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Article

Comparison of Influencing Factors on Safety Behavior and Perception Between Contractor Managers and Subcontractor Workers at Korean Construction Sites

by
Min-Jun Kim
1,
Sang-Pyeong Ahn
2,
Seung-Hyeon Shin
3,
Min-Guk Kang
4 and
Jeong-Hun Won
4,*
1
Department of Disaster Prevention Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea
2
Samsung C&T Corporation, Seoul 05288, Republic of Korea
3
Department of Big Data, Chungbuk National University, Cheongju 28644, Republic of Korea
4
Department of Safety Engineering, Chungbuk National University, Cheongju 28644, Republic of Korea
*
Author to whom correspondence should be addressed.
Buildings 2025, 15(6), 963; https://doi.org/10.3390/buildings15060963
Submission received: 2 February 2025 / Revised: 24 February 2025 / Accepted: 5 March 2025 / Published: 19 March 2025
(This article belongs to the Special Issue Construction Workplace Trends and Work Health and Safety)
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Versions Notes

Abstract

:
This study compared the influencing factors affecting safety behavior and perception between main contractor managers (CMs) and subcontractor workers (SWs) at Korean construction sites. The safety level, policies, and management capabilities of the main contractor are closely related to the safety behavior of the SWs since CMs have a key role in ensuring work order authority and safety responsibility during the work process. To compare the difference in safety behavior between CMs and SWs, ten hypotheses were prepared, and surveying was conducted. Using the responses of 1219 CMs and 1191 SWs, a frequency analysis, intensive validity analysis, and confirmatory factor analysis were performed; discriminant validity was verified; and a structural equation model was constructed and compared. The results showed that the factors affecting safety behaviors and perceptions were significantly different between CMs and SWs. The WSB (workers’ safety behavior) for CMs was affected, from most to least, by the MSP (manager’s safety perception; 0.382), incentives (0.166), WSP (workers’ safety perception; 0.143), and the MSB (manager’s safety behavior; 0.134). However, for SWs, the WSB was affected, from most to least, by the MSP (0.440), WSP (0.184), the MSB (0.130), and incentives (0.083). Awarding an incentive as a way to encourage safe behavior is an important influencing factor affecting safety behavior for CMs, while workers’ safety perception (WSP) is an important factor for SWs. The results can contribute to the implementation of effective safety and accident prevention activities at construction sites by comparing the influencing factors for the safety behaviors and perceptions of CMs and SWs, which can increase the effectiveness of the safety climate and reduce the possibility of accidents.

1. Introduction

The construction industry is one of the most hazardous industries in many countries. In the majority of countries, including the United Kingdom and the United States, the construction industry has a higher accident rate than other industries [1,2]. In South Korea, nearly half of fatal work-related injuries occur in the construction industry. The work-related fatality rate in the construction industry in South Korea is generally higher than those in all other industries [3]. Above all, half of the accident-related fatalities occur in the construction industry, which is a serious social problem. Consequently, the South Korean government has pursued a strong regulatory policy to reduce the number of accident-related fatalities. In 2018, the South Korean government implemented a “Declaration to reduce occupational accident deaths”, which was intended to create a workplace where workers’ lives and safety are guaranteed.
The environment of a construction workplace is a significant factor in preventing accidents, although there are numerous causes of workplace accidents [4]. Construction workers are exposed to various hazards at sites, ranging from underground to high-ground-level sites, and they are exposed to many risks compared to other industries [5]. Often, in the construction industry, the project contractor employs subcontractors of various engineering types on the site to provide expertise, and they constitute up to 90% of the labor force. Carrying out most of the work through subcontractors is a strategic approach used by contractors to achieve better cost control and risk management and to obtain necessary construction services [6]. However, it should be noted that subcontractors are relatively small companies operating in poor working conditions, for example, with low budgets, insufficient human resources, and a lack of professional safety managers [7]. These poor working conditions tend to lead to vulnerability in the safety and health management of workers. To compensate for this, safety and health management, safety technology research, and safety and health regulations are being implemented to improve the safety of subcontractor workers (SWs) [8,9,10,11,12].
According to a study sponsored by the U.S. Construction Industry Research Institute, the safety of subcontractors was more affected by contractors than the subcontractors themselves [13]. Wu et al. [14] stated that, in the construction industry, the obligations and policies of the client affected the safety priorities, attitudes, and behaviors of workers employed by contractors and subcontractors. The roles of contractors and subcontractors are important in managing the safety of subcontractors [12]. To reduce fundamental industrial accidents at construction sites, it is important to improve the safety behaviors and safety perceptions of all workers employed by contractors and subcontractors. In South Korea in particular, site contractor managers (CMs) deeply affect the safety behaviors and safety perceptions of SWs by controlling safety behavior during the work process. Thus, it is helpful to analyze the differences in safety behavior between CMs and SWs to prevent construction site accidents.
Recently, occupational safety and health issues have been integrated into fundamental safety behaviors and perceptions, moving away from engineering concerns, which are simply regarded as technical issues. The influence of the organizational climate or relationships between individuals within an organization on the safety behaviors of the workers has been a primary topic of research in recent years [15]. Since most of the victims of accidents at construction sites are SWs, it is important to improve the safety behaviors and perceptions of SWs. The safety and health management at construction sites is mainly carried out by CMs, but the differences in safety behaviors and perceptions between CMs and SWs act as an obstacle to improving the effectiveness of the safety climate. However, few studies have been performed that compare the safety climate between managers and workers at construction sites, even though most accidents occur at the worker level [16]. In particular, there are not many studies in Korea that reveal these differences.
In this study, the factors affecting workers’ safety behavior and workers’ safety perception were analyzed, targeting CMs and SWs, to solve problems with the safety climate. A correlation analysis between the factors affecting the safety behaviors and safety perceptions of CMs and SWs was performed by utilizing a structural equation model to analyze results obtained through a questionnaire. Structural equation models are often used in research to analyze the factors affecting workers’ safety behavior and safety performance, so a structural equation model was also applied in this study [17,18,19]. The questionnaire was reconstructed by utilizing the NOSACQ-50 and S-CAT programs. The Smart PLS 3.0 program was used for the survey analysis, and the safety factors were constructed in accordance with the structural equation model.
Contractors are required to provide a safe workplace for subcontractor managers and workers and work instructions, as well as provide safety education on work procedures and accident prevention measures for SWs. Since the importance of the cooperative relationship between the contractor and subcontractor increases in safety management, differences in safety behavior and perception between CMs and SWs can reduce the level of the safety climate at construction sites and increase the possibility of accidents. To reduce accidents at construction sites, it is necessary to investigate the factors affecting safety behavior and perception between CMs and SWs. Most of all, the primary consideration of a contractor’s safety management may not fully reflect the thoughts and requirements of SWs. The factors that influence safety behavior from the perspective of a CM, who has many regular employees, and a SW, who has mostly irregular employees, may be different. However, few studies have been conducted to understand the gap between CMs and SWs for workers’ safety behavior and perception [16]. The results of this study can contribute to the implementation of effective safety accident prevention activities at construction sites by comparing the factors that affect the safety behaviors and perceptions of CMs and SWs.

