SUBMITTED BY: GROUP 1UM14004 Abinash MallickUM14001 Abhijeet DashUM14024 Esha EpsitaUM14025 Tarakshewar RaoUM14029 Krishna KumariUM14032 Kundan MohapatraUM14036 Nitika BaraliaUM14038 Padmalaya MallickUM14037 Goutam Prasad RaoUM14056 Sukanya Dash

Employee Satisfaction

OBJECTIVE

OBJECTIVE

To gather information on employee satisfaction in an organization.

This survey focuses on how employees feel about their job description, position

within the company, relationships with colleagues and superiors, advancement

opportunities and overall satisfaction.

METHODOLOGY

• Problem Identification

• A population and sample were identified.

• A set of questionnaire was prepared to get the responses of the sample population.

• An online survey was conducted using Qualtrics application, which formed the basis for the primary research. Social networking sites and email were used as medium for creating awareness about the survey.

• Convenient sampling technique was used for sampling the population data.

• Analysis of the collected data using SPSS.• Interpretation/inferences of the result obtained.

A priori Reasoning

An employee’s job satisfaction quotient depends on factors

such as job description, position within the company,

relationships with colleagues and superiors, future

prospects and overall satisfaction.

All these variables which contribute to the overall

satisfaction of an employee can define one particular

construct.

HYPOTHESIS

Ho: Income level and employee satisfaction are not

dependent

H1: Income level and employee satisfaction are dependent

ANALYSIS

The following analysis was carried out on the data collected using SPSS package.

Univariate Analysis on the demographic variables

Bivariate Analysis – Linear Regression

Multivariate AnalysisMultiple RegressionFactor Analysis of the Likert Scale dataCluster Analysis of the demographic variables

