Paciﬁc University Journal of Social Sciences · 2020-07-24 · Dr. Rashmi Singh Demographic...

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Issue 1 25 November, 2019 UdaipurVol. 4

Indexed in : Scientific Indexing Services | Scientific Journal Impact Factor (4.729)

Does Economic Scale of Family Affects Emotional Competencies? - A Study

Related To Youth

Dr. Rashmi Singh

Demographic Composition and Employment Security in Informal Sector :

Empirical Evidence from India

Prof. Sudhir Chandra Das, Shweta

Social Intelligence Effect on Binge Eating Disorder

Upasna Singh, Dr. Rashmi Singh

1

13

18

Factors Drive Satisfaction in Shopping Malls – A Study on Customers' of

Udaipur (Rajasthan)

Nikhil Menaria, Dr. Anurag Mehta

24

An Empirical Study on M-commerce Adoption By Rural Businessmen in South

Rajasthan

Dr. Khushbu Agarwal

33

Corporate Social Responsibility of Vedanta : A Study on Improvement in

Health Condition in Tribal area of South Rajasthan

Prof. Pushpkant Shakdwipee

40

Pacic University Journal of Social Sciences Vol. 4, Issue 1 25 November, 2019, Udaipur

[email protected]

India home to 28 percent of the world's multidimensional poor: Human Development Index 2019.The human development index of 2019 points that India is home to 28 percent of the world's poor people, despite the fact that between 2005 and 2015, the country has lifted 271 million people out of poverty.India shifted one spot up from 130 positions to the rank of 129 among 189 countries in the 2019 Human Development Index released by the United Nations Development Programme (UNDP). India's HDI value is up by 50 percent with an increase of 0.647 in 2018 from 0.43 in 1990, placing India above average among countries in the medium human development group as well as above the average for other South Asian countries. However, in recent years the annual HDI growth in India has slowed down. India's average annual HDI growth was 1.43 percent between 1990 -2000 which increased to 1.57 percent during 2000 - 2010 which, between 2010 to 2018 came down to 1.34 percent. Among the neighboring countries of India, Pakistan ranked 152 and Bangladesh ranked 135, Sri Lanka, and the Maldives ranked 71 and 104 respectively with China at 85th position. With 46 percent growth rate during 1990- 2018 South Asia became the fastest-growing region followed by East Asia and Pacific at 43 percent growth. In global parlance, Norway is at first rank with an HDI value of 0.954 while Burundi with the value of 0.423 is at the bottom of the list.

Significance of the Report

The report highlights the inequality in development beyond income that exists in the society as well as to measure loss in human development progress due to inequalities along with emphasizing the gender gaps in development. The Human Development Report considers four parameters viz; per capita gross national income, life expectancy at birth, mean years of schooling, and expected years of schooling for evaluating the performance of the countries.

According to the report, India ranks at 122 positions out of 162 countries on the Gender Development Index and is only marginally better than the South Asian average (0.829 vs 0.828). In India, per capita income increased over 250 percent while life expectancy at birth rose by 11.6 years, mean years of schooling increased by 3.5 years whereas the expected years of schooling grew by 4.7 years between 1990-2018. Despite the growth in basic standards, group-based inequalities especially affecting women and girls persist in India.HDI report indicates towards enormous high incidences of multidimensional poverty across countries. Out of 1.3 billion multidimensional poor living in the 101 countries of the world, almost half of it amounting to 661 million are from Asia and the Pacific region. The world is moving towards a new set of inequalities, from access to health services and education to inequalities based on climate education and technology. The irony is that in India despite a reduction in absolute poverty, the old and new inequalities are on the rise. Older inequalities in terms of access to healthcare and education and the next generation of inequalities specifically around the climatic crisis, education, and technological divide are becoming the roadblocks for achieving the agenda for sustainable development. In India the development initiatives for financial inclusion like the Pradhan Mantri Jan Dhan Yojana and for universal health care, Ayushman Bharat are the crucial campaigns for bridging an equal distribution of wealth and power in the Indian society and are essential for achieving the agenda for sustainable development and for ensuring the quality of human life.

Editorial

Prof. Mahima BirlaDean

Faculty of Management


1

Abstract

The informal sector provides employment opportunity to a large proportion of population in India, still majority of informal workers do not have employment security. Employment security is the protection against loss of employment and the ensured possibility of continuing employment, even though not in the same job. In this paper an attempt was made to assess the demographic composition of informal sector in India and the level of employment security at individual level with the help of empirical evidence from India. The findings suggested that demographic have significant inuence on economic security (Duration of current Employment, Expectations to continue current Employment, Perceived security of current employment, Expected time to find alternate employment, Easiness to Obtain alternate employment )of informal sector workers.

Key Words : Informal Sector, Employment Security, Demographic Composition, Informal Workers

Introduction

The informal sector plays an elemental role in providing employment opportunities to a large proportion of population in the country. A high proportion of socially and economically under privileged sections of society are concentrated in the informal activities in India. ILO launched the concept of the “informal sector” in development policy debate in a report published in 1972 following a multidisciplinary employment mission to Kenya. . The term informal sector was first used by British anthropologist Keith Hart in 1971 during a study on Ghana and described informal sector as that part of the urban labour force which falls outside the organised labour market (Hart, 1973).

Kantor (1997) stated that the informal sector workers include all workers in informal enterprises, some workers in formal enterprises, self- employed workers, and those doing contract work for informal and formal enterprises and contractors (ILO, 2002). The informal economy thrives in a context of high unemployment, underemployment, poverty, gender inequality and precarious work. It plays a significant role in such circumstances, especially

Demographic Composition and Employment Security in Informal

Sector : Empirical Evidence from India

Prof. Sudhir Chandra DasProfessor of OB and HR, Faculty of Commerce, BHU, Varanasi (U.P.)


ShwetaResearch Scholar, Faculty of Commerce, BHU (U.P.)

2

in income generation, because of the relative ease of entry and low requirements for education, skills, technology and capital. ILO defined the term “informal economy” as “all economic activities by workers and economic units that are not covered or insufficiently covered by formal arrangements. In 2003 the ILO adopted the concept of “informal employment” based on job characteristics, for which reason informality can exist in both the informal and formal sectors.

Unni and Rani (2003) described the nature of Indian informal workforce as heterogeneous and diverse. The Indian informal worker includes street vendors, daily-wage construction workers, domestic workers, small-scale entrepreneurs, p iece -workers , as wel l as middle -c lass professionals running businesses from their homes. Despite variation in the type of work, earnings and education-levels, the majority of informal workers in India makes low earnings and lacks the benefits, social security and legal protections available to workers in formal employment. Chen (2005) stated that the India's informal economy can be understood as encompassing entrepreneurial as well as exploitative and dependent forms of economic activity. The main characteristics of informal sector are ease of entry, smaller scale of operation, local ownership, uncertain legal status, labour-intensive and operating using lower technology based methods, inadequate access to government schemes, finance and government aid (Raju, 1989). The mass of new employment in recent years, particularly in developing and transition countries, has been in the informal economy. Despite their large proportion, the majority of informal workers do not have stable avenues of employment (ILO, 2002). The seven essential securities for decent work are often denied to the informal sector workers; namely-labour market security, employment security, job security, work security, skill reproduction security, income security and representation security.

Objectives of Study

i) The objective of this paper is to inquire into the demographic composition of informal workers in informal sector workers,

ii) T o a s s e s s t h e r e l a t i o n s h i p b e t w e e n demographic composition and employment security among informal sector workers.

iii) To ascertain the inuence of demographic variables on economic security of informal sector workers.

Hypothesis Formulation

On the basis of above objectives following hypothesis were formulated:

H T h e r e i s n o r e l a t i o n s h i p b e t w e e n 01

demographic composition and employment security among informal sector workers.

H There is no inuence of demographic 02

variables on economic security of informal sector workers.

Demographic Composition of Informal Sector

61.2 percent of the world's employed population amounting to two billion workers are engaged in informal employment. A considerably high rate of informality exists in developing countries as compared to developed ones. Disassembling the share of employment according to gender, 63 percent of men across the globe are engaged in informal employment which is higher than the 58.1 percent of women. Young person and old people are found to be more affected by informality than persons belonging to the age group of 25 and 64 years (ILO, 2018).

Around the world, increase in level of education is related to decrease in the level of informality. People residing in rural areas are twice as likely to be in rural employment (80 per cent) than those residing in urban areas (43.7 percent). Formality of employment is positively related to socioeconomic development The majority of workers in India are in informal employment, behind this there are two diverging trends- the decrease in the share of informal worker in informal sector, the share of workers in the informal sector fell from 86.3 percent in 2004-05 to 82.2 percent in 2011-12 and the increase in share of informal workers (i.e. workers without access to social security) in organised sector; the share of informal workers in


3

the organized sector increased significantly because of a greater use of contract and other forms of casual labour. Because of these countervailing trends, the overall proportion of informal workers in total employment (informal sector workers plus informal workers in the organized sector) has remained relatively stable, at around 92 percent (ILO, 2017).

Employment Security In Informal Sector

Employment Security is concerned with the protection of workers against uctuations in earned income as a result of job loss. Job loss can be the result of economic downturns, as a part of restructuring, or due to various reasons of dismissal. ILO has included it as one of the seven forms of security for decent work.

According to ILO (1995), “Employment security means that worker has protection against arbitrary and short-notice dismissal from employment, as well as having long term employment relations that avoid casualisation”. This definition covers only wage and salary workers excluding self-employed workers. Self employed category includes, employers, own account works and various other non- standard workers like contract workers.

According to ILO (2004), “Employment security is the protection against loss of income-earning work. For wage and salary workers, employment security exists in organizations and countries, in where there is strong protection against unfair or arbitrary dismissal and where workers can redress unfair dismissal. For the self- employed, it means protection against sudden loss of independent work, and/or business failure.” Dasgupta (2001) defined employment security as protection against loss of employment and the ensured possibility of continuing employment, even though not in the same job.

Materials and Methods

The research design of the paper is as follows:

1. Participants - The participants were labourers, working in the informal sector of

Varanasi district, Uttar Pradesh. Varanasi district ranks 18th in the terms of population in Uttar Pradesh. It ranks 9th in literacy with 75.6 % which is higher than state average of 67.7% (Census 2011). Varanasi's economy is hugely dependent on informal sector which accounts for over a third of the workforce (Kumar, 2016). Simple random sampling technique is followed in the current paper with sample size of 200 workers.

2. Measures - To ascertain the demographic c o m p o s i t i o n o f i n f o r m a l s e c t o r , 8 demographic variables were used, namely- Gender, Age, Education level, Occupation, Employment status, Marital Status, Religion, Caste. For assessing the employment security of workers informal sector at individual level following variable were used: Perception of employment security it includes two variables-Perceived security of current employment and Seasonal nature of work; Perception of likelihood of finding alternate employment, it includes two variables-Expected time to find alternate employment and Ease of finding alternate employment. The economic security variables used here are t a k e n f r o m “ E m p l o y m e n t S e c u r i t y : Conceptual and Statistical Issues” by (Dasgupta, 2001).

3. Analysis Procedure - Data is collected from respondents using a questionnaire. It analysed using SPSS version 21. Descriptive s t a t i s t i c s , P e a r s o n C o r r e l a t i o n a n d Multivariate analysis of variance (MANOVA) were used to analyse the data

Statistical Analysis and Discussions

Two set of variable demographic variables and employment security variable were analysed and the results are presented below:

1. Demographic Variables - Following variables were selected to ascertain the demographic composition of informal sector workers:-

(i) Gender : Out of the total respondents 69.5 percent were male and 30.5 percent were female. 24 percent respondents belonged to the 18-25 year age group in which, 29 percent


4

respondents were in the 25- 35 year age group in which, 30.5 respondents were in 35-45 year age group in which and 16.5 respondents were in 45-60 age group.

(ii) Age : Majority of respondents 30.5 percent belonged to 35-45 year age group, 29 percent respondent belonged to 25- 35 year age group, 24 percent comes under the category of 18 to 25 years of age group and rest 16.5 percent came from 45-60 years age group.

(iii) Educational Level : 25.5 per cent of respondents were educated till high school, 25.5 percent were uneducated, 22 percent received primary education, 13 percent were intermediate, 9.5 percent were graduates and 4.5 percent were post graduate.


Demographic Variables Frequency Demographic Variables Frequency

Gender Male 139 Occupation Vendor 31

Female 61 Rickshaw Puller 35

Age 18-25 48 Carpenter 15

25-35 58 Rajgir 23

35-45 61 Weaver 24

45-60 33 Waiter 7

Education Illiterate 51 Ward Boy 3

Primary 44 Worker 56

High School 51 Paint worker 3

Intermediate 26 Electrician 1

Graduate 19 Other 2

Post Graduate 9 Marital Status

Married 139

Employment Status

Self Employed

82 Unmarried 43

Widow/Widower 15

Wage Worker 89 Separated 3

Caste General 16

Home Based Worker

29 OBC 79

SC 98

Religion Hindu 176 ST 7

Muslim 24

Source: Field Data

Table 1 : Demographic Composition of Unorganized Labourers

(iv) Occupation: The participants were engaged in employment as construction workers, vendors, rickshaw pullers, rajgir, paint workers, ward boy, weaver, waiter, carpenter, electrician and other categories. The majority of respondents 28 percent belonged to construction workers.

(v) E m p l o y m e n t S t a t u s : 4 4 . 5 p e r c e n t respondents were wage workers, 41 per cent respondents belonged to self employed category, and 14.5 percent respondents were home based workers.

