Aligning with SDGs in Construction: The Foreman as a Key Lever for Reducing Worker Risk-Taking

Abstract

Improving occupational health and safety (OHS) in the construction industry can contribute to the advancement of the Sustainable Development Goals (SDGs), particularly Goals 3 (Good Health and Well-being) and 8 (Decent Work and Economic Growth). Yet, workers’ risk-taking behaviors (RTBs) remain a persistent challenge. Drawing on Social Cognitive Theory and Social Information Processing Theory, this study develops and tests a social influence model to examine how foremen’s safety attitudes (SAs) shape workers’ RTBs. Drawing on survey data from 301 construction workers in China, structural equation modeling reveals that foremen’s SAs significantly and negatively predict workers’ RTBs. However, the three dimensions of SAs—cognitive, affective, and behavioral—exert their influence through different pathways. Risk perception (RP) plays a key mediating role, particularly for the cognitive and behavioral dimensions. Furthermore, interpersonal trust (IPT) functions as a significant moderator in some of these relationships. By identifying the micro-social pathways that link foremen’s attitudes to workers’ safety behaviors, this study offers a testable theoretical framework for implementing the Sustainable Development Goals (particularly Goals 3 and 8) at the frontline workplace level. The findings provide empirical support for organizations to move beyond rule-based management and instead build more resilient OHS governance systems by systematically cultivating the multidimensional attitudes of frontline leaders.

1. Introduction

OHS constitutes a fundamental pillar of sustainable development, playing a critical role in advancing the SDGs, particularly those related to Good Health and Well-being (SDG 3) and Decent Work and Economic Growth (SDG 8) [1,2]. However, within the construction industry—a labor-intensive and high-risk sector—OHS management has long faced formidable challenges [3,4]. Data from the U.S. Bureau of Labor Statistics indicate that the construction industry accounts for a disproportionately high rate of fatal occupational injuries, representing 20% of all workplace fatalities [5]. Similarly, data from the Ministry of Housing and Urban-Rural Development of China reveal the persistent frequency of occupational accidents in municipal and housing construction projects [6]. These frequent incidents not only undermine the fundamental rights, health, and dignity of workers but also inflict substantial economic losses. Therefore, enhancing OHS in the construction industry is not merely a practical imperative to safeguard workers’ lives but also a critical pathway toward fostering the sector’s sustainable development.

The prevalence of unsafe behaviors among on-site workers remains a major contributor to occupational injuries [7,8,9], with RTBs posing a particularly critical challenge to OHS management in the construction industry [10,11]. RTBs are defined as intentional deviations from established operational procedures or safety regulations, characterized by their strategic and goal-oriented nature [12,13]. In the loosely structured, highly mobile, and temporary context of construction sites [14,15], such behaviors are difficult to mitigate through formal rules and routine safety training alone. Within this context, foremen can occupy a pivotal position. They maintain close interactions with workers by assigning tasks and offering on-site support, making them not only vital organizational intermediaries but also informal “opinion leaders” within crews [16,17]. This dual role grants them considerable influence over workers’ safety-related behaviors [18,19,20]. As such, leveraging the foremen’s leadership as a proactive force in OHS management presents a promising pathway for addressing persistent safety challenges and advancing the construction sector’s contributions to SDGs 3 and 8.

Prior research has acknowledged the multidimensional nature of foremen’s influence on workers’ safety-related behaviors, extending beyond behavioral modeling and task-related support to encompass knowledge transmission and cognitive guidance [19,20,21,22]. For example, improving foremen’s competence in safety communication has been shown to significantly enhance workers’ safety performance [21], and foremen’s safety-oriented leadership can shape workers’ operational behaviors by shaping their safety-related cognition [20]. These studies not only provide empirical validation of the foreman’s pivotal role in construction OHS management but also draw on theoretical perspectives such as opinion leadership and attachment theory to elucidate the mechanisms underlying foremen’s impact on workers’ safety behaviors. Despite these contributions, the majority of existing studies examine individual dimensions of foremen’s influence in isolation, thereby neglecting the potential coupling and interaction effects among multiple foreman roles. Moreover, current research often treats workers as passive recipients of influence, failing to acknowledge their agency and strategic intent in risk-taking decisions. As such, prior studies remain insufficient to comprehensively explain how foremen shape workers’ RTBs, marking a significant theoretical and practical gap in the literature.

To systematically examine how foremen influence workers’ RTBs, this study introduces foremen’s SAs as a core construct and investigates its effects across three dimensions: cognition, behavioral, and affective. In contrast to the Theory of Planned Behavior, which primarily emphasizes the role of individual behavioral intentions, the influence of foremen on workers’ RTBs reflects the socially constructed and context-dependent nature of workplace actions [23]. As salient others on the construction site, foremen’s safety attitudes not only serve as behavioral referents for workers but also function as key social cues through which workers interpret safety-related situations and infer organizational expectations. These social signals, in turn, shape workers’ RP and behavioral decisions. Accordingly, this study integrates Social Learning Theory and Social Information Processing Theory to construct a more explanatory framework. Specifically, RP is introduced as a mediating variable, and IPT is incorporated as a moderating variable. This integrated model aims to systematically examine the following research questions:

(1)

Does the SAs of foremen influence workers’ RTBs?

(2)

What role does rp play in mediating the relationship between foremen’s SAs and workers’ RTBs?

(3)

Does this influence differ across varying levels of IPT?

By uncovering the mechanisms through which foremen’s safety attitudes influence construction workers’ RTBs, this study aims to deepen the understanding of micro-level determinants in OHS within the construction industry. It further seeks to provide a theoretical foundation and empirical evidence to support the advancement of SDG3 and SDG8 in the construction sector. The paper is structured as follows: Section 2 establishes the theoretical framework, followed by methodological details in Section 3. Section 4 presents empirical findings, with discussion in Section 5 and conclusions in Section 6.

2. Theory and Hypotheses

2.1. Foremen’s SAs and Workers’ RTBs

SAs refer to individuals’ beliefs and emotional evaluations concerning safety policies, procedures, and operational practices [24]. They are typically conceptualized as comprising three interrelated dimensions: cognitive (CSA), affective (ASA), and behavioral (BSA) components [25]. Within the construction context, foremen’s safety attitudes not only serve as important predictors of their behaviors related to safety management and production but also reflect their subjective orientation regarding the trade-off between safety and efficiency.