UNIVARIATE ANALYSIS

GENDER FREQUEN

CY

PERCENT

AGE

Male 81 66.94 %

Female 40 33.06 %

44

77

MARITAL STATUS

MARRIED

SINGLE

MARITAL

STATUS

FREQUEN

CY

PERCENT

AGE

Married 44 36.36 %

Single 77 63.64 %

MARITAL STATUS

Mean 1.636364

Standard Error 0.043913

Median 2

Mode 2

Standard

Deviation

0.483046

Sample Variance 0.233333

Kurtosis -1.69878

Skewness -0.57409

Range 1

Minimum 1

Maximum 2

Sum 198

Count 121

85

21

105

AGE GROUP

20 - 30

30 - 40

40 - 50

50 plus

AGE

GROUP

FREQUEN

CY

PERCENT

AGE

20 – 30 85 70.25 %

30 – 40 21 17.35 %

40 – 50 10 8.26 %

50 Plus 05 4.13%

AGE GROUP

Mean 1.46281

Standard Error 0.074265

Median 1

Mode 1

Standard

Deviation

0.816918

Sample Variance 0.667355

Kurtosis 2.208654

Skewness 1.753446

Range 3

Minimum 1

Maximum 4

Sum 177

Count 121

31

45

28

107

INCOME LEVEL

< 5 Lakhs

5 - 10 Lakhs

10 - 15 Lakhs

15 - 20 Lakhs

> 20 Lakhs

INCOME

LEVEL

FREQUENC

Y

PERCENTA

GE

< 5 lakhs 31 25.62 %

< 5 – 10

Lakhs

45 37.19 %

< 10 – 15

Lakhs

28 23.14 %

< 15 – 20

Lakhs

10 8.26 %

> 20 Lakhs 7 5.78 %

INCOME LEVEL

Mean 2.31405

Standard Error 0.101662

Median 2

Mode 2

Standard Deviation 1.11828

Sample Variance 1.250551

Kurtosis -0.02803

Skewness 0.732344

Range 4

Minimum 1

Maximum 5

Sum 280

Count 121

5

23

3715

18

14

9

Position in the Organization

Management Trainee

Junior engineer

Senior Engineer

Entry-level manager

Mid-level manager

Executive

Other

POSITION FREQUENCY

PERCENTA

GE

Management

Trainee 5 4.13%

Junior engineer 23 19.01%

Senior Engineer 37 30.58%

Entry-level

manager 15 12.40%

Mid-level

manager 18 14.88%

Executive 14 11.57%

Other 9 7.44%

29

14

3

786

54

CURRENT DEPARTMENT

Operations

Finance

Sales

Marketing

Strategy

Procurement

Other

CURRENT

DEPARTMENT

FREQUENC

Y PERCENTAGE

Operations 29 23.97%

Finance 14 11.57%

Sales 3 2.48%

Marketing 7 5.79%

Strategy 8 6.61%

Procurement 6 4.96%

Other 54 44.63%

0

5

10

15

20

25

30

35

40

45

50

Primary Work Location

Primary Work Location 0

2

4

6

8

10

12

Pu

ne

Ko

ta

Rou

rkel

a

Sw

eden

Bhu

ban

esw

ar

Tal

cher

Vis

hak

hap

atnam

Hy

der

abad

Du

rgap

ur

My

sore

Bhil

lai

No

ida

Ph

oen

ix U

SA

Jam

shed

pur

Od

ish

a

Ah

med

abad

Other Cities

Other Cities

PRIMARY WORK

LOCATION FREQUENCY PERCENTAGE

Delhi 17 14.05%

Chennai 7 5.79%

Mumbai 13 10.74%

Bangalore 29 23.97%

Kolkata 9 7.44%

Others 46 38.02%

16

64

24

17

NO. OF YEARS IN THE COMPANY

Less than ayear

1 - 3 years

4 - 6 years

More than sixyears

NO. OF

YEARS IN

THE

ORGANIZ

ATION

FREQUEN

CY

PERCENT

AGE

Less than a

year 16 13.22%

1 - 3 years 64 52.89%

4 - 6 years 24 19.83%

More than

six years 17 14.05%

Less than a

year 16 13.22%

11

24

2147

18

Overall Employee Satisfaction Level

Extremely Dissatisfied

Somewhat Dissatisfied

Neutral

Somewhat Satisfied

Extremely Satisfied

OVERALL

SATISFAC

TION

LEVEL

FREQUEN

CY

PERCENT

AGE

Extremely

Dissatisfied 11 9.09%

Somewhat

Dissatisfied 24 19.83%

Neutral 21 17.36%

Somewhat

Satisfied 47 38.84%

Extremely

Satisfied 18 14.88%

BIVARIATE ANALYSIS

Objective(to find

relationship b/w)

Dependent

variable

Independent

Variable

A Priori Reasoning

Income & satisfaction

Overall how

satisfied you are

with the company

Income level Higher income level

generates higher

satisfaction

No of years in the

organization &

satisfaction

Overall how

satisfied you are

with the company

How long have you

been working in the

organization

A satisfied

employees stays

longer in the

organization

Position & Income Income level Your current

position in the

company

Higher position

leads to higher

income

BIVARIATE ANALYSIS

HYPOTHESIS

Null Hypothesis: There is no relationship between income level and satisfaction.

Alternative Hypothesis: There is a relationship between income level and satisfaction.

Income & satisfaction

BIVARIATE ANALYSIS

HYPOTHESIS

Null Hypothesis: There is no relationship between number of years in an organization and satisfaction.

Alternative Hypothesis: There is a relationship between number of years in an organization and satisfaction.

No of years & satisfaction

BIVARIATE ANALYSIS

HYPOTHESIS

Null Hypothesis: There is no relationship between income level and position.

Alternative Hypothesis: There is a relationship between income level and position.

Income & position

Position vs. satisfied

Age Vs satisfaction

BIVARIATE ANALYSIS (income vs. satisfaction)

Conclusion

Income level is statistically significant in explaining the satisfactionlevel of the employee, but due to the smaller value of R² , we cannotgeneralize our hypothesis.

Equation Alpha Beta1 Beta2 Beta3 R Squared

Simple

Linear

2.22222

(0.0000)

0.462

(0.000)

0.113

Log Linear 0.900

(0.000)

0.287

(0.001)

0.096

Quadratic 2.332

(0.000)

0.456

(0.249)

-0.012

(0.861)

0.13

Cubic 2.664

(0.016)

-0.24

(0.987)

0.183

(0.754)

-0.023

(0.736)

0.130

CROSS Tabulation (income vs. satisfaction)

CROSS Tabulation (Years in organization vs. Satisfaction )

CROSS Tabulation (Position in organization vs. Income )

MULTIPLE REGRESSION

OBJECTIVE

To find a relationship of the overall employee satisfaction with gender, age group, income level, no. of years working in the organization, appraisal process involvement, and degree of freedom

A PRIORI REASONING

•As income level increases ,employee satisfaction level increases.•People working for more number of years in an organization having transparent appraisal process and when given optimum degree of freedom, tend to be more satisfied.

MULTIPLE REGRESSION

HYPOTHESIS

Null Hypothesis: There is no relationship of the overall employee satisfaction with gender, age group, income level, no. of years working in the organization, appraisal process involvement, and degree of freedom

Alternative Hypothesis: There is a relationship of the overall employee satisfaction with gender, age group, income level, no. of years working in the organization, appraisal process involvement, and degree of freedom

Variables Used

• Variables-

• Dependent variable

• Y = Satisfaction Level in Company

• Independent Variables

• Q1 = Gender

• Q3 = Age group

• Q4 = Income level

• Q7 = No. of years working in the organization

• Q9 = Appraisal process involvement

• Q10 = Degree of freedom

Analysis By Multiplicative model

Iteration Log(

Constant

)

Log

Q1

(β1)

Log

Q3

(β2)

Log

Q4

(β3)

Log

Q7

(β4)

Log Q9

(β5)

Log

Q10

(β6)

