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. 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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