2. Accident Statistics and Literature Review

2.1. Accident Statistics in Korea

Figure 1 presents the change in the fatality rate per 10,000 workers and the number of accident fatalities. In the figure, the fatality rate in the construction industry is higher than those in other industries in South Korea. The fatality rates per 10,000 workers are steadily decreasing in all industries as a whole and in the manufacturing industry specifically. However, the fatality rate per 10,000 workers in the construction industry has fluctuated repeatedly and only recently decreased. Comparing the accident fatality rates per 10,000 workers in 2023, the construction industry has a 3.9 times higher fatality rate than the manufacturing industry. Most of the fatal accidents occurring in the construction industry in South Korea involve individuals working for/as subcontractors.

2.2. Literature Review

The unsafe behavior of workers is known to be a direct cause of accidents, and accidents can be predicted based on the safety climate [21]. Since the concept of safety climate was proposed by Zohar [22], numerous studies have been conducted on it because there is a high positive correlation between the safety climate and the safety behavior of workers. According to the results of a systematic literature review, the number of papers on the safety climate has increased continuously [23,24]. The safety climate is sometimes used in a similar sense to safety culture, but many researchers consider the safety climate to be a sub-part of safety culture and describe it as a brief image of the on-site safety culture [25,26].
The safety climate is the common perceptions of employees about the general importance of and priorities for safety policies, procedures, practices, and safety in the workplace [27,28]. One reliable indicator of various safety-related outcomes is the safety climate of the organization, since numerous studies demonstrate the importance of the safety climate, even though there have been many problems with applying scientific results to the field [29,30,31,32,33,34]. In addition, the evaluation of the safety climate is considered to be the most economical and time-saving way to prevent accidents [35]. Improving the safety climate in the construction industry is considered a way to improve its poor safety and health system [36]. The safety climate can be used as a diagnostic tool to identify the strengths and vulnerabilities of safety and health systems in the construction industry so that strategies can be developed to improve safety performance [36]. Thus, research is continuously being conducted to investigate various factors affecting the safety climates at construction sites [26,37,38] and develop safety climate evaluation models [39,40,41,42,43]. Various factors affect the safety climate in the construction industry, such as management commitment, workers’ commitment to safety, safety communication, safety perception, safety behavior, safety training, and others.
Most research on the construction of the safety climate has traditionally focused on the “organization” as the unit of analysis [39]. However, in recent years, the safety climate has been proposed as a multilevel concept, including organizational and group levels [39,44]. For example, Kim [41] suggested significant factors in enhancing the safety climate from the perspective of four groups (managers, superintendents, skilled laborers, and general helpers): from the managers’ perspective, a combination of improvement in the support environment and a reduction in work pressure; for superintendents and skilled laborers, increasing worker competence; and for general laborers, increasing worker involvement. In the construction industry, the concept of “organization” is somewhat ambiguous, and the structure of subcontracting must be reflected in the safety climate. The “organization” is more unclear to SWs than to CMs [43]. Thus, when considering the improvement of the safety climate at construction sites, it is necessary to at least distinguish between SWs and CMs.
Managers’ interest in and willingness to improve safety could be the starting point for increasing the level of the safety climate. Zohar [22] claimed that the importance level of the safety of managers and the safety climate in the organization depend on the interest of key stakeholders, such as the chief manager and safety manager. Brown and Holmes [45] empirically delineated the sub-factors affecting the safety atmosphere into three factors: the manager’s attitude, the manager’s behavior, and the worker risk level. Management safety empowerment is also an important key factor in improving the level of the safety climate [45]. Makki et al. [40] suggested that top management at construction sites are key players in safety management for preventing accidents that result from the workers’ unsafe behavior.
Managers’ safety perception factors, such as management safety justice, affect the level of the safety climate at a construction site. Dejoy et al. [46] stated that the more managers engage with and take responsibility for safety, the more the workers will perceive the overall stability of the workplace and connect it to safe working behavior. Some researchers demonstrated that persistent unsafe situations are mainly caused by the mismanagement of responsibilities and the actions of subordinates [47,48,49,50].
For workers’ safety commitment and effort, the recognition of workers is a very important factor for safety regulations and procedures established and implemented within an organization [51]. For workers to prioritize safety and ensure risk non-acceptance, managers’ participation in safety activities is important, but individual workers’ perceptions of safety are also very important [52]. In addition, it was predicted that workers could be linked to safe work behavior if they were better aware of current safety rules and procedures and tried to implement them. Workers’ perceptions of safety in the workplace are directly correlated with occupational accidents. Workers who understand occupational safety are less likely to have an accident than workers who perceive their jobs as relatively dangerous [26]. In addition, workers who perceive themselves as working in a safe environment show relatively low levels of work-related stress [53].
From the study of safety communication, learning, and trust in co-workers’ safety competence, open communication and information sharing within the organization raised workers’ safety perception [51]. The way to increase the effectiveness of safety communication is for managers to effectively communicate safety-related guidelines to workers and receive active feedback [54]. In addition, the continuous interaction between management and workers is significant for safety performance at construction sites. Managers should constantly check and communicate whether workers have complied with the safety protocols contained in the safety manual [55].
Trust in the efficacy of safety systems could affect the level of the safety climate. The establishment of an organization’s safety-related system could lead to positive safety behavior among workers [22,56]. Even though effective application of the safety management system is important, identifying possible operational problems and continuous improvement are also very important. Therefore, activities to identify problems with the organization’s safety system should be systematically carried out through safety inspections by safety and health managers and advice from external experts. Inspection activities for these systems can greatly contribute to the creation of a high level of safety atmosphere.
Narrowing the gap in the perception of the safety climate between workers and managers is critical for mitigating and preventing accidents at construction sites, especially in developing countries where safety conditions are poor and unpredictable and, in most cases, safety measures are insufficient [26]. Cao et al. [57] point out that managers’ management behavior significantly affects workers’ safety knowledge and safety motivation, which in turn affects workers’ safety behaviors. However, few studies have compared the safety climate between managers and workers at construction sites, even though most accidents have occurred at the worker level [16].

