Safety climate,safety behavior,injury experience,and musculoskeletal disorders among Iranian manufacturing employees: A structural equation modeling study

Abstract

Background

While safety climate and performance are extensively studied, few research studies have explored the relationship between safety climate and both retrospective and prospective indicators of safety performance.

Objective

This study aims to investigate the relationships between safety climate, safety behavior, musculoskeletal disorder (MSD) symptoms, and injury experience in manufacturing using structural equation modeling (SEM).

Methods

The study enrolled 409 employees from 15 manufacturing companies in Urmia, Iran in 2021. Data were collected using safety climate, safety behavior, and Nordic musculoskeletal disorders questionnaires. Occupational injury experiences from the past year were also recorded. SEM was used to analyze safety climate's relationships with performance variables.

Results

A positive relationship was observed between safety climate and safety behavior (r = 0.38, p < 0.01). Symptoms of MSD correlated significantly with safety climate (r = −0.12, p < 0.05) and injury experience (r = 0.10, p < 0.05). A significant association was found between safety climate and safety behavior factors in the first model (ß = 0.19, p = 0.001). The overall fit indices for the proposed model were acceptable. SEM confirmed a significant association between safety climate and safety behavior (ß = 0.20, p = 0.001). However, no relationship was found between safety climate/behavior and injury experience or MSD symptoms.

Conclusions

The safety climate influenced safety behavior but had no effect on employees’ self-reported injuries or MSD symptoms. Enhancing workplaces safety climate could prevent unsafe behaviors. This study recommends exploring the association between safety climate and safety performance indicators, considering potential mediators and moderators.

Keywords

safety performance injury climate behavior modeling

Introduction

The assessment of safety performance significantly influences decision-making regarding safety in the workplace. Employing a suitable set of safety indicators involves consistently inputting valid information into the Safety Management System (SMS). This system furnishes details on both actual safety performance, represented by retrospective or lagging indicators, and SMS operation, encompassing prospective or leading indicators.^1,2 A conventional approach to appraising safety performance involves gathering and scrutinizing incident-related data, such as the frequency and severity of accidents, known as lagging indicators. However, these indicators have faced criticism for measuring system failures without revealing causal links that could drive system improvement. In response to this critique, leading indicators, such as safety climate and safety behavior surveys, have been developed. These leading indicators fill the information gap left by incident-based measurements, aligning with current organizational and safety management trends.³ The combination of lagging and leading indicators empowers managers to make well-informed decisions to enhance workplace safety.

The safety climate within an organization is defined as employees’ participative understanding of safety management, serving as an indicator of their accurate and clear beliefs regarding safety and the effective application of safety management in the workplace.^4–6 Numerous studies have consistently identified a correlation between safety climate and safety performance.^7,8 It stands out as the most robust predictor of organizational safety performance, exerting a substantial impact on employee performance.⁹ Furthermore, the safety climate serves as an early indicator, capable of highlighting potential safety problems before they result in injuries.^8,10–12 It plays an important role in the occurrence of accidents.^13,14 Past research has proposed that the safety climate is a reliable predictor of safety-related outcomes, including accidents or injuries.^11,12,15,16 Therefore, the following hypothesis was proposed: H₁a:

Safety climate negatively influences injury experience.

Musculoskeletal disorders (MSDs) are globally recognized as significant occupational concerns. In Iran, they stand out as the most prevalent work-related injuries, leading to considerable downtime and costs.¹⁷ MSDs encompass disabilities affecting muscles, tendons, ligaments, joints, blood vessels, and peripheral nerves, resulting in pain or discomfort.¹⁸ Various workplace activities, including heavy lifting, extended shifts, lack of rest schedules, repetitive movements, poor posture, vibration, and extreme static hand and arm postures, are acknowledged risk factors for MSDs.¹⁹ Demographic characteristics and psychological factors also emerge as noteworthy predictors for MSDs.^20,21 Despite the critical impact of MSDs, only a limited number of studies have explored the relationship between safety climate and MSDs.²² Research indicates that lower levels of organizational climate in the workplace are associated with increased MSD prevalence, subsequently contributing to absenteeism.²³ Another study demonstrates that enhancing the safety climate has led to a 15% reduction in MSDs, significantly lowering work-related injuries and diseases. Consequently, a suboptimal safety climate in the workplace can result in adverse health outcomes, including MSDs, injuries, and work-related diseases.²⁴ Notably, healthcare workers with a better safety climate exhibit lower symptoms of MSDs.²⁵ Accordingly, the following hypothesis was proposed as follows: H₂a:

Safety climate may negatively influences the symptoms of MSDs.

Safety behavior is defined as “the actions or behaviors that individuals exhibit in almost all jobs to promote the health and safety of workers, clients, the public, and the environment”.^26,27 It is widely believed that employee safety behavior is influenced by the safety climate, either directly or indirectly.^28,29 Mohamed (2002) utilized a Structural Equation Modeling (SEM) to explore the connection between safety climate and safe work behavior in the construction industry, concluding that safe work behavior results from a positive safety climate.³⁰ A significant proportion of workplace accidents can be attributed to unsafe behaviors.^31,32 Neal et al. (2000) found that workplace safety climate was associated with self-reporting of compliance with safety regulations and procedures, as well as engagement in safety-related activities. Safety-related behaviors, including safety compliance (typically mandatory) and safety participation (often voluntary), can be considered integral components of safety performance, even though standard safety performance measures primarily rely on accident or injury data. Employee behavior that enhances occupational health and safety (OHS) is termed safety compliance, encompassing actions such as adhering to safety procedures and working in a safe manner. Contributions such as assisting coworkers, promoting safety programs, taking initiative, and enhancing workplace safety are all facets of a comprehensive workplace safety program.³³ Another crucial aspect is safety participation, where employee behavior contributes to the safety of coworkers while aligning with the stated goals and objectives of the organization.³⁴ Employees with a positive perception of the safety climate are more likely to engage in safety compliance and participation behaviors, linked to reduced negative safety outcomes like injuries and accidents.²⁶ Therefore, an additional hypothesis is proposed as follows: H₃a:

Safety climate positively influences safety behavior.

Occupational injury is defined as any wound or damage to the body caused by an event in the workplace.³⁵ However, few studies have delved into the mechanisms mediating the association between safety climate and safety outcomes, such as injuries and MSDs.^36,37 Unsafe behaviors of employees were identified as the main cause of accidents.^38,39 Employees’ behaviors are crucial for ensuring their safety and preventing MSDs.⁴⁰ In a manufacturing context, Johnsen (2007) found an association between safety climate and injury frequency, a relationship mediated by safety behavior.¹⁰ Hence, we propose that safety climate influences injury experience and symptoms of MSDs through the mediation of safety behavior, as follows: H₁b:

Safety behavior negatively affects injury experience.

H₂b:

Safety behavior negatively affects symptoms of MSDs.

Few investigations, particularly in non-English speaking countries, have explored the relationships between safety climate, safety behavior, and safety outcomes, such as injury experience and symptoms of MSDs.⁴¹ At the time of developing this study, no similar studies utilizing the hypothetical model have been reported in Iran (Figure 1). Therefore, in this study, our aim was to investigate the association among safety climate, safety behavior, injury experience, and symptoms of MSDs.

Figure 1.

SEM model of safety climate, safety behavior, injury experience, and MSD symptoms.

