• 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
ebazco@gmail.com
ORCID nº: https://orcid.org/0000-
0003-2376-0531
Victoria
Sanagustín-Fons
Universidad de Zaragoza
vitico@unizar.es
ORCID nº: https://orcid.org/0000-
0002-3957-2466
David
Almorza Gomar
Universidad de Cádiz
david.almorza@gm.uca.es
ORCID nº: https://orcid.org/0000-
0002-2004-2799
Abstract
Contemporary lifestyles are characterized
by a multitude of factors that contribute to
elevated levels of stress in the population,
with work being a signicant factor. This
study addresses an important gap in social
science research by examining work-
related 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 socio-
demographic 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 ofce workers. However,
there was a signicant 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 Demand-
Control-Social Support Model, job stress,
Karasek, seniority, Spain.
Resumen
Los estilos de vida contemporáneos se ca-
racterizan por una serie de factores que con-
tribuyen a elevar los niveles de estrés en la
población, siendo el trabajo un factor signi-
cativo. Este estudio aborda una importante
laguna en la investigación en ciencias socia-
les al examinar el estrés laboral en trabaja-
dores industriales. La empresa participante
fue seleccionada por ser emblemática en la
gestión de recursos humanos y la preven-
ción de riesgos psicosociales. Para evaluar
el nivel de estrés laboral se administró el
Cuestionario de Demandas-Control-Apoyo,
y se consideraron las variables sociodemo-
grácas de género, edad y antigüedad. Los
resultados revelaron que los trabajadores
de fábrica presentaban niveles mínimos de
estrés laboral, sin diferencias signicativas
en comparación con los trabajadores de o-
cina. Sin embargo, se observó una discre-
pancia signicativa 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 De-
manda de Trabajo-Control-Apoyo Social, es-
trés 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
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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).
Specically, 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 elds 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 specic 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 specically examining how gender, age, and seniority inuence 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 denitions 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 dened as the response a worker experiences
to a specic situation in their work environment, both in the short and long term,
manifesting itself as a result of that specic condition.
The absence of a comprehensive and global denition of work-related stress
represents a signicant obstacle that hinders the progress and development of
research in this eld of study (Gunasekara & Perera, 2023).
The European Agency for Safety and Health at Work (EU-OSHA, 2016) identies
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; Seraca 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 specic 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 &
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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 conicts 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
insufcient 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 inuence in the scientic eld (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 signicant
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
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identify a negative correlation between age and the number of stress sources, job
prole 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
efcient coping strategies (Aldwin et al., 2021). Furthermore, they do not consider
that work-related stress has a signicant 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 Grifths 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 signicant
gender differences in their perception of work-related psychosocial factors and work-
related injury outcomes (Baidwan et al., 2019).
Finally, the socio-demographic variable of seniority was shown to have a signicant
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, rstly, 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 (ofce). These groups may have different stress
levels due to their completely different roles. It is also important to examine how
socio-demographic variables inuence 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 (ofce 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 ofce
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 seniority-
related stress patterns observed in service-oriented occupations are consistent in
production-oriented workplaces, which have different working conditions, physical
demands and organisational structures.
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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 condentiality, 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 ve 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, nally, (iv)
discussion and conclusions.
In the rst 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 ofce 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 inuential in explaining the effects of job stress, supported by signicant
scientic evidence (Fila et al., 2017; Osorio & Cárdenas, 2017; Spiegelaere et al.,
2015). Moreover, studies indicate that the test-retest reliability over a period of ve
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 Escribà-
Agüir 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
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coefcient for each of the three dimensions (0.83-0.87) and Cronbach’s alpha (0.74-
0.88). 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 classied 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
classied as follows:
• Type of work: factory and ofce.
