Measures to Improve the Mental Health of Construction Personnel Based on Expert Opinions
Publication: Journal of Management in Engineering
Volume 38, Issue 4
Abstract
Increasing rates of depression, anxiety, and suicide in the construction industry have drawn the attention of researchers to consider mental health as an integral part of health and safety. However, prior research has focused mainly on determining the sources of work stress, with a paucity of studies related to measures to improve mental health. This study aims to fill this gap by evaluating the mix of measures within an integrated approach that can be adopted to promote good mental health. Surveys were collected from 62 construction experts based in 4 countries. The data were analyzed using descriptive statistics, structural equation modeling (SEM), and a post-survey interview. SEM showed that secondary intervention measures such as those focused on healthy coping and individual resilience do not necessarily mitigate mental health stressors; it also signals the importance of including primary intervention measures in a workplace mental health intervention. These findings highlight intervention measures that could be implemented to create a psychologically healthy workplace. These measures can guide policy-making to boost job satisfaction, mental health, safety, and performance. Furthermore, these results provide a compass for building construction organizations to determine which measures are yet to be implemented in their workplaces and need to be explored.
Introduction
Globally, in the workplace, increased work speed and low job control causes job dissatisfaction and heightens perceived stress (Burke 2019). The dynamic nature of construction projects elevates the stressful nature of the industry, thus exposing construction professionals to workplace stress and poor mental health (Liang et al. 2021; Bowen et al. 2013). In the construction industry, stressors such as high job demand, poor interpersonal relationships, bullying, and harassment constitute psychosocial risk factors for mental distress and mental illness symptoms (Sunindijo and Kamardeen 2017; Chan et al. 2020; Leung et al. 2016). Mental illness, when left unattended, is a precursor to suicidality (Sunindijo and Kamardeen 2017), accidents, and presenteeism (Siu et al. 2004). Therefore, increasing the mental health of personnel holds excellent benefits to the industry.
Stress reactions have prompted countries, such as Australia, Canada, the United Kingdom, and the United States, to put measures in place to promote mental health. In the UK, for example, workdays lost to mental health problems cost employers about £43 billion per annum due to presenteeism and sickness leave (Ajayi et al. 2019). Although presently data on mental health in the construction industry is not readily available for all countries, international research has shown that compared to the general population, the construction industry suffers higher rates of poor mental health and suicide risks (Bryson and Duncan 2018; Milner et al. 2017). Mental illness symptoms and suicidal ideation are prevalent in the construction industry. For instance, in the Netherlands, among building construction supervisors, there was an 18% and 20% prevalence rate of depression and post-traumatic stress disorder symptoms, respectively; 11% and 7% among skilled workers, respectively (Boschman et al. 2013). Among building construction practitioners pooled from seven countries, the prevalence rate for anxiety and depression was 87% and 70%, respectively (Rees-Evans 2020). To effectively deal with mental health concerns at work, occupational health researchers have advocated adopting an integrated intervention approach (see LaMontagne et al. 2014). According to LaMontagne et al. (2018), “an integrated intervention approach to mental health” or “integrated mental health framework” involves adopting measures that (1) protects employee’s mental health by reducing work and nonwork risk factors for mental health problems; (2) promote employee’s mental health by developing the positive aspects of work, as well as the strengths and positive capacities of the employee; and (3) respond to mental health problems that manifest in employees at work regardless of cause, whether work or nonwork-related.”
Creating a workplace that considers the well-being of its employees will lead to greater job satisfaction, improved safety, mental health, performance, and organizational productivity (Burke 2019). Previous studies on mental health in the construction industry focused on determining mental health symptoms and their stressors (Boschman et al. 2013; Sunindijo and Kamardeen 2017). The studies provide a foundation for this present research. Although there exists research on mental health in the construction industry, empirical evidence on measures required to improve the mental health of on-site construction personnel remains insufficient. Additionally, existing studies on mental health among construction personnel have not employed an integrated approach to mitigate or prevent mental health problems.
While evidence shows that measures to improve mental health are available in other sectors, solutions are unique to the workplace context (LaMontagne et al. 2014), as the one-size-fits-all interventions do not apply to mental health problems (Rebar and Taylor 2017). For example, measures for job redesign in the construction industry for site-based personnel may differ from those required in the banking sector. Likewise, given the nature of the construction industry, the combination of measures necessary to make the construction workplace psychologically healthy and their importance will vary from those needed for other industries (Nwaogu and Chan 2021). This further emphasizes the need for context-specific solutions, e.g., the measures needed for construction personnel would differ from those needed for office clerks.
Toward informing efforts to make the construction workplace psychologically safe and healthy, the aim of this study is to determine the mix of measures that can be adopted to promote good mental health in the construction industry. To achieve the aim, the following objectives are set out: (1) to identify measures necessary for mental health promotion and their importance; and (2) to model the influence of the measures on stressors quantitatively. This study provides valuable initial evidence on primary, secondary, and tertiary intervention measures that can be implemented in the construction industry to create a psychologically safe and healthy workplace. The study will guide policies for boosting job satisfaction, mental health and well-being, safety, and performance in the construction workplace. Furthermore, the study potentially provides a checklist to construction organizations on measures yet to be implemented in their workplaces and need to be explored.
Literature Review
Types of Interventions
Effective workplace interventions that organizations can adopt within an integrated approach to mental health would consider combining primary, secondary, and tertiary interventions. Such workplace interventions would address work-related stress, build individual resilience or coping measures, detect mental health problems, and recommend an appropriate treatment. Intervention measures designed to prevent the development of work-related mental health problems are primary interventions (LaMontagne et al. 2014; Pignata et al. 2017). Primary interventions are directed toward eliminating or reducing stressors and sources of mental health problems (LaMontagne et al. 2014; Pignata et al. 2017).
Intervention measures directly channeled toward the employees are known as secondary interventions (LaMontagne et al. 2014). Secondary interventions include measures to reduce mental health problems by modifying how employees respond to or deal effectively with stressors (Pignata et al. 2017). Tertiary interventions are reactive in nature, as they involve responding to mental health problems by treating employees, offering counseling or financial assistance (LaMontagne et al. 2014; Nwaogu and Chan 2021). Intervention measures (or measures) refer to strategies that can be initiated or facilitated to prevent depression, anxiety, or both, and treat or rehabilitate a worker with diagnosed mental illness symptoms (Joyce et al. 2016). Stressor refers to a cause of stress, a potential mental illness risk factor; it is a threat to an individual (Murison 2016).
The Research Problem
Previous studies on measures to improve mental health focused on job stress mitigation (e.g., Havermans et al. 2018; Pignata et al. 2017, 2018; Yip and Rowlinson 2009) and mental illness symptoms (e.g., Joyce et al. 2010, 2016; Tan et al. 2014; Gullestrup et al. 2011; Lingard et al. 2007). Pignata et al. (2017), examining employees in the education sector, deduced that academic staff employed mostly secondary interventions such as coping measures to reduce stress. In contrast, nonacademic staff relied on primary intervention measures implemented by the organization to reduce and mange stress among employees. Pignata et al. (2018), surveying five Australian universities, deduced that measures implemented to reduce stress include increased salary, recognition practices, fairness, career development, and improved work-life balance. The findings in Pignata et al. (2017, 2018) emphasize that a single-mode intervention (i.e., secondary, primary, or tertiary) is not adequate for stress mitigation and mental health management.
Havermans et al. (2018) reported that “communication about stress,” “having a supportive workplace,” and “the availability of stress prevention measures” in the workplace were some measures perceived by employees to reduce stress. Generally, a meta-analysis by Tan et al. (2014) showed that most organizations employed secondary interventions to mitigate mental health problems among workers. While secondary interventions assist in coping and resilience building, they are ineffective in modifying risk factors as their effect wears out in a short time (Joyce et al. 2016; LaMontagne et al. 2014). Unlike this study, Pignata et al. (2017, 2018), and Havermans et al. (2018) were related to employees in sectors that are not related to construction. Solutions to make a workplace psychologically healthy and safe are context-specific (LaMontagne et al. 2014), as the one-size-fits-all interventions are not appropriate with mental health problems (Rebar and Taylor 2017). For example, measures for job redesign in the construction industry may differ from those required in nonconstruction related occupations (e.g., health or education). Also, the studies neither identified the level of importance of the measures nor examined their perceived impact on identified stressors.