Methods

Study design and participants

The current study enrolled 409 participants employed at 15 manufacturing companies in Urmia, Iran. These companies were engaged in the production of tobacco, cement, food, textiles, and wood products. Participants were selected using the quota random sampling method from each company and unit. Inclusion criteria comprised a minimum of one year of work experience in a manufacturing company, working a minimum of 40 h per week, and proficiency in the Persian language with at least primary education. Exclusion criteria included incomplete questionnaire responses. During the administration of the questionnaires, participants were briefed on the study's purpose, and they were assured that the information collected would be treated with confidentiality. All procedures involving human subjects were conducted in accordance with the ethical standards of the Institutional Review Board/Ethics Committee of Urmia University of Medical Sciences, Iran (IR.UMSU.REC.1401.317) and with the Declaration of Helsinki of 1964 and its later amendments. Informed consent was obtained from all participants prior to their participation in the study.

Study instruments

The data were gathered using safety climate, safety behavior, and Nordic MSDs questionnaires. Additionally, participants were queried about workplace injuries in the previous 12 months and whether they had undergone any safety training courses. The safety climate scale, developed by Ghahramani and Khalkhali (2015), served as a tool for assessing safety climate within manufacturing companies. This scale underwent rigorous evaluation for face and content validity, as well as reliability, by a panel consisting of university scholars, safety officials, and manufacturing company employees. Quantitative content validity analysis revealed a satisfactory content validity index (CVI ≥ 0.78) and content validity ratio (CVR > 0.38). Construct validity was confirmed through factorial analysis, and the scale exhibited high reliability with a calculated Cronbach's alpha of 0.96. The safety climate questionnaire encompassed demographic data and comprised 45 questions across seven factors: safety commitment and communication (16 items), safety involvement and training (8 items), positive safety practices (8 items), safety competency (3 items), safety procedures (4 items), accountability and responsibility (3 items), and a supportive environment (3 items).⁴² Using a Likert scale and with 45 questions, the safety climate score ranged from 45 to 225 across all participants. The scores were categorized into three groups: weak (less than 135), medium (135–180), and strong (greater than 180).

The safety behavior questionnaire utilized identical items as those employed by Vinodkumar and Bhasi (2010). After translating the questionnaire into Persian and undergoing a review by two authors, the tool underwent evaluation by an OHS specialist. Necessary revisions were implemented based on the provided comments. The content of the questionnaire items in the initial version was derived from existing standard tools, and subsequent qualitative validation was conducted. The questionnaire demonstrated a comparative fit index value exceeding 0.95, indicating strong construct validity. Moreover, the internal consistency of the questionnaire, assessed by Cronbach's α, exceeded 0.65, signifying a satisfactory level of reliability. The safety behavior questionnaire comprised items related to safety compliance (4 items) and safety participation (4 items).⁴³ Additionally, due to the inclusion of eight questions in the safety behavior questionnaire (n = 8) and the application of a Likert scale, the safety behavior score demonstrated variability among participants, ranging from 8 to 40. These scores were categorized into three groups: weak safety behavior (less than 24), medium (24–32), and strong (more than 32). To assess safety climate and safety behavior, respondents have to indicate their agreement on a five-point Likert scale, ranging from 1, representing strongly disagree, to 5, representing strongly agree.

The Nordic questionnaire was used to record the symptoms of MSDs of participants in 9 body regions. This questionnaire is a validated and reliable tool widely used for assessing the prevalence of MSDs. This questionnaire was developed in 1987 by Kuorinka et al. at the Nordic Institute for Advanced Training in Occupational Health.⁴⁴ The questionnaire, translated and localized by Mokhtarinia et al. (2015) for use in Iran, received confirmation of face validity for all included items based on the researchers’ findings. The intraclass correlation coefficient (ICC) was less than 0.70, indicating an acceptable level of agreement.⁴⁵ The standard error of measurement fell within the acceptable range, ranging from 0.56 to 1.76. Additionally, Cohen's kappa value ranged from 0.78 to 1, further supporting an acceptable level of agreement.⁴⁶