(vi) Marital Status : Majority of participants, 69.5 percent were currently married; few, 21.5 per cent were unmarried and remainder, 7.5 percent were formerly married (widow/widower) and 1.5 percent were separated.

(vii) Religion : 88 percent of respondent belonged to the Hindu religion and 12 percent respondent were Muslim.

(viii) Caste : The majority of respondents, 49

percent belonged to scheduled caste category, 39.5 percent belonged to other backward caste category, 8 percent respondents were of general category and 3.5 percent belonged to scheduled tribe category.

5


1. E m p l o y m e n t S e c u r i t y V a r i a b l e s - Employment security can be assessed at three levels- national level, industry level and at individual level. At the individual level employment security can be assessed by two types of indicators- subjective indicators and objective indicators (Dasgupta, 2001).

2.1 Subjective Indicator : Subjective indicator of employment security is measured through perception of continuance of individual's employment. It includes:

(a) Perception of employment security- At individual level , perception about employment security is the feeling of an i n d i v i d u a l a b o u t c o n t i n u i n g h i s employment. In survey, respondents were asked about how secure they feel about their current employment.

Table 2: Perceived Security of Current Employment within Employment Status

Employment Status

Perceived Security of Current Employment Very

Insecure

Quite

Insecure

Quite

Secure

Very Secure Total

Self Employed 21

25.6%

28

34.1%

12

14.6%

21

25.6%

82

100.0%

Wage

Workers

24

27.0%

23

25.8%

21

23.6%

21

23.6%

89

100.0%

Home Based

Worker

7

24.1%

12

41.4%

7

24.1%

3

10.3%

29

100.0%

Total 52

26.0%

63

31.5%

40

20.0%

45

22.5%

200

100.0%

Table 2 shows the frequency distribution of perceived security of current employment within employment status. Majority of workers, 31.5 per cent responded that they felt quite insecure about continuing their employment, 26 per cent felt very insecure, 20 percent felt quite secure and 22.5 per cent were feeling very secure about continuing their employment. Among self employed workers 25.6 per cent workers felt very insecure about continuing their employment as compared to 27 per cent of wage workers and 24.1 per cent of home based workers. 34.1 per cent self employed workers were quite insecure as compared with 25.8 percent wage workers and 41.4 per cent of home based workers. 14.6 per cent self employed

workers felt quite secure as compared with 23.6 percent of wage workers and 24.1 per cent of home based workers. 25.6 per cent self employed workers felt very secure about continuing their employment as against 23.6 per cent of the wage workers and 10.3 per cent home based workers.

The feeling of security may be affected by various factors; it includes the threat of job loss. The continuation of employment also depends upon the seasonal nature of employment. Majority of workers i.e. 76.8 per cent of wage workers; 83.1 per cent of self- employed and 75.9 percent of home based workers stated that their work is not of seasonal nature, which can be a sign of stability of continuance of their employment.

(b) Perception of likelihood of finding alternate employment : Following two indicators can be used to assess the likelihood of finding alternate employment:

I. E x p e c t e d T i m e t o F i n d A l t e r n a t e Employment - Majority of workers, 29 per cent workers did not know after how much time they wil l get a l ternate employment . Uncertainty has been identified as a major component in the experience of employment insecurity (Dasgupta, 2001). 20.5 percent workers responded that to find alternate employment they will require very long time, constituting of 24.4 percent of self employed, 19.1 percent of wage workers and 13.8 percent of home based workers. 22.5 per cent workers s ta ted that , they wi l l find a l ternate employment after long time; comprising of 17.1 percent self employed workers, 29.2 per

Source : Field Data

6


cent of wage workers and 17.2 per cent of home based workers. 16.5 per cent worker were of opinion that quite long time will be needed for finding alternate employment and 11.5 per cent of workers responded that it will

be not long before they will find alternate employment, among which 11 percent were self employed, 7.9 per cent were wage worker and 17.2 per cent were home based worker (Table 3).

Table 3: Expected Time to find Alternate employment within Employment Status

Employment Status

Expected Time to find Alternate employment

Very Long Time

Long Time Quite Long

Not Long Don’t Know

Total

Self Employed 20 24.4%

14 17.1%

10 12.2%

9 11.0%

29 35.4%

82 100.0%

Wage Workers 17

19.1%

26

29.2%

15

16.9%

7

7.9%

24

27.0%

89

100.0%

Home Based Worker

4

13.8%

5

17.2%

8

27.6%

7

24.1%

5

17.2%

29

100.0%

Total 41 20.5%

45 22.5%

33 16.5%

23 11.5%

58 29.0%

200 100.0%

Source: Field data

Table 4: Ease of Finding Alternate Employment within Employment Status

Employment Status

Ease of Finding Alternate Employment

Very Easy Easy Hard Very Hard Don’t Know

Total

Self Employed 1 1.2%

7 8.5%

20 24.4%

34 41.5%

20 24.4%

82 100.0%

Wage Workers 3

3.4%

10

11.2%

36

40.4%

29

32.6%

11

12.4%

89

100.0%

Home Based Worker

0

0.0%

0

0.0%

18

62.1%

9

31.0%

2

6.9%

29

100.0%

Total 4 2.0%

17 8.5%

74 37.0%

72 36.0%

33 16.5%

200 100.0%

Source: Field data

7

II. Ease of Finding Alternate Employment - 36 per cent workers responded that it will be very hard to find alternate employment, among them 41.5 percent were self employed, 32.6 per cent wage workers and 31 per cent were home based workers. 37 per cent responded it will be hard to find alternate employment, which can be disaggregated into 24.4 percent of self employed, 40.4 per cent of wage workers and 62.1 percent of home based workers.16.5 percent worker did not know, constituting of 24.4 per cent of self employed 12.4 per cent of wage worker and 6.9 per cent of home based worker. 8.5 percent workers were of opinion that they will find alternate employment easily, in which 8.5 per cent were self employed and 112.2 percent of wage workers. Only 2 per cent workers stated that finding alternate employment will be very easy (Table: 4).

Objective Indicators of Employment Security - Objective indicators of employment security


inc ludes- contrac tual , behavioura l and governance indicators. Behavioural indicators include length of present employment and skills. Governance indicator includes the institutional support for individual for continuation of employment. This paper uses only behavioural indicators of employment security:

(a) Duration of Current Employment - As shown in Table 5, 7.5 percent workers were in their current employment for 0-1 years. 24 percent were in their employment for 1-5 years. 31 percent for 5-10 years, 11.5 per cent were working for 10-15 years and more 26 per cent of workers were in employment for more than 15 years. (b) Skills: 77 percent participants responded that they did not receive training for their work and only 23 percent received training. However, this does not state that informal labours are unskilled but that there is a need to recognize that their skills are acquired outside the formal education system.

Table 5 : Duration of Current Employment within Employment Status

Employment Status

Duration of Current Employment

0-1 1-5 5-10 10-15 More than 15 Total

Self Employed

4

4.9%

19

23.2%

24

29.3%

12

14.6%

23

28.0%

82

100.0%

Wage Workers 8 9.0%

23 25.8%

27 30.3%

9 10.1%

22 24.7

89 100.0%

Home Based Worker

3

10.3%

6

20.7%

11

37.9%

2

6.9%

7

24.1%

29

100.0%

Total 15

7.5%

48

24.0%

62

31.0%

23

11.5%

52

26.0%

200

100.0%

Source: Field Data

8


Tab

le 6

: Rel

atio

nsh

ip b

etw

een

Dem

og

rap

hic

Co

mp

osi

tio

n a

nd

Em

plo

ym

ent

Sec

uri

ty

Var

iab

les

M

ean

S

.D.

1.Gender(G)

2. Age (A)

3. Education (ED)

4. Occupation (O)

5. Employment (E)

6. Marital Status (MS)

7. Religion (R)

8. Caste (C)

9. Seasonal nature of work

( SNW)

10. Duration of current Employment (DCE)

11. Expectations to continue current

12. Perceived security of current employment

13. Expected time to find alternate employment

14. Easiness to Obtain alternate employment

1. G

end

er(G

) .3

050

.4

6156

1

2. A

ge(

A)

3.39

50

1.02

676

.4

47

1

3. E

du

cati

on

(ED

)

2.72

50

1.44

562

.1

31

.0

00**

1

4. O

ccu

pat

ion

(O)

4.

6350

2.

7676

8

.050

.807

.080

1

5. E

mp

loy

men

t (E

)

1.73

50

.698

01

.000

**

.0

22*

.250

.000

**

1

6. M

arit

al S

tatu

s (M

S)

1.

4100

.6

9593

.0

01**

.000

**

.0

03**

.0

43*

.0

00**

1

7. R

elig

ion

(R

) 1.

1200

.3

2578

.0

83

.002

**

.254

.4

69

.296

.1

94

1

8. C

aste

(C

) 2.

4800

.6

9427

.8

74

.3

68

.001

**

.9

72

.1

68

.842

.0

00**

1

9. S

NW

.7

950

.4

0471

.5

69

.5

18

.2

62

.4

50

.777

.7

65

.303

.5

60

1

10. D

CE

2.

475

1.

5528

8

.620

.000

**

.0

00**

.287

.1

05

.000

**

.006

**

.254

.9

57

1

11. E

CC

E

3.11

1.

1725

1

.004

*

.0

84

.0

00**

.8

97

.380

.0

00**

.0

02**

.0

00**

0.

43*

.0

00**

1

12. P

SC

E

2.39

1.

1018

0

.599

.910

.191

.6

35

.561

.3

11

.132

.0

23

.000

**

.434

.0

04*

*

1

13. E

TF

AE

3.

06

1.52

579

.5

70

.0

38*

.009

**

.076

.6

99

.598

.0

00**

.0

46*

.8

60

.154

.0

34*

.126

1

14. E

TO

AE

3.

565

.9

3281

.9

30

.0

51

.011

*

.000

**

.016

*

.045

*

.738

.8

92

.731

.0

01**

.978

.038

* .024

*

1

*

. C

orr

elat

ion

is

sig

nifi

can

t at

th

e 0.

05 l

evel

(2-

tail

ed).

*

*. C

orr

elat

ion

is

sig

nifi

can

t at

th

e 0.

01 l

evel

(2-

tail

ed).

So

urc

e : S

tati

stic

al A

nal

ysi

s

9

Relationship between Demographic C o m p o s i t i o n a n d E m p l o y m e n t Security

A Pearson Product Movement Correlation was run to determine the relationship between demographic variables and economic security variables. As shown in Table 6,the relationship of Expectations To Continue Current Employment (ECCE) with Gender (G); Expected Time To Find Alternate Employment (ETFAE) with Age (A) and Caste (C); Easiness To Obtain Alternate Employment (ETOAE) with Education (ED), Employment (E) and Marital Status (MS) were found significant at 5% level of significance with p value less than.05.

The relationship of Duration of current employment (DCE) with Age (A) , Education (ED), Marital Status (MS) and Religion (R); Expectations to continue current employment (ECCE) with Education (ED), Marital Status (MS), Religion (R) and Caste (C); Easiness to find alternate employment (ETFAE) with Education (ED) and Religion; Expected time to find alternate employment (ETFAE) with Education (ED) and Religion (R); Easiness to obtain alternate employment (ETOAE) with Occupation (O) were found significant at 1% level of significance with p value less than .01. On the basis of above analysis, the H01 There is no relationship between demographic composition and employment security among informal sector workers has been rejected.

Inuence of Demographic Composition on Employment Security

To ascertain the inuence of demographic variables on economic security variables

MANOVA has been carried out. The one- way MANOVA is used to determine whether there are any differences between Independent groups on more than one continuous dependent variable.

For the analysis, five (5) economic security variables (Duration of current Employment, Expectations to continue current Employment, Perceived security of current employment, Expected time to find alternate employment, Easiness to Obtain alternate employment) are considered as dependent variables and 8 demographic variables (Gender, Age, Education, Occupation, Employment , Marital Status ,Religion, Caste) are considered as independent variables.

Results of MANOVA (Table:7) shows that there was a statistically significant difference in economic security based on age, F(15,582)=4.579; p<.005; Wilk's Lambda= .715, partial n2=.101., respondents belonging to the age group of 45-60 had highest mean score of 3.9610, from which it can be concluded that workers belonging to 45-60 age group have more economic security.

There was a statistical significant difference in economic security based on education level, F(25,707.321)=2.462, p<.005;Wilk's Lambda=.733, partial n2=.060, respondents having education up to primary level had highest mean score of 3.777, depicting inuence of primary education on employment security ; occupation, F (50,847) = 2.016, p<.005; Wilk's Lambda = .599, partial n2=.097 and marital s tatus, F(15,582) = 3.959,p<.005; Wilk's Lambda = .746, partial n2 = .093, married respondents had the highest mean score of 3.6835, portraying married workers have more economic security.


Independent Variable

Dependent Variable

Levene’s Test

Test of between subject effect Box Test

Multivariate Test

F d.f.

