Appril june issue of GJMMS pdf

Volume 1 Number 2, April-Jun,2015

I

GLOBAL JOURNAL OF

MULTIDISCIPLINARY AND

MULTIDIMENSIONAL STUDIES

Dr. N.M.Lall

B.com, M.A.(Eco), Ph.D.

FRAS (LONDON)

Patron

Dr. A.K.Jha

PGDM, M.A.(Eco), Ph.D.

Managing Cum Chief Editor

Dr. Suresh Sachdeva Dr. Brajesh Mishra

M.A.(Eco), Ph.D. MOT

Prof. of Economics HOD (OT)

Govt. SLP College, Govt. SLP College Smt.K.P.P.I.P.O

Gwalior (M.P.) Gwalior (M.P.) Annand (GUJRAT)

Editor Editor Editor

ISSN No.2394-8965

SHRUTAAYUSH PUBLICATION

GREATER NOIDA

II

Member of Editorial Board

---------------------------------------- Dr.V. D. Sharma

(M.Sc. M.A, B.Ed, PGDFM, Ph.D) A Gandhian Professor,

Faculty of Management Studies & Ex Proctor Gen. Secy, Rashtriya Shaikshik Mahsangh (University Campus)

VBS Purvanchal University Jaunpur-222003 (UP) Dr. H.K.S.Kumar Chunduri

Sr. Faculty Member, Department of Business Studies,

Ibra College of Technology, IBRA, Sultanate of Oman

Dr. Violetta Gassiy Associate professor,

Public administration department, Kuban State Univer-sity, 149,

Stavropolskaya st., Krasnodar Russia Prof (Dr) Ramesh Balkrishna Kasetwar (Retd Colonel)

Ex professor

Bharati Vidyapeeth University, New Delhi Vineet Jain,

Asstt. Prof. (Mechanical) Amity University Haryana, Gurgaon

Dr. Rushiraj Upadhyay, Asst. Professor, M.S.W Department,

Gujarat University, Ahmedabad Deepak Pathak Assistant Professor,

Mechanical Engg Dept., FET Agra College Agra Mahendra N. UmareAssociate

Professor & HOD (Civil) at NIT, Nagpur ROB WOOD

Department of Global Strategy & Management 2010 presentWestern Carolina University, Cullowhee, NC

Judi Krzyzanowski B.Sc, M.SC., Environmental scientist

Dr. Vijay Pithadia, PhD., MBA, Electronics Technician

Director & Professor, SHG MBA Women college, Amreli Dr. Dheeraj Pawar

III

Assistant Professor, Amity Institute of Telecom Engineering and Management,

Amity University, Noida

Raymond W. Thron, Ph.D Faculty

College of Health Sciences, Walden University

Dr. Mwafaq M. Dandan

Associate Professor Department of Banking and Financial Sciences

Amman University College for Banking and Financial and Sciences

Albalqa applied universityJordan

Professor (Dr) Rajesh Arora Director

Dr D Y Patil Institute of Management Studies, Pune.

Dr. L. Govinda Rao, PG in Mgt.(XLRI), Ph.D.,

Chairman & CEO,

Matrix Institute of Development Studies, Kameswari Kuteer,

Secunderabad 500 011 AP India.

Shailkh.Shoeb Anwer Aurangabad

Dr.C.B.Singh, Ph D, M A, (Economics), M Sc (Ag Eco.), MBA (FM, MM),

Associate Professor

Institute oF Economics & Finance

Bundelkhand University, Jhansi 284128 (UP) India

Dr. David Nickerson, Distinguished Professor,

Department of Finance and Real Estate,

Rogers School of Management,

Ryerson UniversityToronto, Ontario M5B 2K3, Canada

Dr Dilip Kumar Vinnakota Principal,

Govt Junior College SATHUPALLY

Khammam District, Telangana State

Steve Pyser Fellow, Caux Round Table and Lecturer (PTL),

Rutgers University School of Business - Camden

Bocar Samba Ba (Research scholar Economics) 2 place viala, 34060 montpellier, France

IV

Mahendra N. UmareAssociate Professor & HOD (Civil) at NIT, Nagpur

Charles "Randy" Nichols, Ph.D., Louisville, KY, Professor of Management Author, Educator, Speaker

Shabnam Siddiqui, Assistant Professor,

FMS-WISDOM, Banasthali University,

P.O. Banasthali Vidyapith 304022Rajasthan, INDIA,

Monika Hudson, DM Assistant Professor, Director,

Gellert Family Business Resource Center/

Public Service Internship Program,

University of San Francisco

Juan Carlos WANDEMBERG – Ph.D. WANDEMBERG Sustainable Development Quito -

ROB WOOD Associate - Graduate Faculty;

Department of Global Strategy & Management 2010

presentWestern Carolina University, Cullowhee, NC

Dr. Mohammed Rizwan Alam, Assistant Professor

Marketing University Of Modern Science Dubai

Avil sinha Fellow (ECONOMICS), IIM, Indore

Mary Manana University of South Wales

Dr. Stefan Walter, Heidenrod, Germany (Economics and Management)

C.H.Raj Marketing professional Noida

Greg Benzmiller Ph.D. MA, MBA Colorado Springs, CO 80919

Hazra Imran (PhD) Post-Doctoral Fellow,

Funded by MITACS Elevate (Canada),

Athabasca University, Edmonton, AB, Canada

Indrajit Bandyopadhyay, Registrar, Usha Martin Academy, Kolkata, India

Rijo Tom, Asst. Professor , Dept. of ECE, Kalaivani College of Technology, Coimbatore

V

S.Praveen – HR & Administration Executive – FDC International FZE (Dubai)

Anil kumar. S Hagargi, Research scholar,

Dept of Management Studies and Research,

Gulbarga University,Gulbarga, Karnataka, Ihor Yaskal,

PhD in Economics,

Yuriy Fedkovych Chernivtsi National University, Ukraine

Nilesh Borde, Assistant Professor at Goa University

Dr. Kiran Mehta, Associate Professor (Finance),

Chitkara Business School, Chitkara University

Dr. Renuka Sharma, Associate Professor (Finance),

Chitkara Business School, Chitkara University

Pradeep Kumar Owner ASPIRE OVERSEAS CO, Noida

Dr.prof.V.Raghu Raman, Senior Faculty (Business Studies),

IBRA COLLEGE OF TECHNOLOGY, OMAN

PAZIENZA, Department of Economics,

University of Foggia, Foggia, Italy

Dr. Tiyas Biswas, Assistant Professor

Department of Business Administration

Bengal College of Engineering and Technology, Durgapur

Devanathan Elamparuthy B.E.,M.B.A.,M.Phil.,P.G.D.P.E.,D.I.S.,(P.hd).,

Asst.Professor Business Administration, Annamalai University

MUFTI MD. IBRAHIM, Faculty of Education

,Ahsanullah University of Science and Education.

Ahsanullah Teachers’ Training College,Dhaka

SUDHASHREE PARVATI, Lecturer,

Department of English,

VI

Adi Keih College of Arts and Social Sciences,

Adi Keih, Zoba: Debub, State of Eritrea, N.E.Africa

Dr. SHAUKAT ALI, M.Con., M.Phil., Ph.D.

Associate Professor and Head, Commerce Department.

Anjuman-I-Islam’s Akbar Peerbhoy College of Commerce & Economics,

University of Mumbai, Mumbai

Indrani Ganguly, M.A. B.Ed. (Geography),

Principal of Shri Shikshayatan School., Kolkata.

Nagori Viral Y., Assistant Professor

GLS Institute of Computer Technology (MCA), Ahmedabad .

VII

Editorial

-------------

The current changes and challenges experienced by the

contemporary world have been an inspiration for us in elaborating

this new forum of dis-cussions on the real world issues affecting or

having a meaningful impact on the different segment of society and

on our lives. This is an attempt of boldly and unrestrictedly

contributing to new Ideas through research findings and doing things

differently, thereby providing quality and value. Scholars, re-

searchers, young researchers worldwide are encouraged to join

efforts in find-ing solutions for the common issues raised by the

recent social and environ-mental changes. It aims to be a dialogue

between the scientific community and the citizens, as a testimony of

their concern to place the results of their work in the service of the

society. A new orientation in research policy is imperative to respond

to the new needs of the society to guarantee environ-mental

sustainability and economic growth in the knowledge society. The

purpose of the Global Journal of Multidisciplinary and

Multidimenstional Studies is to make an area of free circulation of

ideas and knowledge, of shar-ing experience and finding effective

solutions for real-life problems, to under-stand their causes and

foresee the consequences. While the society needs and calls for

research, research needs to be accountable to society. To this end, the

journal publishes Research papers, survey, articles, research findings,

book reviews, and annotations of new books. Dr.A.K.Jha Managing and Chief Editor GJMMS

VIII

GLOBAL JOURNAL OF MULTIDISCIPLINARY

AND MULTIDIMENSIONAL STUDIES

Vol. 1 Issue No. 2 April-

June2015

1 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY 1

OF COTTON FARMERS IN PAKISTAN: A CASE STUDY

OF DISTRICT DERA GHAZI KHAN

ABDUL HAMEED

2 IDENTIFICATION OF FACTORS INFLUENCING 24

PREFERENCES FOR GREEN PRODUCTS: A STUDY IN

AND AROUND KOLKATA (INDIA)

Prof. Sudipta Majumdar & Dr. Sukanta Chandra Swain

3 Performance of financial markets in Indian Economy 35 Dr.A.K.Jha & Viriender Pal Singh

4 DIFFICULTIES IN IMPLEMENTING IFRS: 44

A STUDY ON PERCEPTION OF CA STUDENTS

IN KOLKATA

Surajit Das

5 FINITELY GENERATED FREE ABELIAN 53

GROUPS AND THEIR APPLICATIONS

Simon Eze Ejiofor 6 Software of the Mind: Culture-Strategy Fit a 62

Trump card for Multinational Corporations - A study Mrs. Sheela Reddy

1 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN

PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN

ISSN NO. 2394- 8965 GJMMS

Vol. – 1, Issue – 2, April- June-2015

ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON

FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN

ABDUL HAMEED

Pakistan institute of Development Economics

ABSTRACT

This paper investigates technical, allocative and economic efficiency of cotton farmers of

district Dera Ghazi Khan using data envelopment analysis technique. Structured

questionnaire is used to collect the data of cotton farmers. Data collection is carried out for

Kharif season of 2012. A stratified random sampling selection technique was used to

collect the data. The results reveal that mean total technical, pure technical, allocative,

economic and scale efficiencies are 0.67, 0.94, 0.57, 0.54 and 0.71 respectively. It also

shows that cotton farmer can produce average 22.5 mounds per acre seed cotton without

reducing the inputs and technology. It also concludes that education, experience and

contact with extension workers are significant determinants of technical efficiency of the

cotton farmers.