Within the construction industry, there have been a few single-mode intervention studies on mental health. For instance, Gullestrup et al. (2011) adopted secondary intervention measures to mitigate suicide among tradesmen in the Australian construction industry (Nwaogu and Chan 2021). The measures included mental health literacy, stimulating helping behaviors, and some aspects of employee assistance programs. However, while the interventions increased mental health literacy, it did not mitigate mental illness and suicide. Additionally, studies in the construction industry have evaluated primary intervention measures such as job redesign measures (see Yip and Rowlinson 2009; Lingard et al. 2007). Lingard et al. (2007) achieved increased work-life balance and productivity using a compressed working week arrangement. Yip and Rowlinson (2009) reported mild effectiveness against the sources of burnout (emotional exhaustion, cynicism, and professional efficacy) using a reduced workday and fortnight off-work measure. However, both studies did not provide for nonwork factors that can cause or worsen mental illness, to which the primary interventions may be ineffective. Therefore, to ensure a sustainable mental health promotion in the industry, adopting an integrated approach to mental health management is more reliable than a single-mode approach because it can modify and mitigate risk factors.
Although previous research in the construction industry examined the single-mode approach to stress prevention or mental health promotion among personnel, Nwaogu and Chan (2021) moved the conversation forward by examining potential multimodal measures for mental health promotion among construction personnel in Nigeria. However, the study was based on contributions from experts in a single country. Little is known about the opinion of experts in other climes on the measures. Also, like previous studies (Havermans et al. 2018; Yip and Rowlinson 2009; Pignata et al. 2017, 2018; King et al. 2018; Gullestrup et al. 2011), Nwaogu and Chan (2021) did not examine the perceived impact of the measures on identified stressors. Based on the preceding, this present study is intended to fill the gap and advance Nwaogu and Chan (2021) by determining the impact of those multimodal measures on perceived stressors based on the experiences of construction experts from a variety of climes.
Theoretical Background
Since the integrated approach to mental health holds a promising mix of solutions to mental health in the construction industry, this study adopted the Job Demand-Resources (JD-R) model to evaluate the impact of the measures on mitigating stressors. Other models that have been employed in the stress and health literature include the Demand-Control (DCM) and Effort-Reward Imbalance (ERI) models. In contrast to prior models that are restricted to particular job demands or resources, the scope of JD-R is broader (Schaufeli and Taris 2014). The JD-R model is considered because it encompasses a wide range of work-related factors and can be tailored to different workgroups (Schaufeli and Taris 2014). The JD-R assumes that every occupation has unique resources, risk factors, and personal characteristics that may influence an employee’s health, well-being, and motivation (Bakker and Demerouti 2007; Schaufeli and Taris 2014).
This study uses the JD-R model and the integrated mental health as guiding principles on measures needed to eliminate or mitigate job demand and strain reactions to boost good health, work engagement, and job performance. In order to meet the integrated approach to mental health, the measures identified from existing literature (e.g., Enns et al. 2016; Hanisch et al. 2016; Gillen et al. 2017; Aguinis et al. 2012; Joyce et al. 2010; Ahola et al. 2012) were a mix of primary, secondary, and tertiary intervention measures (see Table 1). The measures were later grouped into seven constructs following Nwaogu and Chan (2021). The JD-R model served as a guide for selecting the measures that can motivate employees (e.g., IM19, IM20, IM22) and testing their impact on stressors. In Table 1, primary intervention measures, such as IM19 (job sculpting), IM22 (job crafting), IM23, and IM24, can boost job autonomy which would in turn increase motivation among on-site personnel, thereby, fulfilling the motivation aspect of the JD-R model (Bakker and Demerouti 2017), as improving motivation will boost health and productivity (Johari and Jha 2020), whereas secondary intervention measures such as IM01, IM02, IM03, IM04, and IM06 aimed at building coping mechanisms and individual resilience in construction personnel would meet the personal resources aspect of the JD-R model.
| Code | Measures to improve mental health | References | Type of intervention |
|---|---|---|---|
| Healthy coping and individual resilience focused measures | Secondary | ||
| IM01 | Empower staff to be individually more resilient through resilience training programs | Enns et al. (2016) and Tan et al. (2014) | |
| IM02 | Introduce wellness programs to workplaces/site offices | Burke (2019) and Enns et al. (2016) | |
| IM03 | Promote talks about antistigma (antistigma campaign) | Gullestrup et al. (2011) and Hanisch et al. (2016) | |
| IM04 | Stimulate helping behaviors toward people suffering from mental health problems through mental health first aid | LaMontagne et al. (2018) and Gullestrup et al. (2011) | |
| IM05 | Put measures in place for exercises such as exercise weekends | Havermans et al. (2018) and Enns et al. (2016) | |
| IM06 | Provide employees with competence training | Pignata et al. (2018) and Enns et al. (2016) | |
| Stress control focused measures | Secondary, tertiary | ||
| IM07 | Promote mental health awareness through literacy programs | LaMontagne et al. (2018) and Gullestrup et al. (2011) | |
| IM08 | Provide practical stress management training | Havermans et al. (2018) and Enns et al. (2016) | |
| IM09 | Put better education policies in place (e.g., providing subsidies for/encouraging employee career development) | Pignata et al. (2018) and Enns et al. (2016) | |
| IM10 | Conduct regular team meetings with supervisors and subordinates focused on addressing work stress | Havermans et al. (2018) | |
| IM11 | Promote communication about work stress from supervisors or subordinates without penalty | Pignata et al. (2018) and Havermans et al. (2018) | |
| IM12 | Offer assistance to nonwork stressors such as marital challenges | Pignata et al. (2018) and LaMontagne et al. (2014) | |
| IM13 | Provide aid for stressors such as financial challenges | Pignata et al. (2018) and LaMontagne et al. (2014) | |
| IM14 | Offer a sustainable retirement plan for employees | Pignata et al. (2018) and LaMontagne et al. (2014) | |
| Workplace (organizational) justice focused measures | Primary | ||
| IM15 | Create policies to eliminate bullying | Pignata et al. (2018) and Gillen et al. (2017) | |
| IM16 | Create policies to eliminate harassment | Pignata et al. (2018) | |
| IM17 | Promote equality policies irrespective of gender and age | Pignata et al. (2018) and Enns et al. (2016) | |
| IM18 | Reduce threatening of staff with disengagement when they make mistakes | Havermans et al. (2018) | |
| Employee morale and engagement focused strategies | Primary, secondary | ||
| IM19 | Promote employees’ deeply embedded life interest (i.e., job sculpting) | Hlanganipai and Mazanai (2014) and Aguinis et al. (2012) | |
| IM20 | Give constructive feedback instead of reprimanding | Havermans et al. (2018) | |
| IM21 | Celebrate employees’ success | Havermans et al. (2018) | |
| Job redesign and control focused measures | Primary | ||
| IM22 | Employees should be allowed some flexibility to design their job roles and tasks, while human resources approves it, in line with the job position and goals of the organization (i.e., job crafting) | Pignata et al. (2018) and Joyce et al. (2010) | |
| IM23 | The workplace should allow site employees to a flexible work schedule, with regards to work time and duration with no intention to reduce productivity or performance (i.e., flexitime) | Pignata et al. (2018) and Joyce et al. (2010) | |
| IM24 | Offer employees opportunities to balance work and life using compressed working week arrangements | Pignata et al. (2018) and Lingard et al. (2007) | |
| Interpersonal relationship related measures | Primary, secondary | ||
| IM25 | Ensure swift conflict resolution | Havermans et al. (2018) | |
| IM26 | Supporting improved relationships at work | Enns et al. (2016) and Ahola et al. (2012) | |
| IM27 | Put in place measures that increase cooperation between colleagues | Pignata et al. (2018) and Havermans et al. (2018) | |
| Job demand and satisfaction focused measures | Primary | ||
| IM28 | Allow the taking of regular breaks to enable rest | Havermans et al. (2018) | |
| IM29 | Better planning of work tasks and shifts | Havermans et al. (2018) | |
| IM30 | Hire more personnel to reduce the workload | Havermans et al. (2018) | |
| IM31 | Conduct employee satisfaction surveys | Havermans et al. (2018) |
Since the measures are needed to mitigate poor mental health, understanding the perceived impact of the measures on some stressors could improve decision-making. Therefore, this study investigates the influence of the measures on some stressors adapted from Chan et al. (2020) (see Table 2). To test the relationship, it is hypothesized that each measure construct will be negatively associated with the stressors. This implies that on implementing the measures, the stressors and their impact should reduce. Although the study is focused on measures to improve mental health, the purpose of the stressor questions was to aid confirmatory analysis of the intensity of the measures.
