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Multi-criteria analysis of barriers
to building information modeling
(BIM) adoption for SMEs in
New Zealand construction industry
Andrew Thomas Hall and Serdar Durdyev
Department of Engineering and Architectural Studies, Ara Institute of Canterbury,
Christchurch, New Zealand
Kerim Koc
Department of Civil Engineering, Yıldız Technical University, Istanbul, Turkey
Omer Ekmekcioglu
Faculty of Civil Engineering, Istanbul Technical University, Istanbul, Turkey, and
Laura Tupenaite
Department of Construction Management and Real Estate,
Vilnius Gediminas Technical University, Vilnius, Lithuania
Abstract
Purpose –Building information modeling (BIM) is a prominent concept to digitalize data collection and
analysis processes. Small and medium-sized enterprises (SMEs) account for a considerable percentage of the
works performed in the construction industry. The adoption rate of BIM by SMEs is still, however, not at the
desired level in the New Zealand construction industry. This study aims to evaluate barriers to BIM
implementation for SMEs in the New Zealand construction industry.
Design/methodology/approach –This study adopted four-step methodology to evaluate barriers to BIM
adoption for SMEs. First, a comprehensive literature review, followed by a focus group discussion was
performed to identify barriers to BIM adoption. Then, analytical hierarchy process (AHP) was used to assess
identified barriers. Finally, experts’agreements (both internal and external) were ensured by consistency
analysis and Kendall’s coefficient of concordance (Kendall’s W) tests.
Findings –The findings indicate that (1) interoperability between software platforms, (2) lack of government
mandate on BIM usage at project level, (3) high cost of acquiring the software and licensing required to use BIM
and (4) lack of client demand for adopting BIM were the most significant barriers in terms of technological,
governmental, resource and cultural categories, respectively. Further investigation of the expert evaluation
showed strong consistencies (each expert separately) and agreements (among experts) in each AHP matrix.
Practical implications –Primary focus should be training of local market (particularly SMEs) professionals
as the shortage in qualified professionals makes the country-wide adoption challenging. The publicity in the
local market can help SMEs understand how BIM is leveraged for further improvements in project
performance.
Originality/value –Overall, this research not only provides a roadmap for the widespread adoption of BIM
within SMEs in New Zealand through analysis of the barriers encountered but also highlights the power that
policymakers hold over the mass adoption of BIM within SMEs.
Keywords Building information modeling, Innovation, Digitalization, Digitization, Small to medium
enterprises, Analytical hierarchy process
Paper type Research paper
1. Introduction
Construction industry plays a driver role in most of the countries across the globe (Ozturk
et al., 2020). The industry is also a major contributor to the GDP of New Zealand over the last
two decades accounting for more than 6% of the total GDP of $306Bn NZD in 2019.
Meanwhile, the New Zealand Government announced $12 b funding toward national
Multi-criteria
analysis
of barriers
to BIM
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0969-9988.htm
Received 20 March 2022
Revised 17 May 2022
Accepted 10 June 2022
Engineering, Construction and
Architectural Management
© Emerald Publishing Limited
0969-9988
DOI 10.1108/ECAM-03-2022-0215
infrastructure projects in 2020, including new education and healthcare facilities and
transportation upgrades (Veldhuizen et al., 2019). In addition to this, the number of building
consents granted in New Zealand has increased by 31% from 2017 to 2020 with a total of
39,881 per year. It is also worth noting that the construction industry is under pressure to
meet the increase in urban demand and population growth, while facing cost, time and quality
constraints (Durdyev and Hosseini, 2019). There are plenty of administrative challenges, i.e.
complicated work processes and tiresome managerial activities, leading to inefficiencies in
the construction industry (Durdyev et al., 2018). One class of the solutions asserted to
overcome these challenges is the use of digital information technology and automation tools
in managerial activities for the construction industry, such as document and process
management, project planning and control, and risk management (Jahanger et al., 2021;
Durdyev et al., 2019).
Digital technology currently adopted in the construction industry includes the use of
digital twins, smart buildings, smart contracts, artificial intelligence, modern methods of
construction, advanced building materials and virtual or augmented reality (Omar and
Nehdi, 2016). All these concepts require large amounts of data that need to be integrated with
a common data environment (CDE) to ensure there is no conflict or duplication, such that the
collected data can be used by project stakeholders to improve work efficiency (Han and Leite,
2021). In this sense, the building information modeling (BIM), which chiefly aims to provide a
model of a project connected to a wide range of planning and construction elements, has
become fundamental in terms of digital transformation strategy (Van Tam et al., 2021;
Durdyev et al., 2021). Even though the need for the construction industry for adapting to new
opportunities is multi-faceted; such as evolving expectations of the clients, market pressures,
availability of new technology, new generation of professionals within the industry and the
implementation of legal framework (Wyman, 2018), digitalization accommodates several
opportunities including enhanced customer satisfaction, simulation and digital modeling of
various scenarios, accurate forecasting, and advanced document management systems
(Sezer et al., 2021). Furthermore, digitalization also offers new partnership models, high level
performance tracking and enhanced project deliverables (Stoyanova, 2020).
Despite the pertinent literature shows the benefits of digitalization and large companies
have been integrating the new technologies into their systems, only 60% of the projects
executed by large and conglomerate businesses (30þStaff) used BIM according to the New
Zealand BIM benchmark survey (EBOSS, 2020). Hence, the challenges encountered during
transformation to a digitalized model can be associated with poor company and workforce
culture, data sharing issues, lack of collaboration and a lack of awareness of the benefits of a
digitalized business structure. While these issues are apparent for large companies, Kılıçet al.
