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Article

Impact of Resilience Engineering on Physical Symptoms of Construction Workers

by
Zhen Hu
,
Heng Zhong
,
Siyuan Li
,
Siyi Li
,
Yuzhong Shen
*,
Changquan He
* and
Zhizhou Xu
College of Civil Engineering, Shanghai Normal University, Shanghai 201418, China
*
Authors to whom correspondence should be addressed.
Buildings 2024, 14(12), 4056; https://doi.org/10.3390/buildings14124056
Submission received: 14 October 2024 / Revised: 5 December 2024 / Accepted: 15 December 2024 / Published: 20 December 2024
(This article belongs to the Section Construction Management, and Computers & Digitization)
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Abstract

Physical symptoms plague construction workers and pose threats to safety performance and productivity. Following the resilience engineering (RE) principles, recent construction safety management practices enhance construction workers’ safety capability and safety management system resilience. This paper established an exploratory structural model explaining how construction workers’ safety capability alleviates their physical and psychological symptoms through safety management system resilience. To validate and estimate the structural model, 741 valid responses from construction workers based in Shanghai, China were obtained. Given no established scales for the constructs in the model, a cross-validation procedure, consisting of exploratory and confirmatory factor analysis and path analysis, was performed. The results showed that although neither safety capability nor safety management system resilience has direct negative impacts on physical symptoms, they can reduce physical symptoms via alleviating psychological symptoms. Furthermore, safety capability can reduce psychological and physical symptoms via safety management system resilience. This paper therefore suggests that cultivating construction workers’ safety capability would be the first step in implementing resilience engineering principles in construction. The continuous implementation of cost-effective and tailored resilience training programs are suggested to enhance construction workers’ safety capability. Safety management systems are suggested to improve with the fostering of a just culture and emerging technologies.

1. Introduction

The construction industry plays a vital role in economic development, especially in developing countries/regions [1]. In mainland China, the construction industry’s value in 2018 was RMB 6.2 trillion (around 0.8 trillion Euros), 445 times higher than that in 1978. However, the numbers of accidents and fatalities in the construction industry have always been above the all-industry average as well. Mainland China has seen an upward trend in the number of construction-related accidents and fatalities from 2016 to 2019. In 2019 alone, there were 773 accidents and 904 fatalities in housing and municipal projects, an increase of 5.3% and 7.0%, respectively, compared with those in 2018. Construction is thus widely known as one of the most dangerous sectors [2].
As consequences of accidents, injuries and illnesses occur frequently during the daily work of construction workers and injured workers have to endure both psychological and physical symptoms, such as serious anxiety, depression, lung disease, and hearing loss. For example, dust-related lung disease and noise-induced hearing loss are two of the most common physical symptoms that plague construction workers [3,4]. The exposure to coarse particles in the dust can affect the lungs and heart. Ringen and colleagues [5] assessed the construction workers’ risk of dust-related lung disease, chronic obstructive pulmonary disease, and hearing loss in their working life. The results showed that among construction workers, 16.0% developed chronic obstructive pulmonary disease, 11.0% developed parenchymal radiological abnormality, and 73.8% developed hearing loss. Occupational diseases among construction workers were 2–6 times higher than those in other industries. Traumas caused by falling from a height and being hit by sharp objects are also common injuries in construction. Recent years have seen a decline in the number of construction workers in mainland China. Despite that, in the year 2023, more than 45 million workers made a living through construction [6]. Almost all of these construction workers were from rural areas, pursuing a non-agricultural job in urban areas. According to a recent survey with construction workers in South China [7], 57.9% of the participants were subject to work-related musculoskeletal disorders (WMSDs). Both chronic diseases and direct injuries cause suffering, disability, decreased productivity and also impose indirect burdens on both their families and the society [8].
Extensive research has been conducted to address construction workers’ physical symptoms. According to Abbe and colleagues [9], traditional approaches to construction workers’ safety focused on improving the physical and biomechanical aspects of work and making efforts to enhance the tools, equipment, and methods for accomplishing tasks. Recent efforts in this area turned to the influence of psychosocial factors on workers’ safety. For example, safety climate and safety culture are extensively embraced in the construction sector as theories and methodologies for managing safety. They are often considered as lagging indicators due to prior accidents/injuries [10]. The correlations among safety climate, safety performance, safety behavior and safety outcomes have been researched widely [11,12,13]. However, although safety climate has been found to be indicative of both past and future safety events, previous studies have predominantly portrayed it as a lagging indicator [10]. Therefore, the impact of proactive safety management approaches on workers’ psychological and physical symptoms warrants research efforts.
There are two perspectives of safety, i.e., Safety-I and Safety-II. Safety-I assumes that all accidents are caused by the failed components of a system, and if the set of causes of an accident can be eliminated, the same or similar accidents can be avoided. A find-and-fix reactive approach embodies the Safety-I perspective and is very useful in a purely technical system [14]. However, Safety-I and its approaches are limited when dealing with the increasingly complex socio-technical systems. Safety-II focuses on success and tries to ensure a successful work situation by monitoring the work system and its surroundings vigilantly and responding to changes proactively [14]. Resilience engineering (RE), which focuses on proactivity and aims to provide tools to manage risk and is hence one application of the Safety-II philosophy, is becoming a prevalent agenda in safety research and management practice [15]. Through resilience engineering, an organization or a system can achieve resilience. Although resilience engineering was originally developed for systems or organizations, it can also be applied to enhance an individual’s resilience. An organization or a system is resilient if it can adjust its functioning before, during, or after changes and disturbances, and operate as required under both expected and unexpected situations [14,15]. Similarly, an individual is resilient if s/he has the ability to anticipate, adapt, learn, and recover under both expected and unexpected scenarios. Resilience engineering has been applied in many high-risk industries, such as aviation, healthcare, nuclear power, construction, oil and gas, emergency services, transportation, cybersecurity, etc.
Adopting resilience engineering is especially relevant for the Chinese construction industry. First, the increasing complexity poses challenges to the safety performance of construction projects around the globe, including China. Second, the regulatory regime for construction safety management practice in Mainland China is reactive and prescriptive in nature, as enterprises not strictly following the rules and regulations is often cited as the critical problem in practice [16]. In Mainland China, there are two important laws related to occupational health and safety, i.e., the Work Safety Law and the Law on Prevention and Control of Occupational Diseases. Under these laws, the State Council promulgates the National Program on Prevention and Control of Occupational Diseases (2021–2025), which states that, among others, the lax enforcement of the law and regulations by governmental agencies and the non-compliance with rules and regulations by enterprises are prominent in current occupational disease prevention efforts [17]. Therefore, it can be inferred that, in general, the current construction project safety management approach in Mainland China can be described as reactive, or “Safety-I” [18]. Third, the safety management system has been introduced and implemented in construction since the 1980s. It brings about benefits and encounters obstacles as well [19]. Current safety management systems are often criticized as having a lack of resilience and ability to respond to the varying and unforeseen safety risks [20]. Fourth, mental health issues pose a severe threat to productivity in construction [21], and the impact of resilience engineering on workers’ psychological symptoms warrants an investigation. Hence, it is fitting for this paper to investigate the impact of resilience engineering on workers’ psychological and physical symptoms, and hence propose resilience strategies to enhance construction projects’ safety performance and alleviate construction workers’ psychological and physical suffering. Resilience can be applied at any level, from individuals to organizations [15]. This paper examines the impact of resilience at two levels (i.e., safety capability at the individual level and safety management system resilience at the system level) on workers’ psychological and physical symptoms.
The remainder of this article is structured as follows: Section 2 provides an overview of the existing literature and presents the hypotheses; Section 3 outlines the methodologies employed in this study; Section 4 reports the findings; Section 5 discusses the implications of these findings and suggests directions for future research; and finally, Section 6 concludes this study.

