Perceived Leadership Support, Safety Citizenship, and Employee Safety Behavior in the Construction Industry: The Role of Safety Learning

Abstract

The construction industry faces significant challenges in ensuring worker safety, encompassing both physical hazards and mental health concerns. Drawing on Social Exchange Theory (SET), this study explores the impact of perceived leadership support (PLS) on employee safety behavior (ESB) and safety citizenship behavior (SCB), focusing on the mediating role of SCB and the moderating effect of safety learning (SL). A quantitative approach was employed, collecting a sample size of 410 construction workers from various companies within the Turkish construction sector. Data were collected through electronic questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results reveal that PLS positively influences both ESB and SCB. Additionally, SCB mediates the relationship between PLS and ESB, while SL moderates the effect of PLS on both SCB and ESB, further strengthening the positive relationships. This study highlights the critical role of leadership support and safety learning in promoting safer behaviors within the construction industry, suggesting that organizations should cultivate a proactive safety culture to enhance safety outcomes.

1. Introduction

The construction industry is one of the most hazardous sectors globally, with higher fatality rates than other fields [1,2]. Many countries, including Turkey, report significant safety incidents, underscoring the critical importance of safety management within construction [3]. In Turkey, the construction sector has grown substantially over the past decade, with Turkish contractors achieving notable success in global markets, supported by technological advancements and the socio-economic needs that drive the sector [4]. Despite these advances, unsafe behaviors remain a leading cause of severe accidents in the industry [5]. As construction workers play a pivotal role in implementing engineering tasks, their attitudes toward safety are essential for managing workplace safety effectively [6]. This study examines the relationships between PLS, SCB, ESB, and SL within the construction industry. PLS refers to how workers feel their leaders prioritize and promote safety initiatives. SCB involves voluntary safety-related behaviors that go beyond basic requirements, contributing to a safer work environment. ESB reflects actions and practices that adhere to safety protocols and procedures [7]. SL encompasses how employees acquire, share, and apply safety-related knowledge to improve their safety performance.

The primary goal of this research is to investigate the influence of PLS on SCB and ESB, while also examining the moderating role of SL. By doing so, this study seeks to offer insights into how leadership and safety learning can shape safety outcomes in the construction industry. The study makes a unique contribution to the existing literature by exploring these relationships in the context of the Turkish construction sector, where achieving optimal safety performance remains a challenge. It fills a gap in research by connecting leadership support, safety learning, and employee behaviors to safety outcomes in construction. Utilizing Social Exchange Theory (SET), this research emphasizes the importance of involving all stakeholders, employees, leaders, and external parties in safety decision-making processes [8,9]. SET suggests that effective leadership and safety learning can foster a collaborative approach to safety, encouraging SCB and proactive ESB [2,10].

By integrating SET into construction safety management, this study aims to enhance our understanding of the key factors influencing safety behavior. It offers practical recommendations for organizations to foster a proactive safety culture by leveraging leadership support and safety learning, thereby improving safety outcomes. This research also contributes theoretically by applying SET to the safety management context, demonstrating how leadership and learning processes can collectively enhance safety performance. The study addresses the following questions:

How does perceived leadership support influence safety citizenship behavior and employee safety behavior?

What role does safety learning play in moderating these relationships?

How can organizations in the construction sector leverage leadership and learning to improve safety outcomes?

Through these questions, this study provides a comprehensive model for understanding and improving safety performance in the construction industry.

This research paper focuses on these questions, using SET to identify the relationship between safety citizenship and the safety behavior of construction workers in Turkey’s construction sector. A literature review was conducted, followed by hypothesis testing. The final stage of the study explores the practical application of these concepts.

The remaining section offers a comprehensive literature review and introduces the conceptual framework, highlighting key variables. Based on SET, we formulate several hypotheses, illustrated in the proposed research model (Figure 1). These hypotheses examine both direct and indirect effects, with a more significant influence observed in this research. Section 3 details the research methodology, outlining the methodological processes adopted. Section 4 presents the results, while Section 5 discusses the findings, Section 6 drawing conclusions and addressing implications, limitations, and future research directions.

2. Theoretical Background and Hypotheses Development

2.1. Social Exchange Theory

Social Exchange Theory (SET) offers a robust framework for understanding the dynamics between employees and their organizations, particularly in safety contexts. SET is grounded in the principle of reciprocity, which posits that individuals are more likely to engage in positive behaviors when they perceive favorable treatment from others in their social environment [11]. In the context of the construction industry, where workplace safety is paramount, SET provides a lens through which we can understand how safety behaviors emerge as a response to perceived organizational support. For example, when employees perceive that their leaders are committed to safety, they are likely to reciprocate by engaging in safety-enhancing behaviors, such as compliance with safety rules and proactive participation in safety initiatives [12].

