Job Stress in Industrial Company: the Impact of Gender, Age and Seniority on Tension Levels

• 85 • Anduli Revista Andaluza de Ciencias Sociales ISSN: 1696-0270 • e-ISSN: 2340-4973 JOB STRESS IN INDUSTRIAL COMPANY: THE IMPACT OF GENDER, AGE AND SENIORITY ON TENSION LEVELS. ESTRÉS LABORAL EN LA EMPRESA INDUSTRIAL: EL IMPACTO DEL GÉNERO, LA EDAD Y LA ANTIGÜEDAD EN LOS NIVELES DE TENSIÓN. Ester Bazco Nogueras Universidad de Zaragoza [email protected] ORCID nº: https://orcid.org/00000003-2376-0531 Victoria Sanagustín-Fons Universidad de Zaragoza [email protected] ORCID nº: https://orcid.org/00000002-3957-2466 David Almorza Gomar Universidad de Cádiz [email protected] ORCID nº: https://orcid.org/00000002-2004-2799 Abstract Contemporary lifestyles are characterized by a multitude of factors that contribute to elevated levels of stress in the population, with work being a significant factor. This study addresses an important gap in social science research by examining workrelated stress among industrial workers. The participating company was selected because it is emblematic in human resource management and psychosocial risk prevention. The Demands-Control-Support Questionnaire was used to determine the degree of job stress, and the sociodemographic variables of gender, age and seniority were also considered. The results showed that factory workers had minimal levels of job stress, with no differences between them and office workers. However, there was a significant discrepancy in job stress levels between men and women, with women having higher levels. Regarding age and seniority, job stress levels tend to decrease with increasing age and seniority. Keywords: age, gender, Job DemandControl-Social Support Model, job stress, Karasek, seniority, Spain. Resumen Los estilos de vida contemporáneos se caracterizan por una serie de factores que contribuyen a elevar los niveles de estrés en la población, siendo el trabajo un factor significativo. Este estudio aborda una importante laguna en la investigación en ciencias sociales al examinar el estrés laboral en trabajadores industriales. La empresa participante fue seleccionada por ser emblemática en la gestión de recursos humanos y la prevención de riesgos psicosociales. Para evaluar el nivel de estrés laboral se administró el Cuestionario de Demandas-Control-Apoyo, y se consideraron las variables sociodemográficas de género, edad y antigüedad. Los resultados revelaron que los trabajadores de fábrica presentaban niveles mínimos de estrés laboral, sin diferencias significativas en comparación con los trabajadores de oficina. Sin embargo, se observó una discrepancia significativa en los niveles de estrés laboral entre hombres y mujeres, siendo este último grupo el que presentaba niveles más elevados. En lo que respecta a la edad y la antigüedad, los niveles de estrés laboral tienden a disminuir. Palabras clave: edad, género, Modelo Demanda de Trabajo-Control-Apoyo Social, estrés laboral, Karasek, antigüedad, España. Citation/ como citar este artículo: Bazco-Nogueras, Ester; Sanagustín-Fons, Victoria; Almorza-Gomar, David (2025). Job stress in industrial company: the impact of gender, age and seniority on tension levels. ANDULI 28 (2025), pp. 85-114 https://doi.org//10.12795/anduli.2025.i28.04 Recibido 5.06.2024 Revisado: 21.02.2025 Aprobado: 14.05.2025 Anduli • Revista Andaluza de Ciencias Sociales Nº 28 - 2025 • 86 • 1. INTRODUCTION Stress is a prevalent social and health problem in contemporary societies, and some authors have designated it the ‘epidemic of the 21st century’ (Aguiló, 2019). Specifically, a study conducted by IPSOS states that in the year 2024, 33% of Spanish workers reported feeling stressed one or more times in the last year to the point of being unable to go to work for a period of time (IPSOS, 2024). This research examines job stress in an industrial company, an area that has received less attention compared to other fields such as health or education. According to Azevedo et al. (2019), the sectors that have been the focus of the majority of research on job stress are the health sector (47%), the education sector (14%), and the military (10%). While there are numerous studies on job stress in general, there is a specific gap in the literature regarding job stress in industrial settings, particularly concerning the interaction between socio-demographic variables and stress levels in manufacturing environments. Most existing research has focused on service-oriented sectors like healthcare and education, with industrial workers remaining relatively understudied despite their unique workplace conditions and stressors. Our study addresses this gap by specifically examining how gender, age, and seniority influence stress levels in an industrial context, where physical demands often combine with organisational pressures in ways distinct from other sectors. 