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Construction Management and Economics
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rcme20
Technological, organisational and environmental
determinants of smart contracts adoption: UK
construction sector viewpoint
Sulafa Badi , Edward Ochieng , Mohamed Nasaj & Maria Papadaki
To cite this article: Sulafa Badi , Edward Ochieng , Mohamed Nasaj & Maria Papadaki
(2020): Technological, organisational and environmental determinants of smart contracts
adoption: UK construction sector viewpoint, Construction Management and Economics, DOI:
10.1080/01446193.2020.1819549
To link to this article: https://doi.org/10.1080/01446193.2020.1819549
Published online: 22 Sep 2020.
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Technological, organisational and environmental determinants of smart
contracts adoption: UK construction sector viewpoint
Sulafa Badi, Edward Ochieng, Mohamed Nasaj and Maria Papadaki
Faculty of Business & Law, The British University in Dubai (BUiD), Dubai, United Arab Emirates
ABSTRACT
This study aims to identify the factors that influence the adoption of smart contracts in the UK
construction sector. A deductive questionnaire-based approach informed by the technology-
organisation-environment (TOE) model is adopted. The framework is comprised of twelve inde-
pendent variables and one dependent variable of smart contracts use intention. Ten hypotheses
are developed to statistically test the causal relationships between the eleven variables of the
research model. The study adopts a convenience sampling approach, with the population of
interest being primarily drawn from among UK construction practitioners. The results generated
from linear regression analysis suggest that the following four factors have a significant influ-
ence on the adoption of smart contracts: supply chain pressure, competitive pressure, top man-
agement support, and observability. The descriptive statistics obtained also offer a greater
understanding of the perceptions and attitudes towards smart contracts across the UK construc-
tion sector. The results demonstrate the usefulness of a perception-based model that utilises
the TOE framework to assess facets that influence the adoption of smart contracts. The study
contributes to innovation diffusion studies in construction project management and supports
“early adopters”at the footfall of the technology’s diffusion curve.
ARTICLE HISTORY
Received 26 May 2020
Accepted 31 August 2020
KEYWORDS
Smart contract; use
intention; technological;
organisational; environmen-
tal; the construction
industry
Introduction
A smart contract, introduced in the mid-1990s (Szabo
1997), is an automated, fully executable transaction
protocol that operates without the need for human
intervention. A smart contract is self-reinforcing in that
legally binding contractual conditions are coded in the
program and are executed only once the coded
clauses are met. Transaction information such as pay-
ment amounts is embedded in smart contracts, and
funds are automatically transferred between the con-
tracting parties once the stipulated instructions are
satisfied. The general objectives of smart contract
design are to satisfy common contractual conditions
(such as payment terms), align expectations, and min-
imise the need for trusted intermediaries (Mason 2017,
Uccuru 2017). The disruptive nature of smart contracts
is more evidenced in the banking and financial sector:
the European Commission Trend Report (2016) esti-
mated that smart contracts could reduce bank infra-
structure costs by as much as EUR 18.4 billion per
annum by 2022.
Smart contracts are claimed to disrupt the
“traditional”models of contracting in the construction
sector (Mik 2017, Savelyev 2017). Moreover, smart con-
tracts can offer significant benefits in the construction
sector, particularly in terms of protection against late
payments among contracting parties, and such con-
tracts may thus reduce the prevalent contractual dis-
putes, cash flow problems and business insolvencies
in the industry (Sambasivan and Soon 2007, Tran and
Carmichael 2012). Indeed, studies by Assaf and Al-Hejji
(2006) and Kim et al.(2009) found that late payments
and withheld or refused payments are among the
major causes of delay in the delivery of construction
projects. Thus, smart contracts are advocated to offer
significant time and cost efficiencies. Nevertheless,
while the trustworthy payment environment facilitated
through smart contracts has substantial advantages,
smart contract adoption remains in its embryonic
stages in the construction sector.
To comprehend the diffusion of technology among
construction organisations, the proponents of smart
contracts in the construction industry need to be
appraised. As established from the reviewed literature,
no existing research study has fully endeavoured to
address this issue. Hence, this research study seeks to
fill this empirical gap. The main objective of this study
CONTACT Sulafa Badi Sulafa.badi@buid.ac.ae Faculty of Business & Law, The British University in Dubai (BUiD), Dubai, United Arab Emirates
ß2020 Informa UK Limited, trading as Taylor & Francis Group
CONSTRUCTION MANAGEMENT AND ECONOMICS
https://doi.org/10.1080/01446193.2020.1819549
is to examine the adoption of smart contracts in the
UK construction industry. Specifically, the study is
underpinned by one main research question: what are
the factors that influence managers’decision to adopt
smart contracts in the UK construction industry? To
answer this question, the research study utilises the
technology-organisation-environment (TOE) framework
(DePietro et al.1990) and proposes a perception-based
model that provides thoughtful adoption intentions
and attitudes across the construction sector towards
smart contracts. In particular, the proposed framework
is intended to aid in the identification of techno-
logical, organisational, and environmental facets that
influence the adoption of smart contracts in the con-
struction sector.
The article is organised as follows. The next section
provides a detailed review of contracting systems in
the construction industry and delineates the theoret-
ical foundations of the research by introducing the
TOE framework. This overview is followed by an
account of the method used to address the research
question. The subsequent sections present the find-
ings and conclusions drawn from the primary and sec-
ondary data.
Conceptual underpinnings
Procurement and contracts in the
construction sector
Procurement refers to the process established by the
client to acquire a building or other construction proj-
ects (Morledge and Smith 2013). The procurement
process can be divided into several stages such as for-
mulating the procurement strategy or approach, the
tendering process, the bidding process, subcontractor
selection, the type of payment, collaborative tools,
and performance evaluation (Eriksson and Westerberg
2011). In terms of procurement strategies, there are
several possibilities including traditional approaches
such as design-bid-build (DBB); integrated approaches
such as design and build (DB); construction manage-
ment (CM) and management contracting (MC); and
collaborative approaches such as public-private part-
nerships (PPP) (Morledge and Smith 2013). The chosen
strategy should follow the overall strategic objectives
of the client’s business plan (Briscoe et al.2004).
According to Zaghloul and Hartman (2003), construc-
tion contracts are “the written agreements signed by
the contracting parties (mainly an owner and a contrac-
tor), which bind them, defining relationships and
obligations”(p. 419). In any given construction project,
and to achieve the client’s goals, the choice of contract
type should ensure the alignment between the client’s
objectives and the motivations of the contractor
through adequate risk allocation (Kozek and Hebberd
1989). There are several types of contract in use in the
construction sector, and the most suited contract type
depends on the completeness of the information avail-
able to bidders at the tender stage and the risk appetite
of the client (Gordon 1994, Antoniou and Aretoulis
2018). A study by Zaghloul and Hartman (2003) involv-
ing 300 respondents from across the construction indus-
trysupplychainfoundthatmorethan74%ofthe
contracts employed in construction are prepared con-
tracts written by one of the contracting parties, often
the owner, and not negotiated.
Smart contracts in the construction sector
The concept of smart contracts was first promoted
by computer scientist Nick Szabo in 1997 (Schneider
et al.2018). Szabo defined a smart contract as a
“computerised transaction protocol that executes the
terms of a contract”(Schneider et al.2018). Smart con-
tracts are fully executable and self-enforcing contracts
without the need for human intervention (Mason and
Escott 2018).
Smart contracts are enabled by the fast diffusion
of blockchain technology (Schneider et al.2018).
Blockchain is a digital ledger technology that holds a
group of registers and records with a high level of
integrity. When the recorded transactions are approved,
blockchain applies a global distributed digital network
that does not allow changes or alterations. This restric-
tion is safeguarded by using a hash function, which
ensures that every block has a unique fingerprint proc-
essed and created by a mathematical formula (Kokina
et al.2017). Beck et al.(2018, p. 1021), have described
blockchain as a “decentralised, transactional database
technology that facilitates validated, tamper-resistant
transactions that are consistent across a large number
of network participants called nodes”.Insimpleterms,
the fundamental objective of blockchain is to facilitate
transparency and trust between anonymous individuals
without the use of intermediaries (Surujnath 2017).
