See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/362227260
PESTEL Analysis of Mental Health Management of Project Management
Practitioners (PMPs) in Architecture, Engineering and Construction (AEC)
Project Organization
Article in Smart and Sustainable Built Environment · July 2022
DOI: 10.1108/SASBE-04-2022-0074
CITATION
READS
1
1,531
3 authors:
Bashir Tijani
Jin Xiaohua
NSW Department of Education and Communities
Western Sydney University
14 PUBLICATIONS 98 CITATIONS
65 PUBLICATIONS 574 CITATIONS
SEE PROFILE
SEE PROFILE
Osei-Kyei Robert
Western Sydney University
118 PUBLICATIONS 2,800 CITATIONS
SEE PROFILE
Some of the authors of this publication are also working on these related projects:
Smart Modern Construction Typologies in 21st Century View project
Methodology for estimating embodied carbon through a blockchain platform for construction supply chains View project
All content following this page was uploaded by Bashir Tijani on 19 August 2022.
The user has requested enhancement of the downloaded file.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2046-6099.htm
PESTEL analysis of mental health
management of project
management practitioners (PMPs)
in architecture, engineering and
construction (AEC)
project organization
Bashir Tijani, Xiao-Hua Jin and Robert Osei-Kyei
PESTEL
analysis
Received 28 April 2022
Revised 14 June 2022
23 June 2022
24 July 2022
24 July 2022
Accepted 24 July 2022
School of Engineering, Design and Built Environment, Western Sydney University,
Sydney, Australia
Abstract
Purpose – Architectural, engineering and construction (AEC) project organizations are under constant
pressure to improve the mental health of project management practitioners (PMPs) due to complexity and
dynamism involved in project management practices. Drawing on institutional theory, this research explores
how external environmental factors, political factors, economic factors, social factors, technological factors,
environmental factors and legal factors (PESTEL), influence mental health management indicators that
contribute to positive mental health.
Design/methodology/approach – Purposive sampling method was used to collect survey data from 82
PMPs in 60 AEC firms in Australia. Structural equation modelling was used to test the hypotheses based on 82
items of data collected from PMPs.
Findings – Overall, this study revealed interesting findings on the impact of external environmental factors on
mental health. The hypothesized positive association between political factors and mental health management
indicators was rejected. The data supported the proposed hypothetical correlation between economic factors
and mental health management indicators and the influence of social factors on mental health management
indicators. Moreover, a hypothetical relationship between technological factors and mental health management
indicators was supported. The significant positive impact of environmental factors on mental health
management indicators proposed was supported, and legal factors’ positive correlation on mental health
management indicators was also supported.
Originality/value – Despite the limitations, the present findings suggest that all the external environment
factors except political factors shape mental health management outcomes.
Keywords AEC, Mental health management, PMPs, PESTEL, Institutional theory
Paper type Research paper
1. Introduction
The architectural, engineering and construction (AEC) project organization is notorious for
poor mental health among project management practitioners (PMPs) due to inherent project
management activities that require management of multiple projects concurrently,
engagement with various stakeholders and coordinating multiple contractors within
stringent time, budget and standards (Pinto, 2014; Zika-Viktorsson et al., 2006).
These project management activities exposed PMPs to poor mental health through
psychosocial risks, including long work hours, project overload and inter-role conflict (Tijani
et al., 2020c; Haynes and Love, 2004; Bowen et al., 2014b). Therefore, it is apparent that the
This study is based on research supported by Western Sydney University PhD research scholarship
scheme.
Smart and Sustainable Built
Environment
© Emerald Publishing Limited
2046-6099
DOI 10.1108/SASBE-04-2022-0074
SASBE
design of the AEC project organization is a precursor to poor mental health because of the
relationship between project management practices and organizational design
(Zika-Viktorsson et al., 2006; Gustavsson, 2016).
Poor mental health among PMPs is a significant and unresolved problem in project
management due to the negative repercussions on project performance and scanty project
management studies focussing on the issue (Wang et al., 2017). Owing to the negative
consequences, scholars adopted various concepts at individual (Haynes and Love, 2004;
Leung et al., 2006) and organizational level to mitigate the psychosocial risks and promote
positive mental health (Yang et al., 2017; Love and Edwards, 2005). Their perception of the
problem was underpinned by job demand-resources (JDR) theory which posited that poor
mental health is a consequential effect of the imbalance between job demand and resources
(Pinto et al., 2016; Love et al., 2010). Drawing upon the theoretical concept, Love and Edwards
(2005) investigated the influence of job demand, job control and social support on the mental
health of project managers. The study found that job control and social supports alleviate
psychosocial risks and promote mental health.
Similarly, Leung et al. (2008), Bowen et al. (2014b) and Love et al. (2010) confirmed
organizational support as a resource for reducing psychosocial risks and improving the
mental health of project managers. Scholars further intensified their efforts in investigating
individual strategies to adjust or moderate the effects of poor mental health among PMPs
(Haynes and Love, 2004; Bowen et al., 2014a; Leung et al., 2006). Coping mechanisms are the
individual strategies that provide temporary solution towards the mental health problems as
they lack the capacity to address the psychosocial risks (Bowen et al., 2014a).
Extant studies unfolded the proximal job demand, proximal job resources and
individual resources contributing to the mental health of PMPs to establish the importance
of focussing on job demands and resources in AEC project organizations (Koch and
Schermuly, 2021). Proximal job demands, including project overload, long working hours
and project uncertainty, are related to project management tasks performed in a project
setting (Pak et al., 2019; Dollard et al., 2013). Proximal job resources: including
organizational support, feedback and job autonomy, are resources that immediately
influence the project management tasks performed in a project setting (Pak et al., 2019;
Tijani et al., 2021b). Individual resources refer to the workers’ resources: coping
mechanisms and personality applied to moderate the negative effects of work stress
(Kamardeen and Sunindijo, 2017; Tijani et al., 2020a). No doubt previous studies
contributed to the body of literature; nevertheless, a considerable literature has
challenged the concentration on proximal demand, proximal job resources and
individual resources because of limitations in elucidating the impact of the external
environment on organizational project practices, contributing to poor mental health among
PMPs (Koch and Schermuly, 2021; Dollard et al., 2013; Pak et al., 2019). Dollard et al. (2013)
argued mental health research must take a new dimension by moving from the utilization of
proximal factors and individual resources to external environmental implications on
organizational practices in establishing solutions to poor mental health. Viewing the
problem through the lens of the impact of external environmental factors on organizational
practices would capture the macro-environmental sources of poor mental health, which
have not been considered in the AEC project organizations (Parker et al., 2001; Dollard et al.,
2013). Despite the call for a paradigm shift from proximal factors to external environmental
factors in addressing mental health problems, there are scanty studies that explore how
external environmental factors shape mental health management outcomes contributing to
positive mental health. Examining external environmental factors on the mental health of
PMPs would unfold how the dynamic environment in which AEC project operates imposes
institutional forces to control the organizational project management practice responsible
for mental health.
Given that AEC projects do not operate in a vacuum, external environmental conditions
contribute to project management practices embraced by the governance institutions in
project delivery, which indirectly impact mental health. Continuous operation of AEC
projects in a dynamic and complex environment demands understanding external
environmental factors controlling organizational project management activities that shape
mental health. The external environment determines the organizational project management
practices through forces exerted on the organization to meet external environment demands
to have a competitive advantage. Through exploring the link between external
environmental factors and mental health, a new research scholarship showing how
external environmental factors contribute to mental health in a project environment would be
established, which has received limited attention among project management scholars.
Considering external environmental factors in investigating mental health problems in AEC
firms would reveal dynamic environmental factors controlling and coordinating the
organizational project management practices affecting mental health. Although previous
studies established the organizational and individual factors impacting mental health;
however, external environmental factors influencing the organizational factors remain underexplored, limiting the extant studies to explain the proximal factors contributing to mental
health. Exploration of the external environmental factors bridges the limitations of previous
studies and extends current mental health management knowledge in a project environment.
Therefore, this study aims to address this significant gap by addressing the main research
question.
What are the relationships between external environmental factors: political factors, economic
factors, social factors, technological factors, environmental factors and legal factors and mental
health management indicators in AEC project organizations?
This research question is answered by empirically testing the association between
external environmental factors and mental health management indicators. Afterwards,
underlying theoretical and methodological assumptions were discussed, which, when met,
may provide indicators for a limited causality. Through the hypothetical testing and
assumption, this study would identify dimensions of external environmental factors:
political factors, economic factors, social factors, technological factors, environmental
factors and legal factors that are related to mental health management outcomes in AEC
project organizations.
2. Theoretical background
2.1 Institutional theory
Institutional theory is a macro theory of organization-environment that emerged from the
work of DiMaggio and Powell (1983) and explained the impact of institutions on the
behaviour of organizations. The theoretical understanding is attributed to the pressure
imposed by coercive isomorphism on organizations residing in the environment (DiMaggio
and Powell, 1983). Utilization of institutional theory has grown in project management over a
couple of decades to explain the influence of external environmental factors on project
outcomes (Winch and Mayotorena-Sanchez, 2020); nevertheless, the application of
institutional theory through coercive isomorphism concept is rare in project management
to address the proliferation of mental health problems among PMPs in AEC project
organizations. Nevertheless, this theory is applicable in addressing mental health problems in
PMPs because of its comprehensiveness in explaining the impacts of external institutional
forces on organizational management practices, which shape the mental health of workforces
(Dollard et al., 2013). Based on theoretical principles, this study contributes to mental health
knowledge by empirically exploring relationships between external environment factors:
PESTEL
analysis
SASBE
political, economic, social, technological, environmental and legal (PESTEL) on mental health
management practices in AEC project organization through institutional theory.
3. Literature review and hypotheses development
3.1 Mental health management indicators
The concept of mental health management indicators was derived from workplace well-being
factors to represent mental health policies, strategies and practices contributing to the
positive mental health of workers (Carvajal-Arango et al., 2021). Various terms, including
healthy organizational indicators (Aronsson et al., 1999), healthy work environment index
(Lindberg and Ving
ard, 2012), and management standard indicators (Edwards and Webster,
2012), were used interchangeably to represent mental health management indicators.