RTBs among construction workers are frequently motivated by the desire to save time or reduce physical workload [26]. Although such behaviors stem from individual decision-making processes [10], a substantial body of evidence suggests that these decisions are strongly shaped by social influences. For instance, supervisors’ violations of safety regulations can trigger workers’ unsafe behaviors [27]. Nurses’ SAs have been shown to positively influence patients’ safety-related practices [28]. Similarly, individuals’ propensity to engage in drunk driving is significantly associated with the driving behaviors of their peers [29].

In construction projects, foremen occupy a unique position that bridges the gap between managerial staff and frontline workers. They are not only experienced workers who collaborate closely with their crew members, but also individuals entrusted with the authority to assign tasks and supervise on-site operations [17]. This dual identity, combined with their frequent daily interactions with workers, enables foremen to exert a more direct and effective influence on workers’ on-site behaviors compared to either peers or higher-level managers. As such, foremen often function as “opinion leaders” within their crews [16].

To comprehensively elucidate how foremen, as pivotal frontline leaders, exert influence within construction teams, this study integrates Social Learning Theory and Social Information Processing Theory to reveal the underlying mechanisms from two complementary perspectives.

From the perspective of Social Learning Theory, behavioral modeling is fundamental to the acquisition and reinforcement of workplace practices [30]. On construction sites, foremen—by their status as “technical experts” [31]—serve as the most immediate models for workers. Through their expressions of safety-related cognitions, emotional attitudes, and daily practices, foremen generate a tacit and evolving behavioral framework [32]. This framework functions as a context-sensitive, operational heuristic that guides workers in negotiating the trade-offs between safety compliance and productivity demands. Particularly under ambiguous conditions—such as conflicting task goals or resource constraints—this modeling pathway constitutes a critical channel through which foremen shape risk-related behaviors among workers.

In contrast to the direct imitation emphasized in behavioral modeling, Social Information Processing Theory outlines a more indirect and cognitively mediated pathway of influence [23]. According to this framework, individuals embedded in uncertain environments actively interpret salient social cues to form subjective understandings of situational demands. Construction sites—characterized by fluid workflows, ambiguous directives, and evolving contingencies—serve as quintessential settings for such interpretive processes [33]. Within this context, foremen’s safety attitudes act as salient informational cues that anchor workers’ perceptions of organizational safety priorities [34], inform their appraisal of risk, and shape their behavioral inclinations toward risk-taking. This constitutes the cognitive framing pathway through which foremen exercise influence.

In summary, foremen’s SAs encompass their cognitive understanding and evaluative judgment of safety protocols and play a pivotal role in shaping workers’ RTBs. This influence operates through multiple pathways, including professional guidance, behavioral modeling, and the transmission of safety values. As such, foremen’s attitudes provide a crucial perspective for understanding how frontline RTBs develop. Based on this analysis, the study proposes the following hypotheses:

H1a: 

Foremen’s CSA negatively correlates with construction workers’ RTBs.

H1b: 

Foremen’s ASA negatively correlates with construction workers’ RTBs.

H1c: 

Foremen’s BSA negatively correlates with construction workers’ RTBs.

2.2. Construction Workers’ RP

2.2.1. Foremen’s SAs and Workers’ RP

Risk perception is not an isolated individual cognitive process but a socially embedded constructive phenomenon [35,36]. Workers often interpret risk signals in the on-site environment by observing the behaviors of others—particularly supervisors and peers—and thereby develop their own frameworks for risk assessment [37]. Empirical studies have confirmed that peers exert a significant influence on construction workers’ risk perception [38]. Among them, foremen, as uniquely positioned managers with the most direct contact with workers on site, not only reflect their personal safety attitudes but are also regarded by workers as critical referents for discerning what constitutes “genuine risk” [39].

Construction sites are characterized by high dynamism and heterogeneity, with varying tasks, process stages, and environmental conditions that continuously change [33], making it difficult for workers to accurately identify the primary risks they face based solely on written regulations or verbal instructions. In such contexts of insufficient information or ambiguity, individuals tend to rely on experienced or authoritative others as reference points for judgment [40,41]. Therefore, when these referents demonstrate a positive attitude toward safety regulations, workers are more likely to perceive risks as serious and requiring careful attention; conversely, if they exhibit tolerance or tacit approval of rule violations, workers gradually normalize the presence of risk, resulting in higher risk tolerance.

In construction contexts, risk perception is typically divided into three dimensions: probability judgment, severity assessment, and safety anxiety [42]. The first two dimensions are classified as cognitive risk perception, while “safety anxiety” is regarded as affective risk perception [43]. Sociological and psychological studies indicate that cognitive risk perception is generally limited to individuals with specialized domain knowledge [44]. Although construction workers operate in high-risk environments, their risk judgments are often primarily driven by emotions [45]. Therefore, this study places special emphasis on workers’ affective risk perception.

Given that foremen’s SAs provide workers with critical social cues about risk, the study proposes the following hypotheses:

H2a: 

Foremen’s CSA positively correlates with construction workers’ RP.

H2b: 

Foremen’s ASA positively correlates with construction workers’ RP.

H2c: 

Foremen’s BSA positively correlatesd with construction workers’ RP.

2.2.2. Workers’ RP and RTBs

While Maslow’s hierarchy positions safety as a fundamental human need, prioritizing loss aversion over potential gains from non-compliance [46], empirical studies document frequent RTBs among construction workers [47,48]. This paradox raises a critical question: what mechanisms underlie this divergence between safety needs and actual behaviors, particularly when workers’ safety performance affects not only organizational outcomes but also their personal and familial well-being?

RP may be the core psychological mechanism for explaining the divergence between “safety demand” and actual behavior. Empirical studies across disciplines consistently link RP to behaviors such as non-compliance and risk-taking. For instance, RP significantly affects public acceptance of emerging food technologies [49] and improves driving behaviors through enhanced hazard awareness [50]. In construction safety, RP significantly predicts workers’ safety-related behaviors. A Hong Kong study of 469 workers demonstrated a strong negative correlation between RP and risk-taking, particularly through its emotional dimension [42]. Furthermore, the positive influence of RP on both safety compliance and safety participation behaviors also indirectly reflects its potential in suppressing RTBs [44,51]. As a mechanism of need-based regulation, RP governs behavior by determining whether risks are perceived as acceptable. Accordingly, workers may engage in RTBs due to either underestimating hazards or disregarding safety protocols. RP serves as a key psychological mechanism for explaining the paradox wherein construction workers fundamentally value safety yet frequently engages in RTBs. Accordingly, this study proposes the following hypothesis:

H3: 

RP negatively correlates with construction workers’ RTBs.