As smart contracts are believed to be the future in
many different industries, the construction sector may
not be immune to the diffusion of this technology
(Ashworth and Perera 2018). A smart contract could
be used between any two actors in a construction
supply chain: between the client and the main con-
tractor, the main contractor and subcontractors, the
subcontractors and suppliers, and among other
contractual combinations along the chain. A smart
2 S. BADI ET AL.
contract may be used in payment transactions in the
following ways: first, the principal specifies the num-
ber of conditions that should be fulfilled for the pay-
ment to be issued. At the same time, the payment
would be embedded in the contract as digital curren-
cies (i.e. cryptocurrencies). Once the service provider
fulfils the scope of work specified in the contract, the
payment will be automatically issued. As such, a smart
contract will protect the main contractors, subcontrac-
tors, and suppliers against late-payments and the
insolvency of the principal. Indeed, an inquiry into the
construction industry insolvency in Australia has found
that late payments and withheld payments are the
main cause of business failure in the industry (Collins
2012). Delayed payments were also found to increase
project cost, prolong completion time, and adversely
affect the quality of the project outcomes (Assaf and
Al-Hejji 2006, Gandhak and Sabihuddin 2014).
The construction industry is characterised by highly
opportunistic and adversarial relationships between
the contracting parties (Boukendour 2007). Disputes
among clients, contractors, subcontractors, and other
supply chain actors often reach the courts of law
and are dealt with through measures such as adjudica-
tion, party-to-party negotiation, and arbitration
(Fenn et al.1997, Cheung and Yiu 2006). The Arcadis’
report “Global Construction Disputes Report 2020:
Collaborating to achieve project excellence”(Arcadis
2020) have shown that the average value of disputes
in the UK construction industry reached $17.8 million
in 2019 and took an average of 9.8 months to be
resolved. In studies that set out to determine the
main causes of disputes in the construction industry,
Cheung and Yiu (2006), Cakmak and Cakmak (2014)
established that contractual problems are the most
common causes of dispute in projects. Payment-
related triggering events were found to be at the core
of these disputes (Carmichael 2002, Carmichael and
Balatbat 2010). Interestingly, attitude towards contrac-
tual claims was found to vary across the global con-
struction industry with differences between countries
attributed to culture, construction practices, regula-
tions, and the political environment (Chan and Yeong
1995). Nevertheless, the study by Cheung and Yiu
(2006) argued for the inevitability of disputes in
the industry and recommended that construction pro-
fessionals adopt a proactive approach to dispute man-
agement. The need for a shift from dispute resolution
to dispute mitigation is, hence, evident and we posit
that smart contracts could form part of such proactive
dispute management measures. Indeed, the recent
Arcadis report mentioned above (Arcadis 2020), have
shown that a staggering 60% of respondents agree
that effective contract administration would have the
single most significant impact in avoiding disputes in
construction. Smart contracts will mitigate against dis-
putes among the contractual parties by reducing con-
flicts about progress, automating payments, and
increasing the transparency of contractual clauses
(Mason and Escott 2018). Several studies such as those
by Cheung and Pang (2013) and Shen et al.(2017)
have underlined human factors such as erroneous esti-
mation, deficient documentation, and opportunistic
behaviours as major ingredients of construction dis-
putes. Hence, the automated decision-making system
embedded in smart contracts may eliminate human
interfacing and human misinterpretation of contractual
clauses that may lead to disputes. According to
Ortolani (2019), the unparalleled contractual automa-
tion facilitated by smart contracts may allow for pri-
vate, self-enforcing arbitration systems to emerge and
result in the marginalisation of state courts.
Trust is another important concept in inherently
risky contractual transactions (Schoorman et al. 2007,
Lau and Rowlinson 2009); however, the building of
trust is often seen as problematic among supply chain
actors working on construction projects (Khalfan et al.
2007, Wong et al.2008, Badenfelt 2010). Low trust
relationships between clients and contractors were
found to increase project costs by 8–20% in risk pre-
miums linked to disclaimer clauses (Zaghloul and
Hartman 2003). Smart contacts, being self-executing
and self-enforcing, promise to help in the develop-
ment of trust by shifting the emphasis from “relational
trust”to “digital trust”(Shin 2019) based on the confi-
dence in the “rules-of-the-code”(De Filippi and Hassan
2018, Hawlitschek et al.2018). Wang et al.(2017)
argue that trust in the technology itself will bypass
the need for assessments of an exchange partner’s
honesty, integrity, and truthfulness. A trustworthy net-
work of payments could be established by the con-
tractual terms and conditions being coded in the
smart contracts (Kinnaird and Geipel 2017). Payments
can then be paid automatically once the stipulated
conditions are fulfilled. This trust-building capacity of
smart contracts is particularly salient in dealing with
the legal and contractual barriers associated with
building information modelling (BIM) adoption
(Winfield and Rock 2018). According to a report by
Winfield and Rock (2018), smart contacts will increase
the trust between the contracting parties due to the
data records’real-time availability, immutability, secur-
ity, and transparency.
CONSTRUCTION MANAGEMENT AND ECONOMICS 3
Smart contracts are espoused to generate greater
efficiency by minimising overall costs and reducing
waste in the construction industry (Ashworth and
Perera 2018). From the reviewed literature, it was
found that smart contracts can reduce administrative
processes, which would allow billing and checking
subsequent calculations of costs to be made simpler,
much faster, and more accurately (Kinnaird and Geipel
2017). Smart contracts also eliminate the need for a
physical paper-based contract and the associated
administrative costs of writing, storing, and archiving
such documents (Cohn et al.2017). The digital envir-
onment supporting smart contracts may also result in
third-party intermediaries such as lawyers and author-
ities being less critical in governing contractual terms,
hence, reducing transaction costs (Mik 2017, Savelyev
2017). In addition to being used to purchase, track
and verify goods and services between supply chain
actors; smart contracts could be used in transactions
involving the ownership transfer of energy, intellectual
property rights, and property and land titles (Kinnaird
and Geipel 2017).
Notwithstanding the above benefits, smart contract
adoption in the construction sector may require fun-
damental shifts in the contractual relationship
between firms. A change in mindset is needed to con-
vince construction firms to switch from their existing
procurement models of traditional paper-based con-
tracts to a conventional blockchain-enabled smart
model (Mason 2017). The complexity of smart con-
tracts is also heightened by the need for exceptionally
accurate coding from the outset, given that the con-
tract will forever reside in the public ledger (Frantz
and Nowostawski 2016). Moreover, construction proj-
ects are complex undertakings, and many common
events such as on-site accidents, new regulations, and
force majeure may prove difficult to capture in com-
puter codes (Thompson Reuters 2018). Smart contracts
also come with challenges such as limitations over
storage capacity, interoperability, data reliability, and
confidentiality (Li et al.2019).
Given the abovementioned benefits and challenges
of smart contracts in the construction sector, their
adoption remains in its infancy phase, and it is widely
believed that a long road remains ahead to convince
the different parties involved about the benefits
afforded by a smart contract (e.g., Cardeira 2015).
Hence, this research aims to provide insights into the
factors that influence the smart contract adoption
intentions of construction industry supply chain actors.
The theoretical perspective from which the study is
conducted is provided in the subsequent sections.
Technology-organisation-environment
(TOE) framework
Several theories have been developed to examine the
concept of innovation adoption such as the technol-
ogy acceptance model (TAM)(Davis 1989), the theory
of planning behaviour (TPB) (Ajzen 1991), the unified
theory of acceptance and use of technology (UTAUT)
(Venkatesh et al. 2003), the diffusion of innovation
(DOI) theory (Rogers 2003) and the technology-organ-
isation-environment framework (TOE) (DePietro et al.
