Prapti - kgcas.ac.in · Problems towards FMCG Product have been analysed and ... consumer electronics, ... consumers preference and satisfaction towards Amway

KGCAS - Prapti ISSN:2456-8708 Vol 1. Issue 2 Dec 2017

Prapti

Research Journal

Vol. 1 Issue-2 December, 2017

1. A study on problems faced by the customers towards FMCG products in Coimbatore

district..........................................................................................................................................1 - 6

Dr. M Thiyagaraj and S.Rajam

2. Indian Tax System........................................................................................................................7 - 11

J.Balakrishnan

3. Goods And Service Tax In India................................................................................................12 - 15

S.Karthik

4. An Improvised Energy-Efficient LEACH for Wireless Sensor Network...............................16 - 22

A.Krishnakumar, Dr.V.Anuratha

5. A study on account holders satisfaction towards service rendered by Keernatham agricultural

credit society, Coimbatore..........................................................................................................23 - 31

L.Lovely Lourds Preethi

6. A study pertaining to test of equality of selected indian pharmaceutical Companies ….....33 - 42

Dr..Ramya

7. A study on investors perception towards derivative market at angel broking, Salem….….43- 50

M.Praveen

8. Fault Prediction Using Fuzzy Set Based K-Means Clustering Algorithm…………………..51-56

M. Jasmine Sagaya Jonita


1

A Study on Problems Faced by the Customers

towards FMCG Products in Coimbatore District Dr. M. Thiyagaraj1 , S.Rajam2

Abstract

In Indian economy, the Fast Moving Consumer Goods sector

experienced outstanding growth in the past decade. This sector

is an important contributor to India’s Gross Domestic Product.

This industry in Coimbatore district is shaping up under the

umbrella of organized sector and it is distinctly classified into

four different segments like Food and Beverage industry,

Cleaning, Disinfectants and Home Care, Personal care and

Electronics. The study is descriptive in nature both primary

and secondary data to be considered for this analysis. 750

samples are randomly selected in the rural area of Coimbatore

District and analysed for the study. Objectives are framed and

required tools to be applied for this study. Through this study

Problems towards FMCG Product have been analysed and

provide the suitable suggestions to improve the services.

Keywords: Fast Moving, Gross Domestic Product, Indian

economy, Food and Beverage, Problems.

1. INTRODUCTION

India is one of the largest economies in the world in terms

Associate Professor1,

Department of Commerce,

Dr.S.N.S Rajalaskmi Arts and Science College,

Chinavedampatti (PO). Coimbatore.

Professor2,


Kongunadu Arts and Science College,

Kovundampalayam, Coimbatore.

of purchasing power and increasing consumer spending,

next to China.The National Council of Applied Economic

Research (NCAER) survey report says that there are 720

million consumers across the villages in rural India. Hence,

the development of the nation largely depends upon the

development of the rural population. The Indian rural

marketing environment increased the awareness along with

rise in income levels.

In Indian economy, the Fast Moving Consumer

Goods (FMCG) sector experienced outstanding growth in

the past decade. FMCG sector is an important contributor to

India’s Gross Domestic Product (GDP).

It is the fourth largest sector in the Indian economy.

This sector also creates employment for around three

million people in downstream activities, which are generally

carried out in smaller towns of rural India.

The development of consumerism in the Coimbatore

district is due to Industrialization. This increased the growth

of FMCG market in Coimbatore district. The FMCG

industry in Coimbatore district is shaping up under the

umbrella of organized sector and it is distinctly classified

into four different segments like Food and Beverage

industry, Cleaning, Disinfectants and Home Care, Personal

care and Electronics.


2

1.1 FAST MOVING CONSUMER GOODS (FMCG)

Products which have a quick turnover, and relatively

low cost are known as Fast Moving Consumer Goods

(FMCG). FMCG products are those that get replaced within

a year. Examples of FMCG generally include a wide range

of frequently purchased consumer products such as

toiletries, soap, cosmetics, tooth cleaning products, shaving

product and detergents, as well as other non-durables such

as glassware, bulbs, batteries, paper products, and plastic

goods.

FMCG products may also include pharmaceuticals,

consumer electronics, packaged food products, soft drinks,

tissue paper, and chocolate bars. A subset of FMCG

products is Fast Moving Consumer Electronics which

includes innovative electronic products such as mobile

phones, MP3 players, digital cameras, GPS Systems and

Laptops. These are replaced more frequently than other

electronic products. White goods in FMCG refer to

household electronic items such as Refrigerators, T.Vs,

Music Systems, etc.,

2. REVIEW OF LITERATURE

Mona Chaudhary and SnehaGhai (2014) conducted a study

to find out the perception of youngsters towards cause-

related marketing of FMCG category and its impact on their

buying behaviour. It was found that the perception of youth

was the most important aspect to contribute to the society.

The researchers appreciate the initiatives taken by the

marketers who join hands with the NGO working for a

noble cause. A well-designed cause-marketing campaign

can bring benefits to the company. It works as a great

differentiator in FMCG as there are so many similar

offerings in the market. A well-rated and effectively

communicated cause marketing campaign creates a positive

impact on buying behaviour of the young consumers in

favour of the brand.

Bloom et al. (2006) have clearly written in their

study that companies have been able to use cause-related

marketing to make a distinction of their brands from

competitors in consumers' minds and to get desirable

effects, including greater efficiency for other marketing

efforts, an ability to charge higher prices, increased market

share, greater brand loyalty and better stakeholders

management.

Cheron Emmanuel et al. (2012) carried out a study

that aimed to examine the effect of brand-cause fit and

campaign duration on company and brand image,

commercial objectives and buying intention as perceived by

Japanese consumers and aimed to evaluate the moderating

role of gender and participation in philanthropic activities

on the impact of cause-related marketing (CRM)

programmes in Japan. An experimental design was used

with 196 Japanese subjects completing a survey online.

Results showed that a high brand-cause fit was found to

elicit more positive attitudes towards the CRM programme

than campaign duration. Japanese female respondents were

showing more favourable attitudes than men, confirming

results in previous research studies conducted in the West.

Hou, Jundong et al. (2008) examined several factors

that potentially influenced a consumer's purchasing decision

to participate in cause-related marketing (CRM)

programmes in the Chinese context. This study was also

intended to test empirically the hypothesized relationship


3

between cause's attributes of causes and purchase intention

in such an environment. The results show that the degree of

causes participation for consumer, fit between the brand and

the cause, cause importance, congruence between the firm's

product and the cause. The cause proximity plays an

important role in consumers' attitudes towards the product.

Kalaiselvi.S (2014), conducted “A study on

consumers preference and satisfaction towards Amway

Nutrition Products with special reference to Coimbatore

city, concluded that

3. STATEMENT OF THE PROBLEM

In modern world, the consumption of FMCG

products plays an important role in our day to day life. Rural

customers are facing the problem in quality product,

availability and accessibility. Internationally the market for

FMCG products has expanded significantly during the last

decade. In recent years the demand for FMCG products is

growing among households. FMCG products are produced

by many companies and sold in their brand names. In this

situation it is required to find out and problems faced by the

consumers’.

4. SCOPE OF THE STUDY

The study is conducted to find out the Rural

Consumers’ problems towards the FMCG Products in

Coimbatore districts. Coimbatore is the second largest

district in the state of Tamil Nadu.

Hence the study is very essential to this particular

area and the researcher has focused on rural areas in the

Coimbatore district. To attempt the specified objectives, 750

respondents were selected.

5. OBJECTIVE OF THE STUDY

To analyze the problems faced by the consumers

while using the FMCG products.

6. RESEARCH METHODOLOGY

A pilot study was conducted with 50 consumers of

retail outlets in Coimbatore District. Interview schedules

were used as a major tool to collect first-hand information

from the sample respondents. Field survey technique was

adopted to collect information from the sample respondents.

The interview schedule has been pre-tested and

modified to suit the purposes of this study. Totally 750

respondents were taken for the study. Both primary data and

secondary data have been used in this study. The primary

data have been collected from FMCG consumers in

Coimbatore district, with the help of an Interview Schedule.

The secondary data have been drawn from different

sources like newspapers, magazines, journals, books,

websites and pamphlets. In this study Garrett Ranking

method has been used to findout the problems faced by the

customers towards FMCG products in Rural area.

7. LIMITATIONS OF THE STUDY

The geographical coverage of the study is

restricted only to rural part of Coimbatore District

and hence, the results of the study cannot be

generalized in its original form to other parts of the

state or country.

The study is confined only to the rural people who

are residing in the rural part of the Coimbatore

District. The study in confined only selected

FMCG. All the FMCG were not available in rural

areas.


4

8. DATA ANALYSIS AND INTERPRETATION

8.1 GARRETT’S RANKING TECHNIQUES

With the help of Garrett’s table, the percent

position estimated is converted into scores. Then for each

factor, the scores of each individual are added and then total

value of scores and mean values of score is calculated. The

factors having highest mean value is considered to be the

most important factor. In this study Garrett ranking

technique is used to know the most important problem faced

by the customers while using the FMCG products.

In this study, to know the highest problem faced by the

respondents Garrett’s ranking techniques was used. In this

study FMCG products are Classified three groups

1.Food and Bevearage

2.Personal care Products

3.Health care Products

8.1.1 FOOD AND BEVERAGE PRODUCTS

SOURCE: PRIMARY DATA

INFERENCE

Table no. 8.1.1 reveals that, there are nine major

problems faced by the consumers towards Food and

beverage products. It is observed from the above table,

“Poor response” was ranked first by the respondents with

the total score of 45504 and the mean score of 60.67.

TABLE – 8.1.1

PROBLEMS TOWARDS

FOOD AND BEVERAGE PRODUCTS

S. NO. PROBLEM

FACTORS

TOTAL

SCORE

MEAN

SCORE RANKS

1 Poor Quality 42222 56.30 III

2 High Price 36805 49.07 VI

S. NO. PROBLEM

FACTORS

TOTAL

SCORE

MEAN

SCORE RANKS

3 Low Quantity 42570 56.76 II

4 Non

availability

35491 47.32 VIII

5 Irregular

supply

32743 43.66 IX

6 Duplication 36378 48.50 VII

7 Poor response 45504 60.67 I

8 More

complaints

40330 53.77 IV

9 Poor display 38469 51.29 V

“Low Quantity” was ranked second with the total score of

42570 and the mean score of 56.76. “Poor Quality” was

ranked third with the total score of 42222 and the mean

score of 56.30 “More complaints” was ranked fourth with

the total score of 40330 and the mean score of 53.77. “Poor

Display” was ranked fifth with the total score of 38469 and

the mean score of 51.29.

“High Price” was ranked sixth with the total score

of 36805 and the mean score of 49.07. “Duplication” was

ranked seventh with the total score of 36378 and the mean

score of 48.50. “Non-availability” was ranked eighth with

the total score of 35491 and the mean score of 47.32.

“Irregular supply” was ranked ninth with the total score of

32743 and the mean score of 43.66. It is concluded that the

respondents are highly suffered with the problem factor

such as “Poor response, Low Quantity and Poor quality.


5

These three factors are most important prblems faced by the

FMCG customer towards Food and Beverage Products.

8.1.2 PERSONAL CARE PRODUCTS

TABLE –8.1.2

PROBLEM TOWARDS PERSONAL CARE

PRODUCTS

S.

NO.

PROBLEM

FACTORS

TOTAL

SCORE

MEAN

SCORE RANKS


2 High Price 45504 60.67 I

3 Low Quantity 35491 47.32 VIII

4 Non availability 42570 56.76 II

5 Irregular supply 32743 43.66 IX


7 Poor response 36805 49.07 VI

8 More

complaints 40330 53.77 IV



INFERENCE

Table no.-8.1.2 reveals that, there are nine major

problems faced by the consumers towards personal care. It

is observed from the above table, “High Price” was ranked

first by the respondents with the total score of 45504 and the

mean score of 60.67. “Non-availability” was ranked second

with the total score of 42570 and the mean score of 56.76.

“Poor Quality” was ranked third with the total score of

42222 and the mean score of 56.30 “More complaints” was

ranked fourth with the total score of 40330 and the mean

score of 53.77. “Poor Display” was ranked fifth with the

total score of 38469 and the mean score of 51.29. “Poor

Response” was ranked sixth with the total score of 36805

and the mean score of 49.07. “Duplication” was ranked

seventh with the total score of 36378 and the mean score of

48.50. “Low quantity” was ranked eighth with the total

score of 35491 and the mean score of 47.32. “Irregular

supply” was ranked ninth with the total score of 32743 and

the mean score of 43.66. It is concluded that the respondents

are highly suffered with the problem factor such as “High

Price, Non-availability and Poor quality. These three factors

are most important problems faced by the FMCG customer

towards Personal care products.

8.1.3 HOUSEHOLD CARE PRODUCTS

TABLE – 8.1.3

PROBLEM TOWARDS HOUSEHOLD CARE

PRODUCTS

S.

NO.

PROBLEM

FACTORS

TOTAL

SCORE

MEAN

SCORE

RANKS


2 High Price 45504 60.67 I

3 Low Quantity 40330 53.77 IV

4 Non

availability 35491 47.32 VIII

5 Irregular

supply 32743 43.66 IX


7 Poor response 36805 49.07 VI

8 More complaints

42570 56.76 II



6


INFERENCE

Table no. 8.1.3 reveals that, there are nine major

problems faced by the consumers towards household care

products. It is observed from the above table, “High Price”

was ranked first by the respondents with the total score of

45504 and the mean score of 60.67. “More Complaints” was

ranked second with the total score of 42570 and the mean

score of 56.76. “Poor Quality” was ranked third with the

total score of 42222 and the mean score of 56.30 “Low

Quality” was ranked fourth with the total score of 40330

and the mean score of 53.77.

“Poor Display” was ranked fifth with the total

score of 38469 and the mean score of 51.29. “Poor

Response” was ranked sixth with the total score of 36805

and the mean score of 49.07. “Duplication” was ranked

seventh with the total score of 36378 and the mean score of

48.50. “Non-availability” was ranked eighth with the total

score of 35491 and the mean score of 47.32. “Irregular

Supply” was ranked ninth with the total score of 32743 and

the mean score of 43.66. It is concluded that the respondents

are highly suffered with the problem factor such as “High

Price, More complaints and Poor quality. These three

factors are most important problems faced by the FMCG

customers towards Household care products.