2. Literature Review and Hypotheses

2.1. Safety Capability, Psychological Symptoms, and Physical Symptoms

The construction industry is often described as dynamic, ever changing and inherent with risks [22]. Therefore, the notion of “dynamic safety capability” fits the construction context. Dynamic safety capability refers to “an organization’s capacity to generate, reconfigure, and adapt operational routines to sustain high levels of safety performance in environments characterized by change and uncertainty” [23] (p. 2). Based on this definition, in this paper, we consider safety capability as an individual’s capacity to sense risk and hazards, seize opportunities, and transform processes and procedures to tackle emerging risks on construction sites. By sensing risk and hazards, the individual is able to scan and interpret the external environment for potential safety opportunities and threats. By seizing opportunities, the individual is capable of managing contradictions and competing goals related to safety. By transforming processes and procedures, the individual is able to cultivate new safety capabilities. In other words, safety capability reflects an individual’s resilience in dealing with safety risks.
Building up workers’ dynamic safety capability is an important avenue to improve safety performance in construction, given that most construction accidents can be attributed to unsafe acts. Despite that, safety capability factors were barely considered in identifying the causes of accidents. In view of this deficiency, Lyu and colleagues [24] proposed a 24 model-based causal analysis framework, in which safety capability is a key factor controlling individual behavior. Safety capability influences an organization’s safety culture, safety management system, and operational outcomes by influencing individual acts. The changed safety culture and safety management system, in turn, affect individual safety capabilities, causing individuals to develop new safety capabilities [24]. Other researchers studied specific aspects of safety capability. For example, safety control capability was defined as the ability to manage the system risk level by minimizing the likelihood of accidents and losses [25]. An insufficient safety management capability on construction sites can bring about casualties and the loss of properties [26].
Construction workers usually suffer physical symptoms (PHY), such as headaches, burns, and musculoskeletal tension [7]. In addition, some psychological symptoms (PSY)—e.g., insomnia, emotional exhaustion, stress, depression, and anxiety—plague construction workers and affect their safety performance and productivity. Both physical and psychological symptoms lead to unsafe behaviors [27]. Individuals under high psychological stress are more prone to accidents [28] and unsafe behaviors, which may result in physical symptoms [29]. Lim and colleagues [30] pointed out that construction workers’ psychological symptoms can adversely influence their work performance. Construction workers’ mental health is essential for their productivity and safety [31,32]. Construction workers with more severe psychological symptoms are more likely to be involved in near misses and accidents, and hence, suffer from injuries [33]. This suffering, in turn, aggravates physical symptoms. Construction workers’ mental distress is thus substantially associated with both injury rate and self-reported pain [34].
Construction workers’ psychological and physical symptoms can be reduced by enhancing their resilience, and safety capability, in particular. For other high-risk occupations, like firefighters, resilience is positively associated with their mental health and further mediates the impact of their emotional social support on psychological well-being [35]. With an enhanced safety capability, construction workers are supposed to keenly sense potential risks, forcefully seize opportunities to take safe actions, and transform processes and procedures timely, hence avoiding risks, psychological distress and physical discomfort. Therefore, in line with this reasoning, we propose the following:
H1. 
Psychological symptoms are positively associated with physical symptoms.
H2. 
Safety capability is negatively associated with physical symptoms.
H3. 
Safety capability is negatively associated with psychological symptoms.

2.2. Safety Management System Resilience, Psychological Symptoms, and Physical Symptoms

Since Holling’s [36] seminal work, the concept of resilience has been an attractive idea in many sectors and the word resilience has been modified by different adjectives, such as economic resilience, psychological resilience, organizational resilience and social resilience. Traditionally, the safety science community focused on failures and accidents, and viewed them as system malfunctions. The increasingly complex and dynamic nature of sociotechnical systems brings new risks, which necessitate new management strategies. Cognitive systems engineering researchers, such as Erik Hollnagel and David D. Woods, propose that risk and safety are the products of normal organizational processes, and they reintroduce the concept of resilience engineering to address these new risks. The core proposition of resilience engineering is to build up the organizations’ capability to adapt to unpredictability and complexity [37]. In other words, it is hoped that resilience strategies can cope with unpredictable safety risks more flexibly in the presence of disruption and threat [38].
Consequently, academics and practitioners in construction also turned to the principles of resilience engineering in managing construction project safety performance. Chen and colleagues [39] carried out a study to investigate the influence of individual resilience and safety climate on the safety performance and job stress of construction workers; they defined the concept of individual resilience as “the capacity of individuals to cope successfully in the face of significant change, adversity, or risk” (p. 168). Resilience engineering principles can also aid in addressing risk assessment in sustainable construction [40]. Notably, most of the studies of resilience in the construction domain focus on resilience at the urban and individual levels. The lack of safety resilience in construction projects from a systems perspective may result in stagnant safety performance. Tonetto and colleagues [41] proposed seven suggestions for the enhancement of construction resilience, which emphasize the role of organizational support rather than that on the individual level, as proposed in most current studies. Against this background, safety management system resilience draws more attention from both the academia and industry.
While safety capability reflects a worker’s resilience in dealing with safety risks, safety management system resilience refers to the capacity of the safety management system instituted by an organization to respond to actual accidents, monitor critical threats, anticipate potential risks, and learn from factual evidence. A safety management system is designed to manage safety elements in the workplace, and these elements include policies, plans, procedures, objectives, responsibilities, etc. A resilient safety management system can make reactive, concurrent and proactive adjustments [42]. Reactive adjustments take place after a disturbance and intend to rebound from a disruptive state and recover to the original state. Concurrent adjustments are the immediate reactions in the disruption phase. Proactive adjustments are performed to prepare normal operational functions for any unexpected event in the pre-disruption phase. For instance, personal protective equipment (PPE), as an element of the safety management system, should be used appropriately. Its proper use can protect firefighters from injuries, but if it is used for too long, firefighters may be subject to both psychological stress and physical strain [43]. In this regard, resilience should be engineered into the use of PPE by allowing users to take regular breaks or improving PPE ergonomically. Therefore, a resilient safety management system is expected to ameliorate workers’ physical and psychological symptoms. In this line of reasoning, we hypothesize the following:
H4. 
Safety management system resilience is negatively associated with physical symptoms.
H5. 
Safety management system resilience is negatively associated with psychological symptoms.

