Key competencies for identifying construction activities that produce recyclable materials: an exploratory study

Abstract

Purpose
Construction activities generate overwhelming waste that is typically disposed of in landfills, which has significant environmental consequences and hinders national progress. However, with the appropriate competencies, there is an opportunity to identify construction activities that produce recyclable materials, offering a path to a sustainable future. This study aims to assess the competencies for identifying construction activities that produce recyclable materials. To attain that aim, the study seeks to identify the key competencies and assess the index level of the competencies.

Design/methodology/approach
A systematic literature review was conducted, and 20 competencies were identified and categorized into knowledge, skills, and abilities. A questionnaire survey was developed based on the competencies and completed by 101 individuals. The collected data were analyzed using normalized mean analysis, confirmatory factor analysis, and fuzzy synthetic evaluation (FSE).

Findings
The results revealed that the key competencies are problem-solving skills, communication skills, skills in providing vocational training, and knowledge of the environmental impacts of construction activities. The FSE ranks the constructs in order of skills, knowledge, and abilities. Also, the FSE illustrated that the overall index level is inclined to be important.

Practical implications
This study leads to saving natural resources, using raw materials efficiently, protecting from environmental pollution, and mitigating resource depletion by providing the index level of the competencies.

Originality/value
The findings can guide professionals in effective waste management, policymakers in creating new policies and regulations, and researchers in compiling a list of competencies for identifying construction activities that produce recyclable materials.

Full text

International Journal of Building Pathology and Adaptation
Key Competencies for Identifying Construction Activities
that Produce Recyclable Materials: An Exploratory Study
Journal:
International Journal of Building Pathology and Adaptation
Manuscript ID
IJBPA-10-2023-0148.R3
Manuscript Type:
Original Article
Keywords:
Construction waste, Construction activities, Recyclable Materials, Key
competencies, Exploratory study
International Journal of Building Pathology and Adaptation

International Journal of Building Pathology and Adaptation
1
Key Competencies for Identifying Construction Activities that Produce
Recyclable Materials: An Exploratory Study
Abstract
Purpose ─ Construction activities generate overwhelming waste that is typically disposed of in
landfills, which has significant environmental consequences and hinders national progress. However,
with the appropriate competencies, there is an opportunity to identify construction activities that
produce recyclable materials, offering a path to a sustainable future. This study aims to assess the
competencies for identifying construction activities that produce recyclable materials. To attain that aim,
the study seeks to identify the key competencies and assess the index level of the competencies.
Design/methodology/approach ─ A systematic literature review was conducted, and 20 competencies
were identified and categorized into knowledge, skills, and abilities. A questionnaire survey was
developed based on the competencies and completed by 101 individuals. The collected data were
analyzed using normalized mean analysis, confirmatory factor analysis, and fuzzy synthetic evaluation
(FSE).
Findings ─ The results revealed that the key competencies are problem-solving skills, communication
skills, skills in providing vocational training, and knowledge of the environmental impacts of
construction activities. The FSE ranks the constructs in order of skills, knowledge, and abilities. Also,
the FSE illustrated that the overall index level is inclined to be important.
Practical implications ─ This study leads to saving natural resources, using raw materials efficiently,
protecting from environmental pollution, and mitigating resource depletion by providing the index level
of the competencies.
Originality/value ─ The findings can guide professionals in effective waste management,
policymakers in creating new policies and regulations, and researchers in compiling a list of
competencies for identifying construction activities that produce recyclable materials.
1. Introduction
Construction activities in any project, either small or large size, continually generate massive
amounts of waste (Eze et al., 2022). Construction waste can negatively impact the community
and cause significant issues in different areas globally (Marrero et al., 2017). For instance,
construction waste releases pollutants into the air and water, resulting in health issues among
communities (Ferronato et al., 2021). Furthermore, improper disposal of construction waste
results in environmental issues, including soil contamination, habitat destruction, and disruption
of natural drainage (Kolaventi et al., 2020). Improper disposal of construction waste also affects
the economy by depleting national resources from the need for new raw materials (Bao & Lu,
2021). Therefore, managing construction waste is crucial to avoiding community,
environmental, and economic issues.
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Recycling is one approach to managing construction waste responsibly and sustainably.
However, construction waste recycling (CWR) rates remain low in many countries (Islam et al.,
2019). This low rate is a missed chance to mitigate the negative impacts of construction waste
and realize the economic rewards as well as the environmental benefits of recycling. Also, these
low rates indicate that construction professionals tend to focus on disposing of construction
waste rather than recycling (Wu et al., 2020). This tendency is due to a lack of competencies in
CWR (Bao & Lu, 2021). As a result, construction projects are losing the benefits of CWR,
including producing recycled materials (Bao & Lu, 2021). Furthermore, the lack of
competencies in CWR can lead to higher project costs and increased environmental impacts
(Omer et al. 2022a). In this regard, having the necessary competencies in CWR is valuable for
reducing the negative impact of construction projects.
Competencies are crucial for project success (Ahsan et al., 2013). Specifically, assessing
the competencies of project members is crucial for improving project performance
(Pamidimukkala & Kermanshachi 2013). International Project Management Association (IPMA)
defined competencies as ‘the application of knowledge, skills, and abilities (KSA) to achieve
the desired results’ (IPMA, 2015). Having the right competencies is crucial for sustainable
construction. For example, Cárcel-Carrasco et al. (2021) highlighted the role of knowledge in
understanding the negative impact of waste produced during construction activities on the
environment. Furthermore, Hegazy et al. (2020) identified that specific skills are required for
managing construction activities that produce a high rate of waste. Finally, Hu et al. (2021)
advocated the importance of having the ability to prepare construction activity plans that
produce minimal waste. In this context, having the appropriate competencies can lead to a deep
understanding of construction waste from different perspectives, including regulation, safety,
and management (Aslam et al., 2020). Therefore, having the competencies to identify
construction activities that produce recyclable materials is highly needed.