2.2.3. The Mediating Role of Workers’ RP

Social cognitive theory posits dynamic interactions between environmental factors and individual cognition [23]. Empirical studies have confirmed the mediating role of cognition in this interaction. For example, team leaders influence knowledge sharing by fostering interdependence [52], and group norms shape safety behaviors through social identification [53]. Based on the environmental–cognitive–behavioral pathway, and integrating hypotheses H2 through H3, this study posits that foremen’s SAs can reduce workers’ RTBs not only through direct social influence but also via an indirect cognitive mechanism—by shaping workers’ RP. In this process, RP serves as a critical mediating bridge that links SAs to behavioral outcomes. Accordingly, the following hypotheses are proposed:

H4a: 

Foremen’s CSA negatively correlates with workers’ RTBs through the mediation of RP.

H4b: 

Foremen’s ASA negatively correlates with workers’ RTBs through the mediation of RP.

H4c: 

Foremen’s BSA negatively correlates with workers’ RTBs through the mediation of RP.

2.3. The Moderating Role of IPT

IPT enhances relational security by reducing self-consciousness and psychological defensiveness, thereby facilitating open emotional expression [54]. As a cornerstone of high-quality relationships [55], IPT fosters positive attributions regarding others’ behavioral intentions, which in turn increases individuals’ willingness to respond constructively. Empirical evidence suggests that IPT plays a pivotal role in workplace dynamics—it strengthens the intention to share knowledge and enhances the absorption of shared expertise during communication processes [56,57].

In addition to its direct and mediating effects, IPT also functions as a cognitive moderator by shaping how individuals interpret and evaluate others’ behaviors, thereby influencing the impact of external factors on psychological and behavioral outcomes [58]. For instance, IPT moderates the relationship between employees’ perceptions of corporate social responsibility and their organizational citizenship behaviors, with higher levels of trust amplifying this positive association [54]. In the field of knowledge management, studies have shown that trust in supervisors positively moderates the relationship between employees’ perceptions of performance appraisal, training opportunities, and career development and their willingness to share knowledge [59]. Similarly, research in construction safety has revealed that coworker trust can buffer the negative impact of challenge- and hindrance-related stressors on safety compliance and proactive communication, while also enabling workers to transform job demands into safety-enhancing actions [60].

This cross-domain consistency suggests that trust is not merely a psychological state but functions as a cognitive-behavioral amplifier, systematically shaping how external stimuli are interpreted and enacted. Specifically, in high-trust environments, workers are more likely to perceive safety-related cognitions as credible “authoritative advice,” to empathize with the emotions conveyed, to heighten their vigilance, and to interpret foremen’s safety behaviors as well-intentioned “protective signals.” Conversely, in low-trust relationships, such cognitions may be questioned, emotional resonance may be weak, and the same behaviors could be dismissed as mere “formalities,” thereby significantly diminishing their positive effects. Based on this reasoning, the study proposes the following hypotheses:

H5a: 

IPT positively moderates the relationship between CSA and workers’ RP.

H5b: 

IPT positively moderates the relationship between ASA and workers’ RP.

H5c: 

IPT positively moderates the relationship between BSA and workers’ RP.

Furthermore, integrating the logic of H4 (mediation effects) and H5 (moderation effects), since IPT moderates the strength of the relationship between foremen’s SAs and workers’ RP (the first half of the pathway), and RP serves as the key mediator linking foremen’s attitudes to workers’ RTBs, it can be inferred that the mediation effect via RP is conditional and varies with the level of IPT. Specifically, under high levels of IPT, this indirect effect is likely to be stronger. Therefore, this study proposes the following:

H6a: 

The indirect effect of CSA on RTBs through RP is strengthened by higher levels of IPT.

H6b: 

The indirect effect of ASA on RTBs through RP is strengthened by higher levels of IPT.

H6c: 

The indirect effect of BSA on RTBs through RP is strengthened by higher levels of IPT.

Figure 1 presents the theoretical model, delineating how SAs influence workers’ RTBs through the mediating mechanism of RP, with IPT moderating the antecedent relationships in the mediation process.

3. Methodology

This study is grounded in Social Learning Theory and Social Information Processing Theory, focusing on the impact of foremen’s SAs on construction workers’ RTBs. Through theoretical analysis, research hypotheses are proposed and a theoretical model is constructed. To empirically test the relationships among variables corresponding to different types of hypotheses, a combined methodological approach of Structural Equation Modeling (SEM) and hierarchical regression analysis is employed. Specifically, SEM is well-suited for simultaneously examining causal paths among multiple latent variables and excels in testing and assessing the strength of mediation effects [61]; hierarchical regression analysis is particularly appropriate for detecting moderation effects, effectively revealing boundary conditions (such as the moderating role of interpersonal trust) through interaction terms [62]. Both methods are widely used and well-established statistical tools in the social sciences, enabling a reasonable match and complementarity among research objectives, variable characteristics, and effect types. Although this study adopts statistical analysis rather than experimental design, the joint application of SEM and hierarchical regression can still effectively uncover the complex mechanisms among variables and provide robust empirical support for testing theoretical hypotheses.

3.1. Questionnaire Development and Sample Collection

Given that the variables involved in this study—such as safety attitudes, risk perception, and risk-taking behaviors—primarily pertain to individual subjective psychological or cognitive dimensions and are difficult to directly measure through experimental manipulation, this study employs structured questionnaires to collect relevant data, ensuring the measurability of variables and the systematic nature of the data.

The structured questionnaire comprises three sections: (1) demographic characteristics (gender, age, work experience, and education level), (2) measures of SAs and IPT, and (3) measures of construction workers’ RP and RTBs. Data collection occurred from July to December 2024 across construction projects in Beijing, Hunan, and Guangdong provinces. To enhance sample diversity and reduce selection bias, a hybrid sampling framework integrating snowball recruitment, site-based convenience sampling, and voluntary response mechanisms was deployed, with strict adherence to non-coercive participation protocols.