2.3. Safety Capability and Safety Management System Resilience

Both safety capability and safety management system resilience place significant emphasis on preparing for unforeseen circumstances and existing issues by scanning the external environments and considering internal deficiencies. Safety capability reflects an individual’s resilience in the case of unexpected safety risks, while safety management system resilience ensures that the organization navigates through disruptions. Pilanawithana et al. [42] name the former as people resilience, and the latter as system resilience. Furthermore, they found the positive impact of both people resilience and system resilience on the organization’s safety performance. Therefore, we hypothesize the following:
H6. 
Safety capability is positively associated with safety management system resilience.
Based on the aforementioned hypotheses, the theoretical model is shown in Figure 1. In this model, the relationship among safety management system resilience, safety capability, psychological symptoms and physical symptoms are examined. Furthermore, a possible mediating effect among these variables is also to be validated and estimated.

3. Methods

3.1. Ethical Approval

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Shanghai Normal University (protocol code 2023-018, 23 April 2023). Informed consent was obtained from all respondents involved in the study.

3.2. Questionnaire Design

A survey questionnaire was employed for data collection. The questionnaire had six sections. The first section was to introduce the survey and seek prospective respondents’ consent to participate. The second section was to collect respondents’ demographic information. The other four sections were to measure the four constructs (i.e., safety capability, safety management system resilience, psychological symptoms, and physical symptoms), respectively.
This study tried to explore a less studied mechanism through which safety resilience (in terms of construction workers’ safety capability and safety management system resilience) impacts construction workers’ psychological and physical symptoms. Hence, the study was exploratory in nature. Widely accepted measures for the four constructs in the theoretical model are lacking in the construction management domain. For example, Ranasinghe et al. [44] noted that there is no common set of resilience engineering indicators in construction refurbishment. Therefore, the research team developed measures based on literature reviews and consultations with subject matter experts and industry practitioners. Since Likert-type measures are usually employed “to assess attitudes, individual traits and other psychological factors” [45] (p. 71) and five-point Likert scales are less confusing and more reliable [46], this study used five-point Likert scales to measure the four constructs.
Safety capability has four dimensions (i.e., safety attitude, safety behavior, effective communication, and emergency response capability). These dimensions had been discussed by Griffin et al. [23] and Tang et al. [47]. The items to measure each dimension were adapted from Chen and colleagues [39], and Feng and Trinh [48]. In total, fourteen items were used to measure safety capability, with four items measuring safety attitude and safety behavior, respectively, and three items measuring effective communication and emergency response capability, respectively.
Safety management system resilience has four dimensions (i.e., robustness, adaptability, redundancy, and efficiency). These dimensions had been mentioned by Pilanawithana et al. [20]. Each dimension was measured by two items adapted from Guo and colleagues’ work [49].
Psychological symptoms were measured by six items, which were also common psychological symptoms in construction, such as depression, anxiety, etc.
Physical symptoms were measured by 10 items, which were also common physical symptoms in construction, such as fatigue, headache, hearing loss, etc.

3.3. Data Collection Procedures

A pilot study was conducted prior to the main study. Based on the pilot study, the research team revised the questionnaire to ensure that each statement could be clearly understood by construction workers, who usually received less education. The main study was carried out during the construction workers’ lunch breaks from April to July 2023. At least three members of the research team were present when conducting the survey, so that the problems encountered in completing the questionnaire could be resolved immediately. In total, 1062 hard-copy questionnaires were distributed. After removing invalid questionnaires, 741 valid ones were obtained, with the valid response rate at 69.8%.
Table 1 shows the demographic information of the 741 valid respondents. The majority of the participants were in the age range of 31–50 and around 86.0% were male; about 55.5% of workers graduated from junior middle school. Around 30.0% of the respondents had less than 5 years of industrial experience. The majority had been working for their current employer for less than 3 years, and only 17.0% of the workers had worked for over 3 years for the current employer. Around 98.0% of the respondents had to work for more than 40 h per week. As a result of the highly physical workload, the construction workers are prone to fatigue [50].

3.4. Data Analysis

This study was exploratory in nature and the items used to measure the involved constructs were accumulated from different sources. Therefore, to secure reliable and valid measures of the four constructs, a procedure called cross-validation was followed [51]. Specifically, the sample was randomly split into two groups with the aid of SPSS (version 25), for calibration and validation purposes, respectively. First, with the calibration sample, exploratory factor analysis (EFA) with varimax rotation method was carried out on the indicators for the four constructs. This study employed the varimax rotation method because, compared with other rotation methods, it can achieve a simplified factor structure [52]. Second, with the validation sample, the structural equation modeling (SEM) technique aided by AMOS (Windows version 24) was utilized to validate the measurement model derived from EFA by conducting a confirmatory factor analysis (CFA). The reliability, convergent and discriminant validity of the derived factors were assessed. Third, again with the validation sample, the structural model, which embodies the hypotheses, was assessed and the path coefficients were calculated with a bootstrap procedure [52]. Bootstrapping is a resampling technique which creates subsamples of the original sample and re-estimates the model for each new subsample [52]. The distribution of bootstrapping samples is not affected by normality assumptions. In this study, the number of bootstrapping subsamples was set to 10,000, and the confidence interval was constructed at the level of 95% (i.e., 10,000 bootstrap samples and 95% confidence intervals). As Shen et al. [53] suggested, for both the measurement and structural model, four indices (i.e., comparative fit index (CFI), root-mean-square error of approximation (RMSEA), the Chi-squared (χ2) value and the associated degrees of freedom (df)) are sufficient to measure the overall goodness of fit. Figure 2 illustrates the cross-validation process employed in this study.

4. Results

4.1. Exploratory Factor Analysis Results

Before conducting an EFA, it is essential to assess whether the data are suitable for EFA. The value of the Kaiser–Meyer–Olkin (KMO) was 0.867, which denotes an excellent sampling adequacy. The varimax rotation method, as one of the prevalent orthogonal rotation methods developed by Kaiser [54], was applied. Only the indictors with factor loadings above 0.7 were retained. In total, twenty indicators were retained, and they were the three indicators presupposed to measure safety management system resilience, the eight indicators presupposed to measure safety capability, the four indicators presupposed to measure psychological symptoms, and the five indicators presupposed to measure physical symptoms.