In construction, SET applies directly to safety behaviors, as safety is often a collective responsibility. Workers rely on their leaders and colleagues to create and maintain a safe environment [13]. When leaders demonstrate support for safety through training, resources, and a culture that prioritizes safety, workers are likely to reciprocate by adhering to safety protocols and engaging in SCB that goes beyond mere compliance [14,15]. SET also suggests that, when organizations prioritize safety, employees feel a sense of obligation to maintain those safety standards, thus reducing accidents and enhancing overall safety performance [16,17]. This theory underscores the importance of leadership in fostering a safety culture where employees not only follow safety guidelines but also take proactive steps to ensure the well-being of their colleagues.

2.2. Perceived Leadership Support and Employee Safety Behavior

PLS is a critical factor influencing ESB. PLS refers to employees’ perceptions of how much their leaders prioritize safety and support their efforts to engage in safe work practices [18]. ESB can be categorized into two dimensions: safety compliance and safety participation. Safety compliance refers to following established safety rules and procedures, while safety participation involves proactive behaviors, such as helping colleagues or suggesting improvements to safety protocols [19]. These two facets of ESB are essential for fostering a comprehensive safety culture within organizations.

Several studies have established a positive relationship between PLS and ESB. For instance, ref. [20] found that employees who perceive strong leadership support for safety are more likely to engage in both compliance and participation behaviors. This relationship is grounded in SET, as employees reciprocate their leaders’ commitment to safety with positive safety behaviors [21]. Further research has shown that leadership styles that emphasize transformational leadership, which includes idealized influence and inspirational motivation, are particularly effective in promoting ESB [22]. In the construction industry, where hazards are frequent, leaders who prioritize safety set the tone for their teams, fostering a culture where safety is valued and practiced consistently [23]. Based on this, the following hypothesis is proposed:

H1. 

Perceived leadership support positively influences employee safety behavior.

2.3. Perceived Leadership Support and Employee Safety Citizenship

SCB refers to voluntary, discretionary actions that employees take to promote safety within their work environment. These actions include helping colleagues adhere to safety standards, sharing safety knowledge, and advocating for safety improvements [24]. While ESB focuses on compliance and participation, SCB goes beyond these by emphasizing proactive safety behaviors that are not necessarily required but contribute significantly to a safer workplace [25].

PLS plays a vital role in fostering SCB. Leaders who demonstrate a strong commitment to safety inspire their employees to go beyond basic safety compliance. Research has shown that transformational leadership, which emphasizes inspiration and motivation, can significantly enhance SCB by creating a safety culture where employees feel empowered to take initiative [26]. For example, in high-risk industries like construction, effective safety leadership has been shown to promote SCB, resulting in fewer accidents and improved overall safety performance [27]. Studies also suggest that employees are more likely to engage in SCB when they perceive strong support from their leaders, reinforcing the reciprocal nature of the relationship between leadership and employee behaviors in the context of SET [28]. Based on these insights, the following hypothesis is proposed:

H2. 

Perceived leadership support positively influences employee safety citizenship.

2.4. Safety Citizenship and Employee Safety Behavior

SCB has been found to have a significant impact on ESB, as employees who voluntarily engage in SCB are likely to influence their colleagues to also prioritize safety [29]. By helping others adhere to safety protocols, sharing safety knowledge, and advocating for safety improvements, employees who exhibit SCB set an example that encourages others to follow suit [30]. In this way, SCB acts as a mechanism through which safety behaviors are disseminated throughout the organization, contributing to a safer work environment overall.

Research has shown that SCB is closely linked to safety outcomes, particularly in industries where safety risks are high, such as construction. For instance, a study [31] found that SCB played a critical role in reducing accidents and improving safety performance in construction projects. Employees who engage in SCB not only adhere to safety protocols themselves but also encourage their colleagues to do the same, creating a culture of safety that permeates the entire organization [32]. Thus, SCB serves as an important driver of ESB, helping to ensure that safety standards are consistently maintained across the workforce. Therefore, the following hypothesis is proposed:

H3. 

Safety citizenship positively influences employee safety behavior.

2.5. The Mediation Role of Safety Citizenship

The concept of SCB suggests that it serves as a crucial link between PLS and ESB. SCB mediates the relationship between PLS and ESB by functioning as a conduit through which leadership support influences employees’ safety behavior. When leaders demonstrate a strong commitment to safety, such as providing safety training, involving employees in safety-related decisions, or offering resources for safety improvements, employees are more likely to develop trust in their leadership. This trust fosters a sense of obligation among employees to reciprocate the support they receive from their leaders, in line with the principles of SET. This reciprocity is often manifested in behaviors that exceed basic compliance with safety rules, such as engaging in SCB, which contributes to the overall safety climate [4]. The mediating role of SCB can be better understood through its ability to translate leadership support into tangible safety actions. PLS enhances employees’ feelings of empowerment and responsibility toward safety, encouraging them to take initiative and engage in SCB. For instance, when leaders actively participate in safety initiatives and provide employees with the necessary resources and guidance, employees feel more motivated to reciprocate by contributing to the safety of their workplace through proactive safety behaviors. This reciprocity mechanism helps establish a virtuous cycle where leadership support encourages SCB, which in turn leads to greater engagement in ESB [33].