1.1. Job stress The term 'job stress' encompasses diverse definitions shaped by general perspectives, facing the challenge of occasional imprecision and contradictions due to its inherent complexity (Akanji, 2013). Initially, it was described as 'a situation wherein job-related factors interact with the worker to change his or her psychological and/or physiological condition such that the person is forced to deviate from normal functioning' (Newman & Beehr, 1979:1). In contemporary terms, job stress is characterized as a pattern of physical and psychological responses to uncontrollable external work demands (Patlán, 2019). That is, it can be defined as the response a worker experiences to a specific situation in their work environment, both in the short and long term, manifesting itself as a result of that specific condition. The absence of a comprehensive and global definition of work-related stress represents a significant obstacle that hinders the progress and development of research in this field of study (Gunasekara & Perera, 2023). The European Agency for Safety and Health at Work (EU-OSHA, 2016) identifies stress as the second most prevalent health issue among European workers, adversely affecting organisations. This results in heightened staff turnover, increased absenteeism and presenteeism, more workplace accidents and sick leave, communication challenges, early retirements, reduced business performance, and substantial economic losses estimated in billions (Collins et al., 2018; Götz et al., 2018; Hassard et al., 2018; Kaur et al., 2017; Mäcken, 2019; Serafica et al., 2023). On the other hand, job stress can lead to various health problems, including blood pressure issues, muscle tension, and depression. It can also cause specific challenges such as decreased concentration, altered personality and work habits, low back pain, and the potential for burnout (Gavelin et al., 2022; McTernan et al., 2013; Yang et al., 2023). Osorio & Cardeñas (2017) conducted a review highlighting three predominant explanatory models of job stress in research: the demand-control model (Karasek), the effort-reward imbalance model (Siegrist), and the transactional model (Lazarus & Artículos • Ester Bazco Nogueras, Victoria Sanagustín-Fons, David Almorza Gomar • 87 • Folkman). Karasek’s (1979) model relates job strain to the interaction of job demands and decision/control freedom. The job demand dimension includes psychological stressors related to workload, unanticipated tasks, and conflicts with colleagues and superiors. In contrast, the job control dimension refers to an individual worker’s potential ability to control their tasks and behaviour during the working day (Karasek, 1979). Research has shown that there is a correlation between high job demands, low job control, and increased levels of stress and job dissatisfaction (Fila, 2016; Rigó et al., 2020). Johnson & Hall (1988) expanded the model by integrating the dimension of social support, emphasizing interactions and support from coworkers and supervisors. According to this model, workers facing high demands, low control, and insufficient social support are susceptible to job stress (Karasek & Theorell, 1990), with social support acting as a modulating variable (Chesley, 2014). Stressors are estimated using the Job Content Questionnaire (JCQ) (Karasek et al., 1998). This study adopted Karasek’s model as its theoretical foundation due to its widespread use, recognition, and influence in the scientific field (Babamiri et al., 2022; Luchman & González-Morales, 2013; Taris, 2016). Its frequent application in comparison to other related models has contributed to the consolidation of its position as the most tested (Kain & Jex, 2010). On the other hand, the model assesses dimensions that include aspects related to work relationships, one of the areas in which stress manifests itself. In particular, the dimension of social support stands out, which acts as a modulator of these levels. For many years, studies have included socio-demographic variables as a crucial aspect of job stress assessment and management, acknowledging their significant impact on stress levels (Leka & Jain, 2010). Variables associated with stress response encompass age, gender, marital status, social class, geographical areas, personality, education, income, occupation, workplace, and seniority (Marinaccio et al., 2013; Novais et al., 2016). 