Analyzing the Unsafe Behaviors of Frontline Construction Workers Based on Structural Equation Modeling

Abstract

The unsafe behavior of frontline workers at construction sites is the most important cause of construction accidents. This study proposed a comprehensive model of frontline workers’ unsafe behaviors based on a systems perspective and used structural equation modeling (SEM) to explore the influence mechanisms between the objective conditions (e.g., work environment, work climate, and task complexity), safety management (e.g., safety education and training, safety reward and punishment regulations, safety inspection, safety technology disclosure, and safety warning signs), group influence (propagation of unsafe behaviors among workers), personal perception (subjective judgment of operators on their safety knowledge and skills), and unsafe behaviors. Data from 460 frontline workers were collected through questionnaires and the correlation hypotheses were tested using SPSS 26.0 and Amos 26.0 software. The following conclusions were obtained: (1) objective conditions directly positively influence safety management, group influence, and personal perception but indirectly negatively influence unsafe behavior; (2) safety management not only directly positively affects personal perception but also directly negatively affects unsafe behavior. However, the direct effect of safety management on group influence is not significant; (3) group influence has a direct positive effect on unsafe behavior, but the direct effect on personal perception is not significant; (4) the direct effect of personal perception on unsafe behavior is insignificant. These findings can be used as preliminary data to guide decision makers or managers in construction companies to develop reasonable management plans to curb the unsafe behaviors of frontline workers.

1. Introduction

The safety situation in the global construction industry has always been severe [1,2], and China’s construction industry is no exception. Construction progress and quality are easily affected by objective conditions, as there are many open-air operations. The dangers at construction sites are everywhere [3,4]. Personal injuries and fatalities caused by workers’ unsafe behaviors occur from time to time, and the consequences of these accidents are serious [5,6].

The unsafe behavior of construction workers is the direct cause of accidents and the focus of construction safety management [7,8]. In recent years, many scholars have carried out related research work on unsafe behavior [9]. The research on unsafe behavior has mainly focused on the safety climate during 1991–2000 and then on behavior-based safety (BBS) and organizational factors during 2001–2020. For example, Mattila, M et al. [10] designed a questionnaire to measure the safety climate with questions on safety attitudes and opinions and conducted research on the validity of monitoring behavior on construction sites. Neal, A et al. [11] investigated the mechanisms by which the general organizational climate influences the safety climate and safety performance. Li, H et al. [8] proposed a proactive behavior-based safety (PBBS) approach combining BBS with the Proactive Construction Management System (PCMS), which was applied to a Hong Kong construction project to prove the efficacy of construction accident prevention. One of the hot topics in the current stage of research on unsafe behaviors is exploring the relationship between the factors associated with unsafe behaviors through methods such as structural equations, neural networks, and system dynamics [12].

However, most of the existing research has examined how the individual characteristics of workers led to unsafe behaviors or focused only on the mechanisms by which unsafe behaviors are transmitted in a group. There are not many studies that can comprehensively consider the effects of factors on unsafe behaviors in the dimensions of safety management, interactions among individuals within a workgroup, and the external environment. Therefore, the research goal of this paper is to establish an integrated model and use SEM to verify the relevant hypotheses to explore the influence mechanisms between the external objective environment, safety management measures, interactions among individuals within the frontline workgroups, workers’ perceptions, and unsafe behaviors.

2. Accident Statistics and Literature Review

In this study, 93 construction accident cases in China occurring between October 2016 and October 2021 were analyzed, of which 80 accident investigation reports could be retrieved. After manual collation and statistical analysis of the accident investigation reports, the proportion of accidents directly caused by human, machine, and environmental factors is shown in Figure 1.

2.1. Unsafe Behaviors

Unsafe behavior is often the direct cause of construction accidents. In the 80 investigation reports of construction accidents, the descriptions of unsafe behavior mainly include poor physical and mental conditions (including sudden illness), adventurous work activities (including work without a qualification), the incorrect use of personal protective equipment (PPE) or failure to take safety precautions, unauthorized changes to the construction plan, using unsafe equipment or materials, improper storage of materials, obeying an illegal command, and blind rescue. The corresponding word frequency statistical analysis was performed, in which the largest proportion was the incorrect use of PPE or failure to take safety precautions, followed by unauthorized changes to the construction plan, adventurous work activities, and illegal commands. The percentages of various types of human unsafe behaviors are shown in Figure 2.

2.2. Objective Conditions

A few studies have shown that environmental factors also contribute to construction accidents because most of the construction sites are in open-air environments. Such environmental factors include high temperatures [4], strong winds [13], heavy rain [4,14], and other adverse weather conditions [9], as well as poor working conditions such as limited space [13,14] and poor visibility [4]. The influence of objectively existing environmental constraints on workers is discussed in the relevant reports. For example, R.A. Haslam et al. [13] mention that restricted spaces cause workers to inevitably adopt poor body postures in order to accomplish their tasks. In our accident case analysis, we also found that accidents involving restricted space operations such as basement excavation are often accompanied by unsafe behaviors.

After analyzing 80 cases of construction accidents that had occurred in China in the last five years, we found that crossover operations or joint operations of multiple work types that comply with the requirements of safety standards are common on construction sites. However, when multiple work types perform crossover operations or joint operations, irregularities such as poor risk identification and misuse are more likely to occur. This implies that differences in the magnitude of objective risks across different operation patterns can also have an impact on personnel behavior.

