The correlation between employee mental health and job performance for sustainable human resource management

Abstract

This study examines the relationship between employee psychological health and job performance within sustainable human resource management frameworks, focusing on frontline workers from multi-province oil companies. Through a combination of theoretical and empirical analysis, it was found that four demographic factors, age, education level, income, and job type, have a significant impact on mental health (p < 0.05). The regression results showed a positive correlation between mental health and job performance (adjusted R² = 0.374, β = 0.586, p < 0.05), and all three dimensions of mental health were positively correlated with job outcomes to varying degrees. The research findings emphasize that employees’ mental health is a key determinant of job performance, indicating that sustainable human resource management practices should be incorporated into mental health support mechanisms. For energy sector organizations and other high-stress industries, these results provide empirical justification for investing in workplace mental health initiatives. The study contributes to academic literature and practical human resource management by demonstrating how psychological factors directly influence organizational performance metrics within sustainability-oriented business models.

Keywords

Sustainable human resource management frontline employees mental health job performance relevance

Introduction

The concept of sustainable human resource management (HRM) is increasingly becoming a focal point for organizations to pursue operations consistent with sustainable development principles. This approach goes beyond the traditional HRM concept that focuses primarily on maximizing efficiency and reducing costs, integrating social, environmental, and economic factors into its framework.¹ Sustainable HRM is both a moral imperative and a strategic necessity. Organizations gradually realize that the well-being of employees is inherently linked to the overall health and lifespan of the enterprise. This understanding has led to more research examining the multifaceted relationship between employee mental health and job performance. Psychological health issues may lead to a decrease in productivity, an increase in absenteeism and employee turnover, thereby affecting an organization’s ability to achieve its goals.² Boxall et al. conducted an in-depth analysis of HRM and proposed a high-involvement model of HRM.³ Banmairuroy et al. proposed an HRM organizational and structural equation model of innovation through an analysis of knowledge-based leadership.⁴ Engelsberger et al. proposed a concept of open innovation performance to promote the sustainability of HRM.⁵ Previous studies have identified multiple factors that affect employees’ mental health, including work environment, organizational culture, leadership style, and personal factors such as age, education, and income. Despite increasing awareness of these issues, there is still a significant gap in understanding how frontline employees’ mental health and job performance interact within the sustainable HRM paradigm.⁶ Frontline employees, as the backbone of many organizations, often face unique pressures and challenges that may affect their mental health in special ways. The petroleum industry has inherent high-risk environments, high-intensity workloads, and geographically dispersed operations. All of these factors pose significant psychological demands on frontline workers and provide an ideal environment for such investigations. This study employs the dual-factor model (DFM) of mental health, which emphasizes both the presence of positive psychological traits and the absence of mental illness, providing a more comprehensive perspective on employee mental well-being. The research findings are expected to provide practical insights for human resources practitioners and organizational leaders to develop effective strategies to promote mental health and improve job performance. This can provide a deeper understanding of the role of sustainable HRM in cultivating a healthy, efficient, and resilient workforce. Specifically, this study seeks to answer the following research question: How does the psychological health of frontline employees influence their job performance within the framework of sustainable HRM?

The research is divided into four sections. The first section is a summary of previous research. The second section provides a detailed review of relevant work, examining the existing knowledge system on sustainable HRM, employee mental health, and job performance. In this context, job performance is understood as the scope of activities and behaviors that contribute to achieving organizational goals. Psychological capital refers to an individual’s positive psychological state, characterized by self-efficacy, optimism, hope, and resilience. The third section outlines the research method, including the theoretical framework, sample selection, and data analysis techniques. The fourth section presents the research results and discusses the findings of correlation analysis and regression analysis. The fifth section explores the practical significance of the research and proposes suggestions for future research. The novelty of this study lies in its application of DFM for mental health in high-pressure industrial environments, and the integration of psychological capital as a mediating factor within a sustainable HRM framework. This is a method that has not been extensively explored in previous literature. This study aims to provide a solid foundation for academic and practical contributions in the field of sustainable HRM through this structure.