9. FINDINGS OF THE STUDY

A. Problem faced by the respondents towards food

and beverage products

The respondents are highly suffered with the problem

factor such as “Poor response, Low Quantity and Poor

quality. These three factors are most important

problems faced by the FMCG customer towards Food

and Beverage Products.

B. Problem faced by the respondents towards

personal care products


factor such as “High Price, Non-availability and Poor


problems faced by the FMCG customer towards

Personal care products.

C. Problem faced by the respondents towards house

hold care products


factor such as “High Price, More complaints and Poor


problems faced by the FMCG customers towards

Household care products.

10. SUGGESTIONS

After analyzing the various factors related to the

Problems towards FMCG products, it observed that there is

still scope for improvement. By keeping this view in mind,

the following suggestions are made to improve FMCG

Product Quality and services.

The respondents are facing severe problems such as

“Poor response, Low Quantity and Poor quality towards

food and beverage products . So the FMCG companies

should have better access with the consumers. They

should give immediate responses to the customers’

query and complaints


7

The respondents are facing the problem factor such as

“High Price, Non-availability and Poor quality towards

personal care products the FMCG companies are

recommended to promote FMCG in lines of pricing

strategy rather than just making low price appeals.


factor such as “High Price, More complaints and Poor

quality towards house hold care products. So the

company should take necessary steps in preventing the

quality of the product.

11. CONCLUSION

The FMCG sector has had much better time in the

recent years. The FMCG market is very big in Coimbatore

Districts and it is competitive also. . In recent days we have

seen a lot of innovation in the manufacturing process and

improvement in the quality of FMCG product. With this

revolution in technology the FMCG product also increases.

Logistics companies play important role in the distribution

of FMCG. The rural people are mainly price conscious but

not so brand loyal, so they may switch to other quality

product with low price, they do not consider the taste as

important factor while making purchase decision. As well

the rural consumers are very low effect of brand ambassador

on their buying behaviour but advertisement definitely helps

them to increase their product knowledge.

12. REFERENCES

1. Dr.S.P.Gupta, Statistical Methods, Sultan Chand & Sons

Educational Publishers, New Delhi-2006.

2. J.Llian Mercer, ―Great Customer Service‖, Allen and Unwin

Publishing, New Delhi, Ed-2003.

3. Mona Chaudhary & SnehaGhai, (2014). “Perception of

Young Consumers towards Cause Marketing of FMCG

Brands”, International Journal of Sales & Marketing

Management Research and Development, Vol. 4, Issue 2,

Pp.21-26.

4. Cheron,Emmanuel; Kohlbacher, Florian; Kusuma, Kaoru,

(2012).“The effects of brand-cause fit and campaign duration

on consumer perception of cause-related marketing in

Japan”, The Journal of Consumer Marketing , Vol. 29, Issue

5 : Pp.357-368.

5. Hou, Jundong; Du, Lanying; Li, Jianfeng (2008).“Cause's

attributes influencing consumer's purchasing intention:

empirical evidence from China”, Asia Pacific Journal of

Marketing and Logistics, Vol. 20 Issue 4, Pp.363-380.

6. Kalaiselvi.S (2014), “A study on consumers preference and

satisfication towards Amway Nutrition Products with special

reference to Coimbatore city” Global Journal for Research

Analysis, Vol.3,No.10, ISSN: 2277-8160.

http://search.proquest.com/pubidlinkhandler/sng/pubtitle/The+Journal+of+Consumer+Marketing/$N/37088/DocView/1030152050/abstract/140E89D4FCE23D0E8A0/27?accountid=38609

http://search.proquest.com/pubidlinkhandler/sng/pubtitle/Asia+Pacific+Journal+of+Marketing+and+Logistics/$N/31237/DocView/227365148/abstract/140E89D4FCE23D0E8A0/81?accountid=38609

http://search.proquest.com/pubidlinkhandler/sng/pubtitle/Asia+Pacific+Journal+of+Marketing+and+Logistics/$N/31237/DocView/227365148/abstract/140E89D4FCE23D0E8A0/81?accountid=38609


8

Indian Tax System

J.Balakrishnan

Abstract

Tax estimate the exact value to the tax payer and tax value are

collected based on the Income level of the tax payer. The

economic level are increased by the tax collection of the

government.

1. INTRODUCTION

Role of the Central and State Government

Central government of India levies taxes like as

Income tax, customs duty, service tax and central excise

duty.

State governments to levy income tax on agricultural

income, professional tax, value added tax, state excise duty,

land revenue and stamp duty. The local bodies are allowed

to collect octroi, property tax and other taxes on various

services like drainage and water supply.

Organizational Structure

The CBIT is headed by CBIT chairman and also

comprise six members. Member (Income Tax)

Member (Legislation and Computerization)

Member (Revenue)

Member (personnel & Vigilance)

Member (Investigation)

Member (Audit & Judicial)

Assistant Professor in Commerce,

Bishop Ambrose College,

Coimbatore.

The chairperson holds the rank of special Secretary to

Government of India while the members rank of additional

Secretary to Government of India.

The CBIT chairmen and members of CBIT are selected

from Indian Revenue Services (IRS), a premier civil

services of India, whose members constitute the top

management of Income Tax Department

2. OBJECTIVES OF TAXATION

The following are the main objectives of taxation.

1. Primary 2. Secondary

Primary

Raising more revenues

Preventing the concentration of wealth in a few

hands

Re-distribution of wealth for the common and social

purposes

Maintaining the welfare of the states.

Secondary

Encouraging the essential productions

Maintaining balanced economic growth

Enforcing government policy

Increasing savings and investment by public

Reduction of unemployment problems

Removal of Regional disparities among the states

Importance

There are many responsibilities of state to its

countrymen. State is represented by the government. The

government of any country performs a number of activities


9

in order to maintain law and order, peace and security,

satisfying with the requirement of basic needs of public

utilities. It also indicates various development programmes

and maintains diplomatic and friendly relation with other

nations in the world.

3. BENEFITS OF TAXES

Tax encourages savings and investment if a tax

payer can invest amount to his/her business and

they has to reduce in the tax payment to the

government.

Tax payer has to file tax returns, it helps when you

are in getting loan from bank.

4. ESSENTIALS OF TAX

A tax is a compulsory contribution of a person or

entity to the state as per the rules.

The tax payer does not receive direct and or special

benefit in return.

It is spent by the government for the common

interest and benefit of the people.

It is paid only by those persons and entities who

earn income exceeding a certain specified limit.

Non-Payment Penalty

Suppose the payment of tax is avoiding means the

tax payer has to pay their own money. A customer fail to

pay tax when the tax amount returns or extended and have

time to pay owe money in the same month and has to pay a

non-payment penalty equal to 0.5% of the due tax will be

levied.

Underpayment Penalty

When a customer does not make the whole

payment owed on tax and Taxes must be paid as income is

earned, and most taxpayers comply with the rule for fear

that income tax will be withheld from their pay cheques.

The independent contractors who work side jobs in

addition to their salaried employment or as full time

workers are responsible for ensuring that the tax due on

their earnings are covered through estimated tax payments.

5. TYPES OF TAXES

Two Types of taxes like

1. direct Tax and

2. Indirect Taxes

Direct Tax

Central Board of Direct Taxes (CBDT) is a part of

the Department of Revenue and it overlooks these direct

taxes.

Types of Direct Tax

Income Tax Act

Wealth Tax Act

Gift Tax Act

Interest Tax Ac:

Capital Gains Tax

Perquisite Tax

Corporate Tax – Types

Minimum Alternative Tax

Fringe Benefit Tax

Dividend Distribution Tax

Indirect Tax

Indirect Tax is depending on the goods and

services. It is differ from direct tax and it is not levied on a

person who pays them tax directly to the government.

Examples of Indirect Tax

https://www.bankbazaar.com/income-tax.html

https://www.bankbazaar.com/tax/income-tax-act.html

https://www.bankbazaar.com/tax/wealth-tax-act.html

https://www.bankbazaar.com/tax/capital-gains-tax.html

https://www.bankbazaar.com/tax/taxability-of-perquisites.html

https://www.bankbazaar.com/tax/corporate-tax.html


10

VAT (Value Added Tax), Taxes on Imported Goods, Sales

Tax, etc.

Sales Tax

Sales tax is a tax that is levied on the sale of a

product. The product can be something that was produced in

India or imported and can even cover services rendered. The

sales tax is levied on the seller of the product who then

transfers it onto the person who buys said product with the

sales tax added to the price of the product. The limitation of

the sales tax is that it can be levied only ones for a particular

product, which means that if the product is sold a second

time, sales tax cannot be applied to it.

Service Tax

Goods sold in India is priced in the Sales tax ,so

the service tax added to services provided in India. In

budget 2015, it was announced that the service tax will be

increased from 12.36% to 14%. It is not added on goods but

on companies that provide services and is collected every

month otherwise once every quarter based on how the

services are provided. If the establishment is an individual

service provider then the service tax is paid only once the

customer pays the bills.

The major service which comes under vicinity of

service tax are telephone, tour operator, architect, interior

decorator, advertising, beauty parlor, health center, banking

and financial service, event management, maintenance

service, consultancy service

Current rate of interest on service tax is 14.5%.

GST - Goods and Service Tax

GST is the largest reform in India’s indirect tax

structure since the market started opening up about 25 years

ago. The GST is a consumption-based tax, as it is applicable

when the consumption takes place. The GST is included on

value-added goods and services at each stage of

consumption in the supply chain. The GST payable on the

goods and services can be set off against the GST payable

on the supply of goods and services, the merchant will pay

the applicable GST rate but can claim it back through the

tax credit mechanism.

The Rajya Sabha passed the Constitutional Amendment Bill

required for introduction of GST bills on 3 August 2016

with more than two-third majority.

The IT framework and services for implementation

of the new taxation system will be managed by "Goods and

Services Tax Network (GSTN)", a non-government

company set up by the Centre and states.

GST rates in India ranges from 0% up to 50%:

which includes 0%, 5%, 12%, 18%, 28%, 29%, 31%, 43%,

45%, 48%, 50% {Additional cess includes (28%+1%),

(28%+3%), (28%+15%), (28%+17%), (28%+20%),

(28%+22%)} varying for both goods and services on 1211

items and services.

EV's (Full Electric Vehicles): 12%

Hybrid Vehicles: 43%

Value Added Tax

VAT, is a commercial tax is not applicable on

commodities that are zero rated (eg. food and essential

drugs) or those that fall under exports. The value added tax

is levied at all the stages of the supply chain, right from the

manufacturers, dealers and distributors to the end user.

The value added tax is a tax that is levied at the

discretion of the state government and not all the states

https://www.bankbazaar.com/sales-tax.html

https://www.bankbazaar.com/tax/service-tax.html

https://www.bankbazaar.com/tax/goods-and-services-tax.html

https://www.bankbazaar.com/tax/value-added-tax.html


11

implemented it when it was first announced. The tax is

levied on various goods sold in the state and the amount of

the tax is decided by the state government itself.

Custom duty & Octroi:

When the customer purchases anything that needs

to be imported from another country, a charge is applied on

it and that is the customs duty. The custom applies to all the

products that come in via land, sea or air. Even the customer

bring in products bought in another country to India, a

customs duty can be levied on it. The use of the customs

duty is to ensure that all the goods entering the country are

taxed and paid for. Customs duty ensures that goods for

other countries are taxed.

Octroi is means to ensure that goods crossing state

borders within India are taxed appropriately. Octroi levied

by the state government and functions in much the same

way as customs duty does.

Excise duty

Central Excise Act, 1944, it imposes a duty of excise on goods manufactured or produced in India. Excise duty as a

duty or tax levied upon the manufacture or production of commodities with in the country intended for home consumption.

CENTRAL BOARD OF DIRECT TAXES

Central government of India

S. No. Parliament of India

1 Taxes on income other than agricultural income (List I(Union List), Entry 82)

2 Duties of customs including export duties (List I(Union List), Entry 83)

3

Duties of excise on tobacco and other goods manufactured or produced in India except (i) alcoholic liquor for

human consumption, and (ii) opium, Indian hemp and other narcotic drugs and narcotics, but including

medicinal and toilet preparations containing alcohol or any substance included in (ii). (List I(Union List), Entry

84)

4 Corporation Tax (List I(Union List), Entry 85)

5 Taxes on capital value of assets, exclusive of agricultural land, of individuals and companies,

taxes on capital of companies (List I(Union List), Entry 86)

6 Estate duty in respect of property other than agricultural land (List I(Union List), Entry 87)

7 Duties in respect of succession to property other than agricultural land (List I(Union List), Entry

88)

https://www.bankbazaar.com/tax/custom-duty.html

https://en.wikipedia.org/wiki/File:Indian_Revenue_Service_Logo.png

https://en.wikipedia.org/wiki/Parliament_of_India

https://en.wikipedia.org/wiki/Income_tax_in_India

https://en.wikipedia.org/wiki/Duty_(economics)

https://en.wikipedia.org/wiki/Customs

https://en.wikipedia.org/wiki/Export

https://en.wikipedia.org/wiki/Excise

https://en.wikipedia.org/wiki/Tobacco

https://en.wikipedia.org/wiki/Liquor

https://en.wikipedia.org/wiki/Opium

https://en.wikipedia.org/wiki/Hemp

https://en.wikipedia.org/wiki/Drug

https://en.wikipedia.org/wiki/Corporation_Tax

https://en.wikipedia.org/wiki/Property_tax

https://en.wikipedia.org/wiki/Estate_duty

https://en.wikipedia.org/wiki/Inheritance_tax


12

8 Terminal taxes on goods or passengers, carried by railway, sea or air; taxes on railway fares and

freight (List I(Union List), Entry 89)

9 Taxes on sale or purchase of goods other than newspapers, where such sale or purchase takes

place in the course of inter-State trade or commerce

10 Taxes on the consignment of goods in the course of inter-State trade or commerce

It is an indirect tax which is often passed on to the consumer as part of the price. It levied or imposed at the manufacturing

stage and charged at specific rates. The duties may be levied on or specific basis or an advalorem basis that is Quantity and

Value of the commodities.