In addition, the objective complexity of the task can also affect the quality of personnel operations [13]. Objective complexity in this context means that the operating procedures of different tasks are either complicated or simple, and the more complicated the steps are, the more complex that task is. At the same time, the tools used by workers to complete different tasks are different. For example, lifting operations require the use of cranes [13], whereas plastering operations do not. The complexity of different tasks varies due to the different procedures and tools used. Such complexity exists objectively, and the resulting differences in the magnitude of the objective risk also affect the quality of the personnel’s operations.

2.3. Safety Management

In general, indirect causes in accident reports are characterized by the absence of relevant interventions in daily safety management. Most of the unsafe behaviors were caused by the weak safety awareness of workers, on the one hand due to personal psychological reasons, such as a weak sense of responsibility and a preference for following the crowd, and on the other hand due to the lack of safety education and training [15,16]. Through the statistical analysis of 80 construction accident investigation reports, the safety management factors leading to personal casualties in China’s construction industry mainly include inadequate safety education and training, imperfect or unimplemented safety management regulations, inadequate safety technical briefings, inadequate site safety inspections, inadequate site safety supervision, and failure to set safety warning signs as required. The proportion of each safety management factor in the 80 accident reports is shown in Figure 3.

2.4. Personal Perception

Personal perception in this study refers to the subjective perception of one’s own safety knowledge and safety skills as a result of the intervention of safety management measures [16]. It mainly includes a comprehensive understanding of the possible safety risks and occupational hazards in the work process, proficiency in safety techniques or preventive measures for work risks and occupational hazards, and the ability to use personal protective equipment correctly according to regulations.

2.5. Group Influence

The influence of the organizational climate or person-to-person relationships within an organization on workers’ unsafe behaviors has been one of the hot spots of research in recent years. Cao et al., (2011) point out that managers’ management behaviors have significant effects on both workers’ safety knowledge and safety motivation, which in turn influence workers’ safety behavior choices [17]. Yang and Wei (2017) verified that there is a high positive correlation between the safety climate and safety behaviors [18]. Guan (2020) explored the propagation pattern of unsafe behaviors among construction worker groups by constructing a network model of the propagation path of unsafe behaviors. The results showed that the influence of key figures as well as intimate figures has a certain contribution to the propagation of unsafe behaviors [19].

A workgroup is the most basic organizational cell on a construction site in China and consists of the group leader and the group members. Young et al., (2011) proposed that imitation is a manifestation of learning, that is, the learning of exemplary behavior [20]. In a workgroup, exemplary people include the group leader, the technical backbone, the safety pacesetter, and the mentor. The behavior of group members can also be influenced by other workers on the same site project, such as closely interacting workers and fellow villagers. Psychological studies have shown that herding is a universal social psychological phenomenon, mainly manifested by individuals in a group being guided and pressured by the group to change their perceptions and behaviors and involuntarily align with the majority. Therefore, group members’ choices of unsafe behaviors are also influenced by the behavioral patterns of most workers.

3. Research Method

The research methodology of this study is shown in Figure 4.

Heinrich was the first to propose the accident causation sequence theory in his book Industrial Accident Prevention [21], which studied the relationships between the various factors that lead to injuries and fatalities. The theory holds that (1) injuries and deaths are the result of accidents, (2) accidents are caused directly by human unsafe behaviors or the unsafe states of physical things, (3) human unsafe behaviors and the unsafe states of physical things are caused by human shortcomings, and (4) human shortcomings are induced by adverse environments or by innate genetic factors. This theory is the theoretical basis of the investigation report on construction personal injury and death accidents in China. The direct causes given in the investigation reports correspond to the unsafe behaviors of people or the unsafe state of physical things, the indirect causes correspond to the deficiencies of management, and the environmental factors induce the defects of people. Therefore, we collated the texts of 80 accident investigation reports from which we extracted the relevant factors in four aspects: unsafe behaviors, unsafe states of physical things, undesirable environments, and management deficiencies. With the accident statistics and literature review in Section 2, we proposed a research model, designed an exploratory scale questionnaire, and validated the hypotheses using structural equation modeling (SEM).

SPSS is a commonly used statistical analysis software to characterize data and analyze the correlations between different factors in a statistical sense. AMOS is a structural equation modeling software package. Structural equation modeling (SEM) is a multivariate data analysis method in which researchers import research data after establishing a hypothetical theoretical model, test the influence relationship between variables through factor analysis and path analysis, and then revise the theoretical model. SEM has the advantage of theoretical apriority and is therefore widely used in the fields of psychology, behavior, and sociology. In this study, data analysis and model validation were carried out with the SPSS 26.0 and AMOS 26.0 software packages.

3.1. Research Model

We developed a research model (Figure 5) based on five constructs: objective conditions, safety management, group influence, personal perception, and unsafe behavior.

• The objective conditions refer to the objective factors related to the completion of construction tasks.
• Safety management refers to the safety management measures taken by the project department.
• Group influence refers to the influence of people with different identities in the workgroup on individual workers’ behaviors and attitudes.
• Personal perception refers to the workers’ perceptions of the safety knowledge and safety skills they possess.
• Unsafe behavior refers to the possible unsafe behavior of workers in the process of completing construction tasks.

Figure 5. Hypothetical model.

Figure 5. Hypothetical model.

