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Comparative Analysis of Technostress Among Banking Employees
in Himachal Pradesh
1Pankaj Thakur, 2Dr. Santosh Kumari
1Research Scholar in Himachal Pradesh University Business School, HPU Shimla, India
2Assistant Professor in Management at Himachal Pradesh University Business School, HPU Shimla, India
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1409000072
Abstract: The banking industry has been at the forefront of this change, as digital technology have quickly changed how businesses
work throughout the world. Digitalization has improved efficiency and accessibility, but it has also created psychological problems
for workers, such as technostress, which Brod (1984) originally described as a contemporary sickness caused by not being able to
handle technological demands. This research investigates the differential effects of technostress among banking personnel in the
public and private sectors of Himachal Pradesh, India. Utilizing primary data from 400 respondents across chosen banks (PNB,
SBI, HDFC, ICICI) in four districts (Shimla, Solan, Mandi, Kangra), the study applies validated measures for technostress (Ragu-
Nathan et al., 2008). A multistage sampling method was employed to carry out the research. Statistical investigation using SPSS,
encompassing Mann–Whitney U and MANOVA tests, indicates that private bank personnel endure markedly elevated levels of
technostress in comparison to their public sector colleagues (U = 14547.000, Z = -4.207, p < 0.05). Even though they are under a
lot of stress, private bank workers say they feel like they are getting more out of life, which may be because of performance bonuses.
Multivariate research substantiates that the job sector has a substantial effect on stress, work speed, and life perspective (Wilks’
Lambda = .929, F = 10.048, p < .001). These results highlight the need for specific treatments to alleviate technostress and enhance
staff well-being in technology-driven banking settings.
Keywords: Technostress, Individual Work-Performance, Well-Being and Banking
I. Introduction:
There is change in working pattern across the globe in organizations. The advancement in technology has brought the seismic shift
in all organizations and it has become a pervasive function. The internet technology, digitization and artificial intelligence have
created both opportunities and challenges. Organizations have acknowledged that without adopting technology it is not possible for
them gain competitive advantage and operational efficiency. This technological transformation is crucial for them and they are
investing in digital transformation projects. In an organizational setup, the human component is in contrast with the technological
revolution and faces challenges in adjusting to it, leading to psychological strain. This strain was named “technostress” by Brod in
1984. Brod called it a disease, and people associated with it are facing problems coping with it. It is manifesting as anxiety among
employees and brings resistance towards technology. With the passage of time, technology has become more complex and
pervasive, and it has become more important to understand the complexities of technostress. Technostress is a stress that originates
due to continuous adoption of new technologies, digital overload and pressure to remain connected. Brod has called it a modern
disease wherein people cannot handle technology efficiently.
Among the business sectors of a nation, the banking sector is one of the cornerstones that lies at the forefront of digital adoption.
The digital adoption has replaced the paper-based and ledger-based transactions. Nowadays, banks are fully automated, and they
are working through an advanced core banking system along with data analytical systems. The government mandate, ease of use,
and stiff competition in the banking sector are the driving forces behind the adoption of digital technology. Despite the ease of use
and more accessibility, these advancements have increased the pressure of work among banking employees.
Frontline banking staff interact with customers through various digital channels, and perform complex digital transactions, and to
remain efficient, they continuously update their digital proficiency and skills in a rapidly growing technological environment. Back-
office professionals manage complex IT infrastructure and data, remaining vigilant and proficient at all times.