3. Research Methodology

3.1. Research Procedure

The research procedure of this study is shown in Figure 2. By analyzing the literature on safe climates, a questionnaire was developed on the safety behaviors and perceptions of CMs and SWs. The questionnaire was reconstructed from the previously known programs NOSACQ-50 and S-CAT. After setting the hypotheses, a survey was conducted among CMs and SWs. Using the Smart PLS 3.0 program, the validation of the structural equation model was carried out, and the influencing factors were analyzed.

3.2. Composition of the Questionnaire

Survey studies provide another source of raw data to understand organizational culture rather than producing processed climate or cultural outcomes [58]. In the study of safety culture, safety climate questionnaires have been the main means of measurement [59]. Many studies have made significant efforts to build valid and reliable safety climate questionnaires [58]. The development of questionnaires capable of sufficiently producing valid information could be useful for situational analysis, such as corrective action or the identification of related problems. In this study, a questionnaire was reconstructed by modifying necessary parts from the NOSACQ-50 and S-CAT surveys, which have been widely used. Marín et al. [60] stated that NOSACQ-50 is a reliable and valid tool for predicting safety motivation, the perceived safety level, and self-assessed safety behavior. Probst et al. [61] stated that the S-CAT questionnaire could evaluate the organization and work site safety environments of construction companies of all sizes. By utilizing the NOSACQ-50 and S-CAT questionnaires, influencing factors that affect the safety behaviors and safety perceptions of construction site workers were selected, and the questionnaire was reconstructed.
NOSACQ-50 can assess an organization’s safety climate, and it has been validated in more than 25 languages [62]. Yousefi et al. [63] emphasized that NOSACQ-50 is a reliable and valid tool for predicting safety motivation, the perceived safety level, and self-assessed safety behavior. NOSACQ-50 consists of 50 questionnaire items, divided into 7 categories, as shown in Table 1, to measure safety climates in various fields with objective figures. Detailed information related to this issue can be found at https://nfa.dk/vaerktoejer/spoergeskemaer/safety-climate-questionnaire-nosacq-50 (accessed on 25 February 2025). In Table 1, related studies are also included. The seven items are divided into six to eight detailed items to measure, quantify, and analyze the level of common perception related to the safety climates of respondents. It was possible to measure a complex structure by compressing a lot of information into several manageable elements [64].
The S-CAT was developed as a safety climate assessment tool that companies can use to improve their safety culture and provide information to improve safety-related side effects [61]. This is particularly important given recent empirical evidence demonstrating the importance of developing valid industry-specific measures for safe climates [34].
This study constructed a questionnaire by modifying NOSACQ-50 and S-CAT to confirm the correlations between factors influencing construction workers (Figure 3), since surveys can have the effect of maximizing generalizability across the industry of the organization regarding safety issues [71]. Among the survey questions in each category of NOSACQ-50 and S-CAT, questions related to this study were classified into MSB (manager’s safety behavior), MSP (manager’s safety perception), WSB (workers’ safety behavior), WSP (workers’ safety perception), and incentives. After reviewing the categorized questions, the authors reconstructed the questionnaire based on theoretical content about NOSACQ-50 and S-CAT and references related to the safety climate. As shown in Table 2, the questionnaire consists of nine detailed questions for five fields. For all measurement items, a 5-point Likert scale (1: Not at all~5: Very much) is used.

3.3. Hypotheses

To understand the safety factors affecting construction workers and the correlations among affecting factors, the following ten hypotheses were established. In the hypotheses, MSP and MSB denote the manager’s safety perception and the manager’s safety behavior, respectively. WSB and WSP mean workers’ safety behavior and workers’ safety perception. The structural equation model is shown in Figure 4.
Hypothesis 1-1.
The MSP has a positive effect on WSB.
Hypothesis 1-2.
The MSP has a positive effect on the MSB.
Hypothesis 1-3.
The MSP has a positive effect on WSP.
Hypothesis 2-1.
The MSB has a positive effect on WSB.
Hypothesis 2-2.
The MSB has a positive effect on WSP.
Hypothesis 3.
WSP has a positive effect on WSB.
Hypothesis 4-1.
Incentives have a positive effect on WSB.
Hypothesis 4-2.
Incentives have a positive effect on WSP.
Hypothesis 4-3.
Incentives have a positive effect on the MSB.
Hypothesis 4-4.
Incentives have a positive effect on the MSP.