(2014) discovered that smaller to medium enterprises (SMEs) have less resources available to
be able to support their digital transformation. This can be attributed to the high cost of initial
investment in digital technologies and equipment, and unperceived benefits and return on
investment to the business (Sategna et al., 2019). Besides, another obstacle in the adoption of a
fully digitalized project is the gap between the level of the main contractors and the SMEs;
such that an SME cannot allocate resources for digitalization while the main contractor may
have a high level of digital maturity (Tezel et al., 2020). Considering the fact that large
contractors often rely on SMEs to carry out specialist works in the construction sector and
SMEs contributed to 37% of the industry operating profit compared to large businesses
contributing 14%, SMEs play a vital role in the construction sector in New Zealand and
further efforts need to be undertaken to allow them to digitalize, delivering buildings at a
faster pace with more efficient use of resources, that surpass customer expectations and meet
new levels of sustainability.
The vast majority of the literature focused on diverging objectives while investigating the
adoption of BIM for SMEs. On the other hand, several researchers also investigated the
ECAM
barriers or challenges to BIM adoption for SMEs in several countries such as China (Li et al.,
2019) and Australia (Hosseini et al., 2016), or without focusing on particular countries
(Saka and Chan, 2020a). However, there is a need for a country-specific (due to unique cultural
and operational context where the industry is operated) exploration of barriers that are
hindering BIM adoption for SMEs, which differentiates the present study from those reported
in the literature. In addition, existing literature adopted several methods to address BIM
adoption of SMEs such as innovation diffusion theory (Hosseini et al., 2016), social network
analysis (Li et al., 2019), Bayesian belief networks (Reza Hosseini et al., 2018) and statistical
analysis (Ayinla and Adamu, 2018). However, the literature lacks using multi-criteria
decision-making (MCDM) methods to address barriers to BIM adoption for SMEs. MCDM
methods are widely considered to provide effective ground to analyze criteria, factors or
barriers and have widely been applied to a variety of research questions in the construction
management literature (Mahdiyar et al.,2020;Tabatabaee et al., 2019). Thus, this study aims to
evaluate barriers to BIM adoption for SMEs in New Zealand construction industry using the
analytical hierarchy process (AHP) method. Internal consistencies (each expert individually) of
experts from the New Zealand construction industry were ensured through consistency ratio,
while external consistencies (agreement level between experts) within the expert group were
checked via Kendall’s W test. Overall, this study differs from its counterparts in three aspects.
First, this study focuses on the SMEs from New Zealand construction industry, which has not
been reported in the literature. Second, this study adopts one of the most widely used MCDM
methods (i.e. AHP), while the implementation of MCDM methods has been overlooked in the
pertinent literature. Finally, the consistencies and reliability of the attained results are
addressed explicitly through a two-step consistency evaluation (Step 1: consistency analysis;
Step 2: Kendall’s W). Overall, the findings of this research are expected to contribute to the
SMEs that are aiming to enhance their digitalization capacity, with a particular focus on BIM
utilization, in the New Zealand construction industry.
2. Literature review
Industry 4.0 refers to the fourth industrial revolution that is currently driving the changes in
the construction industry toward a more automated and data-driven environment (Marr,
2017). Industry 4.0 can be leveraged to create a digitized value chain, allowing the
communication of data and information between the environment, the business and its key
stakeholders (Alaloul et al., 2020). In addition, there are many tools, such as big data analytics,
autonomous robots, simulations and system integrations, Internet of Things (IoT), cloud
computing and augmented reality, that enable the transformation of business management
and operational facilities into digitalized processes (Gbadamosi et al., 2021;Omar and Nehdi,
2016;Pan et al., 2020). Here, digitalization refers to the process of converting knowledge from
an analogous into a digital method. For instance, handwritten documents can be superseded
by digital versions and traditional paper-based document management systems can be
replaced by cloud-based solutions in this regard. The digitalization of these processes has a
significant potential not only in leading to the reduction of business administrative costs but
also in increasing the productivity and performance of projects allowing to adapt to the new
digital solutions (Chowdhury et al., 2019). Furthermore, opportunities for digitalization
include improved customer experience through new business channels, enhanced
visualization, 3D simulation tools, simplified administrative work loads and improved
customer satisfaction tracking and placing a focus on client centricity (Wyman, 2018).
Digitalization also makes smart buildings and smart infrastructure more achievable and
helps collaborative partnerships and innovative financing options be more available (Coupry
et al., 2021). Digitalization requires businesses to utilize connected systems at every link of
their value chain. For a business to digitalize, it requires the development and transformation
Multi-criteria
analysis
of barriers
to BIM
of its business model through the adoption of digital technologies, allowing it to generate
more revenue and provide a higher value to all players involved in the supply chain (Berlak
et al., 2021). Through the digitalization of a business model, benefits included are increased
communication, a higher level of efficiency in the management of the construction process,
higher levels of sustainability and increased safety. In this context, BIM is the preliminary
step for SMEs to render their business in a way to enhance digitalization.
2.1 BIM and higher level of BIM usage
BIM is a key component in the digitalization of the construction industry. The BIM can take
traditional silos of data and integrate them into a unified system that is able to be modified
and analyzed in real time (Wyman, 2018). Manata et al. (2018) suggest that the value of BIM is
not necessarily in the visual model it creates, but in the ability for the model to analyze the
effects of changing diverse elements of the building before construction occurs. The gaps
between the maturity levels of BIM have recently been explored in the industry from Level
0 BIM (manual drawings) to Level 1 (2D drawings), Level 2 (3D drawings, common data
environment) and Level 3 (open data standards, new contractual framework, etc.) (Ayinla and
Adamu, 2018). Particularly interoperability challenges in the adoption of BIM were addressed
for the practical and efficient use of BIM at Level 2 and Level 3 to facilitate sustainable design
by integrated use of BIM (Arayici et al., 2018).
