and sanitation), SDG7 (affordable and clean
energy), SDG9 (industry, innovation, and infrastructure), SD11 (sustainable cities and communities)
and SDG13 (climate change).
Relation to the research question: The data suggest that engineers do rate the ‘use of SDG for
measuring impact’ as an important issue and have a clear sense of priority as to which of the 17 goals
they believe are more relevant/important to them and their business. This provides a starting point for
delving deeper into the specific goals and what mechanisms they have to measure the impact. .
Differences analysis: The results also showed that there was a marked difference in millennial
responses as shown in Figure 9.
4.2.2. Question 2 (Outcomes): What Are the Top 5 SDG Goals Most Relevant to Measuring Impact
of Your Infrastructure Projects and Programmes?
Data overview: The survey results showed that engineers have a strong focus on five priority
SDGs (shown in Figure 8), namely SDG6 (clean water and sanitation), SDG7 (affordable and clean
energy),
Adm. SDG9
Sci. 2020, (industry, innovation, and infrastructure), SD11 (sustainable cities and communities)
10, 55 17 of 39
and SDG13 (climate change).

Figure 8. Responses of participants to their five top SDGs that engineering projects should measure
impact against, showing the top seven list (with six and seven being significantly less popular).

Relation to the research question: The data suggest that engineers rate the use of SDG for
measuring impact is important ,and this provides a starting point for delving deeper into the context,
mechanism and outcomes issues that affect its application.
Differences
Figure analysis:
8. Responses The resultstoalso
of participants theirshowed
five topthat
SDGsthere was a marked
that engineering difference
projects should in millennial
measure
responses as shown in Figure 9.
impact against, showing the top seven list (with six and seven
seven being
being significantly
significantly less
less popular).
popular).

Relation to the research question: The data suggest that engineers rate the use of SDG for
measuring impact is important ,and this provides a starting point for delving deeper into the context,
mechanism and outcomes issues that affect its application.
Differences analysis: The results also showed that there was a marked difference in millennial
responses as shown in Figure 9.

Figure 9.
Figure 9. Preference of the
Preference of the SDGs
SDGs 13
13 and
and 15;
15; differentiating
differentiating between
between millennials.
millennials.

4.2.3. Question 3 (Mechanism): Do Commercial Realities Dictate the SDGs You Pick?
The next question probed the way in which commercial realities influence the selection of SDGs
to measure (see Figure 10).

Figure 9. Preference of the SDGs 13 and 15; differentiating between millennials.

Adm. Sci. 2020, 10, x FOR PEER REVIEW 17 of 38

4.2.3. Question 3 (Mechanism): Do Commercial Realities Dictate the SDGs You Pick?
The2020,
Adm. Sci. next10,question
55 probed the way in which commercial realities influence the selection of18SDGs
of 39
to measure (see Figure 10).

Figure 10. Response to Question 3: Do commercial realities dictate the SDGs you pick?
Figure 10. Response to Question 3: Do commercial realities dictate the SDGs you pick?

Data overview:
Data overview: Respondents
Respondents were were nearly
nearly equally
equally split on this
split on this issue;
issue; 36%
36% either
either disagreed
disagreed oror
strongly disagreed that SDG choice was influenced by commercial realities,
strongly disagreed that SDG choice was influenced by commercial realities, while 39% agreed or while 39% agreed or
strongly agreed,
strongly agreed, with
with 21%
21% non-committal.
non-committal.
Relation to the research question: The
Relation to the research question: The responses
responses cancan be
be linked
linked to to the
the second
second question
question inin Figure
Figure 1,
1,
that seeks
that seeks to
to identify
identify problem
problem areas.
areas. There
There is is evidently
evidently anan issue
issue that
that many
many engineers
engineers feel
feel that
that the
the
businesses they
businesses they work
work for for can
can be be too
too commercially
commercially orientated.
orientated. This
This is not aa consistent
is not consistent view
view but
but
potentially an area for further exploration.
potentially an area for further exploration.
Differences analysis:
Differences analysis: ItIt is
is likely
likely that
that the
the ‘agree’
‘agree’ type
type of
of responses,
responses, as as well
well as
as the
the non-committal
non-committal
responses, could reflect the difficulty of interpreting the question. For example,
responses, could reflect the difficulty of interpreting the question. For example, if the respondent if the respondent
identified that the use of SDGs was a secondary consideration after ensuring
identified that the use of SDGs was a secondary consideration after ensuring ‘business survival’,‘business survival’, they
mightmight
they have have
agreed with the
agreed withproposition, whereas
the proposition, an alternative
whereas position
an alternative might have
position might been
haveto been
suggest
to
SDGs are good for business based on the wider ‘societal shared
suggest SDGs are good for business based on the wider ‘societal shared values’. values’.

4.2.4. Question
4.2.4. Question4 4(Mechanism):
(Mechanism):DoDo
YouYou
WantWant to Know
to Know moremore
aboutabout Measuring
Measuring SDG on
SDG Impact Impact
Your on Your
Projects?
Projects?
Data overview: In addition, the overwhelming majority of engineers wanted to know more about
how to Data overview:
measure SDG In addition,
impact the projects
on their overwhelming majority
better (83% of engineers
vs. 17%), especiallywanted
among to
theknow more
millennial
about how(see
generation to measure SDG impact on their projects better (83% vs. 17%), especially among the
Figure 11).
millennial generation (see Figure 11).
Adm. Sci. 2020, 10, 55 19 of 39
Adm. Sci. 2020, 10, x FOR PEER REVIEW 18 of 38

Figure
Figure 11. Response
11. Response to Question
to Question 4: Do
4: Do you youtowant
want knowtomore
know more
about about measuring
measuring SDGonimpact
SDG impact on
your projects?
your projects?
Relation to the research question: this supports the rationale for deepening the research and
provides
Dataaoverview:
useful wayeighty-seven
to seek interviews with
per cent ofCEOs in the follow-on
respondents stage or
either agreed of this research
agreed programme.
strongly that this
Differences analysis: there were few differences which strengthened the findings
was important. Again, millennials rated this more importantly, at 94%, versus 82% for non-millennials and provided
evidence
(see Figurefor further evaluation into this area in future research.
11).
Data overview: eighty-seven
Relation to the research per cent
question: Theofsurvey
respondents either agreed
respondents or agreed
gave very strongstrongly
supportthat thisview
to the was
important.
that it was Again, millennials
important rated this more
that engineering importantly,
businesses at 94%,
seek ways to versus
measure82%andfor report
non-millennials
SDG impact.(see
Figure 11).
This consolidated the views expressed in earlier questions that this was a matter of import and that
they Relation
wanted to tobe
theengaged
researchinquestion: The surveyand
future knowledge respondents gave very The
learning activities. strong supportwould
responses to the view
help
that it wasfuture
galvanise important that engineering
engagement businesses
in this research seek ways to measure and report SDG impact. This
project.
consolidated
Differencestheanalysis:
views expressed
millennialsin earlier questions
were stronger in that
theirthis was a matter
responses but notofatimport and that
a significant they
level.
wanted to be engaged in future knowledge and learning activities. The responses would help
4.2.5. Question
galvanise future5 engagement
(Mechanism):inWhat Is the Engineers’
this research project. View on Current Infrastructure Projects and
TheirDifferences
Achievement of the SDGs?
analysis: millennials were stronger in their responses but not at a significant level.
Data overview: Only 34% of engineers believed that ‘there is strong evidence that we have a
“fit forQuestion
4.2.5. purpose”5 SDG
(Mechanism): What
measuring Is the Engineers’
approach to track ourView on Current
projects’ impact Infrastructure Projects and
on SDGs’. Thirty-seven per
Theirneither
cent Achievement of the
disagreed norSDGs?
agreed, probably due to the fact that it is such a complex and difficult
challenge to measureOnly
Data overview: impact
34%and to-date, the
of engineers industry
believed thatcontinues to struggle
‘there is strong to find
evidence thata we
practical
have aand
“fit
workable solution
for purpose” SDG to this issueapproach
measuring (Merry 2019; Fukuda-Parr
to track andimpact
our projects’ McNeill on2019).
SDGs’. Thirty-seven per cent
Relation
neither to thenor
disagreed research
agreed,question:
probablyThe
dueresults
to thereveal some
fact that it isareas
suchwhere the current
a complex measurement
and difficult of
challenge
projects’ SDG impact needs improvement (see Figure 12), despite the strong support
to measure impact and to-date, the industry continues to struggle to find a practical and workablefor the importance
of measuring
solution and
to this reporting
issue (MerrySDG impact,
2019; and the clear
Fukunda-Parr and identification
McNeill 2019). of five priority SDGs for the sector.
Relation to the research question: The results reveal some areas where the current measurement
of projects’ SDG impact needs improvement (see Figure 12), despite the strong support for the
Adm. Sci. 2020, 10, x FOR PEER REVIEW 19 of 38

importance
Adm. of measuring
Sci. 2020, 10, 55 and reporting SDG impact, and the clear identification of five priority20SDGs
of 39
for the sector.

Figure 12. Response to Question 5: Do we have a ‘fit for purpose’ measuring approach to track
Figure 12. Response to Question 5: Do we have a ‘fit for purpose’ measuring approach to track projects’
projects’ SDG impact?
SDG impact?

Differences analysis:
Differences analysis: there
there were
were significant
significant differences
differences but
but across
across aa balanced
balanced response
response curve.
curve.
Thiscould
This couldbebeinterpreted
interpretedpositively
positively(only
(only24%
24%did
didnot
notagree)
agree)or
ornegatively
negatively(only
(only34%
34%agreed).
agreed).

