INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025  
Impact of Employee Welfare Measures on Workforce Well-Being in  
SBI: Insights from the Public Banking Sector  
1 Dr. D. Appala Raju, 2 Dr. P. Sree Devi  
1 Assistant Professor AKNUCE, Adikavi Nannaya University, Rajamundry  
2 Assistant Professor JNTUGV, Vizianagaram  
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1411000003  
Received: 20 November 2025; Accepted: 27 November 2025; Published: 29 November 2025  
ABSTRACT:  
Employee welfare plays a crucial role in enhancing job satisfaction, productivity, and organizational loyalty,  
especially in service-oriented sectors such as banking. This study evaluates the welfare measures implemented  
by the State Bank of India (SBI) with specific reference to employees in the Visakhapatnam district. The  
research examines employee awareness, utilization, accessibility, satisfaction, and effectiveness of welfare  
schemes. Using a descriptive and analytical research design, data was collected through structured  
questionnaires and analyzed using statistical techniques such as Cronbach’s Alpha, KMO and Bartlett’s Tests,  
Principal Component Analysis (PCA), communalities, and variance extraction. The results indicate that the  
measurement scale is reliable (α = 0.745), and the dataset is suitable for factor analysis. Eight major factors  
were identified, explaining 77.05% of total variance, highlighting dimensions such as welfare awareness, HR  
support, and adequacy of schemes, accessibility, and employee outcomes like morale and work-life balance.  
Findings show that although employees demonstrate awareness and satisfaction with welfare schemes,  
challenges remain in communication, procedural clarity, and equitable access. The study emphasizes the need  
for improved dissemination mechanisms, simplified procedures, and enhanced HR support to strengthen  
welfare effectiveness. Overall, the research underscores the importance of aligning welfare policies with  
evolving employee expectations to enhance satisfaction and organizational performance.  
Key words: Welfare schemes, Socio-culture, work life balance, Employee well-being.  
INTRODUCTION  
Employee welfare is a flexible and evolving concept that varies significantly across time, regions, industries,  
and socio-cultural settings. It is influenced by factors such as social values, customs, and the degree of  
industrialization. Welfare provisions are shaped by the age, gender, socio-cultural background, marital status,  
economic conditions, and educational levels of employees. They encompass both social and economic  
dimensions, reflecting the prevailing value systems and societal development. As an integral component of  
human resource management, employee welfare directly affects satisfaction, commitment, and performance. In  
service-oriented industries particularly in the banking sector employees are the organization’s most valuable  
asset, representing the institution in every customer interaction. Therefore, effective welfare measures are  
essential to sustain a motivated, efficient, and loyal workforce.  
Public Sector Banks (PSBs) in India, including the State Bank of India (SBI), employ lakhs of individuals  
across diverse regions and job profiles. The welfare of these employees is critical not only for their personal  
well-being but also for ensuring efficient banking operations, customer satisfaction, and institutional stability.  
Over the years, PSBs have introduced a wide range of welfare schemes such as health insurance, housing  
facilities, staff loans, educational assistance for children, pension schemes, and recreation facilities.  
State Bank of India being the largest public sector bank in India, has always been at the forefront in  
implementing progressive employee welfare policies. Through initiatives like the staff welfare fund, medical  
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benefits, holiday homes, educational scholarships, and training and development programs, the Bank seeks to  
create a conducive and supportive work environment. However, despite these measures, there are ongoing  
challenges related to workload stress, work-life balance, transparency in welfare distribution, and awareness  
among employees about the benefits available to them.  
In the era of digitalization and rapid transformation in banking services, it becomes essential to periodically  
study and evaluate the effectiveness of these welfare measures. Understanding the perceptions of employees  
towards these initiatives helps management redesign welfare policies to match current employee needs and  
expectations. Hence, this research focuses on assessing the employee welfare measures in public sector banks  
with specific reference to the State Bank of India, identifying the satisfaction level of employees, and  
exploring areas for further improvement.  
LITERATURE REVIEW  
Kumar and Kumar (2018) examined employee attitudes toward welfare activities provided by the Singareni  
Collieries Company Limited (SCCL) in Kothagudem. Their study also focused on assessing the satisfaction  
levels of employees with regard to the welfare initiatives implemented by the organization. The findings  
revealed that SCCL had established and maintained comprehensive welfare facilities for its employees, and the  
majority of respondents expressed satisfaction with the welfare measures offered to them.  