1.2. Job stress and socio-demographic variables This study explores the relationship between perceived job stress, following Karasek’s model, and three of these variables: gender, age, and seniority. In the context of the socio-demographic variable of sex, extant literature suggests that women often experience higher job stress levels in comparison to men (Mensah, 2021; Solanki & Mandaviya, 2021; Wiegner et al., 2015). Numerous studies suggest that adult women are more susceptible to anxiety and stress disorders, with some reporting two to three times higher likelihood, particularly in the workplace (Christiansen, 2015). With the growing integration of women in the labour market, the analysis of the correlation between this variable and stress has acquired increasing importance (Fila et al., 2017). The varying stress levels between genders are attributed to risk factors, with women reporting more stress sources, interpersonal stimuli, and a greater impact on their health (Ramos & Jordão, 2014; Richardsen et al., 2016). Additionally, anatomical differences in men and women’s brains may contribute to diverse stress responses (Novais et al., 2016). Regarding age, job stress is notably linked to younger workers globally (Harter, 2023; Parry et al., 2022). Research indicates that older workers are less affected by intense negative daily work situations, maintaining high attention levels and experiencing lower negative impacts on such days (Scheibe, 2021). Kushal et al. (2018) similarly Anduli • Revista Andaluza de Ciencias Sociales Nº 28 - 2025 • 88 • identify a negative correlation between age and the number of stress sources, job profile and stress, and work experience and stress. Younger employees often require substantial assistance with tasks upon joining a position, and their perception of and response to stress varies according to individual differences in self-regulation and personality traits (Nowak, 2023). Alternatively, older individuals have been shown to possess enhanced coping capacity, elevated internal locus of control, and more efficient coping strategies (Aldwin et al., 2021). Furthermore, they do not consider that work-related stress has a significant impact on their job performance (Lee et al., 2025). Conversely, Rauschenbach & Hertel (2011) demonstrate an inverted U-shaped relationship between age and stress, supported by higher environmental demands on middle-aged workers. A literature review by Griffiths et al. (2009) concurs, indicating that workers aged 50-55 years experience the highest stress levels, which diminish as they approach retirement age. It is worth noting that older workers exhibit significant gender differences in their perception of work-related psychosocial factors and workrelated injury outcomes (Baidwan et al., 2019). Finally, the socio-demographic variable of seniority was shown to have a significant negative relationship with stress. That is, the longer the seniority, the lower the stress levels of workers (Azofeifa et al., 2016; Kushal et al., 2018). This phenomenon can be attributed, firstly, to a reduction in job demands (Hessels & van der Zwan, 2019) and, secondly, to an increase in effective coping strategies (Kruczek et al., 2020). Furthermore, Casu & Giaquinto (2018) concluded that job seniority is a moderating variable between entrapment and sources of job stress. Cardoso et al. (2018) proposed that new age management policies implemented by human resources departments represent the optimal approach to address the challenges posed by an ageing workforce. Investigating the manifestation of job stress in manufacturing companies in the industrial sector is crucial due to limited research in this area. Additionally, it is essential to differentiate between the two main groups of workers in this business sector: direct workers (factory) and indirect workers (office). These groups may have different stress levels due to their completely different roles. It is also important to examine how socio-demographic variables influence these dynamics. Analysing these dimensions in detail can help develop effective strategies to manage and prevent stress in work environments, promoting both well-being and professional effectiveness. The aim of the research is to analyse the level of stress perception among employees. To achieve this, the levels of occupational stress will be compared between two groups of workers (office workers and factory workers) in an organisation in the industrial sector. The relationship between stress and socio-demographic variables such as gender, age, and seniority will also be explored. Our research hypotheses were: (1) The factory group experiences higher levels of job stress compared to the office group. (2) Women have higher levels of job stress than men. (3) The variable ‘age’ has a negative relationship with stress levels. (4) Similarly, the variable ‘seniority’ is negatively associated with stress levels. These hypotheses, while previously examined in other sectors such as health care and education, have rarely been systematically tested in industrial manufacturing settings. By applying stress models applied to the industrial sector, this study aims to verify whether the gender, age and seniorityrelated stress patterns observed in service-oriented occupations are