This study aims to assess the competencies for identifying construction activities that
produce recyclable materials. To achieve that aim, the study seeks to identify the key
competencies and assess the index level of the competencies. The findings can assist industry
professionals and policymakers in managing construction activities that produce recyclable
materials, thereby supporting efficient waste management within construction projects.
Furthermore, this study provides researchers with a fresh list of competencies for in-depth
exploration. The importance of this study lies in providing the key and level of importance of
the competencies. To the authors’ knowledge, this is the first study that assesses the importance
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of competencies in identifying construction activities that produce recyclable materials.
Therefore, the study provides a foundation that paves the way to increase the production of
recyclable materials that can be used in existing or future construction projects.
2. Research background
2.1 Construction activities that produce recyclable materials
Construction activities generate waste that contains different materials that can be recycled. For
instance, creating new concrete can generate waste that can be used as recycled aggregates (Ma
et al., 2022). Project stakeholders need well-managed construction activities to benefit from the
generated waste (Islam et al., 2019). The design stage can facilitate managing construction
projects by prioritizing the usage of recyclable materials (Kabirifar et al., 2020). Besides, proper
planning can ease the identification of recyclable materials during construction. It also aligns
with the need to gain better enhancement of environmental, economic, and social objectives
(Kabirifar et al., 2020). In contrast, construction activities that do not produce non-recyclable
materials lead to being disposed of in landfills, which raises waste management problems (Wang
et al., 2023). At the same time, this will lead the practitioners to skip identifying construction
activities that produce recyclable materials. In this context, adopting recycled materials in the
design of construction projects reflects growth in the demand for sustainable products
(Shooshtarian et al., 2020). Therefore, supporting construction activities by using recycled
materials is crucial.
2.2 Competencies in theory: The concept of knowledge, skills, and abilities
According to the Organization for Economic Co-operation and Development (OECD),
competencies consist of three components – knowledge, skills, and abilities (OECD, 2003).
Furthermore, Employment and Social Development Canada also emphasized that competencies
consist of the same three components (i.e., knowledge, skills, and abilities) (Employment and
Social Development Canada, 2023). Also, IPMA acknowledged that competencies consist of
knowledge, skills, and abilities (IPMA, 2015). Furthermore, recent works in construction project
management also used KSA as the underlying component of competencies (e.g., Bademosi &
Issa, 2022; Kesavan et al., 2021; Pathuri et al., 2022). In summary, international and national
associations, as well as recent research, recognize KSA as the underlying component of
competencies.
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2.3 Knowledge, skills, and abilities related to construction waste recycling
2.3.1 Knowledge. Prior research suggests that knowledge is necessary to identify construction
activities that produce recyclable materials in practice (Baldwin et al., 2007). Without the proper
knowledge, project team members might overlook the reusability of the waste as recyclable
materials. Furthermore, knowledge of construction waste clauses in contract documents can
assist in using recyclable materials (Ajayi et al., 2017). However, these clauses may be disabled
because of the abilities and skills limitations of construction workers. Eadie et al. (2013)
mentioned that knowledge of building information modeling (BIM) can also assist in identifying
construction activities that produce recyclable materials.
2.3.2 Skills. Zubir et al. (2021) highlighted the importance of having the required skills to
identify construction activities that produce recyclable materials. Specifically, Botchway et al.
(2023) and Johari & Jha (2021) advocated that communication skills can facilitate the
identification process. Furthermore, although most architects focus on building aesthetics,
design skills do play a crucial role in identifying construction activities that produce recyclable
materials (Dokter et al., 2021). Finally, Amoah & Bikitsha (2021) indicated the importance of
planning skills in managing, including identifying construction activities that produce recyclable
materials.
2.3.3 Abilities. Finally, prior research has also investigated abilities related to CWR. Prior
research has found that the ability to establish project goals that align with sustainable
construction is crucial for developing effective recycling procedures (Wang et al., 2014).
Furthermore, the ability to align different design options with potential construction waste
outputs is also crucial (Wang et al., 2015). The ability to integrate advanced technologies among
construction workers is also crucial to recycling construction waste (Ganiyu et al., 2020).
Without such ability, construction workers are limited in using advanced technologies for CWR.
The ability to differentiate material types for construction material procurement is also crucial
in CWR (Kar & Jha, 2022). Finally, the ability to use BIM can highly support CWR in projects
(Ganiyu et al., 2020).
2.4 Research Gap
Although there is research on different competencies for managing construction activities, the
existing body of knowledge on competencies for identifying construction activities that produce
recyclable materials is still limited. This gap can lead to increased waste generation in
construction projects. The gap also contributes to the loss of benefits associated with CWR,
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including producing recycled materials. In other words, the current literature lacks information
that can assist professionals and policymakers in managing construction waste efficiently. For
this purpose, this study aims to assess the competencies for identifying construction activities
that produce recyclable materials.
3. Methodology
3.1 Systematic literature review
A systematic literature review (SLR) was employed to identify the competencies for identifying
construction activities that produce recyclable materials. SLR is a distinguished literature review
approach as it involves a clear and rigorous method, which helps mitigate bias and allows future
replications (Moher et al., 2015). The SLR was conducted as per the guidelines outlined in the
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), as it is a
widely accepted and comprehensive standard procedure for conducting SLRs (Moher et al.,
2015). As a result, the scoping, planning, searching, screening, and eligibility procedures were
implemented during the SLR. The Scopus database was used as it is massive compared to other
databases (Kumar et al., 2021). The search string used for the search was TITLE-ABS-KEY
(construction AND waste AND management AND skill* OR knowledge OR abilit*).