4. Data Collection

The survey was conducted on people of various ages and experience levels working for the contractors and subcontractors of one large construction company in Korea. The selected company was estimated to have a high safety level for CMs. The company represents the general characteristics of the Korean construction industry since it is a large construction company that works with many subcontractors. Thus, we decided that the selected company was suitable for analyzing the difference in perception between SWs and CMs. The results of the survey are limited in applicability to small construction companies.
The survey was conducted at all sites of the considered company, and the number of valid respondents was 1219 CMs and 1191 SWs. The general characteristics of the respondents participating in the survey are shown in Table 3. For respondents of CMs, 507 managers (41.6%) in their 30s accounted for the largest percentage, followed by 407 managers (33.4%) in their 40s, 214 managers (17.5%) in their 50s, 65 managers (5.3%) under 30, and 26 managers (2.1%) in their 60s or older. In the case of SWs, 472 workers (39.6%) in their 50s accounted for the largest proportion, followed by 283 workers (23.8%) in their 40s, 169 workers (14.2%) in their 30s, 137 workers (11.5%) in their 20s, and 129 workers (10.8%) in their 60s or older.
For the experience of respondents, the highest percentage consisted of managers or workers who had worked for more than 10 years for both CMs and SWs. In detail, 687 managers (56.4%) for contractors had the maximum experience of over 10 years, 373 managers (30.6%) had 5 to 10 years of experience, 85 managers (7.0%) had 3 to 5 years of experience, 53 managers (4.3%) had 1 to 3 years of experience, and 16 managers (1.3%) had less than 1 year of experience. The number of workers working for subcontractors was as follows: 458 workers (38.5%) had more than 10 years of experience, 211 workers (17.7%) had 5 to 10 years of experience, 182 workers (15.3%) had 1 to 3 years of experience, and the numbers were the same for 3 to 5 years of experience and less than a year of experience, i.e., 170 workers (14.3%) each.
The number of years of service for the company showed a clear difference between CMs and SWs. The highest percentage for contractors consisted of 506 managers (41.5%) with 7 to 15 years of service, followed by 254 managers (20.8%) with 15 years or more, 209 managers (17.1%) with between 3 and 7 years, 108 managers (8.9%) with between 1 and 3 years, and 52 managers (4.3%) with 1 year or less. The highest percentage for subcontractors was 764 workers (64.1%) working for less than a year, followed by 271 workers (22.8%) for 1 to 3 years, 84 workers (7.1%) for 3 to 7 years, 43 workers (3.6%) for 7 to 15 years, and 25 workers (2.1%) for more than 15 years.
The workers of respondents belonging to the CM (contractor manager) category were in their 30s, 40s, 50s, 20s, and 60s, but those of SWs (subcontractor workers) were primarily in their 50s, followed by 40s, 30s, 20s, and 60s. For career experience, there was no significant difference between CMs and SWs. Of course, the years of service at the company showed a huge difference since construction workers were mainly employed in the project unit. CMs had most commonly worked for 7 to 15 years (41.5%), and SWs had most commonly worked for less than one year (64.1%). Most respondents for CMs were in their 30s; if the managers had obtained a job in their 20s and continued in this career, they would have 7 to 15 years of work experience. Most SWs were in their 50s; however, those with less than one year of work experience were the most common. This result means that SWs work on short-term contracts on a construction project or on a daily basis. The working period at a company may vary depending on the scale and size of the construction project, but there are many short-term construction projects that finish within one year, and subcontractors are divided by process. In addition, we found that work experience at the company was short because the subcontracting company has many freelancers focused on professional engineering.

5. Results

5.1. Model Validation

The Smart PLS 3.0 program was used to analyze the survey through the structural equation model. PLS analysis requires the verification of internal consistency, convergent validity, and discriminant validity as measurement questions and constituent concepts. The internal consistency of the measurement questions was verified via Fornell and Larker’s composite reliability [72], as shown in Table 4. As a result of verifying the data of the CMs (contractor managers), the lowest value for the combined reliability of the CMs was 0.772, which was greater than the combined reliability standard of 0.7 claimed by Nunnally [73] and Goodhue and Thompson [74], so the combined reliability value was satisfactory.
The average variance extracted (AVE) was found to have a very high internal relevance, as the minimum value was 0.631 for WSB, satisfying the standard value of 0.5 or more claimed by Fornell and Lacker [72] and Chin [75]. As a result of verifying the data of SWs, the combined reliability of SWs was calculated to be 0.675 and did not reach the standard value of 0.7, but the remaining four fields were calculated to be 0.816, reaching the minimum value of 0.7 or higher. AVE was found to be 0.5 or higher, meeting the standard claimed by Fornell and Lacker [72] and Chin [75]. Even though WSP had a combined reliability of under 0.7, it was not a small number, being 0.675, and we determined that the internal consistency was acceptable because AVE was satisfied.
Intensive validity was verified with factor loading values and T-values for the configuration concept using the bootstrap method of Smart PLS. Fornell and Larker [72] recommended that the factor loading value of the measurement item be 0.7 or more and the T-value be 1.96 or more. The factor loading values and T-values of CMs and SWs are shown in Table 5. The factor loading values of CMs were more than 0.7, except for Q6, and the Q6 item was only slightly insufficient at 0.698, indicating that intensive validity was observed. Since the factor loading value of SWs was 0.404 in Q7, Q7 did not meet the criterion, so the analysis was conducted on eight items, excluding Q7. Smart PLS analysis requires confirmatory factor analysis rather than exploratory factor analysis [76]. The confirmatory factor analyses of the contractor and subcontractor respondents are shown in Table 6 and Table 7.
In confirmatory factor analysis, the factor loading value for the constituent concept must be greater than the factor loading values for other constituent concepts. As a result of the analysis, both respondents of contractors and subcontractors met the requirements because all survey items had higher values than other factor loading values. Discriminant validity is found to be acceptable when the square root value of the AVE displayed on the diagonal axis of the correlation coefficient between constituent concepts is higher than those between other constituent concepts [72]. Table 8 shows the discriminant validity of CMs. Among the correlation coefficients between the constituent concepts of the CMs, the lowest value of the square root of AVE was 0.794, and the highest value of the correlation coefficients of other constituent concepts was 0.662, so the discriminant validity of the CMs was acceptable.
Table 9 shows the discriminant validity of SWs. Among the correlation coefficients between the constituent concepts of subcontractors, the lowest square root value of the AVE was 0.831, and the highest correlation coefficient of the other constituent concepts was 0.636, indicating that the discriminant validity of the subcontractors was acceptable.

