Construction SMEs labour productivity: causal layered analysis

Abstract

Purpose
Small and medium-sized contractors are critical to micro and macroeconomic performance. These contractors in South Africa have long been confronted with the problem of business failure because of a plethora of factors, including poor productivity. The purpose of this study is to investigate salient issues undermining the productivity of small and medium-sized contractors in South Africa. This study proposes alternative possibilities to engender productivity improvement.

Design/methodology/approach
Qualitative data were collected using semi-structured interviews with 15 contractors in Gauteng Province, South Africa. The research data were analysed using content and causal layered analyses.

Findings
Challenges to contractors’ productivity were associated with inadequately skilled workers, management competence and political factors. Skills development, construction business and political factors were dominant stakeholders’ perceptions. Metaphors for construction labour productivity are presented and reconstructed as alternative directions for productivity improvement.

Practical implications
Contractors lose a substantial amount of South African Rand to poor productivity. Alternative directions provided in this study can be leveraged to increase profitability in construction organizations, enhance the social well-being of South Africans and ultimately improve the contribution of contractors to the South African economy.

Originality/value
The causal layered analysis (CLA) applied in this study is novel to construction labour productivity research. The four connected layers of CLA, which make a greater depth of inquiry possible, were explored to investigate labour productivity in construction organizations.

Full text

Construction SMEs labour
productivity: causal
layered analysis
Oluseyi Julius Adebowale and Justus Ngala Agumba
Department of Building Sciences, Tshwane University of Technology, Pretoria,
South Africa
Abstract
Purpose –Small and medium-sized contractors are critical to micro and macroeconomic performance.
These contractors in South Africa have long been confronted with the problem of business failure because of a
plethora of factors, including poor productivity. The purpose of this study is to investigate salient issues
undermining the productivity of small and medium-sized contractors in South Africa. This study proposes
alternative possibilities to engender productivity improvement.
Design/methodology/approach –Qualitative data were collected using semi-structured interviews
with 15 contractors in Gauteng Province, South Africa. The research data were analysed using content and
causal layered analyses.
Findings –Challenges to contractors’productivity were associated with inadequately skilled workers,
management competence and political factors. Skills development, construction business and political factors
were dominant stakeholders’perceptions. Metaphors for construction labour productivity are presented and
reconstructed as alternative directions for productivity improvement.
Practical implications –Contractors lose a substantial amount of South African Rand to poor
productivity. Alternative directions provided in this study can be leveraged to increase profitability in
construction organizations, enhance the social well-being of South Africans and ultimately improve the
contribution of contractors to the South African economy.
Originality/value –The causal layered analysis (CLA) applied in this study is novel to construction
labour productivity research. The four connected layers of CLA, which make a greater depth of inquiry
possible, were explored to investigate labour productivity in construction organizations.
Keywords Causal layered analysis, Construction business, Construction project, Contractor,
Labour productivity, SMEs
Paper type Research paper
Introduction
South Africa invests billions of Rand in the construction industry every year. Small and
medium-sized enterprises (SMEs) benefit from a significant share of the spending because of
their role in economic prosperity (Barbosa et al., 2017). The construction engineering sub-
sector is the second largest employer among SMEs, accounting for about 34.2% of total
small business employment (Adediran and Windapo, 2017). SMEs are mandated by the
government to significantly reduce unemployment in South Africa (Balogun et al.,2016).
They have always been the leading employment generator in South Africa. South African
SMEs are recently experiencing poor performance (Wentzel et al.,2016), leading to a high
The authors acknowledge the financial support provided by Tshwane University of Technology for
this study.
Conflict of interest: The authors have no competing interests to declare.
Construction
SMEs labour
productivity
Received 20 November2022
Revised 11 February2023
31 March 2023
26 April 2023
17 May 2023
10 June 2023
Accepted 19 June2023
Journal of Engineering, Design
and Technology
© Emerald Publishing Limited
1726-0531
DOI 10.1108/JEDT-11-2022-0583
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1726-0531.htm

rate of business failure. Some construction organizations were compelled to lay off a number
of their employees (Aghimien et al.,2019). The sector continues to experience a decline in
employment capacity (Wentzel et al., 2016). Their growth and strength have been hampered,
limiting their contribution to the country’s economy (Balogun et al., 2016). Several factors
are reportedly responsible for their poor performance, including poor productivity. Studies
have examined the performance of South African construction SMEs (Aghimien et al.,2019;
Adediran and Windapo, 2017;Wentzel et al.,2016;Aigbavboa et al.,2014).
Recommendations were proposed in these studies without resulting in a significant
improvement in SME performance. To improve existing SME performance studies, it is
important to address the key parameters that are critical to SME project performance,
including productivity. Productivity is an essential indicator for determining project
performance (Gurmu, 2019;Karthik and Rao, 2019). Construction labour costs account for
30%–50% of the total cost; therefore, CLP largely determines the profitability of
construction organizations (Jarkas and Bitar, 2012). It is estimated that every 10% increase
in CLP in the UK saves the equivalent of £1.5bn (Lu et al.,2021).
South Africa’s construction labour productivity (CLP) was at its lowest level in 46 years
(Bierman et al.,2016). A 15% productivity loss was similarly reported in the Indian
construction sector (Karthik and Rao, 2019). While existing productivity studies emanating
from South Africa have widely reported inadequate skills as a leading factor contributing to
poor productivity (Adebowale and Smallwood, 2020;Bierman et al.,2016), Indian CLP loss
has been largely associated with the time spent on non-productive tasks. Productivity in
developed and developing countries has not been too impressive. On average, 51% of hours
are lost on sites weekly in Iran (Goodarzizad et al.,2023). The situation is similar in the USA
and Canada, where the average hour lost in projects was estimated at 19.7% and 13.6% for
projects with skills shortage and projects without skills shortage, respectively (Karimi et al.,
2017). Reports from the USA and Canada suggest that beyond skills shortage, there are
other major factors contributing to poor productivity on construction projects. Worker
efficiency and working time distributions on commercial construction projects in Alberta
showed an average direct effective working time between 49.5% and 52.1% (Hewage and
Ruwanpura, 2006), which indicates a significant time loss. The global growth of CLP is
lower than the average annual growth of around 16% in many industries (Hai and Van
Tam, 2019). Considering these statistics, it is not surprising that the construction sector only
accounts for about 85% of the productivity in other industries (Lu et al.,2021), which
necessitates the need for increased efforts for improvement in the construction sector.
Because of the prevalence of poor productivity in construction projects, studies have
examined CLP in developed and developing countries (Agrawal and Halder, 2020;Durdyev
and Ismail, 2016;Hiyassat et al.,2016;Jarkas, 2015;Jarkas et al.,2015;Sebastian and
Raghavan, 2015;Thomas and Sudhakumar, 2013;Jarkas and Bitar, 2012;Olomolaiye et al.,
1987). Studies have identified factors affecting CLP (Adebowale and Agumba, 2022b;
Gurmu, 2021;Tam et al., 2021;Alaghbari et al., 2019;Shoar and Banaitis, 2019). The effects
of heat (Yi and Chan, 2017) and Building Information Modelling (BIM) (Wong et al.,2020)on
CLP have been studied. Some researchers developed quantitative models (Sarihi et al.,2023;
Selvam et al., 2022;Tsehayae and Fayek, 2018) and qualitative models (Jalal and Shoar,
2019;Palikhe et al.,2019;Nojedehi and Nasirzadeh, 2017). Information technology has been
leveraged to predict CLP (Goodarzizad et al., 2023;Karatas and Budak, 2022;Mlybari, 2020).
Some methodologies, which include importance rating and risk mapping (Gunduz and Abu-
Hijleh, 2020), self-motivation theory (Tam et al., 2022), conditional frontier theory (Ma et al.,
2016) and probability theories (Kubeckova and Smugala, 2021), have been used to
investigate CLP. A number of research approaches and methodologies highlighted above
JEDT