1990). While the TAM, TPB, and UTAUT explore adop-
tion at the individual level of analysis, DOI and the
TOE framework are focussed on the level of the organ-
isation as a decision-making unit. Since this study
aims at examining adoption at the organisational
level, the TOE framework is chosen as the main theor-
etical lens.
The TOE framework was first introduced by DePietro
and colleagues in 1990 (DePietro et al.1990). This
framework explicates the main variables influencing
the adoption of technological innovations at the organ-
isational level by looking at three components:
technological factors, organisational factors, and envir-
onmental factors (Fu and Su 2014). A large number of
studies have used the TOE framework to evaluate
technological innovations adoption within firms
(Oliveira and Martins 2010,ChandraandKumar2018).
For instance, it has been utilised to examine the adop-
tion of electronic data interchanges (Kuan and Chau
2001), information systems implementation (Thong
1999), e-business (Zhu et al.2004), cloud computing
(Gutierrez et al.2015), e-commerce (Hong and Zhu
2006), e-procurement (Teoa et al.2009) and enterprise
resource planning (Pan and Jang 2008). In construction
industry research, several studies have utilised the TOE
framework in the study of e-procurement (Tran et al.
2014, Ibem et al.2016) BIM implementation (Ahuja
et al.2016,Chenet al.2019) and in the assessment of
project complexity (Bosch-Rekveldt et al.2011,
Pe~
naloza et al.2020). The aforementioned studies dem-
onstrate the valuable insights offered by the TOE
framework into understanding the adoption of techno-
logical innovations across a variety of contexts and
industries. The decision to adopt the TOE approach in
this research study is driven by its coherence and con-
sistency with other frameworks such as the DOI theory
(Ramdani et al.2013,Sila2013). Hence, following the
TOE framework, we propose a perception-based model
that postulates that the degree at which a construction
organisation decides to adopt and implement a smart
contract is influenced by three main groups of determi-
nants: technological, organisational and environmental
4 S. BADI ET AL.
factors (Soto Acosta et al.2016). These three groups of
factors are examined thoroughly below and then linked
to the construction industry context to build the con-
ceptual framework for this study.
Technological characteristics
The technological context of the TOE framework is
concerned with both internal and external technolo-
gies that are relevant to the firm (Gutierrez et al.
2015). This includes technologies that exist in the
marketplace as well as technologies that are being uti-
lised within the firm (Zhu et al.2004, Gutierrez et al.
2015). The technological factors that influence adop-
tion decisions originate from Rogers’s(2003) DOI the-
ory and include relative advantage, compatibility,
complexity, trialability, and observability.
The first technological factor, relative advantage, is
defined by Rogers (2003, p. 229) as “thedegreetowhich
an innovation is perceived as being better than the idea
it supersedes”. What defines this relative advantage is
contingent on the specific characteristics of the innov-
ation (Rogers 2003)andiswidelyseenasafundamental
indicator for the adoption of innovations (Gutierrez et al.
2015). In Lee’s(2004) empirical study on technology
adoption behaviour, the relative advantage was found
to be a significant indicator with the probability of an
innovation being adopted significantly increasing as a
result of its perceived relative advantage. Within the con-
text of the construction domain, a study by Mason and
Escott (2018) examining the perceptions of the key
stakeholders of smart contracts in the UK construction
sector has highlighted several potential advantages to
their adoption. Among the possible benefits are the
automation of contract formation, the automation of
updates and payments, and securing payment to con-
tracted parties. Another major advantage is minimising
the occurrence of disputes (Mason and Escott 2018), as
smart contracts may provide a sense of security and
trust among participants in the project because all rights
are protected through the embedded automated pay-
ment mechanisms (Cardeira 2015). Based on these con-
siderations, the following hypothesis is proposed:
H1: Perceived relative advantage is positively related
to smart contract adoption intention.
Compatibility is the second technological factor. It
is defined by Rogers (2003, p. 240) as “the degree to
which an innovation is perceived as consistent with
the existing values, past experiences, and needs of
potential adopters”. Given that adopting new advan-
ces has the potential to bring substantial changes to
the organisation and its practices, its adoption of
novel solutions is often met with resistance by
organisational members (Ramdani et al.2013).
Consequently, the new changes brought by the adop-
tion must be compatible with the existing infrastruc-
ture of the organisation (Ramdani et al.2013).
Gutierrez et al.(2015) affirmed that the probability of
an organisation to adopt an innovation is higher if the
technology is perceived to be compatible with the
organisation’s currently existing systems and its values
and beliefs. In the case of a smart contract in the con-
struction sector, compatibility is likely to influence its
adoption intention by construction practitioners.
Accordingly, a smart contract needs to be compatible
with the existing contract management systems in the
organisation as well as its contract management
needs. Compatibility with the organisation’s existing
values and beliefs is also required. Thus, we postulate
the following hypothesis:
H2: Perceived compatibility is positively related to
smart contract adoption intention.
Complexity is the third technological factor. It is
defined by Rogers (2003 p. 257) as “the degree to
which an innovation is perceived as relatively difficult
to understand and use”. Uncertainty is often associ-
ated with complex technologies and can hinder their
successful implementation. Indeed, adoption is more
likely to succeed for less complex innovations (Sila
2013). Within the context of the construction sector,
Mason and Escott (2018) underlined the non-complex-
ity of smart contracts from a user’s point of view.
Given the characteristics of the pre-coded smart con-
tract, Mason and Escott (2018) suggest that under-
standing how the technology works or the particulars
of the coding process is not a necessity for contract
users. Organisations can use and trust the technology
without concerning themselves with the complex
technical details. The day-to-day users of smart con-
tracts are not required to understand the coding struc-
ture and the algorithms behind the technology.
Hence, we hypothesise the following:
H3: Perceived non-complexity is positively related to
smart contract adoption intention.
The fourth technological factor is trialability, which
is defined as “the degree to which an innovation may
be experimented with on a limited basis”(Rogers
2003, p. 258). As specified by Lin and Chen (2012),
when individuals and organisations are provided with
the opportunity to trial an innovation before its
actual adoption, the likelihood of successful adoption
increases. A study by Ramdani et al.(2013) found
that trialability is an important factor that influences
CONSTRUCTION MANAGEMENT AND ECONOMICS 5
the adoption of enterprise applications by small
and medium-sized enterprises. In their study of
e-commerce adoption, Kendall et al.(2001) also found
trialability to be a significant factor influencing the
adoption of technologies. Considering the construc-
tion sector, Mason and Escott (2018) highlighted the
need for a trial period of testing before the actual
implementation of smart contracts. As construction
organisations move from the traditional form of
contracts to smart contracts, these new contractual
systems and technologies will need to be tested to
support the development of user confidence and trust
in such technologies. Trials will help to avoid failure
and glitches (Mason and Escott 2018). Since other
researchers such as Kendall et al.(2001) and Ramdani
et al. (2013) have shown that trialability is relevant to
technological innovation adoption, it is evident that
trialability is equally likely to influence smart contract
adoption in construction. Therefore, our fourth
hypothesis is formulated as follows:
H4: Perceived trialability is positively related to smart
contract adoption intention.
Observability is the fifth technological factor. It is
defined by Rogers (2003,p.258)as“the degree to
which the results of an innovation are visible to oth-
ers”. It is generally believed that organisations will be
more inclined to adopt new technology if they can
observe the benefits that other organisations are
reaping from adopting that particular technology. Lin
and Chen (2012) study of the adoption of cloud com-
puting stated that observability is a prerequisite for
the promotion of new technologies. The researchers
point out the need to provide organisations with
models of successful business cases of cloud adop-
tion. In the case of a smart contract in construction,
observability could be facilitated by generating good
publicity about the positive effects of such contracts,
while information regarding successful use cases
could be made widely available to enable a clear
understanding of the advantages of smart contracts
as opposed to traditional contracting systems. Based
on the above assertions, the following hypothesis
is assumed:
H5: Perceived observability is positively related to
smart contract adoption intention.
