In the context of industrialization, modernization, and international integration, industrial parks play a crucial role in Vietnam’s socio-economic development. Hai Phong city is currently one of the localities experiencing rapid industrial park growth, attracting tens of thousands of workers, especially in manufacturing, processing, and production industries. This growth creates many job opportunities but also presents challenges in human resource management, particularly regarding work pressure for employees. Work pressure not only impacts productivity and work quality but also directly affects employees’ physical and mental health and their engagement with the business (Lazarus & Folkman, 1984).

Many studies worldwide have examined factors that lead to stress, pressure, and burnout at work, such as the Job Demands-Resources Model by Demerouti et al. (2001), which indicates that work pressure results from an imbalance between job demands and the resources workers use to cope. Other research highlights the importance of the work environment, workload, relationships with supervisors and coworkers, as well as income and well-being (Karasek & Theorell, 1990). However, most of these studies are mainly conducted in developed countries or within the service and administrative sectors. Research focused on industrial park workers in emerging countries, especially in Vietnam, remains relatively limited.

Furthermore, workers in industrial parks in Hai Phong face various pressures such as high work intensity, long shifts, unequal income, and tough labor competition. Some domestic studies have only focused on job satisfaction or employee engagement (Nguyen & Tran, 2020), but have not thoroughly analyzed the factors that create pressure and how they influence workers. Therefore, both theoretical gaps, like the absence of a specific analytical framework for Vietnam’s industrial parks, and practical gaps, such as the lack of empirical evidence in Hai Phong, need to be addressed.

The purpose of the study is to examine the factors influencing workers’ work pressure in Hai Phong city’s industrial parks and to propose suitable human resource management recommendations.

Work pressure

According to Montgomery et al. (1996), work pressure is a disorderly feeling of discomfort that originates from events and situations in the organization, leading to negative physiological and psychological reactions. When the work environment creates many complex and urgent demands, and employees lack control or sufficient resources to handle them, pressure develops and gradually builds over time. When work demands surpass a worker’s ability to perform, the resulting negative reactions are a sign of work pressure (Westman, 2005). Oke and Dawson (2008) view work pressure as the interaction between the individual and the work environment, arguing that it is a response to working conditions perceived as unsuitable or beyond one's tolerance. These reactions can be emotional, behavioral, or physiological, depending on the level of stress and the individual's capacity to cope. Malek (2010) states that work pressure is a common phenomenon, but its manifestation varies based on the work situation and the specific group of workers. This suggests that personal factors such as adaptability, psychological background, and professional experience significantly influence how individuals perceive and respond to pressure. Nguyen and Uong (2023) assert that work pressure causes workers to be less willing to engage with their work over time, leading to intentions to quit and reduced job satisfaction.

Thus, work pressure is a common psychological phenomenon that reflects the stress workers feel when work demands surpass their abilities, resources, or personal tolerance limits. It arises when there is an imbalance between job expectations and employees' ability to meet them. This stress not only shows itself through mental states such as anxiety, fatigue, and irritability but also causes physiological reactions like headaches, insomnia, or long-term health issues. Additionally, work pressure can also result from unclear task assignments, role conflicts, workplace conflicts, or a lack of support from supervisors and colleagues.

Analytical framework

This study applies the Job Demands-Resources Model by Demerouti et al. (2001) as the basis for developing the analytical framework. Work pressure results from an imbalance between job demands, such as high workload, time pressure, role ambiguity, work performance, and a work environment, and job resources such as support from colleagues, favorable working conditions, workplace relationships, and development opportunities. When demands surpass resources, workers are more likely to experience high stress and pressure. The Job Demands-Resources Model explains how workload, work environment, role ambiguity, workplace relationships, and work performance influence work pressure. Figure 1 illustrates the analysis framework as follows:

Figure 1: Analytical framework

Source: Construction by the author

Research hypothesis

The work environment includes physical factors such as lighting, noise, temperature, humidity, air quality, safety levels, and exposure to hazardous elements at work. It is a key factor that directly influences employees' feelings, attitudes, and stress levels while working. According to the National Institute of Occupational Safety and Health, 54% of employees and 52% of HR professionals believe that feeling unsafe at work is a major cause of increased work pressure (Elisa, 2007). An unsafe work environment can cause employees to feel anxious and distracted, making it difficult for them to maintain their work efficiency over time. More importantly, in industrial parks, the working environment has a significant impact on workers' stress levels (Nguyen & Nguyen, 2015). The study by Mansoor (2011) suggests that poor working conditions, including high temperatures, loud noise, or inadequate ventilation, can lead to stress and reduced work performance. Conversely, when employees see their work environment as positive, with good lighting, fresh air, and comfortable temperature and humidity, they tend to be more satisfied and feel more at ease during work (Srivastava, 2008). Based on the above arguments, the research hypothesis is proposed as follows:

H1: A good working environment is negatively related to workers’ work pressure in industrial parks.