have been used by scholars to address productivity in construction. Arising from these
studies are several interventions to improve human resource productivity. Despite the field
of construction human resource productivity being widely researched, there is a paucity of
research that apply the four layers of causal layered analysis (CLA) to study productivity in
construction. Most existing productivity studies are based on the litany and systems of CLP,
which are the first two layers of CLA. Addressing the last two layers of CLA, which are the
worldview and metaphor of CLP, would provide deeper insights into CLP research. This
study addresses this gap by using CLA to investigate and analyse productivity issues in
South African construction SMEs. CLA offers a new perspective to analysing problems to
ultimately create alternative possibilities.
Construction labour productivity
Studies from both developed and developing countries have examined factors that influence
CLP. Studies have been conducted in the USA, Australia, Saudi Arabia and India
(Kermanshachi et al.,2022;Gurmu, 2021;Tam et al.,2021;Thomas and Sudhakumar, 2013).
Some research has conducted systematic reviews to examine factors affecting CLP
(Adebowale and Agumba, 2022b;Adebowale and Agumba, 2022a;Hamza et al., 2022;
Adebowale and Agumba, 2021). The reviews presented scientometric analysis, systematic
reviews, CLA and metadata analysis of the literature related to CLP research. Insights were
provided into new areas of knowledge in construction productivity research. CLA presents a
transformed future for construction productivity in developed and developing countries.
Factors affecting CLP in high-rise buildings have been identified (Gurmu, 2020;Shoar and
Banaitis, 2019). Shoar and Banaitis (2019) prioritize the most important drivers of labour
productivity in a high-rise building. Building material management practices that increase
labour productivity in multi-story construction projects have been studied (Gurmu, 2020).
Bhilwade et al. (2022) showed high accuracy in predicting labour productivity for formwork
activities in high-rise construction. Gurmu and Aibinu (2018) report on management
practices to increase labour productivity in multi-story construction projects. Nguyen and
Nguyen (2013) advise practitioners to consider the relationship between building floor and
labour productivity when planning workforce and construction activities.
The impact of skilled labour availability on the productivity of North American
construction projects has been studied (Karimi et al., 2017). Projects with craft shortages had
lower productivity compared to projects with sufficient labour. Aghayeva and Slusarczyk
(2019) considered the importance of motivated human resources for the productivity of
construction organizations and reported a hierarchy of motivating and demotivating factors
for workers. Tam et al. (2022) used a self-determination theory to examine the motivation for
improving construction productivity in Vietnam. Jarkas and Horner (2015) establish a
baseline for labour productivity in building construction. The study used metrics specificto
Kuwait, but the principles of data collection and analysis are general and could be applied in
other countries. Statistical analysis and plastering probability theories have been applied to
predict the time required to complete construction works (Kubeckova and Smugala, 2021). In
the USA, Kermanshachi et al. (2022) developed management guidelines and analysed the
impact of change orders on CLP. Gunduz and Abu-Hijleh (2020) used the importance
assessment and risk mapping method to assess human resource productivity drivers in
construction. Chaparro et al. (2020) examined the impact of personnel transport on CLP in
Australia. Yi and Chan (2017) studied the effects of heat stress on CLP in Hong Kong. Wong
et al. (2020) assessed the impact of BIM on CLP in Malaysia. The research results can be
used by construction organizations to minimize the negative impact of commuting and heat
stress on worker productivity while leveraging the practical application of BIM. The
Construction
SMEs labour
productivity

conditional limit theory was used to study the convergence of CLP. Error correction models
are implemented to identify the long-term equilibrium and dynamics of CLP (Ma et al.,2016).
The development of CLP models and the lack of frameworks for adapting existing or
original models limit the possibility of reusing existing models. Tsehayae and Fayek (2018)
evolve a context adaptation framework that helps to adapt existing or original CLP models.
In Iran, Sarihi et al. (2023) developed, optimized and validated CLP models to address the
challenges of a systematic approach to measuring CLP while accounting for the complex
relationships between multiple factors. Selvam et al. (2022) proposed a model that can be
used effectively to determine a real-time project duration considering factors affecting
labour productivity and project constraints. Dijkhuizen et al. (2021) considered the
importance of off-site construction to increase labour productivity. The study developed a
conceptual model that describes critical factors affecting off-site construction. System
dynamics are widely used to model various causal relationships between factors that
interact with labour productivity (Jalal and Shoar, 2019;Palikhe et al.,2019;Khanzadi et al.,
2017;Nojedehi and Nasirzadeh, 2017). The system dynamics help to identify the
relationships between factors that influence productivity. System dynamics models could
inform policymaking for decision-makers (Palikhe et al.,2019). Table 1 summarizes the key
factors affecting construction productivity.
An overview of South African construction small and medium-sized
enterprises
SMEs in South Africa are contractors with 250 full-time employees and an annual turnover
of less than R220m (Renault et al.,2020). The poor performance of SME contractors
undermines their potential to contribute meaningfully to job creation (Fatoki, 2014).
Construction SMEs account for a significant number of contractors in South Africa (Balogun
et al.,2016). Until 2016, more than 50% of construction SMEs are owned by previously
disadvantaged South Africans (George, 2016). Studies have shown that large contractors
generally perform better than SMEs in terms of achieving project objectives (Wentzel et al.,
2016), while SMEs are more strategic for job creation and poverty reduction. Although the
government has spent a considerable amount to boost performance, present performance
does not justify such spending (Mafundu and Mafini, 2019;Aigbavboa et al.,2014). Current
SMEs’challenges result from a combination of issues, including poor productivity
(Adebowale and Agumba, 2022a).
Studies have reported salient factors hindering performance in South African
construction SMEs. Chimucheka (2013) identifies insufficient education and low
entrepreneurial skills. Aghimien et al. (2019) recognize the need for capacity building of
business owners, especially in corporate governance. Management, strategic planning and
inadequate funding are reported by Wentzel et al. (2016).Olawale and Garwe (2010) found
problems related to financial support, education and training. Wentzel et al. (2016) and
Aigbavboa et al. (2014) maintain that contractors receive significant financial support to
succeed, but poor financial management is rather the issue. Fatoki (2014) reports the need
for SMEs to cultivate a positive attitude towards training. Aigbavboa et al. (2014) believe
that leadership training will give SMEs a competitive advantage, while Abor and Quartey
(2010) recommend the participation of governmental and non-governmental organizations.
Challenges confronting construction SMEs are both internal and external (Fatoki, 2014).
Internal challenges include management functions, employee development and attitude
towards customers. External challenges include competition, rising costs of doing business,
finance and crime. Access to funding is becoming increasingly difficult for contractors
because of rising interest rates (Aghimien et al., 2019). These and other challenges make it
JEDT

Author Region
No. of factors
identified Major finding
Alinaitwe et al.
(2002)
Uganda 36 Incompetent supervisors, lack of skills from the workers, rework, lack of tools/equipment and
poor construction methods
Kazaz and Ulubeyli
(2007)
Turkey 9 Working at similar activities, design complexity, error tolerance, weather conditions and
disruptions
Rivas et al. (2011) Chile 38 Materials, tools, rework, equipment, truck availability and the workers’motivational dynamics
Jarkas and Bitar
(2012)
Kuwait 45 Clarity of technical specifications, the extent of variation/change orders during execution,
coordination level among design disciplines, lack of workers’supervision and proportion of work
subcontracted
Mahamid (2013) Palestine 31 Rework, lack of cooperation and communication between construction parties, the owner’s
financial status, lack of worker experience and lack of materials
El-Gohary and Aziz
(2014)
Egypt 30 Workers’experience and skill, incentive programs, availability of materials and their ease of
handling, leadership and construction management competency and workers’supervision
Odesola and Idoro
(2014)
Nigeria 15 Craft workers’pride in their work, lack of skills, rework, incompetent supervisors and workers’
personal problems/poor economic conditions
Jarkas (2015) Bahrain 37 Workers’skills, coordination among design disciplines, lack of workers’supervision, errors and
omissions in design drawings and delay in responding to requests for information
Jarkas et al. (2015) Oman 33 Errors and omission in design drawings, change to orders during execution, delay in responding
to requests for information, lack of workers’supervision and clarity of project specifications
Afolabi et al. (2018) Nigeria 17 Availability of equipment and material, supervision, payment method, welfare on-site and
weather conditions
Jalal and Shoar
(2019)
Iran 60 Fatigue, lack of workers’motivation, lack of skill, schedule delay and inflation in the cost of
execution
Source: Author
Table 1.
A review of factors
affecting
construction labour
productivity
Construction
SMEs labour
productivity

difficult for SMEs to compete with large construction organizations in terms of performance
(Chimucheka, 2013).
Research methodology
The research scope
The research was conducted with SMEs in Gauteng province, South Africa.
According to the Construction Industry Development Board (CIDB), four provinces
(Eastern Cape, Gauteng, Kwazulu Natal and Western Cape) account for 76% of the
total capital outlay, which implies there is more construction activity in these regions.
Gauteng province has the highest construction capital outlay [Construction Industry
Development Board (CIDB), 2015]. Over the year, the province recorded more
economic activities than other provinces in South Africa, making it the economic hub
of South Africa. Grades 1–5 contractors registered with Construction Industry
Development Board in Gauteng province make up the research population. These
contractors represent the lowest to medium-capacity SMEs in the construction sector.
The research approach
The research used a qualitative approach to collect data from SMEs in construction. Most
existing productivity studies have used quantitative research to address their research
problems (Agrawal and Halder, 2020;Gurmu and Aibinu, 2018;Jarkas and Bitar, 2012;
Durdyev and Mbachu, 2011). Given this research design, a quantitative investigation is
suitable for inquiries at the litany and systems layers. However, quantitative research could
result in weak and unreliable data at the worldview and metaphor layers. Interviews offered
the benefit of more robust data at these deeper layers of the inquiry (Sim et al.,2018).
Consequently, qualitative research was preferred. CLA is useful to deepen an inquiry from a
multi-stakeholder perspective (Inayatullah, 2008). In its application, the world is examined
through four layers. The layers are not discrete categories but are dynamically connected to
allow vertical movements between layers and horizontal movement within a layer
(Inayatullah, 2004). Litany, the first layer, is concerned with the conventional perceptions of
reality as it appears to be (Inayatullah, 2019). The systemic layer of CLA involves examining
the factors that influence the litany (MacGill, 2015). This level focuses on trends and drivers
of change (Inayatullah, 2008). Worldview, the third level, deals with the assumptions that
drive dominant social causes and perspectives (Inayatullah, 1998). Worldview informs,
supports and co-creates the systemic layer (Inayatullah, 2004). The last layer, the metaphor,
represents the deepest level of the CLA pyramid: deep stories and collective archetypes can
be deeply felt but are not necessarily available for conscious understanding or control
(Inayatullah, 2019).
Design of interview guide
The interview guide was divided into two sections. The first section dealt with the socio-
demographic information of the respondents. Respondents were asked about their years of
experience, their organization’s CIDB rating and the time their organizations have been
conducting construction projects. In the second part, respondents were asked four different
questions related to each CLA level. They were asked to describe labour productivity in
SME projects (Litany), identify key factors responsible for such productivity performance
(Systems), discuss shared beliefs of stakeholders (Worldview) and describe their
organization’s productivity using metaphorical expressions (Metaphors). Before
respondents were presented with the second part of the research questions, the definition of
productivity applicable to this study was explained to avoid misrepresentations.
JEDT