4.2.6. Question
4.2.6. Question 66 (Context):
(Context): What Are the Greatest
Greatest Challenges
Challenges for
for Measuring
Measuring SDG
SDG Impact?
Impact?
Data overview:
Data overview: The
The respondents
respondents toto the
the exploratory
exploratory survey
survey said
said that
that the
the four
four greatest
greatest challenges
challenges
were(see
were (seeFigure
Figure13):
13): success
success definition
definition(56%),
(56%), business
businesspriorities
priorities(55%),
(55%),leadership
leadership(52%)(52%)and
andaa focus
focus
on outputs
on outputsrather
ratherthan
thanoutcomes
outcomes(46%).
(46%).
Relation to the research question: These responses address the aim to identify contextual issues
(as shown in 2.2 in Figure 1). This starts to build a nodal framework for further investigation and also
provides links to the ‘outcomes’ issues (2.4 and 2.5 of Figure 1).
Differences analysis: the four issues with the highest incidence are all within 42% to 58%. Each of
the margins between the issues is 4–6%, which shows a balanced and consistent view without outliers.
This could be interpreted positively (only 24% did not agree) or negatively (only 34% agreed).

4.2.6. Question 6 (Context): What Are the Greatest Challenges for Measuring SDG Impact?
Data overview: The respondents to the exploratory survey said that the four greatest challenges
wereSci.
Adm. (see Figure
2020, 10, 55 13): success definition (56%), business priorities (55%), leadership (52%) and a21
focus
of 39
on outputs rather than outcomes (46%).

Adm. Sci. 2020, 10, x FOR PEER REVIEW 20 of 38

Figure 13. Responses to Question 6: What are the greatest challenges for measuring SDG impact? (See
Appendix B for full data).

Relation to the research question: These responses address the aim to identify contextual issues
(as shown in 2.2 in Figure 1). This starts to build a nodal framework for further investigation and also
provides links to the ‘outcomes’ issues (2.4 and 2.5 of Figure 1).
Figure 13. Responses to Question 6: What are the greatest challenges for measuring SDG impact?
Differences
(See Appendixanalysis: the
B for full four issues with the highest incidence are all within 42% to 58%. Each of
data).
the margins between the issues is 4–6%, which shows a balanced and consistent view without outliers.
The four
fourtop
topchallenges
challenges identified
identified in results
in the the results can
can also bealso be interpreted
interpreted as the
as reflecting reflecting the
difficulties
difficulties of integrating business needs with the SDGs in the absence of shared
of integrating business needs with the SDGs in the absence of shared value business strategies, value business
strategies, although
although ‘success ‘success could
definition’ definition’ could the
also reflect also reflect
lack the tolack
of KPIs of KPIs
measure SDGtoperformance
measure SDG on
performance on engineering projects.
engineering projects.

4.2.7. Question
Question 77 (Context):
(Context): How
How Could
Could the
the Achievement
Achievement of
of the
the SDGs
SDGs on
on Future
Future Infrastructure
Infrastructure
Projects Be Improved?
Projects Be Improved?
Data overview:
Data overview:This This exploratory
exploratory research
research showsshows (in Figure
(in Figure 14)inthat
14) that termsin ofterms of the
the greatest
greatest opportunities within engineering firms, the top four opportunities were leadership
opportunities within engineering firms, the top four opportunities were leadership (57%), increased (57%),
increased education
education and trainingand
intraining in SDG
SDG impact impact
skills skills
(57%), use (57%), use ofand
of a simple a simple
widelyand widely
used used tool
tool (55%) and
(55%) and business skills
business skills (48%). (48%).

Figure
Figure 14.
14. Responses
Responses to
to Question
Question 7:
7: What
What are
are the
the greatest
greatest opportunities
opportunities for
for measuring
measuring SDG
SDG impact?
impact?
(See Appendix C for full data).
(See Appendix C for full data).

The challenges were also compared to the ‘opportunities’ in Figure 15 to assess the the respondent’s
respondent’s
understanding
understanding of ofwhether
whetherthe same
the samethemes were
themes noted
were as both
noted a challenge
as both and anand
a challenge opportunity. There
an opportunity.
were
Theredata
werelinkages betweenbetween
data linkages the two results,
the twowith ‘leadership’
results, and ‘business
with ‘leadership’ andskills/success definition’
‘business skills/success
appearing
definition’ in both of the
appearing top four
in both responses.
of the top four responses.
Adm. Sci. 2020, 10, 55 22 of 39
Adm. Sci. 2020, 10, x FOR PEER REVIEW 21 of 38

From Survey: Challenges/opportunities

to measuring the projects’ SDG impact:

Challenges:
1. Success definition (56%),
2. Business priorities (55%),
3. Leadership (52%)
4. Focus on outputs rather
than outcomes (46%)

Opportunities
1. Leadership (57%)
2. Increase education and
training in SDG impact
skills (57%),
3. Build a simple tool (55%)
4. Business skills (48%).

Figure
Figure 15.
15. Responses
Responsesto toQuestion
Question66 (opportunities)
(opportunities)and
and 77 (challenges)
(challenges) for
for measuring
measuring SDG
SDG impact?
impact?
(See Appendix B for full data).
(See Appendix B for full data).

4.3. Inferential
4.3. InferentialStatistics
Statistics
Chi-Square analysis
Chi-Square analysisprovided
provided a p-value
a p-value between between 0.001
0.001 and and
0.132 (see0.132 (see Appendix
Appendix C).
C). Therefore,
Therefore,
the thevalidation
statistical statistical validation was not
was not found found
to be to be consistent,
consistent, andthe
and as such, as such, the findings
findings should be should
viewedbe
viewed
as as an indicator
an indicator of whereof further
where further research
research can becan be focused.
focused. However,
However, the survey
the survey has been
has been followed
followed by
by interviews
40 40 interviewswithwith
CEOsCEOsandand Heads
Heads of of Sustainability,and
Sustainability, andwhile
whileanalysis
analysisofofthis
this data
data remains
remains
underway, ititprovides
underway, providesincreased confidence
increased thatthat
confidence therethere
is a gap
is between current practices
a gap between and engineers’
current practices and
strong desire
engineers’ to improve
strong desire tohow sustainability
improve is addressedisinaddressed
how sustainability the contextin of
theinfrastructure projects.
context of infrastructure
projects.
5. Discussion, Framework Development and Policy Implications
5. Discussion, Framework
The survey Development
has captured engineers’and Policy
views on Implications
the measurement of SDG impact on projects.
Therefore, it has provided an individualistic and
The survey has captured engineers’ views on the rich perspective
measurement on the
of views of engineers
SDG impact on the
on projects.
importance of SDG measurement. The survey also specifically examined the mechanism and
Therefore, it has provided an individualistic and rich perspective on the views of engineers on the context
of SDG measurement
importance to identify strengths
of SDG measurement. andalso
The survey weaknesses of employing
specifically such
examined the an approach.
mechanism andIn doing
context
so, the survey identified that the overwhelming majority (87%) of engineers surveyed
of SDG measurement to identify strengths and weaknesses of employing such an approach. In doing have a strong
so, the survey identified that the overwhelming majority (87%) of engineers surveyed have a strong
appetite for action on the SDGs. From the engineers surveyed, millennial engineers are 15–20% more
Adm. Sci. 2020, 10, 55 23 of 39

appetite for action on the SDGs. From the engineers surveyed, millennial engineers are 15–20%
more likely than non-millennials to want to work on projects that deliver according to the SDGs.
However, this strong focus and desire, almost commitment, to the SDGs and their materialisation was
accompanied by an equally strong frustration with the lack of solutions that are fit for purpose and
currently available for use in industry.

5.1. Analysis of Results and Development of a Future Research Model

The survey highlighted four critical success factors to improve the effectiveness of SDG
measurement at the sub-national levels: (1) tools and methods, (2) suitable training geared towards
an understanding of the SDGs, (3) definition of business success that differentiates between outputs
and outcomes, and finally, (4) the most frequently identified success factor was the leadership and
governance tailored to driving change under the SDG framework. This led to eight core themes that
emerged from the results, which are discussed within the three categories of Context, Mechanism and
Outcomes, consistent with the C–M–O Realist Evaluation approach in Figure 4 (Zachariadis et al. 2013).
The first category of Context had four emergent themes: (C1) leadership and governance, (C2) business
skills for engineers, (C3)performance measurement tools, and (C4) millennials. The second category of
Mechanism had three emergent themes: (M1) prioritisation of SDGs and (M2) adapting to levels of
portfolio–programme–project (P3M) complexity. The third category was Outcomes, which had two
themes: (O1) outputs versus outcomes as definition of project success and (O2) adopting a Creating
Shared Value approach using the Theory of Change and the Triple Bottom Line.

5.2. Context

5.2.1. Leadership—Governance (C1)

Unsurprisingly, leadership was identified as a dominant contextual issue in the measurement
of SDG impacts at organisational and project levels. Whilst the results from the engineers’ survey
are insightful, the data need triangulation by seeking the senior leaders’ attitudes in the follow-on
interview stage of this research. This will capture views of 40 CEOs and senior executives to gain an
organisational perspective on the dominant contextual issues. It is expected that leadership will be a
prominent theme because of its role in transformation, which lies at the core of adopting the SDG lens
to measure business and project success.
As Metcalf and Benn noted (2013), leadership is a well-recognised success factor in the
implementation of sustainability and CSR in businesses. The SDGs have similar complexity patterns
to sustainability, with an equal need for adaptive systems that place an extraordinary demand on
leaders (Metcalf and Benn 2013). These leaders need the skill sets that can balance the complexities of
achieving the economic business success of profitability with the increasing demands on co-balancing
with environmental and social objectives. The visionary leaders will be the ones that harness the
CSV mindset and then can empower and align their organisations with the people, profit and planet
thematics of TBL, thinking and acting within ‘systems of systems’ models that seek innovative solutions
to the SDG challenges (Fullan 2005).