Organisational characteristics
The organisational context is concerned with the char-
acteristics of the organisation itself, such as size and
scope (Zhu et al.2004, Oliveira and Martins 2010). In
this study, the organisational context includes two
main characteristics: top management support and
organisational information technology (IT) readiness
(Sila 2013). Top management support is seen to
reduce the salience of the forces working against the
change and help overcome internal resistance.
The support of top management can also influence
the adoption process by stimulating change through
communicating and reinforcing the values and vision
of the firm (Ramdani et al.2013). In Chandra and
Kumar (2018) empirical study of augmented reality
adoption in e-commerce, the authors underlined the
importance of top management support in developing
a positive perception towards innovation adoption
and in providing the needed resources and monitory
support. Thus, the following hypothesis is formulated:
H6: Perceived top management support is positively
related to smart contract adoption intention.
The second factor is organisational readiness,
defined by Ramdani et al.(2013, p. 738) as “the avail-
ability of the needed organisational resources for
adoption”. The concept of readiness is concerned with
the availability of the necessary skills, IT systems, and
resources required to adopt the new technology
(Ramdani et al.2013). When the organisation has the
needed technological infrastructure, this infrastructure
provides the platform required to build further tech-
nologies (Oliveira and Martins 2010). Furthermore, the
IT knowledge of employees facilitates the develop-
ment of new applications. Therefore, when an organ-
isation has both the needed infrastructure and IT
skills, new technologies can be more effectively inte-
grated and adopted (Oliveira and Martins 2010).
Therefore, the following hypothesis is proposed:
H7: Perceived IT readiness is positively related to
smart contract adoption intention.
Environmental characteristics
Zhu et al.(2004, p. 20) defined the environmental con-
text as “the arena in which a firm conducts its busi-
ness—its industry, competitors, access to resources
supplied by others, and dealings with government”.A
major environmental factor highlighted by several
studies is competitive pressure (Soto Acosta et al.
2016). The number of organisations utilising new tech-
nology in a particular sector was found to largely
impact innovation diffusion, as firms compete to be
the pioneers of the latest innovations to secure their
competitive advantage. Once there is such competi-
tion, the need to adopt new technology is intensified
in the business environment (Chandra and Kumar
2018). This fierce competition requires organisations to
6 S. BADI ET AL.
adopt new technologies to increase quality, reduce
cost, and improve their efficiency and effectiveness
(Chandra and Kumar 2018). In their study of 2459
firms across the European Union, Oliveira and Martins
(2010) found competitive pressure to be a key facilita-
tor for e-business adoption in the telecommunications
and tourism industries. They referred to competitive
pressure as one of the most important drivers of e-
business adoption. Hence, the following hypothesis
is devised:
H8: Perceived competitive pressure is positively
related to smart contract adoption intention.
Another environmental factor that is known to
encourage the adoption of new technology is supply
chain partners’pressure. Indeed, the behaviour of
organisations largely depends on the demands of their
customers and suppliers. According to Sila (2013), the
pressure from partners can be in the form of force,
threats, persuasion, or invitations. As a result, supply
chain pressure is identified as a factor that influences
the decision of adoption. Accordingly, many studies
have highlighted the important role of partners in the
successful implementation of technological advances
(Powell et al.1996, Soosay et al.2008, Garc
ıa-Moreno
et al.2016). Thus, the following hypothesis is posited:
H9: Perceived supply chain partners’pressure is
positively related to smart contract adoption intention.
The third environmental factor pertinent to this study
is the government support in the form of incentives, sub-
sidies, and regulations, which is known to be an important
factor in individual firms’technology adoption decisions
(Gibbs and Kraemer 2004). Gibbs and Kraemer (2004)
emphasised that in newly industrialising countries such as
Singapore, as well as in developing countries such as
India, the role of such government support is far more sig-
nificant than in developed countries such as France and
Germany. Government regulations and support can work
to either encourage or discourage organisations from
adopting new technologies (Chong and Olesen 2017). In
a study examining e-business adoption across 113 firms,
Al-Zoubi (2013) found government support to be among
the most important factors associated with e-business
adoption. The following is, thus, hypothesised:
H10: Perceived government regulatory support is
positively related to smart contract adoption intention.
In summary, the TOE framework is applied in this
study to develop a perception-based model that ascer-
tains the variables influencing the use intention of
smart contracts in the construction sector, as shown in
Figure 1. The framework comprises of twelve
independent variables and one dependent variable of
smart contracts use intention. Ten hypotheses are
developed to statistically test the causal relationships
between the eleven variables of the research model.
The next section provides further details on the
method and research approach adopted to achieve
the study objectives.
Method
A research methodology defines the principles and
procedures followed to execute a particular scholarly
inquiry, beginning from the researchers’ontological
and epistemological positions and extending to the
finer details of how data will be collected and ana-
lysed. In this study, as illustrated in Figure 2,we
assume a positivist ontological stance in which real-
ity is seen as objective and independent from the
observer. In turn, the study is epistemologically
deductive in that the factors influencing the adop-
tion intention of smart contracts in construction are
identified and the relationship between them estab-
lished by reviewing existing theories and developing
a set of hypotheses. These hypotheses are subse-
quently tested through empirical data collection
andanalysistobeapprovedorrejected(Saunders
et al.2016). In particular, the independent and
dependent variables forming the developed hypoth-
eses are measured numerically and the relationship
between them examined through quantitative
methods including a questionnaire and a subse-
quently performed statistical data analysis, as ration-
alised below.
Sampling and data collection
The study adopted a convenience sampling approach
with the population of interest being the UK
construction practitioners. A survey was designed and
administered through an online survey website (sur-
veymonkey.com). The survey questionnaire elicited
participants from the UK construction sector, and this
process was managed through a pre-qualification
question at the beginning of the questionnaire that
ensured that only respondents who met this criterion
were allowed to proceed with the questionnaire
response. Invitations to participate in the study were
distributed through posts on an online networking
site (www.linkedin.com), an online construction profes-
sional group (Co-operative Network for Building
Researchers [CNBR]), and an academic portal (www.
researchgate.com). Data collection took place over
CONSTRUCTION MANAGEMENT AND ECONOMICS 7
three months from August 2019 to November 2019.
The final database comprised 146 questionnaire
responses, of which 42 were incomplete, leaving
a total of 104 usable responses (i.e., fully completed
questionnaires). The sample was fairly evenly
distributed across organisation type, size, age,
education, and years of experience (i.e., the total
number of years working in the industry), as illustrated
in Table 1.
Measures
A set of operational measurement items were used to
measure the independent and dependent variables.
Figure 2. Methodology flowchart.
Figure 1. A perception-based model for smart contract adoption.
8 S. BADI ET AL.
To increase the accuracy, consistency, and confidence
of measurements, a multiple-item approach was
adopted based on perceptual statements, all of which
were assessed using five-point Likert scales ranging
from 1 ¼Strongly Disagree to 5 ¼Strongly Agree.
Previously validated scales in the literature were uti-
lised to develop all items, with adjustments made as
required to ensure fit with the context of the study.
Table 2 below lists the main constructs and their
measurement items.
Data was also collected concerning the respond-
ents’firm type and firm size, along with demographic
information such as the respondents’age, education
level, and tenure.
Validity and reliability
Several procedures were followed to assess the quality
of the instrument in terms of its validity and reliability.