consistent in production-oriented workplaces, which have different working conditions, physical demands and organisational structures. Artículos • Ester Bazco Nogueras, Victoria Sanagustín-Fons, David Almorza Gomar • 89 • 2. METHOD AND MATERIALS In the initial phase of the study, an exploratory investigation was conducted to identify a leading industrial enterprise that has distinguished itself through exemplary human resources management and psychosocial risk prevention strategies. The selected multinational company, located in the province of Zaragoza (Spain), is part of an industrial sub-sector of great importance and tradition, with a presence in the region for more than 30 years. For reasons of confidentiality, the name of the company remains anonymous. This company was selected because of its importance in the region, its size and the diversity of its functions. It should be noted that although the study is based on convenience sampling, the entire population of employees was informed about the research and informed consent was obtained from those who chose to participate. This contextual information about the study population is essential to understand the scope of the study and the representativeness of the results obtained. In the second phase, after the selection of the company, the quantitative approach is chosen to test the hypotheses, as it is considered the most appropriate method to respond to the objectives of this research. This stage is divided into five sub-stages: (i) selection of the sample and collection of information, (ii) selection of the instrument, (iii) administration of the questionnaire, (iv) analysis of the data and, finally, (iv) discussion and conclusions. In the first sub-phase, the sample is carefully selected and essential information is collected from both the workers and the organisation. The sample was selected by probability sampling from the total population of the company, with a total of 340 participants, divided into two groups: the factory group, consisting of 252 workers (although 296 were initially registered), and the office group, consisting of 88 people. The sample selected represents 28.01% of the total study population. In the second sub-phase, an instrument was selected to assess the risk of job strain among workers. The Job Content Questionnaire (JCQ-29) developed by Karasek (1979) and later extended by Johnson and Hall (1988) was chosen. The questionnaire assesses three dimensions associated with job strain, which comprise various job factors: (i) psychological demands: time pressure, workload, physical demands and job breaks; (ii) control: freedom in decisions and task content; and (iii) social support: peer and supervisor support (Boxall & Macky, 2014; Karasek et al., 1998). The JCQ-29 assesses levels of job strain by combining the three dimensions mentioned above, resulting in different categories of job types. In particular, the category known as isostressful jobs (high job demands, low job control and low social support) is considered to expose workers to a higher risk of psychological distress caused by job strain (Karasek and Theorell, 1990). This instrument is widely regarded as one of the most influential in explaining the effects of job stress, supported by significant scientific evidence (Fila et al., 2017; Osorio & Cárdenas, 2017; Spiegelaere et al., 2015). Moreover, studies indicate that the test-retest reliability over a period of five years is satisfactory among employees whose job responsibilities and ergonomic exposures have remained consistent over time (d’Errico, 2008). The questionnaire utilised in this study is a Spanish version developed by EscribàAgüir et al. (2001) and derived from the book ‘Ergonomía y psicosociología aplicada: Manual para la formación del especialista’ (Llaneza, 2009, pp. 475-477). As demonstrated by Escribà-Agüir et al. (2001), this version exhibits a factor structure comparable to the original version, as evidenced by the high intraclass correlation Anduli • Revista Andaluza de Ciencias Sociales Nº 28 - 2025 • 90 • coefficient for each of the three dimensions (0.83-0.87) and Cronbach’s alpha (0.740.88). Furthermore, the three dimensions of the JCQ also exhibit similar reliability to that of the original American questionnaire, with Cronbach’s alpha values above 0.90 for each of the dimensions. It consists of 29 items, each assessed using a Likert-type response scale ranging from 1 (strongly disagree) to 4 (strongly agree). The items are classified into three dimensions according to Karasek’s model, resulting in three scores: psychological demands (items 10 to 18), control (items 1 to 9), and social support (items 19 to 29). The control dimension is further divided into the content category (items 1, 2, 3, 4, 7 and 9) and the decisions category (items 5, 6 and 8). The social support dimension is