Varadaraj and Charumathi (2019) analyzed the impact of employee welfare activities on employee satisfaction  
at ETA, a construction company. Their study concluded that welfare activities have a direct and positive effect  
on employee performance. They further recommended that organizations should take proactive measures to  
educate employees about the welfare initiatives available to them, thereby enhancing awareness and utilization  
of these benefits.  
Kumari and Kannan (2018) examined the welfare activities provided to employees in the garment industry and  
assessed employee satisfaction with these initiatives. The study also analyzed the employee-employer  
relationship within the sector. The findings suggested that management should enhance welfare measures and  
actively cooperate with employees to improve workplace satisfaction and overall employee well-being.  
Employee welfare measures can be better understood and analyzed by grounding them in strong theoretical  
frameworks. Integrating Maslow’s Hierarchy of Needs, Herzberg’s Two-Factor Theory, and Organizational  
Support Theory provides a comprehensive conceptual foundation for examining how welfare practices  
influence employee satisfaction, motivation, and organizational commitment. These theories help explain the  
psychological and behavioral responses of employees toward welfare initiatives and offer deeper insights that  
strengthen research analysis and interpretation.  
Maslow’s Hierarchy of Needs serves as a valuable framework to understand how welfare measures address  
different levels of employee needs. Welfare facilities such as adequate wages, basic amenities, medical care,  
and safe working conditions fulfill the physiological and safety needs of employees. Programs that encourage  
teamwork, communication, and social interaction meet their belongingness needs. Similarly, recognition  
programs, rewards, and opportunities for skill development contribute to esteem and self-actualization needs.  
By mapping welfare measures against Maslow’s hierarchy, the study can highlight how organizations support  
employees across essential and higher-order needs, leading to overall satisfaction and well-being.  
Herzberg’s Two-Factor Theory offers another perspective by classifying employee welfare measures into  
hygiene factors and motivators. Many welfare initiatives—such as salary, job security, working conditions,  
and safety facilities—serve as hygiene factors that prevent dissatisfaction. While these factors do not create  
high motivation, their absence can lead to dissatisfaction among employees. On the other hand, welfare  
measures like rewards, recognition, career advancement, and developmental opportunities act as motivators  
that enhance employee satisfaction and productivity. Using this theory helps evaluate whether current welfare  
practices merely reduce dissatisfaction or actively motivate employees toward better performance.  
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Organizational Support Theory (OST) further strengthens the understanding of employee welfare by  
emphasizing the role of perceived organizational support. Welfare measures are often interpreted by  
employees as indicators of how much the organization values their contributions and cares for their well-being.  
When employees perceive strong support through benefits such as health schemes, housing assistance, training  
programs, and social security measures, they develop a stronger sense of loyalty, commitment, and trust  
toward the organization. This theory helps explain the positive impact of welfare initiatives on employee  
engagement, morale, and retention.  
Overall, integrating these three frameworks provides a multidimensional understanding of employee welfare  
measures. Maslow explains the fulfilment of human needs, Herzberg clarifies the difference between factors  
preventing dissatisfaction and those promoting satisfaction, and OST highlights the importance of employees’  
perceptions of organizational care. Together, these theories offer a holistic conceptual basis that enriches the  
analysis of welfare practices and strengthens the theoretical robustness of the study.  
Statement of The Problem  
Although the State Bank of India and other public sector banks have well-established welfare policies, a  
notable gap persists between policy design and on-ground implementation. Many employees remain unaware  
of the full range of benefits or feel that existing measures do not adequately meet their changing personal and  
professional needs. With digitalization, rising competition, and increasing customer expectations, employees  
face heavier workloads, longer hours, and higher role stress, which can weaken the positive impact of welfare  
initiatives. Differences in welfare provision across employee categories and regions further create perceptions  
of inequality. Issues such as poor communication, limited access in remote branches, insufficient medical  
coverage, and delays in reimbursements add to employee dissatisfaction. Given the importance of employees  
as key organizational assets, these challenges highlight the need to better understand the factors influencing  
welfare effectiveness. Therefore, this study aims to examine the hygiene and motivational aspects of employee  
welfare in SBI and identify areas for improvement to enhance satisfaction and organizational efficiency.  
Scope of The Study  
The present study seeks to systematically examine the existing employee welfare measures implemented by  
the State Bank of India (SBI). Recognizing that employee safety and welfare constitute critical determinants of  
organizational productivity and operational efficiency, the study undertakes an evaluation of the current  
welfare provisions to assess their effectiveness and their contribution to overall organizational performance. In  
addition, the research aims to analyze employees’ perceptions and levels of satisfaction with the welfare  
initiatives offered by SBI, thereby identifying specific areas that warrant enhancement to improve employee  
well-being, motivation, and productivity.  