| Code | Stressors of mental health |
|---|---|
| CS01 | Physical illness |
| CS02 | Nature of work causing increased mental demand |
| CS03 | Hours worked per day (in excess of 60hrs per week) |
| CS04 | Work overload (too much quantity of work) |
| CS05 | Increased work speed |
| CS06 | Little opportunity/ability to participate in decision-making |
| CS07 | Little social support from colleagues/immediate supervisors |
| CS08 | Little relationship with colleagues/coworkers |
| CS09 | Occupational injury/hazards |
| CS10 | Poor working conditions (such as no leave, or leave without allowances, no housing allowances) |
| CS11 | Job insecurity (fear and/or uncertainty about the work) |
| CS12 | Strict adherence to the time or schedule (you cannot decide the timing for executing a task) |
| CS13 | Fatigue resulting from work causing poor sleep and recovery |
| CS14 | Criticisms from boss and colleagues |
| CS15 | Lack of feedback mechanism in place |
| CS16 | Low socioeconomic status (your position relative to your peers) |
| CS17 | Over-promotion- the job task is more than your experience with no mentoring |
| CS18 | Little task control, responsibility, or authority |
| CS19 | Fear of failure |
| CS20 | Interpersonal conflict |
| CS21 | Musculoskeletal pain and injuries |
| CS22 | Poor physical working condition |
| CS23 | Lack of respect from subordinates |
| CS24 | Workplace harassment |
| CS25 | Workplace bullying |
| CS26 | Work-home conflict/life imbalance (lack of time for family and other leisure due to work) |
| CS27 | Low income causing financial insecurity |
| CS28 | Wages not paid on time |
| CS29 | Unsatisfactory living condition at home |
| CS30 | Marital relationship challenges |
| CS31 | Poor family connection/relationships |
| CS32 | Increased level of education not relative to getting better jobs & income leading to frustration and worries |
| CS33 | Lack of medical subsidies for you or your family |
| CS34 | Lack of subsidies for family travel fees |
| CS35 | Lack of opportunity for career development while you still work on a particular job (such as furthering your studies) |
| CS36 | Lack of opportunity for promotion |
| CS37 | Lack of team, departmental, or company social get togethers |
| CS38 | Past traumatic experiences (death of a relative,accident, or bad happening) |
Note: CS = cause of stress (i.e., stressor).
Methodology
Survey Instrument
The research instrument is an online administered questionnaire developed by adapting measures and perceived stressors in the construction industry (see Appendix). The questionnaire is divided into three parts. Part A solicited demographic questions, while Part B elicited questions relating to perceived stressors in the industry. Part C consisted of measures required to mitigate the stressors and their impact. The respondents were required to indicate their level of agreement with each stressor or measure on a four-point Likert scale with 1 = “strongly disagree,” and 4 = “strongly agree.”
Face and Content Validity
A panel of five experts was used to conduct face and content validity on the draft questionnaire. The panelists consisted of 3 occupational health psychologists and 2 construction professionals with over 18 publications in the field of occupational health and safety. The validity was conducted involving a three-stage review. Upon receiving feedback from each stage, the questionnaire was redeveloped based on panelists’ comments, and the new draft was sent to them for perusal. Upon approval, the final questionnaire underwent pilot testing. The pilot testing involved 10 corporate members of the Chartered Institute of Building (CIOB) and Royal Institute of Chartered Surveyors (RICS). The professionals were asked to comment on their understanding of the questions and the time taken to complete the survey. All participants indicated the questions were appropriate, understandable, and took approximately 7 minutes to complete.
Data Collection
The questionnaire was used to elicit expert opinions from a purposive sample of construction practitioners chosen based on the criteria that they hold a position as a construction professional occupying a policy-making role in the construction industry. An expert refers to a person with the skill or knowledge exhibited in leadership positions or who presents in conventions or is recognized by journal publications (Darko and Chan 2018). Therefore, for this study, an expert refers to a construction professional with the skills and pedigree in building production and management policy-making roles within the building construction workplace. The experts are targeted as respondents because they have risen from the lower level to top management level and key policymakers within the construction industry. The term “personnel” refer to “on-site supervisory personnel (e.g., the site engineers, site supervisors) on a building construction project.”
The experts for the questionnaire survey were purposively selected from (1) the Royal Institution of Chartered Surveyors website, (2) websites of construction companies, or (3) a compiled list of construction practitioners who partook in a previous survey conducted by the researchers or their network. The websites of other professional bodies in the Architectural, Engineering, and Construction (AEC) industry were consulted; however, only RICS afforded the possibility of finding members and getting their email addresses. For RICS, only members engaged in health and safety, building surveying, and construction positions were surveyed. After identifying the participants, the questionnaire was administered through an email containing a brief introduction to the study and a link to access the survey.
All participants are members of a professional body engaged in construction management, health, and safety. Albeit the subject of making the workplace psychologically healthy and safe seems a general concern that any construction professional can answer, it is not, considering the constraints of time, cost, and quality when organizing construction activities. Based on convenience and purposive sampling, approximately 247 questionnaires were sent to potential respondents. Finally, to assess the findings and gain better insights into the study, a post-survey interview was conducted with 5 experts who partook in the survey and indicated a further interest in the research. To aid the post-survey interview, the result obtained from the survey alongside a brief explanation was provided to the experts (see Hwang et al. 2020). The interviewees were asked to assess and give their suggestions on the findings with reference to the purpose of the study.
Data Analysis Methods
The data collected were analyzed using mean score ranking via SPSS 26.0 package and structural equation modeling performed using the SmartPLS 3.3.3 software.
Consistency Reliability of Experts’ Ranking
Prior to conducting the analysis, internal consistency reliability was determined. Internal consistency reliability determines the understanding of the questions used for measuring a phenomenon among a sample of respondents (Taber 2018). As stated by Flo et al. (2018), “a commonly accepted rule for describing internal consistency when using Cronbach’s alpha includes: = excellent, = good, and = unacceptable.”
Mean Ranking of the Measures
The measures were ranked using their mean score and standard deviation (SD) values to determine their significance in achieving the aim of the study. This was employed as it is the most commonly used descriptive statistics to rank measures perceived by respondents in a quantitative study (Chan and Adabre 2019; Darko and Chan 2018). In a case where two or more measures had the same mean, the measure with the lowest standard deviation is ranked highest, following Darko and Chan (2018) approach.
Kruskal-Wallis Test
The Kruskal-Wallis test was used to determine whether experts’ opinions from the countries differed regarding a particular intervention measure. Kruskal-Wallis test is a nonparametric test suitable for assessing the difference among three or more independently sampled groups (Nwaogu and Chan 2021; McKight and Najab 2010). With a significance level of 0.05, the null hypothesis () in the Kruskal-Wallis test holds that “there is no difference in the mean ranks of the groups” (Nwaogu 2021; Nwaogu and Chan 2021; McKight and Najab 2010). Therefore, if the p-value is greater than 0.05, there is no statistically significant difference in the experts’ opinion regarding a particular intervention measure. However, if the p-value is less than 0.05, the is rejected, indicating a statistically significant difference in their opinion.