Empirical evidence supports this mediation model, highlighting SCB as a pivotal factor in translating leadership behaviors into improved safety outcomes. Research investigating high-performance work systems, which include elements of transformational leadership, found that SCB played a significant mediating role in the relationship between leadership support and safety outcomes. Employees who perceived strong leadership support for safety were more likely to engage in SCB, which subsequently led to enhanced ESB and reduced safety incidents. This suggests that SCB acts as the behavioral mechanism through which leadership influence is transmitted to employees’ safety actions [33]. Trust is a key factor in this mediated relationship. Employees who trust their leaders are more likely to embrace safety as part of their daily routine and engage in SCB, further reinforcing safety norms within the organization. Leaders who actively participate in safety initiatives set a positive example, promoting a culture of safety that employees are motivated to support and uphold. However, the strength of the mediated relationship can vary depending on the specificity of the behavioral measures used. Studies have suggested that using safety-specific trust measures, rather than generic behavioral measures, can enhance the strength of the mediation effect by better capturing the nuances of the safety dynamics at play [33].

Overall, SCB serves as an intermediary construct that is shaped by employees’ perceptions of leadership support and subsequently influences their safety behaviors. The mediation model underscores the critical role of SCB in ensuring that leadership support for safety is effectively translated into employee actions that contribute to a safer workplace. Based on these insights, the following hypothesis is proposed:

H4. 

Safety citizenship mediates the relationship between perceived leadership support and employee safety behavior.

2.6. The Moderation Role of Safety Learning

In high-risk industries such as construction, ensuring workplace safety is paramount. As part of this effort, it is essential to understand how factors like PLS and SL can enhance ESB. This section explores the moderating role of SL on the relationship between PLS and SCB, as well as its effect on the indirect relationship between PLS and ESB through SCB. In this study, SL is measured as a continuous construct, which allows us to assess varying levels of safety learning across different organizations. By considering SL as a continuous moderator, we can observe how incremental improvements in safety knowledge influence the relationships between leadership support and safety behaviors. SL is operationalized through safety training programs, workshops, safety drills, and informal knowledge-sharing sessions that occur within organizations. These activities contribute to building a collective safety knowledge base that employees can draw upon to enhance their ESBs. The decision to include SL as a moderator is grounded in its critical role in amplifying the effects of leadership support on safety outcomes. SL directly influences how well employees internalize and act on the safety values emphasized by their leaders. In firms where SL is high, employees are better equipped to understand and implement safety protocols, which strengthens the positive influence of PLS on SCB. This amplification occurs because employees who are continuously learning about safety are more likely to engage in proactive safety behaviors, such as assisting colleagues with safety issues and advocating for safer work practices. In contrast, firms with lower levels of SL may struggle to fully leverage the benefits of PLS, as employees lack the necessary knowledge and skills to translate leadership support into meaningful safety actions [34].

Empirical research supports this moderating role of SL. Studies have shown that organizations that prioritize safety learning create a more informed workforce that is not only aware of safety procedures but also more motivated to engage in discretionary safety behaviors (SCB). For instance, a study by [35] found that construction firms with comprehensive safety learning programs observed a stronger relationship between PLS and SCB, as employees in such environments were more likely to embrace safety initiatives and exceed mandatory safety requirements. These findings align with the principles of SET, which posits that employees reciprocate leadership support by engaging in behaviors that benefit the organization. When safety learning is emphasized, employees perceive greater value and support from their leaders, which further motivates them to engage in both SCB and ESB [15]. SL also plays a critical role in moderating the indirect relationship between PLS and ESB through SCB. In firms with high levels of SL, the indirect effect of PLS on ESB via SCB is strengthened. This occurs because employees with robust safety knowledge are better able to take initiative in promoting safety, thus reinforcing the positive impact of leadership support on their safety behaviors. In contrast, in firms with lower SL, employees may not fully grasp the safety principles promoted by their leaders, weakening both the direct and indirect relationships between PLS, SCB, and ESB [26,36]. The inclusion of SL as a moderator is supported by its demonstrated ability to magnify the positive effects of leadership on safety behaviors. By fostering a culture of continuous learning, construction firms can significantly improve their safety outcomes, reduce accidents, and enhance overall workplace safety. Based on this, the following hypotheses are proposed:

H5. 

The relationship between perceived leadership support and safety citizenship is moderated by safety learning. That is, the positive relationship is stronger for construction firms with high safety learning.

H6. 

The relationship between perceived leadership support and employee safety behavior is moderated by safety learning. That is, the positive relationship is stronger for construction firms with high safety learning.

H7. 

Safety learning moderates the indirect relationship between perceived leadership support and employee safety behavior through safety citizenship, such that the indirect relationship is the strongest when safety learning is high.

The proposed research model, as presented in Figure 1, illustrates the hypothesized relationships between perceived leadership support, safety citizenship behavior, and employee safety behavior, moderated by safety learning.

3. Methods

3.1. Participants

In line with the research objectives of investigating perceived leadership support, safety citizenship, ESB, and SL within the Turkish construction industry, a rigorous data collection and sampling procedure were implemented.