5.2.2. Business Skills for Engineers (C2)

Following on from the previous theme of defining success through outcomes and not just through
the traditional outputs of time, cost and scope (and quality), the survey results also indicated the need
to build capability and capacity amongst engineers. This complements the work by Zahra et al. (2006)
into entrepreneurship and dynamic capabilities needed of engineers beyond their traditional technical
expertise. Others (Armanios et al. 2017) propose that the broadening of skill sets to include business
skills of innovation and definition of broader TBL success will play a more dominant part of the
education and learning syllabus in future. In this regard, it was suggested by a number of respondents
that embedding business skills learning within core engineering educational programmes would
Adm. Sci. 2020, 10, 55 24 of 39

help provide opportunities for meaningful improvements in the measurement of SDG performance
on projects.

5.2.3. Measurement Tools and Processes (Methodologies) (C3)

There are many authors that recognise the need for using benefits and impact
performance measurement to ensure strategic plans are delivered effectively and efficiently
(Micheli and Manzoni 2010; Prasad et al. 2003). The survey responses also showed that there is
significant room for improvement on availability of ‘fit for purpose’ engineering tools and methodologies
to measure SDGs. These results highlight the need for a new simple tool (such as a set of KPIs linked to
the SDG indicators) developed with a global sustainability body, such as the Global Reporting Initiative
(GRI). This might allow the engineering community to align projects’ SDG reporting with the growing
trend of using global standards to report sustainability, with 93% of the world’s largest 250 corporations
reporting on their sustainability performance in 2018 and 82% (Global Reporting Initiative 2016) of
these using GRI Sustainability Reporting Guidelines to do so.

5.2.4. Millennials (C4)

In addressing the research question on engineers’ use and rating of SDG measurement at local
levels, a further investigation was carried out to explore potential differences between millennials and
others. In a highly encouraging note, the survey results indicate that millennial engineers are 15–20%
more likely than non-millennials to want to work on projects that deliver the SDGs. This is a key
insight for engineering business leaders to take note of (such as CEOs and other managers) because in
the UK alone, 50,000 engineers (all forms) will have to be recruited per year until 2022 to meet the
projected level of demand for qualified engineers (Institution of Civil Engineers 2018). These talent
management priorities will be important to business resilience in the near future. Indeed, the identified
lack of alignment between millennials’ perceptions of businesses’ motivations balanced against their
own imperatives is typically shown in their allegiance to employers (Deloitte 2018b). Simply stated,
if businesses do not make a greater effort to demonstrate their ability to create shared value that
achieves financial, but also environmental and social, outcomes, then it will not engender loyalty.
In turn, this may result in higher staff turnover, thereby damaging its business interests as well as
negatively impacting the projects’ delivery of SDGs.
The survey results are further evidenced by a report from Deloitte (2018a) on findings from
over 10,000 millennials. The Deloitte (2018b) survey indicated a distinct, negative shift in millennials’
attitudes on CEO’s and business’ motivations and ethics. Today, less than half of millennials believe
businesses operate ethically (48% balanced against 65% in 2017) as well as a drop in the number of
them that believe that CEOs are committed to supporting society (47% balanced against 62% in 2017).
This highlights that there is a significant mismatch between what millennials define as responsible
organisations and the people that lead them, in terms of what responsible businesses should aspire to
achieve. The message is clear that millennials want business leaders to be proactive about making a
positive impact on society. Measuring projects’ SDG impact is a way to do this.

5.3. Mechanism

5.3.1. Prioritisation of SDGs (M1)

The survey highlighted a clear preference for measuring just five SDG goals (there was a 50%
reduction in the preferences for the next two SDG goals). This indicated a long tail of ten further goals
that did not appear to resonate with participants. The literature review also noted that there is a growing
body of evidence (Allen et al. 2019; Bali Swain and Yang-Wallentin 2020; Jones and Comfort 2020) that
suggests that the complexity of the 17 Goals and the 169 targets needs to be simplified and a reduced
selection prioritised for measurement. Combined, the findings from the survey on the top five SDGs
and the complexity noted in the literature review is consistent with the advice given by the UN
Adm. Sci. 2020, 10, 55 25 of 39

Global Compact in their proposed methodology (Global Reporting Initiative 2015). This indicates an
important area for further research to assess how this simplification can be achieved at organisational
and project levels.

5.3.2. Organisational, Portfolio, Programme and Project (P3M) Complexity (M2)

The survey questions did not specifically address the separate levels of organisations and projects,
which in the project management discipline is referred to as portfolio–programme–project management
(P3M) levels. This becomes important in the development of a prototype since the measurement of
SDGs at the enterprise level (the portfolio) will likely be different from that at programme and project
levels. This requires further research. The non-response error, noted in Section 3.3, highlighted that the
survey of organisational level had not achieved its objectives and that this should be addressed in the
next research stage of the forty interviews, which will introduce the wider issues of the P3M structure.

5.4. Outcomes

5.4.1. Defining Success—Outputs Versus Outcome (O1)

The survey showed that engineers agreed that too often, projects define success by traditional
outputs using the so-called ‘iron triangle’ of time/cost/scope (and quality) dimensions to deliver on
the SDG goals. Instead, the majority of survey respondents agreed that the engineering and project
management communities need to place a greater emphasis on the achievement of long-term outcomes
and a corresponding broader definition of success.

5.4.2. Creating Shared Value using Theory of Change and Triple Bottom Line (O2)
As noted above (Section 5.4.1), the survey showed that many engineers agreed that the choice
of SDG goals and targets should be primarily selected on the basis of business profitability. This is
counter to CSV and TBL. As a result, the longer-term value of making investment decisions based
on broader TBL principles could be weakened. It is therefore proposed that the next research stage,
to inform the development of the prototype, investigates how the TBL could be integrated with the
measurement of SDGs.

5.5. Development of Conceptual Framework for Measuring the SDG Performance of Infrastructure Projects
The identification of the four context issues discussed in Section 5.2 above offers insights into the
situational effects on the likely success of the mechanism. Stated slightly differently, for the measurement
of SDG impacts to deliver the outcomes expected, the contextual situation needs to be appropriate.
For example, without the requisite organisational leadership inter- and intra-businesses, from the
government as well as executives, the measurement of SDGs will not be successful. This survey therefore
informs the next stage of the Sequential Explanatory Design (Creswell and Creswell 2017), shown in
Figure 16, that involves interviews of forty senior executives. It is proposed to deepen the exploration of
the four context issues by using a nodal coding system (Creswell and Creswell 2017) for the interviews.
This will strengthen the analysis and, combined with the literature review to enable the triangulation of
data, will enable the development of a robust prototype to test the approach with practitioners.
Four of the mechanism and outcome C–M–O themes, noted in Sections 5.3 and 5.4, are further
explored below in Table 3 to identify themes that can be included in future research. As a deduction,
it is posited that the shared value approach aligns individual business priorities of specific firms
with sustainable development imperatives. Consequently, adopting an enhanced SDG measurement
approach is capable of releasing the energies of businesses to pursue competitive advantage and the
SDGs through integrated business strategies. As a way of summarising the conceptual development of
a prototype SDG measurement model, based on the survey findings presented in this study, a tabulated
compendium is shown below that includes some exploratory questions, with derived assumptions
and supporting literature references.
Adm. Sci. 2020, 10, 55 26 of 39

Table 3. Summary of conceptual development of a future prototype model for SDG measurement, based on the survey results in this study, including questions &
sub-questions, supporting literature and corresponding concepts.
C–M–O Future Questions for Next Stage of
Assumptions Derived from Stage 1 Research Supporting Literature
Research Focus Research
Assumptions:
1. Only a small proportion of the 232 SDG indicators are
currently being measured at the project level, and
consequently, there is a large gap between global definitions (Klopp and Petretta 2017; Donohue et al.
Q1: How can a simplified
of SDG objectives and project-level definitions of action. 2016; Nerini et al. 2018; Allen et al. 2016;
Prioritisation of selection of goals and targets be
2. The evidence of the difficulty to use the existing 169 targets IPCC 2018; Swain 2018; United Nations
SDGs (M1) identified at organisational and
and 232 indicators suggests that the derived model should 2018; Hall et al. 2016; Martens and
project levels?
recognise that a contextual perspective needs to be adopted Carvalho 2016)
to keep it simple for practitioners who are already heavily
committed to other performance measurement frameworks.

Q2: Can a measurement system

be designed to address different
Assumptions: (Morris 2013; Cooke-Davies 2007; Morris P3M levels?
1. Measurement of SDG performance should accommodate the 2017); NAO Report Projects (National Q3: Can an MREL (monitoring,
required different organisational levels, namely portfolio, Audit Office 2005); (Silvius and Schipper reporting, evaluation and
programme and project levels. 2014; Silvius et al. 2017; Martens and learning) learning loop be
Organisational 2. That existing sustainability measurement at organisational Carvalho 2016; Økland 2015; Silvius and established that is suitable for
P3M Complexity and project levels is well established, and therefore, SDG Schipper 2014; Association for Project each of the levels from
and measurement should be aligned to existing successful Management 2019; Sawaf and Gabrielle organisational level to portfolio,
Sustainability approaches, not created as an ‘add-on’ (i.e., the organisational 2014; Schaltegger and Burritt 2000; Eccles programme and project levels?
(M2) level will likely have different SDG imperatives and and Krzus 2010; Bonini and Emerson Q4: What existing sustainability
reporting requirements, such as using the GRI, from the 2005; Bugg-Levine and Emerson 2011; measurement systems are used at
project level, which might have limited capability and Preston 2012; Malhi et al. 2009; Suess project (e.g., CEEQUAL) and
capacity to track too many targets and indicators.) 1980; Tilt 2009; Perrini and Tencati 2006; organisational (e.g., Global
Kaplan and Norton 1996) Reporting System) levels, and
how might they be used to align
with SDG measurement?
Adm. Sci. 2020, 10, 55 27 of 39

Table 3. Cont.
C–M–O Future Questions for Next Stage of
Assumptions Derived from Stage 1 Research Supporting Literature
Research Focus Research
Assumptions:
1. Measurement of SDG performance should be viewed from a
systemic perspective and thereby move beyond the
traditional ‘iron triangle’ view of projects in the short term Q5: Can the existing causal value
(i.e., according to schedule, budget, scope and quality chain of the benefits approach
Theory of Change and Logic Model:
Defining performance) and additionally, take account of longer-term (from project inputs through
Stein and Valters 2012; Weiss 1995;
Success—Outputs project outcomes and impacts. activities, outputs, outcomes and
Project Success: Thiry 2004;
Versus Outcome 2. The model should harness the core concepts of the Theory of impacts) be used to build a
Themistocleous and Themistocleous and
(O1) Change and the Logic Model, with their focus on outcomes commonly understood view of
Wearne 2000
measurement, including the analysis of causal linkages, what future SDG measurement
engagement of stakeholders and strategic design with the success looks like?
‘ends’ being the starting point for a right to left
causal mapping.