• 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) classication; 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 condentially. 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/ofce, 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 signicant levels of job strain. As there is no consensus regarding the denition
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
coefcient of variation was calculated. As the coefcient 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 signicant 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 condence level of 95%. In contrast, to analyse Hypotheses 3 and 4 (age and
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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 nally,
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 nally 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 ofce 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
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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 ofce 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 classied 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 ofce group scored higher on all three dimensions, particularly on
the control dimension (factory group=17; ofce 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
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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 ofce
group. The test was conducted with a sample of 340 individuals at a 95% condence
level. The analysis showed no signicant difference in job stress levels between the
factory and ofce 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
Ofce 5 83 88
Total 32 308 340
Source: Own elaboration based on employee questionnaire (11 September 2023).
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Table 5 presents the relationship between the dimensions of the JCQ questionnaire
and the socio-demographic variables in the study, categorized into factory and ofce
groups. In the female cohort of the factory group, the control variable is less than 16
(15.8) and the demands are greater than 10 (12.6), indicating potentially high levels of
stress. However, as reported in previous studies (Gillman et al., 2023; Nahum-Shani
& Bamberger, 2011), the moderating variable of social support (21.06) alleviates
these levels.
Table 5. The relationship between the dimensions of
the JCQ Questionnaire and the variables sociodemographic.
CONTROL DEMANDS SUPPORT
FACTORY
WOMEN 15.81 12.65 21.06
MEN 18.77 13.03 23.35
AGE 1 17.2 12.4 21.2
AGE 2 17.07 13.09 21.56
AGE 3 19.44 12.22 26.56
SENIORITY 1 17.47 12.65 23.35
SENIORITY 2 16.08 13.11 20.61
SENIORITY 3 21.4 12 26.3
OFF ICE
WOMEN 25 16 26
MEN 25.82 15.06 25.06
AGE 1 26 17 29
AGE 2 25.18 15.45 25
AGE 3 26.25 14.5 25.5
SENIORITY 1 24.71 15.57 24.71
SENIORITY 2 26.25 16 23.5
SENIORITY 3 26 14.82 26.27
Source: Own elaboration based on employee questionnaire (11 September 2023).
A test of equality of proportions was conducted to examine hypothesis 2, which
suggests that the socio-demographic variable ‘female’ is linked to higher levels of job
stress than the variable ‘male’ (see Table 6). The sample size was 336 (comprising
of factory and ofce groups), as four participants did not respond to this information.
A condence level of 95% was used. The analysis revealed signicant differences,
indicating higher stress levels for women in general (p ≈ 1).
Table 6. Contingency table: distribution of cases of work-related stress by work environment.
JOB STRESS
Yes No Total
Sex Women 28 116 144
Men 4 188 192
Total 32 304 336
Source: Own elaboration based on employee questionnaire (11 September 2023).
In relation to the socio-demographic variables of age and seniority, a covariance
study was carried out to determine whether there is a direct or inverse relationship
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with each of the dimensions of the model. As can be seen in Table 7, there is a
clear direct relationship between age and seniority and the dimensions of control
and social support. The values of these two dimensions increase with increasing age
and seniority. In contrast, there is an inverse relationship between age and seniority
and the job demands dimension. As age and seniority increase, the value of the
dimension decreases. This indicates that the factors that contribute to higher levels
of job stress decrease as the age of the workers and the number of years in the
company increase.
Table 7. Covariance between age and seniority,
and the dimensions of the JCQ questionnaire.
AGE SENIORITY
FACTORY
Control 6.92 6.84
Demands - 0.70 - 1.37
Support 7.27 5.18
OFFICE
Control 1.16 7.60
Demands - 7.59 - 4.32
Support 1.12 7.04
WOMEN
Control - 9.98 - 12.68
Demands - 4.41 - 6.06
Support - 6.57 - 10.57
MEN
Control 22.31 20.26
Demands 1.88 1.43
Support 15.29 16.00
Source: Own elaboration based on employee questionnaire (11 September 2023).
Subsequently, an analysis of covariance (ANCOVA) was performed in order to
examine the impact of the independent variables (work environment, gender, age
and seniority of employees) on the dependent variable, the level of job stress. The
regression model (Table 8) shows a multiple correlation coefcient of 0.324, indicating
a moderately low relationship between the independent variables and cases of job
stress. The coefcient of determination R^2 is 0.105, suggesting that approximately
10.5% of the variability in job stress can be explained by the variables considered
in the model. The adjusted R^2 is slightly lower (0.094), indicating that some of the
variables may not be contributing signicantly to the explanation of this variability.