Based on the content of the previous case study and literature review, we defined objective conditions as exogenous variables, whereas safety management, group influence, and personal perception were defined as mediating variables. Unsafe behavior was the dependent variable. It is necessary to note that the objective conditions in the model include the adverse environmental factors, operation methods, and task complexity that cannot be improved by managers. According to Heinrich, unexpected changes in weather conditions that are not predicted by the meteorological department and restricted workspace can induce human defects. As discussed in Section 2.2, operation methods and task complexity also induce human defects. Thus, objective conditions are defined as an exogenous variable that can have impacts on safety management, personal perceptions, group influences, and unsafe behaviors.

The following hypothetical model demonstrates the hypotheses of the influencing factors and their interrelationships on the unsafe behaviors of group workers in Chinese construction projects in this study.

Hypothesis 1 (H1). 

Objective conditions affect safety management.

Hypothesis 2 (H2). 

Objective conditions affect group influence.

Hypothesis 3 (H3). 

Objective conditions affect personal perception.

Hypothesis 4 (H4). 

Safety management affects group influence.

Hypothesis 5 (H5). 

Safety management affects personal perception.

Hypothesis 6 (H6). 

Group influence affects personal perception.

Hypothesis 7 (H7). 

Group influence affects unsafe behaviors.

Hypothesis 8 (H8). 

Personal perception affects unsafe behaviors.

Hypothesis 9 (H9). 

Safety management affects unsafe behaviors.

Hypothesis 10 (H10). 

Objective conditions affect unsafe behaviors.

3.2. Questionnaire Structure

A questionnaire was developed based on a statistical analysis of accident cases in the Chinese construction industry and relevant influencing factors from the literature review, as shown in

Table 1. List of measurements.

Constructs	Label	Question Contents	Valid/ Not Valid
Objective Conditions (OC)	OC1	Operation method [13]	Valid
	OC2	Task complexity [13]	Valid
	OC3	Weather [22]	Valid
	OC4	Operational environmental constraints [22]	Valid
Safety Management (SM)	SM1	Safety education and training *	Valid
	SM2	Safety responsibility regulations *	Valid
	SM3	Safety rewards and penalties regulations *	Valid
	SM4	Penalty strength [23]	Not Valid
	SM5	The practicality of the regulations [24]	Not Valid
	SM6	Safety technology disclosure *	Valid
	SM7	Safety inspection [23,25]	Valid
	SM8	Safety supervision [23,25]	Valid
	SM9	Safety warning mark [26]	Valid
Personal Perception (PP)	PP1	Safety knowledge of the occupational hazards [16]	Valid
	PP2	Safety knowledge of the technique and precautions [16]	Valid
	PP3	The use of personal protective equipment (PPE) [16]	Valid
	PP4	Experience of being punished for violation of regulations [27]	Not Valid
Group Influence (GI)	GI1	Leaders *	Valid
	GI2	Workgroup leader [28,29]	Valid
	GI3	Technical backbone [19]	Valid
	GI4	Safety pacesetter [19]	Valid
	GI5	Master worker [19]	Valid
	GI6	Co-workers [19]	Valid
	GI7	Co-workers with close contacts [19]	Valid
	GI8	Co-workers from the same hometown [19,30]	Valid
	GI9	Most of the co-workers [19]	Valid
Unsafe Behaviors (UB)	UB1	Insisting on working though knowing the physical and mental condition of oneself is poor *	Valid
	UB2	Adventurous work activities [31]	Valid
	UB3	Using unsafe equipment *	Valid
	UB4	Improper storage of materials *	Valid
	UB5	Disregard for the unsafe behavior of others [25,32]	Valid
	UB6	Tendency to follow the use of PPE [25]	Valid
	UB7	Tendency to follow adventurous activities [19]	Valid
	UB8	Save others hastily *	Valid
	UB9	Change the construction plan without authorization *	Valid
	UB10	Obey illegal commands *	Valid

* These items were extracted from Section 2: Accident Statistics and Literature Review.

.

The initial questionnaire had 36 items. Four experts with several years of experience, who were primarily responsible for construction safety management and had been involved in large infrastructure projects in Beijing, Shenzhen, Tianjin, and other cities in China, were invited to validate the question set. The work experience of each expert is presented in

Table 2. Briefings on the work experience of each expert.

Expert	Brief Description of Work Experience
Expert A (Mr. Zhang)	Mr. Zhang joined a subsidiary of China State Construction after graduating with a bachelor’s degree in Safety Engineering in 2015 and was engaged in on-site safety management of construction projects until 2021.
Expert B (Ms. Yan)	Ms. Yan joined a subsidiary of China State Construction after graduating with a bachelor’s Degree in Safety Engineering (Construction Safety Direction) in 2015 and was engaged in on-site safety management of construction projects until 2023.
Expert C (Ms. Wang)	Ms. Wang joined a subsidiary of China Nuclear Engineering & Construction after graduating with a bachelor’s degree in Safety Engineering (Construction Safety Direction) in 2015 and was engaged in on-site safety management of construction projects until 2021.
Expert D (Ms. Fang)	Ms. Fang joined a subsidiary of China State Construction after graduating with a bachelor’s degree in Safety Engineering (Construction Safety Direction) in 2015 and was engaged in on-site safety management of construction projects until 2019.

. Each item of the initial questionnaire was set with three options: (1) valid, (2) not sure, and (3) not valid. The main purpose of this step was to ensure the feasibility and applicability of the exploratory questionnaire. Three items in the initial questionnaire were deleted after expert validation. The questionnaire consisted of two parts: (1) basic information about the respondents (9 questions in total) and (2) questions about personal perceptions, unsafe behavior, objective conditions, safety management, and group influence (33 questions in total). It should be noted that the questionnaire was designed with positive and negative questions as well as trap questions (e.g., position level), a step that was taken to facilitate the subsequent screening of the raw data to maximize the exclusion of invalid data.