Examples of mental health management indicators include good reward system, social
relationships and work–life balance policy (Lindberg and Ving
ard, 2012; Areskoug Josefsson
et al., 2018; Edwards and Webster, 2012). They are regarded as organizational level indicators
that define an organization’s environment promoting the positive mental health of workers
(Raya and Panneerselvam, 2013). These management indicators cover the primary sources of
work stress and explain the characteristics of a workplace in which risks from work-related
stress are effectively managed (Edwards and Webster, 2012). Mental health management
indicators prevent negative implications affecting individual’s health in the workplace and
have a promotional effect on the mental health and well-being of the workers (Lindberg et al.,
2015). The theoretical underpinning covering mental health and stress management
contributed to mental health management through the explanation of proximal psychosocial
risks and imbalance between the demand of work environment and workers’ resources
causing poor mental health (Tijani et al., 2021a; Zika-Viktorsson et al., 2006). Integration of the
theoretical concepts underpinned the development of mental health management indicators
capturing organizational and project sources impacting mental health (Gustavsson, 2016).
Psychosocial risks are prominent in a project based organization due to the complexity of its
operation; hence the stress management control theories see the tackling of psychosocial
risks as effective mental health management (Pinto et al., 2016). Moreover, viewing the mental
health management through the prism of imbalance between job demand and resources also
covers the resources for addressing excessive project demands. Therefore, the combination of
theories contributed to the development of mental health management indicators for
promoting positive mental health.
Table 1 depicts the list of the indicators.
3.2 External environmental factors (PESTEL)
Drawing inspiration from Dollard et al. (2013) and Parker et al. (2001) studies, external
environmental factors exert pressure on organizations, which transfers to the human
component of the organizations. The consequence of this pressure is manifested in the mental
health of project teams that constitute human resources components of construction projects.
Based on this perspective, this study examined political, economic, social, technological,
environmental and legal (PESTEL) factors. PESTEL analysis is a strategic management
technique used as an external analysis tool when conducting market research in business but
is also used by organizations to manage different projects strategically. Therefore, it is an
important market and environmental analysis tool to support decision-making (Narayanan
and Fahey, 2001).
3.2.1 Political factors. The construction industry operates within a political atmosphere
with specific restrictions and regulations to guide the activities of organizations working in
the environment (Khalid and Rahman, 2019). The central government, local government,
Code
MHI1
MHI2
MHI3
MHI4
MHI5
MHI6
MHI7
MHI8
Mental health management
definitions
Effective project governance
Project leadership
Social relationship with colleagues
Relationship with project
stakeholders
Employee recognition in the
organization
Working time schedule is flexible
MHM12
MHMI13
MHM14
Fairness in the organization
Strong teamwork in the
organization
Good reward system
Good Upskilling
Effective project management
office
Clear project information system
Role clarity
Appropriate staffing
MHM15
Project workload
MHI9
MHM10
MHI11
Relevant literature
PESTEL
analysis
Yang et al. (2017)
Agervold (1991), Baptiste (2009), Kroth et al. (2007)
Bergh et al. (2014)
Aronsson et al. (1999), Unterhitzenberger et al. (2020),
Wreder et al. (2008)
Aronsson et al. (1999), Unterhitzenberger et al. (2020),
Wreder et al. (2008)
Arwedson et al. (2007), Baptiste (2009), MacDermid et al.
(2008)
Agervold (1991), Kroth et al. (2007), Quick (1989)
Agervold (1991), Baptiste (2009), Kroth et al. (2007)
Baptiste (2009), Quick (1989)
Kroth et al. (2007)
Yang et al. (2017)
Gustavsson (2016)
Kroth et al. (2007)
Arwedson et al. (2007), Kroth et al. (2007), Lowe and Bennett
(2003)
Arnetz and Blomkvist (2007), MacDermid et al. (1999)
national government, and other governmental agencies play pivotal roles in the construction
industry. The national government in every economy represents regulators and main clients
in the construction project environment (Ansah et al., 2016). The government launched
initiatives, decisions, policies and schemes to stimulate, improve or protect the economy and
fulfil specific purposes. Some of these policies positively change the operational process of the
construction industry working in the economy (Ansah et al., 2016).
Scholars have explored the positive consequences of political factors on construction
projects (Ansah et al., 2016); nevertheless, its positive consequences on the mental health of
PMPs, who are at the forefront of construction projects, remain unknown. Political factors
influence mental health management outcomes in a project organization by shaping the
behaviour of corporate governance in designing governance structures aligned with the
demand of political affairs (Parker et al., 2017). Political environmental demands such as
government regulations, tax policies, and labour migration shape corporate governance
principles in managing the organizations and stakeholders (Ansah et al., 2016). Corporate
governance abides by political environmental demands to set structure and boundaries
communicated through policies and processes to project governance to perform project
management works (Muller et al., 2019). Implementing corporate governance policies and
practices by project governance in coordinating project management activities contributes to
the vulnerability of PMPs to work stress in the project environment (Yang et al., 2017; Koch
and Schermuly, 2021). Therefore, sources of poor mental health can be traced to political
environmental atmosphere exerting pressure on project organizations, which influences
decisions of corporate governance in the development of policies and practices (Tijani et al.,
2021a). The policies and practices guided various governance tasks, including monitoring
and controlling project performance, developing project management competencies and
organizational learning at the project management office (PMO) that informs project
management practices allocated to PMPs (Muller et al., 2019). The project management
Table 1.
List of mental health
management
indicators with
references
SASBE
activities assigned to PMPs determine the exposure of PMPs to excessive work stress
contributing to mental health (Yang et al., 2017; Haynes and Love, 2004). Institutional theory
complements the arguments of previous studies on the critical role of external environmental
factors in shaping project management practices that contributes to the mental health of
PMPs. Consideration of psychological health of PMPs by political factors would determine
coercive isomorphism imposed on the organization in developing project management
activities that enhance positive mental health (Tijani et al., 2021a). Therefore, it is
hypothesized that:
H1. Political factors are positively associated with mental health management indicators
3.2.2 Economic factors. The economic atmosphere in which the AEC industry operates
significantly affects the stakeholders involved in AEC projects because a healthy economy
usually fosters project performance (Khalid and Rahman, 2019; Ansah et al., 2016). Policy
instruments focussing on economic growth, price fluctuation, market competition, resource
availability, and taxation have direct or indirect implications on the construction process
(Khalid and Rahman, 2019). Moreover, positive changes in monetary policies by governments
to regulate the supply of money, exchange rates, interest policies and supply-side policies can
influence project performance (Pulaj and Kume, 2013).
The economic situation often drives clients’ budgets for construction projects that later
transfer to cost and time constraints imposing production pressure on project managers
(Sherratt, 2017). Production pressure is a threat to the mental distress of PMPs because their
concentration on completion of projects at unrealistic times and budget exposes them to
excessive work stress (Ajayi et al., 2019). Similarly, inaccessibility to bank loans and financial
aid from the government due to economic turmoil poses a threat to the cash flow of project
organizations by affecting suppliers of labour, materials and equipment necessary for project
delivery (Sui Pheng and Shing Hou, 2019). The limited supply of labour, materials and
equipment for project delivery subjects PMO to stringent project performance monitoring,
resource allocation and standardized methodologies, which affect the project management
activities assigned to PMPs (Zika-Viktorsson et al., 2006). Given the relationship between
project management activities and work stress, linking mental health management practices
to economic condition in which the AEC project resides is a logical way of extending current
mental health knowledge. Hence, it is hypothesized that:
H2. Economic factors are positively associated with mental health management
indicators
3.2.3 Social factors. In terms of the social dimension, organizations need to understand social
factors inherent in the external environment shaping project performance because of the
interaction between organization and society. Pressures from the social environment hamper
growth and subsequently stifle project development (Ansah et al., 2016; Vintila et al., 2017).
Various studies have investigated social factors such as community involvement, non-profit
organization commitment, norms, and aged population contributing to organizational
performance to establish links between social factors and organization (Hughes, 1989; Khalid
and Rahman, 2019). Nevertheless, there are no blanket social factors affecting organizations
as the purpose defines specific factors shaping the organizations; thus, organizations must
investigate the relevant social factors (Pulaj and Kume, 2013).
Social relationships between external project stakeholders such as regulatory agencies,
local communities, and project organizations are another social dimension that predicts the
smooth implementation of project management practices (Di Maddaloni and Davis, 2017).
Social relationships assist PMPs in engaging external stakeholders in organizational
activities and project management arrangements that generate values for the external
stakeholders (Aragones-Beltran et al., 2017; Nguyen et al., 2019). When mutual relationships
between external stakeholders and project organizations are established, PMPs engage
external stakeholders effectively without excessive work stress through effective
communication and understanding of values required by the stakeholders (Yang et al.,
2017). Previous studies confirmed that PMPs exposed job burnout because of managing
multiple project stakeholders as they tend to balance their needs in project delivery (Yang
et al., 2017; Pinto, 2014). Based on extant studies, it is hypothesized that:
H3. Social factors are positively associated with mental health management indicators
3.2.4 Technological factors. Innovative technologies, particularly BIM and Blockchain
technology, are revolutionizing conventional practices and industrializing the construction
supply chain (Chiang et al., 2006). The impact on productivity enhances communication
among parties and connects project teams across distance and boundaries (Pan et al., 2019).
As the proliferation of technologies continues, there is no indication of stopping in the nearest
future; therefore, the construction industry needs to leverage the opportunities of technology
to address organizational problems. Sherratt (2017) stated that lack of technological
innovation is one of the causes of poor occupational health in the construction industry. In
another sector, organizational inability to adopt technology to improve work process result in
work stress among workers, signifying the potential of technology on employee well-being
(Tarafdar et al., 2007; Nixon and Spector, 2013).
Technology enhances workers’ well-being and satisfaction by increasing workers’ control
over when and where they complete their work (Day et al., 2010), and increasing the
communication between employees and managers (Zaccaro and Bader, 2003). In addition,
flexible technology allows organizations to meet market demand through greater product
customization, giving rise to work characteristics that predict positive mental health (Parker
et al., 2001). However, in the context of project management, scanty studies focused on the
implication of technological factors on the mental health management practices contributing
to the mental health of PMPs. However, drawing on previous studies from other sectors, it can
be hypothesized that technological advancement influences mental health management in
AEC project organizations. Furthermore, institutional theory substantiates the findings of
previous studies by explaining the impact of technology advancement on project
organizations on mental health management practices shaping the mental health of PMPs.
Therefore, it is hypothesized that:
H4. Technological factors are positively associated with mental health management
indicators
3.2.5 Environmental factors. Harmful effects of construction projects on the environment
prompted the evolution of construction sustainability as a mechanism to regularize
activities of construction projects against environmental damage (Ansah et al., 2016).