Keywords: Tchnical Efficiency, Allocative Efficiency, Economic Efficiency, DEA, Cotton

INTRODUCTION Agricultural is a key sector in Pakistan. Its share in Gross Domestic Product

(GDP) is 21.4 percent with absorption of 45 percent of labor force. Population residing in

rural areas of country is directly or indirectly linked with agriculture sector, which is

almost 60 percent of total population (GOP, 2012). The major crops of Pakistan are wheat,

cotton, rice and sugarcane and these contribute 25.24 % to entire agricultural sector.

Cotton is the most important cash crop in Pakistan. It accounts for 7% in the entire

agricultural sector. It is sown in 2879 thousand hectares1. In 2012, cotton production was

predicted at 14 million bales (GOP, 2012).

Figure 1 shows different sectors share in GDP. The overall agriculture share in

GDP decreased from 1960 to 2011 but still it is higher than manufacturing sector. In 1960

agriculture share in GDP was 46.2 percent and in 2011 it came at 21.6 percent (World

Bank, 2013). The area of cotton increased in Pakistan over the last three decades but every

time its yield has been threatened.

In developing countries majority of the population lives in rural areas

and faces extreme poverty. Pakistan is one of those countries where the huge

population is linked directly or indirectly with agriculture sector. In the

agriculture sector cotton is the one of major cash crops in Pakistan. It is very

important crop to earn the money and reduce the poverty level and in

1 Hectare: one hectare is equal to 2.471 acres

ABDUL AMEED

2

improving living standard in rural areas. But unfortunately cotton production has not

improved

significantly. Farmers practice subsistence and traditional farming with low productivity.

“This may be attributed to higher inefficiencies (technical and allocative) because farmers

have less access to resources and extension services to guide them for commercial

production” Javed, et al (2009). Cotton is also the most important and major crop of

District Dera Ghazi Khan. Recently, cotton crop decreased 6.61 percent due to the some

crop related and climate change problems (Pakistan Cotton Ginners Association).

Table-1 gives the cotton production of District Dera Ghazi Khan. It was 515000

bales in 2005-06 and 519000 bales in 2006-07. It was highest in 2006-07 and yet has not

reached again up to this level. The condition of cotton production in Dera Ghazi Khan is

similar to other districts of Punjab. Mostly increase in production is due to increase in

planted area (Directorate of Agriculture Crop Reporting Service, Punjab). It seems other

factors have not major role. Such conclusions cannot be drawn until we assume that

farmers are technically efficient.

This study selected cotton crop to find total technical, pure technical, allocative,

economic and scale efficiencies of its farmers. The efficiency indices computed will make

known the extent of technical and allocative inefficiencies among cotton farmers. It would reflect existing potential for farmers to improve output without changing the

combination of inputs or produce the same output with fewer inputs than they are

currently using. Farm and farmer characteristics observed among efficient farmers will be

used to formulate policy recommendations that will help policy makers to develop

3 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF

COTTON FARMERS IN PAKISTAN: A CASE STUDY OF

DISTRICT DERA GHAZI KHAN

Table-1: Cotton Production of District Dera Ghazi Khan

Years Production In „000‟ Bales

2005-06 515

2006-07 519

2007-08 464

2008-09 233.18

2009-10 356.16

2010-11 212.50

2011-12 344.25

Note: Bale=170 KG Source: Directorate of Agriculture, Crop Reporting Service, Punjab

strategies that may help inefficient farmers. This will also be important in extension work

as it will highlight farm and farmer characteristics more likely to enhance productivity

among the farmers. It also analyzes the

determinants of technical efficiency. Furthermore, it helps in understanding the core

problems which are being faced by cotton farmers of District Dera Ghazi Khan. The other

reason of selection of cotton crop is its importance in textile, food of animals and edible

oil.

1. LITERATURE REVIEW

A number of studies are available in literature that present technical efficiency of cotton

and other agriculture crops like wheat, sugarcane, tomato etc. Sohail, et al (2012)

estimates the technical efficiency for wheat production of district Sargodha, Pakistan.

They use data envelopment analysis (DEA) methodology for estimation of efficiencies and

Tobit regression to find out the determinants of efficiency. The study finds that efficiency

varies from 0.6 to 1. It also examines dependence of efficiency on farm specific variable

such as experience, education, villages distance, household size and farm size. The results

show that farm size and village distance are negatively related with technical efficiency. A similar study using DEA is conducted by Javed, et al (2009) estimates

technical, allocative and economic efficiencies of cotton and wheat farmers in Punjab,

Pakistan. Result shows that average technical, allocative and economic efficiencies are

0.87, 0.44 percent and 0.37 respectively. It also indicates that farmers‟ education and

extension agents are negatively related with inefficiency of cotton and wheat farming. Gul,

et al (2009) estimates the determinants of technical efficiency of cotton growing farms in

ABDUL AMEED

4

Turkey. They use DEA methodology to find efficiencies and Tobit analysis to analyze the

determinants of efficiency. Result shows that on average technical efficiency is 0.79. It

means 21 percent capacity is available for increasing cotton production without changing

inputs and technology.

Gwandi, et al (2010) estimates efficiency of cotton production in Gassol local

Government area of Taraba state, Nigeria. The stochastic frontier analysis (SFA) is used to

determine technical, allocative and economic efficiencies. The result shows that 82 percent

of the variation in output of cotton is explained by input factors. The farm size and family

labour have negative impact. The result also shows that resources are over utilized in

cotton production so farmers need more knowledge on input use. Sylvain, et al (2010) also

use the SFA to estimate determinants of the technical efficiency of cotton farmers in

Northern Cameroon. The result gives technical efficiency indices vary from 11 percent to

91 percent.

OGUNNIYI, et al (2011) “estimates the technical efficiency of tomato production in Oyo

state Nigeria”. The 150 random samples were collected by using multi-stage sampling

technique.

The DEA methodology used to estimate technical efficiency and Tobit analysis used to

determinants of efficiency. Result show that the technical efficiency varies 31 percent to

100 percent .The on average technical and scale efficiencies are 42 percent and 82 percent.

The study also shows that there is small scale inefficiency due to excess use for all inputs

especially for fertilizer, family and hired labor. The determinants of technical efficiency

are education, experience, marital status and gender. EBONG, et al (2009) “estimates the

determinants of technical efficiency of urban farming in Uyo metropolis of AkawaIbom

state, Nigeria”. A simple random sampling procedure was employed in the selection of 75

urban farmers from the four designated locations in the study area and Maximum

likelihood estimation (MLE) procedure was used. The SFA used to determents technical

efficiency of urban farming The result shows that the coefficients of farm size, capital,

labor and planting materials were all positive and significant with technical efficiency.

According to the inefficiency analysis age, farming experience, education, extension

contract and household size has influence the inefficiency of the farmer. The Farmers

technical efficiency index varies 0.10 to 0.95 and with on average 81 percent.

DLAMINI, et al (2010) “estimates the technical efficiency of small scale sugarcane

farmers in Swaziland state, Africa”. A stratified random sample size of 75 farmers was

obtained. The well structure questionnaire used to collect the data. The result of SFA and

inefficiency model indicated that elasticity of fertilizer variable for the VUVULANE small

scale sugarcane farmers was higher 0.536 and the labour, herbicides were positive, age

and land was negative influence.Overall technical efficiency mean is 73.6 percent to

86.7 percent.

5 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN

.HAJIAN, et al (2013) “estimates the total factor productivity and efficiency in Iranian

crop productivity through the data envelopment analysis”. The study consists on the penal

data of 1995 to 2009. The result shows that productivity rise in this period. Technical

efficiencies level higher but allocative and

economic efficiencies are in lower level.

On other hand, Abid, et al (2011) conduct resource use efficiency analysis of small BT

cotton farmers in Punjab, Pakistan by using Cobb-Douglas production function. They find

that BT cotton production has an increasing return to scale with elasticity of production

1.16 of small farmers. All production inputs i.e. pesticides, irrigation; fertilizers and labour

were underutilized because ratio of MVP/ MFC2 was greater than unity i.e. 3.94, 2.01, 1.5

and 1.27, respectively.

In previous studies descriptive statistics analysis is used to describe socioeconomics

characteristics of cotton farmers while the DEA and SFA are commonly used to analyze

the productivity and efficiencies (technical, allocative and economic). These studies also

show that the determinants of inefficiency of productivity and technical efficiency are

estimated with the help of (Tobin, 1995) regression and Coelli, et al (2005) model. These

all studies help us in making the design of our study analysis.

2. MATERIAL AND METHODS The Dera Ghazi Khan District is enclosed in the north by Dera Ismail Khan

District of Khyber Pakhtoonkhwa (KPK) and it‟s bordering Tribal Area, on the west by

Musa Khel and Barkhan districts of Baluchistan Province, on the south by Rajanpur and

on the east by Muzaffargarh and Layya.

Sample size and Sampling design The District Dera Ghazi Khan was purposively (study area of cotton analysis)

selected for this study. The Dera Ghazi Khan consists of the 41 union councils. We

dropped 7 urban union councils and one Sakhi Sarwar union council because cotton crop

is not sown there because of arid area. A random sampling technique is used to select the

union councils, 08 union councils are randomly selected from 33 union councils. Fifteen

sample farmers from each union council are selected from randomly selected villages

based on the share of different categories small, medium and large farmers. Total 120

farmers are interview by stratified sampling technique. The data are collected for the crop year 2012 (Kharif 2012). The cotton farmers are categorized as small, medium and large

farmers. The categories are given below:

1- Category (A): Small farmers 1 to 3 acres under cotton area 2- Category (B): Medium

farmers 3 to 6 acres under cotton area 3- Category (C): Large farmers above than 6 acres

under cotton area 2

MVP is marginal value of product is the value of additional unit of input is equal to the price of output multiplied by

marginal product of factor of production and MFC is marginal factor cost indicates how the total factor cost affected

by one or more change in inputs.

ABDUL AMEED

6

Source: Survey of Pakistan

Data Limitation This study has some weaknesses related to survey interviews; data accurateness is

depended on respondent skill to remember earlier period information and to answer the

survey questions. In district Dera Ghazi Khan, most of the farmers are illiterate and they

do not keep the records of inputs and outputs. Therefore, after the first interview some

information was again collected by re-interviewing the farmers to minimize the errors.

However, some errors and inconsistencies are unavoidable in this kind of study.

3. ANALYSIS This paper uses the data envelopment analysis (DEA) under assumption of

constant return to scale (CRS) and variable return to scale (VRS) to estimate the technical,

allocative and economic efficiencies and Tobit regression to find out determinants of

technical efficiency.

Objective: in ,

Subject to: yiY0

xi X 0

0

represents the inputs vector of X1i, X2i . . . X8i

X1i represents the crop area of the ith farm in acres

X2i represents the total quantity of seed per acre used on the ith farm in kilogram

X3i represents the total quantity of nitrogen per acre used on the ith farm in kilogram.