Some governments around the world have initiated important steps to improve sectoral
adoption of BIM. For instance, the use of Level 2 BIM was mandated by the UK government in
all public projects by 2016 (HM Government - Digital Built Britain, 2015). Hence, the UK has
now competitive advantage in the digitalization of the construction sector through BIM and
put several efforts to establish a “Digital Built Britain (DBB)”as part of Level 3 BIM (NBS,
2017). Level 3 vision brings several concepts together such as construction 4.0, smart cities
strategy and the information economy strategy. Hence, many construction processes are
expected to be evolving with this vision such as performance management, interoperable
cross sector, dependency analytics using big data. In the Level 3 vision, Level 3 A stands for
improvement in Level 2 with industry foundation class (IFC) data exchange, Level 3 B is
related to enabling new technologies and systems through infrastructure IFC, dictionaries
and ontologies; Level 3C covers Level 3 B plus IoT, telemetry and high security; and Level 3D
addresses internationalization and semantic contracts. Despite not mandating Level 2 BIM
like in the UK, the Netherlands also put several efforts to support the use of BIM. For instance,
a recent “National Model BIM Protocol”was published by the Building Information Council/
BIM desk to minimize ambiguity in BIM protocols (CMS, 2017). A report by European
Commission (2021) indicates that the Netherlands and Austria are the only EU member with
Open BIM standards and the Netherlands is working on linking BIM and their digital
building permit system.
2.2 What barriers do SMEs face when trying to digitalize?
Despite the BIM practices are one of the significant tools for the digitalization in the
construction industry through offering a wide spectrum of solutions and opportunities, there
are many barriers and challenges that stand for going through digitalization as well as
materializing digital innovation technologies (Saka and Chan, 2021). In this context, barriers
faced for BIM implementation can be described in a variety of aspects such as technological,
economic and environmental (Ramilo, 2014). For instance, inadequate specialist tools
including the lack of access to software on a construction site were among the biggest barriers
in terms of technological manner (Ayinla and Adamu, 2018). In addition, constant
introduction of new technologies and software has directly contributed to the growing
lack of technical knowledge among the team struggling to keep up with the pace of change
ECAM
(Tezel et al., 2020). This can also be correlated with the costs incurred during the management
of the models as the insufficient budget for the digital transformation is exacerbated within
the company to purchase the essential software and hardware (Saka and Chan, 2020b). It is
important noting that the significant costs encountered at the onset of projects are also
compounded by the necessity for all stakeholders to use compatible software and data
formats (Li et al., 2019). Compared to the larger companies, the SMEs possess smaller
cashflows leading to serious financial barriers to adopt new technologies with regards to
software, hardware and licensing to procure and maintain (Alaloul et al., 2020). The rate of
change, and equipment obsolescence, particularly for the computers also contributes to the
added cost of adopting BIM. To overcome this challenge, one particular solution offered by
Sategna et al. (2019) proposes that the use of a top-down approach for funding the digital
technology in companies would help to ease some of the investment costs associated with
BIM adoption for SMEs. Accordingly, Parida et al. (2019) underlined that the BIM can often
contain a wide number of functionalities that did not see value by the customers or are not
worth to invest. In addition, environmental factors include the fear of failure and productivity
losses when adopting a new business model. Accordingly, there is a significant gap in the
level of innovation between small and large companies; such that the greater the level of
leadership and support that provided by the company, the faster that the digital innovation
was adopted (Parida et al., 2019). However Christensen (2013) argued that large organizations
are not as likely to develop innovations due to the risks involved in changing their business
model. It is also worth mentioning that one of the environmental issues in terms of digital
transformation is the social factors, which have the greatest influence toward successful
implementation of digitalization as a plethora of factors are inter-related and need to be
addressed simultaneously (Alaloul et al., 2020). Hence, the widespread adoption would have
positive effects throughout the industry, resulting in greater collaboration, enhanced
customer relationships and a more innovative working environment. In this vein, closing the
deficit of collaboration between the parties is essential in terms of the data fragmentation and
the lack of training and expertise within the industry (Okumu, 2019). Overall, recent evidence
suggests that the lack of BIM maturity seriously hinders the ability for the company to look
toward more innovative solutions (Stoyanova, 2020;VanDerHorn and Mahadevan, 2021).
2.3 Level of adoption of BIM in New Zealand
The adoption of the BIM strategies is critical to a line of business becoming digitalized as all
the digital tools that can assist in a business undergoing more productive and efficient rely
on the use of BIM as a backbone. Therefore, the adoption rate of BIM in New Zealand can be
viewed as a starting point for understanding the level of digitalization of construction
companies. A BIM benchmark survey completed annually by EBOSS (2020) shows that the
use of BIM is becoming more prevalent throughout the industry with 57% of
subcontractors reporting the use of BIM in 2020, up from 43% in 2019. While 90% of the
industry appeared in the survey report regarding the use of BIM for design, only 25% for
facilities management, which is expected to increase to 45% in 2021. These records could be
indicative for the entire industry in New Zealand; however, the sub-contractor group
surveyed in the corresponding report was made up of 21 companies, of which only three
were SMEs. These insights can further be explored with the rate of using 4D BIM tools;
such that 8% of the sub-contractors have been using 4D BIM for phase planning.
Accordingly, the past efforts discovered that BIM is most used for 3D coordination,
suggesting that the level of BIM maturity within New Zealand is still low. Hence, the
present research is aimed to gain insights into the level of digital tools and digitalization
using BIM that has been adopted at SME level by manifesting the barriers and challenges
observed within a broader perspective.
Multi-criteria
analysis
of barriers
to BIM
3. Methodology
3.1 Research framework
This study evaluates the barriers to BIM implementation for SMEs in New Zealand
construction industry through an MCDM approach. In this vein, an eight-step methodology
was adopted to address the research question as illustrated in Figure 1. First, barriers to BIM
adoption for SMEs were identified through a systematic literature review by using the
Scopus search engine (Step 1). Identified barriers were then synthesized and fixed in a way
that are most suitable to SMEs in New Zealand (Step 2). In this step, focus group discussion
(FGD) was performed with either industry representatives or academicians with a high level
of BIM expertise. In Step 3, a final list of barriers was assessed by experts with individual
judgments through the AHP method. Gathered data were then checked in terms of
consistency ratio (CR) with respect to each AHP matrix (Step 4), and inconsistent assessments
were sent to the corresponding experts who were inquired for revised judgments (Step 5).