Among the initially collected 436 questionnaires, data cleaning and screening were conducted based on the following criteria:

(1) First, questionnaires with missing data on core measurement scales were excluded due to incomplete responses (n = 37);

(2) Second, invalid responses exhibiting a clear “straight-lining” pattern (i.e., selecting the same option for all items) were identified and removed through standard deviation checks and manual inspection (n = 51);

(3) Finally, questionnaires that failed the consistency check involving reverse-coded items were excluded (n = 47).

Following this screening process, a total of 135 invalid responses were removed, resulting in 301 valid questionnaires, with an effective response rate of 69%.

3.2. Measures

To ensure the psychometric soundness of the survey instrument, this study adapted validated measurement scales to align with the research context. All variables were measured using a 7-point Likert scale, ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). SAs were conceptualized along three dimensions, adapted from the work of Zhao et al. [63], who did not report reliability coefficients, and Li et al. [64], who reported Cronbach’s α values of 0.813, 0.765, and 0.762 for the cognitive, affective, and behavioral dimensions, respectively. The cognitive dimension (5 items) included statements such as the following: “Supervisors acknowledge that safety management requires collaborative responsibility among all operational staff.” The affective dimension (4 items) included statements such as the following: “Foremen recognize that safety management is a shared responsibility among all personnel involved in operations.” The behavioral dimension (4 items) included statements such as the following: “Foremen immediately intervene to stop unsafe behaviors observed among crew members.” To verify the consistency of respondents’ answers, several reverse-coded items were embedded in selected scales.

RP was operationalized using a 6-item scale developed by Ram and Chand [50] (Cronbach’s α = 0.922), which predominantly captures the emotional dimension of risk appraisal. Sample items include statements such as “Improperly fastened safety helmets create significant unease,” reflecting workers’ affective reactions to unsafe conditions.

RTBs were measured using Man et al.’s [11] 6-item scale (Cronbach’s α 0.953), with items such as the following: “I may disregard safety protocols to complete tasks on schedule”.

IPT was measured using McAllister’s [65] 5-item scale (Cronbach’s α 0.9), with items such as the following: “You can freely share your ideas and opinions with your foreman.”

To ensure linguistic and conceptual equivalence, the original English scale underwent a standardized back-translation procedure. First, bilingual researchers translated the scale into Chinese. Subsequently, an independent translator with expertise in English linguistics back-translated the Chinese version into English. Discrepancies between the original and back-translated versions were resolved through iterative discussions to preserve semantic consistency. Following this, two construction site managers with over a decade of fieldwork experience reviewed the Chinese scale. They refined item phrasing to align with the linguistic patterns and comprehension levels of Chinese construction workers.

3.3. Data Analysis

A total of 301 valid questionnaires were collected, meeting the sample size requirements for SEM analysis (200–400 participants) and adhering to the recommended ratio of observed variables to sample size (1:10–1:15) [66]. The data analysis process is mainly divided into the following three stages.

First, based on the formal sample, reliability tests were conducted for each measurement scale to ensure the internal consistency of items and the stability of scale measurement. According to the established screening criteria—(1) the corrected item-total correlation coefficient is below 0.40, and (2) the Cronbach’s α coefficient of the scale increases substantially after the item is deleted—we refined the initial scales. For example, in the foremen’s CSA scale, item 5 had a corrected item-total correlation of 0.406, and its deletion increased the α coefficient from 0.803 to 0.819; therefore, this item was removed. After this round of screening, a total of five items were deleted (see Appendix A,

Table A1. Safety attitude scale (foremen).

Dimension	Item Code	Item Description
Cognitive	CSA1	The foreman believes that safety management is closely related to workers’ own behaviors.
	CSA2	The foreman believes that injuries on construction sites are mainly due to bad luck.
	CSA3	The foreman believes that risks and dangers are unavoidable on construction sites.
	CSA4	The foreman believes that safety procedures are too complicated and delay work progress.
	CSA5	The foreman believes that safety only means wearing helmets and fastening safety belts.
Affective	ASA1	The foreman feels that safety officers are responsible and helpful to our work.
	ASA2	The foreman feels it is safer to work with colleagues who actively participate in safety training.
	ASA3	The foreman cares about the safety difficulties we encounter during tasks.
	ASA4	The foreman is willing to communicate with us about safety issues in the work process.
Behavioral	BSA1	The foreman promptly stops unsafe behaviors of team members.
	BSA2	The foreman does not tolerate unsafe behaviors among team members.
	BSA3	The foreman never violates safety regulations, even in special circumstances.
	BSA4	The foreman refuses to work when safety measures are inadequate.

,

Table A2. Risk perception scale.

Item Code	Item Description
RP1	I believe that others may be involved in safety accidents.
RP2	I feel unsafe if my helmet is not worn properly.
RP3	I am worried that I might be involved in a safety accident.
RP4	I am concerned about getting injured in an accident.
RP5	I believe others are at high risk of injury in safety accidents.
RP6	I always worry about safety when working at height.

,

Table A3. Risk-taking behaviors scale.

Item Code	Item Description
RTBs1	If necessary, I would violate operational procedures to complete tasks.
RTBs2	If given the chance, I would engage in behaviors that are prohibited on-site.
RTBs3	To save energy, I would walk through restricted areas.
RTBs4	In some situations, I do not use safety helmets and harnesses as required.
RTBs5	I sometimes ignore safety requirements in order to finish the work faster.
RTBs6	I often perform job duties in an incorrect way just to get the job done.

and

Table A4. Interpersonal trust scale.

Item Code	Item Description
ITP1	I can freely discuss work-related difficulties with the foreman, and he listens attentively.
ITP2	The foreman provides reasonable suggestions and solutions to my safety concerns.
ITP3	Based on experience, the foreman is capable of handling various safety issues at work.
ITP4	The foreman is a trustworthy and respectable person.
ITP5	I have developed a good personal relationship with the foreman.

, for the full measurement scales). The Cronbach’s α coefficients of all the remaining scales exceeded the recommended threshold of 0.70, indicating good internal consistency.

Second, confirmatory factor analysis (CFA) was conducted using AMOS 24.0 to assess the reliability and validity of the measurement model. Convergent validity was evaluated by examining composite reliability (CR) and average variance extracted (AVE), while discriminant validity was assessed by comparing the square root of each construct’s AVE with its correlations with other constructs. This step ensured that all measured constructs were statistically reliable and distinct, thereby laying a solid foundation for subsequent structural model analysis. On this basis, model fit was further evaluated using fit indices such as χ²/df, CFI, TLI, and RMSEA to verify the overall adequacy of the measurement model.