Mean square

Sig. Partial ETA Squared

d.f. F P WilksLambda

PartialETA

Gender

Duration of current Employment (DCE)

.474 .145 1 .225 .704 .001

317

5;194

2.064

.072

.949 .051

Expectations to continue current Employment (ECCE)

.003

8.387

1

11.117

.004

.041

Perceived security of current employment (PSCE)

.079

.278

1

.339

.599

.001

Expected time to find alternate employment (ETFAE)

.428

.323

1

.756

.570

.002

Easiness to Obtain alternate employment (ETOAE)

.691

.008

1

.007

.930

.000

Age

DCE

.061

16.446

3

21.920

.000

.201

.106

15;582

4.579

.000

.715 .101

ECCE

.003

8.161

3

10.127

.000

.111

PSCE

.596

.206

3

.253

.892

.003

ETFAE

.065

2.007

3

4.603

.114

.030

ETOAE

.640

1.373

3

1.188

.252

.021

EducationalLevel

DCE

.112

4.876

5

7.302

.000

.112

.087

25;707

2.462

.000

.733 .060

ECCE

.000

5.124

5

6.383

.000

.117

PSCE .844 7.50 5 .917 .587 .019 ETFAE .006 2.181 5 4.931 .058 .053

ETOAE

.759

1.829

5

1.559

.109

.045

Occupation

DCE

.879

1.052

10

1.725

.401

.053

.165

50;847

2.016

.000

.599 .097

ECCE

.855

2.427

10

3.114

.010

.114

PSCE

.069

1.255

10

1.504

.259

.062

ETFAE

.015

3.054

10

6.445

.001

.139

ETOAE

.102

2.913

10

2.312

.002

.134

Employment

Status

DCE

.879

.749

2

1.234

.474

.008

.273

10;386

1.443

.159

.929 .036

ECCE

.855

2.211

2

3.003

.112

.022

PSCE

.069

.488

2

.596

.614

.005

ETFAE

.015

.464

2

1.087

.629

.005

ETOAE

.102

4.317

2

3.636

.015

.042

Marital Status

DCE

0.13

10.588

3

15.201

.000

.139

.118

15;582

3.959

.000

.746 .093

ECCE

.011

6.667

3

8.444

.000

.093

PSCE

.025

1.401

3

1.690

.244

.021

ETFAE

.038

2.727

3

6.188

.045

.040

ETOAE

.023

2.985

3

2.522

.032

.044

Religion

DCE

.191

5.682

1

9.122

0.18

.028

.013

5;194

5.391

.000

.878 .122

ECCE

.796

9.966

1

13.110

.002

.048

PSCE

.158

2.291

1

2.764

.132

.011

ETFAE

.001

13.008

1

28.560

.000

.062

ETOAE .019 .112 1 .098 .738 .001

Caste

DCE .858 1.398 3 2.284 .245 .021

.027 15;582 4.277 .000 .728 .101

ECCE .000 6.479 3 8.227 .000 .090PSCE .225 2.248 3 2.678 .084 .033ETFAE .020 5.852 3 12.695 .001 .082ETOAE .015 5.096 3 4.176 .002 .072

10


Table 7 : Inuence of Demographic Composition on Employment Security

Source : Field data, Statistical Analysis

11


There was a statistical significant difference in e c o n o m i c s e c u r i t y b a s e d o n r e l i g i o n , F(5,194)=5.391,p<.005; Wilk's Lambda=.878, partial n2=.122; respondents belonging to Muslim community had highest mean score of 4.0833, from which it can ascertained that Muslim workers have more economic security same was the case with c a s t e , F ( 1 5 , 5 8 2 ) = 4 . 2 7 7 , p < . 0 0 5 ; W i l k ' s Lambda=.728.,partial n2=101, respondent belonging to the OBC category had highest mean score of 3.6962, portraying OBC category workers have more economic security.

The difference in economic security based on the gender was found statistically not significant, F (5,194) =2.064, p>.005; Wilk's Lambda= .949, partial n2=.051, same was the case with employment, F (10; 386) =1.443, p>.005; Wilk's Lambda=.929, partial n2 =.036.

On the basis of above discussion, the hypothesis: H 2.There is no inuence of demographic variables 0

on economic security of informal sector workers, is rejected.

Conclusion

From the analysis it can be concluded there is an existence of relationship between demographic composition and employment security among informal sector workers. Further, economic security is significantly inuenced by the age, education level, occupation, employment, marital status, religion and caste. However, gender and employment status does not significantly inuence economic security.

F u r t h e r R e s e a r c h a n d P o l i c y Implications

This paper tried to assess the demographic features of informal workers and the level of employment security of informal sector workers at individual level through the subjective and objective indicators of employment security. Future research prospects include assessing the level of employment security among informal sector in India at macro level through behavioural, contractual and governance indicators. The lack of reliable statistics on the size, distribution and economic contribution of the sector has been a major constraint in proving a realistic understanding of the Indian economy, leading to its

neglect in development planning (NCEUS, 2008).

Due to the heterogeneity of informal sector in India, a single policy to solve all the concerned problems of informal sector is not feasible. This paper made an addition in the literature of unorganised employment and also contributes in the policy making by providing data about the employment security of informal workers at individual level which can be used by the concerned authorities to formulate tailored policy according to the various needs of informal sector workers.

References

Banerjee, S. (2016). Aadhaar: Digital Inclusion and Public Services in India. World Development Report.

Chen, M. A. (2005). Rethinking the informal economy: Linkages with the formal economy and the formal regulatory environment. Tokyo: United Nations University, World Institute for Development Economics Research.

Dasgupta, S. (2001). Employment security: conceptual and statistical issues (Vol. 10). International Labour Office Geneva.

Dutta, G. (2012). A Socio Legal Study on the Right to Food in Assam with Special Reference to Kamrup District. Gauhati University.

Hart, K. (1973). Informal income opportunities and urban employment in Ghana. The journal of modern African studies, 11(01), 61-89.

ILO.(2004). Economic security for a better world. International Labour Office, Geneva.

I L O . ( 1 9 9 5 ) . L a b o u r M a r k e t I n d i c a t o r s Questionnaire 1995 (Geneva, EMPFORM, ILO)

ILO.(2002). Conclusions concerning decent work and the informal economy, International Labour Conference, 90th Session, Geneva.

ILO.(2002). Decent work and informal economy (Report No. 6).International Labour Office, Geneva.

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ILO.(2017). Indian Labour Market Update, International Labour Office, Country Office f o r I n d i a . R e t r i e v e d f r o m http://www.ilo.org/wcmsp5/groups/publ i c / - - - a s i a / - - - r o - b a n g k o k / - - - s r o -new_delhi/ documents/ publication/ wcms_496510.pdf

ILO. (2018). Women and men in the informal economy. A statistical picture. International Labour Organization.

Kumar, T.M.V. (2016). Smart Economy in Smart Cities: International Collaborative Research: Ottawa, St.Louis, Stuttgart, Bologna, Cape Town, Nairobi, Dakar, Lagos, New Delhi, Varanasi, Vijayawada, Kozhikode, Hong Kong. Advances in 21st Century Human Settlements. Springer Singapore. Retrieved from https:// books. google. co.in/ books? id= fhDpDAAAQBAJ.

Kantor, P. (1997). Informal Sector: Lifting the Shroud. Economic and Political Weekly, 2512-2515.

Kavitha, T. (2014). Developing Urban Informal Sector A socio Economic Study of Home Based Workers in Hydrabad. Osmania University.

Malik, S., & Khan, T. (2009). Family Size, Composition and Women Work in Informal Sector. Pakistan Journal of Social Sciences (PJSS), 29(2).

Mitra, R. K. (2009). Trash has Crashed: Impact of Financial Crisis on Waste Pickers of Ahmedabad City. UNDP India.

Mohanty, R. K. (2009). Craft Artisans in Urban Informal Sector. Anamika Publishers & D i s t r i b u t o r s . R e t r i e v e d f r o m https://books.google.co.in/books?id=4lWt6REp-rgC

Narayana , N. (2015) . Impact Of Publ ic Distribution System On Food Secuirty And Economic Empowerment Of Weaker Sections In Karnataka – A Case Study Of Mysore District. University of Mysore.

NCEUS. (2008). Report on Definitional and Statistical Issues Relating to Informal Economy, New Delhi, http:// nceus.gov.in/ Report_Statistical_ Issues_ Informal_ Economy.pdf

Raju, R. S. (1989). Urban Unorganised Sector in India. Mittal Publications. Retrieved from https:// books. google. co.in/ books?id= K7fDs9fV3TsC

Robert, S. P. (2010). A Study on the socio economic status of the s treet vendors in the u n o r g a n i s e d / i n f o r m a l s e c t o r a t Tiruchirappalli Town, Tamil Nadu, India. Bharathidasan University.

S a s t r y , N . ( 2 0 0 4 ) . E s t i m a t i n g i n f o r m a l employment & poverty in India. Human Development Resource Centre New Delhi, India.

Shinde, B. S. (2016). Social Changes In The Nandiwale Tribe In Western Maharashtra With Special Reference To Sangli, Satara And Kolhapur District. Shri Jagdish Prasad Jhabarmal Tibrewala University, Rajasthan.

Unni, J., & Rani, U. (2003). Social protection for informal workers in India: Insecurities, instruments and institutional mechanisms. Development and Change, 34(1), 127-161.

Venkatesh, P., & others. (2013). Recent trends in rural employment and wages in India: has the growth benefitted the agricultural labours? Agricultural Economics Research Review, 26(2013).


13

Abstract

Once a leader said that money is not God but not less than God. We have heard in life that many things are dependent on money. Money proves helpful for many of our physical facilities. It gives us the satisfaction to fulfill our needs whether it is the physical needs or psychological needs. The present paper aims to study that whether the economic scale of the family affects the emotional competence. It is a comparative study of the emotional competencies between the youths belonging to poor families and youths belonging to the middle-class families. Emotional competencies are the social skills which are necessary to know, analyze, and responds to emotions in our self and others.It is also the other term used for emotional intelligence. For this study the total of 50 youths of age group 17 to 21 years were taken. 25 youths from poor class family (income of family is below then 1.5lakhs per annum) and 25 youth from middle class family (income of family is above then 3.5 lakh per annum). The emotional competencies were measured by the scale of emotional competencies made by Dr. H.C. Sharma and Dr. R.L.Bhardwaj. This scale measures various competencies that is adequate depth of feeling, adequate expression and control of emotions, ability to function with motions ability to cope with problem emotions, enhancement of positive emotions. The findings shows that there were significant differences found in various competencies between poor class and middle class youth.

Keywords : Economic Scale, Income, Poor Class, Middle Class, Emotional Competencies, Youth.

Introduction

According to NCAER-CMCR 2010 annual income data is classified that below Rs. 1.5 lakh is considered as the deprived population and Rs. 3.4 – 17 lakh is considered as middle class people. So, by this we have also selected our two groups as low and middle class adults.

According to Allport (1961) “to achieve and maintain a feeling of adequacy. The individual has to acquire a few workable assumptions about the world, where need for competence emerges as most of the fundamental motive of life, because we survive through competence and actualize ourselves through competence.”

Does Economic Scale of Family Affects Emotional Competencies? - A Study

Related To Youth

Dr. Rashmi SinghAssistant Professor, Dept. of Psychology

UCSSH, MLSU, Udaipur (Rajasthan)


14

Emotional competence is define as the type of skill which aims us to do the highest quality work. It is a learned skill. It is also known as the ability to recognize and to manage oneself emotion.

According to Coleman (1970), “emotional competencies is an efficiency to deal effectively with several dissociable but related processes is a blending of five competencies.”

a) Adequate depth of feeling (ADF) - the adequate depth of feeling can be defined as the understanding of the reality of the feelings inside of oneself. It gives the sense of confidence and capability in the personality integration.

b) Adequate Expression and control of Emotions (AEC) - Adequate expression and control of emotions is defined as how we control and express our emotions. When there is problem in control and expression of the emotions i t may lead to personality disintegration.

c) Ability to functions with emotions (AFE) - When we are in emotional situations it is very difficult to enroll in routine life.When we are competent emotionally it helps us to carry out work efficiently even when we are in bad emotional conditions.

d) Ability to cope with problem emotions (ACPE) - there are also some situations which leads us to very bad emotional stage which can cause very harmful effect on our life. So emotional competency save us from harmful effects of such types of emotions which play negative role in our life.

e) Enhancement of positive emotions (EPE) – There are many types of positive emotions like love, care etc. which is very useful and a must for our behavior and personality. This type of emotions make life more balanced and emotional competence helps in developing and enhancing this type of emotions.

Review of Literature

Jamadar, Chandrakant & Sindhu A. (2016) investigated the tribal students to see the effect on

the social economic status on the prediction of creativity and emotional intelligence. 100 adolescents were taken from the tribes of Mysorefrom which 50 were boys and 50 were girls of 8th, 9th and 10th class. Various scales were used like socio economic status scale was made by Meenakshi (2004), emotional intelligence made by UpinderDhar (2010), creativity test made by Wallach- Kogan. The results stated that the students belonging to high socio economic status were found to have more emotional intelligence and creativitywhen compared with low socio economic status students. There was no gender difference found in girls and boys in respect to intelligence and creativity.

Alicea J. Davis (2012) examined the effect of emotional intelligence on the maturational process. Gender and adolescents were the main sample population. The participants were of fifth grade students aged 10 to 13 years. The test used was Bar- on emotional quotient inventory. In this study the descriptive type of statistics was used to interpret about the sample population.The girls generally were found to have higher stress management abilities higher as compared to the boys. There was more adaptation found in the higher socio economic status.

Objective

To study and compare the five spectrums of emotional competencies that is

1. Adequate depth of feeling

2. Adequate expression and control of emotions

3. Ability to function with emotions

4. Ability to cope with problem emotions

5. Enhancement of positive emotions

Between the low economic youths and medium economic youths.