X4i represents the total quantity of phosphate per acre used on the ith farm in kilogram


X5i shows the total tractor hours for all farm operations (which used in land preparation,

weeding, planting, etc)

X6i represents the total quantity of pesticides per acre used on the ith farm in litre. X7i

represents the total number of irrigation per acre used on the ith farm in hours.

X8i represents the total labour (family and hired) as the total number of man-days3 used on

the ith farm.

To estimate the pure technical efficiency DEA is used following, Coelli, et al (2005) with

assumption of VRS:

Objective in ,

Subject to yiY0

xi X 0

N1 1

0

Where:

N1 represents a convexity constraint which ensures that inefficient farm is only

benchmarked against farm of a similar size.

This paper also uses DEA cost minimization method following Coelli, et al

(2005) with assumption of VRS for estimation of cost efficiency:

Objective in ,

Subject to yiY0

xi X 0

N1 1

0

Where: WI is vector of input price w1i, w2i, w3i . . . w12i of the ith farm,

xi Is the cost minimizing vector of input quantizes for the ith farm, N refers to total

number of farms in the sample,

W1i represents the per acre land cost of the ith farm in rupees,

W2i represents the total cost of seed per acre used on the ith farm in rupees,

W3i represents the total cost of nitrogen per acre used on the ith farm in rupees,

W4i represents the total cost of phosphate per acre used on the ith farm in rupees,

W5i shows the total cost of tractor hours for all farm operations (which used in land

preparation, weeding, planting, etc.),

W6i represents the total cost of pesticides per acre used on the ith farm in rupees, W7i

represents the total cost of irrigation per acre used on the ith farm in rupees,

3 Man-day is a number of labor days while one day equals to 8 hours

ABDUL AMEED

8

W8i represents the total cost of labors family and hired as the total number of man-days

used on the ith farm.

Cost efficiency is the ratio between the minimum possible cost and the observed cost.

CE wixi E wixi

Allocative efficiency is estimated by dividing the cost efficiency with the technical

efficiency.

AE =

Scale efficiency is estimated by dividing the technical efficiency of constant return to scale

and technical efficiency of variable return to scale.

Scale efficiency score varies from zero to one, if scale efficiency equal to one

indicate efficiency and less than one indicate inefficiency. The scale efficiency less than

one due to increasing return to scale or decreasing return to scale and equal to one due to

constant return to scale. Technical, allocative, economic and scale efficiencies scores will be estimated by using the computer software DEAP 2.1.

A second step regression model was applied to determine the farm specific

attributes in illumination efficiency in this study. Alternatively, the factors can be

integrated directly into the model and some external factors influence the technical

efficiency of cotton farmers so in order to investigate these external factors. The study

applied second step approach by using a Tobit regression.

i01122334455i

Where: i represent the ith farm in sample, i

Represent the technical efficiency of the ith farm, 1

Represents the education of the ith farmer in years of schooling,

2 Represents the farming experience of the ith farmer in years,

3 Represents the farm size of the ith farm in acres,

4 Represents the access to extension services of the ith farmer in the cotton season,

5 Represents the distance of the ith farm from main market in kilometers,

' s are unknown parameters to be estimated,

i is the error term.

GRETL computer software will be used to estimate Tobit regression model.

4. RESULTS AND DISCUSSION

A review of key variables integrated in data envelopment analysis is given in table-A1.


The table-A1 is specified on per acre inputs quantities and per acre

4 cost basis. These

results are calculated from 119 samples while one sample is dropped due to outlier. Total

technical, pure technical, allocative, economic and scale efficiencies are presented in

table-A2 and table-2. The estimation gives 0.67 mean of total technical efficiency of

sample farmer, which varies from 0.21 to 1.0. These results show that if sample farmer

operate at full efficiency level they can reduce, on an average, their inputs use by 33

percent to produce the same level of output. Decomposition of technical efficiency shows

that, on average, the sample farmers are more scale efficient than they are technically

efficient. The mean pure technical efficiency of sample farmer is 0.94, which varies from

0.64 to 1.0. The mean scale efficiency is 0.71, which varies from 0.26 to 1.00 and only 15

percent farmers are scale efficient while remaining 85 percent sample farmers are scale

inefficient. Ninety-nine percent of these scales inefficient farmers operate under increasing

returns to scale while remaining only 1 percent of these scale inefficient farmers operate

under decreasing return to scale. The mean allocative efficiency of sample farmer is 0.57,

with a minimum of 0.18 and a maximum of 1.00. The combined effect of technical and

allocative efficiencies shows that mean economic efficiency of is 0.54, with a minimum of

0.17 and maximum of 1.00.

The results show that cotton farmers are not fully efficient. Therefore, if the farmers

operate at full efficiency level they can reduce their cost of production by 46 percent

without reducing the level of output and with the existing technology because their

economic efficiency is 54 percent and allocative efficiency shows that the considerable

room is available to enhance the productivity of sample farmers because 43 percent cost of

inputs used in wrong direction and improve it. Frequency distribution of technical,

allocative and economic efficiencies estimates of sample farmers in cotton system are

given in figures 2 to 6 and appendix table-A1.

It is evident from figure 2 that total technical efficiency of the sample farmers varies from

0.21 to 1.00. Most of the farmers‟ (63% out of 119) total technical efficiency is less than

0.80 while only 23% have more than 0.90. The situation seems different in case of pure

technical efficiency (figure 3) here almost 90% farmers have pure technical efficiency

more than 0.90. The pattern of allocate and economic efficiencies are alike (figure 4 and

figure 5) with both average efficiencies around 0.55. Like other efficiencies the farmers

are not scale efficient too (figure 6).

Input Slacks Analysis and Number of Farmers Using Excess Inputs Table-3 indicates that input slacks and number of cotton farmers using excess

inputs. It is evident that the farmers can reduce their cost on inputs by reducing the amount

of slacks without reducing the output. Slacks are observed in irrigation, pesticides,

nitrogen and labor. This is because farmers adopt traditional practices in using the inputs.

4 Acre: one acre is equal to 0.04046 hector.

ABDUL AMEED

10

Table-2: Total Technical, Pure Technical, Allocative, Economic and Scale

Efficiencies5

5 TEcrs mean technical efficiency through constant return to scale, TEvrs mean technical

efficiency through variable returns to scale, AE mean allocative efficiency, EE mean economic

efficiency and SE mean scale efficiency

Efficiencies TEcrs TEvrs AE EE SE

Mean 0.67 0.94 0.57 0.54 0.71

Minimum 0.21 0.64 0.18 0.17 0.26

Maximum 1.00 1.00 1.00 1.00 1.00


They use inputs on the behalf of their father‟s, individual experience and illiterate

pesticides shopkeeper advice. Therefore, it is most important to create awareness about

new technologies and to give them training to improve the use of inputs.

Relationship between efficiencies estimates and cropping area In order to investigated relationship among efficiencies and crop area. The crop area was

categorized into three groups on the basis of operational holdings of farmers. Farm size A

consists of 1 -3 acres under cotton crop considered as small farmers, farm size B consists

of 3-6 acres under cotton crop considered as medium farmers and farm size C consists of

above 6 acres under cotton crop considered as large farmers. The total technical, pure

ABDUL AMEED

12

technical, allocative, economic and scale efficiencies scores relative to the farm size in

cotton crop are presented in table-4

The total technical efficiency, pure technical, allocative, economic and scale

efficiencies of small sample farmers are 0.72, 0.96, 0.55, 0.53 and 0.75 respectively. The

medium sample farmers have total technical, pure technical, allocative, economic and

scale efficiencies are 0.65, 0.93, 0.61, 0.57 and 0.69 respectively. The large sample

farmers have technical, pure technical, allocative, economic and scale efficiencies 0.62,

0.92, 0.55, 0.51 and 0.67 respectively.

In the total technical, allocative, economic and scale efficiencies among cropping

categories, category A farmers are more efficient than category B and

Table- 3: Input Slacks and Number of Farmer Using Excess Inputs

Inputs

Number

of Mean Mean Input Excess

Farmers Slack Use Input Use

(%)

Cotton crop land in acres 13 0.70 6.91 10.09

Seed per acre in kg 15 0.15 5.96 2.58

Nitrogen per acre in kg 26 3.51 55.37 6.34

Phosphate per acre in kg 16 0.47 20.94 2.23

Per acre tractor hours 19 0.09 8.67 1.07

Pesticides per acre in

litres 28 0.64 8.31 7.72

No of irrigation per acre

in hours 34 1.92 13.15 14.59

Labor days per acre man-

days 28 1.68 19.53 8.58

C sample farmers and medium farmers are more efficient than category C farmers. The

small sample farmers total technical, allocative, economic and scale more efficient than

medium farmers because the small sample farmers use small unit, family labor, which all

time work in field and proper management of small unit, less inputs required, easily

control outside factor effect e.g. rain, weather. In the monsoon rain when the water stay in

the field of cotton crop so the small unit of cotton crop easily drain and support to plant

with different ways as compare to large and medium farm size.

In the total technical, allocative, economic and scale efficiencies among cropping


Table- 4: Means of Total Technical (TECRS), Pure Technical (TEVRS), Allocative,

Economic and Scale Efficiencies Estimates according To Farm Size in Cotton crop

categories, category A farmers are more efficient than category B and C sample farmers

and medium farmers are more efficient than category C farmers. The small sample farmers

total technical, allocative, economic and scale more efficient than medium farmers

because the small sample farmers use small unit, family labor, which all time work in field

and proper management of small unit, less inputs required, easily control outside factor

effect e.g. rain, weather. In the monsoon rain when the water stay in the field of cotton

crop so the small unit of cotton crop easily drain and support to plant with different ways

as compare to large and medium farm size.

Table- 5: Scale share in categories, CRS (scale efficient), IRS (increasing return to

scale) and DRS (decreasing return to scale) in cotton crop.

Categories CRS IRS DRS

A (1-3 acres) 20% 80% -

B (3-6 acres) 15% 83% 2%

C (up to 6 acres) 10% 90% -

As presented in table-4 and table-5, scale efficiency of category (A) is 0.75. The 20

percent sample farmers are on constant return to scale while remaining is on increasing

return to scale. It indicates that 80 percent of sample farmers need to increase operational

scale to enhance the productivity and efficiency. While, medium farmers (category-B)

having 0.69 scale efficiency. Among them only 15 percent are on constant return to scale

and remaining 83 percent are on increasing return to scale. The large farmers (category-C)

have scale is 0.67. The only 10 percent of sample farmers are on constant return to scale

and 90 percent are on increasing return to scale the results show that in all categories most

of the farmers are on increasing returns to scale i.e. they can increase their output by

changing their operational scale. It will also enhance their efficiencies

Estimates of Target Output in Cotton Crop

This study also presents target output estimates based on output orientation methodology.

This technique has an advantage of estimating the maximum possible production. Table-6

gives the summary of target output. The target refers to the amount of output the decision

making units should aim at producing given the available unit of inputs and technology.