4.2. Confirmatory Factor Analysis Results

4.2.1. Measurement Model

With the validation subsample, the hypothesized four-factor model, composed of safety management system resilience, safety capability, physical symptoms and psychological symptoms, was tested. The four indices for the measurement model were Chi-square = 408.85, df = 164, RMSEA = 0.06, CFI = 0.95. Together, these indices suggested that the measurement model had achieved a satisfactory goodness of fit.
The factor loadings in the satisfactory measurement model are presented in Table 2. As shown in the table, the factor loadings ranged from 0.58 to 0.87. Further, all the factor loadings were significant (p < 0.001). The squared multiple correlations (SMCs), along with a 95% confidence interval of each item, were shown in Table 3. The SMC of a variable measures the proportion of the variable’s variance that has been explained. For instance, the SMC of item SMSR2 was 0.72, indicating that 72.0% of the variance of SMSR2 was explained by the factor of “safety management system resilience”, on which it was presupposed to load. The SMCs in Table 3 ranged from 0.34 to 0.76.
As shown in Table 4, safety management system resilience and safety capability had negative correlations with psychological symptoms and physical symptoms. Safety management system resilience had positive correlations with safety capability. Psychological symptoms were also positively related to physical symptoms. All the correlations between variables were significant (p < 0.001).
Reliability refers to the degree of stability of the measurement results, and it is an index reflecting the size of the random error in measurement. In this research, the reliability was assessed using internal consistency reliability. Cronbach’s alpha was used to assess the internal consistency reliability of a scale [55]. The range of Cronbach’s alpha lies from 0 to 1. A Cronbach’s alpha value exceeding 0.7 suggests a satisfactory reliability [56,57]. As shown in Table 4, Cronbach’s alpha values exceeded 0.7 and varied from 0.81 to 0.91.
Convergent validity is used to assess the consistency of multiple indicators of the same construct [58]. It can be measured by the average variance extracted (AVE). If the AVE of a construct is larger than 0.5, the construct exhibits convergent validity [53,59]. As shown in Table 4, the minimum AVE value of the four constructs was 0.53, suggesting that all of the four constructs have convergent validity.
Discriminant validity refers to the extent of dissimilarity between two constructs [58]. If the square root of the AVE of a construct is bigger than any correlation coefficient between the construct and other constructs, then the discriminant validity of the construct is established. As shown in Table 4, all the square roots of the AVEs were larger than the correlation coefficients. Hence, all the constructs had exhibited discriminant validity.
Until now, the measurement model exhibited reliability and validity, and the four-factor model derived from EFA with the calibration subsample is confirmed by the validation subsample. Hence, the cross-validation process was concluded.

4.2.2. Structural Model

The four measures of the goodness of fit for the structural model were Chi-square = 408.80, df = 164, RMSEA = 0.06, and CFI = 0.95. The indices suggested that the structural model, as shown in Figure 3, fits the data satisfactorily.
Bootstrapped estimates of the path coefficients and SMC of endogenous constructs in the structural model are shown in Figure 3. Table 5 shows the bootstrapping results for direct effects. If the 95% confidence interval with 10,000 samples contains zero, the corresponding effect is nonsignificant. As shown in Table 5, two hypotheses (i.e., H2 and H4) were not supported. H2 (safety capability is negatively associated with physical symptoms) was not supported because the path coefficient was positive, rather than negative as hypothesized. H4 (safety management system resilience is negatively associated with physical symptoms) was not supported as the path coefficient was insignificant.
Besides direct effects, there were three indirect effects of safety capability on physical symptoms (i.e., SC → SMSR → PHY, SC → PSY → PHY, and SC → SMSR → PSY → PHY). Table 6 shows the bootstrapping results for these indirect effects. As shown in Table 6, the indirect effect of SC → SMSR → PHY was insignificant. However, the other two indirect effects (i.e., SC → PSY → PHY, and SC → SMSR → PSY → PHY) were significant. Further, it seemed that the effect size of the latter is larger than the former (|−0.04| > |−0.03|).
Moreover, the SMCs of safety management system resilience, psychological symptoms and psychological symptoms were 0.40, 0.29, 0.28, respectively. 40.0% of the variance of the construct of safety management system resilience was explained by safety capability; 29.0% of the variance of the construct of psychological symptoms was explained by safety capability and safety management system resilience; and 28.0% of the variance of the construct of physical symptoms was explained by safety capability and psychological symptoms.

5. Discussion

Physical symptoms plague construction workers and pose threats to productivity and safety performance. Safety management has been traditionally associated with centralization, standardization, and compliance. This approach, described as the centralized control mode or “Safety-I”, believes accidents and near misses can be detected and eliminated as long as there is no deviation from the prescribed work [60]. Given the inevitability of variation in increasingly complex socio-technical systems, during the 1990s and 2000s, safety scholars (e.g., Rasmussen, Woods, Hollnagel, Dekker, Amalberti, and Leveson) called for more attention to be paid to operators’ adaptability as a key to the system’s safety and proposed that safety management should focus on guiding and facilitating operators’ adaptability to complexities. This approach of safety management has been labeled as the guided adaptability mode or “Safety-II”. Among others, resilience engineering is one of the approaches in “Safety-II”, and it aims to provide tools to proactively manage risk, without sacrificing performance [61,62]. Resilience engineering has been widely accepted in high-risk sectors, such as aviation, healthcare, nuclear power, oil and gas, emergency services, transportation, cybersecurity, etc.
The organizational and systems-oriented resilience engineering perspective is relatively new in the construction sector and has not drawn much attention from academia and practice yet [63]. Although the application of resilience engineering principles in construction has seen many barriers, there are also opportunities to incorporate these principles at an employee-centered level [62]. This paper introduced two consequences of resilience engineering in construction safety management practices (i.e., safety capability at the individual level and safety management system resilience at the system level) and proposed an exploratory model to depict the mechanism by which safety capability helps to alleviate construction workers’ physical symptoms. To validate and estimate the model, a cross-validation procedure was carried out based on 741 valid responses from construction workers based in Shanghai, China.
The results support the positive association between psychological symptoms and physical symptoms (i.e., H1), and between safety capability and safety management system resilience (i.e., H6). They also support the negative association between safety capability and psychological symptoms (i.e., H3), and between safety management system resilience and psychological symptoms (i.e., H5). However, they failed to support the negative association between safety capability and physical symptoms (i.e., H2), and between safety management system resilience and physical symptoms (i.e., H4). The failure to support H2 and H4 suggests that a simple linear relationship cannot capture the complex impact of resilience on physical symptoms. This is understandable. Just like the prolonged use of personal protective equipment can bring forth physical strain, there is probably an optimal level of resilience, be it at the individual level or system level, which can efficiently and effectively suppress physical symptoms.
Despite that, the results suggest that safety capability and safety management system resilience can alleviate construction workers’ physical symptoms via psychological symptoms. Interestingly, the indirect effect of the path SC → SMSR → PSY → PHY seemed greater than that of the path SC → PSY → PHY. This highlighted the mediating role of safety management system resilience, in that safety capability can better reduce negative psychological and physical symptoms via safety management system resilience strategies at the project level. A typical safety management system usually covers 14 elements (i.e., safety policy, safety organization structure, safety and health training program, in-house safety and health rules, safety inspection program, hazard control program, incident investigation program, emergency preparedness, management of sub-contractors, safety committees, evaluation of job-related hazards, safety and health awareness program, accident control and hazard elimination program, and occupational health assurance program) [64]. Some elements, such as the emergency preparedness and safety and health awareness programs, which are of a proactive nature, have already been incorporated into a typical safety management system. Despite that, in order to enhance safety management system resilience, we suggest using predictive analytics aided by artificial intelligence in uplifting each element. For example, Yin et al. [65] have developed a classification framework for construction personnel’s safety behavior based on machine learning. The classification framework can be used to identify the construction workers who are more inclined to engage in unsafe behavior. With the aid of such a classification framework, the identified construction workers shall be trained more intensively before commencing work and reminded more frequently during their working hours.
The mediating role of psychological symptoms was also highlighted. This finding is consistent with Kolk et al. [66], who found the mediating role of a negative mood in the relationship between age and physical symptoms. Recently, construction workers’ psychological health has drawn much attention [67,68]. The mental health problems in different working mode groups are different; workers who work exclusively during the day have a lower level of mental well-being compared to those with mixed work schedules [67]. The factors that contribute to mental health issues include work-related stress, emotional and physical strain, as well as instances of bullying and harassment [67]. Furthermore, supervisors had an important role in alleviating the negative effects of mental health problems [68].
The findings have practical implications. The results suggest that in implementing resilience engineering in construction, a viable first step is to build up frontline workers’ safety capability. An effective way to enhance such a capability is through resilience training. Traditional training focuses on a compliance with rules and regulations, and reactive responding and monitoring activities [69]. In addition to traditional training, resilience training, which aims to enhance construction workers’ situational awareness, learning ability and adaptability, should be provided. Another way to enhance safety capability is to empower frontline workers and grant them permission to discontinue work for safety reasons. At the system level, one way to enhance safety management system resilience is to upgrade the accident reporting system through fostering a just culture and the incorporation of artificial intelligence. Furthermore, creating a psychologically favorable workplace through well-being programs, such as team building and family day, can alleviate workers’ stress and hence, their physical symptoms as well. It should be reminded that effective resilience training programs need to be tailored to the audience and sustained through efforts. At the same time, the cost should also be considered in initiating and implementing those programs.
The findings, however, should be interpreted with some limitations in mind. First, given the exploratory nature and time constraint, this paper employed a cross-sectional design, which prevents drawing causal conclusions. For example, safety capability contributes to safety management system resilience, and the latter can also contribute to the former. In order to establish causality, a longitudinal research design should be adopted in future. Second, as the data were collected in Shanghai, China, the findings may not be generalizable to other regions. In future, more generalizable conclusions can be drawn by conducting comparative studies with data from other cultural backgrounds. Third, resilience engineering has been introduced as an innovation in managing safety in industries riddled with high risks, complexity and hazardous technologies, like aviation, healthcare, chemical and petrochemical industry, nuclear power plants and railways [70]. Other industries, such as construction and mining, have also seen the occasional application of resilience engineering principles in safety management [20,71]. The findings were derived from the construction sector, and whether they can be extrapolated to other industries deserves more comparative research efforts. Fourth, the empirical evidence to directly link resilience to physical symptoms is limited. This study failed to support such a direct link, since both hypotheses 2 and 4 were not supported. Instead, this study found that psychological symptoms mediate the impact of resilience engineering on physical symptoms. The exploration of other mechanisms accounting for the complex impact of resilience on physical symptoms is encouraged, given the prevalence of physical symptoms.