Assumptions:
1. Measurement of SDG performance should accommodate the Q6: Can the prototype model
perspective of Creating Shared Value (CSV) (i.e., seeking include the TBL ‘golden thread’
solutions that are good for business in the short and longer Creating Shared Value: (Porter and to establish a pathway through
term through balance of profit–planet–people objectives). Kramer 2011; Elkington 1994, 2018; the project SDG measurement in
Creating Shared 2. Measurement of SDG performance should accommodate the Organisation for Economic Co-Operation a way that practitioners can use
Value using Triple perspective of the Triple Bottom Line (i.e., social, and Development 2015; United Nations effectively and efficiently?
Bottom Line (O2) environmental and economic performance). This will drive a 2018); Q7: Is the concept of CSV
broader definition of project sustainability that includes the Triple Bottom Line: (Elkington 1994, recognised and valued by
three pillars (i.e., social, environmental and economic 2018; Griggs et al. 2013) executives, and does it offer a
performance). It provides simplicity and structure for the route to integrated SDG
analysis in regard to selecting and measuring SDGs. measurement?
 Triple Bottom 1. Measurement of SDG Kramer 2011; ‘golden thread’ to
Line (O2) performance should Elkington 1994, 2018; establish a pathway
accommodate the OECD 2019; UN through the project SDG
perspective of Creating 2018); measurement in a way
Shared Value (CSV) (i.e., Triple Bottom Line: that practitioners can use
Adm. Sci. 2020, 10, 55 (Elkington 1994, 2018; effectively and 28 of 39
seeking solutions that are
Adm. Sci. 2020, 10, x FOR PEERgood REVIEW Griggs et al. 2013) efficiently? 25 of 38
for business in the short
Q7: Is the concept of CSV
and longer term through
1. Outcome:
recognised and valued
balance
Central Research Question of profit–planet– by executives, andand does it
for next stage (Creswell,
Measurement of SDG • What is your company’s awareness
Impact at
2017):
people objectives). organisational & application ofoffer
SDGs?a(3 levels)to integrated
route
Using a Realist Evaluation project levels
2. Measurement
(Context-Mechanism-Outcome),
of SDG SDG measurement?
How do performance
engineers in the should
2. Mechanism: Level
construction sector rate and
of SDG measurement
• What level of maturity are your SDG
accommodate
use global UN SDG goals for the maturity
process measurement processes? (4 levels)
infrastructure investment
Results of the survey
decisionsperspective
at local level? of the Triple
3. Context:
• What are the contextual issues that affect SDG
of 325 Engineers Bottom
Sub-Question Line (i.e., social,
for next stage: organisational measurement?
inform the question
environmental and economic environment • Knowledge (incl: understanding what defines
and analysis success; understanding of SDGs; business skills to
structure of the Sub-Question 1: (context) What are
the performance).
contextual issues that affect the This will drive define investment decision at start of project;
interviews Strengths education & training)
likely success of SDG measurement?
a broader definition of project • Leadership & Strategy (incl: stakeholder
Sub-Question 2: (mechanism) What engagement; planning horizons)
Weaknesses
mechanism sustainability
does your organisation that includes • Tools, Processes & techniques (incl what to
use for SDG measurement? measure)
the three pillars (i.e., social,
Sub-Question 3: (outcomes) What Opportunities • Culture (incl millennials; change management)
outcomes environmental
are anticipated from the and economic • Cost / profit (incl: business success; balance
use of this mechanism across Triple Bottom Line)
(measurement of SDG)?
performance). It provides Threats • Other
simplicity and structure for
the analysis in regard to
Figure 16. 16.Proposed
Proposednodal nodalevaluation
evaluation framework
framework for sequential explanatory design of the next
Figure selecting and measuring for sequential explanatory design of the next
stage—the semi-structured interviews of forty executives.
stage—the semi-structured SDGs. interviews of forty executives.

TheFour
The eightof the
eight
mechanism anddiscussed
emergent
emergentthemes
outcome C–M–O themes,
themes discussedearlier, earlier, which
noted in Sections
which informed
informed the
5.3 and 5.4, of
the identification
identification arethe
of the
further
seven
seven
explored
questions below
shown in
aboveTable
in 3 to
Table identify
3, have themes
been that
further can be
developed included
intoina future
conceptual research. As
framework a deduction,
for future
questions shown above in Table 3, have been further developed into a conceptual framework for
it is posited that the shared value approach aligns individual business priorities of specific firms with
research,
future shown
research, inshown
Figurein17Figure
below.17 below.
sustainable development imperatives. Consequently, adopting an enhanced SDG measurement
approach is capable
Critical Realist’s of releasing the energies of businesses to pursue competitive advantage and the
CMO Approach:
SDGs through integrated business strategies. As a way of summarising the conceptual development
Context (C)SDG measurement Mechanism
of a prototype model, based (M) on the survey findings Outcomes presented in(O)this study, a
tabulated compendium is shown below
M1: Prioritisation of that M2:
includes some exploratory questions, with derived
Organisational
O1: Defining project O1: Creating Shared Value
SDGs P3M* Complexity
assumptions and supporting literature
Research themes references. success - outputs using Theory of Change and
emerging from Survey Other SDGs versus outcomes Triple Bottom Line
of 325 Engineers:
Table 3. Summary of conceptual development of a future prototype model for SDG measurement,
based on the
C1: Leadership & survey results in this study, including questions & sub-questions, supporting literature
What ways can
Governance
and corresponding concepts. businesses
C2: Business Skills broaden
& Capabilities economic
C–M–O Future Assumptions Derived from Stage Questions investment
for Next
Supporting Literature
Research Focus
C3: Process and 1 Research Stage ofdecision approach
Research
Tools to include
Assumptions: environment &
society?
C4: Millennials 1. Only a small proportion of
the 1554 SDG indicators are
Built environment
themes currently being measured at Design & select Case Study
digital communications
the project level, and to deepen research into
emerging projects’ SDG
waste water energy consequently, there is a large measurement themes
infrastructure governance gap between global (Klopp and Petretta
How can a few prioritised ‘global’ SDG goals2017;
and targets
Donohue et al.
solid waste transport
definitions
be measuredofatSDG objectives& project levels
‘local’ organisational Q1: How can a simplified
2016; Nerini et al.
consistent with existing benefits approach? Develop a Test the
and project-level definitions selection Model
of goals and
water supply 2018; Allen et al. Model
(P3M* = Project, Programme & Portfolio) 2016;
Prioritisation of of action. targets be identified at
IPCC 2018; Swain
SDGs (M1) 17. Conceptual
Figure 2. Theframework
evidence offor thenext
difficulty
phase of research using organisational and
Figure 17. Conceptual framework for next phase of research 2018; UN 2018;
using Hallthe
the critical
critical
etrealist realist Context–
Context–Mechanism
to use the existing 169 targets
Mechanism–Outcome Approach for future qualitative investigation project levels?
of forty senior executive interviews.
–Outcome Approach forand future al.of
2016; Martens
senior and
232qualitative
indicators investigation
suggests Carvalho forty
2016a,
executive interviews.
that the derived model 2016b)
5.6. Policy Implications
should recognise that a
This paper has evaluated the potential
contextual perspective of linking
needs the SDG global level goals to the local level delivery
on infrastructure projects. The survey has provided
to be adopted to keep it an individualistic view on what is important but
has not given an authoritative simple for practitioners
organisational whoDespite not having the organisational perspective,
view.
there are some important implications are already
that can heavilyaffect the design of policies aimed at achieving sustainable
development. These need further committed researchtobut are other
summarised at a high level in Table 4 below.
Adm. Sci. 2020, 10, 55 29 of 39

Table 4. Policy implications of research.

No. Policy Area Research Implications Affecting Policy Design

Harnessing the power of millennials to • The survey highlights the appetite and demand for change in the improved and
1 increased pace of response to the SDG agenda. This needs to be acknowledged
drive the SDG and climate change agenda.
and harnessed, using the power of the Greta Thunberg-generation to drive action.

• Governments often use financial-centric models and methods to make

return-on-investment decisions when deciding on infrastructure investments. The
2 Redefining success research indicates that this needs to be broadened to include a balance with
environmental and social factors.
• Policy should be reviewed on what outcomes define success and which should be
longer-term and incorporate SDG impacts at the local and organisational level.

• What tools, processes and systems are needed to support policy? For example,
Engineering organisational and project whilst many nations are setting Net Zero targets (e.g., UK in 2050), there are
3 context—designing measuring insufficient tools, systems or processes to measure this in a consistent way.
mechanisms that work • The research indicates that more education is needed in understanding projects’
outcomes–benefits management and how this might affect the successful
measurement of SDGs on projects.