Once all the experts were consistent in their judgments regarding any matrices, individual
judgments (barrier weights) were aggregated to attain the final evaluation results (Step 6). To
ensure the agreement level of overall expert evaluations, Kendall’s coefficient of concordance
(also known as Kendall’s W) test was conducted (Step 7). In case of disagreement in any of the
matrices, the final evaluations were sent to the experts, who were inquired to check the overall
assessment and revise their judgments if they opt for changing their evaluations (Step 8). It is
important to note that after revised judgments for overall agreement, CR of each expert was
also checked (through Step 4). The following sections describe the procedures adopted to
extract the final list of barriers (Section 3.2), AHP method (Section 3.3) and Kendall’s W test
(Section 3.4), respectively.
3.2 Barrier extraction
This study performed a systematic literature review to identify significant barriers for SMEs
to adopt BIM in New Zealand construction industry. To achieve this objective, a three-step
search methodology was performed. First, Scopus search engine was used to collect
Step 1: Identification of the barriers to BIM for SMEs through literature review
Step 2: Refinement of barriers for SMEs in New Zealand
Step 3: Assessment of barriers via AHP analysis for each expert individually
Step 4: Checking the consistency of each expert’s judgements (CR)
Step 5: Revised judgements from experts (if not consistent)
Step 6: Aggregation of the significance of barriers
Step 7: Checking the consistencies of overall experts’ judgements through Kendall’s W
Step 8: Revised judgements from experts (if agreement is not satisfied)
Figure 1.
Application of the
AHP method
ECAM
preliminary sources to set initial list of barriers. The reasons to use Scopus were its coverage,
popularity and effectiveness (Graham et al., 2020;Durdyev, 2020). In this context, SME and
BIM were searched in the Title/Abstract/Keywords section of the search engine, which
resulted in 75 documents. In the second step, the search results were limited to article and
review studies, reducing the number of studies to 42. At the final step of the searching,
abstract-level investigation was performed to identify studies that can be used to extract
barriers for BIM adoption. Here, the following three criteria were used during the abstract
level investigation: (1) articles that are not directly related to BIM and SMEs were excluded,
(2) articles that address solutions for particular problems of SMEs (such as cost) through BIM
implementation were excluded and (3) articles that focus on other aspects between SMEs and
BIM rather than focusing on barriers, challenges, drivers or risks were also disregarded.
Collected materials were then discussed with seven experts (four from industry and three
from universities) through focus group discussion (FGD) to ensure their legitimacy in New
Zealand construction industry. The selection criterion for FGD was having at least 10-year
experience on BIM for academicians and having at least 5-year experience on BIM
implementation for industry representatives. FGD is particularly useful (as compared to
structured or semi-structured interviews) to extract new ideas through a vigorous discussion
section (Endo et al., 2017). In addition, exploration of the subject is possible with deep and free
discussions without any criteria that restrict the diffusion of new ideas (Nyumba et al., 2018).
During the discussion, each barrier addressed in the literature was discussed and a complete
list of barriers were ensured that are suitable to New Zealand SMEs. In general, many of the
barriers addressed in the literature were synthesized to maintain the AHP structure at a
manageable level, while linguistic changes were made nearly in all of the identified barriers.
At the end of the discussion, 20 barriers were ensured and categorized into four categories, i.e.
technological, governmental (legal), resource and culture and knowledge. Table 1 shows
identified barriers to BIM adoption for SMEs.
3.3 Analytical hierarchy process
AHP method, developed by Saaty (1990), was used to determine the significances of the
distinguished barriers and their categories. The method was selected to achieve the study
objective due to several reasons: (1) it allows the decomposition of the problem to the certain
hierarchy of categories and criteria, converting subjective evaluations of their relative
importance into a set of general scores (significances); (2) consistency of the experts can be
calculated through consistency analysis, (3) it is one of the most frequently used MCDM
methods in the literature and (4) the method does not require a large sample size (Mohandes
et al., 2020;Budayan, 2019). In total, 20 barriers in four barrier categories were assessed using
the AHP method. The following four analysis steps were adopted to perform the AHP
method:
Step 1: To assess the importance of each barrier and category, the questionnaires
containing pairwise comparison matrices were prepared and provided to eight experts
(Table 2). Experts were selected by using purposive sampling as the quality of the
participants was sought in the AHP method rather than quantity (Durdyev et al., 2022).
Major selection criteria to select participants were (1) currently working for SMEs in New
Zealand and (2) using BIM in the current project. Each expert performed pair-wise
comparisons of the barriers and categories individually, based on the 1-9 scale proposed
by Saaty (1990), as presented in Table 3.
Step 2: Judgment matrices (assessments) of each expert were used to calculate the
significance of each barrier in each category as well as the significances of categories
according to the calculation steps addressed by Saaty (1990).
Multi-criteria
analysis
of barriers
to BIM
Main barrier Barrier ID A B C D E F G H I J K
Technological
barriers (T)
Interoperability between
software platforms
T1 ✔✔✔✔✔✔X✔✔✔✔
Software market’s limitation to
satisfy the specific requirements
for SMEs
T2XXX✔X✔XXX✔X
Lack of access to digital tools T3 ✔✔✔XXX✔XXXX
Lack of technical infrastructure
(such as compatibility, high-
speed Internet etc.)