Table 8. Covariance between age and seniority,
and the dimensions of the JCQ questionnaire.
Regression statistics
Multiple correlation coefcient 0,323896
Coefcient of determination R^2 0,104909
R^2 adjusted 0,094092
Standard error 0,279809
Source: Own elaboration (14 December 2023).
The analysis of variance (ANOVA) demonstrates that the model is statistically
signicant (F= 9.699, p < 0.001), accounting for approximately 9.9% of the
variance in the stress cases (see Table 9). This nding indicates that at least one
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of the independent variables has a signicant impact on the employees’ level of job
stress. The examination of the individual coefcients discloses that gender exerts a
signicant and positive effect (β = 0.162882, p < 0.001), while the work environment
demonstrates a signicant negative relationship (β = -0.084, p = 0.023). The analysis
indicates that age does not demonstrate a signicant effect (β = -0.001, p = 0.549),
and seniority exhibits a marginally signicant positive relationship (β = 0.003, p =
0.277). These ndings indicate that, when the independent variables of age and
seniority are controlled for, gender and work environment emerge as the primary
factors inuencing stress levels. Specically, it is observed that women report higher
levels of stress, while lower levels of stress are reported in more favourable work
environments.
Table 9. Covariance between age and seniority, and the dimensions of the JCQ
questionnaire.
Coefcient p
Interception 0,073215 0,476669
Work place -0,084364 0,023018
Sex 0,162882 0,0000018
Age -0,001754 0,549473
Seniority 0,003016 0,277097
Source: Own elaboration (14 December 2023).
4. DISCUSSION
Concern about psychosocial risks at work, including job stress, is not a novelty, but it
has been worrying scholars for years, but nowadays more attention is being paid to
them, encouraging companies to assess and contain them (Macías, 2019; Moreno,
2014). A study conducted by Miranda & Gonçalves revealed that psychosocial factors
have been identied as the most signicant risk factors for workers (Miranda &
Gonçalves, 2024). In addition, levels of job stress have shown a consistent upward
trend over time, exacerbated in recent years by the COVID-19 pandemic crisis (Cigna,
2020). This escalation is particularly pronounced among workers in disadvantaged
occupations, as observed by Rigó et al. (2020).
4.1. Levels of job stress
After analysing the stress levels derived from the JCQ questionnaire, it can be
concluded that there are no high stress levels in the studied groups (factory group
and ofce group) of the organisation. Neither group meets the criteria considered
to be at risk [low control (≤16), high demands (≥10) and low social support (≤18)],
indicating an adequate balance between the dimensions of the JDCS model. The
values obtained (Factory: control (17.3), demands (12.81) and social support (22.25);
Ofce: control (25.64), demands (15.27) and social support (25.27)) suggest that the
control and social support variables moderate the high level of the demands variable.
However, a subgroup of 32 workers was identied as having elevated levels of stress,
predominantly comprising women in the factory group. However, the analysis of
proportions revealed no statistically signicant differences in stress levels between
the factory and ofce groups (p = 0.1096).
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These ndings corroborate earlier research indicating that elevated levels of
control and social support can serve as mitigating factors against high demands
and are associated with enhanced workers’ well-being (Fila, 2016; Rodwell et al.,
2011). Additionally, it is noteworthy that in developed countries, the quality of social
relationships constitutes the vast majority of sources of chronic stress (Wilkinson &
Pickett, 2013).
The results obtained (absence of stress in both factory and ofce groups) contrast
with other research, which suggests that both factory and ofce workers experience
signicant job stress, but differ in the sources of stress. For Bolliger et al. (2022),
ofce workers tend to face high work intensity, time pressure and social environmental
stressors. In contrast, factory workers, particularly those engaged in metalworking, are
more likely to experience physical and psychological stress, as well as high oxidative
stress (Tatzber et al., 2022). Other research indicates that stressors specic to ofce
workers include performance appraisal and accountability for business results (Shin
& Son, 2015); while job insecurity, low wages, heavy workloads and long working
hours are common sources of stress for workers in general (Tausig & Fenwick, 2011).