The usage of ‘construction,’ ‘waste,’ ‘management,’ ‘knowledge,’ ‘skills,’ and ‘abilities’
as the search terms aimed to encompass all papers incorporating those words in the title, abstract,
and keywords. This selection of search terms ensures a comprehensive coverage of papers that
align with the adopted search string. Additionally, the asterisk symbol ‘*’ was used to emphasize
the inclusion of related keywords (e.g., ability or abilities). Also, the search string was not
limited to a specific period. The search identified 898 documents, and several requirements were
used to filter the documents. The process began by filtering to journal articles. It is also followed
by a filter to subject areas of engineering, economics, econometrics and finance, social sciences,
business, management and accounting, and decision sciences. As a result, 258 documents were
identified. The titles, abstracts, and keywords of the documents were scrutinized to assess their
relevance. A total of 68 articles were selected for further investigation based on their potential
contribution to the study. Of the 68 documents, 23 were included in the final list of articles. A
total of 20 competencies were identified from the 23 articles and categorized into KSA. The
final list of the competencies includes 6 for knowledge, 9 for skills, and 5 for abilities (see
Appendix 1).
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3.2 Survey development
The list of competencies was used to design a survey comprising three sections. The first section
is the cover letter of the survey, and the second section aims to collect individual and
organizational backgrounds of the respondents. The third section contains the competencies,
which involve acquiring the thoughts of the respondents on the importance of the competencies
using Likert scales. Albaum (1997) characterizes the Likert scale as a suitable approach for
measuring the thoughts of survey respondents. A five-point Likert scale was used: 1 = not at all
important, 2 = slightly important, 3 = moderately important, 4 = important, and 5 = extremely
important. Armstrong (1987) advocated that five-point Likert scales are suitable for improving
data reliability and minimizing social desirability bias. Before distributing the survey to the
target population, a pilot study was conducted with five experts, two from academia and three
from industry, to confirm the applicability of the survey. The authors invited experts with
doctorate degrees and appropriate years of experience to comment on the clarity and
understandability of the survey. Finally, after multiple rounds of revising per the expert
comments, the survey was deemed ready to be distributed to the target population. The findings
from the pilot study indicate that the survey is clear, and respondents do not require assistance
in completing it.
3.3 Data collection
Having survey respondents with relevant expertise is crucial to ensuring that suitable and
knowledgeable respondents are involved. In this regard, individuals with a background in civil
engineering and industrial experience were the target population as these individuals have
valuable practical knowledge of the subject matter. Specifically, the study includes respondents
enrolled in the final year of a civil engineering undergraduate program. These individuals were
intentionally chosen for their exposure to the subject matter, facilitated by practical training.
This sets these respondents apart from others in the earlier stages with the same academic
background. The study conducted two discussion sessions to provide an overview of the study
aim and survey. These sessions ensure that respondents provide qualified survey responses.
Subsequently, the survey was distributed during the latter discussion session. A convenience
sampling method was employed to gather data from the targeted population due to its flexibility
in engaging potential respondents. Convenience sampling is an unrestrictive technique;
therefore, it was adopted to reach the survey respondents (Etikan et al., 2016). As the nature of
this study is exploratory, the convenience sampling technique is considered justifiable (Ferber,
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1977). The collected sample comprised 110 responses; however, with nine incomplete
responses, this study retained 101 valid responses for further data analyses.
3.4 Data analysis
3.4.1 Normalized mean analysis. The normalized mean analysis involves calculating the
normalized values (NVs) of all mean values between 0.000 and 1.000. Here, variables with the
highest mean values are transformed to 1.000, and variables with the lowest mean values are
transformed to 0.000. This study used the threshold of NV ≥ 0.600 to identify the key
competencies. Hu et al. (2015) noted that the threshold of NV ≥ 0.600 represents the third level
on a five-point Likert scale. In addition, the threshold has been used in prior research in
construction project management to denote critical items (Omer et al., 2022a, 2022b). Equation
1 provides the formula used for the normalized mean analysis.
NV
=
Mean value of the competency
-
Minimum mean value
Maximum mean value
-
Minimum mean value
(1)
3.4.2 Confirmatory factor analysis. Confirmatory factor analysis (CFA) is a statistical technique
for validating the interrelationships between parameters. It is also frequently applied to examine
the underlying structure of a given set of variables concerning a given hypothesis (Hair et al.,
2021). In addition, CFA allows exploring the degree to which the measured variables accurately
represent the latent variables. The CFA was performed to verify the validity of the constructs
(i.e., KSA) (Hair et al., 2021). The CFA is applied using the partial least squares structural
equation modeling (PLS-SEM), considering the total sample size is 101 responses. This sample
size was considered satisfactory because more than 30 survey samples were needed to conduct
the statistical analyses (Field, 2013). Also, the sample has more than 100 responses to be
considered sufficient for performing CFA using PLS-SEM (Hair et al., 2021). PLS-SEM was
selected to explore theories in this study as it performs better with complex models and small
samples and has more flexibility (Hair et al., 2021). Therefore, the CFA was performed via PLS-
SEM using the measurement assessment as well as convergent and discriminant validity
analysis.
3.4.3 Fuzzy synthetic evaluation. The fuzzy synthetic evaluation (FSE) is a fuzzy logic method
that can be used as a multi-criteria decision-making procedure in many fields (Xu et al., 2010).
It is considered acceptable compared to the traditional weighted method due to its potential for
making subjective judgments, which are part of how humans think (Sadiq & Rodriguez, 2004).