Environmental regulation and rating systems for construction sustainability put
immense pressure on project managers because of legal and economic consequences on
construction organizations (Hwang and Ng, 2013). The importance of complying with
environmental regulation and rating system escalate the stress level of project manager,
which, if unmanaged, can lead to burnout (Yang et al., 2017). Therefore, environmental
regulatory standards are required to simplify the process of achieving green construction
to reduce the stress level encountered by the PMPs in implementing standards
(Tijani et al., 2021a). Institutional theory further illuminates the impact of coercive
pressure from regulators in changing organizational behaviour (DiMaggio and Powell,
1983). The AEC project organization’s mental health management practices can be
controlled by regulatory standards developed by the institution bodies. Hence, it can be
hypothesized that:
PESTEL
analysis
SASBE
H5. Environmental factors are positively associated with mental health management
indicators
3.2.6 Legal factors. Institutional theory provides theoretical underpinning on why legal
factors exert forces on inner organizational components. Legal are established regulations,
rules and principles promulgated through legislation to guide activities of organizations and
individuals in society (Ansah et al., 2016). National, state and local governments pass various
legislation to protect individual rights and construction organizations against illegal
transactions (Khalid and Rahman, 2019). The employment and labour act represent a
legislative act the define employment right of the employees and employers to ensure justice
and fairness in contractual agreements (Parker et al., 2017). The effects of legal factors are
experienced mainly during the contractual arrangements, code of practices and employment
regulations. During disputes among parties or unlawful employment termination, the legal
framework guides conflict resolutions (Ansah et al., 2016). Extant studies demonstrated the
crucial role of employment regulation in workers’ mental health (Alrasheed, 2015; Parker
et al., 2017). The legal framework guides the design of workplaces that inherit specific
characteristics of workers’ well-being. Processes and operations promoting positive wellbeing are contained in the framework to assist organizations in resource allocation,
employment contracts, training, rewards and responsibilities to workers (Parker and
Cordery, 2007). Legal policies enforce organizations to implement work design framework
designed to assist in achieving healthy workplace that enhance well-being (Parker et al.,
2017). Thus, it can be hypothesized that:
H6. Legal factors are positively associated with mental health management indicators
Based on the proposed hypothetical relationships underpinned by institutional theory and
previous studies, it can be inferred that political, economic, social, technological,
environmental, and legal factors positively impact mental health management indicators.
Although these are just hypothetical relationships, analysing the data collected would test the
proposed hypotheses.
Figure 1 depicts the theoretical model for this research.
4. Research methodology
4.1 Design
The research question and hypotheses indicate that this study aims to discover relationships
between seven variables (i.e. political, economic, social, technological, environmental, legal,
and mental health management indicators). A deductive approach was selected for a robust
design that included existing organizational theory and new empirical evidence. Based on the
deductive approach, a survey design was chosen to collect quantitative data from different
PMPs in AEC firms in order to gain the widest coverage of the resulting organizational
theory.
4.2 Questionnaire development
Three sets of questions were included in the questionnaire. The first set included
respondents’ demographic information (e.g. educational qualification, work experience,
project-related experience) and characteristics of the given AEC projects (e.g. project
monetary value, type of client, project duration). The next two sets covered information on
seven variables: political factors, economic factors, social factors, technological factors,
environmental factors, legal factors and mental health management indicators.
The questionnaire followed the recommendations of Kumar (2019) to ensure that the
scales, criteria and wordings were consistent and clear.
PESTEL
analysis
Figure 1.
Theoretical model for
the hypothetical
relationships
The political factors questions were developed based on an extensive literature review of
extant studies (Vintila et al., 2017; Ansah et al., 2016; Khalid and Rahman, 2019). Based on
literature review of previous studies, measurement items for economic factors were developed
(Khalid and Rahman, 2019; Sui Pheng and Shing Hou, 2019; Hughes, 1989). The social factors
dimensions were developed based on the review of previous studies (Alrasheed, 2015; Ansah
et al., 2016). Literature review of extant studies underpins the development of a questionnaire
for technological factors (Pan et al., 2019; Chiang et al., 2006). The environmental factors
questions were taken from Pan et al. (2019) and Vintila et al. (2017). The legal factors
dimensions were based on literature review (Parker et al., 2001, 2017). To measure mental
health management indicators, questions were based on an extensive review of literature
review (Baptiste, 2009; Kroth et al., 2007; Bergh et al., 2014; Aronsson et al., 1999; Agervold,
1991). All the measurement items were reworded to reflect project management. Accordingly, a
five-point Likert scale (between 1 5 strongly disagree and 5 5 strongly agree) was adopted for
all the variables measuring scales through which respondents were required to show their
agreement level for each item found in the section. Please refer to Appendix for the survey.
4.3 Validity and reliability of questionnaire
Content validity of the questionnaire was theoretically established on the basis that (1) the
design and measurement items concerning the constructs were underpinned by a critical
review of previous studies and (2) multiple measurement scales that captured all of the parts
of the definition of the constructs were developed. Face validity of the data collection
SASBE
instrument was examined through a pilot study in which the drafted questionnaires were
sent to experts via email to identify problems concerning the clarity of instructions, wordings,
questions and statements. The questionnaire was then revisited and finalized based on
experts’ feedback.
The reliability of the questionnaire can be confirmed with all constructs with Cronbach’s
alpha values higher than 0.7 (Hair et al., 2019). Using SPSS version 27, reliabilities of the items
were assessed and generated Cronbach’s alpha value for the latent variables: political factors
(0.819), economic factors (0.829), social factors (0.737), technological factors (0.853),
environmental factors (0.725), legal factors (0.845) and mental health management
indicators (0.942). Please refer to Table 4. The statistical values generated confirmed the
reliability of measurement items.
4.4 Sampling and data collection
Given the absence of government or construction associations register on the numbers of
AEC firms operating in Australia, purposive sampling was chosen in recruiting PMPs to
provide reliable information concerning completed AEC projects in Australia.
Of the 360 online surveys distributed among 60 AEC firms in Australia, 90 responses were
received with a response rate of 25%. The completed surveys were scrutinized to detect
unreliable and invalid responses, which were discarded due to short response duration as
depicted by Qualtrics survey software (5 questionnaires) and the same choices for all the
questions (3 questionnaires). As a result, the information of 82 project management
practitioners (PMPs) was input into a database. The response rate is sufficient based on the
established mathematical equation Kamardeen and Sunindijo (2017) used for work stress in
Australia’s construction industry.
Mathematical formula for sample size is shown below:
N¼
t 2 X s2
d2
(1)
N 5 sample size; t 5 value of significance level of 0.05, which is 5 1.96, s 5 estimated
variance deviation for the scale used for data collection (5 points Likert scale used in this
study); d 5 number of points on the primary scale multiplied by the acceptable margin error
of 5%. The value of d is calculated by multiplying the number of points on the instrument
Likert scale by the marginal error.
N¼
1:962 x12
ð5x 0:05Þ2
¼ 61
Based on the calculation, a minimum of 61 PMPs involved in AEC projects are needed;
however, this study used 82 samples, indicating the sufficiency of the sample size.
4.5 Analysis methods
Structural equation modelling (SEM) has been widely used in mental health studies as an
analytical tool to examine relationships among research constructs (Bowen et al., 2018; Leung
et al., 2011). SEM is characterized by its potential to estimate multiple and interdependent
relationships, presentation of latent constructs in interdependent relationships, and account
for measurement error in the estimation process (DiLalla et al., 2000). In this research, SEM
was applied to examine the impacts of political, economic, social, technological,
environmental, and legal factors on the mental health management indicators in AEC
projects.
PLS-SEM is assessed in two stages: measurement model and structural model.
Measurement model concentrated on assessing the adequacy of individual measurement
items in capturing their related latent variables through evaluating internal consistency,
convergent validity, and discriminant validity of the specified constructs. Structural model
focuses on assessment of the relationships between constructs that formulated the models
(Hair et al., 2012).
4.6 Measurement model assessment
Reliability and validity of the constructs were first evaluated to assess the measurement
model. Then confirmatory factor analysis (CFA) was embraced for assessing internal
consistency reliability, convergent validity and discriminant validity of constructs to
evaluate the capacity of measurement items in capturing their constructs (Anderson and
Gerbing, 1988; Hair et al., 2016).
4.7 Internal consistency reliability
Cronbach’s alpha (Cronbach, 1951), and composite reliability (Werts et al., 1974), are methods
for assessing the internal consistency reliability of reflective models. The Cronbach’s alpha
reliability and composite reliability values are within the range of 0 and 1; a higher value shows
higher reliability level. The acceptable value for Cronbach’s alpha reliability and composite
reliability for this study will be 0.7, based on the recommendation by Hair et al. (2016).
4.8 Convergent validity
Outer loadings of the indicators and average variance extracted (AVE) were used to evaluate
the convergent validity of constructs in this research. The acceptable value of outer loading to
retain a construct is 0.4, based on the suggestion by Hair et al. (2016). The threshold value for
the AVE of each latent variable is 0.5 based on the recommendation of (Kline, 2015).
4.9 Discriminant validity
Cross-loadings and Heterotrait-monotrait (HTMT) ratio of correlation are two measures used
to establish discriminant validity of the constructs (Hair et al., 2016; Henseler et al., 2015).
Discriminant validity of latent constructs is confirmed if indicators loaded higher on the
variable they were specified to measure compared to others in the theoretical model.
Discriminant validity of constructs was further established through HTMT. HTMT value for
correlation between two latent variables should be not greater than 0.9 (Henseler et al., 2015).
5. Results and hypotheses testing
This section presents demographic characteristics and results of the measurement and
structural models.
5.1 Demographic and project characteristics of the respondents
Demographic characteristics of the respondents who completed the questionnaire survey and
project information are shown in Tables 2 and 3. Most respondents possess work experience
with a range of 0–5 years (59.85%), and the least possessed 21 years above (6.1%). Most
participants completed Bachelor degree (35.4%) and the minority of them possessed high
school certificate (3.7%). The value of the project completed by most of the respondents is
A$1.6 million – A$25 million. Public projects accounted for the most completed project
(54.9%). Majority of the respondents involved in the project completed within the range of
12–24 months.
PESTEL
analysis
SASBE
Table 2.