Productivity was evaluated based on the cost and time efficiencies of projects undertaken by
construction SMEs.
Data collection
The research questions were emailed to the SMEs. The research questions were structured
to gather data for the different layers of CLA. Moving among the layers creates a deeper
understanding of possibilities for productivity in construction. It highlights what
Inayatuallah (2009) describes as the transformative dimension –to deconstruct so that
alternative futures can be explored and desired futures can be created. The content validity
of the data collection tool was ensured before data collection. The purpose of content
validation is to minimize the potential error associated with operationalizing research tools
and increase the likelihood of obtaining a supportive and valid construct in a study
(Shrotryia and Dhanda, 2019). One academic and three practitioners were administered the
data collection tool to ascertain the clarity and appropriateness of research questions to
address the research problem. The three practitioners found the research questions to be
clear and appropriate, but the academic suggested revisions to enhance clarity. The
interview questions were revised in light of the input. A purposive sampling technique was
used to select research participants from each construction organization. Purposive
sampling allows researchers to select knowledgeable participants and can provide relevant
information on any topic under investigation (Etikan et al.,2016). Directors, site managers
and forepersons were considered appropriate to provide information on productivity, so
their opinions were sought. Semi-structured interviews were conducted with the
respondents. Semi-structured interviews allow respondents to demonstrate their unique
way of looking at the world –their definition of situations (Silverman, 1993). On-site and
online interviews were conducted with the respondents. Eight interview sessions were
conducted on-site, while seven were conducted online via Microsoft (MS) teams. The COVID-
19 regulations regarding physical distancing required online interviews to collect some
research data. The interview lasted over five months (October 4, 2021–February 8, 2022).
Each session of the interviews was recorded andthen transcribed.
Sample size
There are several recommendations from qualitative researchers regarding adequate sample
sizes for qualitative studies. There are arguments for determining an appropriate sample size for
qualitative studies before starting data collection (Francis et al.,2010). However, some scholars
believe that deciding on an appropriate sample size is iterative and contextual. Such a decision is
made during the analytical process as researchers develop an increasingly comprehensive picture
of research themes, the relationship between the themes and where the conceptual boundaries lie
(Sim et al.,2018). Ando et al. (2014) conducted 12 interviews and recommended the sample size as
sufficient for qualitative studies. Picariello et al. (2017) similarly indicate 12 interviews as the
minimum required to achieve data saturation in qualitative studies. According to Namey et al.
(2016), a sample size of 8–16 interviews is required to adequately answer a research question. In
this study, 15 interviews were conducted. The sample size was considered adequate, as data had
reached saturation, and no new information was available. The list of registered SMEs was
obtained from the CIDB headquarters in Pretoria.
Data analysis
There is no single or correct way to analyse and present qualitative data; qualitative data
analysis and presentation depend on the fitness for purpose (Lacey and Luff, 2001). Content
analysis was used to determine the key themes emerging from the interview data. The
Construction
SMEs labour
productivity

layers of CLA were used for further analysis and discussion. The interview data were
manually classified into relevant themes by identifying key contents in the respondents’
feedback. The litany section was classified as excellent, high, average and low in terms of
SME labour productivity. The systems were classified into skilled-related, management
competence and political causes. The theme for worldview was workers’competence,
construction business and political. The respondents presented only five metaphors;
therefore, it was unnecessary to classify them. Figure 1 shows the research process.
The research findings
The sociodemographic data of the study participants are presented in Table 2. Site
managers constitute 20%, directors 26.7% and forepersons 53.3% of the study participants.
Respondents were aware of site production and its influencing variables, therefore,
considered relevant to the study. The CIDB classification of the participating organizations
ranges from grades 1 to 5, which belongs to the category of South African construction
SMEs. Of the contractors, 53.3% were registered in grades 1–3, classified as small
contractors; while 46.7% were registered in grades 4–5, classified as medium-sized
contractors. Of the respondents, 53.3% have worked in the construction industry for at least
16 years, and 46.7% have 5–13 years of construction experience. The respondents have an
average of 18.3 years of experience in construction. Of the organizations surveyed, 33.3%
have been delivering construction projects for at least 16 years and 66.7% for 3–14 years.
Figure 1.
Research process
Gauteng Province
SMEs
CIDB (Grades 1-5)
Directors
Site managers Foreperson
Purposive sampling
Data collection tool
development
Content validity
Semi-structured interviews
Eight
Onsite
Seven
Online
Content analysis
Causal layered analysis
Source: Author
JEDT

Table 3 contains indicative answers to the question: How would you describe the
productivity of SME construction projects? Based on the responses, the data were classified
into four categories:
(1) excellent productivity;
(2) high productivity;
(3) average productivity; and
(4) low productivity.
The data collected at this level is generally unchallenged and reflects respondents’
perceptions of CLP in SME construction projects. Existing CLP studies have obtained
empirical data to report construction professionals’perceptions of CLP performance across
organizations. The four themes generated for the data classification at litany help to
understand the dominant perception of construction productivity performance among
construction practitioners. The majority of respondents reported low productivity in SME
construction projects and three respondents considered CLP to be average. One respondent
pointed out the inconsistency and unpredictability of CLP in his organization. According to
respondents, the organization experienced low productivity at times and performed better at
other times. Two respondents claimed that some SMEs in construction are very productive,
but new contractors in construction usually have low productivity. One respondent reported
excellent labour productivity in some organizations, while labour productivity was
reportedly poor in some organizations. Although some organizations are reported to have
average to excellent productivity in their projects, the majority of respondents reporting low
productivity point to the prevalence of low productivity in South African construction
SMEs.
Table 4 presents responses to the systemic causes: Which factors are responsible for the
productivity you describe? The first set of themes that emerged at this level are labour-
related, management-related and external drivers. Responses related to the drivers of
change that influence the litany are further classified into three themes:
Table 2.
Respondents’data
Interviewee Position Years of experience CIDB grading Years of operation
Respondent 1 Construction manager 30 6 16
Respondent 2 Director 12 1 3
Respondent 3 Foreman 25 4 11
Respondent 4 Foreman 7 5 13
Respondent 5 Foreman 16 2 9
Respondent 6 Construction manager 9 3 14
Respondent 7 Foreman 13 4 8
Respondent 8 Foreman 23 5 13
Respondent 9 Director 31 4 20
Respondent 10 Foreman 8 3 12
Respondent 11 Construction manager 13 2 13
Respondent 12 Foreman 38 6 32
Respondent 13 Foreman 25 2 20
Respondent 14 Director 5 1 5
Respondent 15 Director 20 2 20
Source: Author
Construction
SMEs labour
productivity

(1) skills-related;
(2) management competence; and
(3) political causes.
Skills-related drivers of litany are associated with construction workers. The majority of
respondents indicated a shortage of construction skills, with only three respondents not
expressing skills shortage as a major factor responsible for the productivity performance
initially defined. The issue related to the directors of construction organizations also
featured as a critical factor responsible for the productivity being recorded in South African
construction SMEs. Most of the issues raised concerning directors are related to their
capacity to champion the required change for CLP productivity improvement in their
organizations. Although not often cited as one of the main factors influencing CLP in
different countries, some CLP studies have described political factors as extrinsic factors
influencing CLP. Political factors have been described in different forms, including political
strikes (Thomas and Sudhakumar, 2013) and political uncertainty (Alaghbari et al.,2019).
The political issue affecting South African construction SMEs is government policies
interfering with productivity in construction organizations.
Table 3.
The litany
Respondent Excellent High Average Low
R1–R15 “generally
speaking,
some
workers are
highly
productive”
(R12)
“.....however, the
SMEs who have
been in operation
for quite some
time have good
productivity”
(R3); “some
SMEs’labour
productivity is
great due to their
experience in
construction”
(R4)
“sometimes we
achieve good
labour
productivity”
(R5); “I will say
productivity of
SME
contractors is
average”(R7);
“I can say it is
medium”(R10)
“...usually below average. Only
about 20% of current SME projects
are completed on actual completion
date”(R1); “SMEs labour
productivity is not encouraging
...”(R2); “productivity of local
contractors who are new in the
industry are poor”(R3); “some
SMEs productivity is really poor”
(R4); “....and sometimes
productivity is terrible”(R5); “poor
labour productivity is one of the
major problems we face [...]”(R6);
“labour productivity is just below
average”(R8, R9); “productivity of
workers is poor. It is usually better
to get foreign guys to do the job
because they have more
experience”(R11); “...and the
productivity of some is very poor”
(R12); “...labour productivity in
construction is not good”(R13);
“workers usually waste time and
they are slow when it comes to
productivity”(R14); and “labour
productivity of workers is nothing
to write home about”(R15)
Respondent R12 R3; and R4 R5; R7; and R10 R1; R2; R3; R4; R5; R6; R8; R9; R11;
R12; R13; R14; and R15
Total 1 2 3 13
Source: Author
JEDT