• What are the core characteristics of monitoring, reporting, evaluation and learning
MREL—Monitoring, reporting, evaluation (MREL) as defined by leading global organisations such as the OECD
4
and learning (Organisation for Economic Co-operation and Development) and the World Bank,
and how might these be applied for MREL of SDGs at project level?
Adm. Sci. 2020, 10, 55 30 of 39

A potential limitation of the study may have emerged as originally the survey was intended
to capture attitudes of both individual engineers as well as organisations. However, this was not
successful because respondents were unable (with only five exceptions) to provide an authoritative
organisational perspective. This was a form of non-response error (Singleton and Straits 2010), and the
strategy to reduce this error was to firstly diagnose the problem and then find ways to mitigate the error.
The reasons given when the researcher followed up with a few known participants who had waivered
their anonymity and volunteered their feedback was that no official statement would be given by large
organisations on a survey without having secured senior leadership sanction. These organisations
were later approached at the interview stage of the research study (not included in this paper),
which involved CEOs and Heads of Sustainability who had the authority to provide a corporate
statement on their organisations’ SDG measurement strategy. Singleton and Straits (2010) highlight the
need for self-awareness of bias when using surveys and actively address these from the start. As an
example, as shown in Table 2, the survey approach addressed four known biases: (1) asking the wrong
question, which was addressed by testing the questions in a pilot stage and getting feedback and
adapting where necessary; (2) surveying an inadequate sample, which was addressed by partnering
with the ICE to benefit from a defined group of engineers (they were all active members of a global
standards body), although ‘opting-in’ meant that the sample was potentially biased in favour of the
survey due to participants being more aware of sustainable development; (3) the single nature of the
survey format did not allow for a free-flowing of ideas, and the time restraint of making it quick to
entice busy professionals to be part of the survey meant it lacked the depth of separate interviews.
This was balanced by having a follow-on phase of 40 interviews with construction company CEOs;
(4) misrepresenting the data results, which was addressed by having forums hosted at the ICE to share
back the findings in discussions (as described in Stage 4 of the Analysis phase in Table 2). This feedback
was used to shape the design requirements of the follow-on interviews.

6. Conclusions and Future Work

This paper started from the premise that there is a problem with linking the SDG global-level goals
to the local-level delivery on infrastructure projects. This potentially manifests in poorer investment
decisions on infrastructure projects because they are too often based on purely financial return on
investment (RoI) instead of a broader set of criteria across the TBL of economic, environment and society.
In order to explore the identified gap in the literature, the research adopted a mixed-method approach
of a Sequential Explanatory Design (Creswell and Creswell 2017). This paper addresses the first stage,
a survey of 325 engineers, to answer the research question of How do engineers in the construction
sector rate and use global UN SDG goals for infrastructure investment decisions at local level? To derive
the answer, it used the Realist Evaluation methodology of the Context–Mechanism–Outcomes model
(Pawson and Tilley 1997) to structure and evaluate practitioners’ views on using SDGs to measure
local success.
The literature review analysed whether the CSR approach to governance and project
decision-making is sufficient to ensure infrastructure projects’ investments maximise their positive
impacts on the UN SDG 2030 targets. The study suggests that the scale and urgency of the SDG
challenge necessitate radical rethinking at the project delivery level, where the majority of the SDG
targets are delivered. Consequently, CSR requires updating by placing socially aware priorities, such as
the SDGs, at the heart of core business thinking and strategies. CSV was offered as a theoretical model
to do this, but it requires practical engagement to implement CSV’s ambitions. In particular, CSV is
dependent on businesses ensuring that there are four key success criteria in place: the leadership,
tools, business skills and a common understanding of the broader definition of success beyond the
‘iron triangle’ of time, cost and scope.
Adm. Sci. 2020, 10, 55 31 of 39

Despite limitation of the sample (all 325 ICE members ‘opted-in’, inferring interest), the results
of the survey show that the vast majority (87%) of engineers surveyed have a strong appetite for
action on the SDGs. From the engineers surveyed, millennial engineers are 15–20% more likely than
non-millennials to want to work on projects that deliver the SDGs. However, this strong focus and
desire, almost commitment, to the SDGs and their materialisation was accompanied by a strong
frustration with the lack of solutions that are fit for purpose. This research identifies that there is
a gap between their perceived importance of measuring SDG impact, contrasted with their current
capabilities (such as skills, knowledge, leadership, tools and approaches) to do so.
The limitations of this exploratory research are that it has not provided definitive findings from
the perspective of organisations. However, it has helped to narrow the scope of future research by
establishing priorities for future research and sign-post for further analysis that deepens the research.
Through use of the refined research framework developed in this paper, with key assumptions and
derived research questions, there is an opportunity to deepen our understanding of this important area.
This can guide the next stage of the forty interviews of engineering organisations’ CEOs and Heads of
Sustainability. The triangulation of data analysis from this paper with the literature review and the
interviews will inform the development of the prototype model, which will subsequently be tested in
case study research. In this way, future research will provide more meaningful insights into how the
use of the SDGs can strengthen future infrastructure investment decisions for people, planet and profit.

Author Contributions: The research was developed by several authors. Their contributions are shown below:
Conceptualization: P.M. Writing—original draft preparation: P.M. Writing—review and editing: P.M., S.P.P. and
E.K. Visualisation: P.M. Supervision: S.P.P. Project administration: P.M. Funding acquisition: S.P.P. Methodology:
P.M. Software and data curation: P.M. Validation: P.M., S.P.P. and E.K. All authors have read and agreed to the
published version of the manuscript.
Funding: This research was indirectly funded by the Nathu Puri Institute for Engineering and Enterprise, School of
Engineering, London South Bank University, through the funding of the doctoral research support to Paul Mansell.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript or in the decision to
publish the results.
Funding: This research was indirectly funded by the Nathu Puri Institute for Engineering and Enterprise, School
of Engineering, London South Bank University, through the funding of the doctoral research support to Paul
Mansell.

Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the
study;
Adm. in2020,
Sci. the collection,
10, 55 analyses, or interpretation of data; in the writing of the manuscript or in the decision to
32 of 39
publish the results.

A. Survey
Appendix A. Survey Questions
Questionsand
andSelection
Selectionof
ofthe
theType
TypeofofQuestion
Question and
and Metric
Metric to to Align
Align with
with
Analysis Requirements for Measuring
Measuring Engineers’
Engineers’ Views
Views on
on Projects’
Projects’ SDG
SDGImpact
Impact

Q# Theme Question Measuring Value

Y / No 1 to 6 text List option
Opening Page With ICE and GEC logos. Thank the respondent for their time. Explain the purpose of the survey.
Who is it for? How long will it take (10 mins)? What will happen with the data? Who is the POC at
ICE.
Are you completing this survey as an individual or for an engineering firm? allow single choice for either individual or as
a
engineering firm
Engineering Organisation -
1a General Data on your
organisation
1.1 Q: What is the name of your organisation. complete text
1.2 Q: In which country is your organisation based? text for country and list for continets
1.3 Q: Name of person completing Survey for firm complete text
1.4 Q: Your role/grade complete text
1.5 Q: Contact details (email) complete text
1.6 Q: Number of Engineers in your firm options include 1-49; 50-99, 100 +etc
1.7 Q: Number of current engineering projects underway (from design through to completion) options include 1-20; 20-49; 50-99, 100 +etc
Q: In which countries do you deliver projects? just home country; 2-5 countries; 6-20 countries;
1.8
20+ countries
1.9 Q: Who is the company's SDG or CSR lead? complete text
Engineering Organisation -
2 SDG Data on your
organisation
Q. How do you plan to assess your impact on the SDGs? select one of the six choices
1 We have no intention to assess our impact on the SDGs
2 We plan to assess our impact on the SDGs but have not thought through how
2.1 3 We plan to assess our impact on some of the SDGs and indicators relevant to our business
4 We plan to assess our impact on all the SDGs and indicators relevant to our business
5 We plan to assess our impact on all 17 SDGs and indicators
6 Don’t know
Q. We fully understand the SDG priorities of the governments in our key markets and countries of 1=strongly agree; 2=agree; 3= neither agree or
2.2
operation. disagree; 4=disagree; 5=strongly disagree 6=n/a
Q. Our company has fully defined the tools that will help it to assess its impact against the SDGs. 1=strongly agree; 2=agree; 3= neither agree or
2.3
disagree; 4=disagree; 5=strongly disagree 6=n/a
Q. We can comprehensively report to governments and other key stakeholders on how our 1=strongly agree; 2=agree; 3= neither agree or
2.4
company is contributing to SDGs. disagree; 4=disagree; 5=strongly disagree 6=n/a
General SDG Data - as a
1b member of the engineering
community
1.1b Q: What is the name of your organisation? complete text
1.2b Q: In which country is your organisation based? text for country and list for continets
1.3b Q: Contact details (email) - optional complete text
1.4b Q: Years of experience as qualified engineer options incl eg 1-5; 5-10; 10-15; 15+; n/a
Q: Are you a millenial (born 1980-2000)? select Y or N; this allows to filter data to assess
1.5b
any difference between them and others
Q. As an engineer I want to know more about UN’s Sustainable Development Goals,and what we 1=strongly agree; 2=agree; 3= neither agree or
1.6b
are doing to measure our impact against them. disagree; 4=disagree; 5=strongly disagree 6=n/a

SDG-Engineering specific
3 Questions
Q. From the list below, please rank the five SDGs where you believe engineers have the greatest list of all 17 Goals with titles
3.1
impact and opportunity.
Q. Having read the UN’s Sustainable Development Goals, do you agree it is important that 1=strongly agree; 2=agree; 3= neither agree or
3.2
engineering business’ sign up to these goals? disagree; 4=disagree; 5=strongly disagree 6=n/a
Q. There is strong evidence that we have a 'fit for purpose' SDG measuring approach to track our 1=strongly agree; 2=agree; 3= neither agree or
3.3
contribution from our projects. disagree; 4=disagree; 5=strongly disagree 6=n/a
Q. Commercial realities dictate that you should cherry pick the best SDGs for your business instead 1=strongly agree; 2=agree; 3= neither agree or
3.4
of the best ones for the planet. disagree; 4=disagree; 5=strongly disagree 6=n/a