4.2. Implications of the theoretical model
The demand-control-support model (DCS) has been extensively employed to identify
job factors associated with job stress (Nordhall et al., 2024; Rodwell et al., 2011) and
has been demonstrated to exhibit a satisfactory level of applicability (Shi et al., 2010).
Although there is no extensive current literature on job stress among employees
in industrial companies (Yan et al., 2022), other research can be found that uses
Karasek’s demand-control-support model, together with the JCQ questionnaire,
to study job stress in industry (Adjobimey et al., 2022; Alias et al., 2022; Juster
et al., 2013). On the other hand, there are also studies that use other theoretical
reference models to assess job stress, such as Lazarus & Folkman’s transactional
model of stress (Kaveh et al., 2023; Marrero et al., 2013) and Siegrist’s effort-reward
imbalance model (Siegrist & Li, 2017; Wang et al., 2020). Moreover, research has
identied instances where more than one model has been employed, as evidenced
by the study conducted by Inoue et al. (2013), which draws upon the demand-control-
support (DCS) model and the effort-reward imbalance (ERI) model.
Although the model has been used in numerous studies to assess levels of job stress,
it has also been criticised for a number of reasons. Firstly, the model’s reliance on
self-reports has been questioned, as well as the variability in the measurement of
demand, control and support variables. In addition, the model has been found to have
inadequate specication and operationalisation of the independent variables (Fila,
2016). The model has also been criticised for its application in cross-sectional designs.
Montero et al. (2013) argue that the primary criticisms relate to the dimensionality of
the scales employed to assess the Job Demands factor, as well as the consistency of
the interactions between the variables.
The necessity for additional investigation into the JDCS model and the JCQ
questionnaire is underscored, as emphasized by Shi et al. (2010). Expanding earlier
suggestions earlier suggestions, there is a need for adopting longitudinal designs,
combining objective and subjective measures, using larger sample sizes, and giving
more attention to diverse types of demands and control., there is a need for adopting
longitudinal designs, combining objective and subjective measures, using larger
sample sizes, and giving more attention to diverse types of demands and control.
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This holistic approach should also encompass individual variables, as highlighted in
studies by Fila (2017) and Luchman & González-Morales (2013).
4.3. Job stress by socio-demographic variables
Despite the absence of signicant differences in job stress between the analysed
environments, and the company’s exemplary status in the prevention of occupational
hazards, the proportions analysis result of the study indicates that there are
discrepancies between the levels of work stress experienced by men and women,
with higher stress experienced by women (p≈1).
This result is consistent with research by Colin-Chevalier et al. (2022), who conclude
that women perceive higher levels of job demands and lower levels of social
support, and Mensah (2021), who conclude that job stress has a direct and negative
relationship with well-being and that women have a greater effect. A number of
potential explanations for these ndings can be proposed. One such explanation is
that women may be reporting higher workloads and emotional experiences (Hagqvist
et al., 2020), which could result in lower levels of control and social support factors
(Marinaccio et al., 2013).
A review of the literature by Singh (2023) also indicates that women report higher
levels of job stress. Nevertheless, they also observe that other studies have failed to
identify signicant differences between the sexes. An example of the latter is research
by Cottingham et al. (2020) and Makhbul & Hasun (2011), which has demonstrated
that there are no statistically signicant differences between men and women in job
stress levels. Ramos and Jordão (2014) also conclude in their research that there
are no signicant gender differences in job stress levels. Nevertheless, the study
demonstrated that men exhibited a signicantly lower prevalence of risk factors and a
greater degree of organisational involvement compared to women. Conversely, some
studies have indicated that men experience higher levels of job stress than women
(Kushal et al., 2018). This discrepancy has been attributed to greater job demands,
particularly greater work-related pressures (Rodríguez et al., 2001).
Research on job stress in industrial workers indicates that both men and women
experience high levels of stress, although the sources and levels of stress may differ.