The scope of this study is limited to employees working in selected branches of the State Bank of India located  
within the Visakhapatnam District of Andhra Pradesh. While geographically confined, the findings derived  
from this study are anticipated to provide meaningful insights that may be relevant and applicable to other  
public sector banks operating under comparable administrative frameworks and institutional environments.  
Through this focused inquiry, the study endeavors to contribute to a deeper understanding of the role and  
impact of welfare measures within India’s public sector banking system.  
Objectives of The Study  
The specific objectives of the present study:  
1. To analyze the various employee welfare measures adopted by the State Bank of India.  
2. To examine the level of awareness and extent of utilization of the various welfare measures provided to  
SBI employees.  
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3. To evaluate the satisfaction of employees with the existing welfare schemes, specifically in terms of  
their adequacy, accessibility, and overall effectiveness.  
4. To offer suggestive measures to improve the welfare measures in SBI.  
RESEARCH METHODOLOGY  
Research Design  
The study follows a descriptive and analytical research design. It describes the existing welfare measures and  
analyses employee perceptions regarding these measures. The design allows the researcher to collect factual  
data, interpret it systematically, and provide meaningful insights.  
Data Sources  
Primary data:  
Primary Data has been collected directly from employees of the State Bank of India through structured  
questionnaires and interviews. Employees from different cadres (officers, clerical staff, sub-staff) and branches  
(urban, semi-urban, and rural) will be included to obtain a comprehensive understanding. A structured  
questionnaire was used containing both closed-ended and open-ended questions. The questionnaire may  
include Likert-scale questions to measure satisfaction levels related to various welfare dimensions like medical  
facilities, housing, education, recreation, etc.  
Secondary Data:  
Secondary Data has been collected from books, journals, periodicals, magzines, HR policy documents and SBI  
Annual Reports. Data collection was processed using statistical tools such as percentage analysis, mean scores,  
and chi-square tests. Graphs, charts, and tables will be used for effective presentation. Statistical software i.e.,  
SPSS can help analyze the relationship between welfare measures and employee satisfaction.  
Sampling Design  
There are 95 SBI branches in Visakhapatnam district area and working nearly 1000 employees are working.  
There are various methods in which sample size can be calculated most commonly used are census for small  
populations, imitating a sample size of similar studies, using published tables, applying formula’s to calculate a  
sample size3. The present research study used published tables for determination of sample size4 is adapted  
from Yamane sample selection table. The populations seize is more than 1000 hence, the researcher determine  
the sample of 286 at five percent level of significance. Simple random sampling may be used to ensure  
representation across different branches.  
Limitations of The Study  
Due to time and resource constraints, the study covers only a limited number of employees and  
branches, which may not represent the entire State Bank of India workforce.  
Employee satisfaction is a subjective concept that may vary according to personal expectations, job  
position, and individual circumstances.  
Since the study focuses on Visakhapatnam region of SBI, variations in welfare implementation across  
different zones may not be fully captured.  
Data Analysis and interpretation Analysis of Reliability Statistics  
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Table .1 Reliability Statistics  
Cronbach's Alpha  
.745  
Cronbach's Alpha Based on Standardized  
Items  
N of Items  
30  
.745  
The table one assess the internal consistency of the measurement instrument, a reliability test was conducted  
using Cronbach’s Alpha. The reliability statistics presented in the table indicate that the 30-item scale yielded a  
Cronbach’s Alpha coefficient of 0.745. According to Nunnally (1978), a minimum threshold of 0.70 is  
considered acceptable for exploratory research, while higher values reflect stronger internal consistency among  
items.  
In the present study, the obtained value of 0.745 demonstrates that the instrument possesses good internal  
reliability, suggesting that the items included in the questionnaire measure the intended constructs in a  
consistent manner. The identical value reported under “Cronbach’s Alpha Based on Standardized Items”  
further confirms that standardization of item variances does not significantly alter the reliability level,  
indicating stability in the scale’s structure.  
Given that the number of items (N = 30) falls within a reasonable range for social science measurement tools,  
the reliability coefficient of 0.745 is sufficient to validate the use of this instrument for subsequent statistical  
analyses. Thus, the scale can be regarded as internally coherent, and the respondents’ patterns of responses are  
reliable for drawing meaningful conclusions in the context of the study.  