Respondents
demographic
profile (N 5 82)
Characteristics
Frequency
Percentage
Respondent experience in current company
0–5 years
6–10 years
11–15 years
16–20 years
21 years or above
49
12
9
7
5
59.8
14.6
11.0
8.5
6.1
Educational background
High school
Diploma
Bachelor’s degree
Master’s degree
Doctoral degree
3
8
29
29
13
3.7
9.8
35.4
35.4
15.9
Respondents experience in AEC projects
1–5 projects
6–10 projects
11–15 projects
16–20 projects
21 projects or above
31
12
5
9
25
37.8
14.6
6.1
11.0
30.5
38
45
14
13
38
6
2
18
10
20.7
24.5
16.7
7.1
20.7
3.3
1.1
9.8
5.4
Project sector
Residential
Commercial
Urban development
Industrial
Infrastructural
Mining and resources
Petrochemical
Institution
Others (commercial, defence, education, government, health, schools, and
government bodies)
5.2 Internal consistency reliability
Table 4 shows the Cronbach’s alpha reliability and composite reliability of latent variables.
The Cronbach’s alpha reliability and composite reliability value for each latent variable are
above 0.7, satisfying the internal consistency reliability rules.
5.3 Convergent validity
As shown in Table 4, The outer loading of each indicator was above 0.4, which is the
acceptable value to be retained from the constructs based on the suggestion by Hair et al.
(2016). In addition, the AVE of each latent variable was above the recommended threshold of
0.5 (Kline, 2015). All these results show that the measurement model satisfied the sufficient
convergent validity rules.
5.4 Discriminant validity
Table 5 depicts the results for the discriminant validity. The results indicate that all the
indicators loaded higher on the variable they were specified to measure when compared to
others in the theoretical model, establishing the discriminant validity of the variables.
Accordingly, the results shown in Table 6 indicate that the discriminant validity is
satisfactory as HTMT values are less than 0.9 and that concept of political factors, economic
Characteristics
Frequency (N)
Percentage (%)
Project value
Less than A$0.2 million
A$0.2 million – A$1.5 million
A$1.6 million – A$25 million
A$26 million – A$50 million
A$51 million – A$150 million
A$151 million – A$500 million
More than A$500 million
3
15
24
10
9
7
14
3.7
18.3
29.3
12.2
11.0
8.5
17.1
Client type
Private
Public
37
45
45.1
54.9
Project duration
0–11 months
12–24 months
25–36 months
More than 36 months
8
40
17
17
9.8
48.8
20.7
20.7
Procurement methods
Traditional method
Design and build procurement
Partnering procurement
Management procurement
Others (hybrid and two stage management contracting)
36
33
3
6
4
43.9
40.2
3.7
7.3
4.9
factors, social factors, technological factors, environmental factors, legal factors and mental
health management indicators are different from each other.
5.5 Structural model assessment
Through bootstrapping that set samples at 5,000 times based on recommended by Hair et al.
(2016), the results of the evaluation of structural model were obtained. The results underlay
the basis for testing the proposed hypotheses and relative strength of the impact of the
exogenous variable on the endogenous variable. The path coefficient reflects the impact of the
independent variable on the dependent variable. The final results indicate that all the six
hypotheses proposed are significant (p < 0.01); see Table 7. R2 is used to evaluate the model’s
predictive power. The R2 value of all the endogenous variables is higher than 0.50, confirming
the theoretical model has higher explanatory power.
In addition to R2 values of all the independent variables, the substantial impact of omitted
independent variables on dependent variables is assessed to establish the predictive accuracy
of the specified theoretical model. Effective size (f2) is used to examine the predictive
accuracy. As indicated in Table 8, the explanatory power of f2 is higher than the threshold
value of 0.02 (Hair et al., 2016), indicating that the theoretical model has explanatory power for
the relationship between the constructs.
5.5.1 Relationship between political factors and mental health management indicators.
The SEM model tested the relationship between political factors and mental health
management indicators. The results show a significant and negative correlation between
political factors and mental health management indicators (β 5 0.064, t-value 5 7.580,
p 5 0.000). This means that political factors negatively affect mental health management
indicators, which provides evidence to reject hypothesis H1.
PESTEL
analysis
Table 3.
Project information
statistics
SASBE
Construct
Measurement items
Political factors
PF1: Report for corruption
management
PF2: Project planning approval
process
PF3: Political stability
PF4: Favourable Australian tax
policies
EF1: Sound Australian
economic policies
EF2: Favourable market
condition
EF3: Accessibility to bank loan
EF4: Provision of financial
support
EF5: Stable inflation
SF1: External project
stakeholders’ support
SF2: Good social relationship
SF3: Availabilities of
construction materials
SF4: Australia national culture
TF1: Clear and effective
governance policies
TF2: Technologies availability
TF3: Utilization of technology
ENF1: Government approval
ENF2: Australia environmental
policies
ENF3: Stable weather condition
LF1: Effective mental health
policies
LF2: Effective Australia work–
life balance policies
LF3: Effective Australia
national code of practices
LF4: Effective Safe work
Australia policy guidance
Economic factors
Social factors
Technological
factors
Environmental
factors
Legal factors
Table 4.
Measurement model
evaluation
Loading
Cronbach’s
alpha
Composite
reliability
AVE
0.409
0.819
0.803
0.645
0.829
0.830
0.596
0.829
0.742
0.561
0.737
0.857
0.775
0.853
0.744
0.657
0.725
0.845
0.685
0.833
0.746
0.811
0.810
0.808
0.608
0.626
0.649
0.677
0.783
0.484
0.630
0.737
0.801
0.904
0.741
0.772
0.583
0.678
0.764
0.762
0.828
5.5.2 Relationship between economic factors and mental health management indicators. The
link between economic factors and mental health management was tested via SEM model.
The results indicate that interaction correlation for economic factors and mental health
management indicators was significant and positive (β 5 0.110, t-value 5 9.811, p 5 0.000).
This result shows that the impact of economic factors on mental health management
indicators is significant. Therefore, hypothesis H2 is supported.
5.5.3 Relationship between social factors and mental health management indicators.
The relationship between social factors and mental health management indicators was tested
using SEM model. The result indicates that there is a significant and positive correlation
between social factors and mental health management indicators (β 5 0.273, t-value 5 9.486,
p 5 0.000). This means that social factors shape mental health management indicators in
AEC projects. The result provides evidence to support H3.
EF1
EF2
EF3
EF4
EF5
ENF1
ENF2
ENF3
LF1
LF2
LF3
LF4
MHI1
MHI10
MHI11
MHI12
MHI13
MHI14
MHI15
MHI2
MHI3
MHI4
MHI5
MHI6
MHI7
MHI8
MHI9
PF1
PF2
PF3
PF4
SF1
SF2
SF3
SF4
TF1
TF2
TF3
Economic
factors
Environmental
factors
Legal
factors
Mental health
management
indicators
0.810
0.808
0.608
0.646
0.649
0.430
0.447
0.249
0.528
0.473
0.412
0.574
0.418
0.608
0.472
0.473
0.499
0.402
0.452
0.349
0.311
0.362
0.494
0.257
0.353
0.423
0.668
0.366
0.407
0.550
0.693
0.519
0.472
0.497
0.564
0.625
0.483
0.443
0.346
0.288
0.386
0.545
0.414
0.741
0.772
0.583
0.442
0.444
0.578
0.531
0.375
0.599
0.480
0.477
0.409
0.395
0.391
0.360
0.401
0.354
0.464
0.445
0.332
0.382
0.424
0.380
0.614
0.382
0.381
0.411
0.400
0.437
0.513
0.663
0.573
0.473
0.506
0.439
0.370
0.488
0.513
0.570
0.571
0.194
0.678
0.764
0.762
0.828
0.472
0.588
0.473
0.398
0.317
0.297
0.347
0.495
0.517
0.551
0.541
0.377
0.393
0.304
0.529
0.409
0.422
0.390
0.481
0.416
0.375
0.484
0.569
0.632
0.501
0.574
0.499
0.498
0.341
0.386
0.400
0.434
0.452
0.341
0.418
0.471
0.469
0.510
0.794
0.852
0.819
0.784
0.686
0.712
0.698
0.687
0.588
0.687
0.880
0.545
0.598
0.548
0.778
0.202
0.411
0.368
0.400
0.485
0.561
0.347
0.452
0.562
0.611
0.690
Political
factors
Social
factors
Technological
factors
0.568
0.405
0.595
0.516
0.435
0.441
0.594
0.201
0.358
0.428
0.409
0.579
0.375
0.475
0.377
0.340
0.306
0.282
0.379
0.404
0.272
0.26
0.543
0.101
0.298
0.184
0.554
0.463
0.833
0.746
0.811
0.427
0.380
0.397
0.741
0.552
0.331
0.358
0.525
0.557
0.561
0.626
0.474
0.517
0.518
0.342
0.507
0.489
0.535
0.576
0.587
0.534
0.563
0.558
0.516
0.545
0.545
0.629
0.319
0.519
0.671
0.311
0.376
0.363
0.566
0.423
0.579
0.521
0.585
0.677
0.783
0.509
0.630
0.669
0.580
0.608
0.406
0.423
0.519
0.518
0.374
0.463
0.536
0.451
0.477
0.547
0.556
0.523
0.639
0.628
0.653
0.62
0.511
0.575
0.529
0.480
0.466
0.509
0.678
0.446
0.480
0.402
0.524
0.322
0.446
0.357
0.324
0.509
0.487
0.442
0.544
0.737
0.801
0.904
5.5.4 Relationship between technological factors and mental health management indicators.
The association between technological factors and mental health management indicators was
tested using SEM model. Technological factors have significant and positive effect on mental
health management indicators (β 5 0.464, t-value 5 7.874, p 5 0.000). Therefore, hypothesis
H4 is supported.
5.5.5 Relationship between environmental factors and mental health management
indicators. SEM model underlay the testing of the relationship between economic
factors and mental health management indicators. The result shows a significant and
positive association between environmental factors and mental health management
indicators (β 5 0.031, t-value 5 7.658, p 5 0.000). The result provides evidence to
support H5.
PESTEL
analysis
Table 5.
Cross loadings
analysis
SASBE
Constructs
Economic
factors
Environmental
factors
Legal factors
Mental health
management
indicators
Political factors
Social factors
Table 6.
Analysis of heterotrait- Technological
factors
monotrait (HTMT)
Hypothesis
Table 7.