First, as mentioned above, the adopted operational
measurement items were borrowed from the previ-
ously published and validated scholarly work (see
Table 1). Second, a pilot testing of the questionnaire
was performed to assess its content validity in terms
of the employed measurement items and scales. This
pilot evaluation was conducted by sending the ques-
tionnaire to three construction practitioners who are
experts in the field. The three practitioners were
requested to fill the questionnaire, noting the time it
took to complete the form and any issues they
encountered in terms of their understanding of the
questionnaire items, how they navigated the online
platform, and the general ease and pleasantness of
the experience. Several changes were made following
the experts’feedback, particularly in terms of the lay-
out, the font sizes, and the sequence of questionnaire
items. Third, measures were taken to ensure the valid-
ity and reliability of the constructs through statistical
testing using IBM SPSS Statistics v.23 software. Validity
indicates the accuracy of the survey questions in
measuring what is intended to be measured, namely
the study constructs and variables (Cooper and
Schindler 2011). On the other hand, construct reliabil-
ity refers to the consistent interpretation of the survey
questions and is defined as the correlation of partici-
pants’answers to each question in the survey with
their answers to other questions within the same sur-
vey (Saunders et al.2016). To examine the constructs’
reliability, Cronbach’s alpha test was adopted. The
results of this testing are illustrated in Table 3. Based
on Mallery and George (2003) scale for evaluating
Cronbach’s alpha results, the construct reliability tests
were found to be ranging between “acceptable”and
“excellent”. One construct, namely PO3, “I have a clear
understanding of the positive effects of a smart con-
tract”, was found to have a reliability of (.582). This
result was deemed unsatisfactory, and PO3 was there-
fore deleted from the scale to enhance the construct
reliability, thereby generating an “acceptable”result
of .684.
To further investigate the variables’reliability, two
additional tests were conducted: the Kaiser-Meyer-Olkin
(KMO) test that measures the sampling adequacy of
the data by examining the correlations between indi-
vidual variables, and the Bartlett Test of Sphericity that
examines the occurrence of correlations. The results of
these two tests are summarised in Table 4.
Based on the above results, the KMO value is
higher than 0.50 and closer to 1, indicating reliable
scales (Kaiser 1974). In terms of the Bartlett Test of
Sphericity, the results were (.000), which are highly
significant and equally indicate reliable scales (Hair
et al.2010). To further validate the quantitative data,
exploratory factor analysis (EFA) was conducted.
Table 1. Participants’demographics.
Characteristics
Respondents
(number)
Respondents
(percentage
of total) (%)
Organisation’s Type:
Client/Owner/Developer 8 7.7
Architecture/Design Consultant 14 13.16
Mangt Consultant/Project Manager 11 10.57
Engineering Consultant 5 4.8
Quantity surveying 7 6.7
Legal Consultant 8 7.7
Main Contractor 22 21.15
Subcontractor 5 4.8
Supplier 1 0.96
Manufacturer 1 0.96
Facility Manager 2 1.92
Academic Institution 6 5.77
Government 14 13.46
Size:
149 24 23.1
50999 37 35.58
10004999 15 14.42
More than 5000 28 26.92
Participants’Years Of Experience:
0–2 7 6.73
3–5 19 18.27
6–10 32 30.77
11–19 22 21.15
20 þ24 23.01
Participants’Education:
High School or Less 2 1.92
College Degree 29 27.88
BA/Higher Diploma 57 54.80
Masters 16 15.38
Participants’Age:
Less than 24 3 2.88
2530 31 29.80
3140 38 36.53
4150 23 22.11
51 or above 9 8.65
CONSTRUCTION MANAGEMENT AND ECONOMICS 9
Table 2. Constructs and measurement items.
Construct Measurement items Source
PART 1: Technology Characteristics
Perceived relative advantage
PRA1 A smart contract reduces payout time. Ilin et al. (2017), Mason
and Escott (2018)PRA2 A smart contract reduces transaction cost.
PRA3 A smart contract provides secured payments.
PRA4 A smart contract protects contracting parties from insolvencies and late payments.
PRA5 A smart contract reduces the occurrence of disputes among contracting parties.
PRA6 A smart contract increases trust among contracting parties.
Perceived compatibility
PC1 A smart contract is compatible with the existing contract management systems in my
organisation.
Gutierrez et al. (2015)
PC2 A smart contract is compatible with the contract management needs of my
organisation.
PC3 A smart contract is consistent with the existing values and believes of my
organisation.
Perceived non-complexity
PNC1 A smart contract is easy to understand. Gutierrez et al. (2015),
Mason and Escott (2018)PNC2 A smart contract is easy to use and is manageable.
PNC3 A smart contract is easy to integrate with existing contractual processes in my
organisation.
Perceived trialability
PT1 I intend to try out a smart contract in a limited scope in my works, before deciding
whether to adopt it in practice.
Ilin et al. (2017)
PT2 A trial period before adopting a smart contract in practice will reduce my
perceived risks.
PT3 Being able to try out a smart contract is important in my decision to adopt it in
the future.
Perceived observability
PO1 There is good publicity about the positive effects of smart contracts. Ilin et al. (2017)
PO2 Other organisations using smart contracts liked using them.
PO3 I have a clear understanding of the positive effects of a smart contract.
PART 2: Organisation characteristics
Perceived top management support
PTMS1 Top management in my organisation is aware of the benefits that smart contracts
can provide.
Gutierrez et al. (2015), Ilin
et al. (2017)
PTMS2 Top management influences employees to increase awareness of the importance/
advantages that smart contracts can bring.
PTMS3 Top management provides adequate resources for employees to adopt
smart contracts.
Perceived organisational readiness
POR1 My organisation has the needed resources to support smart contract adoption. Gutierrez et al. (2015),
Ilin et al. (2017)POR2 Existing technologies in my organisation support smart contract adoption.
POR3 Information Technology (IT) staff within my organisation have the adequate skills and
experience to support smart contract adoption.
PART 3: Environment characteristics
Perceived competitive pressure
PCP1 The use of smart contracts would offer my organisation a stronger
competitive advantage.
Ilin et al. (2017)
PCP2 The use of smart contracts would increase the ability of my organisation to
outperform the competition.
PCP3 The use of smart contracts will allow the generation of higher profits to my
organisation.
PCP4 My organisation have experienced competitive pressure to adopt smart contracts.
PCP5 My organisation would have experienced a competitive disadvantage if smart
contracts had not been adopted.
Perceived government support
PGS1 Government legislation supports the adoption of smart contracts. Ilin et al. (2017)
PGS2 Legislation about smart contracts is transparent.
PGS3 Firms are legally protected through smart contacts.
Perceived supply chain pressure
PSCP1 My organisation’s business partners recommend the adoption of smart contracts. Gutierrez et al. (2015),
Ilin et al. (2017)PSCP2 My organisation’s business partners have requested the adoption of smart contracts.
PSCP3 My organisation have experienced pressure from business partners to adopt
smart contracts.
PART 4: Smart contracts use intention
Use Intention
UI1 My organisation intends to use smart contracts actively. Kim et al.(2016)
UI2 My organisation intends to actively recommend smart contracts to others.
U13 My organisation intends to use smart contracts continuously in various projects.
Respondents were asked to rate the extent to which they agree or disagree with the statements. All responses were measured on a 5 point Likert scale
in which 1 ¼Strongly Disagree, 2 ¼Disagree, 3 ¼Neither agree or disagree, 4 ¼Agree and 5 ¼Strongly Agree.
10 S. BADI ET AL.
Principal components analysis with the varimax rota-
tion method was utilised in extracting the constructs.
The results of the EFA tests for all constructs were
acceptable and proper since each variable result dem-
onstrated that there is only one factor that has an
Eigenvalue of more than (1) that explains the collected
data for this variable. Finally, testing for common
method bias was also performed using the Harman
single-factor test. The results indicated that a single
factor explains only 32.819% of data variance, which is
less than the 50% acceptable threshold advised by
Podsakoff and Organ (1986) and indicates no common
bias in the data used in the study. Based on the tests
above, the data was deemed valid and reliable to per-
form the regression analysis, as will be explained in
the results section that follows.