II. Literature Review:
The concept of technostress dates back to 1984 when Brod coined this term and defined it as a modern disease that stems when a
person fails to cope healthily. In earlier studies, the primary focus has been on job stress, and these studies neglected this crucial
element. Stress is a psychological reaction encountered by people when they fail to meet the circumstances adequately, resulting in
negative outcomes due to insufficient response. Prominent studies have used the “transaction-based stress model” on technostress
(Ragu Nathan et al, 2008; Tarafdar et al., 2010; Alam et al., 2025). Technology-induced stress is characterized by negative
repercussions on human behaviours, attitudes, and cognitive capabilities, according to the research by Tu et al. (2005). Individuals'
productivity drops and their anxiety levels rise as a consequence of this phenomenon (Alam & Hasan, 2025). Two major causes of
stress that come from utilizing ICT at work are too much information and always being available (Ayyagari et al. 2011; La Torre et
al., 2019). Communication overload and internet multitasking created burnout (Alam et al., 2025) and anxiety among people, and
the potential health impairments that resulted from ICT-related strain have a bad impact on their psychological well-being
(Reinecke, L. et al., 2016). Yan et al. (2013) used “Person-Technology (P-T Model)” to identify how computer-mediated
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communication (CMC) technology creates technostress among telemedicine users. Suh & Lee (2017) found in their study that
rapid technological change affects job autonomy and task interdependence. Task interdependence has additive effect on strain; it
increases the workload and invades the privacy of workers, whereas job autonomy reduces the strain. Technostress is a multifaceted
concept that arises when people interact in various ways with technology. Five factors have been commonly mentioned as
"technostress creators" in the literature on technostress are: "techno-complexity, techno-invasion, techno-insecurity, techno-
overload, and techno-uncertainty" (Ragu-Nathan et al., 2008). According to the findings of the review research conducted by La
Torre et al. (2020), there are three distinct forms of technostress: “techno anxiety, techno-fatigue, and techno addiction”, among
others. The researchers have found negative and adverse effects of technostress on work performance (Tams et al., 2018; Borle et
al., 2021; Çini et al., 2023; Fernandez-Fernandez et al. ,2023; Syakina et al., 2023). Technostress literature has reported that it
causes psychological (Yang et al., 2016; Lee, 2016; Srivastava et al., 2015), and physiological strain (Boonjing &
Chanvarasuth,2017; Riedl, 2012; Ayyagari et al., 2011). Carlotto, Wendt, & Jones (2017) explored the relationship between
technostress, career commitment, life satisfaction and work-family interactions. Their study revealed that with the increase in techno
fatigue, techno anxiety increases. Al-Ansari & Alshare (2019) investigated the effect of technostress creators and inhibitors on job
satisfaction, organizational commitment, and perceived performance. The results of their study showed that technostress creators
are negatively associated with job satisfaction. Bourlakis et al. (2023) evaluated the impact of technostress on performance and
well-being. They found that techno-stress is attributed to factors like complexity, insecurity, and excess of technology. Employees
are hooked with technology and they have no leisure time, which in turn has influence on their work-life balance. Jain, Varma,
Vijay, & Cabral (2025) found, in their study on the Indian banking industry, that the use of ICT has elevated technostress levels,
which adversely affect job outcomes. Technostress reduced the innovative behaviour and work engagement of employees, which
in turn caused burnout among them. Khalequzzaman, Wang, Zhang, & Wang (2025) conducted their study on the banking sector in
Bangladesh and found that digital overload and surveillance elevated technostress among employees. Weerawarna & Chandrasekara
(2022), in their study on banking employees of Hambantota district in Sri Lanka, revealed that technostress creators have mixed
results on employee performance. Techno-invasion and techno-complexity negatively influenced the performance of the employees,
and techno-overload positively influenced it.
Research Objective: To analyse comparatively technostress among public and private banking employees.
Hypothesis: H0. There is no significant difference in the technostress level of public and private sector banking employees.
Data and Methods:
Subjects: It is a primary data-based study of selected banks of the public and private sectors (PNB, SBI, HDFC and ICICI) of four
chosen districts (Shimla, Solan, Mandi, and Kangra) of Himachal Pradesh
Data Collection and Procedure: An offline survey was conducted to collect the responses from the respondents. The respondents
constituted the banking employees of selected banks. A total of 400 respondents provided the complete information regarding the
survey. The study has used Ragu-Nathan et al. (2008) scale (5-point Likert Scale) for technostress, Ryff’s Scale (1989) (6-point
Likert Scale) for well-being and Koopman’s (2014) (5-point Likert Scale) Scale of Individual Work Performance. A multi-stage
sampling technique was employed to conduct the study, ensuring a systematic and representative selection of respondents across
various strata.
Table: Nature of Bank
Nature of bank No. of Respondents Percentage
Public 251 62.75%
Private 149 37.25%
Total 400
The sampled respondents belonged to two different types of banks public and private and the table shows the distribution among
the two categories of banks. 62.75% of employees are from public sector and 37.25% are from Private banks.
Table: Gender of Respondents
Gender No. of Respondents Percentage
Male 215 53.75%
Female 185 46.25%
The total no. of respondents was (N=400), among them, males were 215(53.75%) and females were 185 (46.25%).
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Table: Bank you work with /Bank Name
Bank you work with No. of Respondents Percentage
HDFC Bank 69 17.25%
ICICI Bank 80 20%
Punjab National Bank 134 33.5%
State Bank of India 117 29.25%
Total 400
The respondents have been categorized based on their respective banks of employment. The largest segment, comprising 33.5%, is
employed at Punjab National Bank, followed by 27.5% at State Bank of India, 20% at ICICI Bank, and 19% at HDFC Bank.
Overall, approximately 61% of the respondents work in public sector banks, while the remaining 39% are employed in private
sector banks.