The construction industry in Turkey, as one of the nation’s primary economic drivers, has witnessed remarkable growth and development in recent years [37]. Employing approximately 10% of the total labor force and contributing up to 30% of the Turkish economy [38], it stands as a significant sector deserving scholarly attention [39]. To gain a comprehensive understanding of industry dynamics, the questionnaire specifically focused on professionals primarily from the fields of civil engineering and architecture, acknowledging these divisions as central to comprehensively assessing industry behavior [40].

In total, the construction industry in Turkey employed around 2 million individuals in 2022, encompassing a diverse array of technical personnel [41]. To capture a representative sample reflecting this diversity, a questionnaire was distributed to safety experts, site engineers, site supervisors, architects, project managers, and other technical personnel across both public and private sectors [42]. The Chamber of Civil Engineers served as a conduit for disseminating the questionnaire, ensuring access to professionals across various organizational contexts.

3.2. Data Collection

To commence data collection, an email invitation letter was sent to members of a Building Information Centre’s network. Employing a simple random sampling technique, this approach aimed to ensure equal opportunity for participation among eligible individuals within the construction industry. Subsequently, the questionnaire was distributed electronically, utilizing an online form, to a targeted pool of 1200 industry professionals.

Upon completion of the data collection phase, a total of 414 respondents submitted responses to the questionnaire. However, for this research, only 410 meticulously filled-out responses were deemed suitable for analysis, reflecting a response rate of 34.16%. This meticulous selection process aimed to uphold the integrity and validity of the dataset, ensuring that the analyses drew from high-quality responses representative of the target population.

3.3. Respondent Profile

Among the 410 valid participants, a predominant 79.8% identified as male. Moreover, a substantial 81% of respondents fell within the age bracket of 20 to 40 years. Furthermore, the data reveal that 30.7% of respondents reported having less than ten years of experience in the construction sector. The distribution of respondents across various job positions was reasonably balanced, with slight variations among roles (e.g., Architects at 22.7% and Safety experts at 21.2%). Despite these small differences (see Figure 2), the distribution ensures a broad representation of key roles within the industry.

This comprehensive demographic profile, detailed in

Table 1. Demographics profile of the research sample.

Category		Frequency	Percentage (%)
Gender	Male	327	79.8%
	Female	83	20.2%
Age	<20	7	1.7%
	20–less than 30	143	34.9%
	30–less than 40	189	46.1%
	40–less than 50	45	11.0%
	>50	26	6.3%
Experience	<5	83	20.3%
	5–less than 10	126	30.7%
	10–less than 15	73	17.8%
	15–less than 20	79	19.3%
	>20	49	11.9%
Position	Safety experts	87	21.2%
	Site engineers	56	13.7%
	Site supervisors	78	19.0%
	Architects	93	22.7%
	Project managers	96	23.4%
Total		410	100%

, provides a well-rounded understanding of the respondents’ backgrounds, thereby enriching the analysis of safety perceptions and behaviors within the construction context.

3.4. Conceptualization of Variables

Informed by previous research, the survey items were carefully customized to fit the local context and cultural nuances while maintaining the integrity of the original constructs [43]. To ensure face validity, a critical aspect of measurement development, consultations were held with safety officers, representatives, and academicians. Their expertise played a pivotal role in refining the survey items to closely align with the study’s objectives and the distinctive features of the context of the construction industry (Appendix A). Additionally, to adapt the SCB ales to the Turkish context, some items underwent slight revisions and were translated into Turkish. Each measure utilized in the survey employed a consistent 5-point response SCB ale, ranging from “strongly disagree” to “strongly agree”, ensuring uniformity in participant responses across all variables.

The assessment of perceived leader support, a pivotal determinant of organizational climate and employee well-being, relied on a set of 5 items sourced from seminal works in the field [44,45,46]. SL, essential for fostering a culture of continuous improvement and knowledge dissemination, was operationalized using 2 statements adapted from SCB ales developed by [47,48]. Perceived SCB, encompassing employees’ voluntary actions to promote safety beyond formal job requirements, was assessed through a comprehensive set of 12 items informed by the research of [33,49]. Finally, ESB was measured through two distinct dimensions: safety compliance and participation. Safety compliance, encompassing adherence to safety rules and regulations, was evaluated using three items adapted from existing SCB ales [50]. Safety participation, reflecting employees’ active involvement in safety-related initiatives and activities, was also assessed through three items drawn from the same sources. The constructs used in this study, including Perceived Leadership Support (PLS), Safety Citizenship Behavior (SCB), Safety Learning (SL), and Employee Safety Behavior (ESB), were measured using established scales. The detailed survey items for each construct can be found in Appendix A (Survey Measure).

3.5. Common Method Bias Check

The study rigorously addressed the potential common method bias (CMB) issue by following established guidelines and recommended thresholds from the literature [51]. Specifically, we utilized Harman’s single-factor test, a widely recognized technique for detecting this bias [52]. The findings remained robust, as the single factor explained only 27.95% of the total variance, well below the critical threshold of 50%, indicating no significant concern for common method bias [53]. However, we acknowledge the limitations of relying solely on Harman’s test. While it serves as an initial diagnostic tool, its sensitivity to detecting all forms of bias is limited [51]. Given the scope and complexity of our data, we opted not to implement additional methods like the common latent factor approach, but this will be a priority in future studies to further reduce potential bias. Additionally, we conducted a comprehensive collinearity test by estimating variance inflation factors (VIFs) for all constructs, employing the recommended thresholds of 3.3 and 5 [54]. The results, as illustrated in

Table 2. Measurement model assessment.