divided into the supervisor’s category (items 19, 20, 21, 22 and 23) and the coworker’s category (items 24, 25, 26, 27, 28 and 29). To correct the questionnaire, we summed the scores of the items in each dimension, excluding items 4, 12, 13, 14, 21, and 26. The scores for the psychological demand’s variable can range from 5 to 20, for the control variable from 8 to 32, and for the social support variable from 9 to 36. We also collected additional information, including workers’ type of job, gender, age, and years of experience, to compare with stress levels. This information was classified as follows: • Type of work: factory and office. • Gender: women and men. • Age: group 1 (20,35], group 2 (35,50 years] and group 3 (50-65]. Organisation for Economic Co-operation and Development [OECD] (2005) classification; young workers (18-35 years), middle-aged workers (36-50 years) and old workers (51 years and over). • Seniority: group 1 (2,12], group 2 (12,22] and group 3 (22 to 32 years). In the third sub-phase, corresponding to the administration of the questionnaire, the sample participants completed the questionnaire confidentially. The research process was carried out on site in small groups under the exclusive supervision of one of the researchers. Subsequently, we analysed the collected data to determine the presence of stress in various sociodemographic groups, including factory/office, male/female, and age and seniority categories. The theoretical framework posits that a worker will experience job strain when the scores on the control and social support dimensions are low and the scores on the job demands dimension are high. In the fourth sub-phase, the JCQ-29 was employed to identify significant levels of job strain. As there is no consensus regarding the definition of these levels in the questionnaire, it was necessary to identify an appropriate scale. In order to determine whether the mean or the median should be employed, the coefficient of variation was calculated. As the coefficient was found to be below 0.5 in all cases, it was concluded that the arithmetic mean is a representative measure of the research data. Consequently, the mean values of the total score in each of the dimensions were calculated (total control score: 32; total job demands score: 20; total social support score: 36). The stress levels were considered significant if the scores were equal to or less than 16 for the control dimension, equal to or greater than 10 for the job demands dimension, and equal to or less than 18 for the social support dimension. Furthermore, to test Hypotheses 1 and 2, a test of equality of proportions was carried out with a confidence level of 95%. In contrast, to analyse Hypotheses 3 and 4 (age and Artículos • Ester Bazco Nogueras, Victoria Sanagustín-Fons, David Almorza Gomar • 91 • seniority), an analysis of covariance was conducted between these variables and the values of each of the dimensions of the model. 3. RESULTS The sample (N=340) comprises 42.35% women and 56.47% men, with four blank values for this variable (1.18%). The age variable is dominated by Group 2 (35.50], constituting 65.88%, followed by Group 3 (50.65], representing 20%, and finally, Group 1 (20.35], representing 14.12%. In terms of seniority, the largest cohort is group 2 (12.22] (48.24%), followed by group 1 (2.12] (28.23%) and finally group 3 (22.32] (23.53%). After dividing the sample into the study groups (see Table 1), the factory group showed an equal gender distribution, with 50% men and 50% women (four people did not provide this information). The mean age of the workers in this group was 41.78 years and the mean length of service in the company was 15.68 years. On the other hand, the office group was made up of 77.3% men and 22.7% women, with a mean age of 45.59 years and a mean length of service of 18.70 years. The disparity in participation between men and women in this group is explained by the fact that the majority of workers in this group are dedicated to a predominantly male activity, engineering. Table 1. Socio-demographic variables of the research, separated according to study groups. FACTORY OFFICE Frequency % Mean Frequency % Mean GENDER GENDER Female 20 22.73% Female 124 49.21% Male 68 77.27% Male 124 49.21% Blank 4 1.58% AGE 45.59 AGE 41.78 Group 1 (20,35] 12 13.64% Group 1 (20,35] 36 14.28% Group 2 (35,50] 44 50.00% Group 2 (35,50] 180 71.44% Group 3 (50,65] 32 36.36% Group 3 (50,65] 36 14.28% SENIORITY 18.70 SENIORITY 15.68 Group 1 (2,12] 28 31.82% Group 1 (2,12] 68 26.99% Group 2 (12,22] 20 22.73% Group 2 (12,22] 144 57.14% Group 3 (22,32] 40 45.45% Group 3 (22,32] 40 15.87% Source: Own elaboration Table 2 shows the results for each dimension. In the factory group, the mean of the control dimension is 17.3, which means that exceeding the threshold of 16 would not be a risk factor. The mean of the