T4 ✔✔✔XX✔✔✔X✔✔
Technical capability of the
current well-established non-
BIM technology
T5 X ✔XX✔✔✔X✔X✔
Governmental
(legal) barriers (G)
Lack of standards/policies for
adopting BIM at company level
G1XXX✔X✔✔✔✔✔✔
Lack of policy for data security
and ownership of IP
G2 X ✔XXX✔✔XXXX
Lack of governmental support
(e.g. financial) for BIM training
and education
G3 ✔XXX✔✔✔✔X✔✔
Lack of government mandate on
BIM usage at project level
G4 X ✔XXXXXXX✔✔
Resource barriers
(R)
High cost of BIM training R1 ✔✔✔✔✔✔XX✔X✔
High cost of acquiring the
software and licensing required
to use BIM
R2 ✔✔✔✔✔✔✔✔✔X✔
Lack of skilled BIM employees
within the industry/our
company
R3 ✔✔✔✔✔✔XX✔✔✔
Culture and
knowledge
barriers (C)
Lack of knowledge (among
SMEs) of BIM and its benefits
C1 ✔✔✔✔✔✔✔XX✔✔
Lack of collaboration between
key project stakeholders
C2 X ✔✔XXX✔✔✔✔✔
Lack of benefit to adopting BIM
on particular projects
C3 ✔✔✔✔✔✔✔✔✔X✔
Too much effort (time,
complexity etc.) to adopt BIM
C4 X ✔✔✔✔✔✔✔✔✔X
Lack of knowledge in how to
adopt BIM for particular
workflows
C5 ✔✔✔✔X✔✔✔✔✔✔
Lack of the BIM maturity to be
able to benefit from the
technology available
C6 X ✔X✔✔✔✔X✔✔X
Resistance to change (among
SMEs)
C7 X ✔✔✔X✔X✔✔✔✔
Lack of client demand for
adopting BIM
C8 ✔✔✔✔✔X✔✔✔✔✔
Source(s): A5Awwad et al. (2020),B5Ayinla and Adamu (2018),C5Hong et al. (2020),D5Reza Hosseini
et al. (2018),E5Hosseini et al. (2016),F5Li et al. (2019),G5Saka et al. (2022),H5Saka and Chan (2020a),
I5Saka and Chan (2020b),J5Saka et al. (2020),K5Saka and Chan (2021)
Table 1.
Barriers to BIM
adoption for SMEs in
New Zealand
construction industry
ECAM
Step 3: The consistency ratio (CR) of each expert with respect to each matrix was checked
according to Eq (1):
CR ¼CI
RI (1)
where RI is the random consistency index and CI is the consistency index. RI takes distinct
values, changing for the number of criteria/barriers in the matrix while CI is calculated as
follows:
CI ¼λmax n
n1(2)
where nis the number of criteria/barriers in a pairwise comparison matrix and λmax is the
maximum eigenvalue for the corresponding matrix.
Step 4: Weights of barriers determined by each expert separately were aggregated by
taking the average of them. Hence, final barrier importance and rankings were computed.
3.4 Kendall’s test
The level of agreement between experts was measured using a non-parametric Kendall’s
coefficient of concordance (Kendall’s W) test Kendall (1948). It measures the level of
agreement between variables (Ab Wahid and Grigg, 2021), which are experts in this study.
Kendall’s W ranges between 0 and 1 for each measure, such that the higher the value W is, the
stronger the agreement between the judgments of experts (Cruz and Cruz, 2021). Kendall’sW
is calculated with the following Equation:
Expert ID Discipline Role in the organization
Experience in
the construction
industry
BIM use in
the current
project
E1 Design Director 6–10 years Yes
E2 Main contractor Project coordinator 6–10 years Yes
E3 Sub-contractor Project manager >15 years Yes
E4 Main contractor BIM coordinator >15 years Yes
E5 Design BIM technician <5 years Yes
E6 Consultant BIM manager 6–10 years Yes
E7 MEP consultant BIM technician 6–10 years Yes
E8 Sub-contractor BIM coordinator <5 years Yes
Intensity of
importance
Definition
(importance) Explanation
1 Equal Two barriers/categories are equally important
3 Moderate Experience and judgment slightly favor one to another
5 Strong Experience and judgment strongly favor one to another
7 Very strong A barrier/category is strongly favored and its dominance is
demonstrated in practice
9 Absolute The importance of one over another affirmed on the highest
possible order
2, 4, 6, 8 Intermediate values Used to represent compromise
Table 2.
Expert profile
Table 3.
Scale of measurement
in pair-wise
comparison
Multi-criteria
analysis
of barriers
to BIM
W¼12S
r2ðm3mÞrPr
k¼1Tk
;W∈½0;1(3)
where Sis the total square deviation of the rankings of each attribute; T
k
is the index of
reiterated ranks in the rrank, kis the number of experts and mis the number of barriers/
categories. Then, statistical significance of the concordance coefficient is calculated by the
following formula (Kendall, 1948):
χ
2
α
;
υ
¼Wrðm1Þ¼ 12S
rmðmþ1Þ 1
m1Pr
k¼1Tk
(4)
where ais the pre-selected level of significance and
υ
is the degree of freedom. If
χ
2
α
;
υ
is higher
than
χ
2
crit based on
α
significance level (
α
50.05), then the agreement of experts’opinions can
be regarded as satisfactory. Otherwise, the respondents’assessments can be concluded as not
in agreement and need to be harmonized.
4. Results
4.1 Criteria assessment
This study investigates the barriers to BIM adoption for SMEs in New Zealand Construction
industry. Eight experts from the SMEs actively working in New Zealand contributed to this
study for the assessment of barriers. In this vein, one of the most widely used MCDM
methods, AHP was adopted. Assessments of barriers were performed by each expert
individually, resulting in eight different evaluations. Then, the averages of the calculated
weights of barriers were computed to attain the overall barrier weights and rankings. The
results show that “Interoperability between software platforms (T1)”and “Lack of access to
digital tools (T3)”were the most significant technological barriers for SMEs to adopt BIM
tools (Table 4). It is important to note that only one of the experts (i.e. E1) assessed T1 with a
weight value of less than 0.2, showing the consistencies of other experts in this regard. Rather
than T1, Expert 1 considered “Software market’s limitation to satisfy the specific
requirements for SMEs (T2)”as the most significant technological barrier. The reason for
this might be related to the fact that E1 works as a designer with considerably high
experience, which could have provided the expert confidence in terms of interoperability
issues.