Construct	Indicators	Outer Loadings	VIF	Cronbach’s α	CR	AVE
Perceived leader support (PLS)				0.808	0.866	0.564
	PLS1	0.767	1.595
	PLS2	0.669	1.452
	PLS3	0.807	1.738
	PLS4	0.706	1.410
	PLS5	0.797	1.711
Safety citizenship behavior (SCB)				0.797	0.832	0.528
	SCB1	0.824	1.630
	SCB2	0.695	1.201
	SCB3	0.857	2.367
	SCB4	0.821	2.143
	SCB5	0.764	1.992
	SCB6	0.779	2.388
	SCB7	0.872	1.779
	SCB8	0.912	1.552
	SCB9	0.862	1.788
	SCB10	0.908	1.622
	SCB11	0.861	1.469
	SCB12	0.850	1.631
Safety learning (SL)				0.801	0.909	0.834
	SL1	0.917	1.850
	SL2	0.910	1.804
Second-order of Employee Safety Behavior (ESB)				0.896	0.920	0.658
	SC	0.931	-
	SP	0.902	-
Safety compliance (SC)				0.883	0.928	0.811
	SC1	0.912	2.896
	SC2	0.921	3.054
	SC3	0.868	2.053
Safety participation (SP)				0.838	0.903	0.756
	SP1	0.840	1.848
	SP2	0.870	1.956
	SP3	0.897	2.202

Note(s): Variance inflation factor (VIF), Composite reliability (CR), Average variance extracted (AVE).

, demonstrated that all VIFs fell comfortably below these thresholds, ranging from 1.201 to 3.054. This comprehensive assessment, in line with established guidelines and thresholds, provides strong assurance that CMB is not a significant concern in the analysis, enhancing the credibility and reliability of the findings in exploring the relationships between variables.

4. Data Analysis and Results

PLS-SEM was used as the primary data analysis technique. PLS-SEM is widely acknowledged in social science research for its versatility and applicability in analyzing intricate relationships among latent variables [55]. Given our focus on exploring the interplay between various constructs within the construction industry, this methodological approach proved advantageous. Given its robustness in handling missing data and its resilience against violations of normality assumptions [56], PLS-SEM is well suited for analyzing the dataset. This dataset likely captures the inherent complexities and uncertainties of real-world settings. Leveraging PLS-SEM allowed us to rigorously test hypotheses and confidently validate the measurement model [57], enabling insightful analysis of the collected data.

4.1. Validation of the Measurement Model

The measurement model was meticulously validated using Partial Least Squares Structural Equation Modeling (PLS-SEM), a widely recognized method for assessing the reliability and validity of measurement constructs. Initially, we scrutinized the validity and reliability of the measures using established metrics. The internal consistency of the constructs was confirmed, as evidenced by Cronbach’s α and Composite Reliability (CR) values exceeding the threshold of 0.70, indicating reliable measurement instruments [58]. Furthermore, to ensure convergent validity, we assessed the psychometric properties of each construct by examining factor loadings and Average Variance Extracted (AVE) coefficients within the structural model [31]. As indicated in Table 2, all individual factor loadings surpassed the recommended threshold of 0.6, and AVE coefficients were also above 0.5, affirming the convergent validity of the theoretical model. However, we acknowledge that two factor loadings, including PLS2 (0.669) and SCB2 (0.695), were slightly below the 0.7 threshold. Following Hair et al. [59], we retained these items, since they exceeded the acceptable minimum of 0.6 and ensured composite reliability, as demonstrated in Table 2.

Moreover, we conducted a thorough assessment of discriminant validity to ensure that each construct in the model measures a distinct concept [56]. This involved calculating Heterotrait–Monotrait (HTMT) ratios using criteria established by [60]. As illustrated in

Table 3. Discriminant validity of measures.

Factors	ESB	PLS	SCB	SL
Employee Safety Behavior (ESB)	0
Perceived leader support (PLS)	0.464	0
Safety citizenship behaviour (SCB)	0.492	0.523	0
Safety learning (SL)	0.334	0.475	0.459	0

, all HTMT values were below the threshold of 0.850, indicating that multicollinearity among the constructs was not a concern [61]. This robust analysis provides strong evidence of both discriminant and construct validity, further bolstering the credibility and reliability of the measurement model [62]. By employing rigorous validation techniques, we have ensured that the model accurately captures the relationships between key variables, laying a solid foundation for the subsequent analysis of hypotheses and findings in the study.