Related work

Numerous studies have explored sustainable HRM from various perspectives. One prominent stream focuses on the role of green HRM practices in enhancing employee performance and contributing to environmental goals. For instance, Khan et al. and Úbeda García et al. demonstrated positive links between green HRM and organizational results, using statistical and structural methods.^7,8 The second stream of research emphasizes HRM’s role in promoting inclusivity and well-being. Tlaiss et al. examined gender equity in Saudi Arabia, while Kooij et al. highlighted the importance of job crafting for older workers’ motivation.^9,10 Similarly, Wang et al. linked HRM to employee burnout and resilience through high-performance systems.¹¹

Other studies have examined the strategic impact of HRM in specific contexts. Malik et al. focused on reducing employee turnover in multinational firms, while Lee et al. tested theoretical models connecting HRM to firm performance.^12,13 Lu et al. and Kulik addressed mental well-being and workplace safety as essential components of HRM strategy.^14,15

However, despite growing interest in employee well-being, most of these studies have either adopted a broad organizational lens or focused on general populations. There are few studies investigating the mental health status of frontline employees, especially in high-risk industries such as oil. Moreover, previous studies have rarely integrated two factor psychological models or explained how specific mental health components (such as physical, emotional, perceptual, and cognitive) affect work outcomes. This study aims to address these gaps by examining how the mental health of frontline employees measured by DFM affects job performance in the context of sustainable HRM. To frame the relationship between mental health and job performance, this study adopts the DFM of mental health, which combines both positive psychological indicators and the absence of pathology. Tools such as the SCL-90, SDS, and GWB scales are selected based on their alignment with DFM’s dimensional structure. These tools help identify key structures as independent variables, such as body, perception, and emotional cognition. The hypotheses and analytical model are developed by linking these constructs to three components of job performance: task performance, interpersonal facilitation, and work dedication. Therefore, the proposed theoretical model provides information for variable selection and lays the foundation for hypothesis development and regression analysis framework.

Correlation between employee mental health and job performance in HRM

Methodology

Participant Recruitment and Sampling: This study employs a purposive sampling approach targeting frontline employees from petroleum enterprises across multiple provinces in China. Participants are recruited through human resource departments and professional networks. A combination of purposive and convenience sampling is used. Specifically, participants are selected based on their frontline status and accessibility through organizational contacts, without randomization or stratification. Frontline employees are defined as those directly engaged in operations, such as exploration, transportation, sales, and safety roles.

Participants are selected using a combination of purposive and convenience sampling. The selection criteria require respondents to be full-time frontline employees aged 18 or above, directly involved in physical or technical operations of oil companies. Employees in purely administrative, managerial, or remote-support roles are excluded to ensure a consistent occupational exposure profile. Ethical procedures are strictly followed throughout the data collection process.¹⁶ All participants receive an informed consent form outlining the study’s purpose, voluntary nature, and confidentiality assurances. Data are anonymized, and no personally identifiable information is collected.

Sample Size Justification: Although no a priori power analysis is conducted, a minimum sample size of 85 is generally considered acceptable for regression analysis involving three predictors with medium effect size at 80% power and α = 0.05. Therefore, the sample of 98 participants exceeds this threshold and ensures adequate statistical power.

Instrument Reliability and Validity: The survey instruments include the SCL-90 for psychological symptoms, the SDS for depressive tendencies, and the General Well-being Schedule (GWB) for positive mental states.¹⁷ These instruments are selected for their alignment with the DFM of mental health. Reliability coefficients (Cronbach’s alpha) for the three scales are all above 0.85, indicating high internal consistency. The results of confirmatory factor analysis are consistent with previous studies, supporting construct validity.

Operationalization of Constructs: Psychological health is based on three dimensions: physical cognition, emotional cognition, and perceptual cognition, and integrates projects from these three dimensions. Job performance is measured across task performance, interpersonal facilitation, and work dedication, based on existing behavioral performance models. Psychological capital has been integrated as a theoretical construct, but due to limitations in scope, it cannot be directly measured.

Ethical Considerations: All participants are informed of the research purpose and given written consent. The study protocol is reviewed and approved by the Ethics Committee of Shenyang Polytechnic College (Approval No. SPC-2023-HRM06).