Data analysis

The normality test was performed and upon meeting the standard normality criteria, the corresponding statistical tests were performed. Cronbach's alpha coefficient for all safety climate and safety behavior questions and their factors exceeded 0.70, ensuring high internal consistency. he data underwent checks for multicollinearity using tolerance (<30) and the variance inflation factor (<10).⁴⁵ No evidence of multicollinearity among the independent variables was found. To conduct SEM, demographic characteristics, safety climate and safety behavior scores, and the prevalence of MSDs were initially calculated. Subsequently, the relationship between safety performance variables and other variables was assessed using the Pearson correlation coefficient. Then, SEM was used to estimate the relationships between safety climate and safety performance variables. SEM, recognized as a powerful and comprehensive tool for assessing links between observed and latent variables, facilitates the simultaneous examination of multiple dependencies.^47,48 Several criteria were applied to evaluate the fit of the models, including the ratio of χ2 to the degrees of freedom (less than 2), root mean square error of approximation (RMSEA < 0.06), comparative fit index (CFI ≥ 0.90), goodness of fit index (GFI ≥ 0.90), adjusted goodness of fit index (AGFI ≥ 0.90), and normed fit index (NFI ≥ 0.90).^49,50 Statistical analysis was performed using SPSS, while AMOS software was employed for SEM to assess the overall fit indices of the hypothesized model.

Results

Table 1 presents participant demographics, with the majority (34.7%) aged between 30–39 years, and 193 (47.2%) having over ten years of work experience. Approximately 42% had a high school education, 336 (82.2%) were married, and 234 (57.2%) worked in production units. The mean (±SD) age, working experience, and job tenure were 36.31 (±9.61), 10.74 (±6.57), and 9.08 (±6.09) years, respectively. Thirteen of the fifteen companies were privately owned. The majority (92.7%) worked under contract, and 184 (45%) had attended safety training courses.

Table 1.

Personal and occupational characteristics of the participants.

Variable	N	%
Gender
Male	365	89.2
Female	44	10.8
Marital Status
Married	336	82.2
Single	73	17.8
Job Category
Production/service	234	57.2
Maintenance	83	20.3
Office	52	12.7
Warehouse	22	5.4
Other	18	4.4
Education
Elementary	59	14.4
Secondary	63	15.4
High-school	170	41.6
University	117	28.6
Age (year)
<30	112	27.4
30–39	142	34.7
40–49	126	30.8
50–59	26	6.4
≥60	3	0.7
Working Experience (year)
<1	24	5.9
5-Jan	91	22.2
10-Jun	101	24.7
>10	193	47.2
Job Tenure (year)
<1	38	9.3
5-Jan	108	26.4
10-Jun	113	27.6
>10	150	36.7

According to Table 2, among safety climate factors, the highest score was for safety procedures, 3.88 (±0.71), and the lowest was for safety involvement and training, 3.28 (±0.84). Among participants, 110 (26.9%) achieved a strong safety climate score, and 179 (43.8%) attained a moderate safety climate score. The mean score for safety behavior was 4.02 (±0.75), with 207 participants (50.6%) scoring strongly and 151 participants (36.9%) achieving a moderate safety behavior score. The Cronbach's alpha for safety climate and safety behavior questionnaires were 0.97 and 0.93, respectively, indicating acceptable reliability. Table 2 also presents the Cronbach's alpha for the factors of safety climate and safety behavior.

Table 2.

Mean, standard deviation, Cronbach alpha for the factors of safety climate and safety behavior (n = 409).

Factors	Mean	SD	α
Safety climate
Safety commitment and communication	3.64	0.84	0.94
Safety involvement and training	3.28	0.84	0.88
Positive safety practices	3.5	0.92	0.9
Safety competency	3.8	0.79	0.85
Safety procedures	3.88	0.71	0.75
Accountability and responsibility	3.47	0.82	0.68
Supportive environment	3.41	1.08	0.88
Mean safety climate	3.54	0.7	0.97
Safety Behavior
Safety compliance	3.98	0.83	0.91
Safety participation	4.06	0.76	0.88
Mean safety behavior	4.02	0.75	0.93