GEC related Questions: The Global Engineering Congress is being hosted at the ICE from 22-28 Oct 18 (please see details at:
4 Engineering Community – Global Engineering Congress Info Global Engineering Congress Info
Sharing Best Practice
Q. I/we are planning to actively engage with the GEC discussions and support plans to agree and 1=strongly agree; 2=agree; 3= neither agree or
4.1
implement a global engineering response roadmap to the SDGs? disagree; 4=disagree; 5=strongly disagree 6=n/a
Q. As an engineer, I/we support the Global Engineering Congress’ objective to unite the engineering 1=strongly agree; 2=agree; 3= neither agree or
4.2
community to agree and mobilise a response roadmap to the UNSDG? disagree; 4=disagree; 5=strongly disagree 6=n/a
Q. We should look to engineering associations and standards bodies for advice, support and 1=strongly agree; 2=agree; 3= neither agree or
4.3
guidance on measuring project contribution to specific SDGs. disagree; 4=disagree; 5=strongly disagree 6=n/a
Adm. Sci. 2020, 10, 55 33 of 39
Adm. Sci. 2020, 10, x FOR PEER REVIEW 31 of 38

Appendix B.
Appendix B. Data
Data Capture
Capture from
from Survey:
Survey: Select
Selectthe
theSix
SixSDGs
SDGsThat
ThatYou
YouBelieve That
Believe Engineers
That Engineers
Have the Greatest Impact and Opportunity
Have the Greatest Impact and Opportunity
Adm. Sci. 2020, 10, 55 34 of 39
Adm. Sci. 2020, 10, x FOR PEER REVIEW 32 of 38

AppendixC.
Appendix C.Data
DataCapture
Capture from
from thethe Survey’s
Survey’s Chi-Square
Chi-Square TestsTests
(with(with Continuity
Continuity Correction,
Correction,
Likelihood Ratio, and Linear-by-Linear Association)
Likelihood Ratio, and Linear-by-Linear Association)

Question 2: We conducted SPSS analysis to ascertain Chi-Square between the two age groups
Question
(namely millennial 2: Weandconducted SPSS analysis
non-millennial) and theirto ascertain
responses. Chi-Square
There were between the two
159 answers agegave
that groups
a p-
(namely millennial
value (Pearson and non-millennial)
Chi-Square) of 0.136. Thisand wastheir responses.
not fully Theresignificant
statistically were 159 but
answers that agave
indicates a
viable
p-value (Pearson Chi-Square) of 0.136. This was not fully statistically significant
trend that supports further analysis. However, when the optionality of questions was condensed, but indicates a viable
trend that supports
combining the ‘agree’further analysis.
and ‘strongly However,
agree’ categorieswhen the optionality
as well as separatelyofcombining
questionsthe was condensed,
‘disagree’ and
combining the ‘agree’
‘strongly disagree’ and ‘strongly
categories, agree’
the results categories
became more as well as separately
statistically significantcombining
at a p-valuetheof ‘disagree’
0.110.
and ‘strongly
Question disagree’
3: Therecategories, the results
was initially a p-value became moresuggesting
of 0.001, statisticallythat
significant at a p-value of
the non-millennials 0.110.
did not
Question 3: There was initially a p-value of 0.001, suggesting that
have any markedly different opinions on the answers to this question. However, when further the non-millennials did not
have any was
analysis markedly different
conducted opinions on
by combining the answers
agree to thisagree,
and strongly question. However,
the p-value waswhen
0.032,further analysis
indicating that
was conducted by combining agree and strongly agree, the p-value was
the millennials had similar numbers agreeing, but a much higher proportion of millennials were 0.032, indicating that the
millennials had similar
strongly agreeing. It isnumbers
difficult agreeing,
to interpretbutwhat
a muchthishigher proportion
categorically of millennials
means, but it maywere strongly
indicate that
agreeing. It is difficult to interpret what this categorically means, but it may indicate
there is likely to be a stronger viewpoint from a generation that prefer to give higher ratings for an that there is likely
to be athat
issue stronger
has such viewpoint from impacts
catastrophic a generation
if it isthat
not prefer to give
dealt with higher ratings for an issue that has
effectively.
such catastrophic impacts if it is not dealt with effectively.
References
References
(Adshead et al. 2019) Adshead, Daniel, Scott Thacker, Lena I. Fuldauer, and Jim W. Hall. 2019. Delivering on the
Adshead, Daniel, Development
Sustainable Scott Thacker,Goals
Lenathrough
I. Fuldauer, and Jim
long-term W. Hall. planning.
infrastructure 2019. Delivering on the Sustainable
Global Environmental Change
Development
59: 101975. Goals through long-term infrastructure planning. Global Environmental Change 59: 101975.
[CrossRef]et al. 2019) Albuquerque, Rui, Yrjö Koskinen, and Chendi Zhang. 2019. Corporate social
(Albuquerque
Albuquerque, Rui, Yrjö
responsibility andKoskinen,
firm risk:and Chendi
Theory andZhang. 2019.
empirical Corporate
evidence. social responsibility
Management and firm risk: Theory
Science 65: 4451–69.
and
(Allen et empirical Allen, C., Management
al. 2016) evidence. Science
G. Metternicht, and65:
T.4451–69.
Wiedmann.[CrossRef]
2016. National pathways to the Sustainable
Development Goals (SDGs): A comparative review of scenario modelling tools. Environmental Science &
Policy 66: 199–207.
Adm. Sci. 2020, 10, 55 35 of 39

Allen, C., G. Metternicht, and T. Wiedmann. 2016. National pathways to the Sustainable Development Goals
(SDGs): A comparative review of scenario modelling tools. Environmental Science & Policy 66: 199–207.
Allen, C., G. Metternicht, and T. Wiedmann. 2019. Prioritising SDG targets: Assessing baselines, gaps and
interlinkages. Sustainability Science 14: 421–38. [CrossRef]
Armanios, D. E., C. E. Eesley, J. Li, and K. M. Eisenhardt. 2017. How entrepreneurs leverage institutional
intermediaries in emerging economies to acquire public resources. Strategic Management Journal 38: 1373–90.
[CrossRef]
Association for Project Management, UK. 2019. APM Body of Knowledge. Princes Risborough: Buckinghamshire,
ISBN 978-1-903494-83-7.
Astbury, B., and F.L. Leeuw. 2010. Unpacking black boxes: Mechanisms and theory building in evaluation.
American Journal of Evaluation 31: 363–81. [CrossRef]
Baird, C. H. 2015. Myths, exaggerations and uncomfortable truths: The real story behind Millennials in the
workplace. The IBM Institute for Business Value. Available online: https://www.ibm.com/downloads/cas/
Q3ZVGRLP (accessed on 7 August 2020).
Bali Swain, R., and F. Yang-Wallentin. 2020. Achieving sustainable development goals: Predicaments and
strategies. International Journal of Sustainable Development & World Ecology 27: 96–106.
Bénabou, R., and J. Tirole. 2010. Individual and corporate social responsibility. Economica 77: 1–19. [CrossRef]
Berge, M. S. 2017. Telecare–where, when, why and for whom does it work? A realist evaluation of a Norwegian
project. Journal of Rehabilitation and Assistive Technologies Engineering 4: 2055668317693737. [CrossRef]
Bergeron, Dave A., and Isabelle Gaboury. 2020. Challenges related to the analytical process in realist evaluation
and latest developments on the use of NVivo from a realist perspective. International Journal of Social Research
Methodology 23: 355–65. [CrossRef]
Bhaskar, Roy. 2013. A Realist Theory of Science. London and New York: Routledge.
Bhattacharya, A., J. Oppenheim, and N. Stern. 2015. Driving Sustainable Development Through better
Infrastructure: Key Elements of a Transformation Program. Brookings Global Working Paper Series; Washington:
Brookings Institution, Available online: https://g24.org/wp-content/uploads/2016/02/Driving-Sustainable-
Development-Through-Better-Infrastructure-Key-Elements-of-a-Transformation-Program-Bhattacharya-
Oppenheim-Stern-July-2015.pdf (accessed on 10 July 2020).
Bielenberg, A., M. Kerlin, J. Oppenheim, and M. Roberts. 2016. Financing change: How to mobilize private-sector
financing for sustainable infrastructure. McKinsey Center for Business and Environment.
Bonini, S., and J. Emerson. 2005. Maximizing Blended Value–Building Beyond the Blended Value Map to
Sustainable Investing, Philanthropy and Organizations. Available online: http://community-wealth.org
(accessed on 2 April 2020).
Boswell, James F., David R. Kraus, Scott D. Miller, and Michael J. Lambert. 2015. Implementing routine outcome
monitoring in clinical practice: Benefits, challenges, and solutions. Psychotherapy Research 25: 6–19. [CrossRef]
[PubMed]
Brundtland, G. H. 1987. Our Common Future: Report of the World Commission on Environment and Development.
Oxford: Oxford University Press.
Bugg-Levine, Antony, and Jed Emerson. 2011. Impact investing: Transforming how we make money while
making a difference. Innovations: Technology. Governance, Globalization 6: 9–18. [CrossRef]
Constanza, R., L. Fioramonti, and I. Kubiszewski. 2016. The UN sustainable development goals and the dynamics
of well-being. Frontiers in Ecology and the Environment 14: 59. [CrossRef]
Cooke-Davies, Terry. 2007. The real success factors on programmes. International Journal of Programme Management
20: 185–90.
Creswell, John W., and J. David Creswell. 2017. Research Design: Qualitative, Quantitative, and Mixed Methods
Approaches. Thousand Oaks: Sage Publications.
Crosthwaite, C., L. Jolly, L. Brodie, L. Kavanagh, L. Buys, and J. Turner. 2012. Curriculum design and higher order
skills: Challenging assumptions. Paper presented at 5th International Conference: Innovation, Practice and
Research in Engineering Education (EE 2012), Coventry, UK, September 18–20.
Deloitte. 2018a. The Business Case for Inclusive Growth. Available online: https://www2.deloitte.com/content/
dam/Deloitte/global/Documents/About-Deloitte/gx-abt-wef-business-case-inclusive-growth-global%
20report.pdf (accessed on 7 August 2020).
Adm. Sci. 2020, 10, 55 36 of 39