Data Analysis Techniques: Descriptive statistics are used to characterize the sample. Differences between demographic groups are assessed using ANOVA and chi-square tests. Pearson’s correlation coefficients are used to explore associations between variables. Finally, multiple linear regression is applied to test the predictive effect of mental health dimensions on job performance. These methods are chosen for their suitability in detecting group-level differences and testing linear relationships between continuous variables.

Theoretical basis of mental health and sustainable HRM

With the increasingly diverse psychological needs of frontline employees, more business managers are paying attention to employee mental health management. This provides an opportunity for in-depth exploration of mental health issues in organizational environments. Therefore, the study selects a sample of frontline employees from several oil companies in multiple provinces and explores the impact of employee mental health on job performance based on theoretical analysis and empirical research. Psychological health refers to a person’s various psychological activities and behaviors being in a good or normal state. The ideal state of mental health is to maintain a good personality, normal intelligence, correct cognition, appropriate emotion, reasonable will, positive attitude, and appropriate behavior.¹⁸ The current major mental health theories are broadly divided into seven schools of thought, which are shown in Figure 1.

Figure 1.

Content of mental health theory.

From Figure 1, the main theories of mental health include the psychoanalytic view of mental health, personality trait theory, behaviorist theory, cognitive personality theory, humanistic psychology, the “skill perfection” and “new human of the future” models, and the “transcendental human being” model. The health perspective of psychoanalysis holds that psychopathology stems from an imbalance of the “three selves” (self, ego, and superego). A healthy personality is a self-forming force that is not suppressed by the ego or superego. Symptom Checklist-90 (SCL-90), which is widely used today, is developed based on this theory. The personality trait theory focuses on the inner strength of individuals, believing that psychologically healthy individuals have rationality, a clear understanding of motivation formation, the ability to control their own lives, and foresight for the present and future. Behaviorism theory focuses on observable and measurable behavior, and assumes that human behavior is acquired and cultivated. Human physiological and psychological disorders are also acquired through learning, which is a maladaptive or abnormal behavioral response. All abnormal behaviors can be corrected with model changes.

The cognitive personality theory proposes rational emotion therapy, which suggests that everyone is emotionally influenced by irrational beliefs. Therefore, it is important to free oneself from irrational beliefs and maintain rational and logical thinking. Humanistic psychology believes that psychologically healthy people should be full of spirit, full of mind, full of potential, and full of life value. The “improvement of skills” and “future new human models” believe that the key to mental health or “functional individuals” is consistent self-structure and experience. This unconditional positive factor, such as care and an unconditional growth environment, is essential. The “transcendent human” model holds that individuals are free to choose their own behavior in any situation, and psychologically healthy individuals are those who dare to challenge the meaning of life and surpass themselves. These studies and descriptions of mental health from various Western schools of thought are multifaceted and transcend the limitations of the times. Despite the complexity of conclusions and divergent opinions, the definition, structure, and measurement implications for the modern concept of mental health are overwhelming. This also provides a solid theoretical foundation for constructing cross-sectional and multi-factor measurement systems, and further in-depth research is needed.

With the rise of positive psychology, pathological psychology is being used as an important measure. The mainstream view is that even if a person’s psychological problems are cured, they are not necessarily a healthy person. Psychological health must be evaluated through positive factors such as subjective well-being.¹⁹ Therefore, mental health can be better understood if it is evaluated by combining subjective happiness and mental health indices. In this context, the DFM of mental health is gradually taking shape. The contents of the DFM are shown in Figure 2.

Figure 2.

The dual-factor model of mental health.

From Figure 2, the model categorizes individuals based on the interaction between positive and negative psychological indicators, guiding mental health assessment and intervention. The DFM model is constructed based on three main ideas, namely, mental health as a state of perfection, the division of the mental health population, and the guidelines related to prevention and intervention of the psyche. The DFM stands for an inclusive perspective and considers mental health as the perfect combination of being free from any mental illness and being full of happiness. In addition, DFM divides the population into four groups based on mental health and illness status: partially mentally healthy, fully mentally healthy, partially pathological, and fully pathological. At the same time, DFM emphasizes active prevention by distinguishing whether individuals should undergo health prevention and intervention through general measures of their health status and mental health. This model can better distinguish between partial mental health and partial pathology, to achieve better prevention and intervention.²⁰ In enterprises, employee mental health is closely related to the HRM system, making its sustainability an important competitive advantage for human resources.