Categories TEcrs TEvrs AE EE SE

A (1-3 acres) 0.72 0.96 0.55 0.53 0.75

B (3-6 acres) 0.65 0.93 0.61 0.57 0.69

C (up to 6 acres) 0.62 0.92 0.55 0.51 0.67

ABDUL AMEED

14

The minimum output target that some of the decision management unit (DMU) should aim

at producing the target output is 6.8 Mounds per acre. The maximum output target range is

36 Mounds per acre.

The average actual production is 16.46 Mounds per acre, but according to output

orientation analysis the sample farmers can produce on average 22.5 mounds per acre

without reducing or increasing their current level of inputs and technology. According to

this analysis based on actual available inputs and technology to formers 10.9 percent out

of total 119 sample farmers of cotton crop can produce the cotton seed range 6-10 Mounds

per acre, 10.1 percent 11-15 Mounds per acre, 29.5 percent 16-20 Mounds per acre, 11.8

percent Mounds per acre, 14.3 percent 26-30 Mounds per acre and 23.5 percent more than

30 Mounds per acre, respectively.

Table- 6: Frequency Distribution of Output Target in Cotton System

(Mound=40kg)

Range Frequency Percentage

1.00-5.00 0 0.00

6.00-10.00 13 10.9

11.00-15.00 12 10.1

16.00-20.00 35 29.4

21.00-25.00 14 11.8

26.00-30.00 17 14.3

>30 28 23.5

Total 119 100.0

Analysis of Determinants of Technical Efficiency

The socioeconomic factors are expected to affect the level of technical efficiency of

farmers. This study also makes an attempt to find out the sources of technical efficiency

and external factors of cotton crop in District Dera Ghazi Khan. The Tobit regression

model is used to estimate the determinants of technical efficiency and external factors.

Table-7 shows that the coefficients of farmers education (schooling years), experience and

contact with extension agents have positive signs as our priori expectations (positive

related to technical efficiency) and significant. The educated farmers are more technically

efficient than less/no years of schooling cotton farmers. The results are similar to Sohail,

et al (2012), Gul, et al (2009) and Ali and Flinn (1989) who argue that the educated

farmers have better access to information, technology and standard inputs. Moreover, they

can have effective dealing with financial issues. The experience is positive related and

statistical significant which is the same explanation of the Bravo-Uretta (1994), Sohail, et al (2012), Ali and Flinn (1989) and Abid, et al (2011). It is indicating that


farmers experience an important effect on productivity and technical efficiency

of cotton farming. The experienced farmer can manage the farming uncertainty

and different practice in better way. The coefficient of contact with extension

agents has positive and significant effect on the technical efficiency of cotton

sample farmers. Result of this study is in the line with the result of Javed, et al

(2009) when farmers contact with extension agents then they get more information about modern farming, weather condition, cropping preparation, information

about seeds, fertilizers and other requirements.

Table-7: Source of Technical Efficiency of Cotton System with Tobit Analysis

Note: *** significant at 0.01level. **significant at 0.05 level.* significant at 0.10 level

While the variable of farm size negatively related with the technical efficiency of cotton

crop but coefficient is very small. However, on the basis of technology available to

farmers of Dera Gazi Khan bigger farm size can be a cause of low efficiency as proper

management would not be easy. The most farmers used private Muzarey (labor) which are

also illiterate and have financial constraints so they cannot properly manage the large unit.

These distance. The roads and market infrastructure is highly related with the agriculture

production because the outputs are properly reached in market at the proper time and less

destroy with hardship weather.

5. CONCLUSIONS The present study was designed to estimate technical, allocative and economic efficiencies

and also to investigate the determinants of technical efficiency of cotton farmers in District

Dera Ghazi Khan. The data were collected for the crop year 2012 from 120 respondents,

the one respondent drop due to outlier6. The Data envelopment analysis technique used to

estimate the technical, allocative and economic efficiencies and the Tobit regression

analysis was used to estimate the determinants of technical efficiency. Result derived from

DEA models for the cotton crop farmers of District Dera Ghazi Khan indicated that mean

total technical, pure technical, allocative, economic and scale efficiencies were 0.67, 0.94,

ABDUL AMEED

16

0.57, 0.54 and 0.71 respectively. Findings also uncovered that if farmers could manage

optimal levels of inputs, they can reduce 33 percent inputs and 46 percent cost without

changing level of output and technology because the technical and allocative efficiencies

respectively, 67 and 54 percent. The small farmers are more technical, allocative and

economically efficient than category-B (medium) and category-C (large) farmers The

result of target output analysis shows that the sample farmers should produce on average

22.50 Mounds per acre output of seed cotton without reducing the inputs and technology

while the actual output of seed cotton in this study was 16.46 Mounds per acre. The result

of Tobit regression model shows that the education, experience, extension workers have

positive collision on technical efficiency while impact of farm size and market distance

was negative on technical efficiency of cotton crop.

Policy Recommendations According to the finding there are some commendations, which enhance

agricultural efficiency and productivity in District Dera Ghazi Khan.

1-The majority comprehensible consequence is that there is required of echo plan to

encourage formal and technical education in rural area. This will allow situations create

many problems for productivity and efficiency. The variable market distance is used as the

proxy for development of road and market infrastructure. The distance from the village to

main market of agriculture inputs and output is negatively associated with the technical

efficiency. According to the (FAO, 2004) the purchasing of inputs would have been higher

in a developing country if the supply of inputs available at the walking the farmers to

make better technical decision about the farming and best allocation of resources.

2- The study shows that farmers used excess inputs as traditional behavior, individual

experience, believing their parents experience and the local village shopkeeper advice. The

Government should make broadcasting strategy for awareness about the use of agriculture

inputs and resources.

3-The study shows that the farmers having more contact with extension agents are more

efficient than the farmers having low contacts. It is, therefore, recommended that the

policy makers should focus on attractive farmers6 access to information via provision of

better extension services. Government should apportion more funds to make stronger the

extension department and expending net of extension services in the remote areas.

4-The Government should make the strong policy to remove without licenses agro-shops,

because mostly income of the illiterate farmers wastes into the flak seeds, fertilizers,

pesticides and herbicides etc.

5-The study also shows that the farmers located near to the market are more efficient than

the farmers located away from the market. It is, therefore recommended that the policy

makers should focus on the development of market and road infrastructure supply outlets

should be made closer to the farm gate.

6- The Government should issue the licenses to shops for purchase of cotton seeds at the

prescribed rate by government. Further government can generate new revenue way in the


form of license fee. This revenue can be used for the welfare of the farmers.

7- The Government should provide more funds for paka khal system to save the canal

irrigation water. Moreover, there should be proper monitoring by the agriculture

department to check the quality of water, soil, fertilizers, seeds, pesticides and herbicides.

REFERENCES Abid, M., Ashfaq, M., Quddus, A., Tahir, A., & Fatima, N. (2011). A Resource Use.

Efficiency Analysis of Small BT Cotton Farmers in Punjab, Pakistan. Pakistan Journal of

Agriculture, 48(1), 75-81

ALi, M., & Filnn, J. (1989). Profit Efficiency in Basmati Rice Producers in Pakistan‟s

Punjab.

American Journal Of Agricultural Economics, 71, 303-310.

Banker, R.D., Charnes, A., and Cooper, W.W. (1984). Some Models for

Estimating Technical Efficiency and Scale Inefficiencies in Data Envelopment

Analysis. Management Science, 30, 1078-1092.

Bravo-Ureta, B. E., & Evenson, R. E. (1994). Efficiency in Agricultural Production: The

Case of Peasant Farmers in Eastern Paraguay. Journal of Agricultural Economics, 10, 27-

37.

Coelli, T. (1996). A Guide to DEAP Version 2.1 A Data Envelopment Analysis

(Computer) Program. Australian Journal of Agricultural Economic, 39, 219–245.

Coelli, T., Rao, D., & Battese, G. (2005). An Introduction to Efficiency and Productivity

Analysis. Springer.

Dlamini, S., Rugambisa, I., Masuku.B.M, & Belete.A. (2010). Technical Efficiency of The

Small Scale Sugarcane Farmers in Swaziland: A Case Study of Vuvulane and Big Bend

Farmers. African Journal of Agriculture Research, 5(9), 935-940.

Ebong, V., Okoro, U., & Effiong, E. (2009). Determinants of Technical Efficiency of

Urban Farming in Uyo Metropolis of Akwa Ibom State, Nigeria. Journal of Agriculture

and Social Science, 5, 89-92.

FAO. (2004). Fertilizer use by Crop in Pakistan, Land and Plant Nutrition Management

Service, Land and Water Development Division, FAO, Rome. GOP. (2012). Economic Survey of Pakistan. Ministry of Finance,Govt of Pakistan.

Gul, M., Koc, B., Akbinar, G., & Parlakay, O. (2009). Determination of Technical

Efficiency in Cotton Growing Farms in Turkey: A Case Study of Oukurova Region.

African Journal of Agriculture Research, 4(10), 944-949.

Gwandi, O., Bala, M., & Danbaki, J. (2010). Resource Use Efficiency in Cotton

Production in Gassol Local Government Area of Taraba State, Nigeria. Journal of

Agriculture and Social Science, 87-90.

Hajian.Mohammadhadi, S. (2013). Total factor Productivity and Efficiency in Iranian

Crop. Research Journal of Agricultural and Environmental Management, 2(2), 033-043.

Javed, I., Adil, A., Hassan, S., & Ali, A. (2009). An Efficiency of Punjab Cotton-Wheat

System.

The Lahore Journal of Economics, 2(14), 97-124.

6 One sample drop which farm area is 200 acres due to this stander deviation is greater in table-AI

ABDUL AMEED

18

Ogunniyi, L., & Oladejo, J. (2011). Technical Efficiency of Tomato Production in Oyo

State Nigeria. Agricultural Science Research, 1(4), 84-91.

Sohail, N., Latif, K., Abbsa, N., & Shahid, M. (2012). Estimation of Technical Efficiency

and Investigation of Efficiency Variables in Wheat Production: A Case of District

Sargodha (Pakistan). Interdisciplinary Journal of Contemporary Research in Business,

3(10), 897-904.

Sylvain, B. N., C, J. N., & Cletus, N. (2010). The Determinants of The Technical

Efficiency of Cotton Farmers in Northern Cameroon. MPRA, 24814, 50-62.

Tobin, J. (1995). Estimation of Relationship for Limited Dependent Variable.

Econometrica, 26, 24-36.

APPENDICES

Table-A1

Variables Minimum Maximum Mean

Std.