Also, FSE enables the accommodation of fuzziness in survey responses by converting linguistic
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scales into fuzzy numbers (Sadiq & Rodriguez, 2004). Boussabaine (2013) advocated that fuzzy
set theory is well-suited for analyzing data that is affected by inherent fuzziness. Furthermore,
prior research employed FSE in conjunction with descriptive statistics, such as mean value, to
rank the constructs and determine their overall assessment (Farouk et al., 2023; Omer et al.,
2024; Radzi et al., 2024). In this regard, the FSE is used to assess the index level for each
construct of the competencies (i.e., KSA). Also, this study used FSE to assess the overall index
level of the competencies. Dahalan et al. (2023) noted that the FSE can be conducted by
following the steps below:
(1) Computing the weightings. The FSE requires computing the weightings for each
competency and their constructs (i.e., KSA). The weightings can be calculated by using
Equations 2 and 3. The weightings for each competency and construct have a significant role in
providing the accuracy of the FSEs findings. An explanation of Equation 2 is as follows:
Wi
=
M
i
∑
5
i
=
1
M
i
(2)
Where Wi is the weighting, Mi is the mean value, and ∑Mi is the sum of the mean value
for the full set of competencies. Therefore, the weighting function set is following:
W
i
=
(
W
1
,
W
2
,
W
3
,……………,
W
n
)
(3)
(2) Computing the membership functions. The membership functions (MFs) for each
competency and construct (i.e., KSA) can be obtained using the grading alternatives. The
formula for computing MFs can be used in Equation 4. In this regard,
A
1
= not at all important,
A
2
= slightly important,
A
3
= moderately important,
A
4
= important,
A
5
= extremely important.
MF
u
in
=
X
1
u
in
A
1
+
X
2
u
in
A
2
+
X
3
u
in
A
3
+
X
4
u
in
A
4
+
X
5
u
in
A
5
(4)
Also,
MF
𝑢
𝑖𝑛
is the MFs of a given competency;
𝑢
𝑖𝑛
is the KSA;
X
1
u
in
(j=1,2,3,4,5) is the
percentage of the respondents who rated j for the importance of a particular competency, which
measures the grade of MF; and
X
j
u
in
A
i
is the connection between
X
j
u
in
and its degree alternative,
and symbol ‘+’ indicates the fuzzy set notation. Therefore, Equation 5 can reflect the MFs of a
particular competency for identifying construction activities that produce recyclable materials.
MF
u
in
=
(
X
1
u
in
+
X
2
u
in
+
X
3
u
in
+
X
4
u
in
+
X
5
u
in
)
(5)
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Each value of the MF ranges between 0 to 1, and the total of all values is equal to 1. The
MFs and the weightings for each competency were the base to generate the MFs for each
construct (i.e., KSA) (level 2) and overall constructs (level 1).
(3) Computing the multilevel and multi-criteria. The multi-criteria and multilevel could
be generated by following the results of previous equations for the MFs and the weightings.
Therefore, the MFs for the constructs (level 2) can be calculated using Equation 6 as follows:
R
i
=
|
MF
c
i1
MF
c
i2
MF
c
i3
MF
c
i4
⋯
MF
c
in
|
=
|
X
1
u
i1
X
2
u
i1
X
3
u
i1
X
4
u
i1
X
5
u
i1
X
1
u
i2
X
2
u
i2
X
3
u
i2
X
4
u
i2
X
5
u
i2
X
1
u
i3
X
2
u
i3
X
3
u
i3
X
4
u
i3
X
5
u
i3
X
1
u
i4
X
2
u
i4
X
3
u
i4
X
4
u
i4
X
5
u
i4
⋯
⋯
⋯
⋯
⋯
X
1
u
in
X
2
u
in
X
3
u
in
X
4
u
in
X
5
u
in
|
(6)
Regarding Equation 6 and the weightings for the set of competencies within their
constructs (i.e., KSA), the fuzzy matrix was calculated using the next equation:
D
i
=
W
i
●
R
i
=
(
w
1
,
w
2
,
w
3
,…,
w
n
)
(7)
D
i
=
W
i
●
R
i
=
(
w
1
,
w
2
,
w
3
,…,
w
n
) ●
|
X
1
u
i1
X
2
u
i1
X
3
u
i1
X
4
u
i1
X
5
u
i1
X
1
u
i2
X
2
u
i2
X
3
u
i2
X
4
u
i2
X
5
u
i2
X
1
u
i3
X
2
u
i3
X
3
u
i3
X
4
u
i3
X
5
u
i3
X
1
u
i4
X
2
u
i4
X
3
u
i4
X
4
u
i4
X
5
u
i4
⋯
⋯
⋯
⋯
⋯
X
1
u
in
X
2
u
in
X
3
u
in
X
4
u
in
X
5
u
in
|
(8)
=
(
d
i1
,
d
i2
,
d
i3
,…,
d
in
)
Where Di indicates the fuzzy evaluation matrix; Wi is the weighting of each competency
in any given constructs (i.e., KSA); Ri is the membership function of each construct, and ●
connotes the fuzzy composition operator; X1 till X5 are the MFs of the competencies under each
construct; and din refers to the membership degree of the grade alternative.
Overall index level
=
n
i
=
1
(
D
i
×
O
)
=
(
d
1
,
d
2
,
d
3
,
d
4
,
d
5
)
×
(
o
1
,
o
2
,
o
3
,
o
4
,
o
5
)
=
(
d
i1
×
o
i1
)
+
(
d
i2
×
o
i2
)
+
(
d
i3
×
o
i3
)
+
(
d
i4
×
o
i4
)
+
(
d
i5
×
o
i5
)
(9)
4. Results
4.1 Results for normalized mean analysis
Table 1 shows the mean values and NVs of the competencies. The means of each competency
was calculated into NVs to detect the key competencies. The results show that three skills and
one knowledge have NV values > 0.60. However, no ability has NV > 0.60. In other words,
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these three skills and one knowledge are the key competencies for identifying construction
activities that produce recyclable materials.
Table 1
4.2 Results for confirmatory factor analysis
The CFA has validated the suitability of KSA as constructs for the competencies in this study.
The findings of convergent validity show that the value of the reliability test using Cronbach’s
alpha is above the acceptable value of 0.70, indicating acceptable internal consistency
(Cronbach, 1951). Also, the outer loadings are between 0.793 and 0.924, greater than the
required minimum threshold value of 0.70 (Hair et al., 2021). The rho_a coefficient ranges
between 0.933 and 0.943, higher than the cutoff value of 0.70 (Dijkstra & Henseler, 2015). The
rho_c indicated that all values are above the benchmark value of 0.70 (Hair et al., 2021). At the
same time, the AVE value of all constructs is higher than the suggested threshold value of 0.50
(Hair et al., 2021). The summary of CFA values is shown in Table 2.