Hypotheses

Null hypothesis was preferred, so we can say that there was no significant difference found between the low and medium economic group in respect to the five spectrums of emotional competencies that is



15




5. Enhancement of positive emotions.

Variables

Independent Variable- Economic Class (according to the annual income of the family)

a) Low b) Medium

Dependent Variable- Emotional Competencies

a) Adequate depth of feeling

b) Adequate expression and control of emotions

c) Ability to function with emotions

d) Ability to cope with problem emotions

e) Enhancement of positive emotions

Inclusion Criteria

a) Urban Students

b) Literate Students

c) Age range 19- 25 years

d) Economic class (low and medium)

Exclusion Criteria

a) Illiterate students

b) Students below 19 years

c) High economic class youth

d) Rural Students

Methodology

Sample - In accordance with the aim of the present research, sample of 25 youths of low economic class and 25 youth of medium economic class (age 19 to 25years) were taken from Udaipur city (Raj.)

Tools - For present study the emotional competency scale was used which wasmade by Dr. H.C.Sharma & Dr. R.L.Bhardwaj. It measures the emotional competencies on five dimensions which are





5. Enhancement of positive emotions

Talking about the reliability of the inventory by test retest method it is 0.74 and validity of the inventory is .64 and .69.

Procedure - With the help of purposive random sampling 25 youths from low economic class (Family income below 1.5 lakh per annum) and 25 youths from medium economic class(Family income above 3.5 lakh per annum) were selected respectively. After selecting the students, test was distributed to them and instructions were given regarding the test. Administration and scoring was done as per manual of the scale.

Statistical Analysis - Descriptive statistics that is mean and standard deviation was performed. T-Test was used for the significance.


16


Table 1 portrays the various dimensional scores of emotional competencies of different socio economic groups that is low and medium. The results revealed that low economic individuals were almost incompetent in four dimensions of emotional competence.

The possible reasons are that they have less opportunity to learn to regulate ten emotional and also more stress of fulfilment of basic needs can lead to less competent in managing their emotions. The affected health also can decreased their capacity to handle their emotions.

ADF - The mean score of adequate depth of feeling is 18.28 which is higher than low economic status score that is 9.72.They have more resources and have better schooling so are more aware about the situations happening in their side environment.

AEC - the means scores shows that there is high (22.40) adequate expression and control of emotions in medium class adults as compared to low (10.20) class adults.Lower class adults are unable to control the feelings of themselves. The medium class are more adaptive and know the basics of the behavior to live in the society.

AFE - Medium class adults were found to have more ability to functions with emotions as compared to low class adults. Medium class work

more patiently. There is no fear of unknown situations. They are better in taking decisions in the emotional situations. They don't get affected with the emotions when they are in actions as they have more social responsibility.

ACPE- Both were having negligible mean difference between medium and low class adults. And there was no significant difference found between low and medium economic class adults. It means both the groups manage their situations with proper emotions.

EPE- Enhancement of positive emotions were found to be high in medium economic class adults. Medium class enjoys the every phase of life like marriage, entertainment etc. They don't leave any condition to enjoy and they want to remain happy. But low economic class adults have less resources and they fight daily for the needs for their survival so enhancement of positive emotions is very less. According to the Maslow theory also first we have to overcome the need of basic needs and then we can go up to the hierarchy. So it proves that low economic class adults are mainly focus on their livelihood.

According to Zimbardo there is always very serious situations in low economic class and the child developed in that environment lacks in development of essential adjustive skills and

Dimensions of Emotional Competencies

Groups

N

Mean

Std. Deviation

Std. Error Mean

Mean Diff

t

p

value

ADF Low 25 9.72 3.668 .733 7.546 .000

Medium 25 18.28 4.325 .865

AEC Low 25 10.20 2.915 .583

Medium 25 22.40 5.477 1.095 9.83 .000

AFE Low 25 11.88 2.728 .545

Medium 25 22.64 3.450 .690 12.23 .000

ACPE Low 25 19.04 6.803 1.360

Medium 25 19.24 6.495 1.299 .106 .916

EPE Low 25 14.44 3.629 .725

Medium 25 24.44 3.559 .711

8.56

12.20

10.76

.200

10.00 9.835 .000

Results & DiscussionTable 1 : Statistical Summary of the Observations

Source : Statistical Output

17

because of that they are unable to deal with the new- new situations which arrive in the society and hence shows maladjusted behavior.

Naik, Bharat (2014) examined the inuence of sex and socio economic status on emotional i n t e l l i g e n c e o f c o l l e g e s t u d e n t s . 3 6 0 undergraduates college student were selected by using random sampling method. The sample was collected from various colleges of Kanpur and Sangli city. The findings of the study shows that the gender of college students does not affect significantly on emotional intelligence. Socio economic factor have significant effect on emotional intelligence and it was also found that upper socio economic students are having high emotional intelligence than lower socio economic status and middle Socio economic statusstudents.

Implication

This study can be used for creating the awareness to the society that socio economic class matters a lot in the personality and behavior of the individual. There is a need of an hour for the proper education and knowledge for the poor people so they can know about the schemes and can accomplish their basic needs. Knowledge and education is also important for the maturity development. Proper clean, hygienic and healthy atmosphere and living standards should be provided to them.

Conclusion

This research on the topicof emotional competence has focused on various components that have been shown to be significant indicators of emotional competence. The present study was aimed to see the comparison of Emotional competence between low and medium economic adults. Middle economic adults were found to be more emotionally competent then low economic adults.

References

Davis, Alicea J. (2012) "Examining Gender and Socio-Economic Status on the Emotional Intelligence of Early Adolescents". PCOM Psychology Dissertations. Paper 211

Jamadar, Chandrakanth. (2016). The Impact of Socio Economic Status on Emotional Intelligence and Creativity among Tribal Adolescent Students. The International Journal of Indian Psychology,Volume 3, Issue 1, pp 112-125.

Naik, Bharat(2014). Sex and socioeconomic status inuence on emotional intelligence among college students, Indian Journal of Health & Wellbeing, Vol. 5 Issue 7, pp111-113

Sharma, H.C. & Bharadwaj, R.L.Manual for the s c a l e o f E m o t i o n a l C o m p e t e n c i e s . PankajMapan, Agra.


18

Abstract

As we have a common saying that man is a social animal and he always needs a society, company or social interaction to live a happy life. And this interaction directly or indirectly affect the behaviour of individual. Social intelligence is the capacity to know oneself and others and it is developed from the experiences in the social settings and which can help in preventing various disorders such as sleeping disorder, eating disorder, anxiety, depression, stress, personality and etc. There are some factors which inuence social intelligence such as traumatic situation like sexual abusing, social pressure, socioeconomic status, education status, environment, relationship with others, personality and so on. Binge eating disorder is the compulsive overeating or consuming lot amounts of food without having any control on self. Binge eating disorder can be affected by some components which are genetics, psychological and social and cultural like stressful life events, phobia, depression, negative feeling, bullying due to weight, availability of food, boredom, low self-esteem, feeling of inadequacy and loneliness. The purpose of the research is to find out that is social intelligence have any effect on binge eating disorder. The total sample of 120 was collected and it is parted between high social intelligence and low social intelligence with the help of extreme group analysis. 60 males and females were also taken as a sample. 2X2 factorial design was made for this study. Purposive sampling was used in the research and age group was 18-25 adult. In this research independent variable is social intelligence (High and low) and gender (male and female). Binge eating disorder was taken as dependent variable. Two questionnaires have been taken that are Social Intelligence Scale (1986) by N.K Chadha & Usha Ganesan and Binge Eating Scale (1982) by J Gormally et.al. The statistical analysis of data has been done by mean, standard deviation (SD), standard error of mean (SEM) and ANOVA for result. The research has revealed there is significant effect of social intelligence and gender on binge eating disorder.

Keywords : Social Intelligence, Binge Eating Disorder, Happy Life, Behaviour, Social Setting.

Social Intelligence Effect on Binge Eating Disorder

Upasna Singh

Research Scholar

Mohanlal Sukhadia University, Udaipur (Rajasthan)


Dr. Rashmi Singh

Assistant Professor, Mohanlal Sukhadia University, Udaipur (Rajasthan)

19

Introduction

An individual is encircled by people whether they are family member, neighbour, friends and colleagues. An individual needs other people for living happy life as it directly or indirectly is connected with the fulfilment of various psychological needs. A person is surrounded by group of people whether he wants to be a part of that group or not. There is social interaction among people directly or indirectly. Usually social interaction takes place in society. A society setting might affect emotion, behaviour and mood. Social interaction and society behaviourplays a significant role in individual's social intelligence, personality, behaviour, thoughts, emotion and every aspect of the individual.

Social intelligence is the ability to understand oneself and others. This intelligence is learned. Social intelligence is obtained by experience in s o c i a l s i t u a t i o n l i k e s o c i a l a w a r e n e s s , responsibility, success and failure, experiencing emotion, working with others, decision making and etc.

The original terminology and definition was given by Edward Thorndike and it was “the ability to understand and manage men and women and boys and girls, to act wisely in human relations.” (Wikipedia). Psychologists, Nicholas Humphrey believes that it is social intelligence that defines who we are as humans. It mainly develops with the experiences through other people and also from failures and success in social settings. People with high social intelligence have very good social expressiveness skills. They can easily adapt at learning how to play different social roles, they are excellent listeners and they efficiently analyse what makes people tick by paying attention to what they are saying and how they are behaving.

Binge eating disorder (BED) is a basic eating disorder. It is generally called as compulsive overeating or consuming abnormal amounts of food. In this individual are unable to control self by eating and a loss of control and feels distressed during or after eating. It takes place approx. minimum twice per week for six months duration.

Binge eating disorder was explained first in1959 by Albert Stunkard, a psychiatrist and researcher and termed as a night eating syndrome. In BED

person can gain normal weight often it leads to obesity. The negative feeling enforces to continue abnormal eating. It can occur in men and women and in research it is found that they struggle with emotions of disgust and guilt and often have a related co- morbidity, such as depression or anxiety.

Review of Literature

Raj Gnainaiah (2019) investigated differences between obese individuals with a binge eating disorder (BED-O) and obese individuals without a binge eating disorder (Non-BOD-O). the research focussed on various variables that are global emotional intelligence traits & dimensions, the engagement in overeating behaviour i .e. emotional, external and restrained eating, and the engagement in different coping styles. The mass sample was 109 individuals who were taken from a diabetic clinic in Wales. Result revealed that BED-O and Non-BED-O participants did not differ on global emotional intelligence scores, although there were some differences on certain constructs and dimensions of emotional intelligence.

Una Foye (2019) studied the role of emotional intelligence on disorder eating psychopathology. There were 355 participants who completed the Schutte self-report emotional intelligence test and the eating attitude test. The design used was cross-sectional. The results found that individuals with high emotional intelligence had significantly lower disordered eating attitudes while those with low emotional intelligence had significantly higher levels of disordered eating attitudes.

Yongzhan Li (2018) examined the relationship between emotional intelligence, social anxiety and eating disorder risk among adolescents in China. The sample was of 784 high school students. The questionnaire used was Body mass index, Eating attitude test, Wong & law emotional intelligence scale and Liebowitz social anxiety scale. Descriptive statistics, analysis of variance & hierarchical regression have been used. Research resulted that adolescent's emotional intelligence, their social anxiety and eating disorder risk, gender, grade & body size all had main effect on social anxiety & eating disorder risk. There were interaction effects both between gender & grade, and between gender and body size.


20


Krisna Patel et al. (2016) explored the social functioning of 17 inpatients aged 12-17. An opportunity samplingwas used and qualitative research was done. Data was analysed using thematic analysis. A six variables were identified that are group belonging, self-monitoring, social sensitivity, impact of hospitalization, limited coping strategies, strategies for service provision. The result found that adolescents with eating disorders reported social difficulties. They face social difficulties to persist over and above in typical setting.

LeehuZysberg & Einav (2013) studied two hypothesis firstly was, emotional intelligence will positively associate with perceived control and secondly, perceived control will associate positively with anorexia & negatively with bulimia symptomatology. 130 sample was taken and questionnaire were made filled up online by sample. Sample size was taken from Israel. Eating disorder inventory II (Gardener, 1991), Perceived control scale (Hobfoll&Walfish, 1984), the audio-visual test of emotional intelligence (Zysberge et al., 2011), The Schutte emotional intelligence scale (Schutte et al., 2011) were used. The result partial supported emotional intelligence showed a nonlinear association with control, nonlinear association with anorexia scores, positive association with bulimia scores.

Emily B. Ansell et al. (2011) examined the interpersonal model of binge eating which posits that interpersonal problems lead to negative affect which turn into disordered eating. The model has been tested by taking sample of 350 women & assessment was done through internet . Interpersonal problems showed significant effects o n b i n g e e a t i n g a n d e a t i n g d i s o r d e r psychopathology that were statistically mediated by depressive or negative affect& affiliation hadsignificant effects on binge eating and eating disorder psychopathology.

Michele L. Pettit et al. (2010) examined the relationships between perceived emotional intelligence factors and eating disorder symptoms among college students taken as male & female. Sample size was 418. Online survey was completed by sample and Trait meta-mood scale &eating attitude test were taken. Result revealed that gender differences in eating disorder

symptoms and indicate that low levels of perceived emotional intelligence (clarity & repair) are connected with greater risks for bulimia or food preoccupation.