Deviation

Output per acre in kg 240.0 1440.0 657.10 223.0153

Total farm land in acres 1.0 200.0 10.40 19.3541

Land under cotton crop in acres 1.0 40.0 6.91 6.9563

Seed per acre in kg 3.8 10.0 5.96 1.6712

Nitrogen per acre in kg7 9.0 147.0 55.37 23.4862

Phosphate per acre in kg 4.6 69.0 20.94 7.5348

Per acre tractor hours 4.0 14.0 8.67 1.5490

Pesticides per acre in litre 1.5 18.1 8.31 3.1337

No. of Irrigation per acre in hours 4.0 32.0 13.15 7.3619

Labor days per acre man-days 4.5 57.0 19.53 11.2650

Per acre land cost in Rs 5000.0 20000.0 10263.03 2259.1758

Per acre seed cost in Rs 400.0 3000.0 1437.60 574.3237

Per acre nitrogen cost in Rs 684.8 11184.8 4220.16 1792.6342

Per acre phosphate cost in Rs 727.0 9945.7 3052.40 1093.8497

Per acre tractor hours cost in Rs 1200.0 4800.0 2825.63 664.3738

Per acre pesticide cost in Rs 2200.0 13000.0 7058.10 2058.3371

Per acre irrigation cost in Rs 910.0 10533.3 3067.01 1621.5261

Per acre labor cost in Rs 1350.0 17100.0 5858.15 3379.7743

Source: Field survey by author 2012

7 Nitrogen & phosphate amount estimate from ratio of nitrogen in 50 kg bag


Table-A2

Efficiencies of Sample Farmers of District Dera Ghazi Khan

EFFICIENCY

RANGE TECRS TEVRS AE EE

SE

Freq % Freq % Freq % Freq % Freq %

0.01-0.10 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0

0.11-0.20 0 0.0 0 0.0 1 0.8 1 0.8 0 0.0

0.21-0.30 5 4.2 0 0.0 1 0.8 1 0.8 3 2.5

0.31-0.40 11 9.2 0 0.0 10 8.4 24 20.2 8 6.7

0.41-0.50 12 10.1 0 0.0 29 24.4 27 22.7 12 10.1

0.51-0.60 21 17.6 0 0.0 33 27.7 27 22.7 20 16.8

0.61-0.70 21 17.6 2 1.7 27 22.7 25 21.0 15 12.6

0.71-0.80 12 10.1 10 8.4 10 8.4 7 5.9 18 15.1

0.81-0.90 14 11.8 18 15.1 6 5.0 5 4.2 15 12.6

0.91-1.00 23 19.3 89 74.8 2 1.7 2 1.7 28 23.5

TOTAL 119 100 119 100 119 100 119 100 119 100

Majumdar & Swain

20



Identification of factors influencing Preferences for Green

Products: A study in and around Kolkata (India) Prof. Sudipta Majumdar

Faculty Member & Research Scholar

ICFAI University Jharkhand, Ranchi,

India

E-mail: [email protected]

Dr. Sukanta Chandra Swain

Asst. Dean

ICFAI University Jharkhand, Ranchi,

India

E-mail: [email protected]

Abstract

Since the concept of environmental consciousness has become a necessity to save the

mankind, promoting consumption of green products is the need of hour, owing the fact

that green products are environment friendly or sustainable products and are organic in

nature. It is evident that the feeling for the health of environment and consumers, the usage

of green products is emerging at the cost of traditional or conventional products. However,

the magnitude of usage of green products is much behind the ideal one to safeguard the

consumers and environment at large. Thus stretching the incidence and depth of usage of

green products is a must. In order to achieve the pious objective, it is necessary to know

the factors which insisted the users to go for the green products so that the same can be

ventilated to the masses for extending the consumer base for the green products. On this

backdrop, this study has been undertaken to collect responses from the green product users

in and around Kolkata to find out the significant factors, through factor analysis, which

contribute for the popularity of the Green products. The study also tries to find out the

impact of different psychographic variables with respect to popularity of green products.

The findings so obtained will definitely help in augmenting the usage of green products

and hence contribute to safeguard the health of consumers and environment at large.

Key Words: Green Products, Factors, Kolkata, Factor Analysis, Psychographic Variables

1. Introduction

From the last decade onwards people became more concerned about their health as a result

of which they are using more of green products. Green products can be stated as having

less of an impact on the environment and are less damaging to human health than

traditional products. Hence they are also called as sustainable or environment friendly

product. Green products are formed from recycled components, be manufactured in a

more energy-conservative way, or be supplied to the market in more environmental

friendly way [1]. Since people are becoming more aware about the concept of

environmental consciousness, the usage of traditional or conventional products are getting

reduced. Traditional products are those manufactured in the traditional way. They are not

being produced keeping environmental considerations in mind. In today‟s competitive

scenario green products are competing with the conventional or regular products (products

produced by traditional methods).But, this usage pattern is not applicable to all parts of the

society. Knowledge and awareness about the green products play a very vital role in

enabling the customers to use them. But, this awareness and knowledge do not exist, thus

mailto:[email protected]

mailto:[email protected]

21 Identification of factors influencing Preferences for Green Products: A study in and

around Kolkata (India)

restricting the usage of the green products. From the last decade onwards, we have started

using the green products and it will take time before it penetrates to all parts of the society.

The concept of green products is becoming more popular with the aspect of food items.

Since people are becoming more health conscious, they are giving more importance to the

consumable products. People started using more green food products to minimize their

health risk. But, here also like normal green products knowledge and awareness is not

there in all parts of the society. So, these are more being used by the more educated parts

of the society. Also, organizations and government are incapable of promoting the concept

of “Green”. But the best part is, the concept has started and it is penetrating to the society

at a very fast pace. If all the factors which contribute to the popularity of green products,

such as price of the product, its quality, customer‟s perception about the products,

awareness about them, are being handled carefully by the government and the

organizations, then they will become more popular in the society.

As we have been discussing, there are various factors which positively as well as

negatively influence the popularity of green products, both in food and nod-food sector. In

this context, it is important to examine various psychographic factors which influence the

usage of green products, specifically in cosmetics and food sector in Kolkata and around

Kolkata in West Bengal, India. The various psychographic variables, such as

Environmental Consciousness, Health Consciousness, Price Sensitivity, Product

Involvement and Innovation are selected from a through literature review. The consumers‟

perception about each psychographic variable is being understood using specific items.

This paper aims to provide a snapshot of consumers‟ belief about Green Products about

Psychographic variables in India (Kolkata).

2. Review of Literature

From the existing literature, psychographics is being defined as the study of personality,

values, attitudes, interests, and lifestyles (Senise, 2007). This mainly focuses on interests,

activities and opinions (IAO) of the customers. Hence psychographic variables can be

interpreted as combinations of demographic and psychological variables which impact

customer‟s attitude in an overall manner.

It was observed that there is a general perception about organic food products catering

mainly for higher social classes (Harper and Makatouni, 2002). It is further stated in the

same paper that people from those classes have an affordability as well as consciousness

regarding organic products, thus resulting in green food product consumption. Few authors

have also discussed about people‟s tendency towards safe and healthy organic products

intake influencing positively the customers‟ intention to purchase them(Ahmed and Juhdi,

2010). Also, (Davies et al, 1995; Lea and Worsley 2005) in their paper referred that green

consumers prefer buying organic food products for their health concern. So, health is an

important factor driving the customers for green food product consumption. Contradictory

results are also published in a paper by Pickett-Baker and Ozaki (Pickett-Baker and Ozaki,

2008), where authors fail to conclude any positive correlation between positive

environmental beliefs and propensity of the customers to go for buying more green

products.

Majumdar & Swain

22

Environmental knowledge and attitude play a significant role in customers‟ tendency for

green food product purchasing as reported in several papers. Many authors stated that environmental consciousness generates more interest of the customers towards organic

products (Schlegelmilch et al, 1996). Kaiser et al (1999) in their paper reported that

environmental values and environmental knowledge are important factors which affect

ecological behavior intention ultimately helping in building customer‟s attitude towards

organic products. Also Ahmed and Juhdi (2010) referred that customers are positively

inclined towards environment friendly farming because of their environmental

consciousness and it leads to positive customer intention to buy organic products. Lockie

et al , (2002), said that the consumers‟ familiarity with the green products, generate more

interest to consume them. This is common to conventional consumer‟s behavior. They

also stated that the mood of the consumers, i.e., to keep him relax is positively correlated

with organic food consumption. The customers believe that consuming organic food items

make customers stress-free.

Apart from health consciousness and environmental belief, several other psychographic

variables are also tested in literature like customers belief towards information

authenticity, political motivation, skepticism etc. Kozup et al (2003) said that more proper

information from credible sources increase the consumption of organic food products

because of customers‟ environmental belief and authenticity of the information provided.

Similar observation was reported by Schlegelmilch et al (1996), by inferring that more

knowledge, i.e., detail factual information about the organic products improve the chance

of customers‟ buying them. Also , it was said that the customers‟ previous experience of

using some environmental brands i.e., the brands which produce the products in

environment- friendly way have an impact on their chances of selecting those brands only

for repeated usage(Pickett-Baker and Ozaki, 2008). In another paper, it is being stated that

recycling activities positively influences pro-environmental purchasing behavior for those

customers who can dedicate more time and effort(Schlegelmilch et al, 1996). Same papers

also stated that politically motivated activities act positively only for those customers who

are environmentally conscious. In the paper by Chang (Chang , 2011), it is being discussed

that perceived higher price, lower quality and skepticism negatively and perceived

emotional benefits acting positively will create more ambivalence attitudes of the

customers towards buying green products.

In addition to demographic and psychographic variables, the different product specific

variables affect the customers‟ attitude towards green products. The various variables

discussed in the literature are environmental brands, brand name, product type (Green vs.

non-green),preferences for green attributes for the products, green technology, energy

savings .Whereas, with respect to green food products, Heart healthy claim on food

products, nutritional information about the food products, nutritional content of the

alternative products, price, product types (fresh fruit, fresh vegetables, meat, milk and

dairy products, cereals and cereal products) were discussed in the literature.

In the paper by Pickett-Baker and Ozaki(2008), the author stated that environmental

brands, i.e., the brands which produces the products in environmental-friendly manner will

positively influences customers green product purchase decision. In his paper, Mobley et



al (1995) reported that only branded green products create positive impression in the

minds of the customers. Lin and Chang, 2012) had said that green or non-green products

affect the environmental conscious customers‟ usage amount for the products. Olson

(2012) stated that using green technology consumers use more products with energy

efficiency. He also stated that energy savings characteristics of the products positively

influences customers attitude towards green products.

Kozup et al (2003) stated in their paper that heart healthy claim, nutritional information on

the food products partially affect consumer‟s evaluation of the packaged food products.

Also, nutritional content of the alternative food items negatively influences consumer‟s

evaluation of packaged food items. In other papers the authors discussed about the

negative effect of price towards organic food consumption. So, price is a significant

barrier for customer‟s attitude formation towards green food products consumption

(Lockie et al, 2002).

In addition to the demographic, psychographics and product specific variables, there are

various external, i.e., environmental variables which leads to specific customer behavior.