Some explanations of certain questions in Table 1 are necessary based on the discussion in Section 2. The “objective conditions” construct includes adverse environmental factors, operation methods and task complexity that cannot be improved by managers, as stated in Section 2.2 and Section 3.1. In the “Safety Management”, “safety responsibility regulations” is one of the effective management methods proven by China’s long-term practice of safety production, which synthesizes the requirements of various laws and regulations and clearly stipulates the safety responsibilities to be undertaken by leaders at all levels of the operation units, functional departments, engineers, technicians, and workers during the production. “Safety technology disclosure” refers to the technical personnel of the construction company in charge of project management who should give detailed explanations of the safety technology requirements to the construction workgroups and workers before the construction of the construction project; the two sides should sign a document to confirm the explanations were completed. “Safety inspection” refers to the daily safety inspection work of the construction site that is carried out by the construction unit. “Safety supervision” refers to the supervision and guidance of government departments on the safety management of the constructor, the constructor on the contractor, or the contractor on the subcontractor. Related questions under the “Personal Perception” are mainly related to workers’ self-judgment of whether they have fully mastered hazard identification, safety techniques and preventive measures, or the ability to use PPE. In the “Group Influence” construct, “co-workers” refers to workers in the same workgroup, “co-workers with close contacts” refers to workers in the same workgroup who have a good relationship (e.g., they often eat and chat together, etc.), and “co-workers from the same hometown” refers to workers in the same workgroup who come from the same hometown. “The most of the co-workers” construct corresponds to the question of potential herd behavior, i.e., whether the respondents’ judgments and choices are influenced by the common work habits of most of the co-workers in the same workgroup. In the “Unsafe Behaviors” factor, “Insisting on working though knowing the physical and mental condition of oneself is poor” refers to the behavior of insisting on going to work even when one is aware of one’s own physical discomfort. “Adventurous work activities” refers to the behavior of intentionally continuing operations even though one is aware of a potentially hazardous situation. “Disregard for unsafe behavior of others” refers to the behavior of choosing to ignore the unsafe behavior of others. “Tendency to follow” refers to following the adventurous work activities of others or following the behavior of others who do not use PPE. “Save others hastily” refers to a situation where the circumstances at the site are ignored in the rush to help the victims, resulting in a wider range of casualties.

3.3. Data Collection

This study focused on the grassroots teams of construction projects in China. Affected by the COVID-19 epidemic, the questionnaire was administered online to members of the workgroups at three of China State Construction’s current construction projects in Beijing and Shenzhen. The safety officer of the project department explained to the respondents the precautions for completing the questionnaire and the explanation of the scale questions before the questionnaire was distributed to improve the accuracy and validity of the study.

The questionnaire survey started on 19 May 2022 and ended on 25 May 2022, with a final total of 504 questionnaires returned. SEM is a large-sample data analysis technique that generally requires a model with a ratio of observed variables to sample size between 1:10 and 1:15 [33]; therefore, the number of valid questionnaires appropriate for this study should be 350–525. It was verified that 460 respondents had no missing values to retain in the final analysis, which is consistent with the sample size required for SEM.

The demographic characteristics of the 460 respondents are presented in

Table 3. Demographic data for respondents.

Category	Item	Frequency	Percentage
Gender	Male	407	88.5
	Female	53	11.5
Age	≤25	34	7.4
	26–32	73	15.9
	33–39	123	26.7
	40–46	99	21.5
	≥47	131	28.5
Educational background	Primary school or below	68	14.8
	Junior high school	276	60.0
	Higher school	101	22.0
	College degree	8	1.7
	Bachelor’s or above	7	1.5
Experience	≤1	19	4.1
	1–3	78	17.0
	3–5	75	16.3
	5–10	107	23.3
	>10	181	39.3
Positional level	Frontline worker	391	85.0
	Junior manager	69	15.0
Sum		460	100

. A total of 88.5% of the respondents were male, whereas 92.6% of the respondents were over 25 years old and 28.5% were over 47 years old. A total of 60.0% of the respondents had a junior high school education, whereas 62.6% of the respondents had more than 5 years of work experience. A total of 85.0% of the respondents were team members undertaking construction tasks on the frontline.

4. Results

4.1. Exploratory Factor Analysis (EFA)

Generally, researchers develop questionnaires by first identifying the main constructs based on theory and then designing appropriate questions for each construct. For exploratory questionnaires, EFA is used to validate the soundness of the questionnaire structure after data collection is completed [34].

In this study, EFA was conducted using SPSS 26.0 software. Before EFA, it was necessary to determine whether the obtained data were suitable for conducting factor analysis. The judgment was based on the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity. A KMO value of at least 0.6 was required to perform factor analysis. In this study, the KMO value was 0.904, which is much higher than the criterion of 0.6, and the p-value of Bartlett’s test was close to 0 (p < 0.05), indicating that the data were suitable for factor analysis.

Principal component analysis and varimax rotation were performed in EFA. Factors with eigenvalues greater than 1 and factor loadings (FLs) greater than 0.5 in absolute value were retained. The results showed that the cumulative contribution of the total variance explained by the five constructs (67.229%) reached the requirement of being greater than 60%, and the items with FLs less than 0.5 were excluded according to the grouping of the rotated matrix. Thus, after this step, GI1, UB8, and UB10 were removed (see

Table 4. EFA and reliability testing results.