For instance, the ndings of Rivera-Torres et al. (2013) suggest that for men, only
the quantitative demand dimension is statistically signicant in relation to job stress,
whereas for women, the qualitative demands (emotional and intellectual aspects) are
also statistically signicant.
Regarding the age of the workers, the study indicates a direct correlation with the
variables of control and support, and an inverse correlation with the demands variable
in the two groups analysed (Factory: control (6.92), demands (-0.70) and support
(7.27); Ofce: control (1.16), demands (-7.59) and support (1.12)). These results
support those obtained by Marinaccio et al. (2013), but contradict those proposed
by Rodríguez et al. (2001), who suggested a direct correlation between age and
demands and an inverse correlation with social support. Research indicates that the
prevalence of job stress varies by age in the industrial sector. A review of the literature
indicates that the prevalence of job stress varies by age in the industrial sector.
Chitharaj et al. (2016) found that the 30-39 age group had the highest likelihood
of experiencing stress, particularly psychological stress. Maqsoom et al. (2020)
identied the inuence of age and industrial experience on extrinsic psychosocial
stressors. Their ndings indicated that younger workers are more susceptible to
work environment stressors, while less experienced workers are more vulnerable to
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environment stressors. Yaldiz et al. (2018) emphasised the inuence of age on the
relationship between job resources and perceived stress. Their ndings indicated that
older workers were more likely to experience stress when resources were limited. In
addition, Mauno et al. (2019) posited that older employees may experience greater
work intensication and intensied learning demands, which may contribute to job
stress.
Finally, in line with the ndings related to the age variable, the seniority of workers
in the organisation demonstrates a direct correlation with the control and support
variables, and an inverse correlation with the demands variable in the two groups
analysed (Factory: control (6.84), demands (-1.37) and support (5.18); Ofce: control
(7.60), demands (-4.32) and support (7.04)). In the study conducted by Boxall & Macky
(2014), it was established that seniority, when considered as a socio-demographic
variable, was found to have a signicant impact on job stress. No research has been
found that relates seniority of workers in the industrial sector to job stress. In relation
to other sectors, the research conducted, like Marinaccio et al. (2013), shows a direct
relationship between the control variable and the support variable, while an inverse
relationship is observed for seniority and demands. In contrast, Colin-Chevalier et al.
(2022) discovered a direct association between job demands and job stress, coupled
with an inverse relationship between social support and job stress. Research has
identied that employees with over 10 years of seniority are at an increased risk of
job stress, which is consistent with ndings by Birze et al. (2023) and Chen & Cunradi
(2008). Bradley’s (2007) research suggests that the impact of job stress diminishes
with seniority, which is interesting. However, Rogozińska-Pawełczyk (2018) study
revealed that employees with the shortest and longest tenures tend to experience
lower stress levels, while those with 6-15 years of employment report higher stress
and lower satisfaction. Arji et al. (2023), also found that workers with less than 10
years of seniority were more stressed at work. In contrast, a study by Goh et al.
(2021) found that employees with varying levels of experience were more susceptible
to stress and anxiety episodes.
After statistical analysis, it is concluded that the selected socio-demographic variables
explain a small proportion of the variance in job stress (R² = 0.105), with gender (β =
0.16, p < 0.001) and work environment (β = -0.08, p = 0.023) being the main predictors
of stress. This result suggests the presence of other potentially relevant variables that
were not taken into account. To improve the model and better understand the factors
inuencing job stress, it is recommended to incorporate other explanatory variables
and investigate possible interactions between them.
The research has limitations that may affect the obtained results. Firstly, it was a
cross-sectional study that was not followed up over time to determine if the results had
changed. Additionally, work variables associated with stress, such as contract type,
job exibility, or insecurities, were not considered in the study (McGann et al., 2016).
The study did not consider the impact of non-job stress on job stress, as personal
life and job stress can have a reciprocal relationship (Abendroth & Den Dulk, 2011).
Furthermore, it is important to consider the possibility that participants may not have
been entirely truthful when completing the questionnaire.
It is also important to note that the socio-demographic variables of age and seniority
are related to stress, as previously mentioned, provided that the working conditions
have remained consistent over the past few years.