Role ambiguity is a key factor that can negatively influence the level of pressure workers experience in the workplace. It indicates that the worker does not receive complete and accurate information about responsibilities, job scope, and organizational expectations for their role (Kahn et al., 1964). If the job role is not clear, workers are likely to experience confusion and embarrassment about how to do their tasks, which can lower their confidence and increase stress. Sims (2001) asserts that role ambiguity has a strong link to performance, work pressure, and the tendency to procrastinate or give up on tasks. It not only affects individual employees but also harms the overall performance of the organization. Research by Breaugh and Colihan (1994) shows that role ambiguity appears in three main areas: how the work is performed, the criteria for assessing results, and performance. Without clear guidance on these three areas, employees will struggle to align their behavior and decisions with organizational expectations. Muchinsky (1997) also suggested that a lack of transparency in job roles can cause serious psychological effects, such as loss of motivation, ongoing anxiety, and even depression. For workers in industrial park environments, where work requirements are often repetitive, time pressure and productivity are high, so a lack of clarity about roles can increase stress levels, affecting performance and organizational engagement. Based on the above arguments, the research hypothesis is proposed as follows:

H2: Role ambiguity is positively related to workers’ work pressure in industrial parks.

Workload is one of the most common and noticeable factors that pressure workers in today's work environment. When the workload increases or becomes overwhelming compared to their ability to handle it, workers must activate their full thinking abilities to prioritize, decide which tasks to complete first, and which ones can be postponed (Iqbal, 2012). If a heavy workload persists for a long time and continues without pause, it can cause stress, lower energy levels, and negatively impact work performance. Several studies have demonstrated a direct connection between workload and workers' levels of work pressure (Brahmasari et al., 2022; Surya & Rihayana, 2024). Excessive workloads and tight lead time constraints are two factors that significantly affect work pressure (Wilkes et al., 1998), especially in environments with continuous production or high demand schedules. When workers lack enough time to complete assigned tasks, they are more likely to experience prolonged stress, leading to both physical and mental decline. More importantly, in the context of industrial parks where work involves repetition, precision, and tight schedules, large workloads and limited time have increasingly become key factors that cause work pressure, directly impacting workers' psychological health and their ability to sustain performance. Based on the above arguments, the research hypothesis is proposed as follows:

H3: Large workloads are positively linked to workers’ work pressure in industrial parks.

Workplace relations are connections that form and grow over time between individuals or between individuals and organizations. According to Jerald and Robert (2003), workers need to feel supported and helped by colleagues when they are struggling, and they should find friendliness and comfort during the work process. Working with dedicated, goal-oriented colleagues fosters a positive work environment and helps reduce psychological pressure on individuals (Kreitner & Kinicki, 2001). However, if these relationships are not developed positively, a lack of connection, support, and sharing can become a source of stress. Sauter et al. (1990) pointed out that weak or nonexistent relationships between colleagues, superiors, and subordinates are some of the key factors linked to increased work pressure among workers. Additionally, Cox and Griffiths (1995) also emphasized that individuals who are isolated at work, lacking support from colleagues, superiors, or even friends and family, are more likely to experience stress and fatigue at work than those with a positive support network. Based on the above arguments, the research hypothesis is proposed as follows:

H4: Good workplace relations are negatively related to workers’ work pressure in industrial parks.