Table 2 KMO and Bartlett's Test  
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.  
.515  
562.965  
36  
Bartlett's Test of Sphericity Approx. Chi-Square  
Df  
Sig.  
.000  
KMO and Bartlett’s Test  
Table two explains the suitability of the dataset for factor analysis was examined using the Kaiser–Meyer–  
Olkin (KMO) Measure of Sampling Adequacy and Bartlett’s Test of Sphericity. The KMO value obtained for  
the dataset was 0.515, which indicates a poor but acceptable level of sampling adequacy. Although this value  
is on the lower side of the acceptable range, it suggests that the sample size and inter-item correlations are  
sufficiently adequate to proceed with factor analysis. However, a higher KMO value—preferably above 0.60—  
would have indicated stronger shared variance among variables and better suitability for extraction.  
In addition to the KMO value, Bartlett’s Test of Sphericity was applied to assess whether the correlation  
matrix significantly deviates from an identity matrix. The test produced a Chi-square value of 562.965 with 36  
degrees of freedom, and the associated significance value was 0.000, which is well below the threshold of  
0.05. This statistically significant result confirms that meaningful correlations exist among the variables and  
that the correlation matrix is not an identity matrix. Therefore, Bartlett’s Test strongly supports the  
appropriateness of conducting factor analysis.  
Overall, the results of both tests indicate that the data is suitable for factor analysis, though with some caution  
due to the low KMO value. The significant Bartlett’s Test result ensures that the variables share enough  
common variance to justify factor extraction. Thus, factor analysis may be carried out, but further refinement  
of items or an increased sample size could enhance the robustness of the results.  
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Table 3. Communalities  
Initial Extraction  
Gender of Respodent  
1.000  
1.000  
1.000  
1.000  
1.000  
.916  
.769  
.823  
.599  
.785  
Experience  
Income of the Respondent  
I am aware of the various employee welfare measures provided by SBI.  
I came to know about welfare schemes primarily through official  
communication channels.  
My awareness level about SBI’s welfare schemes is high.  
1.000  
1.000  
.653  
.683  
I am aware of all categories of welfare benefits such as health, housing, and  
education.  
SBI provides sufficient communication about welfare schemes.  
1.000  
1.000  
.569  
.683  
I clearly understand the eligibility criteria for availing different welfare  
benefits.  
Extraction Method: Principal Component Analysis.  
Table three portrays the communalities table presents the proportion of variance explained by the extracted  
components for each of the variables included in the Principal Component Analysis (PCA). The initial  
communalities for all variables are equal to 1.000, indicating that before extraction, the total variance in each  
variable is assumed to be fully accounted for. The extraction communalities, however, show the extent to  
which the extracted components are able to retain the variance of each variable, and these values form the  
basis for assessing the adequacy of the factor solution.  
The analysis reveals that the variable “Gender of Respondent” has the highest extraction communality value of  
0.916, demonstrating that a very large portion of its variance is explained by the extracted factors. This is  
followed by “Income of the Respondent” (0.823), “Official communication as the source of welfare  
information” (0.785), and “Experience” (0.769), each reflecting strong representation in the factor structure.  
These results indicate that demographic characteristics and official communication channels play a significant  
role in influencing employees’ awareness and understanding of welfare measures.  
Variables related to welfare awareness also exhibit acceptable extraction values. For instance, awareness of  
welfare scheme categories (0.683), awareness level about SBI’s welfare schemes (0.653), and understanding of  
eligibility criteria (0.683) show moderate to strong communalities, suggesting their reasonable contribution to  
the factor model. However, the variable “SBI provides sufficient communication about welfare schemes”  
records the lowest communality (0.569), though it still exceeds the commonly accepted minimum threshold of  
0.50, indicating that it remains relevant within the factor extraction framework.  
Overall, the extraction communalities range between 0.569 and 0.916, reflecting that all variables retain a  
satisfactory level of explained variance after extraction. As no variable falls below the recommended  
communality cutoff of 0.50, the results confirm that the dataset is suitable for factor analysis. The  
communalities suggest that the derived factors adequately represent the underlying structure of employees’  
welfare awareness perceptions and can be used reliably for further interpretation and component labeling.  
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Thus, the communalities support the adequacy of the PCA model, indicating that the extracted components  
effectively capture a substantial proportion of variance across all variables.  