6. Conclusions

Construction workers’ physical symptoms present challenges to safety performance and productivity in construction projects. Given the increasing complexity of construction projects and its implications on safety performance, resilience engineering principles have recently been introduced into construction safety management practices and are expected to enhance construction workers’ safety capability, safety management system resilience, and organizational safety resilience. As an exploratory effort, this study examined the impact of two resilience related constructs (i.e., safety capability and safety management system resilience) on construction workers’ psychological and physical symptoms. The data supported the hypotheses that safety capability and safety management system resilience are negatively associated with psychological symptoms, safety capability is positively associated with safety management system resilience, and psychological symptoms are positively associated with physical symptoms. However, the data failed to support the hypotheses that safety capability and safety management system resilience are negatively associated with physical symptoms. Furthermore, it was found that safety management system resilience and psychological symptoms mediate the relationship between safety capability and physical symptoms in sequence (i.e., SC → SMSR → PSY → PHY), and psychological symptoms alone mediate the relationship between safety capability and physical symptoms (i.e., SC → PSY → PHY). In other words, psychological symptoms mediate the relationship between safety resilience and physical symptoms. Interestingly, it seemed that safety management system resilience contributes to the mediation of the relationship between safety capability and physical symptoms. This indicated that cultivating frontline workers’ adaptability is a viable way to apply resilience engineering principles in construction. These findings also highlighted the role of safety capability, safety management system resilience and workers’ psychological well-being in alleviating workers’ physical symptoms. In practice, this paper advocates providing resilience training to frontline workers and upgrading safety management system through emerging technologies and a just culture. Psychological symptoms are expected to be alleviated through psychological well-being programs such as team building and family day. All the above safety resilience buildup programs shall be tailored to the recipients, take into account cost, and necessitate sustained efforts.

Author Contributions

Conceptualization, Z.H., Y.S. and C.H.; methodology, Y.S.; software, C.H. and H.Z.; validation, H.Z., S.L. (Siyuan Li) and S.L. (Siyi Li); formal analysis, H.Z.; investigation, C.H., H.Z., S.L. (Siyuan Li) and S.L. (Siyi Li); resources, Z.H.; data curation, Y.S. and Z.X.; writing—original draft preparation, Z.H. and H.Z.; writing—review and editing, Y.S.; visualization, H.Z.; supervision, Z.H.; project administration, Z.H. and Z.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Shanghai Municipal Foundation for Philosophy and Social Science (No. 2020GBL037).

Data Availability Statement

Data are available upon reasonable request from the corresponding authors.