Work performance is a key factor for workers, especially in an industrial production setting, where they are directly involved in activities that produce products and add value for the business. Unlike pure stressors, work efficiency can both motivate positively and potentially lead to fatigue and exhaustion if an individual’s tolerance limit is exceeded. According to Selye (1956), pressure is an unavoidable part of life and does not always carry a negative meaning. In many cases, moderate pressure can serve as a motivator for individuals to boost their productivity. Scott (1966) also noted that as the level of stimulation to enhance work efficiency increases, so does work pressure, and both reach an optimal peak. However, if stimulation levels go beyond the tolerance limit, work efficiency and pressure will both decline. This demonstrates the inverse U-shaped relationship between work pressure and performance. The study by Srivastava and Krishana (1991) in the context of modern industry also confirms that an inverse U-shaped relationship exists between pressure and labor performance. It describes moderate pressure as ideal for optimizing work performance, while too little pressure results in a lack of motivation, and excessive pressure can harm both the physical and mental health of workers. Selye (1975) argued that work performance reaches its peak when the worker is moderately stimulated, meaning at the optimal level. When stimulation is too low, it causes stagnation, and when it's too high, it results in exhaustion and decreased work output. Based on the above arguments, the research hypothesis is proposed as follows:

H5: High work performance is positively related to workers’ work pressure in industrial parks.

Measurement scales

The research scale builds on previous studies by Diem (2003), Elisa (2007), Iqbal (2012), Nguyen, and Nguyen (2015). The author held discussions with several workers in industrial parks in Hai Phong City and consulted with human resources experts to examine the relationship and relevance of the observed variables, ensuring their suitability to the research context before the formal survey. The discussion results indicated that the participants agreed with the proposed scales. The formal scale includes 30 observed variables. The study used a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree).

Sample size

According to Hair et al. (2010), the ideal sample size for exploratory factor analysis (EFA) is 10:1. Therefore, the minimum sample size for this study is 300. However, the author distributed 380 questionnaires to avoid disqualifications during data cleaning, which could have negatively impacted the results. A convenient non-probability sampling method was used for data collection. The questionnaires will be sent online to employees in industrial parks of Hai Phong City for a period of three months, from January 2025 to March 2025. After data cleaning, 355 responses were included. Of these, 53.2% of respondents are female and 46.8% are male. Regarding age, the group from 25 to under 35 years old represented the largest portion at 45.4%, followed by the group from 18 to under 25 years old with 29.6%. The group from 35 to under 45 years old accounted for 18.9%, while those aged 45 and older made up 6.2%. This result aligns with the typical characteristics of industrial parks: most of the workforce is young, of working age, with high work ability and adaptability to industrial production conditions. Regarding education, the majority of workers have a high school diploma, representing 43.9%, followed by those with intermediate or college-level education at 30.1%, individuals with only a primary school education at 20.3%, and those with a university degree or higher at just 5.6%. It reflects the conformity with the labor characteristics of industrial parks, mainly unskilled workers. In terms of working time, the largest proportion of people worked from 1 to less than 3 years (37.2%), followed by those with 3 to less than 5 years (29.6%), less than 1 year (17.7%), and 5 years or more (15.5%). Regarding labor contracts, 57.2% of employees are on fixed-term contracts, 31.3% are on indefinite contracts, and 11.5% work on seasonal or short-term contracts. Therefore, most employees still work under fixed-term agreements, highlighting the lack of stability in labor relations within industrial parks. Regarding income, most employees earn between 7 and less than 10 million VND per month (39.7%), followed by those earning from 5 to less than 7 million VND (30.7%), from 10 to less than 12 million VND (18.9%), over 12 million VND (7.0%), and only 3.7% earn less than 5 million VND.

Data analysis

Hypotheses were tested using multivariate regression. The data analysis process includes reliability analysis, exploratory factor analysis, correlation analysis, and multivariate linear regression analysis. The multivariate regression model is expressed as follows:

• Pres = β0 - β1*WE + β2*RA + β3*WL - β4*WR + β5*WP + e
• In which:
• Pres (dependent variable): Work pressure
• Independent variables (Xi): Work environment (WE), Role ambiguity (RA), Workload (WL), Workplace relations (WR), Work performance (WP).
• βk: Regression coefficient (k = 0, 1, 2....,5).

The descriptive statistical results showed that the worker’s work pressure reached the highest average value (Mean = 4.32), indicating that work pressure is common and severe. They feel overwhelmed, have little time to rest, are asked beyond their abilities, and lack inspiration at work. Next, the workload has a Mean = 4.15, reflecting high-intensity work, rush periods, and the need to complete tasks fully within tight deadlines. This demonstrates that workers are not only physically exhausted but also under significant pressure regarding their progress and the quality of their work. Role ambiguity (Mean = 4.03) is also rated high by employees, indicating that a lack of information, undefined responsibilities, and job expectations still exist. It can cause workers to experience ambiguity, anxiety, and decreased confidence at work. Additionally, the relationship within the workplace (Mean = 3.89) and the working environment (Mean = 3.76) was also rated highly, showing that employees generally feel positive about support from colleagues and supervisors, as well as physical working conditions like lighting, safety, and air quality. However, it still isn't enough to offset the high pressure they face. Finally, work performance (Mean = 3.65) had the lowest average score, indicating that although workers aim to complete tasks on time and meet standards, intense pressure hampers their ability to sustain performance. This could be due to accumulated fatigue, lack of motivation, and an imbalance between expectations and execution (see Table 1).