Deloitte. 2018b. The Deloitte Millennial Survey 2018—Millennials’ Confidence in Business, Loyalty to Employers
Deteriorate. Available online: https://www2.deloitte.com/content/dam/Deloitte/global/Documents/About-
Deloitte/gx-2018-millennial-survey-report.pdf (accessed on 7 August 2020).
Diekhoff, George. 1992. Statistics for the Social and Behavioral Sciences. Univariante, Bivariate, Multivariante.
No. HA29. D46 1992. London: William C Brown Pub, ISBN-10: 0697285146. ISBN-13: 978-0697285140.
Donohue, I., H. Hillebrand, J. M. Montoya, O. L. Petchey, S. L. Pimm, M. S. Fowler, and N. E. O’Connor. 2016.
Navigating the complexity of ecological stability. Ecology Letters 19: 1172–85. [CrossRef]
Easton, Geoff. 2010. Critical realism in case study research. Industrial Marketing Management 39: 118–28. [CrossRef]
Eccles, Robert G., and Michael P. Krzus. 2010. One Report: Integrated Reporting for a Sustainable Strategy.
Hoboken: John Wiley & Sons.
Elkington, John. 1994. Towards the sustainable corporation: Win-win-win business strategies for sustainable
development. California Management Review 36: 90–100. [CrossRef]
Elkington, J. 2018. 25 Years Ago I Coined the Phrase Triple Bottom Line. Here’s Why It’s Time to Rethink It.
Harvard Business Review, June 25.
Emerson, Jed, Jay Wachowicz, and Suzi Chun. 2000. Social return on investment: Exploring aspects of value
creation in the non-profit sector. The Box Set: Social Purpose Enterprises and Venture Philanthropy in the
New Millennium 2: 130–73.
Forum for the Future. 2018. Our Net Positive Approach. Available online: https://www.forumforthefuture.org/
net-positive (accessed on 2 April 2020).
Fukuda-Parr, S., and D. McNeill. 2019. Knowledge and Politics in Setting and Measuring the SDG s: Introduction
to Special Issue. Global Policy 10: 5–15. [CrossRef]
Fullan, M. 2005. Leadership & Sustainability: System Thinkers in Action. Thousand Oaks: Corwin Press.
Galli, Alessandro, Gordana Ðurović, Laurel Hanscom, and Jelena Knežević. 2018. Think globally, act locally:
Implementing the sustainable development goals in Montenegro. Environmental Science & Policy 84: 159–69.
Global Infrastructure Hub. 2019. Infrastructure Investment need in the Compact with African Countries.
Available online: https://outlook.gihub.org/?utm_source=GIHub+Homepage&utm_medium=Project+tile&
utm_campaign=Outlook+GIHub+Tile (accessed on 6 June 2020).
Global Reporting Initiative. 2015. SDG Compass–The Guide for Business Action on the SDGs. SDG Compass.
Available online: https://sdgcompass.org/ (accessed on 11 August 2020).
Global Reporting Initiative. 2016. Carrots and Sticks, Global Trends in Sustainability Reporting Regulation and
Policy’. Available online: https://www.carrotsandsticks.net/wp-content/uploads/2016/05/Carrots-Sticks-2016.
pdf (accessed on 10 July 2020).
Greenhalgh, Trisha, Charlotte Humphrey, Jane Hughes, Fraser Macfarlane, Ceri Butler, and Ray Pawson. 2009.
How do you modernize a health service? A realist evaluation of whole-scale transformation in London.
The Milbank Quarterly 87: 391–416. [CrossRef] [PubMed]
Griggs, D., M. Stafford-Smith, O. Gaffney, J. Rockström, M. C. Öhman, P. Shyamsundar, W. Steffen, G. Glaser,
N. Kanie, and I. Noble. 2013. Policy: Sustainable development goals for people and planet. Nature 495: 305.
[CrossRef] [PubMed]
Hak, T., S. Janoušková, and B. Moldan. 2016. Sustainable Development Goals: A need for relevant indicators.
Ecological Indicators 60: 565–73. [CrossRef]
Hall, J. W., M. Tran, A. J. Hickford, and R. J. Nicholls. 2016. The Future of National Infrastructure: A System of Systems
Approach. Cambridge: Cambridge University Press.
Hayward, R., J. Lee, J. Keeble, R. McNamara, C. Hall, S. Cruse, P. Gupta, and E. Robinson. 2013. The UN global
compact-accenture CEO study on sustainability 2013. UN Global Compact Reports 5: 1–60. [CrossRef]
Holden, E., K. Linnerud, and D. Banister. 2017. The imperatives of sustainable development.
Sustainable Development 25: 213–26. [CrossRef]
Horrocks, Ivan, and Leslie Budd. 2015. Into the void: A realist evaluation of the eGovernment for You (EGOV4U)
project. Evaluation 21: 47–64. [CrossRef]
Howe, Neil, and William Strauss. 1991. Generations: The History of America’s Future, 1584 to 2069. New York:
William Morrow & Company, p. 538.
Hubbard, G. 2009. Measuring organizational performance: Beyond the triple bottom line. Business Strategy and
the Environment 18: 177–91. [CrossRef]
Adm. Sci. 2020, 10, 55 37 of 39

Institution of Civil Engineers. 2018. Project 13 Blueprint and Commercial Handbook. London: Institution of Civil
Engineers, Available online: http://www.p13.org.uk/wp-content/uploads/2018/06/P13-Blueprint-Web.pdf
(accessed on 2 April 2020).
IPCC. 2018. Global Warming of 1.5 ◦ C, an IPCC Special Report on the Impacts of Global Warming of 1.5 ◦ C Above Pre-Industrial
Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the
Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty. Geneva: IPCC.
Jones, P., and D. Comfort. 2020. A commentary on the localisation of the sustainable development goals. Journal of
Public Affairs 20: e1943. [CrossRef]
Joyce, A., and R. L. Paquin. 2016. The triple layered business model canvas: A tool to design more sustainable
business models. Journal of Cleaner Production 135: 1474–86. [CrossRef]
Kaplan, R. S., and D. P. Norton. 1996. Using the Balanced Scorecard as a Strategic Management System. Boston: Harvard
Business School.
Klopp, J. M., and D. L. Petretta. 2017. The urban sustainable development goal: Indicators, complexity and the
politics of measuring cities. Cities 63: 92–97. [CrossRef]
Labor, U. D. 2017. US Bureau of Labor Statistics. Available online: https://www.bls.gov/opub/mlr/2017/home.htm
(accessed on 7 August 2020).
Lenth, Russell V. 2001. Some practical guidelines for effective sample size determination. The American Statistician
55: 187–93. [CrossRef]
Lim, S. S., K. Allen, Z. A. Bhutta, L. Dandona, M. H. Forouzanfar, N. Fullman, P. W. Gething, E. M. Goldberg,
S. I. Hay, M. Holmberg, and et al. 2016. Measuring the health-related Sustainable Development Goals in
188 countries: A baseline analysis from the Global Burden of Disease Study 2015. The Lancet 388: 1813–50.
[CrossRef]
Linsley, Paul, David Howard, and Sara Owen. 2015. The construction of context-mechanisms-outcomes in realistic
evaluation. Nurse Researcher 22: 28–34. [CrossRef] [PubMed]
Malhi, Yadvinder, Luiz Eduardo O. C. Aragão, Daniel B. Metcalfe, Romilda Paiva, Carlos A. Quesada,
Samuel Almeida, Liana Anderson, Paulo Brando, Jeffrey Q. Chambers, Antonio C. L. Da Costa, and et al.
2009. Comprehensive assessment of carbon productivity, allocation and storage in three Amazonian forests.
Global Change Biology 15: 1255–74. [CrossRef]
Martens, M. L., and M. M. Carvalho. 2016. Sustainability and success variables in the project management context:
An expert panel. Project Management Journal 47: 24–43. [CrossRef]
Merry, Sally Engle. 2019. The Sustainable Development Goals Confront the Infrastructure of Measurement.
Global Policy 10: 146–48. [CrossRef]
Metcalf, Louise, and Sue Benn. 2013. Leadership for sustainability: An evolution of leadership ability. Journal of
Business Ethics 112: 369–84. [CrossRef]
Micheli, Pietro, and Jean-Francois Manzoni. 2010. Strategic performance measurement: Benefits, limitations and
paradoxes. Long Range Planning 43: 465–76. [CrossRef]
Millar, Ross, and Kelly Hall. 2013. Social return on investment (SROI) and performance measurement:
The opportunities and barriers for social enterprises in health and social care. Public Management Review
15: 923–41. [CrossRef]
Morris, Peter. 2013. Reconstructing Programme Management. Chichester: John Wiley & Sons.
Morris, Peter. W. G. 2017. Climate Change and What the Project Management Profession Should Be Doing about
It. Princes Risborough, Buckinghamshire: Association for Project Management, Available online: https:
//www.apm.org.uk/media/7496/climate-change-report.pdf (accessed on 2 April 2020).
Morse, S. 2013. Indices and Indicators in Development: An Unhealthy Obsession with Numbers. London: Routledge.
Nardi, P. M. 2015. Doing Survey Research. London: Routledge.
National Audit Office. 2005. Improving Public Services through better Construction. Report by the Comptroller and
Auditor General HC 364-I Session 2004–2005. London: TSO.
Nerini, F. F., J. Tomei, L. S. To, I. Bisaga, P. Parikh, M. Black, A. Borrion, C. Spataru, V. C. Broto, G. Anandarajah,
and et al. 2018. Mapping synergies and trade-offs between energy and the Sustainable Development Goals.
Nature Energy 3: 10–15. [CrossRef]
New Climate Economy. 2016. The Global Commission on the Economy and Climate. Washington: World Resources
Institute, Available online: https://www.deutsches-klima-konsortium.de/fileadmin/user_upload/pdfs/Briefings/
Morgan_12_Nov_15.pdf (accessed on 10 July 2020).
Adm. Sci. 2020, 10, 55 38 of 39