Summary of
hypotheses
Mental health
Economic Environmental Legal management Political Social Technological
factors
factors
factors
indicators
factors factors factors
0.573
0.658
0.602
0.653
0.600
0.607
0.704
0.824
0.651
0.600
0.712
0.727
0.594
0.736
0.700
0.457
0.714
0.755
Path
coefficient
H1: Political factors → Mental health
0.064
management indicators
H2: Economic factors → Mental health
0.110
management indicators
H3: Social factors → Mental health
0.273
management indicators
H4: Technological factors → Mental health
0.464
management indicators
H5: Environmental factors → Mental health
0.031
management indicators
H6: Legal factors → Mental health
0.051
managenent indicators
Note(s): Significant at *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001
Path
Political factors → Mental health management indicators
Economic factors → Mental health management indicators
Social factors → Mental health management indicators
Table 8.
Predictive power from f Technological factors → Mental health management indicators
Environmental factors → Mental health management indicators
square of the
Legal factors → Mental health management indicators
dependent variable
0.755
0.509
0.793
Hypothesis
validation
t Statistics
p Values
7.580
***
Not supported
9.811
***
Supported
9.486
***
Supported
7.874
***
Supported
7.658
***
Supported
9.340
***
Supported
f2
0.040
0.021
0.046
0.188
0.042
0.023
5.5.6 Relationship between legal factors and mental health management indicators.
The interactive relationship between legal factors and mental health management
indicators was tested using SEM model. Legal factors have significant and positive effect
on mental health management indicators (β 5 0.051, t-value 5 9.340, p 5 0.000). Hence,
hypothesis H6 is supported.
Figure 2 shows the final structural model for this research.
PESTEL
analysis
Figure 2.
The final structural
model for this research
6. Discussion
In this study, institutional theory underpins the development of a theoretical model showing
the impact of external environmental factors: political factors, economic factors, social
factors, technological factors, environmental factors, and legal factors on mental health
management indicators. The SEM results show that institutional theory has the potential to
explain how the macro-environment that an AEC project resides shapes its mental health
management outcomes.
6.1 Impacts of political factors on mental health management indicators
Inconsistent with previous studies (Kompier, 2006; Wallis and Dollard, 2008), that established
positive relationship between political factors and mental health management practices, this
study found significant and negative correlation between the political factors and mental health
management indicators. The finding of Parker and Cordery (2007) indicated that political
factors, including political stability and favourable tax rate, can influence work–life balance
practices in a work environment, contributing to mental health. Political pressure for
organizational reforms and policy changes to achieve various goals are significant stressors
SASBE
leading to work stress (Can et al., 2015; Sklansky, 2005). Political climate is a strong force
influencing work practices that predict employees’ mental health. The employees are exposed to
excessive stress due to demands from the political atmosphere (Parker et al., 2001). Hence, it is
likely that the significant and negative relationship between political factors and mental health
management indicators is associated with the research conducted in the working environment.
Previous studies that confirmed the link between political factors and work stress focused on
general workplace; therefore, the negative relationships can be attributed to the project based
organization. Therefore, it is unsurprising to experience differences in findings between current
studies and this research as PMPs engaged in project management activities.
6.2 Impacts of economic factors on mental health management indicators
As predicted in Hypothesis H2, the result confirmed that economic factors are positively
associated with mental health management indicators. This result shows that the economic
situation in which AEC project organizations operates predicts mental health management
outcomes. This finding was explained by Joplin et al. (2003) and Wallis and Dollard (2008),
which posit that economic factors determine work practices adopted by organizations to
promote positive mental health. Good economic conditions that promote a favourable market
situation, sound economic policies, stable inflation and accessibility to financial incentives
encourage organizations to allocate adequate project resources and reduce perceived job
insecurity (van den Bossche et al., 2006; Sui Pheng and Shing Hou, 2019). Albeit economic
conditions are unpredictable; nevertheless, monitoring economic factors, including stable
inflation and accessibility to bank loans, is an innovative strategy to change unhealthy
project management practices exposing PMPs to mental health.
6.3 Impacts of social factors on mental health management indicators
Institutional theory was reflected in preventing poor mental health from comprehending the
relationship between social factors and mental health management indicators. In this research,
social factors were found to be positively correlated with mental health management indicators
in AEC project organizations. This finding is supported by extant studies on the impact of social
factors on mental health management outcomes (Alrasheed, 2015; Yang et al., 2017). Social
factors are strong explanatory constructs of mental health management practices (Tijani et al.,
2021a; Parker et al., 2017). Social factors in the garb of good social relationship between PMPs
and external stakeholders, national culture that prioritizes mentally healthy projects, and
support from social communities in project execution contribute to governance strategy
employed in developing project management activities (Alrasheed, 2015). The project
management practices emanated from the social factors are antecedents to mental health
management practices, including stress-free stakeholders’ engagement, easiness of balancing
different stakeholders’ values and effective communication between PMPs and stakeholders
contributing to positive mental health (Yang et al., 2017). Therefore, paying attention to the
external environment’s social factors can help promote mental health outcomes in AEC projects
that promote positive mental health.
6.4 Impacts of technological factors on mental health management indicators
The finding of this research shows that technological factors are positively correlated with
mental health management indicators. This means that technological factors influence mental
health management practices contributing to the positive mental health of PMPs. Technological
advancement, government regulations on technology implementation and incentives facilitate
effective collaboration between different organizations, which enhances sharing of project
information, resources and knowledge (Ninaus et al., 2015; Pan et al., 2019). Modern technologies
create a platform for sharing project information that supports coordination processes internally
among PMPs and externally with stakeholders (Smith and Carayon, 1995; Kompier, 2006).
Establishing project information sharing central points improves PMPs’ communication
processes and management information flow that prevents role ambiguity and project overload,
which causes poor mental health (Delisle, 2020; Gustavsson, 2016). Resource sharing among
multiple projects and knowledge management with network of organizations often require
technology implementation for efficient resource allocation and sharing of project knowledge for
project delivery (Delisle, 2020; Zika-Viktorsson et al., 2006). Limited application of technology for
resource allocation and knowledge management imposes strain on workers due to excessive
stress inherent in the manual allocation of resources and inaccessibility to project knowledge
(Kompier, 2006; Landsbergis, 2003).
6.5 Impacts of environmental factors on mental health management indicators
Consistent with previous studies (Parker et al., 2017; De Clercq et al., 2021), for which the findings
confirmed the influence of environmental factors on work stress and work–life conflict
management, this research revealed that environmental factors are positively related to mental
health management indicators. The result shows that environmental factors can shape mental
health management outcomes in AEC projects. Drawing on institutional theory, government
regulatory standards on green projects influence project management practices embraced in a
project based organization to meet the regulations (Hwang and Ng, 2013). Demands for projects
with low carbon emissions mandated project managers to design environmental management
plans, select green materials and project management activities for delivering green projects
(Yang et al., 2017). Green projects use processes that are resource-efficient throughout the
project’s life cycle from design, construction, operation, maintenance and renovation to reduce
greenhouse gas emissions (Olubunmi et al., 2016). The stressful processes involved in managing
projects to achieve greening facilities impose strain on project management. Therefore, easy
implementation of the regulatory standards prevents exposure of PMPs to excessive work
stress that spurred project management practices designed for meeting green projects.
6.6 Impacts of legal factors on mental health management indicators
As anticipated in hypothesis H6, the result indicates that legal factors are positively
associated with mental health management indicators. This result indicates that legal factors
can improve mental health management practices in AEC project organizations. Legal
framework for work–life balance and mental health forced project organizations to design
project management practices that promote work–life balance, justice and fairness among
workers and encouragement of stress management programs that mitigate poor mental
health (De Clercq et al., 2021; Landsbergis, 2003). National institutional forces, such as
national employment policies, training systems and health regulations, foster enriched
organization design that promote positive mental health among workers (Parker et al., 2017).
6.6.1 Theoretical and managerial implications. Theoretically, this study contributes to the
body of mental health literature by adopting institutional theory to develop a theoretical
model showing the impact of external environmental factors on mental health management
indicators. Based on the theoretical model, this study revealed the external environmental
factors (PESTEL) that impact mental health. These findings enhance understanding the
influence of political, economic, social, technological and legal factors on mental health
management outcomes. Moreover, this research offers conventional means of viewing mental
health problems in a project based organization by demonstrating how external
environmental factors impose institutional forces on project based organizations to design
organizational management practices contributing to mental health. These are important
findings because they extended current knowledge focussing on organizational and
PESTEL
analysis
SASBE
individual factors to identification of dynamic environmental factors affecting mental health
in a project environment. Additionally, the practical implication lies in understanding the
external environmental demands that AEC firms must develop strategic measures to
promote positive mental health. The proposed external environmental policies can be
implemented through the engagement of PMPs, corporate governance and project
stakeholders at feasibility, design, construction and handover stage of the project delivery.
6.6.2 Limitations and possible future works. Although this study has a robust theoretical
model applied to the under-research domain of mental health management in AEC project
organizations and embraced rigorous multivariance modelling empirical analysis, it also has
limitation that offers a foundation for future research. First, this study was undertaken within
AEC project organization in Australia in the context of PMPs; hence, generalization of
findings to other industries is limited. However, AEC firms across the globe are similar in
operations; hence, our findings are applicable beyond Australian AEC industries. Similar
studies should be conducted in other developed and developing countries to establish the
research findings or reveal any differences in findings.
Second, while this study presented the impact of political, economic, social, technological,
environmental, and legal factors on mental health management indicators, our crosssectional research design cannot prevent the possibility of reverse causality. Therefore, even
though this research was underpinned by strong theoretical arguments for the proposed
hypotheses, future research should adopt a longitudinal research design to develop
interactive relationships between the constructs further. Third, the measurement of key
latent variables was self-reported; therefore, future studies should adopt mixed methods to
obtain the opinion and experiences of PMPs in AEC project organizations. A mixed-method
approach offers detailed insights into how political, economic, social, technological,
environmental, and legal factors shape mental health management indicators.