Striegel Moore et al. (2009) studied gender differences in prevalence of eating disorder incorporate body image concerns, binge eating, & inappropriate compensatory behaviours. A random sample of members ages 18-35 years were taken to complete a survey by mai l or online.Patient Health Questionnaire and The Body Shape Questionnaire were used in study. 3,714 women and 1,808 men haveanswered&men were reported overeatingwhereas women supported loss of control during eating. Although, statistically observed that women significantly more likely then men to report body checking and avoidance, binge eating, fasting and vomiting.

Charlene Boyd (2006) resulted that there is a negative correlation between eating disorders and emotional coping mechanisms, a factor of emotional intelligence. 157 samples were taken from college and all participants were female. This study was designed to discover if women who have a history of eating disorders resemble women who report current or past history of eating disorders & women with current eating problems. Result was found that women had a history of eating problems were similar in control group on most calculate.

Methodology

Objectives

1) To study the effect of Social intelligence on Binge eating.

2) To study the effect of gender on Binge eating.

Hypotheses

1) There is no effect of Social Intelligence on Binge eating.

2) There is no effect of gender on Binge eating.

Sample

The total sample of 120 was collected and it is parted between high social intelligence and low social intelligence with the extreme group

21


analysis. And also gender was taken in consideration. Purposive sampling was used in the research and age group was 18-25 adult. Two questionnaires have been taken that are Social Intelligence Scale (1986) by N.K Chadha & Usha Ganesan and Binge Eating Scale (1982) by Gormally et al.

Design

Variables

Independent Variables

1) Social intelligence

- High

- Low

2) Gender

- Male

- Female

Dependent Variable

1) Binge eating disorder

Exclusion Criteria

1) Rural sector

2) Children and old age

Inclusion Criteria

1) 18 to 25 years youth

2) Males and females

3) Urban sector

Groups High Social

Intelligence

Low Social

Intelligence

Total

Male 30 30 60

Female 30 30 60

Total 60 60 120

Statistical Analysis

To analysis data which was obtained using questionnaire for that statistical analysis was used which are mean, standard deviation (SD), standard error of mean (SEM) and t test.

Result and Discussion

Table 2 : Mean, SD, SEM & T-test of Binge eating disorder among Social Intelligence

Group (High & Low)

Group Mean SD SEM T test

High Social

Intelligence

35.4 5.87 0.75 9.62

Low Social

Intelligence 10.18 4.62 0.59

Source : Field Data (Sig. at 0.05 level)

Table 2, shows the Mean, SD, SEM and T-test of binge eating behaviour among high social intelligence and low social intelligence. There was a significant difference found between high and low social intelligence in reference to binge eating disorder. The people who is having high social intelligence are more social interactive. They love to be around people so, they are hardly spent time alone. In low social intelligence people, they don't like to be with people. They feel conscious when some people are around them. In that case they are alone and engage themselves in food eating and it becomes their daily routine. They get used to have food frequently and this habit leads to Binge Eating Disorder. High social intelligence people concern about society and relationship and they maintain their relationship but it doesn't care about relationship weather they are family member or peer. Owing to this reason, they love to hang out with others. Low social intelligent people don't have patience so they are unable to cope up in the social setting. Binge eating is also associated with the unpleasant feelings and emotions. Low social intelligence has low tactfulness and recognition of social environment. This behaviour is not acceptable by others. People avoid that kind of people which leads to the frustration among individual. So, they indulge themselves to eat more and eat fast. Low social intelligence people

Table 1 : 2x2 Research Design

22


are not having ability to understand social environment so theyfaces negative situation. Sometimes, that situation is hard to tackle. To come out from that situation,and to revive resilience an individual eats large amount of food without having any sense that it can affect negative to his body.

Table 3 : Mean, SD, SEM and T-test of Binge eating disorder among Gender (Male & Female)

intelligent people have more binge eating behaviour.

Females were found to be engaged in more binge eating behaviour as compared to males.

References

Albrech, K. (2014). Social Intelligence Theory. Retrieved from http:// www. karlalbrecht. com/ siprofiletheory. htm

Ansell, E.B. (2011). Examining the interpersonal m o d e l o f b i n g e e a t i n g i n w o m e n . I n t e r n a t i o n a l J o u r n a l o f E a t i n g D i s o r d e r s , 4 5 ( 1 ) . R e t r i e v e d f r o m http://doi.org/10.1002/eat.20897

Boyd, C. (2006) . Coping and Emotional Intelligence in Women with a History of Eating Disorder Behaviour. Mc Nair Scholars,4(1). Retrieved from http:// scholarworks. gvsu. edu/ mcnair/ vol10/ iss1/2

Binge Eating Disorder: Causes, Symptoms, Sign & Treatment help. (2019, June 4). Retrieved from http://www. eatingdisorderhope. com/information/binge-eating-disorder

Foye, U. (2019). Exploring the role of emotional i n t e l l i g e n c e o n d i s o r d e r e a t i n g psychopathology. Eating and Weight D i s o r d e r s , 2 4 ( 2 ) , 2 9 9 - 3 0 6 . D o i : 10.1007/s40519-018-0629-4

Gnainaiah, R. (2019). Emotional intelligence in binge eating disorder among the obese population (Ph.D thesis, University of W o l v e r h a m p t o n ) . R e t r i e v e d f r o m ht tp ://wlv. openresposi tory . com/ bitstream/ handle/ 2436/ 622199/ Gnaniah-P h D % 2 0 T h e s i s . p d f ? s e q u e n c e = 1&isAllowed=y

Li, Y. (2018). Social Anxiety and Eating Disorder Risk Among Chinese Adolescents: The Role of Emotional Intelligence. School Mental Health,10(3), 264-274.

Mandl, E. (2019, December 3). Binge Eating Disorder: Symptoms, Causes, and Asking for H e l p [ H e a l t h l i n e ] . R e t r i e v e d

Group

Mean

SD

SEM

T-test

Male

22.58

13.27

1.71

0.43

Female 23 14.24 1.71

Significant at 0.05 level

Table 3 Shows that there is significant difference in gender that is male and female in reference to binge eating whichdeclares that our second hypothesis has rejected. So it can be said that gender affects the binge eating behaviour. A descriptive analysis has revealed that female have high binge eating disorder than male. There is quite small differences among males and females but it occurs. Some females are involved in their household work, and by this they spend time at home and they have no regularities or we can say schedule of the time of eatingand when they get time they mostly binge eat. When a critical condition or a situation which is not bearable mostly males don't reveal their emotion & reaction but as femalesare found to be more emotional they react by eating and some of the common reason is to neglect that situation is favourite food is taken by them. Females reported that they often feel unable to control that what they are having and how they are having. Some of females think that food should not be wasted and if it's not consumed by other family members it's better to eat by herself. It starts binge eating behaviour. Here prevalent reason of binge eating disorder are to avoid checking body weight and lack of awareness about eating disorders.

Conclusion

High Social intelligent people were found to have less binge eating behaviour and low social

Source : Field Data

23


fromhttp://healthline.com/nutrition/binge- eating - disorder

Moore, S. et al. (2009). Gender Differences in the Prevalence of Eating Disorder Symptoms. I n t e r n a t i o n a l J o u r n a l o f E a t i n g Disorder,42(5), 471-474.

Patel, K. et al. (2016). An Exploration of Social Functioning in Young People with Eating Disorders: A Qualitative Study. PLos One,11(7). Doi: 10.1371/ journal. pone. 0159910

Pettit, M.L. (2010). An Assessment of Perceived

Emotional Intelligence and Eating Attitudes among Students. Am J Health Educ,41(1), 46-52.

Social Intelligence. (n.d.). In Wikipedia. Retrieved December15,2019 from http://en.m. wikipedia.org/wiki/Social_intelligence

Thorndike, E.L. (1920). Intelligence and its use. Harper's Magazine, 140, 227- 235.

Zysberg, L. & Tell , E. (2013). Emotional Intelligence, Perceived Control, and Eating D i s o r d e r s . S a g e J o u r n a l , 1 - 7 . D o i : 10.11.77/2158244013500285

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Abstract

The retail industry in India is experiencing huge changes and improvements in the last few years. The organized retail industry in India is observing a handsome growth at the approximate rate of greater than 20% percent every year and would also observe significant increase or multiple fold increase in the size and business turnover by 2021. In this growth percentage online retailing is fuelling the sector through its sales and handsome discounts over the products which pull the customers to shop with them and the same is followed by shopping malls as well. Retailing industry is crawling towards turning into the one of the biggest revenue industry incorporating employment opportunities as well. In such case working or studying the consumer loyalty or satisfaction is a complex marvel and studying the practices which lead into improving the customer satisfaction is phenomenal. The purchase of any merchandise from any retail point incorporates various factors that could inuence purchasers' decision for selection of the product. Assessing consumer satisfaction is increasingly intricate and much more significant for retailers in now days because retaining the customer is far good rather creating new one. Research objectives of this work were to examine the impacts of sales schemes and some other dimensions impact on consumer satisfaction in shopping malls of Udaipur and to examine the factors across different demographic classes of respondents. For the research work 150 active shoppers from different shopping centres or shopping malls were chosen for study. The sales promotion schemes and other issues impact of consumer satisfaction were identified through a structure survey. The examination will help the administrators of shopping malls to comprehend the fundamental factors that lead into client satisfaction in the shopping malls and help them to create their showcasing systems. Profiling clients by their selection criteria and factors that inuence them to shop give more important approaches to recognize and comprehend different client segments and to focus with progressively engaged techniques. The study concludes that infrastructure, product and their assortments, leisure activities, space and ambience, schemes and promotions are major driving factors for the customer satisfaction and directly associated with the retention of the customer means lead into ensuring the customer revisit to the shopping mall.

Keywords: Consumer Satisfaction, Shopping Malls, Demographics, Organized Retail

Factors Drive Satisfaction in Shopping Malls – A Study on Customers' of

Udaipur (Rajasthan)


Nikhil MenariaResearch Scholar, Faculty of Management, PAHER University, Udaipur (Rajasthan)

Dr. Anurag MehtaPacic Institute of Business Studies, PAHER University, Udaipur (Rajasthan)

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Introduction

The shopping, fundamentally purchasing is one of the non-removable or non-replaceable practice of buyers' daily routine and it is constantly evolving as several new methods of enriching the customer experience at shopping places is followed by retailers, so making the examination and comprehension of this field is significant so that more lovely and comfortable shopping experience and higher clients' satisfaction can be attained by the retailers. The statistical examination of customer behaviour for the shopping and shopping experience is not new but still an area of interest as shopping platforms and process followed by the retailers has been evolved year by year. Shopping malls or houses are not only a place for shopping, clients visit shopping malls not exclusively to make the purchase of some essential or monthly groceries, however they likewise see these shopping malls visits as an amusement movement that gives them fun and joy, and also help them to spend leisure time (Kim, Lee and Kim, 2011). Shopping mall clients or visitors in general took part in different exercises when they visit shopping malls, clients in general buy items and spend some time in shopping malls, which incorporates a mix of shopping and culture, so instead setting off shopping malls for the sole place for buying products would not be a right practice, and more focus should be on making a place to live the way of life at shopping places would be right approach as indicated by Kim et al (2015). In continuation Hunneman et al. (2017) added that buyers in general evaluate store qualities which principally rely on sort of shopping purpose.

A oor with wide product range of all the daily essentials under single roof is shopping mall, is one the most widely accepted authentic commercial marketplace by every class of the society and fundamental reason is one stop shopping and more close experience with products. The shopping mall actually a place with different retail points, services, multiplex, and parking lot, amusement activities which is imagined, developed, and maintained by different management firms as a unit. The ordinary meaning of retail and organized retailing can be depicted as the demonstration of selling of

products and different product from a fixed area. A significant part of the current economic situation in India is the rise of organized and online retail. There has been significant development in organized retailing business especially after 100% FDI in retailing and it is ready for a lot quicker development in coming future with the online retail stores. Major market players and big industries have entered in this business sector and have dec lared extremely future dr iven development plans.

Consumer satisfaction and loyalty, a term as often as possibly examinable because it changes time by time according to different market scenarios, as primarily customer satisfaction and loyalty is associated with price and quality, later on it relies on customer oriented services, for some dimension it depends on customer brand understating and other infrastructural variables. For successive growth and competence with the evolving market retail organizations need to sustain their existing clients as well as should also focus on potential clients. Statistically examining customer satisfaction gives an authoritative indication to know the effective the relationship between customer and variables of organizations incorporating product and service dimensions to the commercial retail point i.e shopping mall. Consumer satisfaction is to be estimated at individual level, yet it is quite often detailed at aggregate level. It tends to be, and frequently estimated along different measurements. The standard proportions of consumer satisfaction include a study with a lot of explanations utilizing a Likert scaling. The frequent shoppers of Udaipur at different shopping malls were approached to assess their satisfaction level for different statements representing shopping malls product and service dimensions and in term of their recognition and desire for execution of the association being estimated. Their satisfaction for different dimension is commonly estimated on a five-point scale.

So, distinctively this research work is a systematic effort to answerer has following research objectives based on research questions stated below through the statistical examination of well produced data set encoded from the responses given by the sampled customers of shopping malls of Udaipur:


26

Research Question 1: Which dimensions of shopping malls ensures customer satisfaction.

Research Question 2: Does demographic variables inuences customer satisfaction at shopping malls.

1. S h o p p i n g m a l l s d i m e n s i o n s l i k e infrastructure, product assortments and categories, leisure activities, space and ambience, schemes and promotions, and other dimensions has direct relationship with the customer and each dimension has its overall impact on customer satisfaction and

2. Demographic characteristics of customers such as age, marital status, locality, occupation has their effect on customer satisfaction in shopping mall or not.