5.2. The Structural Equation Model

In Smart PLS analysis, the explanatory power of the path model is expressed as the R2 value, which is the expanded variance [77]. The R2 values of the CMs (contractor managers) on Smart PLS are shown in Table 10. For hypothesis H1, the MSP was 38.2% in WSB, 33.1% in the MSB, and 12.1% in WSP. The MSB of hypothesis H2 for managers was 13.4% of WSB and 46.5% of WSP. For hypothesis H3, WSP showed 14.3% explanatory power for WSB. Hypothesis H4 on incentives showed an explanatory power of 16.6% in WSB, 26.2% in WSP, 37% in the MSB, and 46.4% in the MSP. All of these met the 10% appropriate power standard suggested by Falk and Miller [78].
Wetzels et al. [79] recommended the Goodness-of-Fit (GoF) of the PLS path model. In this study, the geometric mean of the commonality mean and the R2 mean were used as the GoF test criteria [79,80]. Table 11 shows the R2 values of the SWs (subcontractor workers) based on Smart PLS. Looking at hypothesis H1, the explanatory power of the MSP is shown to be 44% in WSB, 35.2% in the MSB, and 36.6% in WSP. Hypothesis H2 shows that the explanatory power of the MSB is 13% for WSB and 28% for WSP. Hypothesis H4 shows that the explanatory power of incentives is 8% for WSB, 18% for WSP, 22.6% for the MSB, and 40.7% for the MSP. We found that, for hypothesis H4-1, the effect of incentives on WSB was 8%, which did not satisfy the 10% standard for the appropriate power, but the statistical power was confirmed because the remaining nine hypotheses were satisfied.
Next, the significance of the path coefficient was verified. To this end, the path coefficient for the structural model was calculated using the entire sample, and the T-value of the path coefficient was calculated using the bootstrap method provided by Smart PLS. The values of the path coefficients are shown in Table 10 and Table 11. As shown in Table 10, all path coefficient T-values of CMs were found to be greater than 1.96, and hypotheses H1-1 to 4, H2-1, 2, H3, and H4-1 to 4 were accepted at a significance level of 5%. In Table 11, it is shown that the T-values of all route coefficients of SWs are greater than 1.96, and hypotheses H1-1 to 4, H2-1, 2, H3, and H4-1 to 4 were accepted at a significance level of 5%. The GoF impact of workers of CMs was found to be 0.396, the MSP was 0.544, WSP was 0.600, and WSB was 0.533. Since these are larger than 0.36, as suggested by Wetzels et al. [79], the overall suitability of the model is very high. The GoF impact of SWs is 0.346, the MSP is 0.421, WSP is 0.653, and WSB is 0.569, which are larger than those of Wetzels et al. [79], so the overall suitability of the model is confirmed. The visualization of the structural equation model is shown in Figure 5 and Figure 6.

6. Discussion

6.1. Hypothesis Review

Hypothesis 1. The effect of MSP (manager’s safety perception) on WSB (workers’ safety behavior), WSP (workers’ safety perception), and MSB (manager’s safety behavior) was compared between contractor managers (CMs) and subcontractor workers (SWs). For CMs, the MSP affected these factors in the order of WSB, MSB, and WSP. On the other hand, for SWs, the MSP affected these factors in the order of WSB, WSP, and MSB. Since the MSP had the greatest effect on WSB (H1-1), the MSP plays an important role in improving WSB. The second factor affecting MSP is MSB for CMs, while the second factor is WSP for SWs. Among factors, WSP is the lowest factor affected by the MSP for CMs, while the MSB is the lowest factor for SWs. However, comparing the measured values for CMs, the effect of the MSP on WSP (0.121) is significantly less than that of the MSB (0.331). Since there was no significant difference between the MSB (0.352) and WSP (0.366), the MSP of SWs had a large effect on both.
To summarize the results for hypothesis 1, the MSP had the greatest influence on WSP for both CMs and SWs, but the second most significant affecting factor was the MSB in the case of CMs and MSB and WSP for SWs. The effect of the MSP on WSP was low for CMs. Consequently, if the MSP is improved, the MSB and WSB can be enhanced for CMs; for SWs, the MSB, WSB, and WSP can be enhanced by the MSP. This suggests that the level of engagement among safety officers has a positive impact on safety performance, further confirming that an improved MSP contributes to successful safety outcomes [81,82,83].
Hypothesis 2. Comparing the effects of the MSB on WSB and WSP, the MSB had a greater effect on WSB than WSP for both CMs and SWs. However, the effect of the MSB on the WSP of contractors (0.465) was more significant than that of SWs (0.280). Similarly, Li et al. [15] emphasized that managerial safety actions can enhance subcontractors’ safety perception levels. Therefore, CMs need to pay special attention to the MSB.
Hypothesis 3. The effect of WSP on WSB was relatively small, although both CMs (0.143) and SWs (0.184) had some effect [12]. In general, improving workers’ safety perception through education enhances their safety behavior, but the effectiveness is realized only when the education is continuous [84].
Hypothesis 4. The effects of incentives on WSB, WSP, MSB, and MSP were compared. The results show that incentives affected both CMs and SWs in the order of MSP, MSB, WSP, and WSB, indicating that incentives had the greatest impact on the MSP. However, incentives had a weak effect on all WSBs. Chen and Jin [12] suggested that subcontractor workers also preferred improvements in incentive systems.