Suppose the null hypothesis () is rejected after conducting the Kruskal-Wallis test. In that case, a post hoc test must be conducted to determine which group of respondents differ significantly in their opinion concerning a variable (Leon 1998). The post hoc test was conducted using pairwise comparisons of the experts’ opinions for measures with a statistically significant difference. Pairwise comparisons using Dunn’s approach were conducted using SPSS software, as the software automatically produces them for variables (i.e., the measures) with a statistically significant difference.
Structural Equation Modeling
The structural equation modeling (SEM) analytical technique can test hypotheses to establish the relationship between items. The partial least square (PLS-SEM), a type of SEM method, was employed for this analysis because it is suitable for analyzing nonnormal data and can handle a sample size of less than 250 (Darko et al. 2018; Hair et al. 2014). Specifically, PLS is a variance-based SEM method (Hair et al. 2014). It can appropriately handle reflective and formative models for construct measurement and test model fit (Henseler 2017; Hair et al. 2014).
In order to identify the relationship between the variables in PLS-SEM, the process begins by creating a path model that connects variables and constructs based on theory and logic (Hair et al. 2014). One logic to consider is that PLS-SEM can only handle models with no circular relationship between the constructs (Hair et al. 2014). This implies that while PLS-SEM can examine causal relationships, it cannot deduce reverse-causal relationships. The relationship between the constructs is designed as either exogenous or endogenous. Exogenous constructs are independent variables and do not have an arrow pointing at them. In contrast, endogenous constructs are the dependent variables as other constructs explain them; thereby, they have arrows pointing at them (Hair et al. 2014). In this study, the stressors’ construct is endogenous, while the intervention measure constructs (e.g., JRC-M) are exogenous (see Fig. 1).
ME.1943-5479.0001045/asset/d9d6bad0-41fd-47a5-acd3-dd29b7e65e91/assets/images/large/figure1.jpg)
Another logic is specifying the inner and outer models. The outer models are either designed in a reflective or formative manner (Hair et al. 2014). As shown in Fig. 1, the outer model in this study takes the reflective manner, in that the indicators point out from the constructs (e.g., JRC-M) to the variables (i.e., IM12, IM13, IM19). According to Hair et al. (2014), “reflective variables are linked to a construct through loadings, that is, the bivariate correlations between the variable and the construct.” In order to assess reflective outer models, the reliability and validity must be verified. This is done using two major steps. First, Cronbach’s alpha or composite reliability score of 0.70 or higher is used to evaluate the construct’s internal consistency reliability (Hair et al. 2016). The second step involves the assessment of validity, which is examined by construct’s convergent validity and discriminant validity.
Average variance extracted (AVE) score of 0.50 or higher is used to assess a construct’s convergent validity. According to Henseler et al. (2015), “AVE indicates the mean amount of variance that a construct explains in its indicator variables relative to the overall variance of the indicators.” Discriminant validity ensures that a variable in a construct does not correlate too highly with another variable in another construct (Henseler et al. 2015). Henseler and colleagues asserted that if discriminant validity is not fulfilled, the accuracy of results confirming the hypothesized structural paths may not be certain. The discriminant validity can be assessed using the Heterotriat Monotrait (HTMT) criterion at a threshold of less than 0.85 (Henseler et al. 2015).
After sketching the model in the PLS-SEM environment, analysis began by deducing the path coefficients. The analysis involved eliminating all measures and stressors in the model whose factor loading was below the threshold of 0.5 (Hair et al. 2016). This process also aided in ensuring that the construct reliability and discriminant validity of the constructs met minimum requirements. Variables within a reflective outer model are interchangeable and can be eliminated without changing the meaning of the construct because they are highly correlated and consist of a set of possible variables within the conceptual domain of a construct (Hair et al. 2014). To assess the construct reliability and validity, Cronbach’s alpha , composite reliability , and average variance extracted were used (Darko et al. 2018; Hair et al. 2016; Cheung and Zhang 2020). Furthermore, the discriminant validity was assessed using the Heterotriat Monotrait (HTMT) criterion (Henseler et al. 2015).
After confirming the construct reliability and discriminant validity, the path coefficients and effects of the measures on stressors and the hypothesis were tested using bootstrap analysis. The number of bootstrap samples was set at default (5,000) to reduce results’ variations when rerun. The decision on the hypothesis was based on t-values threshold for two-tailed test: 2.58 (at significance ), 1.96 (significance ), and 1.65 (significance ). The R-square, coefficients of p-value, and path coefficients were used for the structural model.
Results
Profile of the Respondents
A total of 62 duly filled questionnaire responses were retrieved from 4 countries (see Table 3), accounting for about a 25.1% response rate. Usually, online surveys face challenges with low response rates (Chan and Adabre 2019). However, the central limit theorem holds that a sample size of at least 30 is valid and sufficient (Chan and Adabre 2019; Nwaogu and Chan 2021). Thus, 62 responses are deemed adequate to the study considering international surveys in the field of construction management, e.g., Owusu et al. (2020) and Chan and Adabre (2019). Owusu et al. (2020) was based on 44 responses from experts based in 18 different countries. Similarly, Chan and Adabre (2019) received 51 responses from 18 countries.
| Description | Number of responses | Percent |
|---|---|---|
| Countries | ||
| South Africa | 19 | — |
| Hong Kong SAR | 18 | — |
| Singapore | 14 | — |
| USA | 11 | — |
| Total | 62 | — |
| Years of experience | ||
| 11–20 | 26 | 41.9 |
| 21–30 | 16 | 25.8 |
| Over 30 | 20 | 32.3 |
| Total | 62 | 100.0 |
| Professional practice | ||
| Industry | 56 | 90.3 |
| Academia/research institute | 6 | 9.7 |
| Total | 62 | 100.0 |
| Profession | ||
| Architects | 2 | 3.2 |
| Civil engineers | 20 | 32.3 |
| Quantity and building surveyors | 2 | 3.2 |
| Construction managers | 38 | 61.3 |
| Total | 62 | 100.0 |
All respondents were affiliated with a professional construction body, and the majority of them (58.1%) had over 20 years of work experience (see Table 3). 56 out of the 62 respondents (90.3%) were actively engaged in industry practice, while 9.7% were in academia or a research institute (see Table 3). The representation of experts was as follows: architects (3.2%), civil engineers (32.3%), quantity and building surveyors (3.2%), and construction managers (61.3%).
Mean Ranking of the Measures to Improve Psychological Health
As shown in Table 4, based on the combined responses from all the countries, the top measures include “celebrate employees’ success (IM21)”, “better planning of work tasks and shifts (IM29)”, “give constructive feedback instead of reprimanding (IM20)”, and “create policies to eliminate harassment (IM16)”, with mean scores of 3.58, 3.56, 3.45, and 3.42, respectively. The Kruskal-Wallis test revealed a statistically significant difference in the experts’ response to 8 measures (i.e., IM07, IM08, IM10, IM13, IM16, IM26, IM27, IM31).