Linear regression analysis
The IBM SPSS Statistics v.23 program was used to ana-
lyse the collected data and verify the study hypotheses.
Linear regression analysis was used to identify the sig-
nificance level of the relation between the predictors’
variables and the smart contracts use intention. The
purpose of using this analysis technique was to
determine which factors have a significant relation with
using smart contracts in the UK construction sector.
The normality assumption was tested for all the
variables and was met, along with the normal plot of
the residuals, as illustrated in the diagram in Figure 3
below. The normal plot of the residuals shows the
points close to a diagonal line; thus, the residual nor-
mality is equally satisfied, and no obvious outlier can
be identified. Hence, regression analysis can be run for
the model. The results of the regression analysis are
shown in Tables 5 and 6.Table 5 demonstrates that
the dependent variable smart contract use intention is
significantly predicted by the independent variables
listed. Table 6 shows the estimated coefficients and
statistics for each of the predictors that were included
in the model, as well as the collinearity test results.
Based on the above results, no apparent multicolli-
nearity problems were found in the model, since none
of the predictor variables has a variance inflation factor
(VIF) greater than 10. In other words, there is no variable
in the model that is measuring the same relationship as
is measured by another variable or group of variables.
Most importantly, the results indicate two significant
Table 4. Kaiser-Meyer-Olkin (KMO) test and Bartlett Test
of Sphericity.
The variables
Kaiser-Meyer-
Olkin
(KMO)
Bartlett’s
Test of
Sphericity
Perceived relative advantage (PRA) .786 .000
Perceived compatibility (PC) .671 .000
Perceived non-complexity (PNC) .653 .000
Perceived trialability (PT) .677 .000
Perceived observability (PO) .500 .000
Perceived Top management support (PTMS) .721 .000
Perceived organisational readiness (POR) .643 .000
Perceived competitive pressure (PCP) .761 .000
Perceived government support (PGS) .709 .000
Perceived supply chain pressure (PSCP) .748 .000
Smart contracts use intention (UI) .751 .000
Figure 3. Plot regression.
Table 5. Regression model ANOVA.
ANOVA
a
Model Sum of squares df Mean square FSig.
1
Regression 51.300 10 5.130 9.228 .000
b
Residual 51.700 93 .556
Total 103.000 103
a
Dependent Variable: SCUI.
b
Predictors: (Constant), PO, PTA, OR, GP, PRA, TMS, PNC, SCP, PC, CP.
Table 3. Cronbach’sascore for each factor of the technol-
ogy-organisation-environment (TOE) framework.
The variables
Number
of items
Cronbach’s
alpha
Perceived relative advantage (PRA) 6 .849
Perceived compatibility (PC) 3 .741
Perceived non-complexity (PNC) 3 .786
Perceived trialability (PT) 3 .791
Perceived observability (PO) 3 .684
Perceived top management Support (PTMS) 3 .860
Perceived organisational readiness (POR) 3 .770
Perceived competitive pressure (PCP) 5 .834
Perceived government support (PGS) 3 .853
Perceived supply chain pressure (PSCP) 3 .923
Smart contracts use intention (UI) 3 .918
CONSTRUCTION MANAGEMENT AND ECONOMICS 11
relations at a 95% confidence level between Top
Management Support and Supply Chain Pressure with
Smart Contract Use Intention. Moreover, another two
significant relations at a 90% confidence level are under-
lined between Perceived Observability and Perceived
Competitive Pressure with Smart Contract Use Intention.
Hence, it can be concluded that supporting evidence
was found to accept the hypotheses H5, H6, H8, and
H9. As shown in Table 6,ofthefoursignificantpredic-
tors of smart contracts use intention, the highest contri-
bution is from Supply Chain Pressure with a positive
value of (.332), followed by Perceived Competitive
Pressure with a positive value of (.217) and Top
Management Support with a positive value of (.212),
whereas only Perceived Observability has a negative
value of (-.184) in relation to the organisation’s
likelihood of using smart contracts.
As Table 6 indicates, there was no identified signifi-
cant relationship between Smart Contract Use
Intention and the other six variables including Relative
Advantage, Compatibility, Complexity, Trialability,
Organisational Readiness, and Government Support.
Hence, it is fair to state that hypotheses H1, H2, H3,
H4, H7, and H10 are not supported. The factors listed
were found not to drive the adoption decision of
smart contracts in this study. Possible explanations for
this outcome will be explored further in the discussion
section below.
Descriptive statistics
Table 7 shows the frequencies of respondents’
answers to the questionnaire variables. The findings in
the table are expanded upon and linked to the regres-
sion analysis findings in the discussion section
that follows.
Discussion
This study was intended to identify significant factors
that influence the adoption of smart contracts in the
UK construction sector. From the linear regression ana-
lysis, environmental factors are found to be the most
significant determinants, as both supply chain pressure
and competitive pressure are underlined as the most
significant determinant for smart contract adoption.
The first determinant for smart contract adoption
intention is found to be supply chain pressure. This
finding is consistent with studies that underline the
important role of supply chain partners in the success-
ful implementation of technological innovation
(Powell et al.1996, Soosay et al.2008, Laforet 2011,
Garc
ıa-Moreno et al.2016). Construction organisations
may exert conformance pressure on each other to fol-
low suit and adopt new technologies. Indeed, as a
smart contract is fundamentally a transactional tech-
nology between supply chain actors, supply chain
pressure is fittingly a key determinant. This finding
also implies that as more supply chain actors adopt
smart contracts, greater conformity forces will be
applied to other supply chain actors to act similarly.
Adopting a supply chain perspective on smart con-
tracts diffusion is warranted, and the applicability of
smart contracts in supply chain management has
been observed across a wide range of sectors includ-
ing food (Hackett 2016), pharmaceuticals (Bocek et al.
2017) and mining (Rizzo 2016), to name a few. Smart
contracts can add value in supply chains by managing
the structural and relational complexity among supply
chain actors through facilitating increased transpar-
ency, traceability, efficiency, and trust among such
supply chain actors (Law 2017).
The second determinant is found to be competitive
pressure. This finding is aligned with previous studies
that underlined competitive pressure as a key
Table 6. Regression model coefficients.
Coefficients
Model
Unstandardised coefficients Standardised Coefficients
t Sig.
Collinearity statistics
B Std. Error Beta Tolerance VIF
1
(Constant) 1.905E-16 .073 .000 1.000
PRA .106 .106 .106 1.001 .320 .480 2.085
PC .153 .106 .153 1.441 .153 .477 2.098
PNC .040 .102 .040 .390 .697 .522 1.916
PT .052 .089 .052 .586 .559 .679 1.473
PTMS .212 .097 .212 2.186 .031 .575 1.739
POR .085 .089 .085 .949 .345 .677 1.477
PCP .217 .118 .217 1.842 .069 .388 2.574
PGS .079 .098 .079 .812 .419 .568 1.761
PSCP .332 .112 .332 2.968 .004 .431 2.318
PO .184 .100 .184 1.852 .067 .545 1.834
aDependent Variable: SCUI.
12 S. BADI ET AL.
Table 7. Descriptive statistics.
Item Number Item Questions
Participants’Answers Percentage
Strongly
Disagree (%) Disagree (%)
Neither
Agree nor
Disagree (%) Agree (%)
Strongly
Agree (%)
PRA1 A smart contract reduces payout time. 4.80 1.90 12.50 50 30.80
PRA2 A smart contract reduces transaction cost. 3.80 12.50 13.50 50 20.20
PRA3 A smart contract provides secured payments. 1.90 6.70 26 46.20 19.20
PRA4 A smart contract protects contracting parties from insolvencies
and late payments.
3.80 14.40 31.70 40.40 9.60
PRA5 A smart contract reduces the occurrence of disputes among
contracting parties.
3.80 10.60 26 39.40 20.20
PRA6 A smart contract increases trust among contracting parties. 2.90 10.60 27.90 42.30 16.30
PC1 A smart contract is compatible with the existing contract
management systems in my organisation.