6.2. Implications of Structural Equation Model Analysis

A comparative analysis was conducted on the influencing factors affecting the dependent variable. The WSB for CMs was affected in the order of MSP, incentives, WSP, and MSB, while SWs were affected in the order of MSP, WSP, MSB, and incentives. Although all the factors considered affected the improvement of WSB, it is necessary to establish separate countermeasures for contractors and subcontractors because their priorities are different. The enhancement of the MSP will greatly improve WSB since the MSP has a significant effect on WSB for both contractors and subcontractors. Even though WSB is influenced by colleagues [69], the decrease in the MSP interrupted WSB, which can directly affect construction accidents.
The second factor that affected the WSB for CMs was incentives, while the effect of incentives on WSB for SWs was the lowest factor among the four influencing factors. Safety competition and reward incentive schemes can effectively stimulate workers’ safety integrity and initiative in safety cooperation to improve safety consciousness and safety consciousness behavior [85]. A sound accident reporting system is an example of construction safety management used to identify the root causes of accidents and prevent future accidents [86]. It is thought that the differences in incentives’ effects between CMs and SWs resulted from the low number of years of service for SWs, as shown in the frequency analysis. The incentives are valid for CMs, but for SWs, the incentives are not valid due to few service years. Li et al. [15] emphasized the importance of providing subcontractors with clear expectations for safe behaviors through safety rewards and penalty regulations. Furthermore, Chen and Jin [12] advocated for strongly encouraging subcontractor employees to provide safety-related feedback and for increasing safety incentives. Therefore, to improve the WSB of subcontractors, firms should consider improving WSB through incentives.
In the case of CMs, WSP was affected in the order of MSB, incentives, and MSP. The effect of the MSB on WSP (0.465) was very large for CMs, and the MSP had a very small effect (0.121). The indirect effect of the MSP was 0.154, slightly larger than the direct effect of 0.121, but there was no significant difference. Since the MSP of CMs greatly affects WSP, it is also necessary to improve the MSP. The time spent by managers in the workplace is related to better safety performance [87]. Therefore, the more safety activities are carried out in the field, the better the WSP can become.
On the other hand, when analyzing SWs, WSP was affected in the order of MSP (0.366), MSB (0.280), and incentives (0.180). SWs have more opportunities for direct communication than CMs because the size of the company is smaller than that of CMs, so they are more affected by the MSP than by the MSB. Therefore, SWs can improve WSP by improving the MSP through manager training. If direct communication is possible, depending on the size of the company, the MSP has a greater effect, and the larger the company, the less opportunity to communicate, so it affects the MSB more than the MSP. Subcontractors have a small number of employees, but direct communication is smooth, so the MSP is affected more than the MSB. On the other hand, contractors have relatively few opportunities for communication due to a large number of people, so it affects the MSB rather than the MSP.
When comparing and analyzing the impact on the MSB, CMs had an effect in the order of incentives and MSP, and SWs’ MSP had more influence than incentives. For CMs and SWs, the MSB and MSP were significantly affected by incentives. As for incentives, both contractors and subcontractors showed large effects on both the MSB (0.370, 0.226) and MSP (0.464, 0.407). However, the effects of incentives on WSB (0.166, 0.080) and WSP (0.262, 0.180) were smaller than for the MSB and MSP. The implementation of workplace safety programs is an effective way to prevent or reduce work-related accidents and injuries [88,89]. Incentives to compensate for safe behavior are effective [90] and encourage workers to work safely to protect their families and loved ones [91].
One factor that affected the MSP was incentives, and CMs were shown to be more influenced by incentives than SWs. When analyzing the impact of the MSP on other factors, in the case of CMs, the impact was in the order of WSB (0.382), MSB (0.331), and WSP (0.121). In the case of SWs, MSP was found to be the same as CMs in the order of WSB (0.440), MSB (0.352), and WSP (0.083), but the degree of influence was found to be greater on WSB and less on WSP compared to CMs.
In summary, among the MSP, the MSB, WSP, and incentives, the one that had the greatest impact on WSB was the MSP, both for CMs and for SWs. Since improving the MSP enhanced WSB, decreasing the probability of construction accidents, construction companies should make efforts to improve the MSP. The incentives’ effect differed between CMs and SWs, even though safety incentives can effectively stimulate workers’ safety behavior and perception. The effect of incentives was significant for CMs, but in the case of SWs, the effect of incentives was relatively small due to a short employment period. When analyzing the factors affecting WSP, CMs were affected in the order of MSB, incentives, and MSP, while SWs were affected in the order of MSP, MSB, and incentives.

7. Conclusions, Implications, and Limitations

The factors affecting the safety behaviors and safety perceptions of construction workers were analyzed by comparing contractor managers (CMs) and subcontractor workers (SWs). To understand the safety climate of construction workers, a questionnaire was conducted for CMs and SWs. Ten hypotheses were established, and a structural equation model was constructed to identify the correlation between factors. Smart PLS 3.0 was used for the structural equation model, and significant structural equation models were created for each of the contractors and subcontractors.
The results showed that the influencing factors affecting the safety behaviors and safety perceptions of CMs and SWs were different. WSB (workers’ safety behavior) for CMs was affected in the order of MSP (manager’s safety perception), incentives, WSP (workers’ safety perception), and MSB (manager’s safety behavior), while SWs were affected in the order of MSP, WSP, MSB, and incentives. CMs’ WSP had an effect in the order of MSB, incentives, and MSP, while SWs’ WSP had an effect in the order of MSP, MSB, and incentives.
It is meaningful to find the differences in the factors affecting the safety perceptions and behaviors of CMs and SWs, as well as to find the priority of access to CMs and SWs for reducing construction accidents. Due to the specialization of various engineering sectors, the relationship between contractors and subcontractors will continue to be important, and the role of the subcontractor will increase. The factor that has the greatest influence on workers’ safety behavior is the safety perception of managers, so it is important to strengthen safety education for managers and ensure that all managers recognize that safety is a basic responsibility. Unlike the managers of the main contractors, workers for subcontractors recognize the effectiveness of incentives as relatively low and workers’ safety perception as an important factor for displaying safe behavior. It is known that the effect of compensation is good, but to increase safety behavior, it is clear that more activities designed to raise workers’ safety perception are needed. The continuous promotion of the safety culture of subcontractors with relatively poor safety culture, education to change workers’ safety attitudes, and activities to raise the perception of the seriousness of accidents utilizing accident cases and virtual experience training are necessary for subcontractor workers.
This study reveals some important means of safety management through surveys of contractor managers and subcontractor workers, but it also has some limitations. First, the survey was conducted only at the sites of a large construction company that has many sites under construction and a relatively high level of safety management to investigate the responses of a large number of managers and workers. Future research should be conducted on various contractors considering the level of contractors’ safety management to examine influencing factors affecting safety behavior and perception between CMs and SWs. In addition, it should consider the size of the construction project and the construction company size since accidents frequently occur at small-scale construction sites. Second, since there is a difference in the degree of risk depending on the type of work, it is necessary to analyze the safety behaviors and perceptions of workers according to the types of work that they perform. Lastly, future research needs to consider the company scale of subcontractors, as this can affect the safety investment and attention at the site.