Adj

R^2

1 Log Y 1.173 (0.160) 0.865 0.118 0.077 0.327 1.383 0.343

Significance 0.002 0.283 0.297 0.246 0.283 0.213 0.295

2 Log Y 0.870 (0.161) 0.233 0.468 0.344 1.349 0.300

Significance 0.019 0.583 0.297 0.072 0.121 0.000

3 Log Y 0.824 0.262 0.457 0.336 1.353 0.305

Significance 0.022 0.224 0.077 0.127 0.000

4 Log Y 0.778 0.568 0.382 1.438 0.302

Significance 0.030 0.019 0.079 0.000

Analysis By Additive modelIteration Constant Q1

(β1)

Q3

(β2)

Q4

(β3)

Q7

(β4)

Q9

(β5)

Q10

(β6)

Adj

R

Sqr

1 Coefficient 0.640 -0.094 0.445 -0.086 0.067 0.125 0.527 0.372

Significanc

e

(0.195) (0.632) (0.013) (0.468) (0.631) (0.130) (0.000)

2 Coefficient 0.499 0.444 (0.76) 0.065 0.122 0.530 0.371

Significanc

e

(0.206) (0.013) (0.514) (0.639) (0.136) (0.000)

3 Coefficient 0.570 0.487 (.072) 0.120 0.535 0.369

Significanc

e

(0.116) (0.001) (0.535) (0.142) (0.000)

4 Coefficient 0.570 0.427 0.112 0.519 0.367

Significanc

e

(0.115) (0.000) (0.163) (0.000)

MULTIPLE REGRESSION

CONCLUSION

Income level and degree of freedom provided by superiors in idea generation are statistically significant in explaining the overall satisfaction level of employees.

However, since the R2 value is not that high there are various other factors which help in explaining the overall satisfaction level of employees.

FACTOR ANALYSIS

• To analyze the different factors that influence the

satisfaction level of an employee in an organization

• To use the factor analysis as a data reduction technique to

group the different manifest variables into constructs and

thus find out the “Factors responsible for satisfaction

level of an employee working in an organization ”

A PRIORI REASONING

• Factor analysis is used to measure various latent variables

that can be measured by multiple observable variables

• The observable variables are

• Proper communication in the company

• Opportunities in the job

• Relations with the supervisor

• Overall facilities available in the company

• These variables are used to measure the overall

satisfaction of the employee

DATA COLLECTION

• Data was collected with the help of a questionnaire where

the responses from the respondents were recorded in a

likert scale (from 1-5)

• Total of 120 responses collected have been tabulated in

excel along with their coded values.

ANALYSIS

• All factor analysis techniques try to clump subgroups of variables together based upon their

correlations and often you can get a feel for what factors are going just by looking at the correlation

matrix and spotting clusters of high correlations between groups of variables

• Here we have four likert table data

• In the first case we have the latent variable of proper communication in the company

This relation is better verified by KMO and Bartlett Test

• Factor loading, communalities and total variance explained

Component Matrix and Rotated Component Matrix

• Here we have four likert table data

• In the second case we have the latent variable of relations with the supervisor

This relation is better verified by KMO and Bartlett Test

• Factor loading, communalities and total variance explained

Component Matrix and Rotated Component Matrix

• Here we have four likert table data

• In the third case we have the latent variable of overall facilities available in the company

This relation is better verified by KMO and Bartlett Test

• Factor loading, communalities and total variance explained

Component Matrix and Rotated Component Matrix

• Here we have four likert table data

• In the fourth case we have the latent variable of opportunities in the job

This relation is better verified by KMO and Bartlett Test

• Factor loading, communalities and total variance explained

Component Matrix and Rotated Component Matrix

FACTOR ANALYSIS

Factor No. Factor Name Variables

1 communicationEmployee-manager (0.880)

Manager-employee (0.813)

2 Clarity of missionClarity to employees (0.798)

Clarity to company (0.826)

3 Relation with superiorsSuperior does Good job (0.777)

Superior enables my best performance (0.798)

4 Evaluation & suggestionRegularity of work evaluation (0.811)

provides actionable solution(0.820)

5 Facilities in the company

Appraisal process (0.809)

Promotion process (0.820)

Health care benefits(0.919)

6 Opportunities in jobRecognition for work (0.822)

Talent utilization(0.790)

CLUSTER ANALYSIS

Demographic variables used for cluster analysis

• Gender

• Marital Status

• Age

• Income Level

• Current Position

• Department

• Number of years

• Primary work Location

• Involvement in Appraisal

• Degree of freedom for Idea generation

• Satisfaction in Company

• Satisfaction in Department

• Recommendation to work in the company

Cluster Centers

CONCLUSION

As observed from different analysis, the satisfaction level of employees changes is very much influenced by their income and degree of freedom provided for idea generation. Apart from that age group, position, the function they perform also play a vital role.

THANK YOU