Table 2
Also, the discriminant validity results are shown in (Appendices 2 and 3). It was found
that for all the competencies, the values are higher than the benchmark for the HTMT and
overlap via cross-loading findings (Hair et al., 2021). Appendix 4 illustrates the results of the
CFA using PLS-SEM.
4.3 Results for fuzzy synthetic evaluation
4.3.1 Results for the weightings. As ‘K05’ has a mean value of 4.36, the weighting of K05 is
calculated as 0.172 using Equation 2. The remaining weightings of the competencies can be
calculated using the same calculation process. Also, the constructs of the competencies are
calculated using the same equation procedure. The weightings of the competencies and the
constructs are presented in Table 3.
4.3.2 Results for the membership functions of the competencies. The ratings obtained for K05
are 1% as ‘not at all important’; 0% as ‘slightly important’; 10% as ‘moderately important’;
38% as ‘important’; and 51% as ‘extremely important’ and was calculated using Equation 5.
Therefore, the MF for K05 is represented as (0.01, 0.00, 0.10, 0.38, 0.51). The MFs for the
remaining competencies were calculated using the same equation and presented in Table 3.
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4.3.3 Results for the multilevel and multi-criteria. The MFs produced in this stage are further
normalized via the weighting functions of the competencies within their specific construct
(Cknowledge) to produce the evaluation matrix. This was calculated through Equation 8 as follows:
D
i
=
(
0.172, 0.167, 0.167, 0.167, 0.166, 0.162) ●
|
0.01
0.00
0.10
0.38
0.51
0.01
0.02
0.14
0.38
0.45
0.02
0.01
0.15
0.35
0.47
0.02
0.01
0.16
0.33
0.48
0.01
0.00
0.16
0.41
0.42
0.02
0.03
0.17
0.37
0.41
|
=
(
0.01, 0.01, 0.15, 0.36, 0.47
)
Therefore, Di = (0.01, 0.01, 0.15, 0.36, 0.47). The fuzzy evaluation matrix for other
constructs is gathered following the same process. Table 3 presents the MFs of the competencies
as level 3, the constructs as level 2, and the overall constructs as level 1.
Table 3
The index level for the set of constructs (i.e., KSA) of the competencies and the overall
index level can be identified using Equation 9 as follows.
I
ndex level
C
Knowledge
=
[(0.01
×
1)
+
(0.01
×
2)
×
(0.15
×
3)
×
(0.36
×
4)
×
(0.47
×
5)]
=
4.270
I
ndex level
C
skills
=
[(0.02
×
1)
+
(0.02
×
2)
×
(0.13
×
3)
×
(0.31
×
4)
×
(0.53
×
5)]
=
4.320
I
ndex level
C
abilities
=
[(0.02
×
1)
+
(0.01
×
2)
×
(0.16
×
3)
×
(0.35
×
4)
×
(0.46
×
5)]
=
4.220
Overall index level
=
[(0.02
×
1)
+
(0.01
×
2)
×
(0.16
×
3)
×
(0.35
×
4)
×
(0.46
×
5)]
=
4.270
The results indicate that the index level for the skills is the highest, followed by
knowledge and ability. Also, the overall index level for the full set of competencies is inclined
to be important.
5. Discussion
5.1 Skills
The study findings suggest that skills are critical to identifying construction activities that
produce recyclable materials. Therefore, professionals leading the identification process must
fulfill the skills needed. The lack of skills, such as poor problem-solving skills, can cause
unexpected project issues (Yap et al., 2019). For example, there are often issues regarding using
mobile recycling machines in construction projects (Ghorbani, 2022). Addressing such issues
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requires specific skills, such as problem-solving skills, to handle them promptly. Having the
required skills hinders the need for problem-solving specialists (i.e., consultants) external to the
project. Moreover, communication skills are also beneficial for managing construction activities
that produce recyclable materials (Ingle & Mahesh, 2022). Communication skills also greatly
support the process of incorporating CWR into projects (Liu et al., 2022). For instance,
construction project managers can apply communication skills to coordinate plans and schedule
activities related to recyclable materials effectively. Furthermore, construction project managers
can also use communication skills to discuss contingency plans and negotiate alternative
recycled materials to minimize project disruptions in case of a shortage of raw materials
(Ghorbani, 2022). Therefore, having the required skills can help support the successful
completion of CWR processes.
5.2 Knowledge
Having the necessary knowledge of the environmental impacts of construction activities can
support the identification of construction activities that produce recyclable materials (Zhao,
2021). This knowledge can assist policymakers in reducing the negative environmental impacts
of construction activities. For example, disposing of construction waste at landfills can harm the
environment and the community (Eze et al., 2022). Islam et al. (2019) suggest that having prior
knowledge of identifying construction activities where recyclable materials could impede the
ongoing generation of construction waste at the project site. Fulfilling knowledge of
construction activities can decrease the risk and achieve sustainable handling of construction
waste. Being knowledgeable about the waste produced by construction activities is crucial for
identifying materials that can be recycled, reducing the reliance on natural resources, and
minimizing the overall environmental impact (Aslam et al., 2020). Also, obtaining knowledge
on construction waste generation allows project managers to understand the factors contributing
to waste generation. Also, the knowledge empowers the development of strategies to reduce
waste and increase recycling opportunities instead of disposing of it at landfills (Eze et al., 2022).
Therefore, possessing the related knowledge can assist professionals in proactively promoting
sustainable practices in construction projects.
5.3 Abilities
The acquisition of the ability to perform construction activities can ensure that the construction
process is well-organized and facilitates the identification and integration of recyclable materials
throughout the project (Bao & Lu, 2021). Also, the ability can enable the project managers to
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emphasize sustainability and adopt recyclable materials instead of raw materials. In addition,
the ability to prepare construction activities plans empowers project managers in developing
project plans that include specific objectives for using recyclable materials (Hu et al., 2023). For
example, tasks such as waste sorting and material tracking should be incorporated to ensure
recycling practices. This incorporation plays a significant role in achieving recyclable materials
identification practices. It also enables the identification of recyclable materials for further
actions such as recycling.