Stakeholders’perceptions were interpreted and situated in discourses that transcend
individual stakeholders. Table 5 presents worldview data collected from the question: What
shared stakeholder beliefs are driving SME productivity? CLP studies have not largely
investigated the field at this level of inquiry. The shared beliefs of stakeholders provide a
framework for understanding the dynamics of CLP. Three themes emerged from the data
collected in this section. These include workers’competence, construction business and
political. Based on the data gathered at this level, stakeholders’beliefs provide a basis for
understanding the competence and ability of construction workers to be productive in
construction tasks. Expression was given to three perceptions that are politically inclined.
These perceptions provide deeper insight into a better understanding of political issues that
undermine the productivity of SMEs in South African construction. These perceptions
include citizens’right to employment, political connection game and employee–employer
relations. The last stakeholders’perception that provides a framework for understanding
CLP is related to the construction business. There is a widespread belief that anyone can
operate a construction business provided there is capital for operation. It is believed that the
construction business is highly profitable. These worldviews are held in society and
constitute the basis of motivation for establishing some construction businesses. While
these perceptions may be true to some extent, especially the second worldview, they are
subject to specific variables that must be considered.
Respondents were requested to describe SMEs’productivity using metaphorical
expressions. Responses are presented in Table 6. The metaphors are imagery presentations,
Table 4.
The systemic causes
Respondent Skill related
Management
competence Political causes
R1–R15 “poor skills of workers”(R1);
“most workers lack requisite
skills”(R2); “the local SMEs
employ cheap labour that
lacks the skills”(R3); “poor
skill, poor skill, and poor skill”
(R5); “there are few qualified
workers”(R7); “most of the
workers lack the
experience...”(R8); “70% of
the workers do not have the
required experience ...”(R9);
“Most of the workers lack
ambition, just working for
money, yet not skillful”(R11);
“...family problem affect their
productivity”(R12); “most
workers do not have the skill
for the work”(R13); “workers
incompetence”(R14); and “...
few skilled workers available
to us”(R15)
“...incompetence of
directors”(R1); “poor
management and
shortage of materials”
(R4); “workers are not
adequately
motivated”(R6);
“owners lack the
discipline to run a
company”(R7); “....
owners sometimes
lack the
professionalism to
manage their
companies”(R9); and
“...the problem of
proper planning”
(R12)
“most workers employed by the
local SMEs are employed through
political influence”(R3); “...the
government forces us to use local
workers residents in job locations”
(R9); “the law of South Africa says
that we must hire local people and
the majority of them lack the
experience”(R10); “...the
government compels us to hire”
(R11); and “late payment by
clients”(R14)
Respondent R1; R2; R3; R5; R7; R8; R9;
R11; R12; R13; R14; and R15
R1; R4; R6; R7; R9;
and R12
R3; R9; R10; R11; and R14
Total 12 6 5
Source: Author
Construction
SMEs labour
productivity

which further define the worldview of CLP. Most of the respondents could not find the
appropriate metaphor to describe their perceptions of CLP. They were willing to present
their thoughts, but they struggle to find an expression to capture their thoughts. It is not
surprising because this is the deepest level of CLA, where ideas beyond the surface are
generated and succinctly presented. Deep stories and collective archetypes can be deeply felt
but are not necessarily available to the conscious mind (Inayatullah, 2019). After struggling
to communicate the imagery of their perceptions of CLP, the respondents have the following
to say: “[...] do not know”,“[...] cannot think of any”,“[...]cannot help any further”, etc.
The five respondents that were able to give metaphoric expression to their perceptions of
CLP indicated: “CLP is a problem with multiple heads”,“the construction sector sometimes
breeds mediocre”,“employers and workers are in compulsory unions”, etc. These
expressions are further discussed in the subsequent section.
Discussion of the findings
This section discusses the key findingsonCLPinSouthAfricanSMEs.Basedontheresearch
data, each layer of CLA is explored to present a new perspective for CLP improvement.
Table 5.
The worldview
Respondent Workers’competence Construction business Political
R1 –R15 “skill development is the
business of some
stakeholders”(R1; R2; and
R3); “contractors cannot
spend a fortune to train
workers we can lose to
another company when
there is no job”(R2; R3;
R10; R11; and R12); and
without the requisite
skills, workers will still
get construction jobs with
contractors with lower
rates (R10; R12; R14; and
R15)
“anyone can do
construction business
when there is capital”
(R1; R3; and R4); and
“construction business
is highly profitable”(R8;
and R9)
“the government must
protect the right of the
citizen for employment”
(R2; R4; R5; R6; and R7);
“...with political
connection, you will get
a job in construction”
(R13); and contractors
must not dismiss
underperforming
workers to avoid
infringing on their right
(R15)
Respondent R1; R2; R3, R10; R11; R12;
R14; and R15
R1; R3; R4; R8; and R9 R2; R4; R5; R6; R7; R13;
and R15
Total 8 5 7
Source: Author
Table 6.
The metaphor
Respondent Response
R1 “...problem with multiple heads”
R5 “...breeds mediocre”
R7 “...epileptic productivity performance”
R8 “...directors in the deep end”
R14 “...compulsory unions...”
Source: Author
JEDT

Litany
The study shows low productivity in most South African construction organizations. This
result is consistent with the results of most existing CLP studies. The construction
management and engineering literature contains several reports on the slow growth and
decline of CLP (Ahmad et al.,2020;Nasir et al., 2014;Moselhi and Khan, 2012). New local
contractors in the South African construction industry are reportedly facing greater
productivity problems. Older contractors outperforming newcomers could result from their
years of experience delivering construction projects. Running a competitive construction
organization requires sufficient funds, among other important considerations. Because of
previous unpleasant experiences of funding organizations, strict measures have been
developed that guarantee the return of the capital. Access to funding has become more
difficult for new entrants (Aghimien et al.,2019), as they struggle to meet funding agency
requirements. Contractors without access to sufficient capital recruit based on their financial
ability. Some of these contractors employ less skilled workers, most of whom are foreign
workers. This increases dependence on foreign workers in South Africa. Dependence on
foreign workers for construction operations is similarly the case in Singapore, where the
construction industry employs over 315,000 foreign workers, accounting for 90% of the
workforce (Shang et al., 2020). Most of these foreign workers are paid less because of their
poor skills (Ofori et al.,2022). Although some foreign workers are more skilled than local
workers, they are in short supply. Skilled workers earn up to 50% more than low-skilled
(Agrawal and Halder, 2020). The few contractors who can afford the services of skilled
workers may be those with moderate to high productivity. Continued reliance on a few
skilled workers is detrimental to the long-term productivity of the South African
construction sector.
Although poor CLP is long-lasting, as demonstrated by decades of research (Kaming
et al., 1997;Zakeri et al., 1996;Olomolaiye et al.,1987) to address the challenge, poor
productivity remains existential in construction organizations. This is an indication that the
existing studies have not brought much benefit to contractors or that the results/
recommendations proposed in these studies have not been taken up in practice. The
prevalence of time and cost overruns (Jarkas et al., 2015) in construction operations further
demonstrates the existence of the problem. The problem of low productivity in construction
SMEs emphasizes the commonplace business failure among small and medium-sized
contractors in South Africa, which is detrimental to the sustainability of construction
organizations. This makes it necessary for construction stakeholders to develop viable
future-oriented frameworks that promote productivity growth in SMEs.
Systems
Skills shortage is the most significant problem for SMEs in the South African construction
sector. Skills shortage is arguably the most commonly reported factor influencing CLP.
Although research has shown that CLP is higher in developed than developing countries
(Chia et al., 2018), both developed and developing countries have records of skill shortages in
construction organizations. Lack of skills in construction has been reported in Singapore
(Hwang et al., 2017), Spain (Robles et al., 2014), New Zealand (Durdyev and Mbachu, 2011)
and the USA (Mojahed and Aghazadeh, 2008). The same has also been reported in some
developing countries, including Nigeria (Odesola and Idoro, 2014) and Uganda (Alinaitwe
et al., 2002). Skills shortage could force contractors to hire available workers (Briscoe et al.,
2000). Most South African construction SMEs tend to hire less skilled workers, giving
construction managers additional responsibility for managing inexperienced workers.
Multiskilling has been recommended as an alternative strategy to manpower use
Construction
SMEs labour
productivity