Central Excise Tariff Act, 1985

Central Excise Valuation (Determination of Price of Excisable Goods) Rules, 2000

Excise Duty:

This is a tax that is levied on all the goods

manufactured or produced in India. It is different from

customs duty because it is applicable only on things

produced in India and is also known as the Central Value

Added Tax or CENVAT. This tax is collected by the

government from the manufacturer of the goods. It can also

be collected from those entities that receive manufactured

goods and employ people to transport the goods from the

manufacturer to them.

Other Taxes in India

Professional Tax

Municipal Tax

Entertainment Tax

Stamp Duty, Registration Fees, Transfer Tax

Education Cess , Surcharge

Toll Tax

Dividend Tax

5. CONCLUSION

Tax is the major maintenance in the government of India

they taking major work in all the sectors like Direct and

Indirect taxes are the collection and filing of tax taking

importance and maintaining all the activities of the tax

payers. Based on the Income the tax payer is paying their

taxes and it is the major responsibility to them. Collection

of tax from the tax payer are using for the welfare of the

Indian economy development sector.

REFERENCES

1) https://www.hdfclife.com/insurance-knowledge-centre/tax-

saving-insurance/Tax-Structure-in-India

2) https://www.bankbazaar.com/tax/penalties-for-not-filing-tax-

how-to-avoid.html

3) http://moneyexcel.com/701/20-types-of-taxes-in-india

4) http://www.economicsdiscussion.net

5) https://taxguru.in/

6) www.google.com

7) Wikipedia

8) IndirectTaxes,,S.Sethurajan & K.Singaravelu, Speed Publishers

https://www.hdfclife.com/insurance-knowledge-centre/tax-saving-insurance/Tax-Structure-in-India

https://www.hdfclife.com/insurance-knowledge-centre/tax-saving-insurance/Tax-Structure-in-India

https://www.bankbazaar.com/tax/penalties-for-not-filing-tax-how-to-avoid.html

https://www.bankbazaar.com/tax/penalties-for-not-filing-tax-how-to-avoid.html

http://moneyexcel.com/701/20-types-of-taxes-in-india

http://www.economicsdiscussion.net/

http://www.google.com/


13

Goods and Service Tax in India S.Karthik

Abstract

The Goods and service Tax plays a vital role in the

Indian economic and also this type of tax are collected from the

tax payer like business peoples when their production depend

on the tax. The goods and services are increasing the tax of the

payer get more Income and the government are also get tax

from the beneficiary.GST create a uniform market throughout

the country from multiple taxes such as excise duty, service tax

in the central level and VAT at the state level.

1. INTRODUCTION

Goods and Services Tax (GST) is an indirect tax

that will be levied on productions, sale and consumption of

goods and services. The use of the GST is that when

applicable it will abolish all indirect taxes.The present

structure of Indirect Taxes is very difficult in India and tax

rates differ from State to State.

The tax payment as ‘Entertainment Tax’ for

watching a movie. We have to pay Value Added Tax

(VAT) on purchasing goods and services. Tax like’s Excise

duties, Import Duties, Luxury Tax, Central Sales Tax, and

Service Tax. GST bring uniformity and reduce the

cascading effect of these taxes by giving input tax credit.

GST objectives:

1. Improvement in the competitiveness of the original

Assistant Professor in Commerce,

Bishop Ambrose College, Coimbatore.

Mob. No: 90037483480,

[email protected]

goods and services, thereby improving the GDP rate

too.

2. Availability of input credit across the value chain.

3. Decrease the complications in tax administration

and compliance.

4. Making a law involving all the tax bases, laws and

administration procedures across the country.

5. Reduce the unhealthy competition among the states

due to taxes and revenues.

6. Adaptation mechanisms and trained staff.

7. The double registration might annoy people. Also,

these registrations result in increase compliances and

cost.

8. Uninformed estimate of the exact impact of GST.

9. No proper mechanisms to control tax evasion

2. IMPORTANCE OF GOODS AND SERVICES TAX

Goods and Services Tax (GST) is an indirect tax

that will be levied on manufacture, sale and consumption of

goods and services. The importance of the GST is that when

applicable it will abolish all indirect taxes. Hence the entire

system of taxation will be simpler.

The previous Government brought a Bill in

the Lok Sabha in 2011, but failed to get it passed. The NDA

Government introduced a “slightly modified” version of the

Bill in Lok Sabha last December. It was completed on May

6, 2016 but for GST to become a reality, the Bill must be

cleared by two-thirds majority by both Houses, and ratified

by 50% of states. It is now pending in the Rajya Sabha.

http://indianexpress.com/tag/lok-sabha/


14

The structure of Indirect Taxes is very complex

in India and tax rates differ from State to State. We payment

of ‘Entertainment Tax’ for watching a movie. We have to

pay Value Added Tax (VAT) on purchasing goods and

services. And there are Excise duties, Import Duties, Luxury

Tax, Central Sales Tax, Service Tax. GST will bring

uniformity and reduce the cascading effect of these taxes by

giving input tax credit.

GST subsumes many indirect and central levies and allows

a producer to claim credit for taxes paid on all inputs,

making production efficient. This is expected to reduce the

cost for consumers.

3. FEATURES OF GST

Subsume following indirect taxes:-

Central Excise duty,

Central Indirect Taxes

Additional duties of excise

CVD&SAD)

Excise duty levied under Medicinal and Toiletries

Preparation Act

Surcharge and Cess .

Service Tax

State Indirect Taxes

VAT/CST

Purchase tax

Entry tax

Octroi

Surcharge and Cess.

GST will have two components comprising

Central GST (CGST)

State GST (SGST)

An additional Tax of 1%

State Taxable supply of Goods by State of Origin and

it would be non CENVATABLE. The additional tax on

supply of goods shall be assigned to the States from where

such supplies originate.

All goods or services

State Excise plus VAT -Alcohol for human

consumption

Electricity Duty -Electricity

Stamp Duty plus Property Taxes -Real Estate

Petroleum Products

Codification is to be specified

It’s all goods and services in the purposed GST

structure. HSN code can be used for classification of goods

and existing accounting code can be used for classification

of services.

Removes

Effect of taxation.

Basics of GST – Implementation In India

Goods and services are the dual system currently India

has a taxation of, it is quite different from dual GST. Taxes

on goods are described as “VAT” Central and State level. It

has adopted value added tax with input tax credit

mechanism for the taxation of goods and services,

respectively, with limited cross-levy set-off.

4. MODELS OF GST

There are three prime models of GST:

Central (Union) Government Level only

State Government Level only

Union and State Government Levels


15

GST work in India:

In a first of its kind initiative, the GST will be

implemented in two components – Central GST or

CGST and State GST or SGST. This dual GST

will be levied on all the supply of goods and

services across the country.

Therefore, if there is a sale within the State, then

the both CGST and SGST will be charged.

However, if the sale is outside the State, then only

the Intra-State GST will be levied by the Centre.

CGST is doing away with indirect taxes such

Central Excise Duty, Service Tax, Addl. Customs

Duty, Special Addl. Customs Duty as well as

Addl. Excise Duty. These indirect taxes are those

that are collected by the Centre.

SGST will remove indirect taxes on goods and

services which are charged by the State such as

VAT, Entertainment Tax, Purchase Tax, Octroi,

Luxury Tax and Entry Tax.

The credits of Input Tax of CGST will be

accessible for settling the output of CGST liability

at every stage. Likewise, in the States, the credits

of SGST taken on the inputs will be made

available for clearing the output of SGST’s

liability at each stage.

GST Rates Reduced from 12% to 5% on 10-11-2017

1. Desiccated coconut

2. Narrow woven fabric including cotton newar [with no

refund of unutilised input tax credit]

3. Idli, dosa butter

4. Finished leather, chamois and composition leather

5. Coir cordage and ropes, jute twine, coir products

6. Fishing net and fishing hooks

7. Worn clothing

8. Fly ash brick

List of all Goods Covered under GST 12%

The GST Council, decision-making body for the

new tax, has fixed the tax framework under the Goods and

Services Tax (GST) which is to be rolled out this July 1.

Tax rates have been finalized for 1,211 items with a

majority of items being kept under the 18 per cent slab.

GST Rates Reduced from 18% to 12% on 10-11-

2017

1. Refined sugar & sugar cubes

2. Medicinal grade oxygen

3. Printing ink

4. Hand bags and shopping bags of jute and cotton

5. Milk

6. Hats

7. Parts of specified agricultural, horticultural,

forestry, harvesting or threshing machinery

8. Specified parts of sewing machine

9. Pasta

10. Curry paste, mayonnaise and salad dressings,

mixed condiments and mixed seasoning

11. Diabetic food

12. Spectacles frames


16

13. Furniture wholly made of bamboo or cane

GST Rates from 12% to 5% on 10-11-2017

1. Idli, dosa batter

2. Finished leather, chamois and composition leather

3. Coir cordage and ropes, jute twine, coir products

4. Desiccated coconut

5. Narrow woven fabric including cotton newar [with

no refund of unutilised input tax credit]

6. Fishing net and fishing hooks

5. CONCLUSION

GST plays a vital role in the Indian government

and the collection of tax amount are increase when the tax

updating coming likewise peoples get problems when

paying the GST at the same time business peoples and

trading peoples like small, medium and large all the peoples

getting affect of the GST. Government have to take step

decrees the tax amount then only the people can get wealth

in their business level.

6. REFERENCES

1. www.quora.com

2. https://gst.caknowledge.in/

3. www.google.com

4. Wikipedia


17

An Improvised Energy-Efficient LEACH for

Wireless Sensor Network

A.Krishnakumar1, Dr. V. Anuratha2

Abstract

Wireless Sensor Network (WSN) becomes an

emerging research area in recent years because of its easy

accessible nature. The Sensor nodes are combined to form a

cluster through which the collected information are

transferred to the Base Station (BS). The clusters are headed

by Cluster Head (CH) which is a normal sensor node.

However, the CH expends more energy level than the

member nodes for its processes which reduces the lifetime of

the network. To improvise the efficiency, the protocol

Energy-Efficient LEACH (EE-LEACH) is proposed. The CH

election is proposed in this protocol and it follows the other

activities as like as LEACH protocol. The evaluation result

shows the effectiveness of the approach used in EE-LEACH

protocol compared to the existing protocols.

1. INTRODUCTION

The sensors are attaining the peak of research in

recent period. Nowadays, the sensors are becoming an

important source in daily life which also creates the need

of developments and updates in tools and algorithms for

the sensor network. Whenever the WSN is under

discussion, the efficiency of utilising the energy (battery)

1.Krishnakumar,

Research Scholar,

2.Dr.V.Anuratha, Head,

Department of PG Computer Science,

Sree Saraswathi Thyagaraja College,

Pollachi.

of a sensor node becomes a top priority.

As long as the sensor node utilising the minimal

energy the lifetime of the network is improved. Therefore,

the energy efficiency in WSN is a serious task. The sensor

nodes are commonly deployed in an area where the human

involvements are very less. The sensor node has to collect

the information like heat, light, and so on from the

environment and forwards it to the BS. As of the dynamic

nature of the sensor nodes the node may long enough to

forward the collected data to the BS. In such situations, the

sensor node forwards the collected data to the nearby

neighbour node. The neighbour node forwards the data to

the BS or other neighbour node. Finally, the data reached

the destination. In this approach, the source sensor node

forwards the data to the neighbour node without analysing

that the node is closer to BS or not which may increase the

difficulties in reaching the data and the source node does

not consider whether the data is reached to the BS or not.

Likewise, there are some other issues which degrade the

performance of the WSN.

To resolve the above discussed issues,

Heinzelman et al., (2000) introduced Low-Energy

Adaptive Clustering Hierarchy (LEACH) protocol in

which the sensor nodes are grouped as clusters. The

clusters are formed based on probability function of


18

LEACH protocol. The probability function or threshold

function is detailed in Eq1.

Where T denotes threshold, n denotes number of

nodes, p denotes probability value. In LEACH the CH is

elected based on the random selection of number. Each

node chosen it value between zero (0) and one (1). Then

the node which holds lesser value than the threshold

function value is elected as CH. Suppose, more than a node

has a lesser value then the node which announces first that

it reaches the lesser value is elected as CH.

The CH then forwards the signal to its neighbour

nodes that it has been elected as CH and asks the

neighbours to join as member nodes. Also, the CH is

incharge for collecting the information from the

environment, aggregating it and forwarding to BS.

Therefore, in comparison to the member nodes the CH

needs more efficiency to complete all the tasks. The cluster

without CH or with a drained CH becomes useless until the

election of another CH. In LEACH protocol the CH is

elected in a round robin basis in which the node which is

elected as CH cannot act as CH for the next round.

LEACH follows Time Division Multiple Access (TDMA)

for round robin.

LEACH follows a convenient cluster formation,

TDMA schedule and so on however; the protocol fails in

electing energy efficient CH. To improvise the CH

election, EE-LEACH is proposed in which an efficient CH

election is attained than the existing protocols.

The section II discusses the review of literature

in which the other existing protocols are discussed. The

proposed protocol EE-LEACH is discussed in section III

where the proposed energy efficient radio model and vice-

CH election are discussed. Section IV discusses the

evaluation results of the proposed protocol and section V

concludes with conclusion and future enhancements.

Figure 1. Cluster formation in LEACH Protocol.


19

Figure 1 shows the cluster formation in LEACH

protocol in which each CH forwards the collected

information from its member nodes to BS.

II. REVIEW OF LITERATURE

Al-Baz & El-Sayed (2017) proposed a new

algorithm for cluster head selection. The algorithm

selects the CH based on its distance and residual energy.

The algorithm also follows a better cluster formation

approach in which the single node cluster formations are

identified as well as rectified. However, the algorithm

lacks in improvising the network lifetime because of not

following the LEACH’s TDMA schedule. The energy

expends for the CH election may reduce because of

relaxing the TDMA schedule but still the rotation of CH

in each round improvise the lifetime and performance of

the network. The algorithm also lacks in intra cluster

communication because of maintaining the same sensor

node for collection of data from the member nodes and

forwarding to BS.

Umar et al., (2017) designs a dynamic re-

clustering LEACH protocol in which the CH is elected

in a dynamic manner. Whenever the energy of a CH

expends to a certain range then the CH election is called

on. The new CH is elected based on its residual energy.