6.70 29.80 30.80 27.90 4.80
PC2 A smart contract is compatible with the contract management
needs of my organisation.
3.80 12.50 25 49 9.60
PC3 A smart contract is consistent with the existing values and
believes of my organisation.
2.90 12.50 26.90 46.20 11.50
PNC1 A smart contract is easy to understand. 3.80 12.50 32.70 42.30 8.70
PNC2 A smart contract is easy to use and is manageable. 2.90 11.50 26 48.10 11.50
PNC3 A smart contract is easy to integrate with existing contractual
processes in my organisation.
5.80 25 26 37.50 5.80
PT1 I intend to try out a smart contract in a limited scope in my
works, before deciding whether to adopt it in practice.
1 10.60 12.50 53.80 22.10
PT2 A trial period before adopting a smart contract in practice will
reduce my perceived risks.
0.00 5.80 12.50 47.10 34.60
PT3 Being able to try out a smart contract is important in my
decision to adopt it in the future.
1 2.90 9.60 50 36.50
PO1 There is good publicity about the positive effects of
smart contracts.
3.80 30.80 30.80 28.80 5.80
PO2 Other organisations using smart contracts liked using them. 4.80 6.70 56.70 26 5.80
PO3 I have a clear understanding of the positive effects of a
smart contract.
5.80 25 26 37.50 5.80
PTMS1 Top management in my organisation is aware of the benefits
that smart contracts can provide.
7.70 28.80 30.80 27.90 4.80
PTMS2 Top management influences employees to increase awareness of
the importance/advantages that smart contracts can bring.
4.80 24 34.60 31.70 4.80
PTMS3 Top management provides adequate resources for employees to
adopt smart contracts.
10.60 24 32.70 28.80 3.80
POR1 My organisation has the needed resources to support smart
contract adoption.
5.80 20.20 26.90 36.50 10.60
POR2 Existing technologies in my organisation support smart
contract adoption.
2.90 26.90 23.10 39.40 7.70
POR3 Information Technology (IT) staff within my organisation have
adequate skills and experience to support smart
contract adoption.
7.70 23.10 29.80 32.70 6.70
PCP1 The use of smart contracts would offer my organisation a
stronger competitive advantage.
5.80 5.80 17.30 43.30 27.90
PCP2 The use of smart contracts would increase the ability of my
organisation to outperform the competition.
2.90 4.80 26 42.30 24
PCP3 The use of smart contracts will allow the generation of higher
profits to my organisation.
4.80 5.80 34.60 36.50 18.30
PCP4 My organisation has experienced competitive pressure to adopt
smart contracts.
11.50 21.20 29.80 27.90 9.60
PCP5 My organisation would have experienced a competitive
disadvantage if smart contracts had not been adopted.
8.70 16.30 39.40 26.90 8.70
PGS1 Government legislation supports the adoption of smart contracts. 7.70 16.30 38.50 30.80 6.70
PGS2 Legislation about smart contracts is transparent. 7.70 16.30 51.90 18.30 5.80
PGS3 Firms are legally protected through smart contacts. 7.70 9.60 49 36 7.70
PSCP1 My organisation’s business partners recommend the adoption of
smart contracts.
7.70 25 35.60 26.90 4.80
PCSP2 My organisation’s business partners have requested the adoption
of smart contracts.
9.60 26 36.50 24 3.80
PCSP3 My organisation has experienced pressure from business partners
to adopt smart contracts.
13.50 22.10 40.40 18.30 5.80
UI1 My organisation intends to use smart contracts actively. 5.80 19.20 43.30 24 7.70
UI2 My organisation intends to actively recommend smart contracts
to others.
5.80 19.20 42.30 26.90 5.80
UI3 My organisation intends to use smart contracts continuously on
various projects.
7.70 20.20 46.20 21.20 4.80
CONSTRUCTION MANAGEMENT AND ECONOMICS 13
facilitator of technology adoption across a wide range
of industries (e.g. Oliveira and Martins 2010, Chandra
and Kumar 2018). In the fiercely competitive environ-
ment of construction, firms may view the adoption of
smart contracts as adding to their ability to serve their
client (Badi et al.2020). In this context, a smart con-
tract may be seen to offer firms an edge that
strengthens their competitive position against rival
firms in the market (Gutierrez et al.2015). Top man-
agement support is found to be the third most signifi-
cant constituent for smart contract adoption. This
finding concurs with previous studies that highlighted
the role of top management support in encouraging
technology adoption in organisations (Low et al.2011,
Chandra and Kumar 2018). Senior managers in con-
struction firms play a major role in raising awareness
of the benefits of smart contracts and act as catalysts
for change in their organisations. Thus, top manage-
ment support can help to reduce internal forces resist-
ing change, cultivate a positive vision for the change,
and communicate this vision among employees
(Hayes 2014). Top management can also allocate the
necessary IT resources and financial support required
for the successful adoption of smart contracts in con-
struction organisations.
Surprisingly, observability is found to be a barrier to
smart contracts in this research study, which contradicts
the findings of previous research on the importance of
observability to technology adoption, such as Lin and
Chen (2012) study of cloud computing. One possible
reason for observability to emerge as a clear barrier to
smart contract adoption is the limited publicity and use
cases available for successful smart contract implementa-
tion in construction projects. Reflecting on the descrip-
tive statistics presented in Table 7, about 35% of the
respondents disagree that there is good publicity about
the positive effects of smart contracts, while almost 57%
do not know whether other organisations using smart
contracts have had a positive experience using such
contracts or not. On the same note, almost 43% of
respondents do not have a clear understanding of the
positive effects of a smart contract. This identified inabil-
ity of construction practitioners to observe the positive
advantages of smart contracts as opposed to traditional
contracting systems will act as a barrier to the wider
adoption of smart contracts in the industry.
Further analysis shows that there is no significant
relationship between the remaining six variables
examined and smart contract adoption, namely relative
advantage, compatibility, non-complexity, trialability,
organisational readiness, and government support.
While these factors are seen to support the adoption of
technologies in other studies (see Section 2 of this
paper), their role in facilitating smart contracts was not
proven in this study. Surprisingly, technological charac-
teristics are found to be deficient in supplementing
smart contract adoption as none of the five determi-
nants examined showed significance. One explanation
for this outcome may relate to the relative immaturity
of the smart contract technology at present (Lauslahti
et al.2017). First, the perceived relative advantage was
not determined to be a constituent of smart contract
adoption. This finding is surprising, as a considerable
proportion of our respondents, as shown in Table 7,
believed that a smart contract reduces payout time
(80%), reduces transaction cost (70%), and provides
secured payments (65%). Moreover, 50% of respondents
stated that a smart contract protects contracting parties
from insolvencies and late payments, 60% viewed smart
contracts as reducing the occurrence of disputes among
contracting parties, and 58% affirmed that such con-
tracts increase trust among contracting parties.
The compatibility of smart contracts with existing
contractual systems is an issue that also requires
attention. Whilst 59% of respondents perceived smart
contracts to be compatible with the contract manage-
ment needs of their organisation, and 58% considered
smart contracts to be consistent with the existing val-
ues and beliefs of their organisations, only 33% of the
respondents acknowledged that smart contracts are
compatible with the existing contract management
systems in their organisation. The compatibility of
smart contracts with existing contractual systems thus
requires further clarification to convince construction
practitioners of their merits (Lauslahti et al.2017). The
compatibility of smart contracts was a concern raised
by a recent European Parliament report (Boucher
2017) which observed that the variation inevitable in
contracts with long durations generates problems for
pre-coded smart contracts. The report also suggests
that the initial costs of putting together a smart con-
tract make these contracts more suitable for repetitive
agreements than for one-off contracts.