Finally, following the hierarchical logic of the hypotheses, different statistical methods were employed for testing:

Main effects and mediation effects testing (H1–H4): SEM was conducted using AMOS 24.0, with Maximum Likelihood estimation to assess the direct effects of foremen’s SAs on RTBs (H1) and RP (H2), as well as the direct effect of RP on RTBs (H3). Meanwhile, the bias-corrected bootstrap method (with 5000 resamples) was used to test the mediating role of RP (H4).

Moderation effects testing (H5) included the following: Hierarchical regression analysis was performed in SPSS 26.0 to examine the moderating effect of IPT (H5). To avoid multicollinearity, all independent and moderating variables were mean-centered prior to creating interaction terms. The analysis proceeded in three steps: first, control variables and independent variables (the dimensions of SAs) were entered. Second, the moderating variable (IPT) was added; third, the interaction terms between independent and moderating variables were included. The significance of the interaction terms was used to determine the presence of moderation effects.

Moderated mediation effects testing (H6) included the following: To test the moderated mediation model (H6), Hayes’ PROCESS macro for SPSS (Model 7) was employed, with a bootstrap sample size set to 5000. This analysis examined whether the indirect effect via RP varies significantly across different levels of IPT.

4. Result

4.1. Descriptive Statistics

A total of 301 valid questionnaires were collected. The demographic characteristics of the sample are presented in

Table 1. Demographic information and proportion of the respondents.

Demographic Variable		N	Percentage
Gender	Male	268	89%
	Female	33	11%
Age	≤18 years old	18	6%
	19–30 years old	34	11%
	31–50 years old	182	60%
	51–60 years old	62	21%
	≥60years old	5	2%
Work experience	≤5 years	81	27%
	5–10 years	102	34%
	≥10 years	118	39%
Education background	Primary school or below	74	25%
	Junior high school	121	40%
	High school	64	21%
	University or above	42	14%

. In terms of gender distribution, the sample was predominantly male, accounting for 89% of the total (N = 268). Regarding age, workers aged between 31 and 50 constituted the majority, representing 60% (N = 182) of the sample. In terms of work experience, most participants were experienced workers, with 73% (N = 220) having more than five years of service; among them, those with over ten years of experience made up the largest proportion (39%, N = 118). Concerning educational background, the sample exhibited generally low education levels, with workers having a junior high school education or below comprising 65% (N = 195) of the total.

Overall, the sample characteristics align with the typical profile of frontline construction workers in China, who are predominantly experienced, middle-aged males with relatively low levels of formal education.

4.2. Model Assessment and Validation

Prior to hypothesis testing, a systematic evaluation of the measurement model was conducted to ensure that all constructs demonstrated sound psychometric properties. This evaluation process involved tests of reliability and validity, as well as an assessment of the overall model fit.

4.2.1. Reliability and Validity Tests

The measurement items were rigorously evaluated through confirmatory factor analysis (CFA) conducted with AMOS, with model fit indices and factor loadings used as criteria to establish psychometrically robust measurement models. As presented in

Table 2. Results of reliability of the scales.

Constructs	Dimensions	Std	Unstd	S.E.	t_Value	p	SMC	CR	AVE
CSA	CSA1	0.733	1.000				0.537	0.821	0.535
	CSA2	0.690	0.975	0.092	10.591	***	0.476
	CSA3	0.804	1.182	0.100	11.768	***	0.646
	CSA4	0.693	1.035	0.097	10.639	***	0.480
ASA	ASA1	0.749	1.000				0.561	0.845	0.580
	ASA2	0.761	1.002	0.080	12.597	***	0.579
	ASA3	0.874	1.174	0.085	13.758	***	0.764
	ASA4	0.646	0.920	0.086	10.661	***	0.417
BSA	BSA1	0.736	1.000				0.542	0.853	0.598
	BSA2	0.864	1.161	0.083	14.049	***	0.746
	BSA3	0.860	1.181	0.084	14.019	***	0.740
	BSA4	0.602	0.807	0.081	9.933	***	0.362
RP	RP2	0.692	1.000				0.479	0.834	0.502
	RP3	0.737	1.170	0.109	10.772	***	0.543
	RP4	0.672	0.916	0.092	10.003	***	0.452
	RP5	0.725	1.027	0.097	10.637	***	0.526
	RP6	0.710	1.166	0.111	10.466	***	0.504
RTBs	RTBs1	0.682	1.000				0.465	0.845	0.581
	RTBs4	0.859	1.213	0.090	13.491	***	0.738
	RTBs5	0.935	1.272	0.089	14.314	***	0.874
	RTBs6	0.855	1.192	0.089	13.428	***	0.731
IPT	IPT2	0.735	1.000				0.540	0.831	0.551
	IPT3	0.802	0.964	0.080	12.037	***	0.643
	IPT4	0.732	0.879	0.078	11.311	***	0.536
	IPT5	0.697	0.876	0.081	10.826	***	0.486

Note: ***: p < 0.001.

, all latent constructs exhibited satisfactory reliability. Standardized factor loadings ranged from 0.65 to 0.95 (p < 0.001), with composite reliability (CR) values above 0.8 and average variance extracted (AVE) exceeding the 0.5 threshold. These results confirm the measurement model’s internal consistency, construct stability, and convergent validity [66].

4.2.2. Discriminant Validity of the Measurement Model

Table 3. Discriminant validity.

	CSA	ASA	BSA	RP	RTBs	IPT
CSA	0.731
ASA	0.377	0.762
BSA	0.461	0.334	0.708
RP	0.612	0.405	0.609	0.709
RTBs	0.520	0.436	0.632	0.715	0.762
IPT	0.596	0.383	0.474	0.709	0.472	0.742

presents the discriminant validity assessment for latent variables. Discriminant validity was evaluated by comparing the statistical differences between constructs, confirming that the constructs are empirically distinct from one another [67]. Specifically, the square root of the average variance extracted (AVE) for each latent variable should exceed its correlations with all other constructs [68]. As detailed in Table 3, the correlation between RP and RTBs marginally exceeded the square root of RP’s AVE by 0.006. Given the theoretical relationship between RP and RTBs, this slight deviation is methodologically justifiable and consistent with established measurement practices. Overall, the discriminant validity of the measurement model was deemed acceptable.