The node which holds high energy level is elected as

CH for the next rounds. LEACH’s TDMA is remodified

according the proposed protocol. The proposed protocol

achieves energy efficiency for a small area network but

for a large area network the proposed protocol fails in

addressing the energy efficient CH election node. In

addition, the dynamic CH election needs to reform the

routing table and routing algorithm to be update fast to

maintains the connectivity which also reduces the

energy level of the CH to some extent.

Bongale et al., (2017) proposed Energy influenced

probability based LEACH protocol. The protocol elects the

CH based on the probability value of the proposed protocol.

Each sensor node is given a probability value based on the

residual energy level. The high residual energy level sensor

node is given a high probability value and low residual

energy level sensor node is assigned with a low probability

value. This probability value is assigned in each round so

that the CH node can be easily elected based on the

probability value. The probability value is also calculated

based on the expending level of energy at each round. The

sensor node which expends minimum energy is given a

higher probability than the node which expends more

energy. The protocol focused in CH election and not

consider effective cluster formation, better data

transmission and so on which drains the energy level in

ease.

Yang et al., (2017) proposed a clustering

algorithm for energy efficient management in WSN in

which the cluster formation is considered as highest

priority than the CH election. The CH is elected based

on the distance parameter and residual energy level. The

sensor node which maintains lesser distance to BS and

highest residual energy is identified and elected as CH.

The elected CH announces its election to the neighbour

nodes and asks to join. The radius which CH forwards


20

the message is longer than the radius of the sensor node.

Therefore, the CH can form with more number of

member nodes and reduces the election of more CH and

cluster formation. The clusters are formed with more

number of nodes which is useful in collecting the

information but it leads to data congestion. The

proposed algorithm lacks in resolving the data

congestion.

Rad et al., (2017) proposed Improved W-

LEACH routing protocol in which the LEACH

protocol’s threshold function is modified to elect the CH

based on the residual energy and the number of

neighbour nodes. The proposed protocol focused in

number of neighbours’ parameter than the residual

energy of a node. The sensor node which holds higher

number of neighbour nodes is elected as CH. This CH

election leads to drains the energy level of CH in

collecting the data form the neighbour node. The

protocol also not follows the TDMA schedule which

also drains the energy level of the sensor node in ease.

3. PROPOSED PROTOCOL

This section details the proposed protocol EE-

LEACH. This proposed protocol focused in designing the

CH election based on the residual energy of a node and

distance parameter. The distance parameter is used in two

ways to form better cluster. This proposed protocol elects

two CHs called: i) CH and ii) Vice-CH.

The CH is in charge for collection and

aggregation of information from the member nodes. As

well as, the vice-CH is in charge to forwards to collected

information to BS.

The distance parameter is considered as distance to

BS for election of CH and distance between neighbours for

election of vice- CH. The residual energy acts as same in

both elections.

1. Inter-cluster communication

The inter-cluster communication of this proposed

protocol forwards the aggregated information from

the vice-CH to the BS or to the neighbour node which

is nearer to BS. For inter-cluster communication, this

proposed protocol elects energy efficient CH using

Eq. 2 and Eq. 3.

a) Residual Energy

The residual energy is identified as in Eq. 2.

Where, present denotes the present energy level of

a node whereas preset denotes the maximum energy level

of a node.

b) Distance to BS

The distance to BS is identified as in Eq. 3.

Where, distancenode denotes distance of a node and

distancefaraway denotes distance of the faraway node


21

2. Intra-cluster communication

The intra-cluster communication of this proposed

protocol collects the data form the member nodes and

aggregates the data and forward it to CH by vice-CH. The

vice-CH is elected using Eq. 2 and Eq. 4.

a) Distance between neighbours

The distance between neighbours is identified as in

Eq. 4.

(4)

Where, distancenode denotes distance of a node and

distancemaximum denotes maximum distance node.

This proposed protocol elects’ CH and vice-CH as

per LEACH’s TDMA schedule. The probability

function of LEACH protocol is modified as in Eq. 5.

(inter-cluster) and Eq.6.(intra-cluster).

and

4. EVALUATION RESULTS

This Protocol EE-LEACH is proposed to achieve the energy efficiency in sensor nodes to extend the lifetime of the network.

This proposed protocol is evaluated with the existing protocol Improved_W-LEACH using Network Simulator 2 (NS2). The

simulation environment parameters are detailed in Table 1.

Table 1. Simulation Parameters

Parameters Value

Number of Nodes 100 (0-99)

Initial energy 1Joule

BS node 100

Packet size 500 bytes

Maximum

simulation time 400 seconds


22

1.Residual Energy

The residual energy is one of major constraints

to identify the lifetime of the network. Therefore, the

residual energy of this proposed protocol EE-LEACH

and existing protocol Improved_W-LEACH is

detailed in Figure 2. It shows the energy expends at

each round between this proposed and existing

protocols.

This proposed protocol expends less energy level

than the existing protocol which prolongs the lifetime

of the network.

0

0.2

0.4

0.6

0.8

1

0 100 200 300 400

Ener

gy (

J)

Simulation Time (S)

EE-LEACH

Improved_W-LEACH

Figure 1. Residual Energy

1. Number of messages received

The number of messages received is to identify

the performance of the network. This proposed

protocol introduces vice-CH for data transmission.

Therefore, performance of this proposed protocol EE-

LEACH and existing protocol Improved_W-LEACH

is detailed in Figure 3.

This proposed protocol receives more messages

than the existing protocol which improves the

performance of the network.

10280

5670

0

2000

4000

6000

8000

10000

12000

Nu

mb

er o

f M

essa

ges

(N)

EE-LEACH Improved_W-LEACH

Figure 2. number of messages received

2. Number of alive nodes

The number of alive nodes is to identify the

performance as well as the lifetime of the network.

The performance of this proposed protocol EE-

LEACH and existing protocol Improved_W-LEACH

in number of alive nodes is detailed in Figure 3.

This proposed protocol maintains more number

of alive nodes than the existing protocol which

improves the overall performance of the network.

Figure 3. number of alive nodes

Figure 1 to Figure 3 shows the efficiency of this

proposed protocol EE-LEACH compared to the

existing protocol Improved_W-LEACH which

outperforms in all constraints.


23

V. CONCLUSION

The improvements in WSN creates the need of energy efficient CH to prolong the lifetime of the network. This

proposed protocol EE-LEACH concentrates in electing CH which carries the aggregated information to BS and this protocol

introduces the vice-CH which is concentrates in collecting and aggregating the information from the member nodes. This

approach improves the performance and prolongs the lifetime of the network. The evaluation results prove the performance

of the proposed protocol EE-LEACH. The future development is to improve the vice-CH functionalities such as cluster

formation, cluster movement and so on.

REFERENCES

1. Heinzelman, W. R., Chandrakasan, A., &

Balakrishnan, H. (2000, January). Energy-efficient

communication protocol for wireless microsensor

networks. In System sciences, 2000. Proceedings of

the 33rd annual Hawaii international conference on

(pp. 10-pp). IEEE.

2. Al‐Baz, A., & El‐Sayed, A. (2017). A new algorithm

for cluster head selection in LEACH protocol for

wireless sensor networks. International Journal of

Communication Systems.

3. Umar, S., Subbarayudu, Y., Kumar, K. K., &

Bashwanth, N. (2017). Designing of Dynamic Re-

clustering Leach Protocol for Calculating Total

Residual Time and Performance. International

Journal of Electrical and Computer Engineering

(IJECE), 7(3).

4. Bongale, A. M., Swarup, A., & Shivam, S. (2017,

February). EiP-LEACH: Energy influenced

probability based LEACH protocol for Wireless

Sensor Network. In Emerging Trends & Innovation

in ICT (ICEI), 2017 International Conference on (pp.

77-81). IEEE.

5. Yang, S. S., Shim, J. S., Jang, Y. H., Ju, Y. W., &

Park, S. C. (2017). Design of Clustering Algorithm

for Efficient Energy Management in Wireless Sensor

Network Environments. In Advanced Multimedia and

Ubiquitous Engineering (pp. 607-612). Springer,

Singapore.

6. Rad, F., Moghtaderinasab, Z., & Parvin, H. (2017).

An Improved W-LEACH Routing Protocol in

Wireless Sensor Network. Journal of Advances in

Computer Research, 8(2), 39-51.


24

A Study on Account Holders Satisfaction

towards Service Rendered by Keeranatham

Agricultural Credit Society, Coimbatore L.Lovely Lourds Preethi,

Abstract

Agriculture is the back bone of our country. Many

villages are based on the income they receive from agriculture

only. But for doing agriculture in now a days the villagers

don’t get much capital. For this problem the Government

started co-operative banks thought the country. The villagers

doing agriculture around the village, Keeranatham was

affected by insufficiency of finance from banks, so they

requested government to start a separate agriculture bank in

their area. As per the farmers request the government started

this bank to provide short term loans, loans with low interest,

quality seeds and good agricultural banking service to the

farmers and develop their income from agriculture.

1. INTRODUCTION

There are 4,595 Primary Agricultural Cooperative Banks at

the village level, providing short term and medium term

credit facilities to the agriculturists. These banks have

covered as on 31.3.02 85.96% of the agricultural

operational holdings in the State of which 79.57% belong to

weaker sections. Distinguishes between agricultural service

cooperatives, which provide various services to their

individually farming members, and agricultural production

cooperatives, where production resources (land, machinery)

are pooled and members farm jointly.

Assistant Professor,



Coimbatore.

Agricultural production cooperatives are relatively rare in

the world, and known examples are limited to

collective in former socialist countries and the kibbutzim in

Israel.

The default meaning of agricultural cooperative in English

is usually an agricultural service cooperative, which is the

numerically dominant form in the world. There are two

primary types of agricultural service cooperatives, supply

cooperative and marketing cooperative. Supply cooperatives

supply their members with inputs for agricultural

production, including seeds, fertilizers, fuel, and machinery.

Marketing cooperatives are established by farmers to

undertake transformation.

The co-operative movement, which is the largest socio-

economic movement in the world, has contributed

significantly to the alleviation of poverty, creation of

productive employment as well as the enhancement of

social integration in the country. The co-operative sector is

mainly concerned with agricultural credit, marketing of

agricultural produce and distribution of fertilizers and

pesticides and other essential commodities.

SHORT-TERM LOANS

The co-operative credit institutions were evolved mainly to

check the spurious practices of the moneylenders and to

http://en.wikipedia.org/wiki/CIS

http://en.wikipedia.org/wiki/Kibbutzim

http://en.wikipedia.org/wiki/Seeds

http://en.wikipedia.org/wiki/Fertilizers

http://en.wikipedia.org/wiki/Fuel


25

provide access to credit to various sections of the population

at reasonable rates of interest. The short term credit

structure in Tamil Nadu has a three-tier structure,

comprising of the Tamil Nadu State Apex Co-operative

Bank, (TNSCB) with 41 branches at the state level, 23

District Central Co-operative Banks (inclusive of one

Industrial Co-operative bank) with 750 branches at the

district level and around447 Primary Agricultural Credit

Societies (PACS) at the grassroot level, catering to the

needs of the farmers in 16317 villages (as on 31.3.2005).

The short term and medium term agricultural loans

provided by the Primary Agricultural Co-operative Banks

during the Tenth Five Year Plan wereRs.5043.93 crore and

Rs.291.51 crore as against the Target of Rs.7500 crore for

Short term and Rs. 625 crore for medium term respectively.

The short fallin achieving the target in short term credit

supply is mainly due to the continuous drought conditions

that prevailed in the state from 2002-03 to2004-05.

LONG TERM LOANS

The Tamilnadu Co-operative State Agriculture and

Rural Development Bank (TNSCARDB) at the state level

and Primary Co-operative Agricultural and Rural

Development banks (PCARDB) at the Taluk / Block level

constitute the two tiers of the long-term credit structure.

These banks provide loans totheir members for

operations related to agriculture and allied activities like

minor irrigation, cultivation of horticulture and Plantation

crops, poultry keeping, dairying, sheep breeding, sericulture

and purchase of tyre carts, tractors, power tillers, laying of

pipelines, construction of cattle shed, farmhouse etc, on a

schematic basis.

The Tamilnadu Co-operative State Agricultural

Rural Development Bank mobilizes the funds required for

loaning operations through floating of Special Development

Debentures. The Central, State Governments and NABARD

subscribe to these debentures.

Company Profile:

Keeranatham primary agriculture co-operative loan

society was Registered at 02.06.1930 and the bank activities

was started at 09.06.1930.

This bank functioning under the co-operative

societies act 1983 and rules 102 (8) of Tamilnadu societies

Rules 1988. The bank is placed under “B” class for the year

under Audit.

There were members 972 at the end of the year as

against 1304 at the beginning of the year. The share capital

of the members amounted to Rs 6, 30,940 againstRs ,56,405

at the beginning of the year.

The value of stock at the beginning of the year was

1,17,357.68 stock of the value of Rs 34,64,627.40 bought

during the year less purchase excluding sales return etc.

amounted to Rs 36,36,569.20. The value of closing stock at

the end of the year was 1, 4,427.47. Net profit should be

disbursed in accordance with the Act rules and by laws of

the society.


26

2. OBJECTIVE OF THE STUDY:

The objectives of the study are:

To study the account holders satisfaction towards

service rendered by bank.

To find out the problems related to A/c holders

To find out the influencing factors of the A/c holders

To offer suggestions to growth and development of

the bank.

3. SCOPE OF THE STUDY:

The study has been under taken:-

To assess the banks real position while providing

services to their customers.

To understand the Account holders satisfaction level

and provide solutions for their problems.

To know the real situation of the bank while

implementing new policies and loans.

To understand the Account holders expectations and

fulfill their needs from this study.

4. LIMITATIONS OF THE STUDY:

Limitations of the study are:

The respondents of this bank would not have

reveled negative issues of the bank.

The satisfaction level of the Account holders

change from time to time, Hence the result of the

project may not be applicable in the long run.

The information provided by the respondents would

be biased to certain extent.

Science most of the respondents are from rural areas

and they would not be exposed to good banking

service, hence they would think the present services

provided by this bank is the best.