Data Analysis and Interpretation
The data collected has been tabulated, analysed and interpreted by using statistical techniques such as descriptive statistical analysis
and other techniques with the help of SPSS.
The Mann–Whitney U test is used to compare two independent groups in cases when the presumptions of parametric tests fail to
hold good. It is a non-parametric alternative to the independent samples t-test. This research uses a scale that is appropriate for non-
parametric analysis, which means that the data on technostress levels may not follow a normal distribution. This method has been
used here to compare two independent groups of public and private bank employees, and the dependent variable is ordinal or does
not have a normal distribution. Therefore, the Mann-Whitney U test has been employed here as a suitable method for comparing
technostress levels among employees of public and private banks. The results drawn from this test will help to provide valuable
insights into the differential levels of technostress felt by banking employees, which will be further helpful for the banking sector
to develop interventions and support systems for their employees to tackle or overcome the difficulties being faced by them.
Mann-Whitney U test
Ranks
Type of Bank N Mean Rank Sum of Ranks
stress level Public bank 251 183.96 46173.00
Private bank 149 228.37 34027.00
Total 400
Test Statisticsa
stress level
Mann-Whitney U 14547.000
Wilcoxon W 46173.000
Z -4.207
Asymp. Sig. (2-tailed) .000
a. Grouping Variable: Type of Banks
Descriptive Statistics
Type of Bank Mean Std. Deviation N
work faster Public bank 2.75 1.297 251
Private bank 2.55 1.407 149
Total 2.68 1.341 400
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stress level Public bank 1.50 .547 251
Private bank 1.76 .589 149
Total 1.60 .576 400
more out of life Public bank 4.27 1.324 251
Private bank 4.59 1.151 149
Total 4.39 1.270 400
Box's Test for Equality of Covariance Matricesa
Box's M 9.046
F 1.494
df1 6
df2 641635.605
Sig. .176
It evaluates the null hypothesis and reflects that the error variance of the
dependent variable in the question is uniform across groups.
a. Design: Intercept + VAR00088
Multivariate Testsa
Effect Value F Hypothesis
df
Error
df
Sig. Partial
Eta
Squared
Noncent.
Parameter
Observed
Powerc
Intercept Pillai's Trace .959 3099.779
b
3.000 396.00
0
.000 .959 9299.337 1.000
Wilks' Lambda .041 3099.779
b
3.000 396.00
0
.000 .959 9299.337 1.000
Hotelling's Trace 23.48
3
3099.779
b
3.000 396.00
0
.000 .959 9299.337 1.000
Roy's Largest Root 23.48
3
3099.779
b
3.000 396.00
0
.000 .959 9299.337 1.000
VAR00088 Pillai's Trace .071 10.048b 3.000 396.00
0
.000 .071 30.143 .998
Wilks' Lambda .929 10.048b 3.000 396.00
0
.000 .071 30.143 .998
Hotelling's Trace .076 10.048b 3.000 396.00
0
.000 .071 30.143 .998
Roy's Largest Root .076 10.048b 3.000 396.00
0
.000 .071 30.143 .998
a. Design: Intercept + VAR00088
b. Exact statistic
c. Computed using alpha = .05
Levene's Test for Equality of Error Variancesa
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F df1 df2 Sig.
work faster 7.088 1 398 .008
stress level 2.159 1 398 .143
more out of life 6.725 1 398 .010
Evaluates the null hypothesis, asserting that the error variance of the
dependent variable is uniform across groups.
a. Design: Intercept + VAR00088
Interpretation
Mann–Whitney U Test
Tests for Examining the Between-Subjects Effects
Source Dependent Variable Type III
Sum of
Squares
df Mean
Square
F Sig. Partial
Eta
Squared
Noncent.