Based on participants’ self-reports, 54 individuals (13.2%) indicated experiencing at least one occupational injury in the last year due to safety hazards. The prevalence of MSD symptoms ranked as follows: neck (20.5%), knees (19.3%), lower back (17.8%), upper back (17.1%), shoulders (12.5%), wrists and hands (10.5%), legs (8.8%), elbows (6.8%), and thighs (6.6%). Upon categorizing participants into two groups, it was observed that 80 individuals (19.6%) reported symptoms in six regions of their body, while the remaining participants reported symptoms spanning across seven to nine regions. The mean of safety climate exhibited a significant positive correlation with gender (r = 0.15, p < 0.01 while it showed negative significant correlations with education (r = −0.13, p < 0.01), and training experience (r = −0.28, p < 0.01). The mean of safety behavior demonstrated significant positive correlations with age (r = 0.17, p < 0.01), working experience (r = 0.13, p < 0.05), job tenure (r = 0.18, p < 0.05), and the mean safety climate (r = 0.38, p < 0.01). Symptoms of MSDs exhibited a significant positive correlation with injury experience (r = 0.10, p < 0.05), but negative correlations with the mean of safety climate (r = −0.12, p < 0.05) and gender (r = −0.18, p < 0.01). Additionally, there were significant positive correlations between injury experience and training experience (r = 0.14, p < 0.01), and education (r = 0.11, p < 0.05), but negative correlations with marital status (r = −0.10, p < 0.05), and working experience (r = −0.11, p < 0.05).

The results of SEM revealed that the overall fit indices for the first model were deemed acceptable (χ²= 328.06, df = 26, p = 0.001, GFI = 0.84, AGFI = 0.73, CFI = 0.89, RMSEA = 0.17, NFI = 0.88). The association between safety climate and safety behavior factors in the structural model was found to be significant (ß = 0.19, p = 0.001). Importantly, all paths were significant in the measurement model (p < 0.001) (Figure 2).

Figure 2.

Structural equation model for safety climate and safety behavior (SCC: safety commitment and communication, SIT: safety involvement and training, PSP: positive safety practices, SC: safety competency, SP: safety procedures, AR: accountability and responsibility, SE: supportive environment, SAPA: safety participation, SACO: safety compliance).

According to SEM findings, the overall fit indices for the proposed model were found to be acceptable (χ²= 4.28, df = 1, p = 0.03, GFI = 0.99, AGFI = 0.95, CFI = 0.95, RMSEA = 0.09, NFI = 0.94). In the model (Figure 3), the path coefficient value of the direct influence of safety climate on injury experience was −0.01, while the indirect effect was 0.11 (0.41 0.27 = 0.11). The direct effect of safety climate on MSD symptoms has a path coefficient of 0.01, whereas the indirect effect has a path coefficient of 0.08 (0.41 0.21 = 0.08). Furthermore, the effect of injury experience on MSD symptoms has a path coefficient of 0.53 (Table 3).

Figure 3.

Structural equation model for safety performance.

Table 3.

Structural equation model for safety climate, safety behavior, injury experience, and symptoms of MSDs.

Hypothesis	Estimate	S.E.	C.R.	P
H₁a: Safety climate Injury experience	−0.008	0.02	−0.31	0.76
H₂a: Safety climate MSD symptoms	0.27	0.13	2.07	0.38
H₃a: Safety climate Safety behavior	0.41	0.05	8.29	0.001
H₁b: Safety behavior MSD symptoms	0.21	0.12	1.68	0.09
H₂b: Safety behavior Injury experience	−0.002	0.02	−0.08	0.93

Discussion

This study aimed to explore the relationship among safety climate, safety behavior, injury experience, and the symptoms of MSDs in manufacturing companies. The results showed that while safety climate influenced safety behavior, there was no significant impact on employees’ self-reported injury experience and MSD symptoms.