(Shang et al., 2020). Multiskilling means focusing on developing workers’skills in a variety
of occupations for flexible use. As skilled workers earn twice as much as the low-skilled,
only the contractors with the financial resources would compete for the scarce skilled
workers. The situation becomes more complicated when some contractors with the financial
means believe that they would save more costs by hiring inexperienced workers (Oswald
et al.,2020). Some contractors support this belief with the intention of dedicating few
resources to training their employees to be fit for purpose. Such training can take time to
produce the desired results after losing a lot of productivity on construction projects.
Construction workers are not motivated for their work, as some of them were more focused
on money than personal development. A systematic approach to skills development for the
South African construction sector should begiven more priority.
Directors of construction organizations in South Africa have a key role to play in
improving human resources in construction (Ghoddousi et al.,2015). Some directors lack the
competence to provide the leadership that generates productivity growth. Aghimien et al.
(2019) report on the need for leaders of construction SMEs to acquire business skills. Some of
the factors highlighted as responsible for directors’low competence are complacency and
the perceived high cost of training. It is important that directors consciously seek
knowledge, not only managerial but also relevant technical knowledge, sufficient to ensure
effective leadershipand provide their organizations with a competitive advantage.
Concerns were raised about political interference in the construction. Issues were raised
ranging from employment to political influence to policies that force contractors to hire
against their will. Some SMEs have expressed frustration with government regulations that
force employers to hire workers in the immediate vicinity of construction operations. The
South African Government’s huge capital investment in construction is a testament to the
government’s commitment to improving the performance of the construction sector.
Therefore, it has instituted organizations, policies and laws to further its performance goals.
Some of the organizations include the Construction Education and Training Authority and
the Construction Industry Development Board charged with the responsibility of promoting
a competitive construction sector. Similar to South Africa, the time construction SMEs
spend navigating government regulations is a major setback for small and medium-sized
construction organizations in Nigeria (Igwe et al., 2019). A study by Ofori et al. (2022) also
reports excessive government regulation of construction activity as a major factor
hampering productivity growth.
Worldview
Key stakeholder beliefs that provide a framework for understanding CLP were obtained.
Training is one of the leading interventions in the literature for improving construction
productivity. Construction organizations need more motivation to implement training
programs (Okorie and Musonda, 2022). The most important reasons for the contractors’
nonchalant attitude towards training are not yet apparent in the literature. Because of the
nature of the construction work, contractors were reluctant to provide training. SMEs avoid
spending a fortune on training workers that their organizations could lose to another
organization. This perception arises from the perspective of the project-based nature
applicable in the construction sector. This can create a dilemma for contractors, as they
must choose between working with less skilled workers or devoting their resources to
training. The situation is exacerbated by the vulnerability of SMEs, where very few survive
because of a lack of access to credit (Balogun et al.,2016). For SMEs to access credit, they
need a good financial history and a viable business plan (Aghimien et al.,2019). These were
a challenge for SMEs, making it difficult to obtain funding from financial institutions. The
JEDT

question then becomes whether the project-based nature of construction should lead to less
emphasis on the need for training in construction. Appropriate industrial relations
legislation and increased awareness of the best and most cost-effective training for different
categories of construction organizations could help improve commitment to training. Some
contractors considered training as a worker and government matter. However, training
remains the responsibility of everyone involved in construction. Construction stakeholders
need to work more collaboratively and effectively to mitigate the factors driving skill
shortages in the South African construction sector. The ability to access capital was seen as
a requirement for owning a construction business. This perception resonates in society;
hence, people believe that capital is what they need to own a construction organization. This
is the main motivation for some contractors, without adequately preparing and recognizing
other critical factors affecting business sustainability. Among other things, contractors
must be able to perform PESTEL and SWOT analyses to determine business risks and
feasibility. A blueprint of technical and managerial skills to run a competitive construction
business is also essential. Some directors believe that the construction business is very
profitable. They rush into a dynamic construction environment without proper preparation,
resulting in low productivity. The final stakeholder perception that shapes the
understanding of CLP is the government’s willingness to create jobs for citizens by enacting
legislation that encourages job creation. Some of the laws are reported to pose challenges for
contractors, as workers develop unnecessary entitlement and are less motivated to engage in
skills development.
Metaphors
Based on the worldview, appropriate metaphors have been used to describe CLP. The
productivity of South Africa’s construction SMEs has been described as a “multi-headed”
problem. This relates to the multiple dimensions of construction productivity challenges.
CLP challenges do not occur in isolation, one problem can be linked to another within the
system (Dai et al., 2009). Factors affecting CLP that are intrinsically and extrinsically
inclined require a holistic and multi-stakeholder approach to address them. “[...] Breeds
mediocre”was another metaphorical term found to be appropriate for describing CLP. This
may refer to construction stakeholders being unable to build a system that meets the
capabilities required by the industry. Construction stakeholders, particularly some primary
stakeholders, are reportedly not engaged in skills development in South Africa. Some
contractors avoid taking responsibility for training because of the nature of the industry,
while some government regulations have not encouraged workers to take responsibility for
personal development. CLP has also been described as epileptic. This refers to
inconsistencies in productivity growth. It was emphasized that older contractors are more
productive than new entrants. Directors were considered to have thrown themselves to the
“deep end”. The foregoing is consistent with the perception that construction activities
require a high level of capital. It also reflects the lack of capacity of directors in terms of
managerial and technical competence. The relationship between some contractors and some
of their workers has been described as a “compulsory union”. This can be interpreted
against the background that the government is forcing contractors to undertake certain
hiring. Government should not back down on measures to encourage job creation, but steps
must be taken to mitigate the disadvantages of such legislation. Table 7 shows the current
and emerging realities of CLP that can drive contractor sustainability and multi-stakeholder
satisfaction. The metaphors are reformulated to show alternative directions for the
productivity of South African construction SMEs. These metaphors offer South African
construction SMEs alternative productivity possibilities:
Construction
SMEs labour
productivity

Productivity challenges should be tackled as a system.
The construction industry should maintain and intensify skills development
programs.
More experienced contractors should mentor new entrants.
Directors should undertake multi-dimensional analyses before starting a
construction business.
Contractors should possess the right to hire based on job requirements and
competency.
Conclusions and implications of the research
Low productivity is one of the main problems faced by contractors in developed and
developing countries. Studies have used different strategies to salvage the situation. Despite
recommendations from existing studies, the productivity of some construction organizations
remains low, which undermines the competitiveness of contractors. This incident indicates that
existing studies have not brought much benefit to contractors or that their results/
Table 7.
Construction labour
productivity
transformed future
Layer Current reality New reality Transformed future
Litany CLP is below expectation in
most construction
organizations
Inter-organization mentorship
programs
CLP improves
System Skills shortage Long-term skill development
for the industry
Improved profitability of
construction businesses
Directors’competence Management and technical
abilities
Political interference Consultations with
stakeholders for policies
reviews
Worldview Workers can be lost to another
company
Appropriate legislation for
employee and employer
relations and cost-effective
training
Increased skilled workers
in construction
Training is the business of
workers and the government
Multistakeholder approach
Capital is the requirement to
float a construction business
PESTEL and SWOT analyses
to ascertain business risks and
feasibility
Reduced business failure
Myth A problem with multiple
heads
Productivity-dependent
factors addressed as a system
Sustainability of
construction businesses
and multistakeholder
satisfaction
Breeds mediocre Sustainable skills
development initiatives
intensified
Deep end Multi-dimensional analyses
precede construction business
Epileptic productivity
performance
Directors and team capacity
development
compulsory union Recruitments driven by job
requirements and competency
Source: Author
JEDT

recommendations have not been taken up in practice. The situation is no different in the South
African construction sector and even worse for small and medium-sized construction
organizations. Small and medium-sized contractors in South Africa account for approximately
95.3% of contractors, indicating their potential for job creation. Job creation potential for
contractors in this category is being truncated by a disturbingly high level of lost businesses,
partly because of poor productivity. This further leads to job losses and negatively impacts
micro and macroeconomic performance. Given the high percentage of South African SMEs in
construction, their current performance and the impact on the well-being of South Africans, it is
important to develop measures to improve their productivity. Although CLP is a widely
researched field, the CLA used in this study is a new methodology in the research field. The
four connected CLA layers, which allow for a greater depth of investigation, were used to study
labour productivity in South African construction organizations. A prevalence of low
productivity has been identified among South African construction SMEs, mainly because of a
shortage of skilled labour. Skill shortages have also been reported as a productivity-related
problem in developed economies, including Singapore, Spain, New Zealand and the USA
(Hwang et al., 2017;Robles et al., 2014;Durdyev and Mbachu, 2011;Mojahed and Aghazadeh,
2008), and even more reported in developing countries, including Iran, Bahrain, Egypt, Nigeria
and Uganda (Jalal and Shoar, 2019;Jarkas, 2015;El-Gohary and Aziz, 2014;Odesola and Idoro,
2014; Alinaitwe et al., 2002). Management and political problems were other factors
contributing to low productivity. It has been recognized that the problem of management is
largely related to most of the factors undermining productivity in the construction sector
(Kermanshachi et al., 2022). Studies conducted in Yemen (Alaghbari et al., 2019) and India
(Thomas and Sudhakumar, 2013) similarly report political uncertainty and political strikes as
major CLP issues in the regions.
The study reported key stakeholders’perceptions and provided a framework for
understanding and promoting social causes of low CLP levels. Perceptions include interest
groups avoiding training commitments, the availability of finance, which is the primary
focus for the construction business, and politically motivated factors. CLP performance was
obtained using metaphorical expressions that define the worldview of CLP. The metaphors
have been reconstructed to show alternative options for South African construction SMEs.
The alternative possibilities include the need to address productivity issues as a system,
maintain and intensify construction industry skills development programs, experienced
contractor mentoring for new entrants, directors conducting appropriate analysis before
starting construction and contractors should be allowed to recruit based on the required
skills and competence. From the analysis of related studies, CLA is still new to the field of
construction, while the results obtained at the last two levels of CLA –worldview and
metaphor are rarely been reported in existing productivity research. This is because existing
studies have widely explored the litany and systemic causes of poor productivity in
construction. The systemic causes have either been quantitatively or qualitatively obtained
to present frameworks and models for productivity improvement for construction
organizations and industry. Although studies have made significant contributions to the
field of human resource productivity, the current construction productivity performance is
an indication that the studies have not significantly benefitted construction organizations or
that the studies’results/recommendations have not been taken up in practice. The novelty of
this study in terms of exploring the worldview and metaphors associated with the
productivity of human resources provided a framework for understanding and further
presents alternative possibilities to engender productivity improvement. The CLA
methodology has implications for theory, as it makes a greater depth of inquiry possible.
Given the importance of construction SMEs to economic prosperity, the outcome of this
Construction
SMEs labour
productivity