Paramete
r
Observe
d Powerd
Corrected
Model
work faster 3.840a 1 3.840 2.142 .144 .005 2.142 .309
stress level 6.146b 1 6.146 19.407 .000 .046 19.407 .993
more out of life 9.319c 1 9.319 5.849 .016 .014 5.849 .675
Intercept work faster 2629.640 1 2629.640 1466.730 .000 .787 1466.730 1.000
stress level 993.886 1 993.886 3138.149 .000 .887 3138.149 1.000
more out of life 7348.639 1 7348.639 4612.756 .000 .921 4612.756 1.000
VAR00088 work faster 3.840 1 3.840 2.142 .144 .005 2.142 .309
stress level 6.146 1 6.146 19.407 .000 .046 19.407 .993
more out of life 9.319 1 9.319 5.849 .016 .014 5.849 .675
Error work faster 713.558 398 1.793
stress level 126.051 398 .317
more out of life 634.059 398 1.593
Total work faster 3585.000 400
stress level 1153.000 400
more out of life 8361.000 400
Corrected
Total
work faster 717.398 399
stress level 132.198 399
more out of life 643.377 399
a. R Squared = .005 (Adjusted R Squared = .003)
b. R Squared = .046 (Adjusted R Squared = .044)
c. R Squared = .014 (Adjusted R Squared = .012)
d. Computed using alpha = .05
Estimated Marginal Means
Estimates
Dependent Variable Type of Bank Mean Std. Error 95% Confidence Interval
Lower Bound Upper Bound
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work faster Public Sector bank 2.753 .085 2.587 2.919
Private Sector bank 2.550 .110 2.335 2.766
stress level Public Sector bank 1.502 .036 1.432 1.572
Private Sector bank 1.758 .046 1.668 1.849
more out of life Public Sector bank 4.275 .080 4.118 4.432
Private Sector bank 4.591 .103 4.387 4.794
Pairwise Comparisons
Dependent Variable (I) Type of
Bank
(J) Type of Bank Mean
Difference
(I-J)
Std.
Error
Sig.b 95% Confidence
Interval for
Difference
Lower
Bound
Upper
Bound
work faster Public bank Private bank .203 .138 .144 -.070 .475
Private bank Public bank -.203 .138 .144 -.475 .070
stress level Public bank Private bank -.256* .058 .000 -.371 -.142
Private bank Public bank .256* .058 .000 .142 .371
more out of life Public bank Private bank -.316* .131 .016 -.572 -.059
Private bank Public bank .316* .131 .016 .059 .572
Conclusions drawn from the estimated marginal means
*. At the 0.05 level of significance, the mean difference is significant.
b. Adjustment for multiple comparisons: Bonferroni method.
Multivariate Tests
Value F Hypothesis
df
Error
df
Sig. Partial
Eta
Squared
Noncent.
Parameter
Observed Powerb
Pillai's trace .071 10.048a 3.000 396.00
0
.000 .071 30.143 .998
Wilks' lambda .929 10.048a 3.000 396.00
0
.000 .071 30.143 .998
Hotelling's trace .076 10.048a 3.000 396.00
0
.000 .071 30.143 .998
Roy's largest root .076 10.048a 3.000 396.00
0
.000 .071 30.143 .998
Each F-test shows the multivariate impact on the Type of Bank. These tests are based on the pairwise comparisons of the
calculated marginal means, which are linearly independent of one another.
a. Exact statistic
b. Calculated with alpha = .05
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Univariate Tests
Dependent Variable Sum of
Squares
df Mean
Square
F Sig. Partial Eta
Squared
Noncent.
Paramete
r
Observed
Powera
work faster Contrast 3.840 1 3.840 2.142 .144 .005 2.142 .309
Error 713.558 398 1.793
stress level Contrast 6.146 1 6.146 19.407 .000 .046 19.407 .993
Error 126.051 398 .317
more out of life Contrast 9.319 1 9.319 5.849 .016 .014 5.849 .675
Error 634.059 398 1.593
The F tests the effect of the variables on the Type of Banks. For these tests, the marginal means that have been estimated and are
linearly independent are compared with each other.
a. Calculated with alpha = .05
Interpretation: To compare the Technostress Level – Public vs. Private Banks, it is applied, and the result can be interpreted as
Mean Ranks
For Public Bank is 183.96, and for Private Bank is 228.37. The calculated value of Mann–Whitney U is 14547.000, Z = -4.207.
Since p < 0.05, the result is statistically significant, which means there is a significant difference in technostress levels between
public and private bank employees. The comparison between Private bank employees shows a higher mean rank, indicating higher
stress levels as compared to public bank employees.
The Descriptive Statistics show that for the variable Work Faster, the Public bank employees (M = 2.75) score is slightly higher
than the private bank employees (M = 2.55). This indicates that public banks employees have slightly higher pressure to work faster
than public employees For Stress Level, Private bank employees (M = 1.76) report higher stress as compared to public bank
employees (M = 1.50), and for More out of Life, Private bank employees (M = 4.59) perceive themselves as gaining more out of
life compared to public bank employees (M = 4.27). .These descriptive results reinforce the Mann–Whitney findings that private
bank employees are under higher stress than their public sector counterparts. Still, they also perceive greater life gains, possibly
due to performance-linked rewards and growth opportunities.
With a value of Box's M = 9.046, p =.176 (>.05), Box's test demonstrates that the covariance matrices are equal. The validity of
multivariate tests is supported by the assumption that the covariance matrices are identical.