Obtaining the abilities, such as implementing lean construction techniques, can allow
for minimizing waste and maximizing the use of recyclable materials. It also comes into play
by ensuring that the project team adheres to the plan and incorporates recyclable materials as
intended (Li et al., 2022). For instance, workers with acquired abilities related to the execution
of construction activities can handle and install recyclable materials and provide potential
opportunities for recycling again (Aldana & Serpell, 2016). Properly sorting construction waste
on-site also enables the identification of recyclable materials for recycling. Also, preparing
sustainable designs empowers designers to contain recyclable materials in the project’s design
(Aldana & Serpell, 2016). Accordingly, the project team can use recyclable materials throughout
the project’s lifecycle. Therefore, acquiring abilities related to construction activities enhances
the integration of recyclable materials in a construction project.
5.4 Study implications
5.4.1 Practical implications. The study provides several implications for practitioners seeking
to advance sustainable construction practices. The study can open doors for professionals’
thoughts about offering recycled materials as an alternative source instead of natural resources.
For example, the list of competencies can help facilitate and promote the idea of having recycled
products by increasing the knowledge-related competencies (e.g., knowledge of the
environmental impacts of construction activities). Practitioners can understand the importance
of CWR and contribute to reducing the negative impacts of construction projects. Also,
acquiring skills-related competencies, such as problem-solving skills, can help practitioners
promptly respond to the need for sustainable construction site practices and avoid complexities
and delays. In addition, professionals can leverage the validated constructs of the competencies
as a framework to manage construction site activities effectively. For example, the framework
can help maximize the productivity of recycled products by specifying the competencies needed
to identify recyclable materials from each construct. Moreover, professionals can benefit from
the index level of FSE sequentially in terms of skills, knowledge, and abilities. This sequence
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can help professionals understand the need for sustainable management of construction
activities by prioritizing constructs with higher index levels. For instance, the index level of
skills is higher than knowledge and ability. Therefore, prioritizing skills over knowledge or
ability might be appropriate if prioritization is necessary. Thus, the study can benefit
professionals who want to manage construction activities sustainably.
5.4.2 Managerial implications. Project managers can use the list of competencies to advance
sustainable practices at construction sites. For instance, skills in providing vocational training
on detecting recyclable and non-recyclable materials at construction sites via self-assessment or
advanced technologies can enhance the production of recycled materials. Project managers can
use the framework based on the validated constructs (i.e., KSA) to guide construction activities
toward site sustainability goals. For example, the framework can underscore the importance of
identifying construction activities that produce recyclable materials in a manageable manner
through adopting KSA at construction sites. In addition, the index level obtained from FSE for
KSA can help prioritize the most important constructs and effectively identify construction
activities that produce recyclable materials. For example, the index level of the skills is the
highest; thereby, project managers can pay more attention to the list of skills to gather recycled
materials. Therefore, the study can aid project managers in managing construction activities
sustainably.
5.4.3 Theoretical implications. The study offers worthwhile theoretical implications for the
current body of knowledge. This study provides a novel list of competencies for identifying
construction activities that produce recyclable materials. This list can be used to extend the effort
toward more profound knowledge of sustainable construction practices. For example, future
research can employ the categorization of KSA to conduct a more in-depth investigation for
each category separately. Another example is that each competency can be a foundation for
deeper knowledge investigation by researchers. Furthermore, the list of competencies can be
used to generate guidelines for construction industry professionals. The framework in this study
can have notable implications, and future research can use it to gain further knowledge by
comparing different types of construction projects (e.g., high-rise and low-rise building projects)
or countries (e.g., countries with different income levels). Although the index level of ability is
the lowest, it can open a new opportunity for future research. For example, researchers can focus
on why the abilities are less important than other constructs. Therefore, this study is a rich
theoretical source for researchers to investigate further.
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5.4.4 Policy implications. The study provides worthwhile implications for policymakers toward
national policy enhancement regarding sustainable construction practices. Policymakers can
benefit from the list of competencies to support sustainable practices at construction sites. For
example, stakeholders can use the competency of ‘ability to prepare construction activities plan’
to ensure the implementation of sustainable practices in advance during construction activities.
Also, policymakers can develop policies relating to ‘knowledge on the environmental impacts
of construction activities’ to understand the importance of reducing the negative impacts on the
environment resulting from construction activities. Meanwhile, policymakers can use ‘skills on
trading construction waste materials’ to recommend generating policies that encourage and
enhance the trading of recycled materials in markets. Policymakers can adopt the output of the
CFA as a framework for guiding construction practices sustainably. For instance, stakeholders
can adopt the framework to guide their policy decisions regarding identifying construction
activities that produce recyclable materials by focusing on each construct of the competencies
to gain further advancement in the identification process. Also, the government can recommend
or force public and private organizations to adopt the framework and achieve the planned
policies to increase recycled materials in the construction industry. Policymakers can use the
validated constructs to continue exploring the competencies needed for identifying construction
activities that produce recyclable materials in future national plans. Therefore, the study
provides notable implications for different parties of policymakers to advance sustainable
construction practices.
5.4.5 Methodological implications. This subsection points out insightful methodological
implications of the study, which can benefit future research. Future research can adapt the search
string of the SLR in this study to extract and categorize the list of competencies into KSA. Also,
the search string can serve as a methodological foundation for conducting SLRs in research
aiming to categorize a list of competencies in other domains. In addition, the rigorous data
analysis procedures used in this study, including NV, CFA, and FSE, can offer methodological
insights for future research on different subject matter. These procedures can provide a sequence
of analysis and facilitate a more in-depth discussion and interpretation of the findings. Therefore,
the study offers valuable methodological implications that researchers can adopt or adapt in
future research.