Sustainable HRM is an HRM activity conducted by enterprises at both the temporal and spatial levels. The core content emphasizes the sustainable development of human resources from the perspective of open systems, including the demographic and capital characteristics of human resources.²¹ Coordinating the time between the company’s lifecycle and an individual’s career cycle at the economic and social levels is key to achieving sustainable development and HEM for the company. Therefore, a general sustainable HRM framework is shown in Figure 3.

Figure 3.

Framework for sustainable HRM.

In Figure 3, only by balancing economic and social issues and solving complex problems can the utilization and development of human resources be addressed from a collaborative and ecological perspective.^22,23 Only through the treatment of these issues can a lasting talent supply system be constructed on the individual, organizational and social levels. At the macro level, it is necessary to integrate the internal labor market of enterprises with the external labor market. For example, increasing the employability (hiring power) of employees has an impact on the link between the external labor market and the internal labor market. If a firm ignores the employability of employees, it will have an effect first on the internal labor market and then on the external labor market. The external labor market will change the composition of human capital and have an impact on its supply, while the internal labor market, on the other hand, has an optimal labor allocation and elasticity. These issues will have a significant impact on the sustainable development of enterprises, thus having far-reaching effects on the realization of enterprises and society.

Definition of the main concepts of the study and hypothesis analysis

Conceptual definition is the prerequisite for constructing reasonable and clear research hypotheses, and the foundation for obtaining scientific research conclusions. The research scope and objectives are different from previous studies, and the related concepts of frontline employees, mental health, job performance psychological capital, etc., also differ greatly from existing theories. Therefore, it is necessary to redefine the above concepts. Traditionally, frontline employees refer to workers in manufacturing industries who are directly engaged in incoming material processing. Currently, they refer to those who are directly engaged in actual work, that is, employees at the operation site. In terms of defining the concepts before and after, the quality of work of frontline employees directly affects the performance of the enterprise. Therefore, considering the huge complexity of oil enterprises, the study delineates frontline employees as the grassroots employees who work on the front line and directly generate economic benefits in oil enterprises. In terms of defining the concept of mental health, this study considers the appropriateness and scientificity of constructing contexts, as well as the combination with the binary concept of mental health, and divides mental health construction into negative and positive indicators. Under the guidance of DFM and construct theory, mental health is measured by referring to the SCL-90 scale, GWB schedule scale, and Self-Rating Depression Scale (SDS) together.

The redefinition of the concept of job performance considers the characteristics of the work environment of frontline employees in oil companies and is based on the behavioral theory view of performance. The redefined job performance refers to all activities done by employees in a certain period that have a desired value for the company.^24,25 The concept of psychological capital has wide application in various economic, social, and organizational aspects. Therefore, the study defines it as a positive mindset and behavior with developmental characteristics that can be measured, developed, invested, and has unlimited potential. It combines the transactional psychological capital of the West with the interpersonal psychological capital of the East, and derives a two factor structure based on the differences between the East and the West. The attribution of some of the factors is also modified to consider the influence of employees’ perceptions and economic development.

The integration of both Western and Eastern mental health models offers a more culturally nuanced foundation for HRM strategy development. Western frameworks, such as the DFM, emphasize individual well-being through diagnostic clarity and structured assessment of psychological states. These models are valuable for building standardized employee wellness programs and quantifiable performance metrics. In contrast, Eastern perspectives, especially those that combine interpersonal psychological capital, focus on relationship harmony, collective support, and emotional resilience in the social environment. This dual theoretical lens allows HRM practices to be both diagnostically rigorous and culturally adaptive. For example, in high-stress industries like petroleum, applying DFM can help identify symptomatic risk factors at the individual level, while Eastern models can inform team-based interventions and peer-support systems aligned with collectivist work cultures. Therefore, by combining these perspectives, human resource managers can design comprehensive plans targeting measurable psychological indicators and informal social support mechanisms. This can bring more sustainable results for employee welfare and organizational performance.