Previous Studies

Substantial amount of research work in the field of customer satisfaction for shopping malls had already been performed but the geographical scope of few studies was Udaipur and Southern Rajasthan and different dimensions have been covered. So, in contrast with the geographical and operational scope of present research work some significant contributions were identified. Some of the research studies performed in the period of 2015-2020 is presented below:

Adholiya et al. (2019) concluded that dimensions incorporating tangibility and intangibility, and competitive advantages offered by the shopping malls to customers drive customer satisfaction very significantly and also ensure customer revisit. It was recommended by the authors that mall should incessantly improve their services and product varieties to get higher satisfaction and loyalty of customers. Makgopa (2018) discovered that demographic characteristics of customers such as their gender class and age group have no impact on customers' loyalty. Author also confirmed that female shoppers take more pleasure in shopping in comparison to the male shoppers and also carry specific intentions for revisiting the same shopping mall.

Hunneman et al. (2017) in their research work concluded that customer with modern shopping practices not only access the products available in

the sore but also access special store characteristics and specialties in order to get higher level of leisure and shopping satisfaction in different way and consider the shopping as a tr ip or entertainment exercise to the shopping mall. Study confirmed the importance of customer satisfaction for the shopping mall economic health and sustainability and same is noted for the customer loyalty as well.

Anselmsson (2016) in recorded if shopping mall administration want to ensure the customers' continuous and frequent visits in the shopping mall then shopping mall administrators has to offer more customer oriented specific service packages to customers and should also focus on all the identified and potential dimensions of customer satisfaction pertained to shopping mall. Study recommended that in order to get the competitive benefits shopping malls should follow and offer competitive services to their customers, that can lead into higher customer satisfaction and to draw advantages of customer loyalty (frequent visits) loyalty benefits should be offered to customers.

It was noticed by Kim et al. (2015) that shopping malls is not a place of shopping only in present days as customers prefer to spend some time for fun and joy and this is becoming the regular practice of each customer who frequently visits shopping malls. Primary purpose of visiting shopping malls by the customers is shopping but spending some time in other activities such as in fun zone, multiplex comes under leisure activities and this is becoming the shopping culture.

Methodology Followed

The geographical area of the study was Udaipur (Rajasthan), which is famous for its lakes and heritages. The customers of the rural, urban and semi-urban Udaipur were chosen for the study purpose. Customers or frequency shoppers of following shopping malls were chosen:

1. Reliance Hyper Mart – Sector 11

2. Vishal Mega Mart

3. Big Bazar

4. V-Mart


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5. Forum Celebration Mall

6. Lake City Mall

7. RK Mall

8. Arvana

9. Mangalam Fun Square

10. Urban Square So, the geographical scope of the present research work was limited for the customers of shopping malls of Udaipur (Rajasthan). Another aspect focusing over the operational variables which participated in the research work were demographic characteristics (age, gender, and occupation) of the customer respondents and product and service dimensions of the shopping mall. Majorly study was intended to determine the effect relationship between the demographic variables of customer and their satisfaction for shopping malls and other objective covering the effect of product and service dimensions effect on customers' satisfaction for shopping malls.

In order to assess the relationship between the variables or effect association between variables primary data was procured from the customers or frequent shoppers of shopping malls through a structured set questionnaire. While distribution of the questionnaire it was under consideration than shopper should be from Udaipur only and should aware of shopping mall products and service dimensions in well form. The nature of the research work is descriptive by nature as research is an effort to describe the relationship between the demographic variables of shopper and product and service dimension of shopping mall. On the basis of aforementioned research question following research objectives and hypotheses would be analytically assessed.

Research Objective 1 : To study the effect of demographic variables on shopping malls' customers' satisfaction.

Research Objective 2 : To study the effect of product and service dimensions of shopping malls on the customers' satisfaction.

H : Age as a demographic variable does not affect 0

shopping malls' customers' satisfaction.

H : Age as a demographic variable significantly a

affects shopping malls' customers' satisfaction.

H : Marital status as a demographic variable does 0

not affect shopping malls' customers' satisfaction.

H : Marital status as a demographic variable a

significantly affects shopping malls' customers' satisfaction.

H : Locality as a demographic variable does not 0

affect shopping malls' customers' satisfaction.

H : Local i ty as a demographic variable a


H : Occupation as a demographic variable does 0

not affect shopping malls' customers' satisfaction.

H : Occupation as a demographic variable a


H : Product and service dimensions of shopping 0

malls' do not inuence customers' satisfaction.

H : Product and service dimensions of shopping a

malls ' s ignificantly inuence customers' satisfaction.

Research Framework

Research framework is the graphical presentation of relationship between the variables participating in the research. Figure 1 presented below depicted association between the demographic variables of customers, product and service dimension of shopping malls, and customer satisfaction for shopping experience at shopping mall.


28


Figure 1: Research Framework – Customer Satisfaction for Shopping Mall

Customer Satisfaction & Loyalty

Age

Occupation

Locality

Marital Status

Product Price and Assortments

Infrastructural and Location Services

Comfort / Leisure and Fun Services

Product Quality and Availability

In order to approach the customers of shopping mall strata and convenience random sampling method is followed. Strata was based on two criteria, one was customers should be from Udaipur and second one was customers should be frequent shopper of the shopping mall. It was also under consideration that customer from all the demographic backgrounds should be covered. In total more than 270 customers were approached of different shopping malls and 130 duly filled questionnaires were collected and used for further statistical analysis. For statistical analysis IBM SPSS 21.0 was used.

Table 1 : Mall wise Number of Respondents

Mall Name N S Mall Name N S

Reliance Hyper Mart 33 16 Lake City Mall 34 19

Vishal Mega Mart 21 7 RK Mall 10 5

Big Bazar 40 15 Arvana 20 15

V-Mart 40 14 Mangalam Fun Square 10 5

Forum Celebration Mall 69 32 Urban Square 9 2

Source: Primary Data

Source : Author's Research Figure

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Above table describes the mall wise customers incorporated in the research work as the respondents and their feedbacks were further used for the statistical analysis to make conclusion of research objectives.

Statistical Analysis and Interpretation

Reliability Analysis : Reliability test is as a measurement of data quality and un-biasness lead into presenting statistical value of overall data quality. As for Indian researches a standard α value of 0.70 is recommend to confirm the data quality. Table 3 of Cronbach alpha statistical value is presented below for the dataset procured from the sampled customer respondents.


Table 2 : Reliability Statistics

Shopping Mall Dimensions α Statistics

Product Dimensions 0.789

Service Dimensions 0.755

Infrastructural and Leisure Dimension 0.726

Customer Satisfaction Dimension 0.779

Source: Statistical Output

The reliability statistics α value of Cronbach test is presented in above table showed that for all the dimension alpha values were ranged in between 0.726 to 0.789 and all are greater than the standard alpha value that is 0.70. This lead into making a conclusion that goodness and reliability of the dataset generated from the responses given by the sampled customer respondents of Udaipur.

Table 3 : Frequency Distribution – Demographic Variables of Customers

Shopping Mall Dimensions

N %

Age

18-30 Years 26 20.00%

30-45 Years 53 40.77%

45-60 years 41 31.54%

60+ Years 10 7.69%

Occupation

Salaried 62 47.69%

Business Class 37 28.46%

Unemployed 15 11.54%

Free Lancer Self Employed 16 12.31%

Locality

Rural 30 23.08%

Urban 64 49.23%

Semi-Urban 36 27.69%

Marital Status Married 32 24.62%

Unmarried 98 75.38%


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From the frequency distribution table it was indentified that majority of customers were from 30-45 years (53, 40.77%) and 45-60 years (41, 31.54%) age group. This signifies that customers from these age groups majorly visit shopping malls. Among different occupation classes it was noticed that most frequent visits to the shopping malls was done by salaried class people (62, 47.69%). A good mix of customers from different localities of Udaipur that are Urban (64, 49.23%), Semi-urban (36, 27.69%), and Rural (30, 23.08%) was noticed. About the marital status of customer respondents it was noticed that among all the sampled customers most of customers were married (98, 75.38%).

Anova (F – Test) - To study the effect of demographic variables on shopping malls' customers' satisfaction – To compare the effect of between the groups and within the groups ANOVA test was used to know the effect of demographic variables on customers' satisfaction at shopping malls. The results of the test are given below:

variables' significance values were found lesser to 0.05. So, it could conclude from the statistics that except locality all the other demographic variables (age, occupation and marital status) significantly inuence satisfactions of customers in shopping malls. The hypotheses established to know about the effect of participated demographic variables on customer satisfaction concluded with following result:


Table 4: F-Test Statistics of Demographic

Variable Effect on Satisfaction

DemographicVariables

Effecton

F Sig.

Age

CustomerSatisfaction

4.365 0.038

Occupation 5.777 0.029

Locality 2.926 0.056

Marital Status 4.792 0.033


From the above F-stat ist ics of ef fect of demographic variables (age, locality, occupation, and marital status) on shopping malls' customers' satisfaction it was noticed that except locality (rural, urban and semi-urban) F test value was 2.926 and its significance value 0.056 was found greater than 0.05. For all the other demographic variables like age (F value = 4.365, Sig. = 0.038), occupation (F value = 5.777, Sig. = 0.029), and marital status (F value = 4.792, Sig. = 0.033) all the

Principal Component Analysis – Factor Extraction

Table 6 : PCA Test Statistics – Item wise Rotated Factor Loadings

Table 5 : Hypotheses Conclusions

Hypotheses F and Sig. Values

Age as a demographic variable signicantly affects shopping malls’ customers’ satisfaction.

4.365, 0.038

Marital status as a demographic variable signicantly affects shopping malls’ customers’ satisfaction.

4.792, 0.033

Locality as a demographic variable does not affect shopping malls’ customers’ satisfaction.

2.926, 0.056

Occupation as a demographic variable signicantly affects shopping malls’ customers’ satisfaction

5.777, 0.029

Source: Table 4

Rotated Factor Loadings

Items 1 2 3 4

PPA1 .874

PPA2 .656

PPA3 .812

PPA4 .702

PQA1 .744

PQA2 .726

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Source : Primary Data, PCA test – Varimax Rotation (KMO Value = 0.789)

From the statistics given above related to twelve participated items in principal components analysis for factor loading following factors or components were found in aggregation:

1. Factor / Component 1: Product Price and Assortments

2. Factor / Component 2: Product Quality and Availability

3. Factor / Component 3: Infrastructural and Location Services

4. Factor / Component 4 : Comfort/ Leisure and Fun Services

Availability, Infrastructural and Location Services, Comfort/ Leisure and Fun Services) customer satisfaction is significantly associated. So, alternative hypothesis is accepted and following conclusion is drawn:

Source: Primary Data


PQA3 .766

ILS1 .633

ILS2 .802

ILS3 .661

CLFS1 .577

CLFS1 .616

Factor / Component 1: Product Price and Assortments incorporates all the statements related to products available at shopping malls such as their prices, categories and assortments, arrangements, displays and other dimensions. Factor / Component 2: Product Quality and

Table 7 : Regression model for Satisfaction

Factors

Satisfaction

Coefcient Sig. Level

Product Price and Assortments

General

0.266

<0.01

Product Quality and Availability General 0.198 <0.01

Infrastructural and Location Services General 0.203 <0.01

Comfort/ Leisure and Fun Services

General

0.179

<0.01

Availability incorporated remaining product characteristics such as product quality, quality assurance, availability, specificity etc. Factor / Component 3: Infrastructural and Location S e r v i c e s i n c o r p o r a t e d s h o p p i n g m a l l s ' infrastructural capacities and facilities like green room, escalator, changing room, displays, parking, and others. Factor / Component 4: Comfort/ Leisure and Fun Services incorporated all the fun rooms and leisure activity points available in the mall.

From the regression coefficients values in correspondence to the general or overall satisfaction of customers' of shopping malls it was noted that the relationship between Product Price and Assortments and satisfaction (0.266, <0.01), Product Quality and Availability and satisfaction (0.198, <0.01), Infrastructural and Location Services and satisfaction (0.203, <0.01), and Comfort/ Leisure and Fun Services and satisfaction (0.179, <0.01) is found significant. So, for the established hypothesis to know about the Product and service dimensions effect on shopping malls' customers' satisfaction is was noted that for all the dimensions (Product Price and Assor tments , Product Qual i ty and

32

Table 8 : Hypotheses Conclusions

Conclusion

The statistical analyses of the research work lead into conclude that customer satisfaction in shopping mall is directly inuenced through their demographic variables such as their age, occupation and marital status. It was also identified that satisfaction of customer does not depend on their locality as satisfaction is directly associated with other dimensions. It was noticed that Udaipur customer satisfaction is directly and significantly associated with the Product Price and Assortments, Product Quality and Availability, Infrastructural and Location Services, Comfort/ Leisure and Fun Services dimensions of shopping mall. So, it is recommended that shopping mall should focus on aforementioned product and service dimension to ensure their customers' satisfaction as it lead into customer revisit and growth of business of shopping malls.

References

Adholiya, A., Adholiya, S. and Chouhan, V. (2019). Shopping Malls' Attributes and Benefitting Characteristics: Determinants of Customer Visit and Satisfaction. Adalya. Vol. 8. pp.655-661.