Acknowledgments

We acknowledge the support from all the respondents and industry partners.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Giang, D.T.; Pheng, L.S. Role of construction in economic development: Review of key concepts in the past 40 years. Habitat. Int. 2011, 35, 118–125. [Google Scholar] [CrossRef]
  2. Mitrevska, C.; Mitrevska, E.; Mitrevski, V. Statistical indicators for accidents at work in construction sector. Ann. Fac. Eng. Hunedoara 2022, 20, 87–90. [Google Scholar]
  3. Boadu, E.F.; Okeke, S.R.; Boadi, C.; Bonsu, E.O.; Addo, I.Y. Work-related respiratory health conditions among construction workers: A systematic narrative review. BMJ Open Respir. Res. 2023, 10, e001736. [Google Scholar] [CrossRef] [PubMed]
  4. Keer, S.; Brooks, C.; Glass, B.; McLean, D.; Harding, E.; Douwes, J. Respiratory symptoms and use of dust-control measures in New Zealand construction workers: A cross-sectional study. PLoS ONE 2022, 17, e0266668. [Google Scholar] [CrossRef]
  5. Ringen, K.; Dement, J.; Welch, L.; Dong, X.S.; Bingham, E.; Quinn, P.S. Risks of a lifetime in construction. Part II: Chronic occupational diseases. Am. J. Ind. Med. 2014, 57, 1235–1245. [Google Scholar] [CrossRef]
  6. National Bureau of Statistics of China. Migrant Workers Monitoring Survey Report 2023. Available online: https://www.stats.gov.cn/xxgk/sjfb/zxfb2020/202404/t20240430_1948783.html (accessed on 5 November 2024).
  7. Lee, Y.-C.; Hong, X.; Man, S.S. Prevalence and associated factors of work-related musculoskeletal disorders symptoms among construction workers: A cross-sectional study in South China. Int. J. Environ. Res. Public Health 2023, 20, 4653. [Google Scholar] [CrossRef]
  8. Tadesse, S.; Israel, D. Occupational injuries among building construction workers in Addis Ababa, Ethiopia. J. Occup. Med. Toxicol. 2016, 11, 16. [Google Scholar] [CrossRef] [PubMed]
  9. Abbe, O.O.; Harvey, C.M.; Ikuma, L.H.; Aghazadeh, F. Modeling the relationship between occupational stressors, psychosocial/physical symptoms and injuries in the construction industry. Int. J. Ind. Ergon. 2011, 41, 106–117. [Google Scholar] [CrossRef]
  10. Payne, S.C.; Bergman, M.E.; Beus, J.M.; Rodríguez, J.M.; Henning, J.B. Safety climate: Leading or lagging indicator of safety outcomes? J. Loss Prev. Process Ind. 2009, 22, 735–739. [Google Scholar] [CrossRef]
  11. Lyu, S.; Hon, C.K.; Chan, A.P.; Wong, F.K.; Javed, A.A. Relationships among safety climate, safety behavior, and safety outcomes for ethnic minority construction workers. Int. J. Environ. Res. Public Health 2018, 15, 484. [Google Scholar] [CrossRef]
  12. Marín, L.S.; Lipscomb, H.; Cifuentes, M.; Punnett, L. Associations between safety climate and safety management practices in the construction industry. Am. J. Ind. Med. 2017, 60, 557–568. [Google Scholar] [CrossRef] [PubMed]
  13. Zahoor, H.; Chan, A.P.; Utama, W.P.; Gao, R.; Zafar, I. Modeling the relationship between safety climate and safety performance in a developing construction industry: A cross-cultural validation study. Int. J. Environ. Res. Public Health 2017, 14, 351. [Google Scholar] [CrossRef]
  14. Ham, D.H. Safety-II and resilience engineering in a nutshell: An introduction guide to their concepts and methods. Saf. Health Work 2021, 12, 10–19. [Google Scholar] [CrossRef]
  15. Bergström, J.; van Winsen, R.; Henriqson, E. On the rationale of resilience in the domain of safety: A literature review. Reliab. Eng. Syst. Saf. 2015, 141, 131–141. [Google Scholar] [CrossRef]
  16. Zhou, Z. Understanding the administrative regulation on occupational health and trend in China. J. Occup. Health 2018, 60, 126–131. [Google Scholar] [CrossRef]
  17. National Health Commission of the People’s Republic of China. Interpretation of the National Program on Prevention and Control of Occupational Diseases (2021–2025). Available online: http://www.nhc.gov.cn/zyjks/zcwj2/202112/7960f4ad8b03466cb34fe537ae7551b5.shtml (accessed on 6 November 2024).
  18. Peñaloza, G.A.; Wasilkiewicz, K.; Saurin, T.A.; Herrera, I.A.; Formoso, C.T. Safety-I and Safety-II: Opportunities for an integrated approach in the construction industry. In Proceedings of the 8th REA Symposium Embracing Resilience: Scaling up and Speeding up, Kalmar, Sweden, 24–27 June 2019. [Google Scholar]
  19. Yiu, N.S.N.; Chan, D.W.M.; Shan, M.; Sze, N.N. Implementation of safety management system in managing construction projects: Benefits and obstacles. Saf. Sci. 2019, 117, 23–32. [Google Scholar] [CrossRef]
  20. Pilanawithana, N.M.; Feng, Y.; London, K.; Zhang, P. Developing resilience for safety management systems in building repair and maintenance: A conceptual model. Saf. Sci. 2022, 152, 105768. [Google Scholar] [CrossRef]
  21. Li, K.; Wang, D.; Sheng, Z.; Griffin, M.A. A deep dive into worker psychological well-being in the construction industry: A systematic review and conceptual framework. J. Manag. Eng. 2022, 38, 04022051. [Google Scholar] [CrossRef]
  22. Jefferies, M.; Brewer, G.J.; Gajendran, T. Using a case study approach to identify critical success factors for alliance contracting. Eng. Constr. Archit. Manag. 2014, 21, 465–480. [Google Scholar] [CrossRef]
  23. Griffin, M.A.; Cordery, J.; Soo, C. Dynamic safety capability. Organ. Psychol. Rev. 2016, 6, 248–272. [Google Scholar] [CrossRef]
  24. Lyu, Q.; Fu, G.; Wang, Y.; Zhang, R.; Wu, Y.; Xie, X. Cause analysis framework from a safety capability perspective: Application to Tianjiayi hazardous chemical explosion accident. Process Saf. Prog. 2023, 42, 567–577. [Google Scholar] [CrossRef]
  25. Liu, Q.; Li, X. Modeling and evaluation of the safety control capability of coal mine based on system safety. J. Clean. Prod. 2014, 84, 797–802. [Google Scholar] [CrossRef]
  26. Yu, C.; Li, A. Evaluation research on safety management capability at construction sites of construction projects. IOP Conf. Ser. Earth Environ. Sci. 2022, 1011, 012037. [Google Scholar] [CrossRef]
  27. Choudhry, R.M.; Fang, D. Why operatives engage in unsafe work behavior: Investigating factors on construction sites. Saf. Sci. 2008, 46, 566–584. [Google Scholar] [CrossRef]
  28. Siu, O.; Phillips, D.R.; Leung, T. Safety climate and safety performance among construction workers in Hong Kong: The role of psychological strains as mediators. Accid. Anal. Prev. 2004, 36, 359–366. [Google Scholar] [CrossRef]
  29. Leung, M.Y.; Liang, Q.; Olomolaiye, P. Impact of job stressors and stress on the safety behavior and accidents of construction workers. J. Manag. Eng. 2016, 32, 04015019. [Google Scholar] [CrossRef]
  30. Lim, S.; Chi, S.; Lee, J.D.; Lee, H.J.; Choi, H. Analyzing psychological conditions of field-workers in the construction industry. Int. J. Occup. Environ. Health 2017, 23, 261–281. [Google Scholar] [CrossRef]