study can be leveraged by South African construction SMEs to promote productivity in
their organizations. Construction stakeholders can consider the results of the study to
formulate a policy framework for SME productivity growth. Such a framework would help
improve the profitability of construction SMEs, which would mitigate business failure and
ultimately help improve the social well-being of South Africans. Considering some of the
operation peculiarities in SMEs and more established contractors, it is recommended that
CLA should be considered to explore productivity in large construction organizations. The
research methodology should also be considered to investigate other construction research
fields considering the dearth of CLA application in the field of construction. Mixed-method
research could also be considered for similar studies in the future. However, such studies
must critically analyse the impact of quantitative data on the third and fourth layers of CLA.
References
Abor, J. and Quartey, P. (2010), “Issues in SME development in Ghana and South Africa”,International
Research Journal of Finance and Economics, Vol. 39 No. 6, pp. 215-228.
Adebowale, O.J. and Agumba, J.N. (2021), “A meta-analysis of factors affecting labour productivity of
construction SMEs in developing countries”,Journal of Engineering, Design and Technology,
pp. 1-20, doi: 10.1108/JEDT-05-2021-0277.
Adebowale, O.J. and Agumba, J.N. (2022a), “A scientometric analysis and review of construction labour
productivity research”,International Journal of Productivity and Performance Management,
pp. 1-21, doi: 10.1108/IJPPM-09-2021-0505.
Adebowale, O.J. and Agumba, J.N. (2022b), “A causal layered analysis of construction labour
productivity in developing countries”,Journal of Futures Studies, Vol. 27 No. 2, pp. 4-17, doi:
10.6531/JFS.202212_27(2).0002.
Adebowale, O.J. and Smallwood, J.J. (2020), “Contractors’perceptions of factors affecting construction
labour productivity in South Africa”,Journal of Contemporary Management, Vol. 17 No. 2,
pp. 326-346, doi: 10.35683/jcm20028.78.
Adediran, A. and Windapo, A.O. (2017), “The influence of government targeted procurement strategies
on the growth performance of construction small and medium sized contractors (SMCs) in South
Africa”,International Journal of Construction Supply Chain Management, Vol. 7 No. 3,
pp. 151-164.
Afolabi, A., Ojelabi, R.A., Omuh, I., Tunji-Olayeni, P. and Adeyemi, M. (2018), “Critical success factors
influencing productivity of construction artisans in the building industry”,International Journal
of Mechanical Engineering and Technology, Vol. 9 No. 8,pp. 858-867.
Aghayeva, K. and Slusarczyk, B. (2019), “Analytic hierarchy of motivating and demotivating factors
affecting labor productivity in the construction industry: the case of Azerbaijan”,Sustainability,
Vol. 11 No. 21, pp. 1-14, doi: 10.3390/su11215975.
Aghimien, D., Aigbavboa, C.O., Gomes, F. and Thwala, W.D. (2019), “Barriers to knowledge
management in small and medium construction companies in South Africa”,Proceedings of the
Creative Construction Conference,Budapest,29June–2 July, pp. 213-219.
Agrawal, A. and Halder, S. (2020), “Identifying factors affecting construction labour productivity in
India and measures to improve productivity”,Asian Journal of Civil Engineering, Vol. 21 No. 4,
pp. 569-579.
Ahmad, S.B.S., Mazhar, M.U., Bruland, A., Andersen, B.S., Langlo, J.A. and Torp, O. (2020), “Labour
productivity statistics: a reality check for the norwegian construction industry”,International
Journal of Construction Management, Vol. 20 No. 1, pp. 39-52.
Aigbavboa, C.O., Tshikhudo, L.M., and Thwala, W.D. (2014), Identification of critical success factors
for the survival of small, medium and micro enterprise contracting firms in the greater
Johannesburg metropolitan area. in Layea, S. and Ibem, E. (Eds), Proceedings 8th Construction
JEDT

Industry Development Board (cidb) postgraduate conference, 10 November University of
Witwaterstrand, Johannesburg, pp. 349-356.
Alaghbari, W., Al-Sakkaf, A.A. and Sultan, B. (2019), “Factors affecting construction labour productivity
in Yemen”,International Journal of Construction Management, Vol. 19 No. 1, pp. 79-91.
Ando, H., Cousins, R. and Young, C. (2014), “Achieving saturation in thematic analysis: development
and refinement of a codebook”,Comprehensive Psychology, Vol. 3, pp. 1-7, doi: 10.2466/03.CP.3.4.
Balogun, O.A., Nazeem, A. and Agumba, J.N. (2016), “Determinants predicting credit accessibility
within small and medium-sized enterprises in the South African construction industry”,
Procedia Engineering, Vol. 164, pp. 473-480.
Barbosa, F., Woetzel, J. and Mischke, J. (2017), Reinventing Construction: A Route of Higher
Productivity, McKinsey Global Institute.
Bhilwade, V., Delhi, V.S.K., Nanthagopalan, P., Das, A.K. and Modi, K. (2022), “Predicting labour
productivity for formwork activities in high-rise building construction: a case study”,Asian
Journal of Civil Engineering, pp. 1-10.
Bierman, M., Marnewick, A. and Pretorius, J.H.C. (2016), “Productivity management in the South
African civil construction industry-factors affecting construction productivity”,Journal of the
South African Institution of Civil Engineering, Vol.58 No.3, pp. 37-44.
Briscoe, G., Dainty, A. and Millett, S. (2000), “The impact of the tax system on self-employment in the
British construction industry”,International Journal of Manpower, Vol. 21 No. 8, pp. 596-614.
Chaparro, D.A., Ying, F.J., Rotimi, F.E. and Egbelakin, T. (2020), “Commute and labour productivity:
investigation of inner city construction sites”,Journal of Engineering, Design and Technology,
Vol. 18 No. 5, pp. 1305-1319.
Chia, F.C., Skitmore, M., Gray, J. and Bridge, A. (2018), “International comparisons of nominal and real
construction labour productivity”,Engineering, Construction and Architectural Management,
Vol. 25 No. 7, pp. 896-915, doi: 10.1108/ECAM-12-2016-0255.
Chimucheka, T. (2013), “Overview and performance of the SMMEs sector in South Africa”,
Mediterranean Journal of Social Sciences, Vol. 4 No. 14, pp. 783-795.
Construction Industry Development Board (CIDB) (2015), Labour and Work Conditions in the South
African Construction Industry, CIDB, Pretoria, South Africa.
Dijkhuizen, M.J., Vrijhoef, R. and Bakker, H.L. (2021), “A conceptual model to determine the impact of off-
site construction on labour productivity”,inAlarcon,L.F.andGonzález,V.A.(Eds),29th Annual
Conference of the International Group for Lean Construction (IGLC29), Vol. 27, pp. 945-955,
10.24928/2021/0121.
Durdyev, S. and Ismail, S. (2016), “On-site construction productivity in Malaysian infrastructure
projects”,Structural Survey, Vol. 34 Nos 4/5.
Durdyev, S. and Mbachu, J. (2011), “On-site labour productivity of New Zealand construction industry:
key constraints and improvement measures”,Construction Economics and Building, Vol. 11
No. 3, pp. 18-33.
El-Gohary, K.M. and Aziz, R.F. (2014), “Factors influencing construction labour productivity in Egypt”,
Journal of Management in Engineering, Vol. 30 No. 1, p. 30-39.
Etikan, I., Musa, S.A. and Alkassim, R.S. (2016), “Comparison of convenience sampling and purposive
sampling”,AmericanJournal of Theoretical and Applied Statistics, Vol. 5 No. 1, pp. 1-4.
Fatoki, O. (2014), “The causes of the failure of new small and medium enterprises in South Africa”,
Mediterranean Journal of Social Sciences, Vol. 5 No. 20, pp. 922.
Francis, J.J., Johnston, M., Robertson, C., Glidewell, L., Entwhistle, V., Eccles, M.P. and Grimshaw, J.M.
(2010), “What is an adequate sample size? Operationalising data saturation for theory-driven
interview studies”,Psychology and Health, Vol. 25 No. 10, pp. 1229-1245, doi: 10.1080/
08870440903194015.
Construction
SMEs labour
productivity