The participants seemed to be undecided regarding
non-complexity, with almost a quarter of respondents
on average neither agreeing nor disagreeing about
the complexity of smart contracts. Almost 50% of par-
ticipants avowed that a smart contract is easy to
understand, whilst 60% considered such contracts
both easy to use and manageable. Forty-three percent
of respondents confirmed that smart contracts are
easy to integrate with existing contractual processes
in their organisations. Based on this finding, it could
be suggested that by switching to smart contracts
14 S. BADI ET AL.
construction organisations can experience automated
and accurate operations and processing, guaranteeing
non-complexity and conformity to regulations and
requirements. For any construction organisation, man-
agement and operations can become a slow, tedious
process susceptible to inaccuracy. Smart contracts can
offer a positive solution by streamlining communica-
tion and providing acute accuracy automation of
information.
Trialability has been identified to increase successful
technology adoption in several studies (e.g., Kendall
et al.2001,Rogers2003,LinandChen2012,Ramdani
et al.2013). Trialability is strongly espoused by our
respondents, with a significant 80% intending to try out
a smart contract in a limited scope in their works before
deciding whether to adopt it in practice, while 82%
suggested that a trial period before adopting a smart
contract in practice would reduce the perceived risks.
Eighty-six percent of respondents agreed that being
able to trial a smart contract is an important factor
in their decision to adopt it in the future. As Table 7
indicates, organisational readiness appeared to be a
contested issue among the respondents, with a quarter
taking the neutral stance of neither agreeing nor dis-
agreeing about whether their organisations have the
needed resources, technologies, or skills to support
smart contracts. That said, some respondents noted that
their organisations have the needed resources (47%),
supporting existing technologies (47%), and adequate IT
staff skills and experience (39%).
The role of government in supporting the adoption
of smart contracts was also observed to be unclear.
Indeed, a considerable amount of our respondents
were unable to provide a definitive view of the role of
government support: 52% remained neutral on
whether legislation regarding smart contracts is trans-
parent, while 49% neither agreed nor disagreed that
construction organisations are legally protected
through smart contracts. Moreover, 38.5% of respond-
ents were unable to ascertain whether government
legislation is supporting the adoption of smart con-
tracts or otherwise. These findings suggest that con-
siderable effort must be undertaken to further
illuminate these issues. While there is some movement
towards increased government regulation in the UK
(for example, see Vos 2019), more work needs to be
done to elucidate the legal aspects of smart contracts.
Conclusion
The present study is designed to identify the key fac-
tors that influence the adoption of smart contracts in
the UK construction sector. Utilising a perception-
based model based on the TOE framework (DePietro
et al.1990), the study appraises factors that influence
the adoption intention of smart contracts at the
organisational level by assessing three primary TOE
components: technological factors (relative advantage,
compatibility, non-complexity, trialability, and observ-
ability), organisational factors (top management sup-
port and organisational readiness) and environmental
factors (competitive pressure, government support,
and supply chain pressure). The study found generally
that the role of environmental and organisational fac-
tors is significant in influencing adoption, and it identi-
fies supply chain pressure, competitive pressure, and
top management support as the most important fac-
ets of smart contract adoption. The findings suggest
that construction practitioners perceived smart con-
tracts to be driven not by their technological charac-
teristics, but rather by how such technology can be
mobilised to implement new approaches to transact-
ing that can add value to the organisation’s competi-
tive advantage and supply chain performance.
The theoretical contribution of the research can be
regarded from three viewpoints:
First, this study makes the first attempt to examine
the drivers of smart contract adoption intention in
the construction sector. Thus, the findings pre-
sented here make a noteworthy contribution to the
growing literature in this fledgling area.
Second, it was found that this study constitutes the
first utilisation of the TOE framework to explore
smart contract adoption, both generally and in the
construction sector specifically. The framework has
proven useful in examining a comprehensive set of
technological, organisational, and environmental
factors that may shape an organisation’s inclusive
decision-making process.
Third, the study has gone some way towards
appraising the role of supply chain pressure, com-
petitive pressure, and top management support in
the decision to adopt smart contracts in the con-
struction sector. Taken together, these findings
suggest a role for environmental and organisational
factors in promoting smart contract adoption deci-
sions in the construction sector.
The findings of this study have several important
implications for future practice in the UK construction
sector. The top management of construction firms
should recognise the vital role they play in driving
smart contract adoption. Top management support
CONSTRUCTION MANAGEMENT AND ECONOMICS 15
and commitment are found to be key constituents for
smart contract adoption as senior managers are seen
as the catalyst for this change in their organisations.
To stay competitive in this increasingly digitised sec-
tor, it may become a requirement for senior managers
to explore the use of smart contracts in their oper-
ation and liaise with their supply chain actors to
streamline their operations and thereby reap the ben-
efits of smart contracts. Top managers should thus
consider enhancing their competence in the applica-
tion of smart contract technology as well as smart
contract management. This objective could be
achieved by adopting a top-down model, with the
knowledge and expertise originating at the upper lev-
els of the organisation being diffused through educa-
tion and communication to the lower-level employees
(Kotter and Schlesinger 1979). Moreover, this study
will be valuable to proponents of smart contracts,
especially those management consultants and law
firms who are exploring the provision of smart con-
tracts as a service to their clients. The study has also
provided useful insight into the factors that are signifi-
cantly associated with smart contract adoption deci-
sions. For instance, it has been shown that consultants
can devote considerable attention to convincing top
management of the benefits of smart contracts and
the related competitive advantages to the firm’s oper-
ations. Furthermore, consultants can accelerate smart
contract adoption by leveraging the power of supply
chain partners and promoting supply chain-wide solu-
tions. The observability of the technological benefits
of smart contracts could also be facilitated by
generating good publicity for successful use cases.
If well-devised, this approach could increase industry
awareness of the advantages of smart contracts as
opposed to traditional contracting systems.
Limitations and further research
The study findings are subject to several limitations that
need to be acknowledged. First, although the scope of
this research was restricted to UK construction practi-
tioners, its limited geographical focus does not invali-
date the study results with respect to other countries.
The fact is that the global construction sector shares
many common fundamental characteristics. The UK
construction sector was simply used as a case study to
examine broader issues and problems of smart contract
adoption. Second, given the limited time and resources
available for conducting the study, convenience sam-
pling was considered the most effective strategy for
selecting a representative group from the population
under study. However, convenience sampling, as a non-
probability sampling approach, could be seen as
“subjective”given that the researchers included a sam-
ple from the selected population following the practical
criteria of accessibility and willingness (Etikan et al.
2016). In this study, the participants included are those
who were readily accessible to the researchers through
their social networks and were willing to contribute
their time to complete the questionnaire.
Finally, the study paves the way for future avenues of
research. Smart contract adoption in construction
remains in its embryonic stages, and future research
could trace the diffusion of the innovation as it moves
across the innovation curve from “early adopters”to
“laggards”(Rogers 2003). In addition, the role of smart
contracts in supply chain management is an area of
potentially significant theoretical and managerial contri-
butions and thus merits considerable attention (see for
example Treiblmaier 2018). Furthermore, the issue of the
legal standing of a smart contract is an unexplored
theme which could be usefully examined. As a mechan-
ism for the administration of contractual processes, a
smart contract is a useful tool. However, there is consid-
erable legal complexity beyond this aspect of smart con-
tracts, which inevitably gives rise to concerns. Indeed,
technological developments of smart contracts need to
be firmly coupled with a greater understanding of the
relevant laws and regulations. Consequently, further
research needs to examine more closely the integration
of smart contracts into project management processes.
A smart contract is only one small part of any technol-
ogy solution for a construction project. A network of
interactive and interacting smart contracts would be
necessary to provide an effective project governance
regimeandshouldbeviewedaspartofthewiderBIM-
led revolution in construction (Mason 2017). BIM
coupled with smart contracts can produce a robust and
trustworthy collaborative platform advocated by many
in the industry (Winfield and Rock 2018).
Disclosure statement
No potential conflict of interest was reported by the author(s).
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