job demands dimension is 12.81, which is above Anduli • Revista Andaluza de Ciencias Sociales Nº 28 - 2025 • 92 • 10 and represents a risk variable when associated with a low level of the control dimension. The mean of the support variable is 22.25, which is above 18, and denotes a positive modulating variable for stress. On the other hand, in the office group, the mean of the control dimension is 25.64, the mean of the job demand dimension is 15.27, and the mean of the support variable is 25.27. The data indicate that none of the groups are experiencing high levels of stress, as they do not meet the three conditions required to be considered as experiencing job strain (low control, high job demands and low social support). Table 2. Variables of the JCQ Questionnaire. FACTORY OFFICE CONTROL DEMANDS SUPPORT CONTROL DEMANDS SUPPORT N252 88 Mean 17.3 12.81 22.25 25.64 15.27 25.27 Median 17 13 22 26 15 26 Mode 18 12 21 26 14 26 Standard deviation 4.39 2.29 3.87 2.19 1.61 3.24 Variance 19.28 5.22 14.97 4.81 2.59 10.49 Range 22 10 19 8 5 13 Minimum Value 8 8 14 22 13 19 Maximum Value 30 18 33 30 18 32 Source: Own elaboration based on employee questionnaire (11 September 2023). Although both groups have elevated levels of the job demands dimension, they also have high levels of control and support variables. According to Karasek’s (1979) categorisation, employees with high levels in the dimensions of demands and control are classified as belonging to the ‘active work’ group. These working conditions promote motivation and the development of new skills, and are associated with high levels of job satisfaction and reduced levels of work-related depression. It is important to note that the office group scored higher on all three dimensions, particularly on the control dimension (factory group=17; office group=26). Despite the fact that the workers as a collective do not evince elevated levels of work-related stress, a total of 32 workers demonstrated elevated levels. The scores obtained in the questionnaire by these workers are displayed in Table 3. Table 3. Contingency table: distribution of cases of work-related stress by work environment. Workers Demands Control Support A11 918 B 12 11 14 C13 12 17 D13 16 18 E15 14 18 F13 14 15 G11 12 17 Artículos • Ester Bazco Nogueras, Victoria Sanagustín-Fons, David Almorza Gomar • 93 • Workers Demands Control Support H 16 15 18 I12 12 17 J11 12 18 K11 918 L 12 11 14 M13 16 18 N15 14 18 Ñ13 14 15 O 16 15 18 P12 13 16 Q 12 11 14 R11 10 18 S13 16 18 T 16 15 18 U 13 14 15 V15 14 17 W12 12 18 X16 15 18 Y11 12 18 Z 13 14 15 AA 15 14 18 AB 18 12 17 AC 14 918 AD 13 16 18 AE 12 11 15 Source: Own elaboration based on employee questionnaire (11 September 2023). Furthermore, workers who met the criteria associated with levels of job strain (control ≤ 16; demands ≥ 10; social support ≤ 18) were selected and a test of equality of proportions was conducted to examine hypothesis 1 (see Table 4), which aimed to determine whether the factory group had higher levels of job stress than the office group. The test was conducted with a sample of 340 individuals at a 95% confidence level. The analysis showed no significant difference in job stress levels between the factory and office groups (p = 0.1096). Table 4. Contingency table: distribution of cases of work-related stress by work environment. JOB STRESS Yes No Total Work place Factory 27 225 252 Office 5 83 88 Total 32 308 340 Source: Own elaboration based on employee questionnaire (11 September 2023). Anduli • Revista Andaluza de Ciencias Sociales Nº 28 - 2025 • 100 • 5. CONCLUSIONS The present research, conducted within an industrial organisation, did not identify elevated levels of job stress among the factory and office groups. However, a subgroup analysis revealed that female factory employees exhibited higher stress levels, indicating substantial disparities between men and women. Our contribution in this area is significant in that it demonstrates the existence of differences between men and women, observing that neither social support nor control over one’s own work neutralises this difference. While previous research has indeed identified gender differences in job stress across various sectors, our study makes a distinct contribution by demonstrating that these gender disparities persist specifically in industrial environments despite the presence of factors that typically moderate stress (control and social support). This is particularly noteworthy as industrial settings have traditionally been male-dominated and less studied from a gender perspective. Our findings confirm that even in workplaces with generally low stress levels and strong organisational support systems, women experience significantly higher stress levels than their male counterparts, suggesting that gender-specific stress factors operate independently of