| Code | Measures to improve mental health | Ranking | Kruskal-Wallis test | Cronbach’s alpha | ||
|---|---|---|---|---|---|---|
| Mean | SD | Rank | ||||
| Healthy coping and individual resilience focused measures | 0.733 | |||||
| IM01 | Empower staff to be individually more resilient through resilience training programs | 3.11 | 0.603 | 23 | 0.339 | |
| IM02 | Introduce wellness programs to workplaces/site offices | 3.40 | 0.586 | 5 | 0.079 | |
| IM03 | Promote talks about antistigma (antistigma campaign) | 3.02 | 0.665 | 25 | 0.335 | |
| IM04 | Stimulate helping behaviors toward people suffering from mental health problems through mental health first aid | 3.18 | 0.641 | 21 | 0.112 | |
| IM05 | Put measures in place for exercises such as exercise weekends | 3.02 | 0.779 | 26 | 0.288 | |
| IM06 | Provide employees with competence training | 3.37 | 0.550 | 7 | 0.506 | |
| Stress control focused measures | 0.840 | |||||
| IM07 | Promote mental health awareness through literacy programs | 3.31 | 0.715 | 18 | 0.000 | |
| IM08 | Provide practical stress management training | 3.32 | 0.594 | 14 | 0.027 | |
| IM09 | Put better education policies in place (e.g., providing subsidies for/encouraging employee career development) | 3.32 | 0.594 | 15 | 0.527 | |
| IM10 | Conduct regular team meetings with supervisors and subordinates focused on addressing work stress | 3.13 | 0.713 | 22 | 0.027 | |
| IM11 | Promote communication about work stress from supervisors or subordinates without penalty | 3.21 | 0.704 | 20 | 0.126 | |
| IM12 | Offer assistance to nonwork stressors such as marital challenges | 3.00 | 0.768 | 27 | 0.099 | |
| IM13 | Provide aid for stressors such as financial challenges | 2.95 | 0.798 | 29 | 0.023 | |
| IM14 | Offer a sustainable retirement plan for employees | 3.31 | 0.667 | 17 | 0.150 | |
| Workplace (organizational) justice focused measures | 0.815 | |||||
| IM15 | Create policies to eliminate bullying | 3.35 | 0.704 | 10 | 0.231 | |
| IM16 | Create policies to eliminate harassment | 3.42 | 0.714 | 4 | 0.019 | |
| IM17 | Promote equality policies irrespective of gender and age | 3.23 | 0.734 | 19 | 0.051 | |
| IM18 | Reduce threatening of staff with disengagement when they make mistakes | 3.34 | 0.723 | 13 | 0.119 | |
| Employee morale and engagement focused measures | ||||||
| IM19 | Promote employees’ deeply embedded life interest (i.e., job sculpting) | 3.34 | 0.651 | 12 | 0.103 | 0.638 |
| IM20 | Give constructive feedback instead of reprimanding | 3.45 | 0.563 | 3 | 0.356 | |
| IM21 | Celebrate employees’ success | 3.58 | 0.529 | 1 | 0.148 | |
| Job redesign and control focused measures | 0.809 | |||||
| IM22 | Employees should be allowed some flexibility to design their job roles and tasks, while human resources approves it, in line with the job position and goals of the organization (i.e., job crafting) | 2.90 | 0.824 | 30 | 0.162 | |
| IM23 | The workplace should allow site employees to a flexible work schedule, with regards to work time and duration with no intention to reduce productivity or performance (i.e., flexitime) | 2.95 | 0.777 | 28 | 0.639 | |
| IM24 | Offer employees opportunities to balance work and life using compressed working week arrangements | 3.37 | 0.607 | 8 | 0.270 | |
| Interpersonal relationship related measures | 0.701 | |||||
| IM25 | Ensure swift conflict resolution | 3.40 | 0.586 | 6 | 0.089 | |
| IM26 | Supporting improved relationships at work | 3.34 | 0.477 | 11 | 0.029 | |
| IM27 | Put in place measures that increase cooperation between colleagues | 3.31 | 0.561 | 16 | 0.031 | |
| Job demand and satisfaction focused measures | 0.769 | |||||
| IM28 | Allow the taking of regular breaks to enable rest | 3.37 | 0.633 | 9 | 0.470 | |
| IM29 | Better planning of work tasks and shifts | 3.56 | 0.562 | 2 | 0.914 | |
| IM30 | Hire more personnel to reduce the workload | 2.89 | 0.851 | 31 | 0.658 | |
| IM31 | Conduct employee satisfaction surveys | 3.03 | 0.746 | 24 | 0.029 | |
Note: SD = standard deviation; bold values are significant at p-value ; and IM = intervention measure.
As shown in Table 5, post hoc testing further showed that while respondents agreed on the importance of most of the measures, experts in Hong Kong seemed to differ on the importance of most of the measures. Overall, the analysis yielded an excellent Cronbach’s alpha () ranging between 0.70 and 0.84 for each construct (see Table 4), indicating that the experts’ understanding of the measures in each construct is consistent. Unlike other measure constructs, “job demand and satisfaction” and “job redesign and control measures” had individual measures with a mean score below 3.00, as approximately 30% of the respondents disagreed about the viability of implementing the measures to achieve a psychologically safe workplace for on-site construction personnel.
| Measures | Kruskal-Wallis test | Countries | Pairwise comparison | Significance level | |||
|---|---|---|---|---|---|---|---|
| US | HK | SA | SG | ||||
| IM16 | 0.019 | 43.05 | 25.17 | 34.08 | 27.07 | HK-US | 0.023 |
| IM26 | 0.029 | 29.61 | 29.16 | 43.55 | 27.64 | SA-US | 0.046 |
| IM08 | 0.027 | 35.59 | 22.06 | 34.24 | 36.71 | HK-SG | 0.048 |
| IM27 | 0.031 | 38.09 | 23.06 | 35.97 | 31.11 | HK-SA | 0.040 |
| IM07 | 0.000 | 34.91 | 17.28 | 36.95 | 39.71 | HK-US | 0.032 |
| IM10 | 0.027 | 39.23 | 22.33 | 35.82 | 31.36 | HK-US | 0.047 |
| IM31 | 0.029 | 37.18 | 22.33 | 36.00 | 32.71 | HK-SA | 0.043 |
| IM13 | 0.023 | 40.23 | 22.53 | 30.97 | 36.89 | HK-US | 0.035 |
Note: IM = intervention measure; US = United States of America; SA = South Africa; SG = Singapore; and HK = Hong Kong.
Structural Equation Modeling
Evaluation of the Model Measurements
With 62 responses, the PLS/SEM was deemed appropriate for the modeling. After eliminating all measures and stressors with factor loading below the threshold of 0.50, only 25 measures and 18 stressors were fit for the analysis (see Fig. 1). The 6 eliminated measures included IM05, IM09, IM10, IM14, IM25, and IM31. The 20 eliminated stressors included CS01, CS02, CS09, CS10, CS13, CS15, CS17, CS18, CS19, CS20, CS21, CS22, CS23, CS24, CS25, CS28, CS30, CS31, CS37, and CS38. Following Hair et al. (2014), the measures and stressors with a factor loading below 0.50 could be eliminated without changing the meaning of the construct in which they were initially situated because they are highly correlated, and the construct consists of a set of possible variables. As shown in Table 6, the constructs had Cronbach’s alpha above 0.70, composite reliability scores above 0.70, and AVE above 0.50. This indicates appropriate construct reliability and validity, as the Cronbach’s alpha, composite reliability, and AVE scores were within the and used for assessing construct reliability and validity (Darko et al. 2018).