  31. Leung, M.; Chan, Y.S.; Yuen, K.W. Impacts of stressors and stress on the injury incidents of construction workers in Hong Kong. J. Constr. Eng. Manag. 2010, 136, 1093–1103. [Google Scholar] [CrossRef]
  32. Blackhall, I.; Littlemore, M. The impacts of personal stress upon critical project decision making in construction. Built Nat. Environ. Res. Pap. 2010, 3, 7–16. [Google Scholar]
  33. Sobeih, T.M.; Salem, O.; Daraiseh, N.; Genaidy, A.; Shell, R. Psychosocial factors and musculoskeletal disorders in the construction industry: A systematic review. Theor. Issues Ergon. Sci. 2006, 7, 329–344. [Google Scholar] [CrossRef]
  34. Jacobsen, H.B.; Caban-Martinez, A.; Onyebeke, L.C.; Sorensen, G.; Dennerlein, J.T.; Reme, S.E. Construction workers struggle with a high prevalence of mental distress and this is associated with their pain and injuries. J. Occup. Environ. Med. 2013, 55, 1197–1204. [Google Scholar] [CrossRef] [PubMed]
  35. Bernabé, M.; Botia, J.M. Resilience as a mediator in emotional social support’s relationship with occupational psychology health in firefighters. J. Health Psychol. 2016, 21, 1778–1786. [Google Scholar] [CrossRef]
  36. Holling, C.S. Resilience and stability of ecological systems. Annu. Rev. Ecol. Syst. 1973, 4, 1–23. [Google Scholar] [CrossRef]
  37. Madni, A.M.; Jackson, S. Towards a conceptual framework for resilience engineering. IEEE Syst. J. 2009, 3, 181–191. [Google Scholar] [CrossRef]
  38. Pęciłło, M. The resilience engineering concept in enterprises with and without occupational safety and health management systems. Saf. Sci. 2016, 82, 190–198. [Google Scholar] [CrossRef]
  39. Chen, Y.; McCabe, B.; Hyatt, D. Impact of individual resilience and safety climate on safety performance and psychological stress of construction workers: A case study of the Ontario construction industry. J. Saf. Res. 2017, 61, 167–176. [Google Scholar] [CrossRef]
  40. Rosa, L.V.; França, J.E.; Haddad, A.N.; Carvalho, P.V. A resilience engineering approach for sustainable safety in green construction. J. Sustain. Dev. Energy Water Environ. Syst. 2017, 5, 480–495. [Google Scholar] [CrossRef]
  41. Tonetto, M.S.; Formoso, C.T.; Saurin, T.A.; Bonesi De Luca, F.; Lora, F.P.; Lantelme, E. Resilient performance on construction projects in the post-pandemic era: An organizational perspective. Eng. Constr. Archit. Manag. 2023; ahead-of-print. [Google Scholar] [CrossRef]
  42. Pilanawithana, N.M.; Feng, Y.; London, K.; Zhang, P. Framework for measuring resilience of safety management systems in Australian building repair and maintenance companies. J. Saf. Res. 2023, 85, 405–418. [Google Scholar] [CrossRef] [PubMed]
  43. Darabont, D.C.; Cioca, L.; Bejinariu, C.; Badea, D.O.; Chivu, O.R.; Chis, T.V. Impact of personal protective equipment use on stress and psychological well-being among firefighters: Systematic review and meta-analysis. Sustainability 2024, 16, 9666. [Google Scholar] [CrossRef]
  44. Ranasinghe, U.; Jefferies, M.; Davis, P.; Pillay, M. Resilience engineering indicators and safety management: A systematic review. Saf. Health Work 2020, 11, 127–135. [Google Scholar] [CrossRef]
  45. Nadler, J.T.; Weston, R.; Voyles, E.C. Stuck in the middle: The use and interpretation of mid-points in items on questionnaires. J. Gen. Psychol. 2015, 142, 71–89. [Google Scholar] [CrossRef] [PubMed]
  46. Revilla, M.A.; Saris, W.E.; Krosnick, J.A. Choosing the number of categories in agree–disagree scales. Sociol. Methods Res. 2014, 43, 73–97. [Google Scholar] [CrossRef]
  47. Tang, S.; Zhu, K.; Guo, P. Research on quantitative assessment and dynamic reasoning method for emergency response capability in prefabricated construction safety. Buildings 2023, 13, 2311. [Google Scholar] [CrossRef]
  48. Feng, Y.; Trinh, M.T. Developing resilient safety culture for construction projects. J. Constr. Eng. Manag. 2019, 145, 04019069. [Google Scholar] [CrossRef]
  49. Guo, Q.; Hao, Q.; Wang, Y.; Wang, J. Subway system resilience evaluation in based on ANP-extension cloud model. J. Syst. Simul. 2021, 33, 943–950. (In Chinese) [Google Scholar]
  50. Park, J.Y.; Lee, J.H.; Kang, M.Y.; Jang, T.W.; Kim, H.R.; Kim, S.Y.; Lee, J. Development of algorithm for work intensity evaluation using excess overwork index of construction workers with real-time heart rate measurement device. Ann. Occup. Environ. Med. 2023, 35, e24. [Google Scholar] [CrossRef]
  51. Zahoor, H.; Chan, A.P.; Utama, W.P.; Gao, R.; Memon, S.A. Determinants of safety climate for building projects: SEM-based cross-validation study. J. Constr. Eng. Manag. 2017, 143, 05017005. [Google Scholar] [CrossRef]
  52. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis, 8th ed.; Cengage Learning EMEA: Andover, UK, 2019; p. 746. [Google Scholar]
  53. Shen, Y.; Koh, T.Y.; Rowlinson, S.; Bridge, A.J. Empirical investigation of factors contributing to the psychological safety climate on construction sites. J. Constr. Eng. Manag. 2015, 141, 04015038. [Google Scholar] [CrossRef]
  54. Kaiser, H.F. The application of electronic computers to factor analysis. Educ. Psychol. Meas. 1960, 20, 141–151. [Google Scholar] [CrossRef]
  55. Cronbach, L.J. Coefficient alpha and the internal structure of tests. Psychometrika 1951, 16, 297–334. [Google Scholar] [CrossRef]
  56. Tavakol, M.; Dennick, R. Making sense of Cronbach’s alpha. Int. J. Med. Educ. 2011, 2, 53–55. [Google Scholar] [CrossRef]
  57. Nunnally, J.C.; Bernstein, I. Psychometric Theory, 3rd ed.; McGraw-Hill: New York, NY, USA, 1994. [Google Scholar]
  58. Ab Hamid, M.R.; Sami, W.; Sidek, M.M. Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. J. Phys. Conf. Ser. 2017, 890, 012063. [Google Scholar]
  59. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  60. Provan, D.J.; Woods, D.D.; Dekker, S.W.; Rae, A.J. Safety II professionals: How resilience engineering can transform safety practice. Reliab. Eng. Syst. Saf. 2020, 195, 106740. [Google Scholar] [CrossRef]
  61. Patriarca, R.; Bergström, J.; Di Gravio, G.; Costantino, F. Resilience engineering: Current status of the research and future challenges. Saf. Sci. 2018, 102, 79–100. [Google Scholar] [CrossRef]
  62. Harvey, E.J.; Waterson, P.; Dainty, A.R. Applying HRO and resilience engineering to construction: Barriers and opportunities. Saf. Sci. 2019, 117, 523–533. [Google Scholar] [CrossRef]
  63. Peñaloza, G.A.; Saurin, T.A.; Formoso, C.T. Monitoring complexity and resilience in construction projects: The contribution of safety performance measurement systems. Appl. Ergon. 2020, 82, 102978. [Google Scholar] [CrossRef] [PubMed]