5.5 Study limitations and future directions
The study has several limitations that can be further explored. First, the data were collected from
a single country, which may limit the generalizability of the study findings. Therefore, future
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research can repeat this study in other countries before using the findings. Furthermore, a
comparative analysis of findings between countries is possible and beneficial for gaining a
deeper understanding of the subject matter. Second, as this study is exploratory, the data
collection was limited to individuals with civil engineering backgrounds. Future research could
target respondents with other academic backgrounds (e.g., architects, quantity surveyors) for
comparison and generalizability. An in-depth understanding of the subject matter can help
develop unique procedures to reduce construction waste generation and increase the demand for
recycled products. Third, the study opted for the convenience sampling technique, which
involves selecting survey respondents who are readily accessible and available (Etikan et al.,
2016). However, considering the exploratory nature of this study, the convenience sampling
technique was deemed suitable (Ferber, 1977). As the convenience sampling approach may
introduce bias, this study took measures by selecting respondents with academic backgrounds
in civil engineering and industrial experience to ensure appropriateness. Furthermore, the
targeted respondents were invited to two discussion sessions to provide an overview of the study
and ensure the survey was understandable. Therefore, the study is still positioned to provide
valuable insights into the sustainable construction body of knowledge even with such limitations.
6. Conclusion
This study has successfully achieved its aim of assessing competencies for identifying
construction activities that produce recyclable materials. The aim was achieved through two
objectives: (1) identifying the key competencies and (2) assessing the index level of the
competencies. The study used SLR to identify a list of competencies and categorized them into
KSA. A survey was developed to assess the competencies and completed by industry
professionals. The study indicates that the key competencies are problem-solving skills,
communication skills, skills in providing vocational training, and knowledge of the
environmental impacts of construction activities. The FSE ranks the constructs in order of skills,
knowledge, and abilities. Additionally, the FSE assessed that the overall index level of the
competencies tends to be important.
The study indicates that competencies are crucial for identifying construction activities that
produce recyclable materials. Also, the findings highlight the importance of understanding the
competencies needed for sustainable practices in the construction industry. Moreover, it
highlights the need for policymakers to formulate policies and regulations for overseeing
construction activities that produce a high rate of waste. The highlights for the related parties
aid in realizing the advantages of CWR. In summary, this study contributed to the existing body
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of knowledge by providing valuable insights into the competencies needed to identify
construction activities that produce recyclable materials.
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Table 1. Results for normalized mean analysis
Codes
Competencies
Mean
SD
NV
CR
OR
─
Knowledge
─
─
─
─
─
K05
Knowledge on the environmental impacts of construction activities
4.386
0.748
0.667
1
3
K06
Knowledge on the practice of sustainable facilities management
4.248
0.841
0.308
2
9
K02
Knowledge on the sustainable characteristics of construction activities
4.248
0.888
0.308
3
9
K03
Knowledge on construction-related waste design
4.248
0.899
0.308
4
9
K01
Knowledge of waste produced by the construction activities
4.238
0.789
0.282
5
10
K04
Knowledge on diagnosing construction waste generation
4.129
0.934
0.000
6
15
─
Skills
─
─
─
─
─
S04
Problem-solving skills
4.515
0.756
1.000
1
1
S05
Communication skills
4.495
0.795
0.949
2
2
S02
Skills on providing vocational training (e.g., safety on worksites)
4.366
0.880
0.615
3
4
S01
Project management skills
4.356
0.855
0.590
4
5
S07
Skills on planning construction activities (e.g., preparing construction sites)
4.297
0.843
0.436
5
6
S03
Skills on lean construction techniques
4.279
0.933
0.436
6
6
S09
Interpersonal skills (e.g., relationship management, self-management)
4.277
0.873
0.385
7
7
S06
Skills on trading construction waste materials
4.139
0.895
0.026
8
13
S08
Skills on designing out waste in construction activities (e.g., using resources
efficiently)
4.139
0.884
0.026
9
13
─
Abilities
─
─
─
─
─
A05
Ability to prepare construction activities plan
4.297
0.807
0.436
1
6
A02
Ability to implement lean construction techniques
4.267
0.893
0.359
2
8
A03
Ability to execute construction activities
4.208
0.898
0.205
3
11
A04
Ability to organize individuals in construction projects
4.198
0.860
0.179
4
12
A01
Ability to prepare sustainable designs
4.129
0.966
0.000
5
15
Notes: SD=Standard deviation; NV=Normalized value = (Mean - Minimum mean value)/(Maximum mean value - Minimum mean value);
CR=Construct ranking; OR=Overall ranking.