| Variable | Construct code | Item code | Loading | Cronbach’s alpha | Composite reliability | Average variance extracted |
|---|---|---|---|---|---|---|
| Measures | EM-M | IM19 | 0.872 | 0.700 | 0.780 | 0.547 |
| IM20 | 0.593 | — | — | — | ||
| IM21 | 0.728 | — | — | — | ||
| HCIR-M | IM01 | 0.634 | 0.799 | 0.837 | 0.510 | |
| IM02 | 0.759 | — | — | — | ||
| IM03 | 0.716 | — | — | — | ||
| IM04 | 0.614 | — | — | — | ||
| IM06 | 0.826 | — | — | — | ||
| IR-M | IM26 | 0.800 | 0.633 | 0.842 | 0.727 | |
| IM27 | 0.902 | — | — | — | ||
| JDS-M | IM28 | 0.932 | 0.775 | 0.855 | 0.665 | |
| IM29 | 0.784 | — | — | — | ||
| IM30 | 0.715 | — | — | — | ||
| JRC-M | IM22 | 0.775 | 0.813 | 0.873 | 0.700 | |
| IM23 | 0.963 | — | — | — | ||
| IM24 | 0.774 | — | — | — | ||
| SC-M | IM11 | 0.774 | 0.863 | 0.893 | 0.627 | |
| IM12 | 0.872 | — | — | — | ||
| IM13 | 0.774 | — | — | — | ||
| IM07 | 0.687 | — | — | — | ||
| IM08 | 0.840 | — | — | — | ||
| WJ-M | IM15 | 0.751 | 0.816 | 0.855 | 0.599 | |
| IM16 | 0.834 | — | — | — | ||
| IM17 | 0.868 | — | — | — | ||
| IM18 | 0.620 | — | — | — | ||
| Stressors | CS11 | 0.648 | 0.816 | 0.855 | 0.599 | |
| CS12 | 0.584 | — | — | — | ||
| CS14 | 0.531 | — | — | — | ||
| CS16 | 0.593 | — | — | — | ||
| CS26 | 0.629 | — | — | — | ||
| CS27 | 0.626 | — | — | — | ||
| CS29 | 0.571 | — | — | — | ||
| CS32 | 0.639 | — | — | — | ||
| CS33 | 0.598 | — | — | — | ||
| CS34 | 0.597 | — | — | — | ||
| CS35 | 0.565 | — | — | — | ||
| CS36 | 0.623 | — | — | — | ||
| CS03 | 0.513 | — | — | — | ||
| CS04 | 0.687 | — | — | — | ||
| CS05 | 0.573 | — | — | — | ||
| CS06 | 0.607 | — | — | — | ||
| CS07 | 0.570 | — | — | — | ||
| CS08 | 0.520 | — | — | — |
Note: EM-M = employee morale and engagement focused measures; HCIR-M = healthy coping and individual resilience focused measures; IR-M = interpersonal relationship related measures; JDS-M = job demand and satisfaction focused measures; SC-M = stress control focused measures; and WJ-M = workplace (organizational) justice focused measures.
The constructs also had acceptable discriminant validity, with the HTMT of the constructs being below 0.85 (see Table 7). According to Henseler et al. (2015), when testing for discriminant validity, the HTMT criterion is .
| Measures | EM-M | HCIR-M | IR-M | JDS-M | JRC-M | SC-M | Stressors | WJ-M |
|---|---|---|---|---|---|---|---|---|
| EM-M | — | — | — | — | — | — | — | |
| HCIR-M | 0.647 | — | — | — | — | — | — | — |
| IR-M | 0.559 | 0.841 | — | — | — | — | — | |
| JDS-M | 0.701 | 0.494 | 0.474 | — | — | — | — | — |
| JRC-M | 0.805 | 0.401 | 0.217 | 0.573 | — | — | — | |
| SC-M | 0.700 | 0.809 | 0.706 | 0.598 | 0.538 | — | — | — |
| Stressors | 0.346 | 0.248 | 0.282 | 0.289 | 0.212 | 0.197 | — | |
| WJ-M | 0.965 | 0.583 | 0.460 | 0.375 | 0.778 | 0.563 | 0.266 |
Note: EM-M = employee morale and engagement focused measures; HCIR-M = healthy coping and individual resilience focused measures; IR-M = interpersonal relationship related measures; JDS-M = job demand and satisfaction focused measures; SC-M = stress control focused measures; and WJ-M = workplace (organizational) justice focused measures.
Evaluation of Structural Model
The bootstrapping result showed that some measures impacted the stressors as hypothesized (see Table 8). In PLS-SEM, for a two-tailed test, the t-values threshold was 2.58 (at significance ), 1.96 (significance ), and 1.65 (significance ) (Darko et al. 2018). The paths testing hypotheses H1, H2, H4, and H7 were significant because they had t-values within the range of 1.65, 1.96, or 2.58 (see Table 8). However, only hypotheses H1, H4, and H7 were supported because it is hypothesized that each measure construct will have a negative relationship with the stressors. Likewise, because the path testing H2 had a positive association with the stressors, H2 was not supported. The positive association shows that although experts perceive that implementing secondary intervention measures to build coping and resilience would more likely improve psychological health among construction personnel, increasing the intensity of the measures alone will not necessarily mitigate the stressors.
| Hypothetical path | Path coefficient | |t-value| | p-value | Level of significance | Hypothesis decision | |
|---|---|---|---|---|---|---|
| H | Relationship | |||||
| H1 | EM-M → stressors | 2.666 | 0.004** | Significant | Supported | |
| H2 | HCIR-M → stressors | 0.559 | 2.300 | 0.020* | Significant | Not supported |
| H3 | IR-M → stressors | 0.347 | 0.880 | Not significant | Not supported | |
| H4 | JDS-M → stressors | 3.388 | 0.000** | Significant | Supported | |
| H5 | JRC-M → stressors | 0.421 | 0.674 | Not significant | Not supported | |
| H6 | SC-M → stressors | 0.294 | 1.279 | 0.578 | Not significant | Not supported |
| H7 | WJ-M → stressors | 1.858 | 0.031* | Significant | Supported | |
Note: *Significant at p-value ; **significant at p-value ; and H = hypothesis.
The model depicting the impact of the measures on stressors had an of 0.561, as shown in Fig. 1. The indicates a satisfactory predictive ability of the model (Hair et al. 2014). The higher the path coefficient, the greater the influence of the independent variable (measure construct) on the dependent variable (the stressors). As noted in Darko et al. (2018), a path coefficient of 0 indicates a weak influence, indicates a moderate influence, and indicates a strong influence. Hypotheses H1, H2, H4 had a path coefficient of 0.639, 0.559, and 0.697, respectively, indicating a strong influence on the stressors, whereas H7, the path linking workplace justice-related measures to the stressors with a path coefficient of 0.462 had a moderate influence on the stressors.
Post-Survey Interviews
All interviewees agreed to the findings of the survey and found them reasonable and practicable. They also provided explanations on the measures that had a lower ranking (i.e., mean score below 3.00). As regards IM02, an interviewee (#2) who is a senior construction project manager, gave an example.
As one of my organization’s mental health and well-being policies, we created indoor games at the head office held every Thursday by 4 p.m. while Fridays are for workout aerobic dance section. However, most site managers who need it are not always available. It is not enough to have such policies; on-site personnel should be encouraged to join or have the arrangement at the site office (Interviewee #2).
Regarding IM30, the interviewees agreed that given the nature of the industry, hiring more personnel depends on the volume of work, contract sum, and firm size. All experts agreed to the findings regarding the job redesign measures (IM22, IM23, and IM24). However, interviewee #4 tried to explain the direction of the survey score for that measure construct.
Both IM22 and IM23 need to be adequately planned and subjected to rigorous experimentation, and that is the reason for the lower mean score. In theory, it seems possible; we want it to work like that. However, unlike other construction team members who are seldom on the job site, site engineers or supervisors need to be around for total quality management, so the compressed workweek arrangement is more practical to be adopted than other job redesign measures (Interviewee #4).
Discussion
Measures such as “celebrate employees’ success,” “better planning of work tasks and shifts,” “give constructive feedback instead of reprimanding,” and “create policies to eliminate harassment” were perceived as most important in order to have a construction workplace that is psychologically healthy and safe. They comprise of measures to boost employee morale, reduce job demand, eliminate injustice, and build coping and resilience among on-site construction professionals. Thus, it highlights the need to adopt measures that are multimodal in nature. This finding corroborates Nwaogu and Chan (2021), where the measures ranked among the most essential to implement.
Construct 1: Stress Control Measures
The measures underlying this construct are secondary and tertiary interventions and include those instrumental in stress control among construction personnel. The PLS-SEM result did not support a negative association between the measures and stressors. This may highlight that secondary and tertiary interventions mainly directed to ease personnel reaction to a stressor are insufficient because some stressors will need primary intervention measures for them to be eliminated or reduced. Thus, this further draws attention to the need for an integrated approach for workplace intervention on mental health.