Table 1: Descriptive statistics

Source: Data processing results (2025)

The results of the analysis in Table 2 show that scales with Cronbach’s Alpha greater than 0.7 exceed the threshold of 0.5 recommended by Hair et al. (2010), demonstrating a high level of intrinsic consistency between observed variables. The scale of workplace relations has the highest Cronbach's Alpha (α = 0.826), followed by workload (α = 0.812), role ambiguity (α = 0.808), working environment (α = 0.795), and working performance (α = 0.788).

Table 2: Cronbach’s Alpha and EFA (independent variables)

Source: Data processing results (2025)

The results of the exploratory factor analysis (EFA) revealed a KMO coefficient of 0.810 and a Sig. value of 0.000 in Bartlett’s Test, demonstrating the suitability of the data for factor analysis. The eigenvalues greater than 1 and a total variance explained of 78.951%, exceeding the 50% threshold, indicate that the extracted factors account for 78.951% of the data's variability. Furthermore, the rotation matrix table shows that the observed variables clustered into five factors as initially hypothesized, with factor loadings surpassing 0.7, thereby satisfying the criteria for both convergent and discriminant validity as recommended by Hair et al. (2010).

Table 3: Cronbach’s Alpha and EFA (the dependent variable)

Source: Data processing results (2025)

The analysis results presented in Table 3 demonstrate that Cronbach’s Alpha reached 0.818, signifying a scale with high internal consistency. Corrected item-total correlations exceeding 0.5 confirm that the observed variables exhibit strong interrelatedness. Furthermore, the factor analysis indicated that the KMO measure was 0.827, and Bartlett’s test yielded a significance value of 0.000, suggesting that the data were highly appropriate for factor analysis. An eigenvalue of 2.105 facilitated the extraction of six observed variables into a single factor, which accounted for 79.584% of the total variance, with factor loadings ranging from 0.729 to 0.802, thereby demonstrating robust convergent validity. No observed variables were eliminated during the analysis, confirming the scale's consistency and stability. Consequently, the dependent scale attains the convergent and discriminant validity as recommended by Hair et al. (2010).

Table 4: Correlation analysis

Source: Data processing results (2025)

The results of the analysis in Table 4 indicate that the independent variables have a positive linear relationship and are statistically significant with the dependent variable because Sig. is less than 0.05 and r is greater than 0.4, meeting the criteria recommended by Hair et al. (2010). Additionally, the correlation coefficient between the independent variables remained within the acceptable threshold and did not show unusually high values, indicating no signs of multicollinearity in the model.

Table 5: Hypothesis testing

Source: Data processing results (2025)

The results of the multivariate linear regression analysis showed that the study model achieved high relevance and a statistically significant R² with an R² of 0.819 and an adjusted R² of 0.803, indicating that the independent variables in the model explained up to 80.3% of the variation in the dependent variable. The analysis also showed that the Durbin-Watson statistic of 1,844 falls within the range of 1.5 to 2.5, indicating no residual autocorrelation in the regression model. The results of the ANOVA analysis and F-test show that the p-value is 0.000, confirming that the linear regression model fits the data well and can be used.

Testing the research hypotheses showed that the Sig. was less than 0.05, indicating the model was statistically significant. Additionally, the variance inflation factor (VIF) of the independent variables is less than 2, so there is no multicollinearity issue. Moreover, regression diagnostic tests such as scatterplots, histograms, and P-P plots demonstrate randomly distributed residuals, standard assumptions are met, and the model does not violate the assumptions of multivariate linear regression. The Scatterplot graph showing the residuals relative to the predicted values indicates that the randomly scattered points around the mean of 0 do not form a regular pattern, confirming that the assumptions of linear relationship and homoscedasticity are valid. Additionally, the histogram of the residuals shows a distribution close to normal, with the curve roughly matching the frequency distribution when the mean is approximately 0 and the standard deviation is near 1, reflecting a residual with a normal distribution. The P-P Plot of the normalized residuals shows the data points are appropriately aligned along the 45-degree diagonal, confirming that the assumption of normal distribution for the residuals is not violated. Additionally, both the Kolmogorov-Smirnov test and the White test results indicated a significance coefficient greater than 0.05. Therefore, the research hypotheses are supported, and the standardized linear regression equation is defined as follows:

Pres = 0.369*WL + 0.325*RA – 0.307*WE + 0.281*WP – 0.276*WR + e

Additionally, the results of the One-sample t-test and ANOVA variance analysis showed that there was no statistically significant difference in work pressure levels among different groups of workers based on demographic factors such as gender, age, education level, seniority, or income level (Sig. > 0.05).

Thus, the factors that influence the work pressure of workers in industrial parks of Hai Phong city, in order of decreasing impact, are: workload, role ambiguity, work environment, work performance, and workplace relations.

Conclusion

This study has identified and analyzed factors influencing the work pressure of workers in industrial parks in Hai Phong city. The results showed that the work environment, role ambiguity, workload, workplace relations, and work performance were strongly linked to the level of pressure workers experienced. Among these, workload and role ambiguity were the most significant factors, reflecting the characteristics of industrial parks, where work is often repetitive, demanding high intensity, and requires strict adherence to schedules.

However, the limitation of the study is that it was only conducted in Hai Phong city, so it does not represent other regions of Vietnam. Therefore, future research should expand the survey to include various industries and areas.

Implications

Theoretically, the study helps clarify the connection between factors in the work stress model, adding empirical support to previous research on occupational stress within the context of industrialization in developing countries.

Practically, the research results are a crucial basis for businesses in Hai Phong Industrial Park to develop effective human resource management policies, especially in designing suitable workloads, clarifying roles, enhancing the environment, and improving workplace relations, thereby reducing pressure, increasing efficiency, and boosting employee cohesion. Based on the study results, some implications are suggested as follows:

First, enterprises need to control and reasonably distribute the workload among employees, making sure that the delivery volume does not exceed each person's capacity and available time. Production and human resources planning should consider employees' physical and mental stamina, develop a production plan, and assign tasks that match each individual's ability. This helps avoid situations where one person has to handle too many stages or complete work in too short a period. Enterprises should regularly evaluate actual labor intensity and listen to employee feedback to adjust assignment goals as needed. Additionally, they can implement flexible policies on working hours, provide rest between shifts, or rotate shifts to help employees restore their physical health and maintain mental well-being.

Second, enterprises need to clarify roles, responsibilities, and expectations for each position by creating clear job descriptions, regularly communicating, and providing feedback from superiors. Additionally, enterprises should develop detailed job descriptions, organize periodic orientation training, and maintain a two-way communication channel between managers and employees. Moreover, enterprises need to publicize clear criteria for evaluating performance, rewards, and punishments so employees understand the organization's expectations and can actively adjust their work behaviors. When workers have a clear sense of their roles, they will feel more secure and experience less psychological pressure.

Third, enterprises need to improve physical working conditions at workshops and factories. They should invest in renovating comfortable workspaces, installing proper ventilation, soundproofing, lighting systems, and enhancing industrial hygiene. Additionally, enterprises need to address psychological aspects of the workplace, such as providing lunch break areas, common spaces, and relaxation zones during shifts. When workers feel safe and comfortable in their environment, they are more likely to handle work pressure positively.

Fourth, enterprises should establish a transparent and positive system for recording and evaluating work performance, helping employees see the value and results of their efforts. However, they must avoid creating excessive pressure when setting targets; instead, they should combine reasonable expectations with support for implementation so that pressure becomes a motivating force rather than a burden. Additionally, enterprises should organize training courses, soft skills consulting, time management, and teamwork development to help employees improve work performance in a positive and autonomous manner. Enterprises need to transform “pressure” into “motivation,” preventing performance from becoming a source of ongoing stress.

Finally, enterprises need to strengthen positive relationships in the workplace, build a corporate culture based on support, respect, and sharing among superiors, subordinates, and colleagues. They should establish a two-way feedback system, transparent communication channels, and internal cohesion activities (like team-building, exchanges, themed activities) to create a friendly working environment, help workers feel connected, and reduce psychological stress caused by isolation or lack of support.

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