7. Conclusions
This research empirically explored the relationship between external environmental factors:
political factors, economic factors, social factors, technological factors, environmental factors,
legal factors and mental health management indicators. A positivist approach tested a
theoretically derived research model. Institutional theory underpinned the theoretical model
guiding this study. The data were collected through a Qualtrics online survey with 90
respondents from 60 AEC firms evenly distributed in all states in Australia. The research
question developed at the beginning of the result can now be answered: The hypotheses
developed for this research were mostly supported. The data did not support the
hypothesized positive correlation between political factors and mental health management
indicators (H1). However, the data support the proposed hypothetical relationship between
economic factors and mental health management indicators (H2), as well as supporting the
influence of social factors on mental health management indicators (H3). In addition,
hypothetical relationships between technological factors and mental health management
indicators were supported (H4). The significant and positive influence of environmental
factors on mental health management indicators proposed was supported (H5), and legal
factors positive correlation on mental health management indicators was also supported.
The research conducted in this study assists in answering the research question
developed at the beginning: What are the relationships between external environmental
factors: political factors, economic factors, social factors, technological factors, environmental
factors and legal factors and mental health management indicators in AEC project
organizations? The findings indicate that, in general, external environmental factors in which
the AEC project resides has positive implication for mental health management outcome.
Nevertheless, it is worth considering the detailed findings revealed in this study. All the
external environmental factors positively impact mental health management indicators,
except pollical factors. This means it is paramount to pay cognizance to the external
environment factors when intended to promote positive mental health among PMPs.
References
Agervold, M. (1991), “Healthy work in a psychosocial perspective”, Nordisk Psykologi, Vol. 43 No. 4,
pp. 249-273, doi: 10.1080/00291463.1991.11675820.
Ajayi, S.O., Jones, W. and Unuigbe, M. (2019), “Occupational stress management for UK construction
professionals: understanding the causes and strategies for improvement”, Journal of
Engineering, Design and Technology, Vol. 17 No. 4, pp. 819-832, doi: 10.1108/JEDT-09-2018-0162.
Alrasheed, H. (2015), “A socio-ecological framework for improving the psychological health of foreign
workers in developing countries: The case of Saudi construction industry”, Nova Science
Publishers, Sydney, 153612074X, 9781536120745.
Anderson, J. and Gerbing, D. (1988), “Structural equation modeling in practice: a review and
recommended two-step approach”, Psychological Bulletin, Vol. 103 No. 3, p. 411, doi: 10.1037/
0033-2909.103.3.411.
Ansah, R., Sorooshian, S., Mustafa, S. and Duvvuru, G. (2016), “An environmental impact framework
for evaluating construction projects delays”, the 2016 International Conference on Industrial
Engineering and Operations Management, Detroit, MI, 23-25 September, available at: http://
ieomsociety.org/ieomdetroit/pdfs/254.pdf (accessed 10 June 2020).
Aragones-Beltran, P., Garcıa-Melon, M. and Montesinos-Valera, J. (2017), “How to assess stakeholders’
influence in project management? A proposal based on the analytic network process”, International
Journal of Project Management, Vol. 35 No. 3, pp. 451-462, doi: 10.1016/j.ijproman.2017.01.001.
Areskoug Josefsson, K., Avby, G., Andersson B€ack, M. and Kjellstr€om, S. (2018), “Workers’
experiences of healthy work environment indicators at well-functioning primary care units in
Sweden: a qualitative study”, Scandinavian Journal of Primary Health Care, Vol. 36 No. 4,
pp. 406-414, doi: 10.1080/02813432.2018.1523987.
Arnetz, B. and Blomkvist, V. (2007), “Leadership, mental health, and organizational efficacy in health
care organizations”, Psychotherapy and Psychosomatics, Vol. 76 No. 4, doi: 10.1159/000101503.
Aronsson, G., Bejerot, E. and H€arenstam, A. (1999), “Healthy work: ideal and reality among public and
private employed academics in Sweden”, Public Personnel Management, Vol. 28 No. 2,
pp. 197-215, doi: 10.1177/009102609902800203.
Arwedson, I., Roos, S. and Bj€orklund, A. (2007), “Constituents of healthy workplaces”, Work, Vol. 28 No. 1,
pp. 3-11, available at: https://pubmed.ncbi.nlm.nih.gov/17264415/#:∼:text5Four%20main%
20categories%20of%20health,in%20the%20psychosocial%20environment%20remains. (accessed
12 April 2018).
Baptiste, N. (2009), “Fun and well-being: insights from senior managers in a local authority”, Employee
Relations, Vol. 31 No. 6, pp. 600-612, doi: 10.1108/01425450910991758.
Bergh, L., Hinna, S., Leka, S. and Jain, A. (2014), “Developing a performance indicator for psychosocial risk
in the oil and gas industry”, Safety Science, Vol. 62 No. 3, pp. 98-106, doi: 10.1016/j.ssci.2013.08.005.
Bowen, P., Edwards, P., Lingard, H. and Cattell, K. (2014a), “Workplace stress, stress effects, and
coping mechanisms in the construction industry”, Journal of Construction Engineering and
Management, Vol. 140 No. 3, pp. 1-15, doi: 10.1061/(ASCE)CO.1943-7862.0000807.
Bowen, P., Edwards, P., Lingard, H. and Cattell, K. (2014b), “Occupational stress and job demand,
control and support factors among construction project consultants”, International Journal of
Project Management, Vol. 32 No. 7, pp. 1273-1284, doi: 10.1016/j.ijproman.2014.01.008.
Bowen, P., Govender, R., Edwards, P. and Cattell, K. (2018), “Work-related contact, work-life conflict,
psychological distress and sleep problems experienced by construction professionals: an
PESTEL
analysis
SASBE
integrated explanatory model”, Construction Management and Economics, Vol. 36 No. 6,
pp. 153-174, doi: 10.1080/01446193.2017.1341638.
Can, S.H., Hendy, H.M. and Karagoz, T. (2015), “LEOSS-R: four types of police stressors and negative
psychosocial outcomes associated with them”, Policing: A Journal of Policy and Practice, Vol. 9
No. 4, pp. 340-351, doi: 10.1093/police/pav011.
Carvajal-Arango, D., Vasquez-Hernandez, A. and Botero-Botero, L.F. (2021), “Assessment of subjective
workplace well-being of construction workers: a bottom-up approach”, Journal of Building
Engineering, Vol. 36, pp. 1-10, 102154, doi: 10.1016/j.jobe.2021.102154.
Chiang, Y.-H., Chan, E.H.-W. and Lok, L.K.-L. (2006), “Prefabrication and barriers to entry—a case
study of public housing and institutional buildings in Hong Kong”, Habitat International,
Vol. 30 No. 3, pp. 482-499, doi: 10.1016/j.habitatint.2004.12.004.
Cronbach, L. (1951), “Coefficient alpha and the internal structure of tests”, Psychometrika, Vol. 16
No. 3, pp. 297-334, doi: 10.1007/BF02310555.
Day, A., Scott, N. and Kelloway, E.K. (2010), “Information and communication technology:
implications for job stress and employee well-being”, in New Developments in Theoretical and
Conceptual Approaches to Job Stress, Emerald Group Publishing, Bingley, pp. 317-350, doi: 10.
1108/S1479-3555(2010)0000008011.
De Clercq, D., Brieger, S.A. and Welzel, C. (2021), “Leveraging the macro-level environment to balance
work and life: an analysis of female entrepreneurs’ job satisfaction”, Small Business Economics,
Vol. 56 No. 4, pp. 1361-1384, doi: 10.1007/s11187-019-00287-x.
Delisle, J. (2020), “Working time in multi-project settings: how project workers manage work
overload”, International Journal of Project Management, Vol. 38 No. 7, pp. 419-428, doi: 10.1016/
j.ijproman.2020.04.001.
Di Maddaloni, F. and Davis, K. (2017), “The influence of local community stakeholders in
megaprojects: rethinking their inclusiveness to improve project performance”, International
Journal of Project Management, Vol. 35 No. 8, pp. 1537-1556, doi: 10.1016/j.ijproman.2017.08.011.
DiLalla, L., Tinsley, H. and Brown, S. (2000), “structural equation modeling: uses and issues”,
Handbook of Applied Multivariate Statistics and Mathematical Modeling, Academic Press,
Illinois. doi: 10.1016/B978-012691360-6/50016-1.
DiMaggio, P.J. and Powell, W.W. (1983), “The iron cage revisited: institutional isomorphism and
collective rationality in organizational fields”, American Sociological Review, Vol. 31 No. 5,
pp. 147-160, doi: 10.17323/1726-3247-2010-1-34-56.
Dollard, M., Osborne, K. and Manning, I. (2013), “Organization–environment adaptation: a macro-level
shift in modeling work distress and morale”, Journal of Organizational Behavior, Vol. 34 No. 5,
pp. 629-647, doi: 10.1002/job.1821.
Edwards, J.A. and Webster, S. (2012), “Psychosocial risk assessment: measurement invariance of the
UK health and safety executive’s management standards indicator tool across public and
private sector organizations”, Work and Stress, Vol. 26 No. 2, pp. 130-142, doi: 10.1080/
02678373.2012.688554.
Gustavsson, T.K. (2016), “Organizing to avoid project overload: the use and risks of narrowing
strategies in multi-project practice”, International Journal of Project Management, Vol. 34 No. 1,
pp. 94-101, doi: 10.1016/j.ijproman.2015.10.002.
Hair, J., Sarstedt, M., Ringle, C. and Mena, J. (2012), “An assessment of the use of partial least squares
structural equation modeling in marketing research”, Journal of the Academy of Marketing
Science, Vol. 40 No. 3, pp. 414-433, doi: 10.1007/s11747-011-0261-6.
Hair, J., Hult, T., Ringle, C. and Sarstedt, M. (2016), A Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), Sage Publications, London, ISBN: 9781483377445.
Hair, J., Black, W., Babin, B., Anderson, R. and Tatham, R. (2019), Multivariate Data Analysis, Cengage
Learning, Hampshire, Berlin, Heidelberg, ISBN: 9781473756540.
Haynes, N.S. and Love, P.E.D. (2004), “Psychological adjustment and coping among construction
project managers”, Construction Management and Economics, Vol. 22 No. 2, pp. 129-140, doi: 10.
1080/0144619042000201330.
Henseler, J., Ringle, C.M. and Sarstedt, M. (2015), “A new criterion for assessing discriminant validity
in variance-based structural equation modeling”, Journal of the Academy of Marketing Science,
Vol. 43 No. 1, pp. 115-135, doi: 10.1007/s11747-014-0403-8.
Hughes, W. (1989), “Identifying the environments of construction projects”, Construction Management
and Economics, Vol. 7 No. 1, pp. 29-40, doi: 10.1080/01446198900000004.