Irrespective of the PLS-SEM result, some of the measures in this construct ranked on the agreement scale. This corroborates Bowen et al. (2014) that recommended stress management workshops and Chan et al. (2020) that posited the need for employee assistance programs (EAPs) in the construction industry to address stressors. Likewise, King et al. (2018) used mental health literacy to shift beliefs regarding suicide and mental health in the Australian construction industry. Measures built around employee assistance models hold an effective intervention to enhance mental health and well-being in the workplace (Saju et al. 2019), as they can be preventive or reactive (LaMontagne et al. 2014; Tan et al. 2014).
Although the experts from Hong Kong differed a little on the need to implement policies for sustainable retirement plans, experts in other countries recommend it. This implies that, overall, such a measure is essential. Considering the dependence of construction companies on project availability, to mitigate the stress that arises from unplanned retirement or job loss, employers should enlighten and enroll their personnel in a variety of available retirement schemes to drive satisfaction benefits and productivity (Marcellus and Osadebe 2014).
The ability to communicate about work stress has been suggested to increase awareness about an individual’s needs, the changes required in a workplace, and the selection of the best mental health intervention (Havermans et al. 2018). Therefore, promoting communication about stress from the personnel is essential. Interventions like mental health literacy among construction personnel should be implemented as they hold the ability to furnish employees with important precursors to help-seeking, particularly the ability to recognize mental illness and identify available intervention options (Moll 2014).
Construct 2: Healthy Coping and Individual Resilience Focused Measures
The measures underlying this construct are aimed at enhancing healthy coping and building individual resilience among construction personnel. They are secondary intervention measures. Tan et al. (2014) deduced that most organizations employed secondary interventions to mitigate mental health problems among workers. However, while secondary interventions assist in coping and resilience building, they are ineffective in modifying risk factors, as their effect wears out in a short time (Joyce et al. 2016; LaMontagne et al. 2014). Consistent with these findings, the structural equation modeling showed that efforts to increase healthy coping and individual resilience did not mitigate the stressors.
With all the measures in this construct ranking on the agreement scale, this result is consistent with prior studies (see Moll 2014; Chen et al. 2017), showing that these measures are essential to improving psychological health. Coping with mental health problems involves several techniques, including seeking professional help or seeking social support from colleagues and family members (Moll 2014; Nwaogu et al. 2021; Nwaogu and Chan 2021). Therefore, measures to eradicate stigma as well as stimulate helping behaviors toward people suffering from mental health problems in the construction workplace should be implemented or reinforced. This is expedient as poor support from managers has been reported to double the risk of a mental illness related sickness (Moll 2014). Colleagues are a significant stakeholder that may first notice changes in an employee’s behaviors, and their attitude can have a considerable impact on whether an employee is supported or discriminated against when they are unwell (Moll 2014).
The level of an individual’s resilience predicts the possibility of developing mental health problems; thus, enhancing resilience appears to be a good target for indicated interventions (Glozier and Brain and Mind Centre 2017). Therefore, empowering employees to be more resilient through resilience training, wellness programs in workplaces, and competence training can allow construction personnel to build relevant resilience and stress-coping skills for mental health management. Additionally, consistent with this study, Havermans et al. (2018) found that competence training will help employees cope with stress, set boundaries, and deal with changes. Pointing to the need for competence training, Haynes and Love (2004) found that older construction personnel suffered job insecurity owing to the difficulty in adopting emerging technologies. Similarly, in this study, providing employees with competence training ranked the fourth significant measure needed for achieving a psychologically healthy workplace in the construction industry. Hence, appropriate competence training is desirable to help personnel cope adequately with changes and trends in technological applications relevant to their jobs, such as the use of cutting-edge technology in carrying out their responsibilities.
Construct 3: Workplace (Organizational) Justice Focused Measures
The measures in this component are mainly primary interventions related to ensuring organizational justice in the construction workplace. The PLS-SEM result supported a negative association between the measures and the stressors. The findings corroborate Nwaogu et al. (2019), which recommended measures to eliminate organizational injustice in the construction workplace. Likewise, among the measures in this group, enforcing policies to mitigate harassment appeared to be an effective measure as they ranked in the top five, pointing to its importance in creating a psychologically healthy and safe construction workplace. The threatening of staff with disengagement when they make mistakes can lead to fear of job insecurity and job dissatisfaction. Thus, measures to reduce this form of workplace injustice are pertinent, as concerns over job insecurity have been found to cause increased poor mental health (Chan et al. 2020). Therefore, ensuring organizational justice through promoting civility can act as a resource to improve mental health and well-being in the construction workplace.
Construct 4: Job Demand and Satisfaction Focused Measures
The measures in this construct are mainly primary intervention measures. The PLS-SEM result supported a negative association between the measures in this construct and the stressors. The path coefficient signaled that the construct is perceived to have a strong impact on mitigating the stressors. This signifies that primary intervention measures that will mitigate stressors and the onset of mental health problems are essential and should be part of mental health intervention. These findings are consistent with Havermans et al. (2018), who found that better planning of work tasks and hiring more personnel are required to effectively reduce job stress. Likewise, in this study, better planning of work tasks ranked in the top five measures needed to create a psychologically healthy construction workplace. However, regarding hiring more personnel, the interviewees explained that given the nature of the industry, the intervention measure would be highly dependent on the volume of work, contract sum, and firm size.
In order to sufficiently mitigate job demand as a risk factor, it is worth conducting an employee satisfaction survey so construction personnel can communicate which workplace psychosocial stressors may impact their mental health and well-being. Thus, corroborating Bowen et al. (2014) that recommended conducting stress appraisal as a way for employers to understand the effect of occupational stress on construction personnel. Also, Havermans et al. (2018) found that there is a need for meetings focused on work stress with employee satisfaction surveys as an effort to prevent work stress. Although employee satisfaction is highly subjective, the information gathered on an individual-to-individual basis will inform the organization on appropriate job demand risk factors that need attention and guide the initiation of necessary measures. Employee satisfaction can be achieved through different channels, particularly minimizing job demand, increasing job control, and providing job support. Ensuring employee satisfaction can positively impact job performance, family satisfaction, and performance (Bakotić 2016; Wu et al. 2016).
Construct 5: Employee Morale and Engagement Focused Measures
The construct is characterized by primary and secondary intervention measures that are related to building employee morale and improving job engagement. This construct comprises measures that ranked the highest, consistent with Nwaogu and Chan (2021). The PLS-SEM result supported a negative association between the measures in this construct and the stressors. Also, the path coefficient showed that the construct is perceived to have a strong impact on mitigating the stressors, pointing to the significance of multimodal measures that incorporate primary and secondary interventions. Havermans et al. (2018) deduced that employees perceived that an organizational culture that celebrates successes helped mitigate job stress. Likewise, in this study, celebrating employees’ success ranked the most significant measure. Therefore, construction organizations should implement policies that celebrate the achievements of employees.
A positive organizational culture that offers employees a sense of respect and encourages constructive criticisms can reduce fears of job insecurity and unemployment (Bryson and Duncan 2018; Havermans et al. 2018). Bryson and Duncan (2018) reported that younger employees required a supportive approach when communicating feedback on job performance, unlike older construction personnel. The differences in communication styles caused more stress to younger personnel leading to absenteeism. Maintaining a supportive organizational culture that creates a feeling of unity, constructive criticism, and focuses on people holds the potential to prevent work stress (Havermans et al. 2018). A primary measure for minimizing job stress for positive mental health achievement is to design job roles aligned with employees’ deeply embedded interests; a technique referred to as job sculpting.