5. RESEARCH METHODOLOGY:

RESEARCH DESIGN:

Research design is the basic frame work which

provides guideline for the rest of research process. It

specifies the methods of data collection and analysis. In this

study descriptive research designing is used.

DESCRIPTIVE RESEARCH DESIGN

Descriptive research design describes the Account

holder’s satisfaction towards service rendered by

Keeranatham primary agriculture bank. The main purpose

of this is to setting knowledge about to subject.

POPULATION

The population (952) specifies that the Account

holders atKeeranatham primary agriculture loan society.

SAMPLING DESIGN:

Sampling size:

This study contains 110 Account holders as

sample size.

DATA COLLECTION METHOD:

For this study two types of data were collected. One is

primary data and another One is the secondary data.


27

Primary data:

The Primary data were collected for the first time and

thus happen to be Original in character. Primary data were

collected through distributing questionnaire to the

respondents.

Secondary data:

The secondary data were already available in the

records of the bank. The Company profile and Account

holder’s details are secondary data of the bank.

6. STATISTICAL TECHNIQUES USED:

Percentage method:

The number of respondents of each category is

summarized to percentage for the convenience to the other

statistical tools namely pie chart, and bar Diagrams.

Chi square:

It is used to find the relationship between two

items.

TABLE 1

Table showing service provided by the bank

Description

Number of

respondent

Percentage

Highly

Satisfied 51 47

Satisfied 26 23

Neutral 33 30

Dissatisfied 0 0

Highly

Dissatisfied 0 0

Total 110 100

Interpretation:

The above table shows that 47% of the

respondents are highly satisfied with service provided by

the bank, 23% of the respondents are satisfied with the

service provided by the bank, and 30% of the respondents

are neutral with the service provided by the bank.

TABLE 2

Table showing the time taken for availing

loans.

Description

Number of

respondent

Percentage

Least time

23

21

Lesser time

31

28

Medium

15

14

Longer

29

27

Too longer

12

10

Total

110

100

Interpretation:

The above table shows that 21% of the

respondents are highly satisfied with the time taken for

availing loans, 28% of the respondents are satisfied with

the time taken for availing loans, 14% of the respondents


28

are neutrally with the time taken for availing loans, 27%

of the respondents are dissatisfied with the time taken for

availing loans and 10% of the respondents are highly

dissatisfied with the time taken for availing loans from

this bank.

CHI- SQUARE TEST

RELATIONSHIP BETWEEN LEVEL OF SATISFACTION TOWARDS

SOLUTION PROVIDING AND APPROACHING THE BANK MANAGER. Null hypothesis (Ho) There is no significant relationship between solution providing and approaching the bank manager.

Alternative Hypothesis (Ha)

There is a significant relationship between solution providing and approaching the bank manager.

TABLE 3

Table showing solution providing and approaching the bank manager.

Solution

provided to

problem

Approaching the bank manager

Highly

satisfied Satisfied Neutral Dissatisfied

Highly

Dissatisfied

Total

Highly Satisfied 5 6 5 4 0 20

Satisfied 5 24 7 5 0 41

Neutral 6 3 3 4 6 22

Dissatisfied 5 5 4 4 0 18

Highly

Dissatisfied 2 0 3 3 1 9

Total 23 38 22 20 7 110

Row total × column total

Expected Frequency = -------------------------------------

Grand Total


29

Observed

frequency

Expected

frequency

( Oi –Ei)

2

(Oi-Ei)

2

(Oi-Ei) /Ei

5

4.18

0.82

0.67

0.16

5 4.00 1.00 1.00 0.25

4 3.63 0.37 0.13 0.03

0 1.27 -1.27 1.61 1.26

5 8.57 -3.57 12.74 1.48

24 14.16 9.84 96.82 6.83

7 8.20 -1.20 1.44 0.17

5 7.45 -2.45 6.00 0.80

0 2.60 -2.60 6.76 2.60

6 4.60 1.4 1.96 0.42

3 7.60 -4.60 21.16 2.78

3 4.40 -1.4 1.96 0.44

4 4.00 0 0 0

6 1.40 4.6 21.16 15.11

5 3.76 1.24 1.53 0.40

5 6.21 -1.21 1.46 0.23

4 3.60 0.4 0.16 0.04

4 3.20 0.8 0.64 0.20

0 1.14 -1.14 1.29 1.13

0 3.10 -3.10 9.61 3.10

3 1.80 1.2 1.44 0.80

3 1.63 1.37 1.87 1.14

1 0.53 0.47 0.22 0.41

Total

45.20

Total: Calculated value = 45.20

Degree of freedom = (5-1) (5-1)

= 4*4

V = 16, Table value (0.05) = 26.03.

The calculated value of chi square is much higher than the table value. Hence the hypothesis is rejected and we

conclude that there is relationship between Level of Satisfaction towards Solution providing and approaching the Bank

Manager.


30

RELATIONSHIP BETWEEN LEVEL OF SATISFACTION TOWARDS

SERVICE PROVIDED AND LOANS AVAILED IN THE BANK

Null hypothesis (Ho)

There is no significant relationship between solution providing and approaching the bank manager.

Alternative Hypothesis (Ha)

There is a significant relationship between solution providing and approaching the bank manager.

TABLE 4

Table showing level of Satisfaction towards Service Provided and Loans Availed in the bank.

Service

Rendered by

The bank

Availed loans in the bank

Yes No Total

Highly Satisfied 32 19 51

Satisfied 16 10 26

Neutral 20 13 33

Dissatisfied 0 0 0

Highly

Dissatisfied 0 0

0

Total

68

42

110

Row total × column total

Expected Frequency = -------------------------------------

Grand Total


31

Observed

frequency

Expected

frequency

( Oi –Ei) 2

(Oi-Ei)

2

(Oi-Ei) /Ei

32

31.57

0.43

0.18

5.70

19

19.47

-0.47

0.22

0.01

16

16.00

0

0

0

10

9.92

0.08

0.64

0.06

20

20.40

-0.4

0.16

7.84

13

12.60

0.4

0.16

0.01

0

0

0

0

0

Total 13.62

Total:

Calculated value = 13.62

Degree of freedom = (2-1) (5-1)

= 1*4

V = 4, Table value (0.05) =9.49.

The calculated value of chi square is much higher than the

table value. Hence the hypothesis is rejected and we

conclude that there is relationship between Level of

Satisfaction towards Service providing and Loans Availed

in the Bank.

CONCLUSION

Out of total population 952 Account holders, a sample

of 110 was selected for the study. With information

collected it was found out that most of the Account

holders are satisfied with the service rendered by this

bank.

If the Bank concentrates the weakest points we noticed

in the finding chapter it will make some positive results to

this bank. The suggestions would create a conducive

climate for the organization to achieve its objectives

effectively.

REFERENCES

1. Jordon, Natarajan, “Banking Theory, Law and

Practice”, Himalaya Publications, 19th Edition,

pp501516.

2. Jayaragavan Itengar, “Introduction to Banking”,

Excel Books, First Edition, New Delhi, pp220-233

https://www.unionbankonline.co.in/Disclaimer/Disclai

me r.htm

3. http://www.unionbankofindia.co.in/ last accessed on

12/09/20122.

4. http://money.rediff.com/companies/union-bank-of-

india/14030018/ratio last accessed on 16/09/20123.

5. http://capitaline.com/user/framepage.asp?id=1 last

accessed on 05/10/2012.

https://www.unionbankonline.co.in/Disclaimer/Disclaime

https://www.unionbankonline.co.in/Disclaimer/Disclaime


32

A Study Pertaining to Test of Equality of

Selected Indian Pharmaceutical Companies Dr.Ramya

1. INTRODUCTION

The Indian pharmaceutical industry currently tops

amongst the India’s science based industries with wide

ranging capabilities in the complex field of drug

manufacture and technology. The pharmaceutical industry

in India is the world’s third largest in terms of volume and

stands 14th terms of value. India’s pharmaceutical market

grew at 15.7 per cent during the year 2011. The Indian

pharmaceutical industry is expected to grow at a rate of 9.9

per cent in 2010 and after that 9.5 per cent till 20151(

www. Indiabiznews.com). The Indian pharmaceutical

market is expected to touch US$ 74 billion sales instead of

US$11 billion sales by 2020. India joined among the

league of top ten global pharmaceutical markets in terms of

sales by 2020 with value reaching US$ 50 billion2. Exports

of pharmaceutical products from India increased from US$

6.23billion in 2006-076 to US$ 8.7 billion in 2008-09. The

Indian pharmaceutical sector is highly split with more than

20,000 registered units. It has expanded hugely in the last

two decades. The pharmaceutical industry in India is an

extremely fragmented market with severe price

competition and government price control.


Department of Commerce (CA),

Sankara college of Science and Commerce,

Coimbatore.

The pharmaceutical industry in India meet around

70% of the country’s demand for bulk drugs, drug

intermediates, pharmaceutical formulations, chemicals,

tablets, capsules, orals and injectable.

There are approximately 250 large units and about

800 small scale units in India. Which from the core of

pharmaceutical industry in India ( including 5 central

sector units). The government stated to encourage the

growth of drug manufacturing by Indian companies in the

early 1960’s and with the patents Act in 1970. However,

economic liberalization in 90’s the former prime minister

P.V. Narashima Rao and then finance minister Dr.

Manmohan singh enabled the industry to become what it is

today3

The pharma companies have started facing challenges in

domestic market due to increase in competition from

unlisted MNCs in this segment. They are rapidly

expanding their field force to extend their geographical

reach and also Pharmaceutical companies entered a

difficult period where shareholders, the market and

regulators have created significant pressures for changing

with in the industry. The pharma industry also have

challenges particularly to improve infrastructure, new

product patent, drug price control and quality management

and R&D programs.

In the 1960’s the government started to encourage

the growth of drug manufacturing by Indian companies,


33

and also passed the patents Act in 1970. India currently

holds a modest 1-2% share, but it has been growing up

approximately 10% per year in terms of global markets.

India with its innovatively engineered genetic drugs and

active pharmaceutical ingredients (API), has gained a good

foothold in the global scene. And today India is seeking to

become a major player in outsourced clinical research as

well as contract and manufacturing and research4

((Indianmirror.com)

Indian pharma Industry emerged as developing industry

which has been able to prepare H1N1 vaccine. The

national institutes of Health, funded the scientists and the

vaccine was developed. This new vaccine works against

the old virus because the year 1918 and the 2009 strains of

H1N1 influenza share features that allow vaccine

generated antibodies both viruses. One more mile stone in

the industry is that India’s first domestic vaccine against

swine flu was made possible.

The demand for pharmaceutical products in India is

significant and is driven by many factors like low drug

penetration, rising middle class and disposable income,

increased government and private spending on healthcare

infrastructure, increasing medical insurance penetration,

changing demographic pattern and rise in chronic lifestyle

related diseases: adoption of product patents, and

aggressive market penetration driven by the relatively

smaller companies5 ( www.Indianmirror.com)

1.2. NEED FOR THE STUDY

In 2010, April 14, the Indian Pharmaceutical

Industry has been placed among the top four emerging

markets in pharma industry by the market research report

published by IMS ( Indian Medical Science ) Health

India. In the last few years, the global pharmaceutical

industry has shown a high interest in the Indian pharma

industry because of its sustained economic growth,

healthcare reforms and patent-related legislation.

Indian domestic pharmaceutical market has seen a growth

at a CAGR of about 12% in the last 5 years. About 67

Million Indians are expected to reach the age of 67 years

by 2011. People of this age group spend around 3 to 4

times more on drugs than people in younger age groups.

This indicates substantial growth of Indian pharmaceutical

industry. Patented drug are expected to have a 10% market

share of pharmaceutical industry in 2010. Every business

organization, whether manufacturing oriented or service

oriented, needs finance, i.e., money for carrying its

activities. Though business organization gets sufficient

money for carrying its activities, success of the business

depends on how well the organization manages them. That

is, it depends on how well a business organization funds its

capital and how efficiently it operates out of the invested

capital and to generate profit. While the success of a

business is also a subjective measure of how well a firm

can finance its assets and make use of the assets to

generate revenues, the business can be stable and healthy if

its financial performance consistently yields profit. These

measures often determine reorganization is considered to

be inefficient, if the performance level is often to be low,

even if it is making profit.

http://www.indianmirror.com/

http://pharmaceutical-drug-manufacturers.com/news/emerging-markets-pharmaindustry.html

http://pharmaceutical-drug-manufacturers.com/news/emerging-markets-pharmaindustry.html


34

1.3. STATEMENT OF THE PROBLEM

The numbers of purely Indian pharmaceutical

companies are low. Indian pharma industry is mainly

operated as well as controlled by dominant foreign

companies having subsidiaries in India due to availability

of cheap labour in India at low cost. In 2002, over 20,000

registered drugs manufactures in India sold $9 billion

worth of formulations and bulk drugs. 85% of these

formulations were sold in India while over 60% of the bulk

drugs were exported ( www.wikipedia.com)

Over the past decade, pharmaceutical companies have

entered a difficult period where shareholders, the market

and regulators have created significant pressures for

changes within the industry. The core issues for most of

drug companies are declining productivity of in-house

R&D, patent expiration of number of block buster drugs,

increasing legal and regulatory concern, and pricing issue.

The larger pharmaceutical companies are shifting to new

business model with greater outsourcing of discovery

services, clinical research and manufacturing.

Current global financial conditions and the threat of a

broad recession accelerated the timetable for implementing

transformational changes in global organizations, as the

industry confronts lower corporate stock prices and an

increasingly cost-averse customer. Leaders of the largest

global pharmaceutical companies recognize the need for

transformational change in their organizations, but will

need to move swiftly to ensure sustained growth. About 67

Million Indians are expected to reach the age of 67 years

by 2011. People of this age group spend around 3 to 4

times more on drugs than people in younger age groups.

This indicates substantial growth of Indian pharmaceutical

industry and the patented drugs are expected to have a 10

per cent market share of pharmaceutical industry in 2010.

The Indian pharmaceutical industry would have to

contend with several challenges particularly

Effects of new product patent, Drug price control,

Regulatory reforms Infrastructure development

Quality management and Conformance to global

standards.

In the above challenges financial performance is

playing a vital role and a sound financial strength is met all

these challenges so it is necessary to find out the overall

financial status of the pharmaceutical industry is essential.