Anselmsson, J. (2006). “Sources of Customer Satisfaction with Shopping Malls: A Comparative Study of Different Customer Segments,” International Review of Retail, Distribution and Consumer Research, Vol. 16, No. 1, pp. 115 – 138.

Hunneman, A., Verhoef, P.C. and Sloot, L.M. (2017). The Moderating Role of Shopping Trip Type in Store Satisfaction Formation. Journal of Business Research, Vol. 78, pp. 133-142.

Kim, J.W., Lee, F. and Suh, Y.G. (2015). “Satisfaction and Loyalty from Shopping Mall Experience and Brand Personality,” Services Marketing Quarterly, Vol. 36, No. 1, pp. 62-76.

Kim, Y.H., Lee, M.Y. and Kim, Y.K. (2011). “A New Shopper Typology: Utilitarian and Hedonic Perspectives,” Journal of Global Academy of Marketing, Vol. 21, No. 2, pp. 102-113.

Makgopa S.S. (2018). “Determining shopping malls customers' satisfaction and loyalty,” Journal of Business and Retail Management Research (JBRMR), Vol. 13, No. 1, pp. 121-130.


Hypotheses Status

Product and service dimensions of shopping malls’ signicantly inuence customers’ satisfaction.

Accepted

Source : Table 7

33

Abstract

India being a developing market for Smartphone users, the focus of M-banking and M-commerce is likely to gain further momentum in the coming years. With the Union Government redrafting the telecom policy in India, broadband through wireless media like mobiles & tablets will have a whirlwind effect on the use of M-banking, m-transaction & M-commerce in India. The revenue is expected to jump from US$ 39 billion in 2017 to US$ 120 billion in 2020, growing at an annual rate of 51 per cent, the highest in the world based on a forecast by India-e-Commerce. In today's age of technology, people prefer to have everything on their finger tips and mobile phone is one solution to that. Internet penetration in the hinterland has led to a host of new developments, with growing access to technology such as broadband and 4G telecom standards , smart phones/ tablets and dongles along with the acceptance of the idea of e shopping which is set to drive future B2C and B2B transactions eco-system.

This paper empirically examines the factors that affect the adoption of mobile banking/m-commerce/m-commerce by rural businessmen of South Rajasthan (Udaipur, Sirohi, Dungarpur and Banswara). This study aims to test five hypotheses on factors that persuade the M-cash adoption with the help of data collected from a sample size of 200 retailers in The factors selected were Security concerns, Comparative advantage, Ease of Use, Rural user's Adaptability, Cost associated to assess their impact on mobile banking/m-commerce adoption for business. The study underlines the importance for rural people in South Rajasthan to adapt for mobile banking/m-commerce.

Keywords: Rural Area, Mobile banking, M-commerce, South Rajasthan

An Empirical Study on M-commerce Adoption By Rural Businessmen in

South Rajasthan


Dr. Khushbu Agarwal

Assistant Professor, Pacic Business School, Udaipur, Rajasthan

Introduction

In today's wired and wireless business scenario, recent studies in Rajasthan on electronic commerce (digital business practices) adoption by rural businessmen have paid attention to the (B2C) part of e business practices because the cyber dispersion rate has increased considerably in the last years. A current statistic by nic.in reveals that the average expansion rate of internet penetration in the country between 2014 and 2018 was 386.8 percent per year and in 2017 total Internet users were 34.8% of the population.

34

The enhanced potential in ease of business has encouraged rural businessmen to shift from conventional methods to the online mode. Thompson and Ranganathan in 2004 had put forth that retailers do have a superior inducement to espouse digital business practices than consumers since it offers numerous benefits to retailers such as immense cost cutting in transaction costs, enhanced competence and strategic litheness by increasing vibrant and exible associations with the main trade partners.

Due to the global reach of digital business practices, retailers in the EM nations have begun to adopt digital business practices in their businesses as per study by (Rao and Metts 2003) but retailers in rural south Rajasthan as other emergent states are still unwilling to use digital business practices in their everyday business operation due to infrastructure impediments and cost overruns.

Hence, it is imperative to make out the factors that control digital business practices adoption among retailers in rural south Rajasthan. These would immensely help the managers who handle the distribution and Logistic support to the companies that supply SKU's to the retailers in predicting digital business usage rates and evaluating the potential growth of digital business practices in the area assigned to the individual manager.

To discover the relationship between various essential factors and intention to adopt mobile banking/m-commerce and analyze the factors that inuences the intent to adopt digital business practices amongst retailers of rural south Rajasthan.

Objectives of the Paper

The rationale of this research is to study the key determinants of mobile banking/m-commerce adoption by businessmen in rural south Rajasthan. What factors determine adoption of mobile banking/m-commerce pract ice by rural businessmen? The paper is purports to look at the factors that persuade or daunt the rural users' adoption of digital business methods for transactions.

The implementation of Information systems is dependent upon “specific social, cultural,

economic, legal and political contexts, which may differ significantly between countries” as per certain research, this “limits the overview of research results from developed countries to developing country contexts” the businessmen awareness, perception or apprehension about his existing and potential use of digital techniques and tools to find out the factors that persuade or discourage digital business adoption.

Researchers have identified quite a few factors that persuade for the adoption of Information Technology systems in various industries, prominent amongst them are (a) The cost of technology (b) Exterior pressure (c) the characteristics of the owner-manager (d) Security etc. Consequently, these factors are creditable to be explored while explaining the adoption blueprint of digital business methods for transactions by retailers in rural south Rajasthan. In addition to these factors the expenditure on adoption and maintenance of the system is also a significant aspect.

The rationale of this study is to identify the key factors persuading the mobile banking/m-commerce adoption by businessmen in South Rajasthan. Security Concerns, Comparative advantage, ease of use, rural user's Adaptability and Cost Associated were found to be significant in envisaging the rural users' intention to use mobile banking/m-commerce practices.

A) Security

In a number of studies it was found that the main barrier in developing Digital or mobile business transactions and practices is the security of using the same. To adopt Digital business protocols information securely, it is crucial for the entity to have reliability of the complete system.

The alarm of losing trade secrets creates disinclination from businessmen to consider getting into the Digital business procedures in the business arena as per Killikanya 2000.

H : Rural businessmen who have a high perceived 01

security risk are less likely to adopt mobile banking/m-commerce


35

B) Comparative Advantage

Comparative advantage has been established as the formidable predictor and also positively associated with an innovation, with a view of the recompense that m-cash offers, it would thus be anticipated that businessmen who perceived e cash as beneficial would probably adopt it.

H : Rural businessmen who have a higher 02

perceived comparative advantage from the implementation of mobile banking/m-commerce are favorably inclined to follow its adoption.

C) Ease of Use

Information systems that the user perceives to be easier to apply in his business as also being less complex , increases the likelihood of its adoption and usage, this would be likely to be accepted by users only when the proper skil ls and consideration of the technology are available .

H : Ease of Use has a positive effect on adoption of 03

mobile banking/m-commerce.

D) Rural Retailer's Adaptability

The level of rural users' adaptability has frequently been identified as a significant factor of I n f o r m a t i o n T e c h n o l o g y a d o p t i o n . A n organization which does not have such a capacity is disinclined to as well as less likely to adopt, the businessmen with deficient inclination may incur elevated initial costs when executing the innovation.

H : Rural businessmen with superior adaptability 04

are more likely inclined to adopt mobile banking/m-commerce practices.

E) Cost Associated

The expenditure on the adoption of the digital practices is a vital factor in use of the digital

platforms. The costs of digital cash practices comprise of investment in the route of its adoption like physical assets of networks, laptops/ tablets/ Smart phones etc there is a straight and major relationship that exists between adoption of technology and the expenditure thereto. The lesser the cost of adoption, higher is the probability of the novel innovation such as the digital business practices being adopted by the entity and vice versa.

H : Rural businessmen who perceive higher costs 05

are involved in the adoption of mobile banking/m-commerce are less likely to adopt the same.

Research Methodology

An experiential analysis was undertaken to test the hypotheses consisting of survey method.

Data collection - The area of the field survey was South Rajasthan (Udaipur, Sirohi, Dungarpur and Banswara) with a focus on the rural businessmen. A total of 320 businessmen were selected through stratified sampling from the sample. A Survey tool comprising of a questionnaire were administered to the selected businessmen of the research sample. A total of 208 filled questionnaires were received, out of which 8 questionnaires were found inadequate due to deficient responses hence, 200 questionnaires were put in use for the subsequent analysis.

Result and Discussion

The profile of the responding businessmen is as below. A majority of the surveyed Rural businessmen i.e. (56%) had been in the business for tenure < 1 year but > 2 years, barely 18% respondents were in the business < 1 year, 35% were in business > five years.


36


Only 7.5 percent of the respondents were Internet users of more than 5 years, only 24.5% rural businessmen were Internet users exceeding 3 years. It emerged that, barely about 38 percent of the responding businessmen were Internet users of >1 yrs but < 3 years. The results point to the fact that majority is highly educated inclusive of 78%

of respondents being either a graduate or a postgraduate.

Measuring Instrument

The respondents were asked to rate the responses on a six-point Likert scale, with 6 as strongly agree and 1 as strongly disagree.

Table 1: Demographic Profile and Descriptive Statistics

No of years in business Frequency Percent

>1 year 18 9

1 year > 5 years 112 56

5 > 10 years 52 26

10 <

18

9

Education level

of Respondents’

Frequency

Percent

>Graduate

44

22

Graduation

130

65

P.G.

26

13

Internet usage

Frequency

Percent

Internet user

124

62

Non Internet user

76

38

Period of Internet usage

Frequency

Percent

>

1 year

60

30

1 >

3

years

76

38

3

>5

years

49

24.5

>5

years

15

7.5

Source: Field Data

Table 2: Multiple Regression Results

Variable Coefcient t-value Sig. Supported

Organizational inclination 0.123 2.459 .015* Yes

Comparative Advantage 0.282 3.951 .000** Yes

Ease of Use 0.247 3.454 .000** Yes

Security – 0.306 3.902 .000** Yes

Cost Associated 0.044 0.585 .559 No

Level of significance of the t-value: *p 0.05; **p .001 ≤ ≤

Source: Statistical Analysis

37

Organizational Inclination

The higher levels of perceived organizational inclination are associated with amplified intentions to adopt digital business practices in the trade. As per data analysis of this study, Organizational inclination demonstrated significant inuence over the adoption of mobile banking/m-commerce business transactions (beta = 0.123, p-value = 0.015).

Comparative Advantage

The results of the analysis indicate that most of the respondents' perceive Comparative advantage to be a considerable forecast for mobile banking/m-commerce adoption for business purpose (beta =

.282; t-value 3.951 significant at p 0.05), which

provides strength to this hypothesis.

Simplicity of Use

The perceived s implic i ty of use is a lso considerably associated with increase in intentions to use mobile banking/m-commerce practices for business purpose. Analysis shows the results of ease of use (beta = 0.247, p-value = .0.000), indicating that ease of use has a positive effect upon mobile banking/m-commerce practices adoption amongst rural businessmen.

A majority of the prior studies put forward that more complex a new technology is perceived to be, less likely it would be adopted. One probable reason is that rural businessmen in South Rajasthan are still reluctant to use Digital/ Electronic Commerce in trade operations. Only about 25 per cent of the businessmen in South Rajasthan have a presence on the www and also utilize IT on a daily basis according to latest UCCI data. Since these rural users are reluctant to use digital business practices, apprehensions that are aggravated by any potential complexity are the main and significant deterrent of digital business practices adoption amongst them.

Security

A series of research concludes that concerns regarding perceived security are the main hurdle to the use of digital business practices by rural

users. Superior levels of perceived security are related to decreased intentions in the adoption of digital business practices initiatives. The analysis illustrates that (beta = –0.306, p-value =.0001), this illustrates that when rural businessmen are fearful about security; the degree of digital business practices adoption is lower.

Cost Associated

Although the findings conclude that perceived cost has a positive relationship with digital business practices vis- a'- vis the intention to adopt, this relationship is not significant (beta = 0.044, p-value =.559).

Conclusion

This paper has endeavored to research the adoption or rejection of mobile banking/m-commerce practices by the rural businessmen in South Rajasthan, analysis illustrates that Comparat ive advantage, organizat ional inclination and security and cost associated are significant elements of digital business practices adoption.

The study concludes that there is a 53 percent variance in the users' intention to adopt mobile banking/m-commerce practices. As we increase our share in the e-biz pie of the country it is imperative that the factors that hold sway over the rural users' adoption of the mobile banking/m-commerce practices be properly understood so that it is put to use in perspective regarding the future trends in digital business practices expansion.

This study has concluded that respondents have cited that digital business practices is a multifaceted structure and the system should be prepared in such a manner which can be as user-friendly as doable, because not all the users in many parts of the country are well acquainted with mobile technology, computers and the www, especially the businessmen who are senior citizens.

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Abstract

Corporate Social Responsibility is understood and implemented differs greatly for each company and country. Moreover, it is a very broad concept that addresses many and various topics such as human rights, corporate governance, health and safety, environmental effects, working conditions and contribution to economic development. Whatever the definition is, the purpose of CSR is to drive change towards sustain ability. Tribes are those who lag behind in the growth part and still fighting for their lively hood and health improvement. This paper analyses the CSR activities conducted by the Vedanta limited in the Tribal belt of Rajasthan for improving health of the Tribal people. A sample of 324 tribes were taken and analysed using Multiple regression method and variables like Imp_Health_1, Imp_Health_10, Imp_Heal th_4 , Imp_Heal th_5 , Imp_Heal th_6 , Imp_Heal th_9 andImp_Health_3 revealed that importance of CSR in the selected geographical area of Rajasthan.