George, H. (2016), “Eliciting the financial challenges facing emerging contractors in developing
countries using the critical incident technique: a case of South African construction industry”,
Business and Management Horizons, Vol. 3 No. 2, pp. 23-33, doi: 10.5296/bmh.v4i2.10289.
Ghoddousi, P., Poorafshar, O., Chileshe, N. and Hosseini, M.R. (2015), “Labour productivity in Iranian
construction projects: Perceptions of chief executive officers”,International Journal of
Productivity and Performance Management, Vol. 64 No. 6, pp. 811-830, doi: 10.1108/IJPPM-10-
2013-0169.
Goodarzizad, P., Mohammadi Golafshani, E. and Arashpour, M. (2023), “Predicting the construction
labour productivity using artificial neural network and grasshopper optimisation algorithm”,
International Journal of Construction Management, pp. 1-17.
Gunduz, M. and Abu-Hijleh, A. (2020), “Assessment of human productivity drivers for construction
labor through importance rating and risk mapping”,Sustainability, Vol. 12 No. 20, pp. 1-18.
Gurmu, A.T. (2019), “Identifying and prioritizing safety practices affecting construction labour
productivity: an empirical study”,International Journal of Productivity and Performance
Management, Vol. 68 No. 8, pp. 1457-1474.
Gurmu, A.T. (2020), “Construction materials management practices enhancing labour productivity in
multi-storey building projects”,International Journal of Construction Management, Vol. 20
No. 1, pp. 77-86.
Gurmu, A.T. (2021), “Fuzzy synthetic evaluation of human resource management practices influencing
construction labour productivity”,International Journal of Productivity and Performance
Management, Vol. 70 No. 2, pp. 256-276.
Gurmu, A.T. and Aibinu, A.A. (2018), “Survey of management practices enhancing labor productivity
in multi-storey building construction projects”,International Journal of Productivity and
Performance Management, Vol. 67 No. 4, pp. 717-735.
Hai, D.T. and Van Tam, N. (2019), “Application of the regression model for evaluating factors affecting
construction workers’labor productivity in vietnam”,The Open Construction and Building
Technology Journal, Vol. 13 No. 1, pp. 353-362, doi: 10.2174/1874836801913010353.
Hamza, M., Shahid, S., Bin Hainin, M.R. and Nashwan, M.S. (2022), “Construction labour productivity:
review of factors identified”,International Journal of Construction Management, Vol. 22 No. 3,
pp. 413-425.
Hewage, K.N. and Ruwanpura, J.Y. (2006), “Carpentry workers issues and efficiencies related to
construction productivity in commercial construction projects in Alberta”,Canadian Journal of
Civil Engineering, Vol. 33 No. 8, pp. 1075-1089.
Hiyassat, M.A., Hiyari, M.A. and Sweis, G.J. (2016), “Factors affecting construction labour productivity: a
case study of Jordan”,International Journal of Construction Management, Vol. 16 No. 2, pp. 138-149.
Hwang, B.G., Zhu, L. and Ming, J.T.T. (2017), “Factors affecting productivity in green building
construction projects: the case of Singapore”,Journal of Management in Engineering, Vol. 33
No. 3, pp. 1-12.
Inayatullah, S. (1998), “Causal layered analysis: poststructuralism as method”,Futures, Vol. 30 No. 8,
pp. 815-829.
Inayatullah, S. (Ed.) (2004), The Causal Layered Analysis (CLA) Reader, Tamkang University Press,
Taipei.
Inayatullah, S. (2008), “Six pillars: futures thinking for transforming”,Foresight, Vol. 10 No. 1, pp. 4-21.
Inayatullah, S. (2019), “Causal layered analysis a four-level approach to alternative futures relevance
and use in foresight”,Futuribles, Vol. 12, pp. 123-135.
Jalal, M.P. and Shoar, S. (2019), “A hybrid framework to model factors affecting construction labour
productivity”,Journal of Financial Management of Property and Construction, pp. 385-409.
Jarkas, A.M. (2015), “Factors influencing labour productivity in Bahrain’s construction industry”,
International Journal of Construction Management, Vol. 15 No. 1, pp. 94-108.
JEDT

Jarkas, A.M. and Bitar, C.G. (2012), “Factors affecting construction labor productivity in Kuwait”,
Journal of Construction Engineering and Management, Vol. 138 No. 7, pp. 811-820.
Jarkas, A.M. and Horner, R.M.W. (2015), “Creating a baseline for labour productivity of reinforced
concrete building construction in Kuwait”,Construction Management and Economics, Vol. 33
No. 8, pp. 625-639, doi: 10.1080/01446193.2015.1085651.
Jarkas, A.M., Al Balushi, R.A. and Raveendranath, P.K. (2015), “Determinants of construction
labour productivity in Oman”,International Journal of Construction Management,Vol.15
No. 4, pp. 332-344.
Kaming, P.F., Olomolaiye, P.O., Holt, G.D. and Harris, F.C. (1997), “Factors influencing craftsmen’s
productivity in Indonesia”,International Journal of Project Management, Vol. 15 No. 1, pp. 21-30.
Karatas, I. and Budak, A. (2022), “Development and comparative of a new meta-ensemble machine
learning model in predicting construction labor productivity”,Engineering, Construction and
Architectural Management, Vol. 1.
Karimi, H., Taylor, T.R. and Goodrum, P.M. (2017), “Analysis of the impact of craft labour availability
on North American construction project productivity and schedule performance”,Construction
Management and Economics, Vol. 35 No. 6, pp. 368-380, doi: 10.1080/01446193.2017.1294257.
Karthik, D. and Rao, C.B., K. (2019), “The analysis of essential factors responsible for loss of labour
productivity in building construction projects in India”,Engineering Journal, Vol. 23 No. 2,
pp. 55-70, doi: 10.4186/ej.2019.23.2.55.
Kazaz, A. and Ulubeyli, S. (2007), “Drivers of productivity among construction workers: a study in a
developing country”,Building and Environment, Vol. 42 No. 5, pp. 2132-2140.
Kermanshachi, S., Rouhanizadeh, B. and Govan, P. (2022), “Developing management policies and
analyzing impact of change orders on labor productivity in construction projects”,Journal of
Engineering, Design and Technology, Vol. 20 No. 5, pp.1257-1279.
Khanzadi, M., Kaveh, A., Alipour, M. and Khanmohammadi, R. (2017), “Assessment of labor
productivity in construction projects using a system dynamic approach”,Scientia Iranica,
pp. 2684-2695.
Kubeckova, D. and Smugala, S. (2021), “Determination of construction process duration based on labor
productivity estimation: a case study”,Organization, Technology and Management in
Construction: An International Journal, Vol. 13 No. 2, pp. 2521-2538.
Lacey, A. and Luff, D. (2001), Qualitative Data Analysis, Trent focus, Sheffield, pp. 320-357.
Lu, H., Zhang, Q., Cui, Q., Luo, Y., Pishdad-Bozorgi, P. and Hu, X. (2021), “How can information
technology use improve construction labor productivity? An empirical analysis from China”,
Sustainability,Vol. 13No.10, pp. 1-13.
MacGill, V. (2015), “Unravelling the myth/metaphor layer in causal layered analysis”,Journal of
Futures Studies, Vol. 20 No. 1, pp. 55-68.
Ma, L., Liu, C. and Mills, A. (2016), “Construction labor productivity convergence: a conditional frontier
approach”,Engineering, Construction and Architectural Management, Vol. 23 No. 3, pp. 283-301,
doi: 10.1108/ECAM-03-2015-0040.
Mafundu, R.H. and Mafini, C. (2019), “Internal constraints to business performance in black-owned
small to medium enterprises in the construction industry”,The Southern African Journal of
Entrepreneurship and Small Business Management, Vol. 11 No. 1, pp. 1-10, doi: 10.4102/sajesbm.
v11i1.165.
Mojahed, S. and Aghazadeh, F. (2008), “Major factors influencing productivity of water and wastewater
treatment plant construction: evidence from the deep South USA”,International Journal of
Project Management, Vol. 26 No. 2, pp. 195-202.
Moselhi, O. and Khan, Z. (2012), “Significance ranking of parameters impacting construction labour
productivity”,Construction Innovation, Vol. 12 No. 3.
Construction
SMEs labour
productivity