From the reviewed literature it was found that customer‟s attitude towards green food

products s being affected by information people have about organic products, tasty,

availability, expensive, food value , natural content, animal welfare, convenience,

environmental protection, food production method, source of information, purchasing

place(hypermarket, supermarket, organic stores, farms), purchasing difficulties(difficult to

find, high prices, poor range of choice), word of mouth, marketing communications,

information about green products, claim Type.

Ahmed and Juhdi (2010) had discussed that information people have about organic food

products negatively influences customer‟s purchase intention towards the products. But

in another paper, the authors had reported that more information people have about the

products , the more customers will be interested to consume them(Chinnici et al ,

2002).Again,Lin and Chang (2012) stated that only the positive information about the

products influences positively user‟s perception of the effectivity of the green products .

Also, Pickett-Baker and Ozaki(2008) stated that effective marketing communications , i.e.,

communicating all the desired information about the product influences positively

consumers‟ green product purchase decision. He had also reported that word of mouth

communication is the most effective tool to convince the

customers about the positive aspects of green products. Chang (2011) had stated that the

claims organizations make about the products have a positive impact towards ad

believability only if they are from authorized sources. Lea and Worsley (2005) had

reported that organic food products tastes better than conventional products and

availability and expense customers have to bear for these acts as barriers towards creating

consumers belief about organic food items. Harper and Makatouni (2002) have concluded

that more environmentally friendly food production method generates positive customers‟

perception about the products. Again more food value creates more positive belief about

the products. More natural content for the organic food items , concern for animal welfare

and environmental protection creates more customers‟ interest towards these

products(Lockie et al , 2002). And the customers were buying more organic food items

Majumdar & Swain

24

from hypermarket, organic stores and farms where they are more motivated towards

buying them by the overall environment.

3. Methodology

The study was based on quantitative data on consumers‟ perception about green products.

Data was collected both in online and offline format. All the respondents were briefed

about the project before they respond.

In case of the online format, the data was collected with the help of mail-based

questionnaire. The questionnaire was sent to many respondents selected randomly. A

cover letter was also sent along with the questionnaire. A total of 100 respondents were

selected randomly and the questionnaires were sent to them. To improve the success rate,

the questionnaires were sent repeatedly to the prospective respondents. Approximately, 65

respondents sent back the filled questionnaires.

The survey was also carried on in the offline format. For that, the questionnaires were

distributed to the respondents selected randomly from the different parts of Kolkata, India

using Green products. The respondents were both green products buyer and non-buyers. A

total of 235 respondents were surveyed for their responses.

So, considering both the online and offline format, 300 respondents were surveyed for

their responses. The questionnaire was formulated from a review of literature based on the

following literatures (e.g. Sanchez, 2010; Hofmester-Toth,2010 ; Grewal, 2000).The

questionnaire‟s main objective is to study the impact of the various psychographic

variables, such as involvement with the product, respondent‟s opinion leadership etc. on

the respondents intention to purchase green products. The paper will be studying the

responses on only two types of green products, namely cosmetics products and food items.

The questionnaire is divided into eight parts. The first part is trying to measure the

environmental consciousness of the respondents with respect to the scales used in the

paper by Sanchez, 2010. The second part is measuring the price sensitivity of the

respondent with respect to the scale used in a paper by Goldsmith, 1991. In the third,

fourth and the fifth part, the respondent‟s opinion leadership, innovativeness and

involvement in buying green products will be studied based on a paper by Grewal, 2000.

In the sixth part, the respondent‟s health consciousness will be studied based on the

concept from the literature by Hong1990.In the seventh part, the respondent‟s reaction to

the different characteristics of the green cosmetics products are studied. The scales are

based on the literatures by Ahmad,2010 ;Chang2011;Davies,1995;Bamberg,2006 and

Lea2005. The eighth part is same as the seventh part. The only difference is that the

products considered here are green food products. The scales are based on the literatures

by Ahmad,2010;Kozup,2003;Davies,1995;Bamberg, 2006; Lin,2012; Chang,2011 and

Lea,2005.All the factors were measured on a seven point scale stating the following

things(1 = Very Strongly Disagree, 2 = Strongly Disagree, 3 = Disagree, 4 = Neither

Agree Nor Disagree, 5 = Agree, 6 = Strongly Agree, 7 = Very Strongly Agree). The socio-

demographic information of the respondents is collected in the ninth part.

The collected data for all the parts of the questionnaire is analyzed using Exploratory

Factor Analysis to to uncover the underlying structure of a relatively large set of variables.

The IBM SPSS (version 19) is used for the purpose.



Variables/Factors(used in the study) contributing for the popularity of Green products

Environmental Consciousness Variable Description

v1 I support different measures to improve water management leading to water conservation

v2 I am aware about the issues and problems related to the environment

v3 I would be willing to pay higher prices for water

v4 It is very difficult for a person like me to do anything about the environment

v5 I believe that using recyclable materials for daily use will improve the environment


Environmental Consciousness

Price Sensitivity

v1 In general the price or cost of buying green products is important to me

v2 I know that a new kind of green product is likely to be more expensive than older ones , but

that does not matter to me

v3 I am less willing to buy a green product if I think that it will be high in price

v4 I don‟t mind paying more to try out a new green product

v5 A really good green product is worth paying a lot of money

v6 I don‟t mind spending a lot of money to buy a green product

Innovativeness v1 I like to take a chance in buying new products

v2 I like to try new and different products

v3 I am the first in my circle of friends to buy a new product when it appears in the market

v4 I am the first in my circle of friends to experiment with the brands of latest products

Involvement v1 I select the green products very carefully

v2 Using branded green products helps me express my personality

v3 You can tell a lot about a person from whether he/she buys green products

v4 I believe different brands of green products would give different amounts of satisfaction

Health consciousness

v1 I worry that there are chemicals in my food.

v2 I worry that there are chemicals in my cosmetic products

v3 I‟m concerned about my drinking water quality.

v4 I avoid foods containing preservatives.

v5 I read more health-related articles than I did 3 years ago.

v6 I‟m interested in information about my health.

v7 I‟m concerned about my health all the time.

v8 Pollution in food and cosmetic products does not bother me.

Majumdar & Swain

26


Environmental Consciousness General characteristics about green cosmetic products v1 Green cosmetic products are safer to use than non-green cosmetic products

v2 Green cosmetic products are of better quality than non-green cosmetic products

v3 Green cosmetic products are more effective than non-green cosmetic products

v4 Branded green cosmetic products are better than non-branded green cosmetic products

v5 Less knowledge about green cosmetic products prevent people from buying them

v6 Less information about green cosmetic products prevent people from buying them

v7 Less availability about green cosmetic products prevent people from buying them

v8 Green cosmetic products are expensive than non-green cosmetic products

General characteristics about green food products v1 Green food products are safer than non- green food products

v2 Green food products are healthier than non-green food products

v3 Green food products have more nutritional value than non-green food products

v4 Green food products are tastier than non-green food products

v5 Less knowledge about green food products prevent people from buying them

v6 Less information about green food products prevent people from buying them

v7 Branded green products are better than non-branded green food products

v8 Green food products do not look good in appearance

v9 Less availability about green food products prevent people from buying them

v10 Green food products are expensive

4. Data Analysis and Findings

Environmental consciousness:

Rotated Component Matrixa

Component

1 2

v4 .692

v5 .662

v1 .761

v3 .792

v2 .771

27

Identification of factors influencing Preferences for Green Products: A study in and


Variable Description Components

v1 I support different measures to improve

water management leading to water

conservation

Environmental Sense(v1 , v2 and

v3)

Environmental Callousness (v4

and v5)

v2 I am aware about the issues and

problems related to the environment

v3 I would be willing to pay higher prices

for water

v4 It is very difficult for a person like me

to do anything about the environment

v5 I believe that using recyclable materials

for daily use will improve the

environment


v1 I support different measures to

improve water management

leading to water conservation

Environmental Sense(v1 , v2

and v3)

Environmental Callousness

(v4 and v5)

v2 I am aware about the issues and

problems related to the

environment

v3 I would be willing to pay higher

prices for water

v4 It is very difficult for a person like

me to do anything about the

environment

v5 I believe that using recyclable

materials for daily use will

improve the environment

From the above table, the variables v1 , v2 ,v3 had more loadings on component 2, thus

making it a Component which can be named as Environmental Sense. Likewise, variables

v4 and v5 have more loadings on component 1 and making it a part of component named

as Environmental Callousness From the above table, it can be stated that the variables v4

and v5 can be combined to be a part of component 1, named as Higher Price. The

variables v1 and v2 can be combined to be part of component 2 named as Price

Sensitivity. Likewise the variables v3 and v5 can be combined to form component 3

named as Price Barrier.

Majumdar & Swain

28

Price sensitivity


Component

1 2 3

v4 .855

v6 .823

v2 .704

v1 .650

v5 .812

v3 .440 .667


v1 In general the price or cost of buying

green products is important to me

Higher Price(v4 and v6)

Price Sensitivity(v1 and v2)

Price Barrier(v3 and v5)

v2 I know that a new kind of green product

is likely to be more expensive than older

ones , but that does not matter to me

v3 I am less willing to buy a green product if

I think that it will be high in price

v4 I don‟t mind paying more to try out a

new green product

v5 A really good green product is worth

paying a lot of money

v6 I don‟t mind spending a lot of money to

buy a green product

Innovativeness


Component

1 2

v1 .868

v2 .803

v3 .399 .386

v4 .935




v1 In general the price or cost of buying

green products is important to me

Higher Price(v4 and v6)

Price Sensitivity(v1 and v2)

Price Barrier(v3 and v5)

v2 I know that a new kind of green product

is likely to be more expensive than older

ones , but that does not matter to me

v3 I am less willing to buy a green product

if I think that it will be high in price

v4 I don‟t mind paying more to try out a

new green product

v5 A really good green product is worth

paying a lot of money

v6 I don‟t mind spending a lot of money to

buy a green product

For the case of Innovativeness, the variables v1, v2 and v3 can be combined to form a

component 1 named as New Product Initiative. The variable 4 alone will be forming

component 2 named as Experimental Attitude.

Involvement:


Component

1 2

v1 .868

v4 .803

v2 .399 .435

v3 .935


v1 I select the green products very

carefully

Satisfaction from

Branded Green products

(v1 and v4)

Branded green products

reveal personality(v2 and

v3)

v2 Using branded green products helps me

express my personality

v3 You can tell a lot about a person from

whether he/she buys green products

v4 I believe different brands of green

products would give different amounts

of satisfaction

Majumdar & Swain

30

From the above table, the variables v1 and v4 can be combined to form a part of

Component 1 , named as Satisfaction from Branded Green products . Likewise, the

variables v2 and v3 are combined to form component 2, named as Branded green products

reveal personality.