4.2.3. Assessment of Overall Model Fit

Adequate model fit is essential for validating theoretical frameworks via SEM [69]. Following established methodological practice [70,71], six commonly used fit indices were employed to evaluate model adequacy: the chi-square statistic normalized by degrees of freedom (CMIN/DF), root mean square error of approximation (RMSEA), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), standardized root mean square residual (SRMR), and comparative fit index (CFI). As shown in

Table 4. The fit indexes of structural models.

Goodness-of-Fit Criteria	χ2/df	RMSEA	GFI	AGFI	SRMR	CFI
Recommended index values	<3	<0.08	>0.9	>0.9	<0.5	>0.9
Model	2.048 (df = 179)	0.059	0.898	0.868	0.054	0.944

, most indices met standard SEM benchmarks. Although the GFI (0.898) and AGFI (0.868) slightly missed the widely cited 0.90 threshold, both exceeded the 0.80 acceptability level recommended by Baumgartner and Homburg [72]. Overall, these results indicate that the structural model demonstrates an acceptable level of statistical fit.

4.3. Hypothesis Testing Results

4.3.1. Test of Main Effects

This section aims to examine the direct effects of foremen’s SAs on workers’ RTBs (H1) and RP (H2), as well as the effect of RP on RTBs (H3). The results of the path analysis are presented in

Table 5. SEM path coefficients.

Path	Std	Unstd	S.E.	t_Value	p	SMC
RP < ---CSA	0.384	0.329	0.062	5.332	***	0.52
RP < ---ASA	0.132	0.089	0.042	2.140	0.032
RP < ---BSA	0.388	0.403	0.076	5.336	***
RTBs < ---CSA	−0.065	−0.057	0.058	−0.979	0.328	0.59
RTBs < ---ASA	−0.134	−0.092	0.037	−2.485	0.013
RTBs < ---BSA	−0.282	−0.297	0.073	−4.073	***
RTBs < ---RP	−0.449	−0.455	0.087	−5.256	***

Note: ***: p < 0.001.

.

Regarding H1, which posits a direct effect of foremen’s SAs on workers’ RTBs, the results indicate that BSA exerts the strongest negative influence on RTBs (β = −0.282, p < 0.001), followed by ASA (β = −0.134, p < 0.05), while the effect of CSA on RTBs is not statistically significant. These findings suggest that both the BSA and ASA of foremen can directly suppress workers’ RTBs, providing partial support for H1.

Regarding H2, which posits that foremen’s safety attitudes influence workers’ risk perception, the results revealed significant positive effects across all three dimensions. Among them, cognitive (β = 0.384) and behavioral (β = 0.388) safety attitudes demonstrated much stronger predictive power than affective attitudes (β = 0.132). These findings suggest that foremen’s beliefs and actions are the primary sources shaping workers’ risk perception. H2 was fully supported.

H3 was confirmed by a robust negative effect of RP on RTBs (β = −0.449, p < 0.001), with RP demonstrating greater explanatory power than CSA, ASA, or BSA. H3 was supported.

The squared multiple correlations (SMC) for both RP (SMC = 0.52) and RTBs (SMC = 0.59) exceeded 0.5, indicating substantial explanatory capacity of their antecedent variables.

4.3.2. Test of Mediating Effects

To examine the mediating role of RP in the proposed model, the study performed 5000 bootstrap resampling iterations. As shown in

Table 6. Standardized mediating effect test.

Path	Effect	Estimate	Bias-Corrected 95% CI
			Low	Up	p
RTBs < ---RP < ---CSA	Direct	−0.057	−0.182	0.067	0.366
	Indirect	−0.150	−0.287	−0.065	***
	Total	−0.206	−0.346	−0.077	0.001
RTBs < ---RP < ---ASA	Direct	−0.092	−0.198	−0.014	0.023
	Indirect	−0.041	−0.107	0.001	0.054
	Total	−0.133	−0.243	−0.032	0.011
RTBs < ---RP < ---BSA	Direct	−0.297	−0.587	−0.092	0.004
	Indirect	−0.183	−0.392	−0.046	0.001
	Total	−0.480	−0.731	−0.285	***

Note: ***: p < 0.001.

, the direct effect of CSA on RTBs was statistically non-significant, whereas the indirect effect was significant (p < 0.001), and the total effect remained significant (p < 0.01). According to Hayes’ mediation criteria [73], these results confirm that RP significantly mediates the CSA–RTBs relationship. Notably, BSA exhibited both significant direct and indirect effects on RTBs (p < 0.01), with the latter accounting for 38% of the total effect. These results suggest that RP partially mediates the BSA–RTBs relationship, with direct pathways also contributing. In contrast, the 95% confidence interval for the indirect effect of ASA on RTBs included zero, providing no empirical support for the mediating role of RP in this path. H4 was partially supported.

4.4. Moderating Effect and Moderated Mediation Effect

To test H5, which proposes the moderating role of IPT in the relationship between SAs and workers’ RP, hierarchical regression analysis was employed. As shown in

Table 7. Standardized moderating effect test.

Variable	M1	M2	M3
CSA	0.273 *** (5.594)
ASA		0.137 *** (3.728)
BSA			0.295 *** (7.868)
IPT	0.433 *** (9.022)	0.505 *** (11.429)	0.435 *** (10.532)
CSA*IPT	0.013 (0.363)
ASA*IPT		0.023 (0.507)
BSA*IPT			0.107 *** (3.248)
R2	0.432	0.404	0.506
R2-chng	0.000	0.001	0.018
F	0.132	0.441	10.552

Note: ***: p < 0.001.

, Models 1 (M1) and 2 (M2) indicated that the interaction terms of IPT × CSA and IPT × ASA were not statistically significant predictors of RP. In contrast, the interaction term of IPT × BSA showed a significant moderating effect (p < 0.001), suggesting that IPT significantly moderates the strength of the BSA–RP relationship. H5 was partially supported.

Figure 2 visually illustrates the moderating effect of IPT. It can be seen that, under H_IPT conditions, the slope representing the influence of BSA is the steepest, indicating that the foremen’s behaviors have a significantly amplified impact on workers’ RP.

To test the moderating role of IPT in the pathway between BSA and workers’ RTBs (H6), this study employed the bootstrap method (5000 resamples) using the PROCESS macro. As presented in

Table 8. Moderated mediation analysis effect test.