Items	Factor Loadings					Cronbach’s α
	Objective Conditions	Safety Management	Personal Perception	Group Influence	Unsafe Behaviors
OC2	0.820					0.879
OC3	0.801
OC4	0.784
OC1	0.710
SM7		0.847				0.906
SM8		0.819
SM1		0.804
SM9		0.797
SM2		0.723
SM6		0.710
SM3		0.657
PP3			0.621			0.704
PP1			0.600
PP2			0.542
GI6				0.862		0.926
GI5				0.834
GI7				0.792
GI2				0.775
GI9				0.772
GI3				0.756
GI8				0.716
GI4				0.511
UB6					0.857	0.895
UB4					0.850
UB1					0.783
UB9					0.766
UB5					0.758
UB3					0.635
UB7					0.604
UB2					0.597

). In addition, a Cronbach’s α value greater than 0.6 indicated that the data were adequate and reliable. It was found that the values of Cronbach’s α for all five constructs were greater than 0.6 (as shown in Table 4), indicating the good internal consistency of the scale.

4.2. Confirmatory Factor Analysis (CFA)

In the social and behavioral sciences, researchers often obtain more information from CFA than SEM, because CFA can provide details about the model setting and the results are more reliable with a structural model adjusted by CFA [34,35]. A good measurement model is a prerequisite for analyzing causal relationships between potential variables, whereas a bad measurement model can lead to erroneous conclusions about the strength and direction of the effects between constructs. Therefore, CFA was conducted using Amos 26.0 software for the five constructs of objective conditions, safety management, individual perceptions, group influence, and unsafe behavior before conducting structural model testing.

Taking the group influence construct as an example, the measurement model of group influence and its factors was constructed in Amos 26.0 software. A factor should be deleted when: (1) the standardized factor loadings (Std. FLs) were less than 0.5 and (2) the squared multiple correlations (SMCs) were less than 0.36 (GI4, UB3). According to the modification indices, items with high residual correlation, which indicates non-independence between the different variables under this construct, were removed (OC1, SM2, SM8, GI3, GI5, GI8, UB7). It is worth noting that the personal perception construct had only three items that were “just determined” in the statistical sense, in which case the items were all retained once the factor loadings were satisfied. By analogy, all the constructs and their items were identified (see

Table 5. Reliability analysis (CR and AVE).

Constructs	Items	Significance Estimation				Std. FL	SMC	CR	AVE
		Unstd.FL	S.E.	t-Value	p
Objective Conditions	OC2	1.000				0.716	0.513	0.892	0.735
	OC3	1.103	0.060	18.531	***	0.933	0.870
	OC4	1.121	0.061	18.509	***	0.907	0.823
Safety Management	SM7	0.926	0.049	19.057	***	0.834	0.696	0.876	0.589
	SM9	0.948	0.050	18.847	***	0.826	0.682
	SM6	1.000				0.796	0.634
	SM1	0.849	0.050	17.062	***	0.759	0.576
	SM3	0.807	0.062	12.927	***	0.599	0.359
Group Influence	GI2	1.000				0.718	0.516	0.902	0.698
	GI6	1.286	0.068	18.855	***	0.923	0.852
	GI7	1.298	0.072	18.042	***	0.871	0.759
	GI9	1.194	0.071	16.907	***	0.815	0.664
Personal Perception	PP1	1.000				0.495	0.245	0.737	0.500
	PP2	1.669	0.223	7.484	***	0.926	0.857
	PP3	1.042	0.113	9.246	***	0.630	0.397
Unsafe Behavior	UB9	1.000				0.742	0.551	0.896	0.596
	UB6	1.302	0.065	20.034	***	0.913	0.834
	UB5	1.168	0.071	16.378	***	0.757	0.573
	UB4	1.228	0.063	19.511	***	0.889	0.790
	UB1	1.056	0.065	16.169	***	0.748	0.560
	UB2	0.643	0.059	10.898	***	0.517	0.267

*** Significance level is p < 0.001.

).

A reliability analysis was performed afterward. The composite reliability (CR) of each construct was greater than 0.7, indicating that the indicators under each construct had good internal consistency. The average variance extracted (AVE) was greater than 0.5, indicating that each construct had high convergent validity (See Table 5).

Thereafter, a discriminant validity analysis was conducted to compare the square root of the AVE with the Pearson correlation coefficient of the latent variable to verify whether the correlation between the indicators within the constructs was higher than the correlation between the constructs. The bolded numbers on the diagonal of

Table 6. Discriminant validity analysis.

Constructs	Unsafe Behaviors	Personal Perception	Group Influence	Safety Management	Objective Conditions
Unsafe Behaviors	0.772
Personal Perception	−0.203	0.707
Group Influence	0.478	0.127	0.835
Safety Management	−0.226	0.775	0.194	0.767
Objective Conditions	0.013	0.550	0.305	0.579	0.857

represent the square root of the AVE, and the values outside the diagonal are the Pearson correlations of constructs. It should be noted that the square root of AVE for personal perception is 0.707, which is slightly smaller than the Pearson correlation value between safety management and personal perception of 0.775. Since the difference between these two values is not significant, it is considered to meet the statistical criteria in this study. The results show that the Pearson correlation value for the constructs was almost smaller than the square root of the AVE for each construct, which indicates that the model has good discriminant validity.