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5. CONCLUSIONS
The present research, conducted within an industrial organisation, did not identify
elevated levels of job stress among the factory and ofce 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 signicant 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 identied
gender differences in job stress across various sectors, our study makes a distinct
contribution by demonstrating that these gender disparities persist specically 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
ndings conrm that even in workplaces with generally low stress levels and strong
organisational support systems, women experience signicantly higher stress levels
than their male counterparts, suggesting that gender-specic 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 benecial 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-nanced 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 signicantly
to the realisation of this article. Special thanks to the participating company for
providing the necessary resources and facilities.
Conict of interest and Ethical clearance statement
The authors declare, in full disclosure and in accordance with ethical standards, that
they have no competing interests, nancial or otherwise, that could inuence 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 identication number.
Availability of deposited data
In the course of this research, private data was collected and obtained following the
signing of a condentiality 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 condentiality agreement previously established
with the organisation.
Declaration of AI use
The present research has employed a variety of articial 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 verication by researchers.
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Annex A
INSTRUCCIONES: Las siguientes armaciones corresponden a diversos aspectos
relacionados con su puesto de trabajo y su entorno laboral. Deberá marcar, con una
X, una sola casilla por armación, según el grado de coincidencia con ella.
1. Mi trabajo requiere que aprenda cosas nuevas.
__ Totalmente en desacuerdo.
__ En desacuerdo
__ De acuerdo
__ Completamente de acuerdo.
2. Mi trabajo necesita un nivel elevado de calicación.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
3. En mi trabajo debo ser creativo.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
4. Mi trabajo consiste en hacer siempre lo mismo.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
5. Tengo libertad de decidir cómo hacer mi trabajo.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
6. Mi trabajo me permite tomar decisiones en forma autónoma.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
7. En el trabajo tengo la oportunidad de hacer cosas diferentes.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
Anduli • Revista Andaluza de Ciencias Sociales Nº 28 - 2025
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8. Tengo inuencia sobre como ocurren las cosas en mi trabajo.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
9. En el trabajo tengo la posibilidad de desarrollar mis habilidades personales.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
10. Mi trabajo exige hacerlo rápidamente.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
11. Mi trabajo exige un gran esfuerzo mental.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
12. En mi trabajo no se me pide hacer una cantidad excesiva.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
13. Dispongo de suciente tiempo para hacer mi trabajo.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
14. No recibo pedidos contradictorios de los demás.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
15. Mi trabajo me obliga a concentrarme durante largos periodos de tiempo.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
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16. Mi tarea es interrumpida a menudo y debo nalizarla más tarde.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
17. Mi trabajo es muy dinámico.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
18. A menudo me retraso en mi trabajo porque debo esperar al trabajo de los
demás.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
19. Mi jefe se preocupa por el bienestar de los trabajadores que están bajo su
supervisión
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
20. Mi jefe presta atención a lo que digo.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
21. Mi jefe tiene una actitud hostil o conictiva hacia mí.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
22. Mi jefe facilita la realización del trabajo.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
23. Mi jefe consigue que la gente trabaje unida.
__ Totalmente en desacuerdo.
__ En desacuerdo.
Anduli • Revista Andaluza de Ciencias Sociales Nº 28 - 2025
• 114 •
__ De acuerdo.
__ Completamente de acuerdo.
24. Las personas con las que trabajo están cualicadas para las tareas que reali-
zan.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
25. Las personas con las que trabajo tienen actitudes hostiles hacia mí.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
26. Las personas con las que trabajo se interesan por mí.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
27. Las personas con las que trabajo son amistosas.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
28. Las personas con las que trabajo se apoyan mutuamente para trabajar juntas.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
29. Las personas con las que trabajo facilitan la realización del trabajo.
__ Totalmente en desacuerdo.
__ En desacuerdo.
__ De acuerdo.
__ Completamente de acuerdo.
EDAD: ________ SEXO _________ PROFESIÓN _________________________
ANTIGÜEDAD _______________ PUESTO________________________________
Gracias por sus respuestas y su tiempo.