Financial performance analysis is the process of

determining the operation and financial characteristics of a

firm from accounting and financial statements. The ability

of an organization is to analyze its financial position is

essential for improving its competitive position in the

market. Through a careful analysis of its financial

performance, the organization can identify opportunities to

improve performance of the industry.

From the above point of view the present study is focused

to examine the impact of selected financial parameters,

their growth performance and their contributions towards

earnings of the particular Indian Pharmaceuticals

Companies.


35

1.4. OBJECTIVES OF THE STUDY

To study the growth and development of Indian

Pharmaceutical companies.

To Test equality of variance

To examine the consistency and growth rate of

selected financial parameters of the selected

Pharmaceutical Companies.

1.5. HYPOTHESES

Null hypothesis were framed for this present study

1.6. METHODOLOGY

1.6.1. SOURCES OF DATA

Secondary data are used in this study, which were

collected from the Capitalineplus corporate database and

PROWESS of the Centre for Monitoring Indian Economy.

Variables pertaining to behavior of liquidity, leverage and

profitability were collected from the balance sheet and

profit and loss account of the selected pharmaceutical

companies for a period of 10 years i.e from 2002-03 to

2011-12.

Besides the corporate database, reports were

collected from Bulletin, Libraries of various institutions

and Research Publications. Editing, classifications and

tabulation of financial data collected from the above

mentioned sources have been done as per the requirements

of the study. In this twenty two bulk companies and ten

formulation companies and three MNC companies were

selected for the study.

1.6.2. SELECTION OF THE SAMPLE

The study is confined to the Pharmaceutical

companies in India. The companies, which have

Continuous data available for all the ten

accounting year.

The companies’ shares were actively trade in

NSE.

The companies for which the data were not

available for one or and more than one year in

between or in the beginning or at the end of the

study period have been ignored.

Totally thirty six pharmaceutical companies were

selected out of 128 companies. Only those companies who

have been in the field for more than ten years and have

valid annual reports are selected for the study.

In this twenty two bulk drugs companies and ten

formulation companies and three MNC companies were

selected for the study. Fifty per cent of companies are

selected for the study on the basis of share capital.

1.6.3. PERIOD OF THE STUDY.

The Indian Pharmaceutical industry increased a domestic

and export share of Rs. 260 billion in the financial year of

2002-03, which accounts for 1.3% of the global

pharmaceutical sector and India exports its pharma

products to various countries around the globe including

highly regulated markets of USA, Europe, Japan and

Australia. It is the reason for the present study to covers a

period from 2002-03 to 2011-12.


36

1.7. SCOPE OF THE STUDY

The study is basically restricted to companies listed in

Bombay Stock Exchange. Physical and market

performance of the companies were not paid any attention

because good performance in them would ultimately be

reflected in the financial variables which is taken for

consideration. A random sample of thirty six

Pharmaceutical Companies has been taken up for the

study. The study has taken financial, accounting and

quantitative data covering a period of ten years from the

financial year 2002-03 to 2011-12.

1.8. LIMITATIONS OF THE STUDY

The study covers a period of ten years from 2002-

03 to 2011-12 for thirty six pharmaceutical companies

were selected. Secondary data were collected from

Centre for Monitoring Indian Economic and Captaline

corporate database due to cost and time constrains.

Considering the availability of continuous data sample

size has been fixed.

1.9. CHAPTER SCHEME

The present study is organized as, introduction,

significance of study, statement of the problem, objectives

of the study, hypotheses framed, sources of data, selection

of sample, period of the study, frame work of analysis,

scope of the study and limitation of the study ,ANOVA

analysis and Summary of findings, conclusion and

suggestions.

II. ANOVA

To test equality of variance and to determine whether

the result of financial analysis from the four groups makes

any difference or not the Hartley’s F max test for

homogeneity of variance is used.

To test the hypotheses of comparing mean performance

among the different groups one-way ANOVA is used and

the results n the test (log transformed) are presented in the

following tables.

1. Hartley’s F Max Test

Hartley’s F Max test for homogeneity of variance is used

to determine whether the result from the four areas of

financial analysis makes any difference or not. Hartley’s F

max test is a simple device to test the equality of variance.

F max statistic can be obtained from Hartley’s F max

distribution with C and (n-1) degree of

F max [C1(n-1)] = S2 max

-----------

S2 min

Where S2 max = largest sample variance

S2 = Smallest sample variance

n-1 = number of period in the test less one

C = number of groups in the test

2. Analysis of Variance (One Way)

Analysis of Variance test based on F – Statistics is

applied to estimate and compare the mean of the selected

variables.

Hypothesis 1

Null Hypothesis :

There is no significant difference in the mean total assets

among different groups of companies.


37

FINANCIAL PERFORMANCE AMONG GROUPS

OF COMPANIES

In order to compare the financial performance

among Bulk(23), Formulation(10) and MNC(3) companies

during the period of study Analysis of Variance is

performed and the results are furnished in the tables given

below:

Null Hypothesis:

There is no significant difference in the mean total

assets among the groups of companies during the study

period.

Table 2.1 ANOVA –Total Assets

SS – Sum of Square DF – Degree of Freedom MS –

Mean Square

Source : Computed

**- Significant at 1 % level

Table 2.1. expose that the F value of 6.60 is

significant the null hypothesis of no difference in the mean

total assets among the groups of companies is rejected and

it is concluded that there is a significant difference in the

mean total assets among the groups of companies. The

mean total assets among the groups are presented in the

table

Source : Computed

The above table showed that the mean total assets,

ranged from Rs.12.74 to Rs.2491.397 and the Formulation

group of companies stood at top whereas the Bulk 5-10 cr

group of companies stood at last.

Null Hypothesis:

There is no significant difference in the mean Net

sales among the groups of companies during the study

period.

SS – Sum of Square DF – Degree of Freedom

MS – Mean Square

Source : Computed


F value of net sales 2.3. which is significant at

1% hence the null hypothesis of no difference in the mean

net sales among the groups are rejected and there is

significant difference in the mean total assets among the

groups of companies. The mean net sales among the

groups are presented in the table 2.4.

Primary data

The above table showed that the mean net sales,

SOURCE S S D F M S F

Between

groups 33423033 6 5570506

6.60*

*

Within

groups 24463073 29 843554.2

Groups of

Companies

No. of

Companies

Mean Total

Assets

Rank

BULK 1-5 CR 4 35.22 6

BULK 5-10 CR 4 12.74 7

BULK 10-15 CR 3 241.17 5

BULK 15-25 CR 8 744.57 3

BULK ABOVE 25

CR 4 1462.72 2

FORMULATION 10 2491.39 1

MNC 3 506.85 4


38

ranged from Rs.16.31 to Rs.1929.76 and the Formulation

group of companies stood at top whereas the Bulk 5-10 cr


Null Hypothesis:

There is no significant difference in the mean net worth

among the groups of companies during the study period.

ANOVA TABLE 2.5.- NET WORTH


Between

groups

13012195.25 6 2168699.2

1 4.38**

Within groups 14360844.84 29 495201.55


Mean Square

Source : Computed


Table 2.5. portrays that the F value is 4.38

significant the null hypothesis of no difference in the mean

Net worth among the groups of companies is rejected and

it is concluded that there is a significant difference in the

mean net worth among the groups of companies. The mean

Net worth among the groups are presented in the table 2.6.

MEAN NET WORTH

Table 2.6. Mean performance of Net Worth.

The table showed that the mean Net worth, ranged from

Rs.7.24 to Rs.1551.60.

Groups

Companies

Mean Net

Worth

Rank

BULK 1-5

CR 4 31.97 6

BULK 5-10

CR 4 7.24 7

BULK 10-15

CR 3 141.06 5

BULK 15-25

CR 8 340.89 3

BULK

ABOVE 25

CR

4 799.03 2

FORMULA

TION 10 1551.60 1

MNC 3 494.08 4

Formulation group of companies stood at top

whereas the Bulk 5-10 cr group of companies stood at last.

Null Hypothesis:

There is no significant difference in the mean

Gross profit among the groups of companies during the

study period.

ANOVA TABLE 2.7.- GROSS PROFIT


Between

groups 693660.53 6 115610.09 3.40**

Within

groups 986614.29 29 34021.18


Mean Square


39

Source : Computed


Table 2.7. confines that the F value 3.40 is

significant at 1% level the null hypothesis of no difference

in the mean Gross profit among the groups of companies is

rejected and it is accepted that there is significant

difference in the mean gross profit among the groups and it

is explained from the mean performance

TABLE 2.8.

GROUPS

COMPANIES

MEAN

GROSS

PROFIT

RANK

BULK 1-5 CR 4 6.64 6

BULK 5-10 CR 4 1.02 7

BULK 10-15 CR 3 25.59 5

BULK 15-25 CR 8 78.70 3

BULK ABOVE

25 CR

4 261.74 2


MNC 3 183.73 4

Source : Computed

The above table showed that the mean Gross

profit, ranged from Rs.1.02 to Rs.346.64 Cr and the

Formulation group of companies stood at top whereas the

Bulk 5-10 Cr group of companies stood at last.

Null Hypothesis:



period.

ANOVA TABLE 2.9.- EBIT

SOURCE S S D

F M S F

Between

groups 879003.21 6 146500.53 4.47**

Within

groups 950371.92 29 32771.45

SS – Sum of Square DF – Degree of Freedom

MS – Mean Square

Source : Computed


From the table 2.9. it is observed that the F value

is significant the null hypothesis of no difference in the

mean EBIT among the groups of companies is rejected and

there is significant difference in the mean total assets

among the groups of companies. The mean EBIT among

the groups is furnished in the tables given below:

TABLE 2.10. MEAN EBIT

GROUPS COMPANIES

MEAN

EBIT

RANK

BULK 1-5 CR 4 6.86 6

BULK 5-10 CR 4 1.51 7

BULK 10-15 CR 3 34.17 5

BULK 15-25 CR 8 104.20 3

BULK ABOVE 25 CR 4 305.24 2


MNC 3 184.28 4


40

Primary data

The above table showed that the mean EBIT,

ranged from Rs.1.51 to 394.55 Cr and the Formulation

group of companies stood at top whereas the Bulk 5-10 Cr


Null Hypothesis:

There is no significant difference in the mean

PAT among the groups of companies during the study

period


Between groups 349390.70 6.00 58231.78 2.58*

Within groups 655805.93 29.00 22614.00


Mean Square

Source : Computed

*- Significant at 5 % level

It is observed from the Table 2.11. F value 2.58 is

significant at 5% level the null hypothesis of no

difference in the mean PAT among the groups of

companies is rejected and there is significant difference

in the mean PAT among the groups of companies. The

mean score of Profit after tax among the group of

companies is furnished in the tables 2.12.

TABLE 2.12. Mean performance of Profit after

Tax(Rs. In crores)

GROUPS

COMPANIES

MEAN

PAT

RANK

BULK 1-5 CR 4 4.18 6

BULK 5-10

CR 4 0.35 7

BULK 10-15

CR 3 13.06 5

GROUPS COMPANIES

MEAN

PAT

RANK

BULK 15-25

CR 8 49.10 3

BULK

ABOVE 25

CR

4 177.00 2

FORMULATI

ON 10 244.39 1

MNC 3 115.01 4

Source : Computed

The above table showed that the mean PAT,

ranged from Rs.0.35 to 244.39 Cr and the Formulation

group of companies stood at top whereas the Bulk 5-10 Cr


Null Hypothesis:


debts among the groups of companies during the study

period.

ANOVA TABLE 2.13.- TOTAL DEBTS


Between

groups 5155148.17 6

859191.

36

1.85

NS

Within groups 13439907.41 29 463445.

08


Mean Square

Source : Computed

Ns-non significant at 5 % level

Since the F value 1.85. is no significant from the

table 2.13. the null hypothesis is accepted and it is

concluded that there is a no significant difference in the


41

mean total debts among the groups of companies. The

mean total debts among the groups are given in table 2.13.

GROUPS

COM

PANI

ES

MEAN

TOTAL

DEBTS

RANK

BULK 1-5 CR 4 3.24 -

BULK 5-10 CR 4 5.30 -

BULK 10-15 CR 3 93.39 -

BULK 15-25 CR 8 756.34 -

BULK ABOVE

25 CR 4 653.54 -

FORMULATIO

N 10 865.72 -

MNC 3 3.66 -

Source : computed

The above table showed that the mean total debts,

ranged from Rs.3.24 to Rs.865.72 Cr and the mean total

debts are on par among the groups of companies.

Null Hypothesis:



period.

ANOVA TABLE 2.15.- EARNINGS PER SHARE


Between

groups 6682.20 6 1113.70

1.59

NS

Within

groups 20299.16 29 699.97

NS- Significant at 5% level

SUMMARY OF FINDINGS, CONCLUSIONS

AND SUGGESTIONS

ANOVA

The mean total assets, ranged from Rs.12.74 to

Rs.2491.397 and the Formulation group of companies

stood at top whereas the Bulk 5-10 cr group of companies

stood at last.

The mean net sales, ranged from Rs.16.31 to



stood at last.

The mean Net worth, ranged from Rs.7.24 to



stood at last.

The mean Gross profit, ranged from Rs.1.02 to

Rs.346.64 Cr and the Formulation group of companies

stood at top whereas the Bulk 5-10 Cr group of

companies stood at last.

The mean EBIT, ranged from Rs.1.51 to 394.55

Cr and the Formulation group of companies stood at top

whereas the Bulk 5-10 Cr group of companies stood at

last.

The mean PAT, ranged from Rs.0.35 to 244.39 Cr

and the Formulation group of companies stood at top

whereas the Bulk 5-10 Cr group of companies stood at

last.

The mean total debts, ranged from Rs.3.24 to

Rs.865.72 Cr and the mean total debts are on par among

the groups of companies.

the mean EPR, ranged from 0.67 to 46.91 and the

mean EPR are on par among the groups of companies.

At corporate level globalization takes place when

companies decided to take part in the emerging global

economy and establish themselves in foreign markets.


42

First they adopt their products or services to the financial

user’s linguistic and cultural requirements, and then they

might take advantage of the internet revolution and

establish a virtual presence on the international market

place with a mulitilingual corporatism.