Hwang, B.-G. and Ng, W.J. (2013), “Project management knowledge and skills for green construction:
overcoming challenges”, International Journal of Project Management, Vol. 31 No. 2,
pp. 272-284, doi: 10.1016/j.ijproman.2012.05.004.
Joplin, J.R., Shaffer, M.A., Francesco, A.M. and Lau, T. (2003), “The macro-environment and workfamily conflict: development of a cross cultural comparative framework”, International
Journal of Cross Cultural Management, Vol. 3 No. 3, pp. 305-328, doi: 10.1177/
1470595803003003004.
Kamardeen, I. and Sunindijo, R.Y. (2017), “Personal characteristics moderate work stress in
construction professionals”, Journal of Construction Engineering and Management, Vol. 143
No. 10, pp. 1-10, doi: 10.1061/(ASCE)CO.1943-7862.0001386.
Khalid, L. and Rahman, I. (2019), “Conceptual model for the external factors affecting project
performance using PESTLE factors”, Journal of Advanced Research in Dynamical and Control
Systems, Vol. 11 No. 3, pp. 246-250, available at: file://ad.uws.edu.au/dfshare/HomesHWK$/
30051911/Downloads/9thICSTSS-18%20(3).pdf (accessed 10 May 2020).
Kline, R.B. (2015), Principles and Practices of Structural Equation Modeling, Guilford Publications, New
York, NY, ISBN: 9781462523351.
Koch, J. and Schermuly, C.C. (2021), “Managing the crisis: how COVID-19 demands interact with agile
project management in predicting employee exhaustion”, British Journal of Management,
Vol. 32 No. 4, pp. 1265-1283, doi: 10.1111/1467-8551.12536.
Kompier, M.A. (2006), “New systems of work organization and workers’ health”, Scandinavian Journal
of Work, Environment and Health, Vol. 10 No. 4, pp. 421-430, doi: 10.1111/1467-8551.12536.
Kroth, M., Boverie, P. and Zondlo, J. (2007), “What managers do to create healthy work environments”,
Journal of Adult Education, Vol. 36 No. 2, pp. 1-12, available at: https://files.eric.ed.gov/fulltext/
EJ891065.pdf (accessed 12 December 2020).
Kumar, R. (2019), Research Methodology: A Step-by-step Guide for Beginners, Sage Publications,
London, ISBN: 1526457083.
Landsbergis, P. (2003), “The changing organization of work and the safety and health of working
people”, Journal of Occupational Environmental Medicine, Vol. 45 No. 1, pp. 61-72, doi: 10.1097/
00043764-200301000-00014.
Leung, M.Y., Liu, A.M.M. and Wong, M.K. (2006), “Impacts of stress-coping behaviours on estimation
performance”, Construction Management and Economics, Vol. 24 No. 1, pp. 55-67, doi: 10.1080/
01446190500228381.
Leung, M.Y., Zhang, H. and Skitmore, M. (2008), “Effects of organizational suports on the stress of
construction estimation participants”, Journal of Construction Engineering and Management,
Vol. 134 No. 2, pp. 84-93, doi: 10.1061/(ASCE)0733-9364(2008)134:2(84).
Leung, M.Y., Chan, I.Y. and Dongyu, C. (2011), “Structural linear relationships between job stress,
burnout, physiological stress, and performance of construction project managers”, Engineering,
Construction and Architectural Management, Vol. 18 No. 3, pp. 312-328, doi: 10.1108/
09699981111126205.
Lindberg, P. and Ving
ard, E. (2012), “Indicators of healthy work environments–a systematic review”,
Work, Vol. 41 No. 1, pp. 3032-3038, doi: 10.3233/wor-2012-0560-3032.
PESTEL
analysis
SASBE
Lindberg, P., Karlsson, T. and Ving
ard, E. (2015), “Determinants for positive mental health and
wellbeing at work–a literature review”, 19th Triennial Congress of the International Ergonomics
Association (IEA 2015), Melbourne, 9-14 August 2015, available at: http://urn.kb.se/resolve?
urn5urn%3Anbn%3Ase%3Ahig%3Adiva-23064 (accessed 20 March 2019).
Love, P.E. and Edwards, D.J. (2005), “Taking the pulse of UK construction project managers’ health:
influence of job demands, job control and social support on psychological wellbeing”,
Engineering, Construction and Architectural Management, Vol. 12 No. 1, pp. 88-101, doi: 10.
1108/09699980510576916.
Love, P.E.D., Edwards, D.J. and Irani, Z. (2010), “Work stress, support, and mental health in
construction”, Journal of Construction Engineering and Management, Vol. 136 No. 6,
pp. 650-658, doi: 10.1061/(ASCE)CO.1943-7862.0000165.
Lowe and Bennett (2003), “Exploring coping reactions to work-stress: application of an
appraisal theory”, Journal of Occupational and Organizational Psychology, Vol. 76 No. 3, pp.
393-400, doi: 10.1348/096317903769647247.
MacDermid, J., Geldart, S., Williams, R., Westmorland, M., Lin, C.-Y. and Shannon, H. (2008), “Work
organization and health: a qualitative study of the perceptions of workers”, Work, Vol. 30 No. 3,
pp. 241-254.
MacDermid, Litchfield and Pitt-Catsouphes (1999), “Organizational size and work-family issues”,
Annals of the American Academy of Political and Social Science, Vol. 562 No. 1, pp. 111-126,
doi: 10.1177/000271629956200108.
Muller, R., Drouin, N. and Sankaran, S. (2019), Organizational Project Management : Theory and
Implication, Edward Elgar Publishing, Cheltenham, ISBN: 9781788110969.
Narayanan, V. and Fahey, L. (2001), “Macroenvironmental analysis: understanding the environment
outside the industry”, The Portable MBA in Strategy, Vol. 47, pp. 189-214.
Nguyen, T.H.D., Chileshe, N., Rameezdeen, R. and Wood, A. (2019), “External stakeholder strategic
actions in projects: a multi-case study”, International Journal of Project Management, Vol. 37
No. 1, pp. 176-191, doi: 10.1016/j.ijproman.2018.12.001.
Ninaus, K., Diehl, S., Terlutter, R., Chan, K. and Huang, A. (2015), “Benefits and stressors–Perceived
effects of ICT use on employee health and work stress: an exploratory study from Austria and
Hong Kong”, International Journal of Qualitative Studies on Health and Well-Being, Vol. 10 No. 1,
p. 28838, doi: 10.3402/qhw.v10.28838.
Nixon, A.E. and Spector, P.E. (2013), “The impact of technology on employee stress, health, and wellbeing”, The Psychology of Workplace Technology, Routledge, pp. 262-284, doi: 2013-07824-011.
Olubunmi, O.A., Xia, P.B. and Skitmore, M. (2016), “Green building incentives: a review”, Renewable
and Sustainable Energy Reviews, Vol. 59 No. 3, pp. 1611-1621, doi: 10.1016/j.rser.2016.01.028.
Pak, K., Kooij, D.T., De Lange, A.H. and Van Veldhoven, M.J. (2019), “Human Resource Management and
the ability, motivation and opportunity to continue working: a review of quantitative studies”,
Human Resource Management Review, Vol. 29 No. 3, pp. 336-352, doi: 10.1016/j.hrmr.2018.07.002.
Pan, W., Chen, L. and Zhan, W. (2019), “PESTEL analysis of construction productivity enhancement
strategies: a case study of three economies”, Journal of Management in Engineering, Vol. 35
No. 1, pp. 1-15, doi: 10.1061/(ASCE)ME.1943-5479.0000662.
Parker, S. and Cordery, J. (2007), Work Organization, Oxford University Press, Oxford, ISBN:
9780199282517.
Parker, S., Wall, T. and Cordery, J. (2001), “Future work design research and practice: towards an
elaborated model of work design”, Journal of Occupational and Organizational Psychology,
Vol. 74 No. 4, pp. 413-440, doi: 10.1348/096317901167460.
Parker, S., Van den Broeck, A. and Holman, D. (2017), “Work design influences: a synthesis of
multilevel factors that affect the design of jobs”, Academy of Management Annals, Vol. 11 No. 1,
pp. 267-308, doi: 10.5465/annals.2014.0054.
Pinto, J. (2014), “Project management, governance, and the normalization of deviance”,
International Journal of Project Management, Vol. 32 No. 3, pp. 376-387, doi: 10.1016/j.
ijproman.2013.06.004.
Pinto, J.K., Patanakul, P. and Pinto, M.B. (2016), “Project personnel, job demands, and workplace
burnout: the differential effects of job title and project type”, IEEE Transactions on Engineering
Management, Vol. 63 No. 1, pp. 91-100, doi: 10.1109/TEM.2015.2509163.
Pulaj, E. and Kume, V. (2013), “How the Albanian external environment affect the construction
industry”, Annales Universitatis Apulensis: Series Oeconomica, Vol. 15 No. 1, p. 295, available at:
http://www.oeconomica.uab.ro/upload/lucrari/1520131/24.pdf (accessed 12 January 2020).
Quick (1989), “An ounce of prevention”, Stress Medicine, Vol. 5 No. 4, pp. 293-299, doi: 10.3928/
01477447-20060601-02.
Raya, R.P. and Panneerselvam, S. (2013), “The healthy organization construct: a review and
research agenda”, Indian Journal of Occupational and Environmental Medicine, Vol. 17 No. 3,
p. 89, doi: 10.4103/0019-5278.130835.
Sherratt, F. (2017), “Shaping the discourse of worker health in the UK construction industry”, Construction
Management and Economics, Vol. 36 No. 3, pp. 141-152, doi: 10.1080/01446193.2017.1337916.
Sklansky, D.A. (2005), “Not your father’s police department: making sense of the new demographics of
law enforcement”, Journal of Criminal Law and Criminology, Vol. 96, p. 1209.
Smith, M.J. and Carayon, P. (1995), “New technology, automation, and work organization: stress
problems and improved technology implementation strategies”, International Journal of Human
Factors in Manufacturing, Vol. 5 No. 1, pp. 99-116, doi: 10.1002/hfm.4530050107.
Sui Pheng, L. and Shing Hou, L. (2019), Construction Quality and the Economy, Springer Nature,
Singapore, ISBN: 978-981-13-5847-0.