workplace interventions that are effective for the general workforce. This opens up new lines of research in the exploration of the relationship between gender and exposure to related stress factors, as well as in the differentiated analysis of the role of both gender and sex in the experience and management of job stress. Furthermore, age and seniority were found to be positively associated with control and social support, and negatively associated with demands. This suggests a possible relationship between work experience and a more favourable perception of the work environment. However, socio-demographic variables explained only a limited part of the observed variability in stress levels, with gender and work environment being the main predictors. The discrepancy between our results and those of other studies can be attributed to the fact that the participating organisation may have an effective occupational risk prevention system, which has the effect of reducing the job stress values observed in another research. It would be beneficial to know the particularities of the company’s occupational health and safety (OHS) system to facilitate the generalisation of the results to similar organisations. It would also be advisable to ascertain the manner in which the participants were recruited by the company, namely the method by which the company selects personnel. It has been demonstrated that selecting workers by assessing personality and locus of control can reduce the levels of job stress within the company, as it acts as a protective factor. With the data obtained, it would be interesting to look more closely at the measures taken by the company studied, with a view to generalising to other companies in the sector, as the main causal factor to intervene in to prevent stress is organisational. Additionally, it is recommended to place greater emphasis on socio-demographic variables when managing job stress. This focus on socio-demographic variables highlights the necessity of a comprehensive understanding of the multifaceted nature of job stress and its impact on individuals within the organisational context. Authors contributions The following sections outline the responsibilities of the various contributors to the project: Concept and design: Ester Bazco Nogueras, Victoria Sanagustín-Fons; Artículos • Ester Bazco Nogueras, Victoria Sanagustín-Fons, David Almorza Gomar • 101 • Methodology: Ester Bazco Nogueras and David Almorza Goma; Software: Ester Bazco Nogueras and David Almorza Gomar; Data collection: Ester Bazco Nogueras; Analysis and interpretation: Ester Bazco Nogueras, Victoria Sanagustín-Fons; Preparation of the original draft: Ester Bazco Nogueras; Proofreading and editing: Ester Bazco Nogueras and Victoria Sanagustín-Fons. Support (Funding) This study has been co-financed by the Regional Government of Aragón within the framework of the Research Group Ref. S33_17R. The research group is entitled “Socio-Economics and Sustainability: Environmental Accounting, Circular Economy and Resources”. Acknowledgments We would like to express our sincere gratitude to all those who contributed significantly to the realisation of this article. Special thanks to the participating company for providing the necessary resources and facilities. Conflict of interest and Ethical clearance statement The authors declare, in full disclosure and in accordance with ethical standards, that they have no competing interests, financial or otherwise, that could influence or bias the objective presentation and interpretation of the results of this article. All study participants gave informed consent to participate in the research. Participants were informed of their right to withdraw from the study at any time. To ensure anonymity, each participant was assigned a unique identification number. Availability of deposited data In the course of this research, private data was collected and obtained following the signing of a confidentiality agreement with the collaborating organisation. This limitation on data disclosure is attributed to corporate policies, in response to the competitive nature of the organisation’s sector. In the event that a researcher expresses interest in the methodology employed for data analysis, they are encouraged to communicate with the corresponding author. The latter would be responsible for evaluating the request, taking as a reference the confidentiality agreement previously established with the organisation. Declaration of AI use The present research has employed a variety of artificial intelligence tools. On the one hand, Elicit was used to support the bibliographic review. Conversely, Chat GPT has been employed to enhance the readability and idiomatic quality of the text, with ongoing supervision and rigorous verification by researchers. 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