Mlybari, E.A. (2020), “Application of soft computing techniques to predict construction labour
productivity in Saudi Arabia”,International Journal of GEOMATE, Vol. 19 No. 71, pp. 203-210.
Namey, E., Guest, G., McKenna, K. and Chen, M. (2016), “Evaluating bang for the buck: a cost-
effectiveness comparison between individual interviews and focus groups based on thematic
saturation levels”,American Journal of Evaluation, Vol. 37 No. 3, pp. 425-440, doi: 10.1177/
1098214016630406.
Nasir, H., Ahmed, H., Haas, C. and Goodrum, P.M. (2014), “An analysis of construction productivity
differences between Canada and the United States”,Construction Management and Economics,
Vol. 32 No. 6, pp. 595-607.
Nojedehi, P. and Nasirzadeh, F. (2017), “A hybrid simulation approach to model and improve construction
labor productivity”,KSCE Journal of Civil Engineering, Vol. 21 No. 5, pp. 1516-1524, doi: 10.1007/
s12205-016-0278-y.
Nguyen, L. and Nguyen, HT. (2013), “Relationship between building floor and construction labor
productivity: a case of structural work”,Engineering, Construction and Architectural
Management, Vol. 20 No. 6, pp. 563-575.
Odesola, I.A. and Idoro, G.I. (2014), “Influence of labour-related factors on construction labour
productivity in the South-South geo-political zone of Nigeria”,Journal of Construction in
Developing Countries, Vol. 19 No. 1, pp. 93-109.
Ofori, G., Zhang, Z. and Ling, F.Y. (2022), “Key barriers to increase construction productivity: the
Singapore case”,International Journal of Construction Management, Vol. 22 No. 14, pp. 2635-2646.
Okorie, V.N. and Musonda, I. (2020), “An investigation on supervisor’s ability and competency to
conduct construction site health and safety induction training in Nigeria”,International Journal
of Construction Management, Vol. 20 No. 5, pp. 357-366.
Olawale, F. and Garwe, D. (2010), “Obstacles to the growth of new SMEs in South Africa: a
principal component analysis approach”,African Journal of Business Management,Vol.4
No. 5, pp. 729-738.
Olomolaiye, P.O., Wahab, K.A. and Price, A.D. (1987), “Problems influencing craftsmen’s productivity
in Nigeria”,Building and Environment, Vol. 22 No. 4, pp. 317-323.
Oswald, D., Ahiaga-Dagbui, D.D., Sherratt, F. and Smith, S.D. (2020), “An industry structured for
unsafety? An exploration of the cost-safety conundrum in construction project delivery”,Safety
Science, Vol. 122, p. 104535.
Palikhe, S., Kim, S. and Kim, J.J. (2019), “Critical success factors and dynamic modeling of construction
labour productivity”,International Journal of Civil Engineering, Vol. 17 No. 3, pp. 427-442.
Picariello, F., Ali, S., Foubister, C. and Chalder, T. (2017), “It feels sometimes like my house has burnt
down, but I can see the sky’: a qualitative study exploring patients’views of cognitive
behavioural therapy for chronic fatigue syndrome”,British Journal of Health Psychology, Vol. 22
No. 3, pp. 383-413, doi: 10.1111/bjhp.2017.22.issue-3.
Renault, B.Y., Agumba, J.N. and Ansary, N. (2020), “Underlying structures of risk response measures
among small and medium contractors in South Africa”,Construction Economics and Building,
Vol. 20 No. 1.
Rivas, R.A., Borcherding, J.D., González, V. and Alarcón, L.F. (2011), “Analysis of factors influencing
productivity using craftsmen questionnaires: case study in a Chilean construction company”,
Journal of Construction Engineering and Management, Vol. 137 No. 4, pp. 312-320.
Robles, G., Stifi, A., Ponz-Tienda, J.L. and Gentes, S. (2014), “Labor productivity in the construction
industry-factors influencing the spanish construction labour productivity”,International Journal
of Civil Structure Construction Architecture Engineering, Vol. 8 No. 10, pp. 1009-1018.
Sarihi, M., Shahhosseini, V. and Banki, M.T. (2023), “Development and comparative analysis of the
fuzzy inference system-based construction labor productivity models”,International Journal of
Construction Management, Vol. 23 No. 3, pp. 423-433.
JEDT

Sebastian, L. and Raghavan, V.S. (2015), “Constraints on labour productivity-a case study”,The
International Journal of Engineering and Science, Vol. 4 No. 4, pp. 88-97.
Selvam, G., Madhavi, T.C., Naazeema Begum, T.P. and Sudheesh, M. (2022), “Impact of labour
productivity in estimating the duration of construction projects”,International Journal of
Construction Management, Vol. 22 No. 12, pp. 2398-2404.
Shang, G., Sui Pheng, L. and Jia Hui, W. (2020), “Drivers and barriers for multiskilling workers in the
Singapore construction industry”,International Journal of Construction Management, Vol. 20
No. 4, pp. 289-304, doi: 10.1080/15623599.2018.1484851.
Shoar, S. and Banaitis, A. (2019), “Application of fuzzy fault tree analysis to identify factors influencing
construction labor productivity: a high-rise building case study”,Journal of Civil Engineering
and Management, Vol. 25 No. 1, pp. 41-52.
Shrotryia, V.K. and Dhanda, U. (2019), “Content validity of assessment instrument for employee
engagement”,SAGE Open, Vol. 9 No. 1, pp. 1-7, doi: 10.1177/2158244018821751.
Silverman, D. (1993), Interpreting Qualitative Data, Sage, London.
Sim, J., Saunders, B., Waterfield, J. and Kingstone, T. (2018), “Can sample size in qualitative research be
determined a priori?”,International Journal of Social Research Methodology, Vol. 21 No. 5,
pp. 619-634.
Tam, N.V., Watanabe, T. and Hai, N.L. (2022), “Importance of autonomous motivation in construction
labor productivity improvement in vietnam: a self-Determination theory perspective”,Buildings,
Vol. 12 No. 6, pp. 1-25.
Tam, N., Quoc Toan, N., Tuan Hai, D. and Le Dinh Quy, N. (2021), “Critical factors affecting
construction labor productivity: a comparison between perceptions of project managers and
contractors”,Cogent Business and Management, Vol. 8 No. 1, pp. 1-18.
Thomas, A.V. and Sudhakumar, J. (2013), “Critical analysis of the key factors affecting construction
labour productivity –an Indian perspective”,International Journal of Construction
Management, Vol. 13 No. 4, pp. 103-125.
Tsehayae, A.A. and Fayek, A.R. (2018), “Context adaptation of fuzzy inference system-based
construction labor productivity models”,Advances in Fuzzy Systems, Vol. 2018, pp. 1-17, doi:
10.1155/2018/5802918.
Wentzel, L., Smallwood, J.J. and Emuze, F.A. (2016), “Improving the business trajectory among small
and medium size construction firms in South Africa”,Journal of Construction Project
Management and Innovation, Vol. 6 No. 2, pp. 1477-1487.
Wong, J.H., Rashidi, A. and Arashpour, M. (2020), “Evaluating the impact of building information modeling
on the labor productivity of construction projects in Malaysia”,Buildings, Vol. 10 No. 4, pp. 1-21.
Yi, W. and Chan, A.P. (2017), “Effects of heat stress on construction labor productivity in Hong Kong: a
case study of rebar workers”,International Journal of Environmental Research and Public
Health, Vol. 14 No. 9, pp. 1-14.
Zakeri, M., Olomolaiye, P.O., Holt, G.D. and Harris, F.C. (1996), “A survey of constraints on Iranian
construction operatives’productivity”,Journal of Construction Management and Economics,
Vol. 14 No. 5, pp. 417-426.
Further reading
Bevan, M. and Steve, J. (2016), “LCI national webinar: preliminary findings from national project
performance research”, available at: www.leanconstruction.org/media/img/safetyweek/FINAL-2016-
05-26_LCI_ National_ Webinar-Owner_Project_Performance_Study_Overview_and_CallToAction.
pdf
Chaturvedi, S., Thakkar, J.J. and Shankar, R. (2018), “Labor productivity in the construction industry:
an evaluation framework for causal relationships”,Benchmarking: An International Journal,
Vol. 25 No. 1, pp. 334-356, doi: 10.1108/BIJ-11-2016-0171.
Construction
SMEs labour
productivity

Creswell, J.W. (2014), Research Design: Qualitative, Quantitative, and Mixed Methods Approaches,4thed.,
Sage, Los Angeles.
Creswell, J.W. (2013), Research Design: Qualitative, Quantitative, and Mixed Methods Approaches,Sage
Publications, Boston.
Hasan, A., Baroudi, B., Elmualim, A. and Rameezdeen, R. (2018), “Factors affecting construction
productivity: a 30 year systematic review”,Engineering, Construction and Architectural
Management, Vol. 25 No. 7, pp. 916-937.
Lans, T., Blok, V. and Wesselink, R. (2014), “Learning apart and together: towards an integrated
competence framework for sustainable entrepreneurship in higher education”,Journal of Cleaner
Production, Vol. 62, pp. 37-47.
Vogl, B. and Abdel-Wahab, M. (2015), “Measuring the construction industry’s productivity
performance: critique of international productivity comparisons at industry level”,Journal of
Construction Engineering and Management, Vol. 141 No. 4, doi: 10.1061/(ASCE)CO.1943-
7862.0000944.
Corresponding author
Oluseyi Julius Adebowale can be contacted at: adebowaleoluseyi@gmail.com
For instructions on how to order reprints of this article, please visit our website:
www.emeraldgrouppublishing.com/licensing/reprints.htm
Or contact us for further details: permissions@emeraldinsight.com
JEDT