IPT	Indirect Effect	BootSE	95% Confidence Interval
−0.888	−0.097 *	0.045	[−0.191, −0.018]
0.000	−0.143 **	0.045	[−0.237, −0.065]
0.888	−0.189 ***	0.056	[−0.309, −0.090]

Note: *: p < 0.05, **: p < 0.01 and ***: p < 0.001.

, the mediating effect of RP remained statistically significant across all IPT levels (−0.888, 0.000, and 0.888), with none of the confidence intervals encompassing zero. Critically, the absolute magnitude of indirect effects intensified with elevated IPT levels (low-to-high difference: Δβ = −0.092), as shown in Figure 3. These results confirm IPT’s positive moderation of the mediation pathway, providing partial support for hypothesis H6c regarding moderated mediation.

5. Discussion

5.1. Summary of Findings

In the context of the construction industry—where OHS management faces particularly significant challenges—mobilizing the proactive role of foremen in OHS management can help the sector address these challenges and accelerate the achievement of SDG 3 and SDG 8. Motivated by this practical need, the study developed a theoretical model positioning foremen’s SAs as the antecedent variable, RP as the mediator, IPT as the moderator, and RTBs as the outcome variable. An empirical investigation was conducted using SEM. The results revealed several key findings.

First, foremen’s SAs were found to be significant negative predictors of workers’ RTBs, with ASA and BSA showing significant negative effects on RTBs at the 0.05 and 0.001 levels, respectively. This finding aligns with existing literature and confirms the critical role of foremen in shaping workers’ safety practices [20,38]. However, by integrating Social Learning Theory and Social Information Processing Theory, this study identifies and empirically validates a dual-pathway mechanism through which foremen’s SAs influence workers’ RTBs: foremen’s SAs not only directly affect workers’ behavioral decisions (direct pathway) but also indirectly shape RTBs by influencing workers’ RP through the informational cues conveyed in foremen’s words and actions (indirect pathway). Compared to prior research that primarily emphasizes the direct influence of foremen on workers’ behaviors [21,74], this study broadens the theoretical perspective on how foremen generate social influence within crews. It highlights their multifaceted roles in workers’ decision-making processes—as behavioral models, information intermediaries, norm interpreters, and emotional climate setters. These insights provide a novel theoretical perspective on the unique value of foremen in frontline OHS management.

Second, the study reveals the multidimensional complexity of the mechanisms through which foremen’s SAs influence workers’ RTBs, with distinct pathways associated with each of the three attitude dimensions. This finding provides an integrated perspective for comprehensively understanding the multifaceted roles of foremen in on-site safety management. Specifically, BSA exert the strongest impact, consistent with prior research [20,74]. BSA shows both a significant direct inhibitory effect on RTBs and an indirect effect via workers’ RP. This supports the theory of “role synergy” [75], which posits supervisors collaboratively shaping individual behaviors through behavioral modeling, procedural monitoring, and capability transmission. In contrast, CSA influence RTBs only indirectly, indicating workers primarily interpret CSA as social cues for risk assessment rather than direct behavioral commands [27]. ASA mainly exert a significant direct effect without a significant indirect one, suggesting workers’ decisions are strongly affected by foremen’s immediate emotional states, likely due to emotional contagion in daily interactions [76,77]. Emotional influences may also have delayed effects requiring more time to translate into risk assessment cues [78], which might explain the non-significant indirect effect found here. In summary, this study deconstructs foremen’s influence from a broad and unified concept into three distinct “influence channels”—behavioral, cognitive, and affective—each operating through different mechanisms. The practical implication is that future OHS interventions should move beyond a one-size-fits-all approach and instead develop differentiated strategies tailored to each specific channel.

Third, this study validates the mediating role of RP in the relationship between foremen’s SAs and workers’ RTBs. Specifically, RP significantly mediates the effects of both CSA and BSA on RTBs (p < 0.01), thereby uncovering the psychological foundation of foremen’s indirect influence. Although the inhibitory effect of RP on RTBs remains contested in some studies [79], such inconsistency may stem from the following: (i) boundary conditions, where contextual factors moderate the effect of RP [80], and (ii) variations in RP types, as rational RP may exert limited influence on workers’ safety behaviors [43]. Nonetheless, the present findings align with the mainstream view that RP significantly reduces risk-taking behavior [43,81], and by confirming its mediating role, the study offers a more nuanced understanding of how foremen exert indirect influence. While RP is inherently subjective, its formation is often shaped by workers’ interpretations of their social environment [82], particularly on construction sites characterized by dynamic, unpredictable, and ambiguous conditions [33]. Thus, RP is not merely a product of individual cognitive processing but also a psychological response triggered by foremen through social influence mechanisms. It plays a pivotal bridging role in the pathway from foremen’s attitudes to workers’ behaviors. This finding provides a clearer roadmap for understanding the complex social influence processes underlying workplace safety.

Fourth, the moderating effect of IPT was significant only in the relationship between foremen’s BSA and workers’ RP (the moderating effect of IPT on the BSA–RP link was significant at the p < 0.001 level), revealing that the influence of foremen extends beyond the domain of interpersonal relationships. Trust fundamentally shapes how individuals perceive and interpret others’ behaviors and intentions [58]. Consequently, in high-trust environments, workers are more inclined to perceive foremen’s behaviors as normative signals or exemplary role models, thereby exerting stronger influence on their RP. In contrast, CSA primarily reflect foremen’s ideological expressions or attitudinal statements. The impact of such cognitive information depends more on the rigor of the informational arguments and the credibility of the source rather than the relational dynamics between sender and receiver [83,84]. This finding conversely highlights the structural power and authority of foremen as “opinion leaders,” whose influence to some extent transcends mere personal likability or trust. Even under low-trust conditions, the cognitive information they convey remains effective. This offers important insights for implementing safety management amid complex interpersonal relationships.

5.2. Theoretical Contribution

This study makes several important theoretical contributions at the intersection of behavioral safety research and sustainable development in the construction industry.

First, this study contributes theoretically by constructing a testable social influence model that links SDGs with frontline OHS practices in the construction sector through a concrete implementation mechanism. Drawing on social cognitive theory and social information processing theory, the model centers on foremen’s SAs, with workers’ RP and IPT serving as key mediating and moderating mechanisms. It offers a conceptual bridge between SDGs (particularly SDG 3 and SDG 8) and frontline RTBs, thereby deepening the understanding of how these global goals can be effectively realized in high-risk work environments.