Author Contributions

M.-J.K.: Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Data Curation, and Writing—Original Draft. S.-P.A.: Validation, Formal Analysis, and Data Curation. S.-H.S.: Software, Validation, and Formal Analysis. M.-G.K.: Investigation and Writing—Review and Editing. J.-H.W.: Conceptualization, Validation, Resources, Writing—Review and Editing, Investigation, Supervision, and Project Administration. All authors have read and agreed to the published version of the manuscript.

Funding

This paper was supported by the Chungbuk National University BK21 program (2022). In addition, this paper was partially supported by Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) through a grant funded by the Korean Government (No. 20224000000070).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

This paper was supported by the Chungbuk National University BK21 program (2022). In addition, this paper was partially supported by Human Resources Development of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) through a grant funded by the Korean Government (No. 20224000000070).

Conflicts of Interest

Author Sang-Pyeong Ahn was employed by the company Samsung C&T Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Buildings 15 00963 g001aBuildings 15 00963 g001b
Figure 1. Comparison of accident fatality statistics in Korea’s construction and manufacturing industries: (a) fatality rate per 10,000 workers (accident + disease); (b) accident fatality rate per 10,000 workers; (c) number of accident fatalities (this figure was reconstructed using data from an analysis of current status of industrial accidents in Korea [20]).
Figure 1. Comparison of accident fatality statistics in Korea’s construction and manufacturing industries: (a) fatality rate per 10,000 workers (accident + disease); (b) accident fatality rate per 10,000 workers; (c) number of accident fatalities (this figure was reconstructed using data from an analysis of current status of industrial accidents in Korea [20]).
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Figure 2. Research procedure.
Figure 2. Research procedure.
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Figure 3. Questionnaire design.
Figure 3. Questionnaire design.
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Figure 4. Hypothesis model.
Figure 4. Hypothesis model.
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Figure 5. The results of the structural equation model for CMs.
Figure 5. The results of the structural equation model for CMs.
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Buildings 15 00963 g006
Figure 6. The results of the structural equation model for SWs.
Figure 6. The results of the structural equation model for SWs.
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Table 1. NOSACQ-50 assessment dimensions.
Table 1. NOSACQ-50 assessment dimensions.
No.Safety Climate DimensionsRelated Studies
1Management safety priority, commitment, and competence (9 items)[22,65,66,67,68]
2Management safety empowerment (7 items)[45]
3Management safety justice (6 items)[46,54,69]
4Workers’ safety commitment (6 items)[11,51,66,68,69]
5Workers’ safety priority and risk non-acceptance (7 items)[52]
6Safety communication, learning, and trust in co-workers’ safety competence (8 items)[51,54,68,70]
7Trust in the efficacy of safety systems (7 items)[22,56]
Table 2. Questionnaire content.
Table 2. Questionnaire content.
CategoryContents
Manager’s safety behavior (MSB)Q1: The manager communicates with workers often about safety-related issues.
Q2: The manager visits the site for safety inspection and directly participates in accident prevention activities.
Manager’s safety perception (MSP)Q3: The manager does not carry out the work when safety is not secured.
Q4: The manager thinks that safety and health is not their job.
Workers’ safety behavior (WSB)Q5: Workers actively report risk factors that can lead to accidents.
Q6: Workers are often found not following safety guidelines.
Workers’ safety perception (WSP)Q7: Workers think that excessive emphasis on safety hinders the work in the field.
Q8: Safety and health staff (managers) help prevent accidents.
IncentivesQ9: To give rewards for safety activities is helpful to motivate safety behavior since the awards make workers proud.
Table 3. General characteristics of survey respondents.
Table 3. General characteristics of survey respondents.
ClassificationCMs
(Contractor Managers)		SWs
(Subcontractor Workers)
Number(%)Number(%)
Age<30 year655.313711.5
30~39 year50741.616914.2
40~49 year40733.428323.8
50~59 year21417.547239.6
>60 year262.112910.8
N/A00.010.1
Years of experience<1 year161.317014.3
1~3 year534.318215.3
3~5 year857.017014.3
5~10 year37330.621117.7
>10 year68756.445838.5
N/A50.400.0
Years of service<1 year524.376464.1
1~3 year1088.927122.8