4.2. Assessment of the Structural Model

In this section, we explore the assessment of the structural model using PLS-SEM, a widely recognized method known for its efficacy in analyzing complex relationships among variables [63]. PLS-SEM is particularly advantageous for theory-building, exploratory research, and predictive modeling, offering a flexible and robust approach to hypothesis testing [64]. Leveraging this methodological framework, we conducted a comprehensive examination of the structural relationships proposed in this study. The use of bootstrapping procedures allowed for estimating standard errors and determining the significance of parameter estimates, ensuring rigorous statistical inference [65]. Additionally, the model fit was assessed using the SRMR and RMSEA values, which were 0.075 and 0.067, respectively, both below the threshold of 0.08, indicating a good fit for the structural model.

In

Table 4. Structural model hypotheses testing results.

Hypotheses	Hypothesized Relationships	Sample Estimate	Standard Error	T-Statistics	p Values	CIs		Decision
						2.5%	97.5%
1	PLS → ESB	0.313 ***	0.077	4.050	0	0.164	0.468	Supported
2	PLS → SCB	0.453 ***	0.063	7.142	0	0.327	0.577	Supported
3	SCB → ESB	0.261 **	0.078	3.359	0.001	0.108	0.413	Supported
4	PLS → SCB → ESB	0.118 **	0.038	3.076	0.002	0.047	0.198	Supported
5	SL × PLS → SCB	0.172 ***	0.048	3.604	0	0.081	0.267	Supported
6	SL × PLS → ESB	0.151 *	0.066	2.307	0.021	0.016	0.277	Supported

Note(s): Absolute values are applied to standardized path coefficients, * statistically significant at p < 0.050, ** statistically significant at p < 0.010, *** statistically significant at p < 0.001.

, we present the findings, including path coefficients and corresponding p-values, which provide insights into the strength and significance of relationships between key variables. By applying PLS-SEM, we enhanced our understanding of the underlying dynamics influencing ESB and leadership support in the construction industry, contributing to knowledge advancement in this field.

In this study, the assessment of the structural model revealed significant findings related to the direct effects of PLS on ESB and SCB within the construction industry. As illustrated in Table 4 and Figure 3, the direct effects analysis provided support for H1, indicating a positive impact of PLS on ESB (β = 0.313, t = 4.050, p = 0). Similarly, H2 was confirmed, demonstrating a positive influence of PLS on SCB (β = 0.453, t = 7.142, p = 0). Additionally, H3 received support, indicating a positive relationship between SCB and ESB (β = 0.261, t = 3.359, p = 0.001).

Building upon these direct effects, H4 proposed that SCB mediates the relationship between PLS and ESB. Following the mediation analysis approach recommended by [66,67,68], bootstrapping was performed to assess the indirect effects. The results indicated a statistically significant indirect effect (β = 0.118, t = 3.076, p = 0.002), providing support for H4.

Furthermore, H5 and H6 proposed moderation effects, suggesting that the relationship between PLS and SCB, as well as ESB, is moderated by SL. To test these moderation effects, the product indicator approach was employed within the PLS-SEM framework, aligning with recommendations by [69,70]. The results revealed significant interaction terms (β = 0.172, t = 3.604, p = 0 for H5; β = 0.151, t = 2.307, p = 0.021 for H6), providing robust evidence in support of both hypotheses. This indicates that the positive relationship between PLS and SCB (Figure 4), as well as between PLS and ESB (Figure 5), is strengthened in construction firms with high levels of SL. Specifically, Figure 4 illustrates that the positive relationship between perceived leadership support and safety citizenship is more pronounced in firms exhibiting high safety learning, while Figure 5 demonstrates that the positive relationship between perceived leadership support and employee safety behavior also intensifies under high safety learning conditions. These findings highlight the significance of accounting for SL as a moderating factor in enhancing the effectiveness of leadership support initiatives aimed at promoting SCB within construction organizations. As a result, these results offer valuable insights for practitioners and policymakers in the construction industry, enabling them to develop more effective strategies for enhancing safety culture and overall performance.

4.3. Moderated Mediation Test

In this study, a moderated mediation analysis was conducted to evaluate H7, which proposes that SL moderates the indirect relationship between PLS and ESB through SCB. Following the approach outlined by [67], specifically using Model 14 in the process macro, the index results presented in

Table 5. Conditional indirect effects results.

Conditional Indirect Effects of Safety Citizenship Behaviour at the Level of Safety Learning			95% BOOT CI
	BOOT Effect	BOOT SE	Lower	Upper
Low safety learning	0.078	0.059	−0.03	0.20
Medium safety learning	0.138	0.043	0.06	0.23
High safety learning	0.199	0.051	0.10	0.31

Note(s): The 95% confidence intervals were derived using bias-corrected bootstrap techniques.

highlight the significance of this moderated-mediation mechanism. The findings reveal that the conditional indirect relationship between PLS and ESB through SCB is indeed moderated by SL. This is evident from the 95% CI not encompassing zero. The bootstrap confidence intervals were calculated using bias-corrected bootstrap techniques, ensuring the robustness of these results. Notably, when Turkish construction employees exhibit a low level of SL, the CI for the mediated moderation model—where PLS influences ESB via SCB—includes zero (β = 0.078, SE = 0.059, [LBCI = −0.03, UBCI = 0.20]). However, when employees demonstrate a high level of SL, the CI no longer includes zero (β = 0.199, SE = 0.051, [LLCI = 0.10, ULCI = 0.31]). Furthermore, the conditional indirect effect index remains non-zero (LLCI = 0.01, ULCI = 0.13), providing further support for H7. These findings underscore the nuanced interplay between perceived leadership support, SCB, and SL in shaping ESB within the Turkish construction industry. They emphasize the importance of considering contextual factors in safety management strategies.