Table 4. Component Matrixa  
Component  
1
2
3
4
Gender of Respodent  
Experience  
.261  
-.168  
.034  
.734  
.658  
-.202  
.822  
.192  
-.037  
.883  
-.254  
-.179  
.406  
.163  
.465  
.008  
Income of the Respondent  
I am aware of the various employee welfare measures  
provided by SBI.  
I came to know about welfare schemes primarily  
through official communication channels.  
.563  
.566  
.736  
.606  
.640  
-.654  
-.408  
.310  
.210  
.250  
.046  
.407  
.207  
.317  
-.454  
.195  
-.006  
-.037  
-.237  
-.069  
My awareness level about SBI’s welfare schemes is  
high.  
I am aware of all categories of welfare benefits such  
as health, housing, and education.  
SBI provides sufficient communication about welfare  
schemes.  
I clearly understand the eligibility criteria for availing  
different welfare benefits.  
Extraction Method: Principal Component Analysis.  
a. 4 components extracted.  
Table four analyzes the component matrix presents the loading values of each variable on the extracted  
principal components. Loadings indicate the strength and direction of the relationship between the variables  
and each component. Higher absolute values suggest that the variable contributes more significantly to the  
underlying factor. Based on the loadings displayed, four components were extracted using Principal  
Component Analysis (PCA).  
The results show that variables related to awareness and understanding of welfare schemes load strongly on  
Component 1. Statements such as “I am aware of all categories of welfare benefits such as health, housing,  
and education” (.736), “I clearly understand the eligibility criteria for availing welfare benefits” (.640), “SBI  
provides sufficient communication about welfare schemes” (.606), and “I came to know about welfare  
schemes primarily through official communication channels” (.563) exhibit high positive loadings on this  
component. This suggests that Component 1 represents a latent construct associated with Welfare Awareness  
and Communication Effectiveness among respondents.  
Component 2 is characterized by the highest loading on Income of the Respondent (.734) and moderate  
loading on awareness of welfare measures provided by SBI (.658), indicating that this factor reflects a  
construct related to Socioeconomic Status and Exposure to Welfare Benefits. Meanwhile, Component 3  
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shows moderate loadings for experience (.822) and awareness level about SBI’s welfare schemes (.407),  
implying that this factor may represent Employee Experience and Welfare Scheme Familiarity.  
Component 4 is primarily defined by a high loading on Gender of Respondent (.883), indicating that gender  
acts as a dominant differentiating factor, independent of other welfare-related variables. This component seems  
to capture Demographic Variation within the respondent group.  
Overall, the component matrix demonstrates that awareness, communication, income, and demographic  
characteristics align into distinct factor dimensions, supporting the factor structure of the PCA. These findings  
suggest that employee welfare perception at SBI is shaped by a combination of information access, benefit  
awareness, financial and experiential background, and demographic differences.  
Thus, the component matrix confirms that the extracted factors provide a meaningful representation of the  
underlying dimensions influencing employees’ awareness and understanding of SBI welfare schemes.  
Table 5. KMO and Bartlett's Test  
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.  
.528  
4810.928  
276  
Bartlett's Test of Sphericity  
Approx. Chi-Square  
Df  
Sig.  
.000  
Table five shows that Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy for the dataset is reported  
as 0.528. This value indicates a moderate level of sampling adequacy. According to standard guidelines, KMO  
values between 0.50 and 0.59 fall within the “poor to mediocre” category, yet they remain acceptable for  
performing factor analysis. Therefore, although the sampling adequacy is not high, the value slightly above  
0.50 confirms that the dataset has the minimum required level of shared variance among variables to proceed  
with factor extraction. This suggests that the factor analysis may still yield meaningful patterns, but the results  
should be interpreted cautiously due to the moderate suitability of the data.  
Bartlett’s Test of Sphericity further supports the appropriateness of applying factor analysis to the dataset. The  
test shows a highly significant chi-square value of 4810.928 with 276 degrees of freedom, and a significance  
level (p-value) of 0.000. Since the significance value is less than 0.05, it indicates that the correlation matrix is  
not an identity matrix. In other words, there are sufficient correlations among the variables, meaning that they  
are related enough to form underlying factors. This strong significance confirms that factor analysis is  
statistically justified.  
Overall, the results of the KMO and Bartlett’s Test collectively demonstrate that the dataset is suitable for  
factor analysis. The moderately adequate KMO value indicates that the variables share some common  
variance, while the highly significant Bartlett’s test confirms the presence of sufficient intercorrelations among  
the items. Although the sampling adequacy is not strong, the statistical requirements for conducting factor  
analysis are met, allowing the researcher to proceed with further extraction and interpretation of latent factors.  