3~7 year20917.1847.1
7~15 year50641.5433.6
>15 year25420.8252.1
N/A907.440.3
Table 4. Internal consistency analysis.
Table 4. Internal consistency analysis.
ClassificationCMs (Contractor Managers)SWs (Subcontractor Workers)
Combined ReliabilityAVECronbach’s AlphaCombined ReliabilityAVECronbach’s Alpha
MSP0.8430.7280.6280.8380.7210.616
MSB0.9030.8230.7850.8490.7370.644
Incentive111111
WSP0.8240.7020.5860.6750.5490.269
WSB0.7720.6310.4320.8160.690.553
Table 5. Intensive validation of survey results.
Table 5. Intensive validation of survey results.
ClassificationCMs (Contractor Managers)SWs (Subcontractor Workers)
Factor Loading ValueT-ValueFactor Loading ValueT-Value
MSPQ30.87481.5830.87287.969
Q40.83243.3820.82649.064
MSBQ10.899111.5950.86775.763
Q20.915157.0350.85058.473
IncentiveQ91.000 1.000
WSPQ70.77137.1790.4047.209
Q80.899132.7170.96699.497
WSBQ60.69821.1420.80141.354
Q50.88177.0790.85976.036
Table 6. Confirmatory factor analysis for respondents of contractor managers (CMs).
Table 6. Confirmatory factor analysis for respondents of contractor managers (CMs).
ClassificationMSPMSBIncentiveWSPWSB
MSPQ30.8740.4910.4120.4480.504
Q40.8320.3580.3780.3590.512
MSBQ10.4710.9150.470.6340.481
Q20.4390.8990.480.5650.438
IncentiveQ90.4640.52310.5610.493
WSPQ70.3150.4030.380.7710.371
Q80.4630.670.540.8990.469
WSBQ60.390.2940.2880.2880.698
Q50.5410.4860.4720.490.881
Table 7. Confirmatory factor analysis for respondents of subcontractor workers (SWs).
Table 7. Confirmatory factor analysis for respondents of subcontractor workers (SWs).
ClassificationMSPMSBIncentiveWSPWSB
MSPQ30.8710.4160.380.510.557
Q40.8270.3340.3080.4430.523
MSBQ10.3630.8520.3320.4360.364
Q20.3990.8650.3030.4370.408
IncentiveQ90.4070.3710.4320.391
WSPQ7-----
Q80.5630.5090.43210.534
WSBQ60.5090.3170.2560.3940.802
Q50.5470.4240.3850.4890.858
Table 8. Discriminant validity analysis of CMs (contractor managers).
Table 8. Discriminant validity analysis of CMs (contractor managers).
ClassificationMSPMSBIncentiveWSPWSB
MSP0.853
MSB0.5020.907
Incentive0.4640.5231
WSP0.4760.6620.5610.838
WSB0.5950.5070.4930.5070.794
Table 9. Discriminant validity analysis of SWs (subcontractor workers).
Table 9. Discriminant validity analysis of SWs (subcontractor workers).
ClassificationMSPMSBIncentiveWSPWSB
MSP0.850
MSB0.4440.858
Incentive0.4070.371
WSP0.5630.5090.4321
WSB0.6360.450.3910.5340.831
Table 10. Calculation of path coefficient, STDEV, T, and p values for workers of CMs.
Table 10. Calculation of path coefficient, STDEV, T, and p values for workers of CMs.
ClassificationPathPath CoefficientSTDEVTp Values
H1-1MSP → WSB0.3820.03311.6770.000 ***
H1-2MSP → MSB0.3310.0310.880.000 ***
H1-3MSP → WSP0.1210.0274.4540.000 ***
H2-1MSB → WSB0.1340.0363.6720.000 ***
H2-2MSB → WSP0.4650.02816.6610.000 ***
H3WSP → WSB0.1430.0344.2020.000 ***
H4-1Incentive → WSB0.1660.0325.2540.000 ***
H4-2Incentive → WSP0.2620.0299.1660.000 ***
H4-3Incentive → MSB0.3700.03111.9350.000 ***
*** p < 0.001.
Table 11. Calculation of path coefficient, STDEV, T, and p values for SWs.
Table 11. Calculation of path coefficient, STDEV, T, and p values for SWs.
ClassificationPathPath CoefficientSTDEVTp Values
H1-1MSP → WSB0.4400.03213.8220.000 ***
H1-2MSP → MSB0.3520.03410.2920
H1-3MSP → WSP0.3660.03211.4900
H2-1MSB → WSB0.1300.034.3630
H2-2MSB → WSP0.2800.0318.9210
H3WSP → WSB0.1840.0355.3110
H4-1Incentive → WSB0.0830.0282.9670.003
H4-2Incentive → WSP0.1800.0335.5300
H4-3Incentive → MSB0.2260.0326.9650
H4-4Incentive → MSP0.4070.02913.8170
*** p < 0.001.
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MDPI and ACS Style

Kim, M.-J.; Ahn, S.-P.; Shin, S.-H.; Kang, M.-G.; Won, J.-H. Comparison of Influencing Factors on Safety Behavior and Perception Between Contractor Managers and Subcontractor Workers at Korean Construction Sites. Buildings 2025, 15, 963. https://doi.org/10.3390/buildings15060963

AMA Style

Kim M-J, Ahn S-P, Shin S-H, Kang M-G, Won J-H. Comparison of Influencing Factors on Safety Behavior and Perception Between Contractor Managers and Subcontractor Workers at Korean Construction Sites. Buildings. 2025; 15(6):963. https://doi.org/10.3390/buildings15060963

Chicago/Turabian Style

Kim, Min-Jun, Sang-Pyeong Ahn, Seung-Hyeon Shin, Min-Guk Kang, and Jeong-Hun Won. 2025. "Comparison of Influencing Factors on Safety Behavior and Perception Between Contractor Managers and Subcontractor Workers at Korean Construction Sites" Buildings 15, no. 6: 963. https://doi.org/10.3390/buildings15060963

APA Style

Kim, M.-J., Ahn, S.-P., Shin, S.-H., Kang, M.-G., & Won, J.-H. (2025). Comparison of Influencing Factors on Safety Behavior and Perception Between Contractor Managers and Subcontractor Workers at Korean Construction Sites. Buildings, 15(6), 963. https://doi.org/10.3390/buildings15060963

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