4.4. Predictive Power Check of the Structural Model

In assessing the predictive power of the structural model, a procedure tailored for the prediction-oriented nature of PLS-SEM [71,72] was employed. Using the ‘perceived leadership support-predict’ approach with a 10-fold procedure, we examined predictive relevance at both the construct and item levels (

Table 6. Perceived leadership support-predict for ESB dimensions.

Items	Q2 Predict	PLS-SEM_RMSE	PLS-SEM_MAE	LM_RMSE	LM_MAE
ESC1	0.156	1.025	0.833	1.053	0.867
ESC2	0.210	0.970	0.780	1.023	0.807
ESC3	0.173	0.958	0.777	0.978	0.782
ESP1	0.108	1.010	0.804	1.027	0.831
ESP2	0.154	1.018	0.828	1.073	0.879
ESP3	0.159	1.037	0.844	1.062	0.862

). A Q² value exceeding 0 is considered a general threshold for predictive relevance because it indicates that the model has predictive accuracy beyond random chance [72]. Specifically, for ‘employee ESB’, the Q² value was 0.197, indicating strong predictive relevance. In comparison to other models in the field, a Q² value in this range demonstrates that the model performs well in predicting key outcomes, such as employee safety behavior, where a Q² above 0.10 is often viewed as sufficient for models aiming to explain behavioral outcomes in organizational settings [56].

Additionally, all item-level Q² values exceeded 0, and the item-level errors of the PLS model were lower than those of the LM, confirming robust predictive power [72]. Notably, we elaborated on specific items, such as ESC1, which exhibited lower errors in the PLS-SEM model compared to the LM model. This difference arises from the predictive orientation of PLS-SEM, which optimizes accuracy at the item level. As all the item-level errors of the PLS model were lower than those of the LM model, we conclude that the model demonstrates strong predictive power, consistent with Shmueli et al. [72].

5. Discussion

The primary objective of this study is to identify key factors that enhance SL, ultimately leading to improved ESB in the construction industry. Grounded in SET, the study investigates how PLS and SL influence employees’ propensity to engage in safe behaviors. By exploring these relationships, the study offers practical insights into fostering a safety-conscious culture within construction firms. A key finding of the study is the pivotal role that PLS plays in prioritizing safety and employee well-being. When leaders align their safety values with those of their employees, they effectively cultivate a culture that encourages safer behaviors. This supports Hypothesis 1 and aligns with existing literature, which underscores the critical role of leadership in shaping safety attitudes and behaviors within organizations [73]. In practice, construction firms can leverage this by ensuring that leadership prioritizes safety, not just in formal protocols, but through active and consistent engagement with employees. This includes regular safety communication, promoting open feedback channels, and visibly championing safety practices. Such initiatives can help reinforce the importance of safety and foster greater employee buy-in.

Furthermore, the study confirms Hypothesis 2, demonstrating a positive correlation between PLS and SCB. Leaders, particularly on-site supervisors, play a crucial role in reinforcing safety by frequently reminding employees to prioritize accident prevention and correcting unsafe behaviors. These supervisors act as key facilitators of safety knowledge, helping employees continuously enhance their safety practices through training and self-directed learning [74]. Construction firms can capitalize on this by integrating supervisors into their broader safety management strategy, empowering them to take ownership of safety training and ensuring they provide ongoing, practical safety guidance.

While Hypotheses 5 and 6 proposed that SL would strengthen the relationship between PLS, SCB, and ESB, the analysis revealed that SL did not have a significant moderating effect. This unexpected finding diverges from previous research that emphasized SL’s potential to enhance safety behaviors by improving employees’ safety knowledge and capabilities [35]. Several theoretical and practical factors might explain this discrepancy. First, it is possible that SL, as operationalized in this study, did not capture the full complexity of learning processes in safety. While the study measured SL as a continuous construct, it may not have fully accounted for the depth and quality of safety training or the cultural context in which it occurred. For instance, firms that implement SL programs without sufficient emphasis on practical application or reinforcement may not see the same benefits in terms of safety behavior improvements [69]. This suggests that the type and delivery method of safety learning, whether formalized training, hands-on experience, or peer-to-peer knowledge sharing, could play a critical role in its effectiveness. Second, other unmeasured factors may be interacting with PLS and SL to influence safety outcomes. For example, individual employee traits such as safety motivation or risk perception could moderate the effects of leadership support on safety behaviors. Alternatively, organizational factors like management commitment to safety or availability of resources for safety initiatives may also play a role in shaping how SL influences safety outcomes [75]. Further research is needed to explore these potential moderators and provide a more comprehensive understanding of SL’s impact on safety behaviors.