Table 6. Communalities  
Initial Extraction  
I have personally availed at least one welfare scheme in the last 3 years.  
I regularly utilize the welfare schemes offered by SBI.  
1.000  
1.000  
.932  
.931  
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The procedures for applying for welfare benefits are simple and user-  
friendly.  
1.000  
1.000  
.796  
.845  
I receive timely information and support from HR regarding welfare  
schemes.  
Lack of awareness is a barrier to using welfare benefits (reverse coded).  
1.000  
1.000  
.745  
.752  
The management encourages employees to make use of welfare  
schemes.  
I am satisfied with the accessibility of welfare schemes in SBI.  
HR plays an active role in promoting welfare schemes.  
1.000  
1.000  
1.000  
.793  
.810  
.636  
Overall, I believe the welfare measures provided by SBI are effectively  
utilized by employees.  
The welfare benefits provided by SBI are adequate to meet employee  
needs.  
1.000  
1.000  
.756  
.728  
The welfare schemes cover all major employee needs such as medical,  
housing, and education.  
The financial limits provided under each scheme are sufficient.  
There is a wide variety of welfare schemes to choose from.  
I am satisfied with the adequacy of the welfare measures.  
1.000  
1.000  
1.000  
1.000  
1.000  
1.000  
1.000  
1.000  
1.000  
1.000  
1.000  
1.000  
1.000  
.749  
.752  
.770  
.642  
.655  
.701  
.779  
.754  
.740  
.807  
.850  
.713  
.856  
Accessing welfare benefits information is easy and convenient.  
The application process for welfare benefits is transparent and fair.  
I rarely face procedural delays while availing welfare schemes.  
The HR department responds promptly to welfare-related queries.  
Welfare benefits are easily accessible to all employees.  
Welfare schemes have improved my overall job satisfaction.  
The welfare measures have positively affected my work-life balance.  
Welfare schemes have improved employee morale and motivation.  
Welfare schemes have helped increase employee retention and loyalty.  
Overall, I am satisfied with the effectiveness of welfare measures  
provided by SBI.  
Extraction Method: Principal Component Analysis.  
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Communalities Analysis  
Table six explain the communalities table presents the proportion of variance in each variable that is explained  
by the extracted factors after applying Principal Component Analysis. The initial communalities for all  
variables are fixed at 1.000, indicating that every variable initially contributes fully to the dataset's total  
variance before extraction. The extraction values, however, reveal how much of each variable’s variance is  
retained by the final factor solution. These values range from moderate to high, demonstrating that the  
extraction process has been effective in explaining a substantial portion of the variance for most items.  
A majority of the variables show high communalities, typically above 0.70, indicating that the extracted factors  
adequately represent these items. Items such as “I have personally availed at least one welfare scheme in the  
last 3 years” (0.932), “I regularly utilize the welfare schemes offered by SBI” (0.931), and “Overall, I am  
satisfied with the effectiveness of welfare measures provided by SBI” (0.856) show very strong extraction  
values. These high communalities suggest that these variables share substantial common variance with the  
underlying factors and contribute meaningfully to the factor structure. Such items are strongly linked with the  
latent constructs being measured—likely employee utilization, satisfaction, or perception of welfare schemes.  
A few items demonstrate moderate communalities, falling in the range of 0.60 to 0.70. Examples include  
“Overall, I believe the welfare measures provided by SBI are effectively utilized by employees” (0.636) and  
“Accessing welfare benefits information is easy and convenient” (0.642). Although lower than others, these  
values still indicate an acceptable level of shared variance. These items contribute to the factor structure but  
may be influenced by unique or variable-specific components. Their moderate communalities suggest that the  
factors partially explain these items, leaving some variance unexplained, which can be attributed to individual  
differences or measurement error.  
Several items related to HR support and procedural ease also reflect strong communalities. For instance, “HR  
plays an active role in promoting welfare schemes” (0.810), “The HR department responds promptly to  
welfare-related queries” (0.779), and “The procedures for applying for welfare benefits are simple and user-  
friendly” (0.796) show high extracted values, indicating that HR-related aspects are well captured by the  
underlying factors. This suggests that employee perceptions of HR responsiveness and process clarity form a  
coherent dimension in the dataset.  