Health consciousness:-

In case of health consciousness of the respondents, the variables 2 and 5 can be combined

to form component 1 , named as Health Sensitivity . The variables v1 , v6 and v7 can be

combined to form component 2 named as Health Concern. Likewise the variable v4 alone

will form component 3 named as


Component

1 2 3 4

v2 .793

v5 -.686

v7 .758

v1 .629

v4 .837

v6 .785

v8 -.313 .378 .487

v3 .375 -.436 .447

Avoid Preservative Food. Lastly, the variables v3 and v8 are combined to form a part of

component 4 named as Food Pollution.


v1 I worry that there are chemicals in my food. Health Sensitivity(v2 and

v5)

Health Concern(v1, v6 and

v7)

Avoid preservative food(v4)

Food pollution(v3 and v8)

v2 I worry that there are chemicals in my

cosmetic products

v3 I‟m concerned about my drinking water

quality.


v5 I read more health-related articles than I did

3 years ago.

v6 I‟m interested in information about my

health.

v7 I‟m concerned about my health all the time.

v8 Pollution in food and cosmetic products

does not bother me.



Characteristics of green cosmetic products:

In case of the Green Cosmetic products, the variables v5 and v6 can be combined to form

component 1 which is named as Green Product Knowledge. The variables v3 and v4 are

combined to form component 2, which is named as


Component

1 2 3 4

v6 .890

v5 .859

v4 .757

v3 -.683

v1 .745 -.337

v2 .612 .437

v7 .434

v8 -.432

Branded Green Cosmetic Products. The third component 3 , component 3 is formed by

combining the variables v1, v2



food.

Health Sensitivity(v2 and

v5)

Health Concern(v1, v6 and

v7)

Avoid preservative

food(v4)

Food pollution(v3 and v8)


cosmetic products

v3 I‟m concerned about my drinking water

quality.


v5 I read more health-related articles than I

did 3 years ago.

v6 I‟m interested in information about my

health.

v7 I‟m concerned about my health all the

time.

v8 Pollution in food and cosmetic products

does not bother me.

and v7 and named as Reliability of Green Cosmetic Product . The remaining variable v8

forms the 4th

component , named as Green Products Expensive .

Characteristics of Green Food Products:- In case of the Green Food products, the variables v3 and v4 are combined to form

component 1, named as Green Food Products Nutritional Taste. The variable v2 forms

component 2, which is named as Green Food Products are Healthier. The variables v5, v6

Majumdar & Swain

32


v1 Green cosmetic products are safer to use

than non-green cosmetic products

Green Product

Knowledge(v5 and v6)

Branded Green Cosmetic

Products(v4 and v3)

Reliability of Green

Cosmetic Product (v7 , v1

and v2)

Green Products

expensive(v8)

v2 Green cosmetic products are of better

quality than non-green cosmetic products

v3 Green cosmetic products are more

effective than non-green cosmetic products

v4 Branded green cosmetic products are better

than non-branded green cosmetic products

v5 Less knowledge about green cosmetic

products prevent people from buying them

v6 Less information about green cosmetic


v7 Less availability about green cosmetic


v8 Green cosmetic products are expensive

than non-green cosmetic products

and v9 are combined to form component 3 which is named as Lack of information and

availability of green Food Products. Likewise the variables v1 and v10 are combined to

form component 4 named as Green Food Products are safe and expensive. Lastly the

variables v7 and v8 are combined to form component 5, which is named as Branded Green

Food Products‟ Look and quality

5. Conclusion

In order to meet the purpose of the study as envisaged in the introduction part of the paper,

factor analysis is used to know important factors which insist buyers to go for green

products and also find out the impact of psychographic variables on the popularity of

green products.

On the basis of analysis done using Exploratory Factor Analysis, huge number of variables

used in the study, to be specific forty five variables, had been scaled down to twenty

variables. Concerning the facet - impact of Environmental consciousness towards

popularity of Green products, factors such as; Environmental Sense and Environmental

Callousness are the most important. Relating to relevance of price towards popularity of

green products, factors such as; Higher Price, Price Sensitivity and Price Barrier plays the

most important role. In the pretext of studying the innovation of the respondents‟ about

buying green products, it has been found that New Product Initiative and Experimental

Attitude are two important factors. Regarding involvement in buying process while

buying green products, factors such as; Satisfaction from Branded Green products and Branded green products reveal personality are the key contributors. About

health consciousness of the respondents in buying green products, factors such



as; Health Sensitivity, Health Concern, Avoid preservative food and Food pollution play

the most important role.

Regarding general factors contributing for the popularity of green cosmetic products,

important factors are; Green Product Knowledge, Branded Green Cosmetic Products,

Reliability of Green Cosmetic Product and Green Products expensive.

Pertaining to general factors impacting green food products, factors such as;

Green Food Products‟ Nutritional Taste, Green Food Products are Healthier, Lack

of information and availability of Green Food Products, Green Food Products are

safe and expensive and Branded

Green Food Products‟ Look and Quality impact the respondents‟ decision for

buying green food products.

Bibliography 1) Ahmad, S. & Juhdi, N.Organic Food: A Study on Demographic Characteristics and

Factors Influencing Purchase Intentions among Consumers in Klang Valley, Malaysia

International Journal of Business and Management, 2010, Vol. 5(2), pp. 105-118

2) Bamberg, S.How does environmental concern influence specific environmentally related behaviors? A new answer to an old question Journal of Environmental Psychology, 2003,

Vol. 23, pp. 21–32

3) Chang, C. Feeling Ambivalent About Going Green Implications for Green Advertising Processing The Journal of Advertising, 2011, Vol. 40(4), pp. 19-31

4) Chinnici, G.and D'Amico, M. & Pecorino, B. A multivariate statistical analysis on the

consumers of organic products British Food Journal, 2002, Vol. 104(3/4/5), pp. 187-199

5) Davies, A., Titterington, A. & Cochrane, C. Who buys organic food? A profile of the

purchasers of organic food in Northern Ireland British Food Journal, 1995, Vol. 97(10),

pp. 17-23

6) Harper, G. & Makatouni, A. Consumer perception of organic food production and farm

animal welfare British Food Journal, 2002, Vol. 104(3/4/5), pp. 287-299

7) Kaiser, F., Wolfing, S. & Fuhrer, U. Environmental attitude and Ecological behavior

Journal of Environmental Psychology, 1999, Vol. 19, pp. 1-19

8) Kozup, J., Creyer, E. & Burton, S. Making Healthful Food Choices: The Influence of

Health Claims and Nutrition Information on Consumers' Evaluation of Packaged Food

Products and Restaurant Menu Items Journal of Marketing, 2003, Vol. 67, pp. 19-34

9) Lea, E. & Worsley, T. Australians’ organic food beliefs, demographics and values British

Food Journal, 2005, Vol. 107(11), pp. 855-869

10) Lin, Y. & Chang.C.A. Doubie Standard: The Role of Environmentai Consciousness in Green Product Usage Journal of Marketing, 2012, Vol. 76, pp. 125-134

11) Lockie, S., Lyons, K., Lawrence, G. & Mummery, K. Eating ‘Green’: Motivations Behind

Organic Food Consumption in Australia European Society for Rural Sociology, 2002,

Vol. 42(1), pp. 23-40

12) Mobley, A., Painter, T., Untch, E. & Unnava, H. Consumer Evaluation of Recycled Products Psychology & Marketing, 1995, Vol. 12(3), pp. 165-176

Majumdar & Swain

34

13) Moisander, J. Motivational complexity of green consumerism International Journal of

Consumer Studies, 2007, Vol. 31, pp. 404-409

14) Olson, E. It’s not easy being green: the effects of attribute tradeoffs on green product

preference and choice J. of the Acad. Mark. Sci., 2012

15) Pickett-Baker, J. & Ozaki, R. Pro-environmental products: marketing influence on

consumer purchase decision Journal of Consumer Marketing, 2008, Vol. 25(5), pp. 281-

293

16) Raghunathan, R., Naylor, R. & Hoyer, W. The Unhealthy = Tasty Intuition and Its Effects

on Taste Inferences, Enjoyment, and Choice of Food Products Journal of Marketing, 2006,

Vol. 70, pp. 170-184

17) Schlegelmilch, B., Arizona, B., Bohlen, G. & Diamantopoulos, A. The link between green

purchasing decisions and measures of environmental consciousness European Journal of

Marketing, 1996, Vol. 30(5), pp. 35-55

18) Razzaque, M. (1995), 'Demographics, Psychographics and Consumer Value Dimensions:

a Study of Consumers in a Traditional Asian Society', European Advances in Consumer

Research 2, 183-192.

19) (http://www.enviro-news.com/glossary/green_products.html)

20) http://www.inc.com/encyclopedia/green-marketing.html

21) http://escholarship.org/uc/item/49n325b7

22) http://www.slideshare.net/f098/green-marketing-5596884

4.139.58.2/ejournalver2/.../PavanMishraandMs.PayalSharma.pdf

http://www.enviro-news.com/glossary/green_products.html

http://www.inc.com/encyclopedia/green-marketing.html

http://escholarship.org/uc/item/49n325b7

http://www.slideshare.net/f098/green-marketing-5596884

35

Performance of financial markets in Indian Economy



Performance of financial markets in Indian Economy Dr.A.K.Jha

Director, CMAT, Gr. Noida

&

Viriender Pal Singh

Research scholar, Mewar University

Abstract

In present circumstances the role of financial market is become larger than other

times. This paper is divided into the two parts one is related with the structures of

financial markets and other is related with performance of financial market.

Structure of financial market in Indian economy

Generally financial markets refers to a place where Buyers and sellers participate in the

trade of financial instruments like commercial bills, Commercial papers, treasury bills and

trade of assets such as equities, bonds, currencies and derivatives. It can be found in nearly

every country in the world. Financial markets is broadly classified into the

1. Capital Market

2. Money Market

3. Insurance market

4. Derivative market

5. Foreign exchange market

6. Commodity market

1. Capital Market: Capital market is the market where individual & Institutions

trades financial securities. It is also known as long term market. A capital market

provides all the facilities and institutional arrangements for borrowings and landings for

Jha & Singh

36

term funds. The capital market further divides into the two parts i.e. Stock market and

Money market.

(a) Stock markets: Stock markets allow investors to buy and sell shares in

publicly traded companies. They are one of the most vital areas of a market economy as

they provide companies with access to capital and investors with a slice of ownership in

the company and the potential of gains based on the company's future performance.

(b) Bond Market: A bond is a debt investment in which an investor loans

money to an entity (corporate or governmental), which borrows the funds for a defined

period of time at a fixed interest rate. Bonds are used by companies and governments to

finance a variety of projects and activities. Bonds can be bought and sold by investors on

credit markets around the world. This market is alternatively referred to as the debt, credit

or fixed-income market. It is much larger in nominal terms that the world's stock markets.

The main categories of bonds are corporate bonds, Government bonds, and Treasury

bonds, notes and bills, which are collectively referred to as simply "Treasuries.