Second, this study enriches the understanding of foremen’s pivotal role in advancing SDGs—particularly SDG 3 and SDG 8—by deconstructing the dual influence pathways of their SAs. Specifically, it distinguishes and empirically tests the differentiated effects of cognitive, affective, and behavioral dimensions. The findings reveal that foremen influence workers’ behaviors not only through direct behavioral modeling and emotional climate shaping but also by transmitting social cues via their behavioral tendencies and cognitive expressions. These cues shape workers’ subjective risk assessments. By systematically uncovering how foremen shape RTBs, the study offers theoretical grounding and practical insights for positioning them as active agents in frontline OHS management.

Finally, the moderation analysis results reveal that foremen’s influence arises not only from relational resources but also from their specific role positioning. The presence of multiple parallel influence pathways broadens our understanding of foremen’s function within OHS management. Furthermore, these insights provide a novel theoretical foundation for leveraging foremen to advance SDGs, particularly SDG 5 and SDG 8, at the frontline level.

5.3. Implications for Practice

In the context of high-risk operations, complex workforce compositions, and limited safety resources, effectively leveraging the potential of foremen in OHS management within the construction industry not only complements formal regulations but also serves as a critical pathway for advancing SDGs—particularly SDG 3 and SDG 8. The mechanisms uncovered in this study concerning foremen’s influence on workers’ RTBs lay a theoretical foundation for targeted intervention strategies aimed at empowering foremen and fostering more resilient OHS management systems:

First, enhance the oversight and guidance of foremen’s safety behaviors, transforming them from “passive supervisors” into “proactive role models.” Enterprises should establish a safety behavior feedback loop by conducting regular behavior observations, documentation, and timely feedback to reinforce foremen’s function as safety exemplars. Specifically, in medium-to-large construction firms, digital technologies can be leveraged for continuous behavior monitoring and anomaly detection, thereby creating a sustainable and automated supervision mechanism. For smaller enterprises with limited resources, cost-effective supervision and guidance can be achieved through routine safety inspections combined with basic feedback mechanisms tailored to their operational capacity.

Second, implement targeted training focused on cognitive restructuring. Building on foundational safety knowledge, enterprises should develop scenario-based, visualized training modules—such as accident simulations and case analyses—to help foremen of diverse educational backgrounds deeply understand the accident causation chain and recognize their central role in fostering a psychologically safe environment within their teams, thereby internalizing safety responsibilities. Medium-to-large construction firms can independently design such modules tailored to their operational characteristics, while small enterprises can enhance traditional training by utilizing online resources.

Furthermore, fostering foremen’s emotional identification with safety management through enhanced communication and engagement is essential. To transform foremen from potential psychological resisters into proactive partners, construction firms should establish routine communication mechanisms. Safety managers are encouraged to engage in regular informal dialogues with foremen, listening attentively to their challenges in balancing safety requirements with production schedules. Involving foremen in discussions on improving safety procedures can cultivate a sense of participation and respect, which in turn strengthens their emotional alignment with organizational safety goals. This emotional resonance facilitates the integration of personal commitment with collective safety objectives, ultimately supporting more effective and sustainable safety governance.

Finally, empower workers by enhancing their RP capabilities to establish a bidirectional safety barrier. Beyond top-down influences, enterprises of all sizes should adopt bottom-up approaches to empower workers. Specific measures may include leveraging VR/AR technologies to visualize hazards in high-risk tasks, thereby enhancing the perceptibility of risks. Additionally, during task assignments, providing clear response strategies and necessary supportive resources can help shift workers’ mindsets from passive compliance (“being told to be safe”) to active commitment (“wanting to be safe”). This transformation fosters synergy with foremen’s safety leadership, contributing to the co-construction of a proactive, adaptive safety culture within the organization.

5.4. Limitations and Future Research

This study has several limitations that warrant acknowledgment. First, the reliance on self-reported cross-sectional data inherently restricts causal inferences and temporal dynamics validation. Given that foremen’s influence on workers constitutes a cumulative process, future studies should adopt longitudinal designs with multi-source data triangulation (e.g., behavioral observations coupled with psychometric assessments) to capture evolving interaction patterns.

Second, treating construction workers as a homogeneous group overlooks potential heterogeneity in foremen’s influence. Although individual characteristics of workers may moderate the effects of foremen’s SAs, meaningful subgroup comparisons or multi-group analyses were not feasible due to imbalanced distributions of these variables in the current sample. Future studies should collect more diverse, representative samples to systematically examine these moderating effects.

Third, critical boundary conditions—including pre-existing social ties (kinship/familiarity) between supervisors and workers—are not incorporated into the analytical framework. Subsequent research should systematically examine how these relational dynamics moderate the association between SAs and RTBs.

Finally, the exclusive focus on Chinese construction contexts limits generalizability. Cross-cultural replications across diverse institutional environments are needed to verify the universality of these findings.

6. Conclusions

To address the urgent need for advancing sustainable development goals (particularly SDG 3 and SDG 8) in the construction industry, this study focuses on foremen as key actors. It systematically explores the social mechanisms through which they influence workers’ risk-taking behaviors. A comprehensive integrated model was developed and tested, revealing differentiated pathways through which the three dimensions of foremen’s safety attitudes—cognitive, affective, and behavioral—impact workers’ risk-taking behaviors. The findings demonstrate that foremen, as pivotal influencers of workers’ production behaviors, exert their influence via dual pathways: direct behavioral demonstration and indirect shaping of risk perception. Among these, the behavioral dimension exhibits the strongest influence with both significant direct and indirect effects; the cognitive dimension affects risk-taking behaviors indirectly through risk perception; and the affective dimension shows a significant but weaker direct effect. The moderating role of interpersonal trust was significant only in the pathway from behavioral safety attitude to risk perception.

The core contribution of this study lies in unveiling the influence pathways of foremen on workers’ RTBs, providing a testable micro-level explanation aligned with the broader sustainability agenda. Given the complexity and breadth of foremen’s influence, enterprises should move beyond traditional safety regulations and systematically cultivate foremen’s cognitive, affective, and behavioral attributes to build an endogenous, proactive, and sustainable safety governance system. This approach offers valuable theoretical insights and practical guidance for optimizing and upgrading OHS governance models within the construction industry.