Tarafdar, M., Tu, Q., Ragu-Nathan, B. and Ragu-Nathan, T. (2007), “The impact of technostress on role
stress and productivity”, Journal of Management Information Systems, Vol. 24 No. 1,
pp. 301-328, doi: 10.2753/MIS0742-1222240109.
Tijani, B., Jin, X. and Osei-kyei, R. (2020a), “A systematic review of mental stressors in the
construction industry”, International Journal of Building Pathology and Adaptation, Vol. 39
No. 2, pp. 433-460, doi: 10.1108/IJBPA-02-2020-0011.
Tijani, B., Jin, X. and Osei-Kyei, R. (2020b), “Critical analysis of mental health research among
construction project professionals”, Journal of Engineering, Design and Technology, Vol. 19
No. 2, pp. 467-496, doi: 10.1108/JEDT-04-2020-0119.
Tijani, B., Jin, X. and Osei-Kyei, R. (2021a), “Theoretical model for mental health management of
project management practitioners in architecture, engineering and construction (AEC) project
organizations”, Engineering Construction and Architectural Management, Vol. 10 No. 4, pp. 1-10,
doi: 10.1108/ECAM-03-2021-0247.
Tijani, B., Nwaeze, J.F., Jin, X. and Osei-Kyei, R. (2021b), “Suicide in the construction industry:
literature review”, International Journal of Construction Management, Vol. 5 No. 2, pp. 1-10,
doi: 10.1080/15623599.2021.2005897.
Unterhitzenberger, C., Wilson, H., Bryde, D., Rost, M. and Joby, R. (2020), “The stakeholder challenge:
dealing with challenging situations involving stakeholders”, Planning Production and Control,
Vol. 32 No. 11, pp. 926-941, doi: 10.1080/09537287.2020.1776907.
van den Bossche, S., Smulders, P. and Houtman, I. (2006), in Smulders, P.G.W. (ed.), Trends and Risk
Groups in Working conditionsWorklife in the Netherlands, TNO, Hoofddorp, pp. 43-64, available
at: file://ad.uws.edu.au/dfshare/HomesHWK$/30051911/Downloads/The_Netherlands_
Working_Conditions_Survey%20(1).pdf (accessed 15 September 2018).
Vintila, D., Filip, C., Stan, M. and Ţenea, D. (2017), “A political, economic, social, technology, legal and
environmental (PESTLE) approach for maritiime spatial planning (MSP) in the Romanian black
sea”, Bucharest, pp. 653-666, available at: https://www.proquest.com/openview/abae2224cc140597
a4be66b716dd9f8c/1?pq-origsite5gscholar%26cbl52032215 (accessed 21 June 2019).
PESTEL
analysis
SASBE
Wallis, A. and Dollard, M. (2008), “Local and global factors in work stress–The Australian dairy
farming examplar”, Scandinavian Journal of Work, Environment and Health, Vol. 34 No. 6,
p. 66, available at: https://www.semanticscholar.org/paper/Local-and-global-factors-in-workstress%E2%80%94the-dairy-Wallis-Dollard/1cecf45b0c14e6a6ecf6a7e5e43b976c05908833
(accessed 10 December 2021).
Wang, C., Mohd-Rahim, F., Chan, Y. and Abdul-Rahman, H. (2017), “Fuzzy mapping on psychological
disorders in construction management”, Journal of Construction Engineering and Management,
Vol. 143 No. 2, pp. 1-20, doi: 10.1061/(ASCE)CO.1943-7862.0001217.
Werts, C., Linn, R. and J€oreskog, K. (1974), “Intraclass reliability estimates: testing structural
assumptions”, Educational and Psychological Measurement, Vol. 34 No. 1, pp. 25-33, doi: 10.
1177/001316447403400104.
Winch, G.M. and Mayotorena-Sanchez, E. (2020), “Institutional projects and contradictory logics:
responding to complexity in institutional field change”, International Journal of Project
Management, Vol. 38 No. 6, pp. 368-378, doi: 10.1016/j.ijproman.2020.08.004.
Wreder, G., Gustavsson, M. and Klefsj€o, B. (2008), “Management for sustainable health: a TQMinspired model based on experiences taken from successful Swedish organizations”,
International Journal of Quality and Reliability Management, Vol. 6, pp. 561-584, doi: 10.1108/
02656710810881881.
Yang, F., Li, X., Zhu, Y., Li, Y. and Wu, C. (2017), “Job burnout of construction project managers in
China: a cross-sectional analysis”, International Journal of Project Management, Vol. 35 No. 7,
pp. 1272-1287, doi: 10.1016/j.ijproman.2017.06.005.
Zaccaro, S.J. and Bader, P. (2003), “E-leadership and the challenges of leading e-teams: minimizing the
bad and maximizing the good”, Organizational Dynamics, Vol. 31 No. 4, pp. 377-387, doi: 10.
1016/S0090-2616(02)00129-8.
Zika-Viktorsson, A., Sundstr€om, P. and Engwall, M. (2006), “Project overload: an exploratory study of
work and management in multi-project settings”, International Journal of Project Management,
Vol. 24 No. 5, pp. 385-394, doi: 10.1016/j.ijproman.2006.02.010.
Appendix
External environmental factors
Political factors
Based on political factors that influenced this project, please specify the extent to which you agree or
disagree with the following statements using the following scale:
1 5 Strongly Disagree; 2 5 Disagree; 3 5 Neutral; 4 5 Agree; 5 5 Strongly Agree.
Statements
(1) Royal Commission into Building and Construction Industry report implemented in the
project facilitates corruption and bribery management, which reduced project
management practitioners work stress
(2) Project planning approval process was easy, which reduced project management
practitioners work stress
(3) There is a political stability during the project execution, which reduced project
management practitioners work stress
(4) Favourable Australian tax policies implemented in the project reduces project
operating cost and income tax, which reduced project management practitioners work
stress
1
2
3
4
5
Economic factors
Based on economic factors influenced this project, please rate the following statements using following
scale:
1 5 Strongly Disagree; 2 5 Disagree; 3 5 Neutral; 4 5 Agree; 5 5 Strongly Agree.
Statements
1
2
3
4
5
(1) Sound Australian economic policies implemented reduces the project cost and
promote retention of project management practitioners, which reduced work stress
(2) Favourable market condition during project execution, which reduced project
management practitioners work stress
(3) Accessibility to bank loan for the project execution improves project financing;
thereby reduced project management practitioners’ work stress
(4) Australia government provided financial support to boost project finance; thereby
reduced project management practitioners work stress
(5) There was stable inflation in Australia during project execution, which reduced
project management practitioners work stress
Social factors
Based on social factors that impact the project, please specify the extent to which you agree or disagree
with the following statements by ticking your responses using the following scale:
1 5 Strongly Disagree; 2 5 Disagree; 3 5 Neutral; 4 5 Agree; 5 5 Strongly Agree.
Statements
1
2
3
4
5
(1) External project stakeholders’ support project management practitioners in the
project execution, which reduced project management practitioners work stress
(2) Good social relationship between project management practitioners and external
project stakeholders, which reduced project management practitioners work stress
(3) Availabilities of construction materials and labour for the project execution, which
reduced project management practitioners’ work stress
(4) Australia national culture assists in designing the mentally healthy project in
promoting the mental health of project management practitioners
Technological factors
Based on technological factors that influenced this project, please specify the extent to which you agree
or disagree with the following statements by ticking your responses using the following scale:
1 5 Strongly Disagree; 2 5 Disagree; 3 5 Neutral; 4 5 Agree; 5 5 Strongly Agree.
Statements
(1) Clear and effective Australian government policies on technology usage implemented
reduced project management practitioners’ work stress
(2) Availability of technologies to improve project management process, which reduced
project management practitioners work stress
(3) Technology was used to improve project management of the project, which reduced
project management practitioners work stress
1
2
3
4
5
PESTEL
analysis
SASBE
Environmental factors
Based on environmental factors influenced this project, please specify the extent to which you agree or
disagree with the following statements by ticking your responses using the following scale:
1 5 Strongly Disagree; 2 5 Disagree; 3 5 Neutral; 4 5 Agree; 5 5 Strongly Agree.
Statements
1
2
3
4
5
(1) Government approval for environmental sustainability was easy to obtain in the
project, which reduced project management practitioners work stress
(2) Australia environmental policies on how to achieve environmental sustainability
implemented in this project were easy, which reduced project management practitioners
work stress
(3) There was a stable weather conditions during the execution of the project, which
reduced project management practitioners work stress
Legal factors
Based on legal policies guiding this project, please specify the extent to which you agree or disagree with
the following statements by ticking your responses using the following scale:
1 5 Strongly Disagree; 2 5 Disagree; 3 5 Neutral; 4 5 Agree; 5 5 Strongly Agree.
Statements
1
2
3
4
5
(1) Effective Australia mental health policies for promoting mental health were
implemented in the project execution
(2) Effective Australia work–life balance policies for balancing work and family life of
project management practitioners were implemented in the project execution
(3) Effective Australia national code of practices for promoting justice and fairness was
implemented in the project
(4) Effective Safe work Australia policy guidance on mentally healthy project
organizational design was implemented in the project
Mental health management indicators
Based on your perception about the working environment of this project, please specify the extent to
which you agree or disagree with the following statements by ticking your responses using the following
scale:
1 5 Strongly Disagree; 2 5 Disagree; 3 5 Neutral; 4 5 Agree; 5 5 Strongly Agree.
Statements
1
2
3
4
5
(1) The project has effective project governance for managing project management
practitioners
(2) Project leadership in the project provides support for project management
practitioners in carrying out their works
(3) There was social relationship among project management practitioners working in
the project
(4) There is a strong relationship between stakeholders and project management
practitioners in the project
(5) Project management practitioners are highly recognized in the project
(6) Work time schedule is flexible during the execution of the project
(7) Project management practitioners are treated fairly in the project
(continued )
Statements
(8) There is a strong teamwork among project management practitioners and other
project teams in the project
(9) Good reward system was offered in the execution of the project
(10) There was good upskilling of project management practitioners based on the
training offered in the project
(11) The project management office was effective in allocating adequate project resources
to the project
(12) There was a clear project information system for sharing information among project
teams in the project
(13) Project roles allocated to project management practitioners were cleared
(14) There was appropriate staffing of project management practitioners in the project
(15) Reasonable project workload was allocated to project management practitioners in
the project
Corresponding author
Bashir Tijani can be contacted at: 17872544@student.westernsydney.edu.au
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
View publication stats
1
2
3
4
5
PESTEL
analysis