The industry today faces the challenges of

competitions from global giants and Indian companies

response the challenges ahead and upgrade them by

moving up the value chain . they have to make a move

from offering low-end services to high-end services such

as product development, research and development,

innovation and end-to-end turnkey solutions.

Performance of a company is measured in

financial terms, the success of the firm depends on how it

is perceived by and reacts to the external economic

markets. The field of managing finance is much more

complicated and faster faces today. Important and swift

development in the field of finance and application of

new theories in decision making environment yield new

challenges and opportunities for financial managers

knowledge of all these developments and their impact is

necessary for the effective management and financial

viability of the modern business firms. Financial

managers need to know how effective decisions can be

made and ineffective ones be avoided.

Objectives of the study were to examine their

financial profile and its growth rate by applying summary

statistical measures, compound growth rate, Hartly

F’max test, one way ANOVA and forecasted trend

pattern of the selected variables by applying Polynominal

Cubic trend are presented in group-wise classification.

From the Hartly’s Fmax test of equality of

variance it is observed that except the total debts, and

earnings per share other selected variables having

homogeneity of variance. Test of significance between

the mean performance among different groups are

analysed by ANOVA, it is observed that the mean

performance of total debts and earnings per share are not

having significant difference and all other variables are

having a significant different between the groups.

6. REFERENCES

[1] Agarwarl, M.P., Analysis of Financial Statements, National

Publishing House, New Delhi, 1981, p.5.

[2] Batty, Management Accounting, McDonald and Evans

Ltd.,1970,p.143.

[3] Pandy, I.M., Elements of Financial Management, Vikas

Publishing House Pvt.Ltd.,1993, pp.1-21.

[4] Srivatsava, R.M., Financial Decision Making –Text Problems

and cases, Streling Publishers Pvt.,1989,pp.49-89.

[5] Krishna Reddy, “Financial Management”, an Analytical and

Conceptual Approach, Chaitanya Publishing House, Allahabad,

1993.

[6]Kuchhal, S.C. “Financial Management”, PRINTWELL,

Jaipur, 1992.

[7]Kullkarini, P.V. “Financial Management”, Himalaya

Publishing House, Mumbai, 1985.

[8]Maheshwari, S.N, “Principles of Management Accounting”,

Sultan Chand and Sons, New Delhi, 1985.

[9]Beaver, W H (2001) „Financial Ratios as Predictors of

Failure‟, Journal of AccountingResearch, spring.

[10]Bauman 2003 Split Information, Stock Returns and Market

Efficiency. Journal of Financial Economics, Vol 6, pp 265-

296.Returns and Market Efficiency. Journal of Financial

Economics, Vol 6, pp 265-296.


43

A Study on Investors Perception towards

Derivative Market at Angel Broking, Salem. Mr.M.Praveen.,

Abstract

The research work is undertaken on “A Study

On Investors Perception Towards Derivative Market With

Special Reference To The Investors Of Angel Brokings”.

This was done by finding out the awareness level and the

interest level among the investors .The main objective of the

research is to identify the awareness level, perception, and

product that are most preferred by the investor.

1. INTRODUCTION

GLOBAL DERIVATIVES MARKETS:

'By far the most significant event in finance

during the past decade has been the extraordinary

development and expansion of financial derivatives.

These instruments enhance the ability to differentiate risk

and allocate it to those investors most able and willing to

take it - a process that has undoubtedly improved national

productivity growth and standards of living.' -- Alan

Greenspan, Chairman, Board of Governors of the US

Federal Reserve System. The past decade has witnessed

an explosive growth in the use of financial derivatives by

a wide range of corporate and financial institutions.

Assistant Professor, Department of Commerce


Sungam By Pass Road,

Coimbatore-45

The following factors which have generally been

identified as the major driving force behind growth of

financial derivatives are the, Increased volatility is asset

prices in financial markets; the increased integration of

national financial markets with the international markets;

the marked improvement in communication facilities and

sharp decline in their costs; the development of more

sophisticated risk management strategies; and the

innovation in the number of financial assets, leading to

higher return, reduced risk as well as transaction cost as

compared to individual financial assets. The growth in

derivatives has run in parallel with increasing direct

reliance of companies on the capital markets as the major

source of long term funding.

In this respect, derivatives have a vital role to

play in enhancing shareholder value by ensuring access to

the cheapest source of funds. Furthermore, active use of

derivative instrument allows the overall business risk

profile to be modified, thereby providing the potential to

improve earnings quality by offsetting undesired risk.

2. DERIVATIVES: AN INNOVATIVE TOOL IN

THE INDIAN MARKET

Keeping in view the experience of even strong

and developed economies the world over, it is no denying

the fact that financial market is extremely volatile by


44

nature. Indian financial market is not an exception to this

phenomenon.

The attendant risk arising out of the volatility

and complexity of the financial market is an important

concern for financial analysts. As a result, the logical need

is for those financial instruments which allow fund

managers to better manage or reduce these risks.

DERIVATIVES DEFINED:

Derivative is a product whose value is derived

from the value of one or more basic variables, called

bases (underlying asset, index, or reference rate), in a

contractual manner. The underlying asset can be equity,

forex, commodity or any other asset. For example, wheat

farmers may wish to sell their harvest at a future date to

eliminate the risk of a change in prices by that date. Such

a transaction is an example of a derivative. The price of

this derivative is driven by the spot price of wheat which

is the “underlying”.

COMPANY PROFILE:

Angel Broking's tryst with excellence in

customer relations began in 1987. Today, Angel has

emerged as one of the most respected Stock-Broking and

Wealth Management Companies in India. With its unique

retail-focused stock trading business model, Angel is

committed to providing ‘Real Value for Money’ to all its

clients.

The Angel Group is a member of the Bombay

Stock Exchange (BSE), National Stock Exchange (NSE)

and the two leading Commodity Exchanges in the

country: NCDEX & MCX. Angel is also registered as a

Depository Participant with CDSL.

OUR BUSINESS

Equity Trading

Commodities

Portfolio Management Services

Mutual Funds

Life Insurance

Personal Loans

IPO

Depository Services

Investment Advisory

ANGEL GROUP

Angel Broking Ltd.

Angel Commodities Broking Ltd.

Angel Securities Ltd

3. OBJECTIVES

Derivative trading in Angel broking have

been started in the year 2001.Hence the company wanted

to know to what extent the people were aware of

derivative market and their perception towards its

products. Investor’s perception is a two-way street of how

they view the derivative segment and translate the

information they receive. Hence this research was

undertaken.


45

Primary Objective

To study the investors perception level and attitude

towards derivative segment

Secondary Objectives

To understand the profile of the investors

To analyze the investing habits of the investor

towards derivative market.

To analyze the factors influencing the investor in

choosing the types of derivative Segment

To analyze investors risk preferences towards

derivative market.

4. SCOPE OF THE STUDY

India is now one of the fastest economically

growing nations. With its vast economy, Indians have a

lot of options to invest their savings.

This study undertaken for Angel broking Pvt Ltd

aims to study the investors’ perception towards derivative

market. The study would also analyze the awareness level

of investors in this segment.

The study has been done by preparing a

questionnaire which contains prospective questions put

forth to the investor’s .The responses help in analyzing

the profile and investing habits of the investor and factors

influencing the investor in investing in derivative

segment.

All this would help in giving suggestions to

Angel broking (P) Ltd, in strengthening their marketing

efforts and in determining the market potential for

investments in derivative market.

LIMITATIONS OF THE STUDY

The area of the study is limited to the investors of

Angel broking, a part of Salem district only. Hence

the results may not be true for other geographical

locations.

Validity and Reliability of the data depends on the

truthfulness of the responses from the public.Time

at the disposal of the researcher is limited.

The size of the sample compared to the population is

very small and hence it may not represent the

whole population.

A structured questionnaire was the basis for

collecting the data, so it has the usual deficiencies

attached to this technique of data collection.

5. REVIEW OF LITERATURE

Kenji Kutsuna, Janet Kiholm Smith and Richard

L.Smith, (2009), examined the determinants of price

formulation from original price to the filling range and

from the filing range to the offer price. The study also

examined the extent to which initial and long term returns

is related to price adjustments. The study came out with

four reasons for price adjustment. First, pricing strategy to

provide incentive to the investors. Second, pricing that

reflects the bias associated with the original offer price.

Third,pricing to match the opportunity cost due to failed

offerings. Fourth, pricing based on other traded shares.

Richard B. Carter, Frederick H. Dark, Ioannis V.

Floros and Travis R. A. Sapp, (2011), studied the long

run performance of 6,686 IPOs from 1981- 2005 and

found that IPOs do not underperform on a risk adjusted


46

basis. The study also found that IPO underperformance is

more during the period 1980s and early 1990s. The IPOs

either perform same as the market or outperform on a risk

adjusted basis from 1998-2005. It also found that IPO

aftermarket returns is based on additional factors like

momentum and liquidity. The liquidity suggests Michael

A. Cusumano, (2012), analyzed the Facebook IPO was

overvalued. The IPO issue price is $38 and the market

value is $104 billion. However the price of share fell 25%

within a week form listing. Yet again the stock price

slipped more than 20% overnight. Analysts believe that

Facebook IPO should not have been priced more than

$13.80. The study points out that there is Uncertainty

about how much and how fast Facebook will grow and

how much will that growth cost to investors. There is no

proper technology to arrive that exact IPO price. However

focus on economics of the business, comparison with

peer films and a forecast about how the company will be

in future will help in arriving at the approximate price that

IPO is tend to suffer because of the heavy trading and

due to negative momentum exposure some investors tend

to sacrifice their returns.

SAMPLING DESIGN

Sampling design is to clearly define the set of objectives,

technically called the universe to be studied. The universe

can be finite or infinite. The nature of the universe studied

for this survey is finite.

Sampling Unit

The number of items selected from the population

constitutes the sample size. The respondents of the study

are present and future investors.

Sample Size

The sample size taken for the study is 150

Sampling Method

Sampling design is to clearly define set of

objects, technically called the universe to be studied. This

research has finite set of universe and the sampling design

used in the study is probability sampling. Simple Random

sampling method is used for the collection of data.

STATISTICAL TOOLS: The data has been mainly

analyzed by using the following methods and tests. Cross

Tabulation andPercentage method supplemented by

appropriate Percentage Analysis, Ranking Method, Chi –

Square Test

ANALYSIS OF DATA

GENDER OF THE RESPONDENTS

INTERPRETATION:

From the above table it is identified that, 80% of the

respondents are male and 20% of the respondents were

female.

GENDER NO OF

RESPONDENTS %

Male 120 80

Female 30 20

Total 150 100.0


47

TABLE 1

AGE LEVEL OF THE RESPONDENTS

S.No. AGE LEVEL

DERIVATIVE USER

No. of Respondents %

1 Below 25 Yrs 10 6.7

2 26-35 Yrs 35 23.33

3 36-45Yrs 39 26

4 46-55 Yrs 42 28

5 Above 55Yrs 24 16

Total 150 100.0

INTERPRETATION:

From the above table it is identified that, 28% of

derivative users are under the age group of 46-55 years.

INTERPRETATION:

From the above table it is identified that,

investors whose income level less than Rs.1 lakhs invest

10 -20%of their saving in this share market, Rs.1 -3 lakhs

invest above 30%of their savings towards share market,

Rs.3 -5 lakhs invest about 20 -30%of their savings in this

market and more than Rs.5 lakhs invest only below 10%.

RANKING ANALYSIS

TABLE 3

RANKING AMONG SECTORS FOR INVESTING

S.No Sectors Weightage

Score

Weighted

Average Rank

1 IT sector 627 4.13 2

2 banking

sector 677 4.46 1

3 Pharmaceuti

cal sector 293 1.93 4

4 Auto sector 205 1.35 5

5 Cement

sector 326 2.15 3

6 Others 149 .98 6

INTERPRETATION:

From the above table, it is inferred that,

The Respondents have ranked banking sector as

First in respect to their preference in investing.

The Respondents have ranked IT sector as Second

in respect to their preference in investing.

The Respondents have ranked cement sector as

Third in respect to their preference in investing.

The Respondents have ranked pharma sector as

fourth in respect to their preference in investing.

The Respondents have ranked Automobile sector as

fifth in respect to their preference in investing.

The Respondents have ranked other sector as

sixth in respect to their preference in investing.

PERCENTAGE OF INVESTMENT

INCOME

LEVEL

Below

10%

10 -

20%

20 -

30%

Above

30%

Less than 1

lakhs

18.37 27.45 17.24 23.81

1 -3 lakhs 20.41 27.45 20.69 28.57

3 -5 lakhs 30.61 29.41 37.93 28.57

More than 5

lakhs 30.61 15.69 24.14 19.05

TOTAL 100.0 100.0 100.0 100.0


48

CHI–SQUARE ANALYSIS

TABLE 4

GENDER AND AWARENESS TOWARDS

DERIVATIVE MARKET

Null Hypothesis (H0) There is no significant relationship

between gender and awareness towards derivative market.

CHI-SQUARE (2) CALCULATION:

Calculated 2 value = 13.42

Degree of freedom = 4

Table value = 9.488

Level of Significance = 5%

INTERPRETATION

It is found from the above analysis that

calculated chi-square value greater than the table value at

4 degree of freedom and null hypothesis rejected. So, we

conclude that, there is close significant relationship

between gender and awareness towards derivative market.

INCOME LEVEL AND USERS TOWARDS

DERIVATIVE MARKET

Null Hypothesis (H0) - There is no significant relationship

between Income level and users towards derivative

market.

CHI-SQUARE (2) CALCULATION:

Calculated 2 value = 10.97

Degree of freedom = 3

Table value = 7.815

Level of Significance = 5%

INTERPRETATION

It is found from the above analysis that

calculated chi-square value greater than the table value at

3 degree of freedom and null hypothesis rejected. So, we

conclude that, there is close significant relationship

between income level and users towards derivative market

SUGGESTION

Among the respondents, the awareness of derivative

segment is high but they were not interested in investing

in this segment. Reasons for not investing are that they

feel it is too riskier, so the company can provide

protective measures for safeguarding them and they can

give guidance and better support.

Most of the respondents agreed that if they are

provide with guidance and support they would invest in

this market. Companies can make use of this and make

many seminars to awake the people regarding their

investment.