Items connected to employee outcomes—such as morale, motivation, satisfaction, work-life balance, and job  
satisfaction—also show high communalities, with extraction values typically above 0.70. For example,  
“Welfare schemes have improved my morale and motivation” (0.850) and “The welfare measures have  
positively affected my work-life balance” (0.807) indicate that these variables are strongly explained by the  
factors. This pattern shows that employee outcome-based variables align closely with the extracted factors,  
making them reliable indicators of the perceived impact of welfare measures.  
Overall, the communalities table demonstrates that the factor analysis is successful in capturing a high  
proportion of variance from the majority of items. Most extraction values exceed 0.70, indicating strong  
representation by the factors and confirming the appropriateness of the factor solution. Only a small number of  
items fall near the lower threshold, but even these maintain acceptable communalities above 0.60. Thus, the  
extracted factor structure is robust, and the indicators used in the study are well-suited for factor analysis,  
collectively explaining substantial shared variance across the dataset.  
Table 7. Total Variance Explained  
Component  
1
Initial Eigenvalues  
Extraction Sums of Squared Loadings  
Total % of Variance Cumulative %  
4.442  
Total  
4.442  
% of Variance Cumulative %  
18.509  
18.509  
18.509  
18.509  
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2
3.377  
2.705  
2.251  
1.745  
1.454  
1.282  
1.236  
.877  
.860  
.710  
.613  
.512  
.375  
.329  
.257  
.222  
.172  
.156  
.132  
.111  
.093  
.057  
.030  
14.069  
11.272  
9.381  
7.270  
6.059  
5.343  
5.152  
3.655  
3.584  
2.960  
2.555  
2.133  
1.564  
1.372  
1.072  
.924  
32.578  
43.850  
53.231  
60.500  
66.559  
71.902  
77.054  
80.709  
84.293  
87.253  
89.808  
91.941  
93.505  
94.877  
95.949  
96.873  
97.589  
98.239  
98.790  
99.251  
99.636  
99.875  
100.000  
3.377  
2.705  
2.251  
1.745  
1.454  
1.282  
1.236  
14.069  
11.272  
9.381  
7.270  
6.059  
5.343  
5.152  
32.578  
43.850  
53.231  
60.500  
66.559  
71.902  
77.054  
3
4
5
6
7
8
9
10  
11  
12  
13  
14  
15  
16  
17  
18  
19  
20  
21  
22  
23  
24  
.716  
.650  
.551  
.461  
.386  
.239  
.125  
Extraction Method: Principal Component Analysis.  
Analysis of Total Variance Explained  
Table seven explain that the Total Variance Explained table summarizes how much variance each extracted  
component accounts for in the dataset. Under the Initial Eigenvalues section, it is observed that the first eight  
components have eigenvalues greater than 1.0, which satisfies the Kaiser criterion for factor retention. These  
eight components collectively explain a substantial proportion of the total variance in the data, indicating that  
they represent the major underlying dimensions of employee perceptions toward welfare schemes.  
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The first component has an eigenvalue of 4.442, explaining 18.509% of the total variance. This suggests that  
the first factor captures a large portion of the shared variance among the items, likely representing the most  
dominant dimension in the welfare-related responses. The second component explains an additional  
14.069%, while the third factor contributes 11.272% of the variance. Together, the first three components  
account for 43.850% of total variance, indicating that a significant portion of the information structure is  
captured within the first few extracted dimensions.  
The table further shows that the fourth to eighth components explain variance ranging from 9.381%  
(Component 4) to 5.152% (Component 8). When combined, these eight components account for 77.054% of  
the cumulative variance. This high cumulative percentage denotes that the factor solution is robust and  
captures more than three-fourths of the overall variability in the dataset. Therefore, the extracted components  
sufficiently represent the structure of the welfare scheme perception variables, reducing data complexity while  
maintaining strong explanatory power.  
Components from 9 to 24 show eigenvalues below 1.0 and contribute very little variance individually. These  
components were therefore not retained in the factor solution, as they do not meaningfully contribute to  
identifying significant underlying patterns. Their minimal variance contribution—less than 4% per  
component—confirms that only the first eight components possess practical significance for interpretation.  
Overall, the Total Variance Explained table confirms that an eight-factor solution is statistically strong and  
theoretically meaningful. The high cumulative variance (77.054%) and the clear drop in eigenvalues after the  
eighth component demonstrate that the retained components provide a comprehensive and reliable  
representation of the latent constructs underlying employee awareness, utilization, satisfaction, HR support,  
and perceptions of welfare schemes. The extraction method using Principal Component Analysis (PCA) has  
thus produced a factor structure that effectively reduces dimensionality without compromising explanatory  
integrity.  