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Table 2. Results for construct reliability and validity
Construct
Competencies
Outer loadings
Cronbach’s Alpha
Rho_a
Rho_c
AVE
K01
0.793
0.929
0.933
0.944
0.738
K02
0.838
−
−
−
−
K03
0.883
−
−
−
−
K04
0.884
−
−
−
−
K05
0.853
−
−
−
−
Knowledge
K06
0.900
−
−
−
−
S01
0.848
0.958
0.960
0.964
0.751
S02
0.867
−
−
−
−
S03
0.898
−
−
−
−
S04
0.879
−
−
−
−
S05
0.799
−
−
−
−
S06
0.889
−
−
−
−
S07
0.918
−
−
−
−
S08
0.817
−
−
−
−
Skills
S09
0.880
−
−
−
−
A01
0.864
0.942
0.943
0.956
0.812
A02
0.924
−
−
−
−
A03
0.937
−
−
−
−
A04
0.869
−
−
−
−
Abilities
A05
0.910
−
−
−
−
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Table 3. Results for weightings and membership functions
Membership functions
Construct
CO
Mean
Weights
Level 3
Level 2
Level 1
─
25.495
0.298
─
0.01, 0.01, 0.15, 0.36, 0.47
─
Knowledge
CKnowledge
CKnowledge 5
4.386
0.172
0.01, 0.00, 0.10, 0.38, 0.51
─
─
CKnowledge 6
4.248
0.167
0.01, 0.02, 0.14, 0.38, 0.45
─
─
CKnowledge 2
4.248
0.167
0.02, 0.01, 0.15, 0.35, 0.47
─
─
CKnowledge 3
4.248
0.167
0.02, 0.01, 0.16, 0.33, 0.48
─
─
CKnowledge 1
4.238
0.166
0.01, 0.00, 0.16, 0.41, 0.42
─
─
CKnowledge 4
4.129
0.162
0.02, 0.03, 0.17, 0.37, 0.41
─
─
─
38.881
0.455
─
0.02, 0.02, 0.013, 0.31, 0.53
─
Skills
Cskills
Cskills 4
4.515
0.116
0.01, 0.00, 0.10, 0.25, 0.64
─
─
Cskills 5
4.495
0.116
0.01, 0.01, 0.10, 0.24, 0.64
─
─
Cskills 2
4.366
0.112
0.01, 0.03, 0.12, 0.27, 0.57
─
─
Cskills 1
4.356
0.112
0.02, 0.00, 0.13, 0.31, 0.54
─
─
Cskills 7
4.297
0.111
0.01, 0.02, 0.13, 0.35, 0.49
─
─
Cskills 3
4.279
0.111
0.03, 0.00, 0.15, 0.29, 0.53
─
─
Cskills 9
4.277
0.110
0.02, 0.01, 0.13, 0.36, 0.48
─
─
Cskills 6
4.139
0.106
0.02, 0.02, 0.16, 0.41, 0.39
─
─
Cskills 8
4.139
0.106
0.03, 0.00, 0.15, 0.45, 0.37
─
─
─
21.099
0.247
─
0.02, 0.01, 0.16, 0.35, 0.46
─
Abilities
Cabilities
Cabilities 5
4.297
0.204
0.01, 0.01, 0.13, 0.38, 0.47
─
─
Cabilities 2
4.267
0.202
0.03, 0.00, 0.12, 0.38, 0.47
─
─
Cabilities 3
4.208
0.199
0.02, 0.00, 0.20, 0.32, 0.46
─
─
Cabilities 4
4.198
0.199
0.02, 0.00, 0.17, 0.39, 0.42
─
─
Cabilities 1
4.129
0.196
0.02, 0.03, 0.20, 0.31, 0.44
─
─
Overall
─
─
─
─
─
0.02, 0.01, 0.16, 0.35, 0.46
Note: CO = Competencies.
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Appendix 1
Table A1. The list of the competencies (knowledge, skills, and abilities)
Constructs
Code
Competencies
Sources
Sum
Knowledge
K01
Knowledge on waste produced by different construction activities (e.g., maintenance
activities)
1
1
K02
Knowledge on the sustainable characteristics of construction activities (e.g., waste
management)
2,10,20,18
4
K03
Knowledge on construction-related waste design
17,18
2
K04
Knowledge on diagnosing construction waste generation
14
1
K05
Knowledge on the environmental impacts of construction activities (e.g., air pollution and
consumption of natural resources)
4, 17
2
K06
Knowledge on the practice of sustainable facilities management
20
1
Skills
S01
Project management skills
9
1
S02
Skills on providing vocational training (e.g., safety on worksites)
4,15,16,17,
19
5
S03
Skills on lean construction techniques (e.g., minimize waste and maximize productivity)
11,13
2
S04
Problem-solving skills
3,12,17
3
S05
Communication skills
6
1
S06
Skills on trading construction waste materials
7
1
S07
Skills on planning construction activities (e.g., preparing construction sites)
18
1
S08
Skills on designing out waste in construction activities (e.g., using resources efficiently)
18
1
S09
Interpersonal skills (e.g., relationship management, self-management)
6,17,18
3
Abilities
A01
Ability to prepare sustainable designs
8
1
A02
Ability to implement lean construction techniques
5
1
A03
Ability to execute construction activities
21,22
2
A04
Ability to organize individuals in construction projects
18
1
A05
Ability to prepare construction activities plan
23
1
Notes: Cárcel-Carrasco et al., 20211; Santos et al., 20212; Nikmehr et al., 20213; Zhao, 20214; Xing et al., 20215; Vaz-Serra and Mitcheltree,
20206; Lu et al., 20207; Celadyn, 20208; Hegazy et al., 20209; Mahmood and Abrishami, 202010; Ahmed et al., 202011; Alhamami et al., 202012;
Herrera et al., 201913; Vieira et al., 201914; Hwang et al., 201815; Arshad et al., 201716; Moreton et al., 201617; Ajayi et al., 201618; Al-Rifai and
Amoudi, 201619; Elmualim et al., 200920; Aldana and Serpell, 201621; Arif et al., 201222; Hu et al., 202123.
References of the table
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Appendix 2
Table A2. Results for discriminant validity (Cross loadings)
Competencies
Knowledge
Skills
Abilities
K01
0.793
0.654
0.697
K02
0.838
0.793
0.779
K03
0.883
0.823
0.863
K04
0.884
0.894
0.898
K05
0.853
0.749
0.778
K06
0.900
0.847
0.868
S01
0.791
0.848
0.747
S02
0.796
0.867
0.774
S03
0.842
0.898
0.865
S04
0.800
0.879
0.833
S05
0.711
0.799
0.718
S06
0.852
0.889
0.886
S07
0.860
0.918
0.883
S08
0.788
0.817
0.838
S09
0.790
0.880
0.824
A01
0.879
0.793
0.864
A02
0.865
0.874
0.924
A03
0.880
0.881
0.937
A04
0.821
0.858
0.869
A05
0.841
0.857
0.910
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Appendix 3
Table A3. Results for discriminant validity (HTMT)
Construct
Ability
Knowledge
Skills
Ability
─
─
─
Knowledge
1.014
─
─
Skills
0.995
0.979
─
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Appendix 4
Figure 1A. Measurement model assessment
Notes: The values of AVE are shown inside the latent variables, while the values of outer loadings are illustrated in
the arrows between the latent and observed variables.
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