  64. Yiu, N.S.; Sze, N.N.; Chan, D.W. Implementation of safety management systems in Hong Kong construction industry: A safety practitioner’s perspective. J. Saf. Res. 2018, 64, 1–9. [Google Scholar] [CrossRef] [PubMed]
  65. Yin, S.; Wu, Y.; Shen, Y.; Rowlinson, S. Development of a classification framework for construction personnel’s safety behavior based on machine learning. Buildings 2023, 13, 43. [Google Scholar] [CrossRef]
  66. Kolk, A.M.; Hanewald, G.J.; Schagen, S.; van Wijk, C.M.G. A symptom perception approach to common physical symptoms. Soc. Sci. Med. 2003, 57, 2343–2354. [Google Scholar] [CrossRef]
  67. Kotera, Y.; Green, P.; Sheffield, D. Work-life balance of UK construction workers: Relationship with mental health. Constr. Manag. Econ. 2020, 38, 291–303. [Google Scholar] [CrossRef]
  68. Rouhanizadeh, B.; Kermanshachi, S. Causes of the mental health challenges in construction workers and their impact on labor productivity. In Proceedings of the Tran-SET 2021, Reston, VA, USA, 3–4 June 2021; pp. 16–26. [Google Scholar]
  69. Bhattacharjee, K.; Bugalia, N.; Mahalingam, A. An analysis of safety practices for small, medium, and large construction projects: A resilience engineering perspective. Saf. Sci. 2024, 169, 106330. [Google Scholar] [CrossRef]
  70. Righi, A.W.; Saurin, T.A.; Wachs, P. A systematic literature review of resilience engineering: Research areas and a research agenda proposal. Reliab. Eng. Syst. Saf. 2015, 141, 142–152. [Google Scholar] [CrossRef]
  71. Pillay, M.; Borys, D.; Else, D.; Tuck, M. Safety culture and resilience engineering-Exploring theory and application in improving gold mining safety. In Proceedings of the Gravity Gold Conference, Melbourne, Australia, 21–22 September 2010. [Google Scholar]
Buildings 14 04056 g001
Figure 1. Theoretical model.
Figure 1. Theoretical model.
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Figure 2. Cross-validation procedure.
Figure 2. Cross-validation procedure.
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Figure 3. Structural model.
Figure 3. Structural model.
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Table 1. Demographic information of the respondents.
Table 1. Demographic information of the respondents.
CharacteristicsCategoryFrequencyPercentage (%)
GenderMale63786.0%
Female10414.0%
Age<25425.7%
25–307710.4%
31–4024332.8%
41–5027537.1%
>5010414.0%
Educational levelPrimary school21128.5%
Junior middle school41155.5%
High school8912.0%
College/University304.0%
Work experience (Years)<522230.0%
5–1017924.2%
11–1514719.8%
16–2013318.0%
21–25375.0%
>25233.0%
Working hours per week (Hours)<40152.0%
41–5014519.6%
51–6030941.7%
60–7018024.3%
>709212.4%
Years with the current employer<118625.1%
1–228238.1%
2–314719.8%
>312617.0%
Table 2. Factor loadings in the satisfactory measurement model.
Table 2. Factor loadings in the satisfactory measurement model.
Scale ItemsSMSRSCPSYPHY
SMSR10.75
SMSR20.85
SMSR30.80
SC1 0.84
SC2 0.77
SC3 0.82
SC4 0.66
SC5 0.65
SC6 0.79
SC7 0.74
SC8 0.78
PSY1 0.58
PSY2 0.70
PSY3 0.78
PSY4 0.83
PHY1 0.85
PHY2 0.67
PHY3 0.65
PHY4 0.87
PHY5 0.85
Note: (1) SC = safety capability; SMSR = safety management system resilience; PSY = psychological symptoms; PHY = physical symptoms. (2) All factor loadings are significant at the level of 0.01.
Table 3. Squared multiple correlations of each item in the satisfactory measurement model.
Table 3. Squared multiple correlations of each item in the satisfactory measurement model.
Scale ItemsSMCs
Estimate10,000 Bootstrapping
95% Confidence Interval
SMSR10.56[0.42, 0.67]
SMSR20.72[0.66, 0.79]
SMSR30.65[0.57, 0.72]
SC10.70[0.64, 0.75]
SC20.60[0.53, 0.66]
SC30.67[0.60, 0.72]
SC40.44[0.32, 0.55]
SC50.42[0.30, 0.53]
SC60.62[0.55, 0.68]
SC70.55[0.48, 0.60]
SC80.60[0.49, 0.68]
PSY10.34[0.24, 0.44]
PSY20.49[0.39, 0.59]
PSY30.60[0.49, 0.70]
PSY40.68[0.57, 0.79]
PHY10.72[0.39, 0.90]
PHY20.45[0.14, 0.76]
PHY30.42[0.14, 0.73]
PHY40.76[0.74, 0.91]
PHY50.72[0.28, 0.87]
Note: SC = safety capability; SMSR = safety management system resilience; PSY = psychological symptoms; PHY = physical symptoms.
Table 4. Correlation matrix, descriptive statistics, reliability and average variances extracted.
Table 4. Correlation matrix, descriptive statistics, reliability and average variances extracted.
MeanS. D.Cronbach’s αSCSMSRPSYPHY
SC3.770.590.910.58
SMSR3.960.690.840.63 ***0.64
PSY1.290.400.81−0.45 ***−0.51 ***0.53
PHY1.040.200.87−0.11 ***−0.17 ***0.51 ***0.61
Note: (1) SC = safety capability; SMSR = safety management system resilience; PSY = psychological symptoms; PHY = physical symptoms. (2) *** p < 0.001. (3) Average variances extracted (AVEs) are listed in bold and italicized.
Table 5. Direct effects of the relationships in the structural model.
Table 5. Direct effects of the relationships in the structural model.
PathEstimate95% Confidence Interval (with 10,000 Bootstrap Samples)Comment
H1: PSY → PHY0.59[0.44, 0.74]H1 supported
H2: SC → PHY0.12[0.05, 0.21]H2 not supported
H3: SC → PSY−0.21[−0.33, −0.10]H3 supported
H4: SMSR → PHY0.05[−0.04, 0.16]H4 not supported
H5: SMSR → PSY−0.38[−0.51, −0.25]H5 supported
H6: SC → SMSR0.63[0.50, 0.74]H6 supported
Note: SC = safety capability; SMSR = safety management system resilience; PSY = psychological symptoms; PHY = physical symptoms.
Table 6. Indirect effects of the relationships in the structural model.
Table 6. Indirect effects of the relationships in the structural model.
PathEstimate95% Confidence Interval (with 10,000 Bootstrap Samples)
SC → SMSR → PHY0.01[−0.01, 0.04]
SC → PSY → PHY−0.03[−0.09, −0.02]
SC → SMSR → PSY → PHY−0.04[−0.10, −0.02]
Note: SC = safety capability; SMSR = safety management system resilience; PSY = psychological symptoms; PHY = physical symptoms.
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Hu, Z.; Zhong, H.; Li, S.; Li, S.; Shen, Y.; He, C.; Xu, Z. Impact of Resilience Engineering on Physical Symptoms of Construction Workers. Buildings 2024, 14, 4056. https://doi.org/10.3390/buildings14124056

AMA Style

Hu Z, Zhong H, Li S, Li S, Shen Y, He C, Xu Z. Impact of Resilience Engineering on Physical Symptoms of Construction Workers. Buildings. 2024; 14(12):4056. https://doi.org/10.3390/buildings14124056

Chicago/Turabian Style

Hu, Zhen, Heng Zhong, Siyuan Li, Siyi Li, Yuzhong Shen, Changquan He, and Zhizhou Xu. 2024. "Impact of Resilience Engineering on Physical Symptoms of Construction Workers" Buildings 14, no. 12: 4056. https://doi.org/10.3390/buildings14124056

APA Style

Hu, Z., Zhong, H., Li, S., Li, S., Shen, Y., He, C., & Xu, Z. (2024). Impact of Resilience Engineering on Physical Symptoms of Construction Workers. Buildings, 14(12), 4056. https://doi.org/10.3390/buildings14124056

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