Multivariate Tests (MANOVA)
The test results are Wilks’ Lambda = .929, F = 10.048, p < .001, which suggests a significant difference between the groups. This
means that whether an employee works in a public or private bank significantly influences the combination of stress level, work
pace, and life perceptions. The Levene’s Test (Equality of Error Variances) results revealed that the Homogeneity of variances
assumption is invalidated and the group comparisons for stress level are more robust.
Work Faster (p = .008) → significant, variance not equal.
Stress Level (p = .143) → not significant, variance equal.
More out of Life (p = .010) → significant, variance not equal.
III. Findings of the Study:
The hypothesis was tested using the Mann–Whitney U test, which considered the dependent variable as technostress level and
public and private bank employees as the independent variables. The result is statistically significant, which rejects the null
hypothesis and confirms that there is a significant difference in the technostress level of public and private bank employees. The
higher mean rank of private bank employees indicates that they have a higher stress level as compared to public sector bank
employees. Both non-parametric and multivariate analyses have confirmed this. This suggests that work environment and
expectations may lead to elevated stress. This might be because of the increased frequency of technology changes, the pressure to
meet tighter deadlines, or the increased demands for performance. Despite higher stress levels, private bank employees are more
satisfied than public bank employees. The gains, like performance-linked incentives and career growth opportunities, make them
more satisfied. The multivariate analysis implies that bank type affects employees’ psychological experiences in a multifaceted
way, extending beyond mere stress. This study examines how technostress impacts the banking sector (both public and private).
The findings of this study are in line with Ragu Nathan et al. (2008), Tarafdar et al. (2007), Ayyagari et al., (2011), Jain, Varma,
Vijay, & Cabral (2025), and Khalequzzaman, Wang, Zhang, & Wang (2025), which show that the negative impacts are created by
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technostress. However, technostress mitigation strategies can be used to reduce the negative impacts of technostress. These
strategies include creating positive emotions and a self-control mechanism (Sriwidharmanely et al., 2021), perceived organizational
support. (Khalequzzaman, Wang, Zhang, & Wang 2025), and autonomy to choose an IT tool they are comfortable with (Rohwer et
al., 2022).
Implications of the Study:
The analysis represents the meaningful differences between the technostress level of public sector and private sector banks. The
findings of this study call for practical implications for organizational policy, employee well-being and human resource strategies
in the banking sector. The statistically higher technostress among private bank employees suggests an urgent need for interventions
that focus on mental health and well-being of employees, stress management workshops, counselling services, and workload
balance mechanisms. The greater life satisfaction experienced by private sector employees due to performance-linked incentives
and growth opportunities can be leveraged to further motivate them, while mitigating technostress. On the other hand, the public
sector can benefit from initiatives that enhance employee engagement and personal fulfilment. The initiatives may include
recognition programs, flexible work arrangements, and career development pathways. The fact that employees in both sectors have
different ideas about how fast work should go, shows that operational efficiency and task allocation need to be looked at again to
make sure productivity stays high.
From a policy point of view, the findings show that it is important to tailor wellness programs and performance frameworks to fit
the culture of the organization. HR teams have to contemplate regular evaluations of employee stress and happiness to guide
strategic choices. Also, the fact that certain variables don't follow the rules of variance shows that we need to do more in-depth
investigation when comparing organizational contexts. Such an approach not only enhances employee well-being but also proves
a strategy for a committed and productive workforce. By prioritizing and emphasizing these assessments, organizations may pave
a path for the betterment of employees and overall success of the organizations. In general, these insights may help banks manage
their employees in a more compassionate and data-driven way.
Limitations:
The study offers valuable insights into the technostress level and workplace perceptions among public and private sector bank
employees. However, the research has certain limitations that must be acknowledged. Firstly, the study was confined to a specific
geographic region and may not reflect the broader workforce across different states or countries. Second, the research only contacted
respondents once in a time span, and it doesn't take into consideration how stress or life satisfaction fluctuates with the seasons,
changes in the economy, or policy changes. Third, there may be response bias at the end of respondents as they underreport or
exaggerate their experiences due to social desirability or personal interpretation. Fourth, the study has focused on variables like
technostress, well-being and individual work-performance. Other variables like leadership style impact, work-life balance have not
been included but they significantly impact employee well-being and performance. Future research may mitigate these constraints
by using longitudinal designs, broadening the variable set, and including a more diverse population.
Declarations
Ethical Approval: This study involved human participants for review.
Funding: No funding or grants from any government or private agency.
Conflict of Interests We state that there is no conflict between authors during the composition and publication of this research
article.
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