Tables 5 and 6 illustrate the results of AHP analysis in terms of governmental (legal)
barriers and resource barriers, respectively. Accordingly, “Lack of government mandate on
BIM usage at project level (G4)”and “Lack of governmental support (e.g. financial) for BIM
training and education (G3)”were the top-level barriers for SMEs to adopt BIM (Table 5).
Interestingly, four of the experts (E1, E4, E6, E7) assessed G4 as even more important than the
sum of the other three governmental barriers by weighting it higher than 0.5 (0.73, 0.58, 0.62,
0.54, respectively). In terms of resource barriers, “High cost of acquiring the software
and licensing required to use BIM (R2)”and “Lack of skilled BIM employees within the
Barriers/Experts E1 E2 E3 E4 E5 E6 E7 E8 Overall weight Rank
T1 0.04 0.33 0.24 0.25 0.44 0.50 0.26 0.40 0.31 1
T2 0.50 0.19 0.14 0.06 0.28 0.17 0.13 0.12 0.20 3
T3 0.32 0.17 0.53 0.56 0.08 0.10 0.28 0.18 0.28 2
T4 0.10 0.17 0.05 0.10 0.17 0.10 0.27 0.04 0.12 4
T5 0.05 0.15 0.04 0.04 0.03 0.13 0.06 0.26 0.09 5
Table 4.
AHP results in terms of
technological barriers
ECAM
industry/our company (R3)”were assessed with 0.48 and 0.41 of weights, leading them to be
the most significant barriers for SMEs.
The cultural category was explained with eight barriers in this study. The findings
demonstrate that “Lack of client demand for adopting BIM (C8)”and “Lack of knowledge
(among SMEs) of BIM and its benefits (C1)”were the most significant barriers with weight
values of 0.22 and 0.20 (Table 7). Other cultural barriers were evaluated with weight values
less than 0.15, separating the top two from them. The findings related to barrier groups also
highlighted the significance of the cultural barrier category. Table 8 shows that cultural (C)
and resource (R) related barriers were the most significant barriers, assessed with weight
values of 0.38 and 0.28, respectively.
4.2 Expert consistencies
Consistency analysis has widely been regarded as one of the most crucial attributes of AHP
analysis. Table 9 shows the CR values of each expert with respect to each barrier category.
The findings highlight that all the experts were found consistent in terms of their inner
Barrier groups/Experts E1 E2 E3 E4 E5 E6 E7 E8 Overall weight Rank
T 0.09 0.13 0.05 0.05 0.38 0.06 0.12 0.09 0.12 4
G 0.09 0.14 0.11 0.53 0.05 0.48 0.04 0.33 0.22 3
R 0.74 0.29 0.21 0.31 0.07 0.12 0.30 0.22 0.28 2
C 0.09 0.45 0.63 0.12 0.51 0.34 0.54 0.36 0.38 1
Barriers/Experts E1 E2 E3 E4 E5 E6 E7 E8 Overall weight Rank
C1 0.38 0.34 0.02 0.21 0.17 0.14 0.20 0.10 0.20 2
C2 0.04 0.11 0.05 0.14 0.09 0.11 0.20 0.22 0.12 4
C3 0.02 0.06 0.02 0.02 0.05 0.03 0.04 0.06 0.04 5
C4 0.03 0.04 0.03 0.06 0.02 0.03 0.03 0.08 0.04 5
C5 0.08 0.22 0.07 0.22 0.19 0.06 0.13 0.07 0.13 3
C6 0.15 0.15 0.17 0.02 0.35 0.05 0.05 0.05 0.13 3
C7 0.24 0.04 0.20 0.05 0.11 0.13 0.18 0.11 0.13 3
C8 0.06 0.04 0.44 0.29 0.02 0.45 0.17 0.30 0.22 1
Barriers/Experts E1 E2 E3 E4 E5 E6 E7 E8 Overall weight Rank
R1 0.22 0.12 0.13 0.07 0.07 0.11 0.07 0.11 0.11 3
R2 0.72 0.20 0.13 0.72 0.73 0.11 0.68 0.58 0.48 1
R3 0.07 0.68 0.75 0.22 0.20 0.78 0.25 0.31 0.41 2
Barriers/Experts E1 E2 E3 E4 E5 E6 E7 E8 Overall weight Rank
G1 0.07 0.45 0.40 0.31 0.20 0.24 0.21 0.27 0.27 3
G2 0.07 0.12 0.09 0.05 0.11 0.05 0.11 0.04 0.08 4
G3 0.14 0.26 0.36 0.07 0.65 0.09 0.14 0.60 0.29 2
G4 0.73 0.17 0.16 0.58 0.05 0.62 0.54 0.09 0.37 1
Table 8.
AHP results in terms of
barrier groups
Table 7.
AHP results in terms of
culture and knowledge
barriers
Table 6.
AHP results in terms of
resource barriers
Table 5.
AHP results in terms of
governmental (legal)
barriers
Multi-criteria
analysis
of barriers
to BIM
judgments in each barrier category. Due to the smaller number of barriers, experts were more
consistent in resource category and barrier groups, such that CR values ranged between
0 and 0.04 in both categories. In contrast, despite all experts being consistent, the extent of
their consistency was the lowest in the culture and knowledge category (C) (ranging between
0.07 and 0.1) owing to the assessment of eight barriers. In addition to the consistency analysis
in the AHP application, a non-parametric statistical test, namely Kendall’s W, was also
performed to ensure the consistency of experts’overall evaluations. Table 10 shows that there
are concordances in experts’evaluations in each barrier category since
χ
2were found as
higher than
χ
2
crit. Kendall’s W was calculated as 0.328, 0.513, 0.439, 0.363 and 0.365, for
technological, governmental, resource, culture and knowledge, and barrier groups,
respectively. The findings show that overall assessment can be considered reliable since
the assessment of experts were similar to each other.