4.3. Structural Equation Modeling (SEM)

SEM is used to verify the relationship between potential variables [25]. Structural models that meet acceptable criteria accurately reflect the relationships between variables. Commonly used fit metrics in SEM include chi-square/degrees of freedom (χ²/df), p-value, the goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), comparative fit index (CFI), normed fit index (NFI), root mean square error of approximation (RMSEA), and standardized root means residual (SRMR) [35]. The fit indices of the final SEM (see Figure 6a) validated by EFA and CFA were within the acceptable range [36], as shown in

Table 7. Model fit indices.

Indices	Fitted Model		Acceptable Values	Supporting Literature
	(a)	(b)
χ2	494.427	497.765	-	-
χ2/df	2.762	2.720	<3	Iacobucci (2010) [37]
p value	0.000	0.000	<0.05	Tanaka (1993); Maruyama (1997) [38,39]
GFI	0.907	0.907	≥0.90	Doll et al., (1994) [40]
AGFI	0.881	0.883	≥0.80	Doll et al., (1994); Arpaci and Baloglu (2016) [36,40]
CFI	0.947	0.947	≥0.90	Iacobucci (2010) [37]
NFI	0.920	0.919	≥0.90	Hair et al., (2006) [33]
RMSEA	0.062	0.061	<0.08	Hair et al., (2006) [33]
SRMR	0.0599	0.0608	<0.10	Iacobucci (2010); Kline (2023) [37,41]

.

The results of the hypothesis validation are elaborated in

Table 8. The final output relationship of multilevel safety intervention with safety behavior.

Hypothesis		Estimate	S.E.	C.R.	p	Result
H1	OC → SM	0.405	0.039	10.395	***	Supported
H2	OC → GI	0.287	0.065	4.451	***	Supported
H3	OC → PP	0.098	0.037	2.674	**	Supported
H4	SM → GI	0.035	0.090	0.393	0.694	Rejected
H5	SM → PP	0.572	0.068	8.367	***	Supported
H6	GI → PP	−0.034	0.027	−1.265	0.206	Rejected
H7	GI → UB	0.773	0.082	9.405	***	Supported
H8	PP → UB	−0.140	0.245	−0.572	0.567	Rejected
H9	SM → UB	−0.677	0.194	−3.498	***	Supported
H10	OC → UB	0.104	0.085	1.223	0.221	Rejected

→ refers to the effect of the construct before the symbol on the construct after the symbol. *** Significance level is p < 0.001. ** Significance level is p < 0.01.

. Four of the ten initial hypotheses (H4, H6, H8, and H10) were rejected, and the remaining six hypotheses proved to be statistically significant. In the path analysis, the influence between variables contains both direct and indirect effects [34], and the total influence values of exogenous variables on endogenous variables are presented in

Table 9. Standardized direct, indirect, and total effects.

Construct A → Construct B		Direct Effects	Indirect Effects	Total Effects
Objective Conditions	Safety Management	0.578	\	0.578
	Group Influence	0.307	\	0.307
	Personal Perception	0.154	0.396	0.550
	Unsafe Behaviors	\	−0.024	−0.024
Group Influence	Unsafe Behaviors	0.540	\	0.540
Safety Management	Personal Perception	0.685	\	0.685
	Unsafe Behaviors	−0.329	\	−0.329

→ refers to the effect of the Construct A on the Construct B.

. The final structural model of safety behavior under the influence of multidimensional factors is shown in Figure 6b.

5. Discussion

• Objective conditions directly and positively affect safety management.

The standardized path coefficient of “Objective Conditions → Safety Management” is 0.578, p < 0.001, and H1 is supported: the objective conditions have a significant positive influence on safety management. The more complex the construction task is, the more obvious the role of safety management measures in restraining the unsafe behavior of frontline workers. Similarly, the harsher the weather or working environment conditions are, the more obvious the role of safety management measures in restraining unsafe behaviors.

• Objective conditions directly and positively affect group influence.

The standardized path coefficient of “Objective Conditions → Group Influence” is 0.307, p < 0.001, and H2 is supported: the objective conditions have a significant positive influence on group influence. The worse the objective conditions (task complexity, weather, or operating environmental conditions) are, the more obvious the interference of the operational behavior of key people on the behavioral decisions of individual workers. In which case, the herd effect of unsafe behaviors is more likely to occur within the workgroup.

• Objective conditions directly and positively affect personal perception.

The standardized path coefficient of “Objective Conditions → Personal Perception” is 0.154, p < 0.05, and H3 is supported: the objective conditions have a significant positive influence on personal perception. Complex construction tasks, sudden weather changes without warning, or limited workspace can motivate workers to proactively enhance their perceptions. That is, workers perceive themselves as being better equipped with sufficient knowledge and competence to do their job safely in such situations. Knowledge and competence in this context refer to the ability to identify potential risks, the ability to correctly implement safety precautions, and the ability to correctly use PPE. Therefore, complex construction tasks, sudden weather changes without warning, or limited workspace may prompt workers to raise their safety awareness and take the necessary precautions to ensure personal safety.

• Safety management directly and positively affects personal perception.

The standardized path coefficient of “Safety Management → Personal Perception” is 0.685, p < 0.001, and H5 is supported: safety management has a significant positive influence on personal perception.