S.No Annual

Income

Derivative

Users

Derivative

Non Users

Total

1 Less than

1 lakhs 2 31 33

2 1 -3 lakhs 14 22 36

3 3 -5 lakhs 14 33 47

4 Above 5

lakhs 12 22 34

Total 42 108 150

S.No. Gender Aware Unaware Total

1 Male 77 43 120

2 Female 13 17 30

TOTAL 90 60 150


49

REFERENCE

1.Kenji Kutsuna, Janet Kiholm Smith and Richard

L.Smith, (2009), "Public Information, IPO Price

Formation and Long-Run Returns: Japanese Evidence", The

Journal of Finance, Vol. LXIV, No 1, pp 505- 546.

2.Richard B. Carter, Frederick H. Dark, Ioannis V. Floros

and Travis R. A. Sapp, (2011), "Characterizing the Risk of

IPO Long-Run Returns: The Impact of Momentum,

Liquidity, Skewness and Investment", Financial

Management, Winter 2011, pp 1067-1086.

3.Michael A. Cusumano, (2012), "Reflecting on the

Facebook IPO", Communications of the ACM, Volume 55,

No -10, pp 20-23.


50

Fault Prediction Using Fuzzy Set Based

K-Means Clustering Algorithm

Jasmine Sagaya Jonita M.,

Abstract

Unsupervised techniques like clustering may be used for

fault prediction in software modules. A fault is a problem in

software that when runs causes failure. Fault proneness is

the likelihood of a piece of software to have faults. Fault

prediction is identified as one major area to predict the

probability that the software contains fault using fuzzy set

based K-Means algorithm, has been applied for predicting

faults in program modules. Software metrics may be used in

fault prediction models to improve software quality by

predicting fault location. The concept of clustering has been

used to determine the quality of clusters for evaluation of the

fuzzy set-based initialization algorithm as compared to other

initialization techniques. The clusters obtained by fuzzy set-

based algorithm were found to have maximum gain values,

whereas Fuzzy Set finds the faults only in the place where it

has occurred. The aim of this paper is fuzzy set are applied

for finding the initial cluster centers to be input to the K-

Means Algorithm. An input threshold parameter governs

the number of initial cluster centers and by varying the user

can generate desired initial cluster centers. This paper aims

to identify software metrics and to assess their applicability

in software fault prediction. By following this technique

time consumption is reduced and the overall error rates of

this prediction approach are compared to other existing

algorithms and are found to be better in most of the cases.


Department of BCA and Information Technology,

Nirmala College for Women,

Coimbatore.

I. INTRODUCTION

K-Means clustering is a nonhierarchical

clustering procedure in which items are moved among

sets of clusters until the desired set is reached. The

partitioning of data set is such that the sum of intra cluster

distances is reduced to an optimum value K-Means is

simple and a widely used clustering algorithm. However,

it has some inherent drawbacks. First, the user has to

initialize the number of clusters which is very difficult to

identify in most of the cases. Second, it requires selection

of the suitable initial cluster centers which is again subject

to error. Since the structure of the clusters depends on the

initial cluster centers this may result in an inefficient

clustering. Third, The K-Means algorithm is very

sensitive to noise. In a method using fuzzy set has been

proposed as an initialization of K-Means algorithm.

The fuzzy set assigns based method the

appropriate initial cluster centers and eliminates the

outliers. Hence overcoming the second and third

drawback of K-Means algorithm. In this study, we focus

on a practical problem that occurs when the fault data for

modules are not available. To solve this challenging

problem, researchers have applied a combination of

clustering techniques to cluster modules, and this process

was followed by an evaluation phase of an expert ,who

was an experienced engineer and labeled each cluster as

fault-prone or not fault-prone by examining not only the


51

representative points of each cluster, but also some

statistical data such as global mean, median, and

percentile of each metric. However, their approach

required a human expert during the prediction process and

it is not always possible to find an experienced expert

who would have the duty to label each cluster. In this

paper, the fuzzy set based K-Means algorithm (FSK) has

been applied for predict pointing the faults in program

modules. The objectives of this paper are as follows:

First, fuzzy set based are applied for finding initial cluster

centers for K-Means algorithm. By varying the value of

threshold parameter a user can generate a desired number

of cluster centers to be used as input to the simple K-

Means algorithm. Second, the Fuzzy set based algorithm

is applied for predicting faults in program modules. The

overall error rates of this prediction approach are

compared to other existing algorithms(Quad tree) and are

found to be better in most of the cases. Clustering gain

values for the best cluster by K-Means and by Fuzzy

based algorithm are very close thereby proving the

effectiveness of the algorithm. To compare the

performance of FSK for initialization of K-Means,

experiments have been conducted in which Fuzzy logic

based algorithm and two other initialization techniques,

Likas et al., Global K-Means algorithm and SAS 2004

have been executed and results are compared on the basis

of evaluation parameters.

The FSK algorithm performs fairly well on all

the parameters. The Global K-Means algorithm considers

each data item in each iteration leading to high

complexity when number of data items and number of

clusters are large and these scalability issues have also

been raised by the authors. The SAS 2004 algorithm even

though being linear does not provide any guidance

regarding the selection of their distance measure [23].The

remaining part of the paper is organized as follows:

Section 2 presents the related work on the topic. Section 3

presents an overview on the theory of fuzzy set and the

initialization algorithm. Section 4 presents the

experimental design. Section 5 presents the conclusion.

II. RELATED WORK

Zhong et al.applied clustering techniques and

expert-based approach for software fault prediction

problem. They applied K-Means and Neural-Gas

techniques on different real data sets and then an expert

explored the representative module of the cluster and

several statistical data in order to label each cluster as

fault-prone or not fault-prone. And based on their

experience Neural-Gas-based prediction approach

performed slightly worse than K-Means clustering-based

approach in terms of the overall error rate on large data

sets. But their approach is dependent on the Availability

and capability of the expert. Seliya and Khoshgoftaar

proposed a constrained based semi-supervised clustering

scheme. They showed that this approach helped the expert

in making better estimations as compared to predictions

made by an unsupervised learning algorithm. Seliya et al.

have proposed a semi-supervised clustering approach for

software quality analysis with limited fault-proneness

data. Most recently Catal et al. proposed a metric

threshold and clustering-based approach forsoftware fault

prediction.


52

The results of their study demonstrate the

effectiveness of metrics threshold and show that the

standalone application of metrics threshold is easier than

the clustering and metrics thresholds-based (two stage)

approach because the selection of number of clusters is

performed heuristically in this clustering-based method.

In our present study we have presented comparative

results performed on same data sets as in Bhattacherjee

and Bishnu have applied unsupervised learning approach

for fault prediction in software module in [28].

In their work, the false negative rates (FNR) for the

clustering-based approach is less than that for metrics-

based approach, while the false positive rates (FPR) are

better for the metrics-based approach. The overall error

rates for both approaches remain the same. Supervised

techniques have however been applied for software fault

prediction [13] and software effort prediction .Several

methods for initialization of K-Means algorithm are

available in literature. Tibshirani et al. suggest a statistical

method based on gap statistic to find the optimal number

of clusters [20]. Pelleg and Moore suggest an algorithm

which efficiently searches the space of cluster locations

and number of clusters to optimize the Bayesian

Information Criterion and Akaike Information.

Figure 1: The prediction of clustering error point

Figure 2: Example of Mouse Dataset:

Criterion . Laszlo and Mukherjee present an approach for

finding the set of centers by constructing a fuzzy logic on

the set of data. Genetic algorithm has been used for

evolving centers in the K-Means algorithm and also for

finding a good partitioning .

An evaluation of several initialization techniques

for K-Means algorithm is presented.

III.OVERVIEW OF FUZZY SETAND

PROPOSEDINITIALIZATION ALGORITHM

A Fuzzy set in one dimensional spaces is a

represents recursive decomposition of space using

separators parallel to the coordinate axis. A fuzzy seta

defined in the universal space .X is a function defined in


53

X which assumes values in the range[0, 1].A fuzzy set A

is written as a set of pairs {x, A(x)} as

A= {{x, A(x)}} , x in the set X. where x is an element of

the universal space X and A(x) is the value of the function

A for this element. The value A(x) is the membership

grade of the element x in a fuzzy set A Example :Set

SMALL in set X consisting of natural number≤to12

Assume:

SMALL(1) = 1, SMALL(2) = 1,

SMALL(3) = 0.9,

SMALL(4) = 0.6,

SMALL(5) = 0.4,

SMALL(6) = 0.3,

SMALL(7) = 0.2,

SMALL(8) = 0.1,

SMALL(u) = 0 for u >= 9.

Then, following the notations describid in the definition

above : Set SMALL = {{1, 1 }, {2, 1 }, {3, 0.9}, {4,

0.6}, {5, 0.4}, {6, 0.3}, {7, 0.2}, {8, 0.1}, {9, 0 }, {10, 0

}, {11, 0}, {12, 0}}

Note that a fuzzy set can be defined precisely by

associating with each x , its grade of membership in

SMALL. Fuzzy sets are sets whose elements have degrees

of membership. Fuzzy sets were introduced by Lofty A.

Zadehand Dieter Klaua in 1965 as an extension of the

classical notion of set.

At the same time, Salii (1965) defined a more general

kind of structures called L -relations, which were studied

by him in an abstract algebraic context. Fuzzy relations,

which are used now in different areas, such as linguistics

(De Cock, et al, 2000), decision-making (Kuzmin, 1982)

and clustering (Bezdek, 1978), are special cases of L-

relations when L is the unit interval [0,1]. If the number of

data sets in the cluster error point any bucket is less than

threshold then the Fuzzy set consists of a single data set

where prediction of the dataset are stored. At each stage

every bucket.Let us consider For n dimensional data set

the buckets will be named as f1af2af3a...f4a.example

:ABS systems and Temperatures.

A. The Proposed Initialization Algorithm

First, some definitions of notations and parameters used

in the initialization algorithm are provided. Parameters

and Definitions.

MIN: user defined threshold for minimum number of data

points in a bucket. MAX: user defined threshold for

maximum number of data point in a bucket.user specified

distance for finding nearest neighbors. set of cluster

centers used for initializing K-Means algorithm.

Algorithm 1 gives the pseudocode for the initialization

algorithm. In lines 1-8 of the algorithm, we are find

The an initial data point or data set into buckets and

continue until all buckets are either Wrong or Right data

set in the buckets as illustrated in Fig.3.

http://en.wikipedia.org/wiki/Lotfi_Asker_Zadeh

http://en.wikipedia.org/wiki/Lotfi_Asker_Zadeh

http://en.wikipedia.org/wiki/Set_%28mathematics%29


54

IV. EXPERIMENTAL DESIGN

We conducted experiments on four real data sets

to test our algorithm. These data sets are: AR3, AR4, AR5

available at [22] and Iris data set . Of these, the first three

data sets are related to software fault prediction. The

synthetic dimensional two class data sets (SYD1 and

SYD2) have been taken to illustrate the initialization

algorithm. For SYD1 we have generated three well

separated clusters with co variances _8:406, 9.483 and

22.585. The mean values of the three clusters for X and Y

attributes are (158.166, 57.062), (102.640, 138.12), and

(24.204, 11.136). For SYD2 we have generated four well-

separated clusters with co variances _0:2025, _7:533,

6.365, and _6:385.

The mean values of the four clusters for X and Y

attributes are (24.77, 3.1), (195.40, 54.312), (92.60,

216.60), and (256.85, 200.10). Out of the total of 163 data

in SYD2, 10 data have been introduced as noise.

Descriptive statistics for all the synthetic data sets are

given in

A. Experimental Setup and Results

Table 3 presents the gain values for all the data

sets as obtained by the simple K-Means algorithm. Values

have been taken for up to12 clusters. For each cluster, six

runs have been executed and the maximum gain value has

been reported. Initialization has been done by random

selection of the initial cluster centers. For the fuzzy set

based algorithm there are input parameters: MIN,MAX,

The value for MIN has been chosen as 5 percent, and for

MAX it is 95 percent. In the Fuzzy set algorithm, for

AR3, AR4,AR5, Iris, SYD1, and SYD2 the values are 40,

80, 40, 0.55, 70, and120, respectively, and the number of

cluster centers obtained was 3,3, 2, 3, 3, and 4,

respectively. Fifth column presents the gain values

obtained by applying algorithm on various data sets. To

be able to compare our clustering quality with the K-

Means algorithm, we adjusted the threshold parameter _

to obtain the same number of clusters (3 for AR3, 3 for

AR4, 2 for AR5, 3 for Iris, 3 for SYD1, and 4 for SYD2)

which gave maximum gain values for K-means algorithm.

TABLE 3

DS PARA FSK

%

KM% CT% CS% NB% DA%

AR3 FPR 34.54 34.54 44.09 43.63 09.00 3.60

FNR 25.00 25.00 25.00 25.00 25.00 75.0

Error 33.33 33.33 41.67 41.27 11.10 12.60

AR4 FPR 4.59 20.00 20.00 20.00 55.00 60.00

FNR 45.0 28.97 32.09 32.14 10.20 14.00

Error 12.14 14.28 28.92 32.14 14.20 07.14

V.CONCLUSION

In this paper, we have evaluated the

effectiveness of Fuzzy set based K-Means clustering

algorithm in predicting faulty software Modules as

compared to the original K-Means algorithm. Fuzzy Set

are applied for finding the initial cluster centers for K-

Means algorithm. In case the user intends to form a

desired number of clusters for K-Means algorithm, the


55

Fuzzy logic based algorithm can give K initial cluster

centers to be used as input to the simple K-Means

algorithm. This is facilitated by varying the value of the

threshold parameter which is input to the Fuzzy set based

algorithm. The overall error rates of software fault

prediction approach by algorithm are found comparable

to other existing algorithms (QUAD TREE) and are

presented in Table 4. In fact, in the case of AR3and AR4

data sets, the overall error rates of FSK are comparable

with the supervised learning approaches NB and DA. The

results of table 4 show that the FSK algorithm works as

an effective initialization algorithm. By following this

technique time consumption is reduced and the

overall error rates of this prediction approach are

compared to other existing algorithms and are found to be

better in most of the cases.

REFERENCES

[1] C. Catal, U. Sevim, and B. Diri, “Clustering and Metrics

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