This study proposes a hypothesis based on the definition of mental health in different parties and related research, combined with the actual characteristics of frontline employees in oil companies and the industry. This hypothesis is based on significant differences in the mental health levels of frontline employees with different organizational and demographic characteristics, including gender, age, education level, education level, years of work experience, monthly income, and job type. In addition, psychological factors directly affect job performance. Paying attention to and maintaining employees’ psychological factors in sustainable HRM efforts can significantly improve employees’ job performance. Employees who are separated from psychological factors, on the other hand, can have a greater impact on job performance in all aspects. Based on this analysis, the study proposes the hypothesis of using DFM, which is about the impact of mental health on job performance. Employees’ physical cognition, perceptual cognition, and emotional cognition have positive effects on task performance, interpersonal facilitation, and work dedication, respectively.

Based on conceptual definitions and research hypotheses, this study constructs a theoretical model based on three dimensions of mental health and job performance, as shown in Figure 4.

Figure 4.

Theoretical model based on three dimensions of mental health, mental capital, and job performance.

In Figure 4, the framework illustrates how the dimensions of mental health (physical, emotional, and perceived cognition) affect job performance (task performance, interpersonal facilitation, and job dedication), while incorporating demographic moderating factors under sustainable HRM. The theoretical model is consistent with the research hypothesis design, which suggests that the mental health level of frontline employees in oil companies has a positive impact on job performance. All dimensions of the mental health of frontline employees have a significant effect on all dimensions of job performance.

Analysis of the correlation between employee mental health and job performance

The study conducts difference, correlation, and regression analyses on the samples to test the proposed hypotheses. The demographic characteristics of the relevant test samples obtained from the study using the sample data collection method are shown in Table 1.

Table 1.

Demographic characteristics of frontline employee sample.

Variable	Category	Frequency (n)	Percentage (%)
Gender	Male	62	63.27%
Gender	Female	36	36.73%
Education	High school and below	24	24.49%
	Technical college	42	42.86%
	Undergraduate	28	28.57%
	Graduate	4	4.08%
Age	≤30	44	44.90%
	31–40	28	28.57%
	41–50	20	20.41%
	≥51	6	6.12%
Years of service	≤3	30	30.61%
	4–8	24	24.49%
	9–15	24	24.49%
	≥16	20	20.41%
Monthly income	≤3000	48	48.98%
	3001–6000	32	32.65%
	6001–9000	10	10.21%
	≥9001	8	8.16%

Table 1 presents the demographic characteristics of the 98 valid frontline employee respondents. The sample includes a higher proportion of male employees (63.27%) and a notable concentration of younger participants, with 44.90% aged 30 or below. Most people have a college degree (42.86%), and nearly half of them have a monthly income of less than 3,000 yuan (48.98%). Work experience is relatively evenly distributed across four experience brackets. These demographic characteristics provide a foundational understanding of the sample composition and are subsequently used to examine whether mental health levels vary significantly across subgroups. Group differences are analyzed using mean difference testing (p < 0.05 indicates significance) and homogeneity of variance tests (p > 0.05 indicates equal variance), which support the correlation analyses that follow in the next sections. Therefore, the results of the significance of differences among frontline employees by gender and age are shown in Figure 5.

Figure 5.

Significance of gender and age differences among frontline employees. (a) Verification results of gender differences of frontline employees. (b) Verification results of differences among front-line employees of different ages, (c) Mental health and levels of various dimensions at different ages.

In Figure 5, A–D indicate mental health, emotional cognition, perceptual cognition, and somatic cognition, respectively. In Figure 5(a), the chi-square validation values and average differences for different ages are all greater than 0.05, indicating the reliability of the research results and that the mental health levels of male and female employees are not significant. This also indicates that the research hypothesis of gender influence is not valid. In Figure 5(b), the minimum value of the mean difference is 0.005, and the values of all four variables are less than 0.05. The chi-square values are all greater than 0.05, indicating the validity of the research hypothesis on the impact of age on mental health levels. In Figure 5(c), frontline employees under the age of 41 meet the characteristics of being physically strong and looking forward to the future. Therefore, their emotional cognitive level is lower, while their physical and sensory cognition are higher, which also conforms to the age hypothesis of this study. In addition, the results of the significance of the differences between different years of work and the education of frontline employees are shown in Figure 6.

Figure 6.

Difference results of frontline employees with different working years and educational backgrounds. (a) Different results of frontline employees with different educational background. (b) Mental health and educational differences of various dimensions. (c) Difference results of frontline employees with different working years.