5. Discussion
Pertinent literature emphasized the importance of the exploration of the barriers to BIM and
digitalization faced by SMEs. Therefore, probing into the problems and challenges that are
encountered in the BIM implementations is essential as suggested by various researchers
(Awwad et al., 2020;Hosseini et al., 2016;Saka and Chan, 2020a). The current research mainly
focalized on the list of barriers/challenges, of which some were selected and modified to
ensure direct relevance to the target audience, i.e. the SMEs in New Zealand. The data from
the survey provided industry relevant data on the significance and the rank of barriers that
the respondents believe to be the most important factors that are hindering the adoption of
BIM and therefore the digitalization of SMEs in the construction industry. Here, the survey
broke the barriers faced by SMEs into four categories, i.e. technological (T) barriers,
governmental (legal) barriers (G), resource (human and cost) barriers (R), and culture and
knowledge barriers (C). The following sub-sections provide the discussions regarding the
analysis results in terms of the above-mentioned four classes of barriers.
5.1 Technological barriers
The results demonstrate that there is still major concern about the interoperability issues
with existing software packages (T1) that are used by the industry. Accordingly, the wide
Barrier category E1 E2 E3 E4 E5 E6 E7 E8
Technological 0.02 0.01 0.05 0.04 0.07 0.02 0.01 0.04
Governmental (legal) 0.04 0.03 0.01 0.06 0.05 0.04 0.03 0.08
Resource 0.04 0.03 0.00 0.04 0.01 0.00 0.01 0.00
Culture and knowledge 0.09 0.08 0.10 0.09 0.09 0.08 0.07 0.10
Barrier groups 0.00 0.02 0.04 0.01 0.04 0.03 0.03 0.03
Barrier category Kendall’sW
χ
2
χ
2
crit
χ
2>
χ
2
crit
Technological 0.328 10.506 9.488 Yes
Governmental (legal) 0.513 12.3 7.815 Yes
Resource 0.439 7.022 5.991 Yes
Culture and knowledge 0.363 20.333 14.067 Yes
Barrier groups 0.365 8.756 7.815 Yes
Table 9.
Consistency ratio (CR)
of expert judgments
Table 10.
Consistencies among
experts
ECAM
variety of file formats and a lack of mandating for IFC files results in multiple project
platforms in use at one time demands a wide skillset to manage the different packages, along
with an increased software subscription cost incurred to the SMEs (Vidalakis et al., 2020).
Ayinla and Adamu (2018) also highlighted the technology and interoperability as a serious
problem that requires significant attention to promote BIM adoption. In addition, a lack of
access to digital tools (T3) such as tablets on the construction site is still a considerable issue
and mirrors the findings of Berger (2016) who reported that over 50% of workers on site do
not have the access to the digital tools which they require on construction sites. The
obsolescence of tools has become a significant concern, particularly for SMEs where the cost
of investment relative to cashflow is significantly higher, and the return on investment (ROI)
of these tools is realized over a short timeframe, such as 2–3 years for digital devices
(VanDerHorn and Mahadevan, 2021;Sategna et al., 2019). From the opposite perspective,
Seyis (2019) found that promoting interoperability is among the most significant benefits of
BIM adoption.
On the other hand, other new and disruptive technologies such as linked data, IoT or cloud
technologies also depend on the BIM technology/maturity level used in construction
companies. There are some industry-related technological inputs/outcomes such as use of
sensors, IoT, cloud services, semantics and transactions as part of smart construction
(HM Government - Digital Built Britain, 2015) that are better implemented if the company
adopts a BIM at the desired level. This can also be related to the limitations of the BIM Level 2,
particularly pertaining to the model integration, combining several types of data, and
knowledge management in databases and/or cloud-based solutions, which are among the
primary focus of BIM Level 3 (HM Government - Digital Built Britain, 2015). It is therefore
recommended that linked data (semantics) or cloud technologies can be integrated to reduce
the investment in development or enhance the usability of the software.
5.2 Governmental (legal) barriers
While the results indicate that governmental factors are still a barrier to the widespread
adoption of BIM, they are only the third most significant. Of these, a lack of government
initiative mandating BIM use on projects (G4) is considered by the respondents to be the
greatest barrier hindering adoption, and therefore the decision makers need to pay significant
attention to mitigate the impacts of these challenges through incentive-based approaches
(ASITE, 2020). At this point, careful consideration needs to be given to the digital divide that
will occur upon mandating use, where those that already have adopted BIM and tailored it to
their workflow will gain more benefit than slow adopters or those forced into the technology.
This is particularly important because SMEs and large companies operate in different
markets and must perform in different ways to adapt to the changes as the companies
inherently need access to different sources of knowledge and the ability to leverage different
technology to succeed to drive successful changes (Sexton et al., 2006). The relevant
authorities should seriously take into account the fact that SMEs having relatively more
initial resources can be early adopters of BIM, gaining more skills and allowing them to
strengthen their competitive position (Dainty et al., 2017). This can increase inequality in the
market, resulting in less competition between SMEs. As a result, BIM could have the potential
to divide the market more than it intends to integrate.
5.3 Resource barriers
The findings show that the high cost of acquiring the software and licensing to use BIM (R2)
is the greatest barrier preventing its widespread adoption (Azhar, 2011;Hosseini et al., 2016).
This contrasts the research conducted by Makowski et al. (2019) which suggest that the
financial aspect of the BIM transformation is one of the least concerning issues for the
Multi-criteria
analysis
of barriers
to BIM
transformation of these companies, even though SMEs generally have lower cash flows and
as a result, reduced investment capabilities into new emerging technology (Ramilo, 2014). The
high cost of software and licensing is also correlated with various technological barriers, such
as the perception that the software market does not satisfy the requirements of SMEs
(ASITE, 2020). One can also argue that if the software satisfied the requirements of the SMEs,
then the cost may be a less prohibitive issue as the SME is able to utilize more of the software
features, generating a higher perceived value (Parida et al., 2019). Large software companies
such as Autodesk, Trimble and Graphisoft currently design several products for complex
and demanding projects that are undertaken by large main contractors and consultants.