The anticipated positive impact of safety climate on safety behavior (H3a) received support as the study found a significant influence of safety climate on safety behavior. Participants reported improved safety behaviors in companies with a strong safety climate. Previous research aligns with these findings, suggesting that workers exhibit better safety behaviors in workplaces with a positive safety climate.^26,51 In the construction industry, Mohamed (2002) identified a positive association between safety climate and safety behavior, emphasizing the role of open expression of safety viewpoints and management's non-punitive approach in fostering a positive safety climate.³⁰ The current study finding underscores the potential of manufacturing companies to positively influence workers’ safety behavior by enhancing organizational safety climate, ultimately contributing to a reduction in occupational injuries. The study suggests that efforts to improve behaviors within companies can contribute to an overall enhancement of the safety climate.

Seo (2005) similarly observed that safety climate directly and indirectly influences safety behavior through the sequential impact of mediating factors.²⁸ In our study, a direct association between safety climate and safety behavior was identified. This indicates that employees in surveyed companies with a robust safety climate may exhibit greater caution when confronted with workplace hazards. It is worth noting that various studies have demonstrated an indirect connection between safety climate and safety behavior in diverse work settings, employing SEM. This indirect link is often mediated by factors such as knowledge and motivation.^33,52,53 Considering the various factors influencing the relationship between safety climate and safety behavior, managers in companies can improve their understanding of the importance of essential activities, including employee training and participation in safety management. This heightened awareness can contribute to more efficient and effective efforts in promoting a safe working environment. In light of the finding that 45% of the study participants actively took part in safety training courses, coupled with previous research indicating lower participation rates among employees in manufacturing companies,^54–56 and considering the documented positive impact of training on enhancing safety climate,⁵⁷ strategic initiatives such as training programs and motivation enhancement emerge as crucial avenues for improving employees’ safety behaviors.

The hypothesized influence of safety climate on symptoms of MSDs (H2a) was not supported. The study revealed that safety climate did not significantly influence MSD symptoms, and participants in companies with a strong safety climate exhibited a higher prevalence of MSD symptoms compared to other participants. In the limited number of studies investigating the relationship between safety climate and MSD symptoms using SEM. For example, Golubovich et al. (2014) found that safety climate was not directly related to upper body musculoskeletal symptoms, but indirectly affected MSD symptoms via frustration. They suggested that a poor safety climate serves as a stressor, leading to frustration and subsequently increasing employees’ reports of MSD symptoms.⁵⁸ The lack of support for the findings in this study concerning the predicted hypothesis may be attributed to the failure to account for other important variables. Therefore, examining the indirect correlation between safety climate and the symptoms of MSDs could offer a more comprehensive understanding of the influence of a robust safety climate in manufacturing companies through the utilization of SEM.

Bivariate correlation analysis indicated that participants reporting higher levels of safety climate had a reduced prevalence of MSD symptoms (r = −0.12, p < 0.05). This aligns with findings in the Norwegian aquaculture industry, where self-reported MSDs in the upper extremities and neck were negatively connected to the safety climate.⁵⁹ Similarly, Arcury et al. (2012) observed that a poor safety climate among migrant farmworkers in North Carolina was linked to health problems, including MSDs, work-related injuries, and illness.²⁴ Their study revealed a significantly lower safety climate in individuals experiencing elevated musculoskeletal discomfort. Additionally, Ghahramani et al. (2021) found that participants in manufacturing companies with better safety climate scores reported a reduced prevalence of MSD symptoms in various body regions, such as the neck, hand, wrist, upper and lower back, as well as one or both thighs.⁵⁷