In Figure 6, 1–4 indicates high school and below, college, undergraduate, and graduate, respectively. In Figure 6(a), the minimum average difference is 0, and all four variables are less than 0.05, indicating significant differences in the impact of education on mental health levels. In Figure 6(b), as the level of education increases, the mental health level and various dimensions of frontline employees significantly improve, indicating that the impact of education on their own psychology is different. There are differences in employees’ psychological perceptions and emotional regulation of themselves. The hypothesis that education has an impact on mental health in the study design is valid. In Figure 6(c), the differences in psychological health and work experience across all dimensions are greater than 0.05, indicating no significant difference. Therefore, the hypothesis in this study that work experience has an impact on employee psychological health is not valid. Meanwhile, the significant results of the differences between different monthly incomes and job types of frontline employees are shown in Figure 7.

Figure 7.

Difference results of different monthly incomes and work types of frontline employees. (a) Difference results of different monthly incomes of frontline employees. (b) Differences between Perceptual Cognition and Mental Health. (c) Different results of different work types of frontline employees. (d) Mental health and differences in job types at all levels of dimensions.

In Figure 7, E and F denote chi-square validation and mean difference validation, respectively. 5–8 denote geological exploration, pipeline transportation, product sales, and safety and security, respectively. From Figure 7(a), the mean difference values of emotional cognition and somatic cognition are 0.116 and 0.153, respectively, which exceed 0.05 and have no significant difference. Perceptual cognition is less than 0.05 and has a significant difference. At this point, according to Figure 7(b), an overall increase in the level of monthly income leads to an increase in the level of mental health and perceptual cognition. The perceptual cognitive level begins to decline after third grade, which is consistent with the income characteristics of frontline employees in oil companies. Some frontline employees have high incomes and high risks, but their sense of happiness is gradually declining. The hypothesis in the study that monthly income can affect mental health is valid.

In Figure 7(c), the average values of the four indicators are all less than 0.05, and the chi-square values are all greater than 0.05, indicating that different types of work have an impact on mental health. At this point, in Figure 7(d), pipeline transportation scores the highest, and the mental health levels of employees in different occupations vary. For example, different job types can cause changes in the work environment, work hazard level, and work intensity. The hypothesis that different types of work will have an impact on the level of mental health in the research design is valid. This study is based on the experimental results of six factors that affect employees’ mental health, and assumes that the level of mental health in the study will have a positive impact on job performance, and conducts regression analysis. Among them, the results of the regression analysis of the effect of mental health on job performance are shown in Figure 8.

Figure 8.

Regression analysis results of the impact of mental health on job performance. (a) Results of correlation coefficient influence of mental health on job performance. (b) Results of the impact of mental health on relevant indicators of job performance.

In Figure 8, the R value indicates the complex correlation coefficient. The t indicates the t-test. The F indicates the variance test. The p indicates the value of rejection of the original hypothesis. In Figure 8(a), the original R-value is 0.378, and after adjusting for variance, it is 0.374, at which point the value of mental health is 0.586. In Figure 8(b), the t-value is 10.765, the F-value is 109.325, and the p-value is 0.000, which is less than 0.05. In summary, the psychological health level of employees has a positive impact, indicating that the hypothesis in the study is valid. Before conducting regression analysis, the standard assumptions are evaluated to ensure the validity of the model. The normality of residuals is confirmed using the Shapiro-Wilk test (p > 0.05), and visual inspection of Q-Q plots indicates no major deviations. Homoscedasticity is evaluated through residual-versus-fitted plots, which shows no heteroscedastic patterns. Multi-collinearity is checked by calculating Variance Inflation Factors (VIF), with all values below 2.0, suggesting no significant collinearity issues among the independent variables.

The relationship between mental health dimensions and job performance meets the assumptions of linearity and independence. Pearson correlation coefficients further support the existence of significant positive relationships between most dimensions of mental health and the three components of job performance. The multiple linear regression model yields an adjusted R² of 0.374, indicating that approximately 37.4% of the variance in job performance is explained by mental health factors. The regression coefficient for overall mental health is β = 0.586, with a standard error of 0.054. The 95% confidence interval for this coefficient ranges from 0.480 to 0.692, and the associated effect size (Cohen’s f²) is 0.60, suggesting a strong effect. These results support the hypothesis that mental health significantly and positively influences job performance among frontline employees. Based on this, the study conducts a deeper analysis of the correlation, and its correlation coefficient matrix is shown in Table 2.