This indicates that the implementation of safety management measures such as safety education and training, safety reward and punishment regulations, safety technical disclosure, safety inspection, and safety warning signs can help workers to improve their personal perception level. Taking safety education and training as an example, in analyzing the 80 construction accident investigation reports we found that 32 accidents (40%) were attributed to the unsafe behavior of the subcontractor’s construction staff. This suggests that safety education and training of subcontractor construction workers is prone to significant deficiencies, which is also supported by the previous literature [14], i.e., subcontractor construction workers are unfamiliar with the contractor’s construction safety regulations and are more likely to be unfamiliar with, or unaware of, the occupational hazards, technique procedures, and precautions on the construction site, which can lead to unsafe behaviors. Therefore, the contractor should focus on supervising and implementing the safety education and training of the construction workers from subcontractors or labor dispatch units and should pay particular attention to reminding the occupational hazards and unifying the construction safety technical requirements and operating procedures to reduce unsafe behaviors.

In addition, both H1 and H5 are supported, which suggests that the objective conditions positively influence personal perception indirectly through safety management (0.578 × 0.685 ≈ 0.396).

• Group influence directly and positively affects unsafe behavior.

The standardized path coefficient of “Group Influence → Unsafe Behavior” is 0.504, p < 0.001, and H7 is supported: group influence has a significant positive influence on unsafe behavior. As the main leader of the workgroup, the group leader has a certain role in demonstrating and guiding the behavioral choices of the workers. Some studies have proved that the safety leadership role of the team leader is conducive to improving workers’ compliance with safety procedures and participation in safety behaviors. However, overemphasizing the safety leadership role of the team leader can also lead to attachment anxiety among workers [6]. Therefore, construction units can appropriately strengthen the safety training and education of team leaders to remind them to play a reasonable role in safety leadership and enhance the safety behavior of team members. In addition, as the most basic unit on the construction site, the workgroup has the most frequent communication among its members and the unsafe behaviors of individual workers are easily spread within the group. Therefore, the construction unit needs to strengthen the group consciousness of the workgroup, optimize the employee safety behavior supervision regulations, and form a positive atmosphere in the workgroup to remind each other and avoid unsafe behaviors together.

• Safety management directly and negatively affects unsafe behavior.

The standardized path coefficient of “Safety Management → Unsafe Behavior” is −0.329, p < 0.001, and H9 is supported: safety management has a significant negative influence on unsafe behavior. The implementation of safety management measures helps to reduce the occurrence of unsafe behaviors among workers. Safety education and training provide frontline workers with the necessary safety knowledge and continuously improve their safety capabilities. According to Table 3, it can be found that construction workers are generally poorly educated and their operational knowledge often comes from previous work experience. However, the risks could vary to a greater or lesser extent from one construction project to another and the safety requirements of contractors are generally different, especially for staff from subcontractors and labor dispatch units. These highlight the importance of safety education and training in reducing unsafe behaviors. On the one hand, construction companies should provide frontline workers with safety training materials that are applicable to the actual working conditions of the construction project [13] and are easy to understand, so that frontline workers are aware of the specific safety regulations [4] and can master the working procedures. On the other hand, accident case-based education and training has been proven to be effective in raising workers’ safety awareness [14], so construction companies should carry out accident prevention education and training on a regular basis. Safety reward and punishment regulations indicate clear safety behavior expectations and requirements for frontline workers that can correct safety motivation and balance safety psychology. Safety technical disclosure is the most common safety management measure on construction sites. The training of workers on safety operation procedures and precautions by management personnel before the commencement of construction operations can effectively reduce the likelihood of accidents caused by non-compliant operations. Daily safety inspections and periodic safety inspections are both necessary safety management measures that can timely detect not only unsafe behaviors of frontline workers but also abnormal states of equipment, unsafe factors in the environment, and deficiencies in construction site management [25]. Safety warning signs can strengthen workers’ safety awareness, remind them of the potential risks in the construction process, and standardize their work behavior.

• The direct effect of the objective conditions on unsafe behavior is not significant.

The p-value of the path “Objective Conditions → Unsafe Behavior” is 0.221 > 0.05, which means that H10 is rejected and the direct effect of the objective conditions on unsafe behavior is not significant. It is important to note that significant and non-significant are relative [42], so the fact that H10 is not supported does not mean that the objective conditions have no influence on the unsafe behavior construct entirely. However, the non-significance of the direct effect is still not consistent with the conclusions of our accident case analysis and literature review sections. Normally, poor objective conditions are supposed to be more likely to trigger workers’ unsafe behaviors. This hypothesis is probably rejected in our model because there are mediating variables for the influence of objective conditions on workers’ unsafe behaviors that we have not addressed in several other constructs, such as workers’ sensitivity to the objective conditions [43] in the personal perception construct. It is necessary for us to supplement the items of the exploratory scale questionnaire in subsequent studies, such as adding the workers’ perception of task complexity and the measure of the individual’s sensitivity to environmental changes in the personal perception construct, to further confirm the relationship between the direct and indirect influence of objective conditions on unsafe behaviors.

• The direct effect of safety management on group influence is not significant.

The p-value of the path “Safety Management → Group Influence” is 0.694 > 0.05, which means that H4 is rejected and the direct effect of safety management on group influence is not significant. However, safety management measures are supposed to help inhibit the spread of unsafe behaviors among workgroup members. There are two possible reasons why the direct effect is not statistically significant: (1) The design of the safety management questions in the questionnaire used in this study mostly involves on-site safety management measures, such as technical safety disclosure, safety inspections, safety warning signs, etc. These safety management measures have a direct effect on individual frontline workers but may have no direct effect on the spread of unsafe behaviors among group members. (2) Safety education and training, including safety rewards and punishments [42], are two of the means to develop a safety culture, which contributes to the formation of a good safety atmosphere and indirectly reduces the spread of unsafe behaviors in the workgroup. Therefore, the safety climate may be a mediating variable between safety management and group influence. Further validation by adding mediating variables to the model will be considered afterward.