Office of National Statistics. 2019. Available online: https://sustainabledevelopment-uk.github.io/reporting-status/

(accessed on 6 June 2020).
Organisation for Economic Co-Operation and Development. 2015. G20/OECD Principles of Corporate Governance.
Paris. Available online: https://www.oecd-ilibrary.org/governance/g20-oecd-principles-of-corporate-governance-
2015_9789264236882-en (accessed on 6 June 2020).
Økland, A. 2015. Gap analysis for incorporating sustainability in project management. Procedia Computer Science
64: 103–9. [CrossRef]
Patel, Zarina, Saskia Greyling, David Simon, Helen Arfvidsson, Nishendra Moodley, Natasha Primo, and
Carol Wright. 2017. Local responses to global sustainability agendas: Learning from experimenting with the
urban sustainable development goal in Cape Town. Sustainability Science 12: 785–97. [CrossRef] [PubMed]
Pawson, R., and N. Tilley. 1997. An introduction to scientific realist evaluation. In Evaluation for the 21st Century:
A Handbook. Thousand Oaks: Sage Publications, pp. 405–18.
Pawson, R., T. Greenhalgh, G. Harvey, and K. Walshe. 2005. Realist review-a new method of systematic review
designed for complex policy interventions. Journal of Health Services Research & Policy 10: 21–34.
Perrini, F., and A. Tencati. 2006. Sustainability and stakeholder management: The need for new corporate
performance evaluation and reporting systems. Business Strategy and the Environment 15: 296–308. [CrossRef]
Peters, L. D., A. D. Pressey, M. Vanharanta, and W. J. Johnston. 2013. Constructivism and critical realism as
alternative approaches to the study of business networks: Convergences and divergences in theory and in
research practice. Industrial Marketing Management 42: 336–46. [CrossRef]
Poirier, E., D. Forgues, and S. Staub-French. 2016. Collaboration through innovation: Implications for expertise in
the AEC sector. Construction Management and Economics 34: 769–89. [CrossRef]
Porter, M. E., and M. R. Kramer. 2011. The Big Idea: Creating Shared Value, Rethinking Capitalism. Harvard Business Review, 89.
Prasad, R., C. Dovrolis, M. Murray, and K. C. Claffy. 2003. Bandwidth estimation: Metrics, measurement
techniques, and tools. IEEE Network 17: 27–35. [CrossRef]
Preston, Felix. 2012. A Global Redesign?: Shaping the Circular Economy. London: Chatham House.
Price Waterhouse Coopers. 2018. From Promise to Reality: Does Business Really Care about the SDGs? London:
PwC, Available online: https://www.pwc.com/gx/en/sustainability/SDG/sdg-reporting-2018.pdf (accessed on
10·July 2020).
Proctor, Enola, Hiie Silmere, Ramesh Raghavan, Peter Hovmand, Greg Aarons, Alicia Bunger, Richard Griffey,
and Melissa Hensley. 2011. Outcomes for implementation research: Conceptual distinctions, measurement
challenges, and research agenda. Administration and Policy in Mental Health and Mental Health Services Research
38: 65–76. [CrossRef]
Rainey, Hugo J., Edward H. B. Pollard, Guy Dutson, Jonathan M. M. Ekstrom, Suzanne R. Livingstone, Helen
J. Temple, and John D. Pilgrim. 2015. A review of corporate goals of No Net Loss and Net Positive Impact on
biodiversity. Oryx 49: 232–38. [CrossRef]
Riley, J. C. 2001. Rising Life Expectancy: A Global History. Cambridge: Cambridge University Press.
Rockström, J., W. Steffen, K. Noone, Å. Persson, F. S. Chapin III, E. Lambin, T. M. Lenton, M. Scheffer, C. Folke,
H. J. Schellnhuber, and et al. 2009. Planetary boundaries: Exploring the safe operating space for humanity.
Ecology and Society, 14.
Sachs, Jeffrey D. 2012. From millennium development goals to sustainable development goals. The Lancet
379: 2206–11. [CrossRef]
Sachs, J. D., G. Schmidt-Traub, and D. Durand-Delacre. 2016. Preliminary sustainable development goal (SDG)
index and dashboard. Sustainable Development Solutions Network, 15.
Sawaf, Ayman, and Rowan Gabrielle. 2014. Sacred Commerce: A Blueprint for a New Humanity, 2nd ed. Sherman Oaks:
EQ Enterprises, pp. 24–28.
Sayer, R. A. 1992. Method in Social Science: A Realist Approach. East Sussex: Psychology Press.
Schaltegger, Stefan, and Roger Burritt. 2000. Contemporary Environmental Accounting: Issues, Concepts and Practice.
Sheffield: Greenleaf Publishing, p. 111.
Scheyvens, Regina, Glenn Banks, and Emma Hughes. 2016. The private sector and the SDGs: The need to move
beyond ‘business as usual’. Sustainable Development 24: 371–82. [CrossRef]
Silvius, A. J., and R. P. Schipper. 2014. Sustainability in project management: A literature review and impact
analysis. Social Business 4: 63–96. [CrossRef]
Adm. Sci. 2020, 10, 55 39 of 39

Silvius, A. G., M. Kampinga, S. Paniagua, and H. Mooi. 2017. Considering sustainability in project management
decision making; An investigation using Q-methodology. International Journal of Project Management
35: 1133–50. [CrossRef]
Singleton, R. A., and B. C. Straits. 2010. Approaches to Social Research. New York: Oxford University Press.
Sosik, John J., and Dongil Jung. 2018. Full Range Leadership Development: Pathways for People, Profit, and Planet.
London and New Tork: Routledge.
Steffen, Will, Katherine Richardson, Johan Rockström, Sarah E. Cornell, Ingo Fetzer, Elena M. Bennett, Reinette Biggs,
Stephen R. Carpenter, Wim De Vries, Cynthia A. De Wit, and et al. 2015. Planetary boundaries: Guiding human
development on a changing planet. Science 347: 1259855. [CrossRef]
Stein, D., and C. Valters. 2012. Understanding Theory of Change in International Development. Available online: http://www.
theoryofchange.org/wp-content/uploads/toco_library/pdf/UNDERSTANDINGTHEORYOFChangeSteinValtersPN.
pdf (accessed on 7 August 2020).
Suess, Erwin. 1980. Particulate organic carbon flux in the oceans—Surface productivity and oxygen utilization.
Nature 288: 260. [CrossRef]
Swain, Ranjula Bali. 2018. A Critical Analysis of the Sustainable Development Goals. In Handbook of Sustainability
Science and Research. Edited by Leal Filho W. World Sustainability Series. Cham: Springer.
Tansey, Oisín. 2007. Process tracing and elite interviewing: A case for non-probability sampling. PS: Political
Science and Politics 40: 765–72.
Thacker, S., and J. Hall. 2018. Engineering for Sustainable Development. Oxford: Infrastructure Transition Research
Consortium (ITRC), University of Oxford.
Thacker, Scott, Daniel Adshead, Marianne Fay, Stéphane Hallegatte, Mark Harvey, Hendrik Meller,
Nicholas O’Regan, Julie Rozenberg, Graham Watkins, and Jim W. Hall. 2019. Infrastructure for sustainable
development. Nature Sustainability 2: 324–31. [CrossRef]
Themistocleous, G., and S. H. Wearne. 2000. Project management topic coverage in journals. International Journal
of Project Management 18: 7–11. [CrossRef]
Thiry, M. 2004. Value Management. In Wiley Guide to Managing Projects. Edited by P. Morris and J. Pinto.
Holbroken: Wiley.
Tilley, Nick. 2016. EMMIE and engineering: What works as evidence to improve decisions? Evaluation 22: 304–22.
[CrossRef]
Tilt, B. 2009. The Struggle for Sustainability in Rural China: Environmental Values and Civil Society. New York:
Columbia University Press.
UK’s Infrastructure and Projects Authority. 2020. The 2020 Annual Report on the Government Major Projects
Portfolio. Available online: https://www.gov.uk/government/publications/infrastructure-and-projects-
authority-annual-report-2020 (accessed on 6 August 2020).
United Nations. 2015. Transforming Our World: The 2030 Agenda for Sustainable Development. Resolution Adopted
by the General Assembly. New York: United Nations.
United Nations. 2018. The Sustainable Development Goals Report 2018. Available online: https://unstats.un.org/
sdgs/files/report/2018/TheSustainableDevelopmentGoalsReport2018-EN.pdf (accessed on 15 July 2019).
US Public Interest Research Group. 2016. Available online: https://uspirg.org/reports/usp/millennials-motion
(accessed on 2 April 2020).
Weiss, C. H. 1995. Nothing as Practical as Good Theory: Exploring Theory-Based Evaluation for Comprehensive
Community Initiatives for Children and Families. New Approaches to Evaluating Community Initiatives:
Concepts, Methods, and Contexts 1: 65–92.
Yamey, Basil S. 1949. Scientific bookkeeping and the rise of capitalism. The Economic History Review 1: 99–113.
[CrossRef]
Zachariadis, Markos, Susan Scott, and Michael Barrett. 2013. Methodological implications of critical realism for
mixed-methods research. MIS Quarterly 2013: 855–79. [CrossRef]
Zahra, Shaker A., Harry J. Sapienza, and Per Davidsson. 2006. Entrepreneurship and dynamic capabilities:
A review, model and research agenda. Journal of Management Studies 43: 917–55. [CrossRef]

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