Table 2.

Correlation coefficient matrix between mental health and job performance.

-	Somatic cognition	Perceptual cognition	Emotional cognition	Task performance	Interpersonal facilitation	Work dedication
Somatic cognition	1	-	-	-	-	-
Perceptual cognition	0.024*	1	-	-	-	-
Emotional cognition	0.031	0.235*	1		-	-
Task performance	0.385*	0.036	0.428**	1	-	-
Interpersonal facilitation	0.011	0.258**	0.365*	0.150*	1	-
Work dedication	0.412**	0.150*	−0.282*	0.356*	0.000	1

In Table 2, * indicates p < 0.05; ** indicates p < 0.01. In Table 2, physical cognition is not correlated with emotional cognition and interpersonal facilitation, but positively correlated with task performance and job dedication. Emotional cognition is significantly negatively correlated with job dedication and positively correlated with other dimensions of job performance. Perceptual cognition is not correlated with task performance and positively correlated with other dimensions of job performance. Task performance is not correlated with perceptual cognition and positively correlated with other dimensions of job performance. Interpersonal facilitation is not correlated with somatic cognition and work dedication, and is significantly and positively correlated with other dimensions. Work dedication is not correlated with interpersonal facilitation and is negatively correlated with emotional cognition. Taken together, the three dimensions of mental health are more or less positively correlated with the two dimensions of task performance, indicating that the mental health of frontline employees has a significant positive impact on job performance.

Discussion

The findings of this study support the hypothesized positive associations between employee mental health and job performance dimensions, particularly within the high-pressure context of the petroleum industry. These results are consistent with previous research, indicating that mental health is a key factor in employee efficiency and organizational sustainability (Wang et al., 2022; Kooij et al., 2022).

Notably, different dimensions of mental health exhibited distinct patterns of association with performance results. Somatic cognition was more strongly related to task performance, while emotional and perceptual cognition were more closely linked to interpersonal facilitation and work dedication. These patterns indicated that physical and psychological vitality worked together in subtle ways to shape behavior in the workplace. The results strengthened the DFM of mental health, emphasizing the importance of a positive psychological state and being asymptomatic.

However, the use of causal terms such as “effect” should be interpreted cautiously. This study uses correlational and regression techniques, which do not establish causal relationships. Rather, the findings suggest statistically significant associations that warrant further investigation. Additionally, the significant role of demographic factors such as age, income, and job type indicates that organizational strategies should be tailored to employee subgroups for optimal outcomes.

By integrating mental health theory with sustainable HRM practices, this study contributes to the growing body of literature advocating for more holistic and employee-centered approaches in high-stress sectors. The results underscore the need for psychological support systems to be embedded in human resource policies, particularly for frontline employees exposed to hazardous environments.

Conclusion

The study revealed a significant positive correlation between the mental health of frontline employees and their job performance, with factors such as age, education level, monthly income, and job type having a significant impact on mental health. The regression analysis further confirmed the positive promoting effect of mental health on job performance, providing empirical evidence for organizations to attach importance to employee mental health in HRM. The research findings emphasized the importance of considering employee mental health in sustainable HRM practices, and pointed out that future research needs to expand the sample size to validate the generalizability of the conclusions of this study.

However, this study is not without limitations. First, the reliance on self-reported data may introduce common method bias, including potential social desirability effects. Second, the sample is drawn exclusively from the petroleum industry, limiting the generalizability of findings to other sectors. Additionally, the cross-sectional design does not allow for causal inferences. Future studies should consider longitudinal methods and incorporate multi-source data, such as supervisor ratings or physiological indicators of stress. Expanding the scope to include other high-stress industries and cultural contexts will also help test the robustness of the observed relationships. Further research may also explore the mediating role of psychological capital or organizational support in enhancing employee well-being and performance under sustainable HRM frameworks.

Footnotes

ORCID iD

Hui Du

Funding

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

Declaration of conflicting interests

The author declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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