• The direct effect of group influence on personal perception is not significant.

The p-value of the path “Group Influence → Personal Perception” is 0.206 > 0.05, which means that H6 is rejected and the direct effect of group influence on personal perception is not significant. According to the survey data of this study, 65.9% of the respondents would follow the work attitude, method, or behavior of the workgroup leader, 64.3% would follow the behavior of the workers they work with, 60.2% would follow the behavior of the workers in the workgroup who are close to them, and 58.5% would follow the behavior of most of the workers performing the construction work. This shows that the work attitude and behavior of the people in key roles in the workgroup have a certain “infectious” effect that does not directly affect the workers’ perception of their safety ability but directly affects the workers’ behavioral choices. Taking peer pressure as an example, Roe, M. [44] highlights that peer pressure may have a positive or negative impact on workers’ attitudes and behaviors in different situations. For example, when the workgroup focuses on accident prevention, peer pressure can have a positive effect, i.e., workers within the crew will be more willing to accept new responsibilities or point out the unsafe behaviors of their co-workers. In contrast, when the workgroup favors risk tolerance, peer pressure has a negative effect. However, this effect is directly related to safety attitudes and safety behaviors rather than personal perceptions. In subsequent studies, we may add dimensions such as safety attitudes to further refine our comprehensive model.

• The direct influence of personal perception on unsafe behavior is not significant.

The p-value of the path “Personal Perception → Unsafe Behavior” is 0.567 > 0.05, which means that H8 is rejected and the direct effect of personal perception on unsafe behavior is not significant. However, the fact that H8 is not supported does not indicate that personal perception has absolutely no effect on unsafe behaviors. Ajzen, I. [45] theorized that perceived behavioral control refers to people’s perception of the ease or difficulty of performing the behavior. However, our exploratory scale questionnaire has three items in the personal perception construct (perceiving that he/she is fully aware of hazards that may be present during operations, perceiving that he/she is fully aware of safety techniques and precautions, and perceiving that he/she is always able to use the PPE correctly) that measure workers’ perceptions of their own level of safety knowledge and capabilities but do not directly measure workers’ perceptions of the ease or difficulty of using them to accomplish a given work task. Therefore, the direct effect of personal perceptions on unsafe behavior is not supported in our model.

In addition, perceived behavioral control can change across situations and actions, which implies that the relationship between personal perception and human behavior is complex and variable [45]. For example, even if workers believe that they have appropriate safety knowledge and that they can use PPE correctly, they may still engage in unsafe behaviors during construction operations due to a sense of chance or a lack of safety motivation to adhere to rules and regulations. Therefore, the design of the questions in this study in terms of personal perception constructs may have some limitations and does not yet cover the measurement of workers’ personal perceptions of the numerous elements involved in the construction operation process. In subsequent studies, we should supplement and enrich the factors in the personal perception construct.

6. Conclusions

This study is based on the unsafe behaviors and influencing factors of workgroup members on construction projects in China. A total of 80 investigation reports on Chinese construction accidents in the past five years were collected and analyzed and common factors such as the objective conditions, safety management, and unsafe behaviors were sorted out. A comprehensive theoretical model was proposed and some hypotheses were verified using SEM to explore the relationships among objective conditions, safety management, group influence, personal perception, and unsafe behaviors. The results show that the direct influence of safety management and group influence on workers’ unsafe behavior is statistically significant, the direct influence of objective conditions and safety management on workers’ personal perception is statistically significant, and the objective conditions influence workers’ unsafe behavior through safety management and group influence. Decision makers and managers of Chinese construction companies can accordingly develop appropriate measures to guide the safety behavior of workgroup workers. For example, complex construction tasks and poor operating conditions will increase the likelihood of spreading unsafe behaviors within a workgroup, in which case decision makers and managers need to pay more attention to monitoring the implementation of safety management measures.

The advantages of this study include the following two points. First, construction accident survey reports in China were collected and analyzed. The objective conditions, safety management, and unsafe behavior factors that are more in line with reality are refined, the questionnaire is easily understood by the respondents, and the data obtained are more reliable. Second, a theoretical model of the influence mechanism of the unsafe behaviors of workgroup personnel is proposed based on the system perspective, which integrates individual, organizational, environmental, and other factors. The influence relationship between these factors was verified through structural modeling. The results of the study can help decision makers or managers understand the influence mechanism of unsafe behavior and take more appropriate measures to guide workers’ safe behavior and provide conditions for construction workers to become a sustainable workforce.

This study also has some limitations. First, the object of this study is frontline workgroup members in a broad sense. However, there may be differences in the influence mechanisms of unsafe behaviors among workers in different geographic regions and job types. Future research needs to further refine the research object, expand the sample size, and even conduct some comparative studies. Second, the question items under the personal perception construct in the comprehensive model need to be supplemented and refined, such as adding a measure of individual safety attitudes. Third, the model validation results of this study indicate that the direct effect of safety management on group influence is not significant. Subsequent studies need to determine the existence of mediating variables and explore the influence patterns.