Keywords : CSR, Health, Tribal Belt, Rajasthan

Corporate Social Responsibility of Vedanta : A Study on Improvement in

Health Condition in Tribal area of South Rajasthan


Introduction

CSR has attracted attention from businesses and stakeholders in regard to its benefits and what it is. CSR has been defined differently by different writers based on what they perceive about the concept. Having learnt from the devastating effects of corporate social irresponsibility, companies are focusing on the impacts of their operations not only on profits but the society and environment at large. Therefore, CSR refers to "the ethical principle that an organization should be responsible for how its behaviour might affect society and the environment"(Jobber & Ellis (2012). From 1960, "corporate social responsibility" has remained a term used indiscriminately by many to cover legal and moral responsibility more narrowly construed (De George, 2011). Investopedia defines that CSR is a corporation's initiatives to assess and take responsibility for the company's effects on environmental and social wellbeing. The term generally applies to efforts that go beyond what may be required by regulators or environmental protection groups.

India is a country of villages and its development is synonymous with the development of the people living in rural areas. India is a vast and second most populous country of the world. Nearly 70 per cent of the country's

Prof. Pushpkant Shakdwipee

Professor, Pacic Business School, Udaipur (Rajasthan)

41

population lives in rural areas where, for the first time since independence, the overall growth rate of rural population has sharply declined, according to the latest Census (Census, 2011). Out of the 121 crore Indians, 83.3 crore live in rural areas while 37.7 crore stay in urban areas, (Census of India's 2011). Provisional Population Totals of Rural-Urban Distribution in the country, has released for the first time since independence, the absolute increase in population is more in urban areas than in rural areas (R.K. Singh, Union Home Secretary). The level of urbanization increased from 27.81 per cent in the 2001 Census to 31.16 per cent in the 2011 Census, while the proportion of rural population declined from 72.19 per cent to 68.84 per cent. But a big part of this population has been leading an uncertain economic life due to non-synchronization of employment opportunities in agriculture sector because of the fast growing population. Rural development has been receiving increasing attention of the governments across the world. In the Indian context rural development assumes special significance for two important reasons. First about two thirds of the population still lives in villages and there cannot be any progress so long as rural areas remain backward. Second, the backwardness of the rural sector would be a major impediment to the overall progress of the economy.

Reviews of Literature

The commitment of Companies on CSR initiatives on rural development is different, the importance of CSR activities on rural areas and its necessity to the companies. 40 Companies were agreed to give their CSR activities on Rural Development. In accordance to the CSR activities in SPSR Nellore Dist companies initially provide 484 solar water motor sets to the rural people for their development relating to livelihood activities. Some companies implement planting, sanitation and road facilities on their rural adopted areas. To know the level of CSR impact on the rural development the researcher should gather the information from the beneficiaries who are adopters by the companies as said in the Stakeholder's theories which are supporting the research aim and objectives (Carroll, 1979). To enhancing the understanding of CSR impact on rural development, research must be focus on the specific CSR initiatives and activities through

which companies try to implement a theoretical and practical commitment to the areas of Livelihood, Educational, Health, environmental and Infrastructure (Arora&Puranik 2007; Wood, 2010). At the moment, there is a recognized dedicated study on this topic (Bhupathi & Guravaiah, 2010).

Alexender Dahlsrud (2008) done research on CSR Dimensions and reported that CSR has five dimensions: Environmental dimension Social dimension Economic dimension Stakeholders dimension Voluntariness dimension. Nilesh R. Berad (2011) done research on and reported that education is the most preferred area of CSR, followed by health care facilities and rural development and livelihood.

Nippatlapalli and Suja S. Nair (2016) in their study on effect of CSR on rural development focused on gaps identified in the literature regarding the implementation of CSR at the social level and the initiatives that inuence this implementation. To measure the impact on rural development ,five areas were classified i.e .education. environment, health, livelihood and infrastructure and analysis made on the basis on multiple correlation. Finally the study found that the effect of CSR on rural development is positive.

Tauffiqu Ahamad, Abhishek and Rajesh Kumar Shastri (2014) found in their study thatthe private sector is more involved in CSR activities than p u b l i c / g o v e r n m e n t s e c t o r f o r R u r a l development. The leading areas that corporations are working for rural development are education, health, environment, livelihood promotion and women's empowerment.

Subhasis Ray (2012) in his study found that all companies focused on education,health and livelihood but there was no example of innovation in service conception and delivery that would result in sustainable change in these areas. Each company would draw up its own CSR plans and programmes that are more aligned to its area of operation.

Research Methodology

Research methodology is a way to systematically and logically solve a problem, understand the process, analyzes methods in addition to the information obtained.


42

Sampling Design

This research includes the CSR spending of top ten cement companies of the country selected on the basis of their sales and production capacity.

Size of sample: the sample includes 10 cement companies and 58 respondents' working in the finance or accounts department in the selected companies.

Data Collection Methods

a) Primary Data: the Primary data on the sat is fact ion were gathered from the respondents working in the companies by using schedule method and their views are gathered by observation.

b) Secondary Data: the secondary data of the company's profitability and CSR spending were gathered by using annual reports of the companies.

Data Analysis

First the Beneficiariessatisfaction were analysed and the views of the respondents

H = There is a significant impact of CSR for 1

improvement in HEALTHcondition as rural development on the satisfaction level of the beneficiaries.

Descriptive statistics shows the regression analysis of impact of CSR activities (for improvement in HEALTH condition as rural development) on the satisfaction level of the beneficiaries. The data was collected from 324 beneficiaries of the selected geographical areas for their satisfaction about the activities done for improvement in HEALTH condition by various tests. The 7 variables entered in the model are satisfaction due to improvement in HEALTH condition and their reasons of the satisfaction. The mean values of the satisfaction were 4.2958 with 0.81322 as standard deviation.


Descriptive Statistics

Variables SPSS Name Mean

Std. Deviation N

Satisfaction

4.2858

0.80322 324

Health check-ups camps

Imp_HEALTH_1

4.0534

0.75324 324

Support & Association with

Special Care hospitals

Imp_HEALTH_2

4.3139

0.68881 324

Water treatment and improved water supply facility

Imp_HEALTH_3

3.821

1.07293 324

Investment in Hospital buildings

Imp_HEALTH_4

4.2928

0.76038 324

Private

Blood Banks

by companies

Imp_HEALTH_5

3.9301

0.97034 324

Mobile clinics

by companies

Imp_HEALTH_6

4.3456

0.73502 324

Encourages safety activities such as accidentprevention initiatives

Imp_HEALTH_7

3.3245

0.90941 324

Provide

medical subsides

Imp_HEALTH_8

2.8386

1.06409 324

Health and safety education programs

Imp_HEALTH_9

2.8527

1.04275 324

Conduct health improvement programs to

encourage awareness for parents and

children health

Imp_HEALTH_10 2.6907 1.0724 324

Table 1 : Multiple Regression for Improvement in Health Condition

43


F. Coefcients a

Model

Unstandardized Coefcients

Standardized Coefcients

t

Sig.

Correlations

Collinearity Statistics

B

Std. Error

Beta

Zero-

order

Partial

Part

Tolerance VIF

7

(Constant)

0.496

0.245

1.972

0.038

Imp_HEALTH_1

0.763

0.036

.725

16.689

-0.01

0.575

0.699

0.606 0.712 1.376Imp_HEALTH_10

-0.352

0.032

-.455

-8.051

-0.01

-0.198

-0.446

-0.307 0.416 2.34Imp_HEALTH_4

0.353

0.039

.344

7.428

-0.01

0.444

0.399

0.264 0.626 1.561Imp_HEALTH_5

-0.24

0.036

-.278

-5.066

-0.01

0.101

-0.301

-0.197 0.442 2.204Imp_HEALTH_6

0.158

0.047

.154

2.926 -0.006 0.113 0.164 0.098 0.485 2.011

Imp_HEALTH_9

0.175 0.037 .2393.943 -0.01

-0.0370.221 0.136 0.361 2.687

Imp_HEALTH_3

-0.1 0.028 -.119-2.392

0.008 0.183 -0.152 -0.098 0.531 1.84

a. Dependent Variable: Satisfaction

Model Summary

Model R R

Square Adjusted

R Square

Std.

Error of

the

Estimate

Change Statistics

R Square

Change F

Change df1 df2 Sig. F

Change

7 .789g 0.614 0.605 0.49467 0.007 5.663 1 323 0.008

g. Predictors: (Constant), Imp_HEALTH_1, Imp_HEALTH_10, Imp_HEALTH_4,

Imp_HEALTH_5, Imp_HEALTH_6, Imp_HEALTH_9, Imp_HEALTH_3

ANOVAh

Model

Sum of

Squares df Mean Square F Sig. 7 Regression 116.85 7 16.684 65.537 .000

g

Residual 70.285 316 0.245

Total 187.145 323

g. Predictors: (Constant), Imp_HEALTH_1, Imp_HEALTH_10, Imp_HEALTH_4, Imp_HEALTH_5,mp_HEALTH_6, Imp_HEALTH_9, Imp_HEALTH_3 h. Dependent Variable: Satisfaction

The numbers of models being entered in the model are seven. R is the Pearson correlation value and r square is the correlation variance between the observed and predicted value. In seventh model the R is valued at 0.789 with 0.614 R square as dependent

variables so the proportionate of variance R-Square in the dependent variable (importance of CSR for rural Community development). This shows overall measure of the strength of association as 36.5 percent in outcome of variances.

44

The adjusted R square is computed with the formula of 1-((1-Rsq)(n-1)/(N-k-1))when k means the importance is 61.4 percent with standard error of 0.49467with a df (1,276) at a significance level of frequency 0.018.Analysis of Variance (ANOVA) between the variables revealed the outcome variables under these categories observed are regression, residual and total with values are consecutively 116.860, 70.295 and 187.155. The total variance explained by the independent variables (regression 116.860) and the variance which is not explained by the dependent variable (Residual 70.295), df (7, 256) with mean square vale of df1 is 116.860 and df2 is 70.295. F is 65.547 (16.694/0.255) which is significant as p<0.05 thus the regression model is found as good (Fit) for the analysis of data. The model is entered into this table is seventh. The variables entered in this analysis are constants, means importance. The first variable is constant, also referred as Y intercept, the high of the regression line when it crosses on Y axis. In the other word, this is the predicted values of the average satisfaction of beneficiaries on CSR for rural community development, when other variables are beta–the value of standardised of the regression equitation for predicting the dependent variable from the independent variables. The regression equation is presented in many different ways as mentioned in the table the coefficient for m e a n i m p o r t a n c e I m p _ H E A L T H _ 1 , I m p _ H E A L T H _ 1 0 , I m p _ H E A L T H _ 4 , I m p _ H E A L T H _ 5 , I m p _ H E A L T H _ 6 , Imp_HEALTH_9 andImp_HEALTH_3.

Conclusion

In the analysis it has found that the relationship between importance and variables of CSR importance is positive which is fit for regression analysis. Therefore regression analysis is presented in the above table clearly shown that the beta coefficient value of 50.6% yield by importance. This means that if these variables increase the satisfaction of tribe's community. Further the respandents revealed that CSR has made improvement for variables like Health check-ups camps (Imp_HEALTH_1), Water treatment and improved water supply facility (Imp_HEALTH_3), Investment in Hospital buildings (Imp_HEALTH_4), Private Blood Banks

by companies (Imp_HEALTH_5), Mobile clinics by companies (Imp_HEALTH_6), Health and safety education programs (Imp_HEALTH_9), and Conduct health improvement programs to encourage awareness for parents and children health (Imp_HEALTH_10). Hence coming towards the significant level there is a significant differences and it accepts the hypothesis. And mean that impact of CSR activities increased satisfaction from rural development activities of the Beneficiary.

References

Alexander, Dahlsrud. (2008). Corporate Social APA Responsibility Is Defined : An Analysis o f 3 7 D e fi n i t i o n s . C o r p o r a t e S o c i a l R e s p o n s i b i l i t y a n d E n v i r o n m e n t a l Management, Vol. 16, No. 1, PP. 1-13 .

Amruth Raj Nippatlapall. (2016). A Study on of Corporate Social responsibility on rural development : Evidences from SPSR Nellore of Andhra Pradesh,International Research Journal of Business and Management. Vol 9, No. 6.

Ahamad, Tauffiqu & Shastri, Rajesh Kumar. Corporate social responsibility: A new pathway for community initiatives and rural development. International Journal of Management Research and Development Vol. 1, No. 5, PP. 185-185.

Dummett K.(2006). Drivers for Corporate Environmental Responsibil i ty (CER), E n v i r o n m e n t , D e v e l o p m e n t a n d Sustainability, Vol 8, No. 3, PP. 375-389.

Kell, G.(2005) The Global Compact. Selected Experiences and Reections. Journal of Business Ethics, 59 (1-2) PP 69-79.

Ruggie, JG. (2011) Global_governance.net : The Global Compact as Learning Network. Global Governance; Vol. 7, No. 4, PP. 371-378.

Ray, S. (2012) .Corporate Social Responsibility in Indian public sector companies: The case of LCM Limited in Social Responsibility, Entrepreneurship and the Common Good, I n t e r n a t i o n a l a n d I n t e r d i s c i p l i n a r y Perspect ives Bas ingstoke : Pa lgrave Macmillan.


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