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

¹Dr. D. Appala Raju, ²Dr. P. Sree Devi

¹Assistant Professor AKNUCE, Adikavi Nannaya University, Rajamundry

²Assistant Professor JNTUGV, Vizianagaram

DOI: https://doi.org/10.51583/IJLTEMAS.2025.1411000003

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.

I. 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 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.

II. 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.

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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025

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.

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.

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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025

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.

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.

III. 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 size⁴is 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

Table .1 Reliability Statistics

Cronbach's Alpha

.745

Cronbach's Alpha Based on Standardized Items

.745

N of Items

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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025

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

Bartlett's Test of Sphericity

Approx. Chi-Square

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.

Table 3. Communalities

Initial

1.000

Extraction

.916

Gender of Respodent

Experience

.769

Income of the Respondent

.823

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.

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.

.599

.785

.653

.683

.569

.683

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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.

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 Matrix^a

Component

Gender of Respodent

Experience

.261

-.168

-.202

.883

-.254

-.179

.406

.034

.734

.658

.822

.192

.163

.465

.008

Income of the Respondent

I am aware of the various employee welfare measures provided

by SBI.

-.037

I came to know about welfare schemes primarily through

official communication channels.

.563

-.654

.046

.195

My awareness level about SBI’s welfare schemes is high.

.566

.736

-.408

.310

.407

.207

-.006

-.037

I am aware of all categories of welfare benefits such as health,

housing, and education.

SBI provides sufficient communication about welfare schemes.

.606

.640

.210

.250

.317

-.237

-.069

I clearly understand the eligibility criteria for availing different

welfare benefits.

-.454

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

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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 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

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

1.000

Extraction

.932

I have personally availed at least one welfare scheme in the last 3 years.

I regularly utilize the welfare schemes offered by SBI.

.931

The procedures for applying for welfare benefits are simple and user-friendly.

I receive timely information and support from HR regarding welfare schemes.

Lack of awareness is a barrier to using welfare benefits (reverse coded).

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.

.796

.845

.745

.752

.793

.810

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Overall, I believe the welfare measures provided by SBI are effectively utilized by

employees.

1.000

.636

The welfare benefits provided by SBI are adequate to meet employee needs.

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

.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.

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

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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

Initial Eigenvalues

Extraction Sums of Squared Loadings

Total % of Variance Cumulative %

Total

4.442

% of Variance Cumulative %

18.509

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

18.509

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

4.442

3.377

2.705

2.251

1.745

1.454

1.282

1.236

18.509

14.069

11.272

9.381

7.270

6.059

5.343

5.152

18.509

32.578

43.850

53.231

60.500

66.559

71.902

77.054

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

.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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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025

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.

IV. 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.

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.

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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,

MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)

ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025

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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