Software companies generally dedicate more resources to satisfy the needs of larger clients;
however, it is important that the requirements of SMEs are not disregarded (Makowski et al.,
2019). Besides, a reported lack of skilled employees (R3) within the industry is still a major
factor in New Zealand, correlating with data gathered by the EBOSS survey, however as the
BIM adoption becomes more prevalent, this barrier is expected to reduce in significance.
5.4 Culture and knowledge barriers
The results acquired within the scope of this research illustrated that a lack of client demand
(C8), followed by a lack of knowledge amongst SMEs of the benefits to BIM (C1) are the two
most significant barriers in the current category; however, many other barriers are inter-
related (Vidalakis et al., 2020). For example, an SME with a lack of knowledge around the
benefits of BIM will also lack an understanding of how they can adapt to new, more efficient
procedures and how these can be leveraged to enhance their position in the market. The
findings obtained according to the AHP results are also in line with the previous attempts;
such that a lack of knowledge and expertise on BIM was found as one of the greatest barriers
faced by the BIM adoption in many other studies (Khosrowshahi and Arayici, 2012). On the
other hand, Hosseini et al. (2016) highlighted that a lack of knowledge and awareness of BIM
is not an influential barrier for Australian SMEs, especially down from the last decade. Still,
this contradiction can be explained by the fact Australia has been having exposure to BIM for
a slightly longer period than New Zealand, leading to a higher-level use within companies.
While the level of BIM maturity and level of use in New Zealand is increasing, SMEs are
currently slow to adopt the new technology (EBOSS, 2020). The findings emerged from this
study also support evidence from previous attempts (Berger, 2016) highlighting that the level
of digital maturity of the SME (C6) is another significant barrier to the adoption of the BIM.
Accordingly, it should be noted that a company is in its early stages of the BIM integration
needs to struggle to realize the additional benefits that advanced levels (5D BIM and/or 6D
BIM) can provide (Wyman, 2018). In addition, the findings of this research are also confirmed
by Hosseini et al. (2016), in which they emphasized that a lack of interest from parties
including the client, and other sub-contractors working for the SMEs are the main barriers to
the widespread adoption of the BIM. There is still regrettably a negative stereotype within the
industry that views BIM as only being compatible with large companies. To help combat this
view, more positive publicity with examples of BIM utilization from the perspective of SMEs
needs to be accomplished (Makowski et al., 2019).
6. Conclusion
This research chiefly aims at the identification of barriers to BIM implementation for SMEs in
New Zealand construction industry. To accomplish this objective, one of the well-known
MCDM methods, the AHP was employed to accommodate the main research question. Thus,
a systematic literature survey was conducted to determine the barriers and challenges in BIM
implementation and a focus group discussion was considered to finalize the list of criteria
ECAM
utilized within the scope of the present study. A total of 20 criteria were divided into four main
categories in terms of technological (5), governmental (4), resources (3), and culture and
knowledge barriers (8), and subjected to the AHP algorithm. The criteria weights were
assigned and the importance of each criterion was determined in this regard. To ensure the
consistency of the judgments and the robustness of the attained results, consistency analysis
(in the AHP algorithm) was checked to ensure the internal consistencies, while Kendall’s
coefficient of concordance test was adopted to ensure the agreement level of overall expert
evaluations. As a result of the analysis, culture and knowledge barriers outperformed among
their counterparts in terms of the highest weight of criterion, followed by the resources
barriers and governmental barriers, whereas the technological barriers category had the
lowest weight. Regarding overall evaluations, a lack of client demand for BIM use, lack of
knowledge around BIM and its benefits, high cost of acquiring the software and licensing,
interoperability between software platforms, and a lack of government initiative in
mandating the use of BIM at a project level were found as the key barriers that require the
utmost attention. In addition, all the judgments of the experts were provided consistent
results to each other according to the employed Kendal’s W test. It is worth mentioning that
this study is the first quantitative attempt within New Zealand focusing on the SMEs in the
construction sector. Overall, even though the culture and knowledge category of barrier is the
most significant, many of the barriers are inter-related and by solving one, others may
inadvertently be resolved. Therefore, the following recommendations can be made in the light
of the conclusions made according to the results of the present study:
(1) An increase in publicity of BIM usage on projects throughout New Zealand is
essential, particularly where SMEs have been involved. The positive publicity will
allow a wide range of SMEs to visualize and understand how BIM was leveraged to
enhance the deliverables and outcomes of the project. This will enhance the
understanding of BIM throughout the industry.
(2) Software companies can ensure that they are catering to the needs of SMEs in
addition to large companies. This requires feedback from SMEs on what they require
from the packages. This will ensure that SMEs, who often have limited resources, pay
for a product that is of value to their business model, rather than having additional
features included in the license fees, but not utilized or required by their business.
(3) The government should further investigate overseas mandates, such as requirements
for all publicly funded infrastructure projects to use BIM (such as in the UK). A top-
down approach would drive the BIM adoption, but further investigation and caution
are recommended to ensure all companies have access to the required skills and
support to stop the inequality in the market that may arise through BIM mandates.
Despite this study contributed a lot to the body of knowledge, the present research is not free
of limitations. For instance, one can treat with some caution due to the limited scope of SMEs
working in New Zealand. In addition, including more experts can be considered for future
research directions as the acquired results significantly depend upon the subjective
judgments of the distinct group of respondents. The precise mechanism of inter-relationships
among criteria remains to be elucidated. Therefore, further attempts may cover the
application of different MCDM algorithms, i.e. analytical network process (ANP) and
decision-making trial and evaluation laboratory (DEMATEL), ensuring the exploratory
nature of the criteria relationships. Future research investigating BIM barriers from a case
study on a large public infrastructure project in New Zealand would provide specific evidence
of the barriers faced by SMEs and can provide insight on some of the methods used to
overcome the issues.
Multi-criteria
analysis
of barriers
to BIM
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Corresponding author
Serdar Durdyev can be contacted at: durdyevs@ara.ac.nz
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Multi-criteria
analysis
of barriers
to BIM