Job sculpting has proved effective in changing the perception of job stress, boosting morale, job performance, job satisfaction, and employee engagement (Hlanganipai and Mazanai 2014; Vanantwerp and Wilson 2018). Hlanganipai and Mazanai (2014) found that most employees were satisfied with job sculpting and recommended its adoption. Similarly, the result of this study showed that the respondents recommend the adoption of job sculpting as a measure to improve mental health within the construction industry. It is expedient that the construction industry becomes more transparent by collecting suggestions on an employee’s embedded life interest to enable building an aspect of the job responsibility to capture such interest. Therefore, ensuring a supportive organizational culture within the construction industry could boost personnel morale and positively affect mental health and well-being.
Construct 6: Job Redesign and Control Focused Measures
The measures in this component are mainly primary interventions aimed at redesigning the work. The measures also allow improving job control and mitigating work-life imbalance. However, the PLS-SEM result did not support a negative association between the measures and the stressors. This may be because some measures in the construct scored the lowest on mean score analysis, which may point to the feasibility concern about adopting those measures for site-based construction professionals as they need to be present for supervisory roles. Nonetheless, similar to Nwaogu and Chan (2021), the measure that involves employing a compressed working week (CWW) arrangement to balance work and life ranked the highest in this construct. The findings further corroborate Lingard et al. (2007), who found CWWs to positively impact work-life balance and be practicable in the construction industry. CWWs ranked within the top ten measures with no difference in experts’ perception about its importance, indicating that the experts believe that adopting a CWW arrangement is more accessible for on-site construction personnel. As emphasized by interviewee #4, the result implies that implementing job crafting and flexitime in the construction industry for site engineers or supervisors could be possible but needs adequate planning. This finding is in line with LaMontagne et al. (2014) that opined that the measures to improve job control for clerks would differ from those to achieve the same for managers. Therefore, due to the industry’s culture, adopting the CWW arrangement is more feasible for construction organizations to improve job perceptions and the well-being of site supervisors.
Construct 7: Interpersonal Relationship Related Measures
This construct contains measures that are related to improving interpersonal relationships between employees. The PLS-SEM result did not support a negative association between the measures and the stressors. This may be because the experts’ responses differed on two measures in the construct. However, one of the measures ranked in the top ten. This signifies the need to improve interpersonal relationships in order to mitigate adverse psychological outcomes in personnel. This finding is consistent with Chen et al. (2017) on occupational health in the construction industry. Consequently, to create quality interpersonal relationships at work, communication and mutual trust need to be encouraged. Furthermore, barriers to interpersonal relationships, such as difficulties in information sharing and complex organizational culture, have to be removed (Migowski et al. 2018). Creating a quality relationship in the workplace can provide compassion, predict job performance, and promote mental health (Chu 2017). Therefore, measures to effectively improve interpersonal relationships like those itemized in this construct can boost trust, confidence, and a sense of security in the construction industry.
Limitation of the Study
This study is not without limitations. One of which is that an online questionnaire survey was employed to gather data from the experts, and the sample was relatively small, relying on the use of purposive sampling techniques. Therefore, these findings likely do not generalize to the entire construction industry, highlighting the need to further investigate these findings. Furthermore, since the survey relied mainly on international experts recruited through email addresses retrieved from various websites, there was no access to other contact details to aid rigorous qualitative inquiry. Moreover, this study is part of a broader research effort, and extensive qualitative structured interviews will be conducted as the second stage of data collection to aid in expanding on the findings presented here. For instance, this will include investigating workable examples of on-site construction activities that could permit flexible work arrangements, especially those that could afford the personnel some form of job control.
Although this study offers an initial step to inform policies necessary to improve mental health, from a broad perspective of experts within the industry, further studies are needed to investigate which of the group of measures are of urgency to the context of specific countries. Such studies will allow for comparability by employers and human resources managers engaged in the decision-making process. This study should be extended by examining the perception of on-site personnel on the combination of measures required in the construction workplace to improve their mental health. The result will aid comparability and the identification of possible bias of the experts and employees concerning the intervention measures. Finally, while the respondents for this study emerged from several countries, the result may not generalize to those climes because of the low response. Nonetheless, the results may serve as a guide to the industry at large.
Conclusions
This study investigated the mix of measures that can improve mental health among on-site construction personnel engaged as site engineers or supervisors from the perspectives of a purposive sample of experts in the construction industry. Based on the findings, the most significant measures needed to improve mental health among the personnel include “celebrate employee success,” “better planning of tasks and work shifts,” “give constructive feedback instead of reprimanding”, and “create policies to eliminate harassment.” Further analysis using structural equation modeling techniques signals the importance of primary intervention measures in a multimodal intervention.
The study also revealed opportunities for job redesign and control measures, with the compressed workweek arrangement as the most viable intervention measure for mental health promotion among on-site construction personnel. The implementation of these measure constructs could help ensure that: (1) risk factors are minimized or modified, (2) the positive aspect of the work and worker’s strength is strengthened, and (3) mental health problems are addressed irrespective of the cause. Therefore, fulfilling the three threads of the integrated mental health framework.
The seven components were developed as measure constructs needed to improve mental health by modifying risk factors related to job demand, job control, workplace support, organization injustice, individual coping, family, welfare, and socioeconomic status. This study is novel as it adapted a mix of measures from an integrated approach to inform practicable construction context-specific decision-making for improving mental health. Another novelty of this study is the introduction of job sculpting to the construction context and body of occupational health as a measure to boost morale and satisfaction. This study bridges the gap between knowledge in occupational health literature and the practicability of the intervention measures in the construction context. The lower mean value of “job crafting,” “flexible work arrangement (particularly flexitime)”, and “hiring of more on-site personnel” may indicate that thorough planning and intervention studies are needed.
Based on the findings of this study, it can be stated that all seven measure constructs are significant and practicable for improving the mental health of on-site personnel in the construction workplace. However, some measures within the job redesign and control measure construct will need proper planning before implementation. As it was deduced that while the CWW arrangement is more practicable, job crafting and flexible work arrangement (particularly flexitime) measures would need thorough planning and intervention studies to determine their viability among the category of construction personnel considered in this study. Therefore, although the job redesign and control focused measures are significant, implementing all the measures in the construct may be less practicable. As regard implementation within the integrated approach to mental health, construction firms will benefit by selecting at least two measures from each of the constructs for implementation.
This study has helped provide valuable initial evidence related to primary, secondary, and tertiary intervention measures that can be implemented in the construction industry at organizational and individual levels to create a psychologically safe and healthy workplace. The measures fit into the integrated intervention approach for sustainable mental health promotion and management. These measures can guide policy-making in the construction workplace to boost job satisfaction, good mental health, well-being, safety, and performance. Furthermore, the study provides a compass to guide construction organizations in determining which measures are yet to be implemented in their workplaces and need to be explored.
More studies that will further examine the validity of the measures in mitigating the stressors and perceptions of experts using SEM techniques are recommended. Finally, future studies will benefit from determining how these measures could apply to varying firm sizes across different contexts, including developed and developing context settings, and importantly, the resulting impact of these measures on employees’ mental health and well-being.
Appendix. Survey Questionnaire
Fig. 2 includes the survey questionnaire and cover letter sent to participants.
ME.1943-5479.0001045/asset/e8567b4d-d8bb-4f03-a8ec-b142246c3ed5/assets/images/large/figure2a.jpg)
ME.1943-5479.0001045/asset/9b7d5a3d-e610-4c56-b462-94af9c322408/assets/images/large/figure2b.jpg)
ME.1943-5479.0001045/asset/02dda499-f45c-4cf1-859f-f88a158fc7fc/assets/images/large/figure2c.jpg)
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This research is part of a doctoral study financially facilitated by the Hong Kong Polytechnic University. Thus, studies that share related backgrounds but with different scopes and methodologies may be produced.
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Journal of Management in Engineering
Volume 38 • Issue 4 • July 2022
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© 2022 American Society of Civil Engineers.
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Received: Mar 19, 2021
Accepted: Jan 21, 2022
Published online: Mar 17, 2022
Published in print: Jul 1, 2022
Discussion open until: Aug 17, 2022
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