SUMMARY  
This study explores the welfare measures provided by the State Bank of India (SBI) and assesses employees’  
awareness, utilization, and satisfaction levels. The research highlights the significance of welfare initiatives in  
ensuring employee well-being and improving organizational outcomes. The introduction emphasizes that  
effective welfare policies are essential for maintaining motivation, reducing stress, and enhancing productivity  
in public sector banks.  
The literature review confirms that welfare measures significantly influence job satisfaction, performance, and  
employer–employee relationships. The statement of the problem indicates that despite the availability of  
comprehensive welfare schemes in SBI, gaps exist between policy formulation and implementation.  
Employees often face issues related to lack of awareness, procedural delays, insufficient communication, and  
unequal access.  
The methodology outlines a descriptive and analytical research design with primary data collected from  
employees across various cadres and branches. Reliability testing shows strong internal consistency  
(Cronbach’s Alpha = 0.745). KMO values, though moderate (0.515 and 0.528), along with significant  
Bartlett’s tests, confirm the data’s suitability for factor analysis.  
Communalities analysis reveals that most variables have strong extraction values (0.60 to 0.93), indicating  
good representation in the factor model. PCA results identify eight key components that together explain  
77.05% of the variance. These components represent dimensions such as awareness and communication,  
utilization, HR support, adequacy of benefits, accessibility, procedural transparency, morale and motivation,  
and job satisfaction.  
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Overall, the study concludes that although SBI’s welfare measures are effective and beneficial, improvements  
are needed in communication, accessibility, and process efficiency to enhance employee satisfaction and  
utilization.  
Suggestions  
The findings of the study highlight several areas where improvements can significantly enhance the  
effectiveness of welfare measures in the State Bank of India. First, communication regarding welfare schemes  
needs to be strengthened, as many employees still remain unaware of the full range of benefits available to  
them. SBI should adopt more systematic and transparent communication practices, using multiple channels  
such as email alerts, digital portals, orientation programs, and periodic welfare awareness sessions to ensure  
that all employees receive timely and accurate information. Additionally, the procedures for applying and  
availing welfare benefits should be simplified to reduce delays and confusion. Digitalizing the application and  
approval processes through a centralized platform would improve user-friendliness, reduce paperwork, and  
enhance transparency.  
Furthermore, the role of the HR department must be strengthened to ensure efficient implementation of welfare  
schemes. HR personnel should be trained to provide guidance, respond to employee queries, and serve as  
proactive facilitators of welfare benefits. Establishing welfare helpdesks or nodal officers at branch and  
regional levels would further improve accessibility. It is also important to address disparities in the availability  
and accessibility of welfare schemes across different regions, particularly in remote and rural branches.  
Ensuring uniform implementation and providing equal access to all employees would reduce perceptions of  
inequality and enhance employee satisfaction.  
In addition, the financial limits and coverage of welfare schemes should be periodically reviewed and  
upgraded to keep pace with inflation and evolving employee needs. Expanding coverage under medical,  
housing, and educational benefits would make welfare schemes more relevant and impactful. Conducting  
regular workshops, training programs, and awareness sessions would ensure continuous engagement and better  
utilization of welfare provisions. Finally, SBI should establish strong monitoring and feedback mechanisms to  
assess the effectiveness of welfare initiatives and understand employee expectations. Collecting and analyzing  
feedback would enable the bank to update welfare policies regularly, improve work-life balance support, and  
strengthen initiatives that enhance employee morale, motivation, satisfaction, and retention.  
REFERENCES  
1. Kumar, G. S. A., and Kumar, K. A. (2018)., "A Study on Labour Welfare Measures in Singareni  
Collieries Company Limited", International Journal of Engineering Technology Science and Research,  
5(3), 1376-1382.  
2. SVaradaraj, A., and Charumathi, D. (2019)., "Impact of Welfare Measures on the Quality of  
Employees Performance with Special reference to Construction Industry", International Journal of  
Management Science and Business Administration, 5(2), 30-36  
3. Kumari, K. P., and Kannan, R. (2018)., "A Study on Statutory Labour Welfare Measures in Garment  
Industry", International Journal for Research Trends and Innovation, 3(2), 45-48.  
4. Yamane, Taro. 1967. Statistics: An Introductory Analysis, 2nd Ed., New York: Harper and Row.  
5. Shaik Rahamath Bee.2017. Intellectual Capital Management in Institutions of Higher Learning in  
Select Universities of Andhra Pradesh, an unpublished thesis.  
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