The statistical analysis supported the mediating role of SCB in the relationship between PLS and ESB, confirming Hypotheses 3 and 4. SCB acts as a bridge between leadership support and safety behaviors, with the standardized path coefficient of 0.261 for H3 indicating a significant improvement in ESB through SCB. Similarly, H4 was confirmed with a standardized path coefficient of 0.118, demonstrating that SCB partially mediates the effect of PLS on ESB. This suggests that, when employees actively engage in SCB, they are more likely to internalize leadership support and translate it into concrete safety behaviors. In practice, this highlights the importance of encouraging SCB, which includes employees voluntarily assisting peers with safety-related tasks and advocating for safer work environments. Organizations can promote SCB by creating a culture where such behaviors are recognized and rewarded. The interaction terms for Hypotheses 5 and 6, based on the product indicator method in the PLS-SEM framework, were significant, with values of 0.172 and 0.151, respectively. Despite the statistical significance, the moderating role of SL was not as robust as expected, prompting the need for further exploration of how SL interacts with leadership support in different organizational settings.

This study references multiple forms of leadership: PLS, empowering leadership, and safety leadership, each of which contributes uniquely to safety outcomes [33]. PLS, as conceptualized in this study, subsumes aspects of both empowering and safety leadership. It emphasizes the extent to which leaders show concern for employee well-being, provide necessary resources, and actively support safety initiatives. Empowering leadership, meanwhile, focuses on giving employees autonomy and responsibility for safety practices, while safety leadership explicitly emphasizes setting and enforcing safety standards. In practice, construction firms should strive to integrate these leadership styles by not only offering support and resources but also encouraging employee involvement in safety decision-making and enforcing compliance with safety protocols. The findings offer several actionable insights for construction firms. First, leadership must be proactive in fostering a safety-conscious culture by visibly prioritizing safety and actively engaging with employees. This can be achieved through regular safety briefings, open communication channels, and involving employees in safety decision-making processes. Second, firms must ensure that SL programs are well designed and practically oriented, with a focus on application rather than just theoretical knowledge. Finally, integrating SCB into organizational safety strategies can enhance the overall safety culture, leading to better safety performance and a reduction in workplace accidents.

6. Conclusions

6.1. Theoretical Contribution

This study contributes significantly to the field of safety behavior in the construction industry by offering a nuanced understanding of how PLS and ESB interact, particularly through the view of SL. By integrating SET [76], the research advances theoretical frameworks on safety management by highlighting the critical role of leadership in fostering safety-conscious behaviors. Unlike prior studies, that focused primarily on individual leadership styles [77], this research delves deeper into how PLS can shape collective safety outcomes through SCB, indicating the mediating role of SCB between PLS and ESB [78]. This enhanced understanding strengthens the theoretical linkages between leadership support and safety outcomes, contributing to the broader discourse on safety management practices within the construction industry.

Additionally, this study clarifies the unique role of SL in enhancing safety behaviors, demonstrating how its influence, while moderated by leadership support, offers practical and theoretical implications for understanding safety practices in construction. By focusing on the Turkish construction industry, the study contextualizes these theoretical contributions, but it also opens pathways for cross-industry and cross-cultural comparisons, enhancing the global applicability of the findings.

6.2. Practical Implications

From a practical standpoint, this research provides actionable insights for construction organizations aiming to improve safety behaviors among employees. PLS emerged as a key facilitator of both SCB and ESB, suggesting that organizations should prioritize leadership training programs that empower leaders to promote a culture of safety learning and proactive safety behavior. Rather than just emphasizing compliance, effective leaders foster environments where employees feel supported in taking ownership of their safety practices. This study shows that clear communication, continuous feedback, and leadership behaviors that emphasize collaboration can significantly improve safety outcomes.

Organizations should develop targeted leadership initiatives that equip supervisors and team leaders with the skills needed to build trust and create open channels for safety-related dialogue. Additionally, implementing feedback mechanisms that allow employees to continuously learn and adapt their safety practices will further embed safety behaviors in daily operations. These practical measures can help organizations not only reduce accidents but also promote long-term sustainability in safety performance, leading to better employee satisfaction, retention, and productivity.

6.3. Limitations and Future Direction

This study has several limitations that future research can address. First, its focus on the Turkish construction industry may limit the generalizability of the findings. To broaden applicability, future studies should explore the dynamics of leadership support and safety behavior in different industries and cultural contexts. Additionally, while the study used a quantitative approach, qualitative research methods like interviews or case studies could provide deeper insights into the underlying mechanisms of PLS and SL. Such an approach would uncover more nuanced factors influencing safety behaviors that surveys alone might not reveal.

Moreover, the research primarily examined short-term safety behaviors, without investigating the sustainability of these practices over time. Future studies should focus on how leadership support can foster long-lasting safety cultures that extend beyond immediate outcomes, possibly by exploring the role of continuous learning and adaptability in maintaining safety standards. Finally, the environmental and organizational factors influencing the sustainability of safety practices, such as resource management and broader workplace conditions, are underexplored in this study. Investigating these elements in future research could provide a more holistic understanding of sustainable safety solutions in the construction sector.