Bailey et al. (2015) and Swanberg et al. (2017) identified a correlation between safety climate and symptoms of MSDs.^60,61 Latino horse farm workers with a poor safety climate experienced increased MSDs in the neck/back and upper extremity regions.⁶¹ The safety climate concerning workers’ psychological health and safety was considered a precursor to psychosocial risks and, subsequently, was linked to MSDs.⁶⁰ Construction workers exhibited a significant negative correlation between the prevalence of MSDs and safety climate.⁶² These findings suggest that a poor safety climate in the workplace may contribute to increased occupational injuries, including MSDs. In other words, enhancing the safety climate in investigated companies holds the potential to reduce occupational injuries like MSDs. However, the present study did not establish a significant indirect relationship between safety climate and MSD symptoms when considering the mediating influence of safety behavior. Furthermore, it revealed no statistically significant direct correlation between safety behavior and MSD symptoms which was initially hypothesized (H2b). This finding contradicts previous studies that demonstrated the effectiveness of implementing safety behavioral modifications in mitigating symptoms of MSDs. This suggests that the current safety climate may not have translated into the implementation of ergonomic practices that effectively alleviate MSD symptoms

The predicted direct (H1a) and indirect (H1b) effects of safety climate on injury experience were not supported, as safety climate and safety behavior were not significantly related to injury experience. Participants in this study expressed that the improvement of safety climate in manufacturing companies did not lead to a decrease in reported injuries. Indeed, numerous studies have demonstrated that a positive safety climate is associated with encouraging safe behavior and is strongly linked to injury prevention.⁷ Typically, unsafe behavior is considered the best predictor of accidents.⁶³ However, the findings of this research differ, possibly due to variations in data sources. Injury data in this study relied on self-reports from organizations with different identities, which might contribute to diverse findings. It's crucial to acknowledge that self-reporting incident involvement is widely accepted as a reliable method for assessing safety outcomes.⁶⁴ Cooper and Phillips (2004) have emphasized the need for extensive data to establish significant associations between variables in safety climate investigations.⁶⁵ Although we collected data at participants’ workplaces in some instances, reliance on self-reported questionnaires could introduce bias and limit the researchers from drawing causal inferences. Nevertheless, self-reporting may offer advantages when faced with constraints in financial and time resources.⁶⁶

While this study integrates both retrospective and prospective safety performance measures within a SEM, it is essential to recognize several limitations associated with the current investigation. The representative sample of manufacturing employees in Urmia may not be optimal due to the specific inclusion/exclusion criteria applied to participating companies and employees. Notably, the selection process was not conducted randomly across all companies and employees in Urmia. Instead, the researchers intentionally chose manufacturing companies with a workforce size ranging from 50 to 300 employees, prioritizing those demonstrating a high level of interest in participating in the study.

The SEM model concentrates on a restricted set of variables at the company level, overlooking potential mediators and moderators that could influence the relationship between these variables. Several occupational, environmental, and organizational factors could act as mediators and moderators in the relationship explored in this study. However, they were not taken into account due to the study's design constraints.

One limitation associated with the findings is the skewed gender distribution among participants, with the majority being male (89.2%). This gender imbalance could potentially impact the study results, as research suggests females are less likely to experience accidents. However, this disparity is expected in manufacturing companies in Iran where the workforce is predominantly male. Additionally, the data are derived from a cross-sectional survey, which introduces the possibility of common method variance. Another limitation pertains to the use of self-report measures, introducing potential biases. To address this, the researchers implemented unique data collection approaches to ensure respondent anonymity and mitigate potential biases.

Conclusion

The study incorporated various retrospective and prospective safety performance indicators. Results revealed a positive correlation between safety climate and safety behavior, suggesting that enhancing workplace safety climate could contribute to better conditions for preventing unsafe behaviors. However, no direct or indirect correlation was found between safety climate and employees’ injury experiences or MSD symptoms in this study. To establish a significant relationship between safety climate and the studied injuries, it appears essential to consider additional factors as potential mediators and moderators. Consequently, future research should delve deeper into exploring the indirect relationship between safety climate and safety performance indicators through the identification of relevant mediators and moderators.

Footnotes

Acknowledgements

The authors thank all participants who took time and effort for this study.

Ethical approval

The research protocol was approved by the ethics committee of Urmia University of Medical Sciences, Iran (IR.UMSU.REC.1401.317).

Informed consent

Informed consent was obtained from all participants prior to their participation in the study.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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