This market can be split into two main sections: the primary market and the secondary

market. The primary market is where new issues are first offered, with any subsequent

trading going on in the secondary market. Indian capital market has grown rapidly during

the last two decade. It has played an important role of India‟s industrial growth.

2. Money Market: The money market is a segment of the financial market in which

financial instruments with high liquidity and very short maturities are traded. It means all

the financial assets or instruments which can be easily converted into the money traded in

this market. The money market is used by participants as a means for borrowing and

lending in the short term, from several days to just under a year. The money market is

further classified in the two parts i.e. Organized and Unorganized market.

37


Money market securities consist of negotiable certificates of deposit (CDs), banker's

acceptances, Treasury bills, commercial paper (Cps), government notes, currency and

repurchase agreements (repos). Money market investments are also called cash

investments because of their short maturities.

3. Insurance market: Insurance market is the market where insurance policy is

bought and sold. The insurance companies working o sale their product in this market.

4. Derivative market: Derivative Market is a market where derivative are

exchanged or traded. The derivative is a special type of security whose market price or its

value is derived from its underlying asset or assets. A derivative is a contract, but in this

case the contract price is determined by the market price of the core asset. Derivative

market can be classified into the tow broad categories i.e. over the counter and exchange

traded market and divided into fiver sub markets i.e. Credit derivative, future contracts,

option, swap and forward contracts.

5. Foreign exchange market: The Foreign exchange market is the financial system

and trading of currencies among banks and financial institutions, excluding retail investors

and smaller trading parties. While some interbank trading is performed by banks on behalf

of large customers, most interbank trading takes place from the banks' own accounts. It is

known as interbank transaction also.

6. Commodity market: A commodity market is a market where the primary

products are bought and sold under legal contracts. These primary products can be part of

the FMCG sector, Metal sector and energy sector etc. The instrument or commodity is

traded in this market by the using of spot trading, Future contracts, Hedging of funds etc.

The exchange can be done through derivative trading or physical exchange of commodity.

Jha & Singh

38

Role and Performance of financial markets in Indian economy

In India, we have traversed a long way since the economic reforms started in the early

1990‟s. The reforms of the early 90‟s were focused on three pillars – Liberalization,

Privatization and Globalization (LPG). The financial sector has also undergone significant

changes during the period to not only to support the rapid growth but also to do so without

disruptive episodes. In this study, The analysis is divided into the four areas. These areas

are

1. Bank based financial sector

2. Debt market based financial sector

3. Foreign exchange market

4. Financial inclusion

1. Bank based financial sector: Indian financial sector has traditionally been bank

based. The banking sector has so far played a seminal role in supporting economic growth

in India. The assets of the banking sector have expanded nearly 11 times from ` 7.5 trillion

at end-March 1998 to ` 83 trillion at end-March 2012. The non-food credit has expanded

by more than 14 times from ` 3.1 trillion 1998 to ` 45.30 trillion during the same period.

The credit to GDP ratio which stood at about five per cent in 1950-51 improved to about

25 per cent in 2000-01 and further to about 52 per cent at the end of 2011-12 (Chart 1).

The pre-emption by way of Statutory Liquidity Ratio (SLR) has declined considerably

from 38.5 per cent in 1991 to 23.0 per cent of the Net Demand & Time Liabilities (NDTL)

in 2013 (Chart 2). All the while, the banking sector has been robust, meeting all prudential

standards as per best international practice. During the recent global financial crisis and

slowdown in the global and domestic economy, the Indian banking sector has proved to be

resilient. There are, however, issues relating to liquidity, asset quality, capital adequacy in

the context of Basel III and earnings which have surfaced in the recent past mainly due to

39


economic slowdown and have to be tackled expeditiously for continued resilience of the

Indian banking system.

Chart 1: Bank credit as a percentage of GDP

2. Debt market based financial sector: The debt market in India has widely

divided into he government bond market and corporate bond market.

(A) Government bond Market: India is a country in which a large segment of

people lived in the underprivileged condition and government has a vital role for the

develop heir livelihood and standard of living. For the fulfillment of this cause

government of India have to be supported by a large government borrowing program. The

outstanding marketable government debt has grown from ` 4.3 trillion in 2000-01 to ` 29.9

trillion in 2012-13. The size of the annual borrowing of the central government through

dated securities has grown from 1.0 trillion to ` 5.6 trillion during this period. It is no

mean achievement to manage such large issuances in a non-disruptive manner in the post

Fiscal Responsibility & Budget Management (FRBM) regime and declining SLR. A large

number of initiatives have been taken over the years, such as, the Primary Dealer (PD)

system, Delivery Vs Payment (DVP), centralized clearing, anonymous dealing system

based on order matching, floating rate bonds, STRIPS, Inflation Index Bonds (IIBs), etc.

The liquidity in the secondary market has also increased significantly from a daily average

trading volume of ` 9 billion in February 2002 to ` 344 billion in March, 2013. The

development of the debt and the derivatives market in India needs to be seen from the

perspective of a central bank and a financial sector regulator which has a mandate to

(B) Corporate bond market: In india the role of debt market for corporate also have

Jha & Singh

40

a vital importance. For business expansion and developmental rolein the economy the

corporate need a huge amount of fund. The impending large scale expenditure for

improving our infrastructure critically depends on the debt market. It is difficult for the

public sector to finance development of a world-class infrastructure of the magnitude

envisaged because of its other commitments and the limitations imposed by fiscal

prudence. Though the banking sector has been playing an active role in infrastructure

finance, there are limitations imposed by Asset Liability Mismatch (ALM) mismatch,

exposure norms, and de-risking the financial system to provide stability to the financial

system. Development of government and corporate debt market may be approached within

a framework of seven key components, viz., Issuers, Investors, Intermediaries,

Infrastructure, Innovation, Incentives and Instruments – what I have called a 7i

framework. Sovereign securities dominate the fixed income markets almost everywhere.

In India too, the central and state governments remain the main issuers. The large supply

of securities, due to enhanced borrowings, has enabled creation of benchmark securities

with sufficient outstanding stock and issuances across the yield curve. The issuances

across the risk-free yield curve in turn, have provided benchmarks for valuation of other

41


bonds/financial assets and benefitted the corporate bond market. Fiscal consolidation

efforts of the Government of India and the State Governments enhance the quality of the

issuers. In the field of corporate bonds besides financial sector entities, large well rated

non-finance companies have also been issuers. The traditional investor base for G-Sec in

India comprised banks, provident funds and insurance companies with a dominance of

domestic investors and limited foreign participation. In the corporate debt market, investor

base is mostly confined to banks, insurance companies, provident funds, PDs and pension

funds. An approach of gradual opening of the domestic bond market to the foreign

investors has been adopted in India keeping in view the macro-economic risks involved in

providing unfettered access to them. Intermediaries play an important role in development

of the market by facilitating the transactions, providing value-added services and

increasing efficacy of the processes. In India, the major intermediaries are the PDs,

industry associations like Fixed Income Money Market & Derivatives

Association/Primary Dealers Association of India, Gilt Mutual Funds and the

Infrastructure Development Funds (IDFs). Infrastructure plays an important role in

development of markets and want of an efficient, transparent and robust infrastructure can

keep market participants away on one extreme or cause market crisis on the other. India

can boast of being one of the few emerging countries with such a state of the art financial

market infrastructure for the G-Sec market. A state-of-the-art primary issuance process

with electronic bidding and fast processing capabilities, dematerialized depository system,

DvP mode of settlement, electronic trading platforms (Negotiated Dealing Systems and

Negotiated Dealing Systems-Order Matching) and a separate Central Counter Party in the

Clearing Corporation of India Ltd. (CCIL) for guaranteed settlement are among the steps

that have been taken by the Reserve Bank over the years towards this end. Financial

innovation is an essential feature in the history of development of financial markets.

Innovations that are motivated by the need to match the needs of the investor and the

issuer or made possible by advancement in technology or knowledge are essential for

evolution of financial markets.

Incentives play a significant role in shaping the development, stability and

functioning of the financial markets. Reserve Bank has been trying to align incentives by

regulation and supervision though regulation itself may have created unintended

incentives/disincentives as in the case of requirement regarding Held To Maturity (HTM)

dispensation. In the process of development of new instruments, Reserve Bank‟s

endeavour has been to ensure calibrated and orderly development of the markets with

emphasis on prudent risk management and promotion of financial stability

3 Foreign exchange market:The foreign exchange market analysis is further

divided into the two parts one is related with current account deficit and second is related

to capital account management.

(A) Current Account deficit : In the context of foreign exchange market, one of the

most talked about issue is the rising current account deficit (CAD). In recent times, CAD

has been increasing and reached a historic high of 6.7 per cent during the quarter October-

December, 2012. It is of course not sustainable in the long run. Therefore, a structural shift

in the composition of our trade account – increasing exports and curtailing non-productive

Jha & Singh

42

imports, such as, gold – is imperative. What needs to be emphasized is that the current

account deficit has so far been successfully financed without any drawdown on our foreign

exchange reserves. This has been possible with sustained capital inflows, which though

not as buoyant as in the pre-crisis days, has still been impressive. Nevertheless capital

inflows cannot be taken for granted and there is a need to create the enabling conditions to

attract and retain long-term foreign capital. The urgency now comes from two sources: the

need to bridge the current account deficit and the need to provide resources – particularly

of the long term type – for infrastructure and other investment needs.

(B) Capital account management: In the Indian context, management of capital

account has been challenging. So far, the approach of Reserve Bank of India has been that

of cautious gradualism, informed by the experience of other developing countries in Latin

America and East Asia who perhaps liberalized their capital account too soon and too fast.

Yet, it should be recognized that as far as the real sector is concerned, the capital account

is substantially open with regard to sourcing debt and equity from abroad as well as

exploiting investment opportunity abroad. The restrictions in capital account are

pronounced as far as the financial sector is concerned. The policy caution is motivated by

apprehensions of sudden cessation or reversal of flows, which some influential

commentators believe to be unfounded. Even within the constraints imposed by a

gradualist approach, Reserve Bank and the Government of India have taken several steps

that include progressive increase in investment limits for Foreign Institutional Investors

(FIIs) in government and corporate debt, introduction of Qualified Foreign Investor (QFIs)

as a separate investor class, etc.

4 Financial inclusion: The weakness of the Indian financial system is lack of depth

of financial inclusion – that needs to be addressed with alacrity. Taking cognizance of the extent of the problem and recognizing importance of financial

inclusion based on the principle of equity and inclusive growth with stability, Reserve

Bank of India has initiated a number of policies. In its recent Annual Monetary Policy

2013-14, Reserve Bank has extended the Lead Bank Scheme to the metropolitan districts

of the country. Extension of the scheme to these areas is expected to provide an

institutional mechanism for co-ordination between government authorities and banks for

reaching doorstep banking to the financial excluded population. To further improve the

banking penetration through alternate channels, Reserve Bank of India has permitted non-

bank entities to set-up White Label ATMs (WLAs), to operate as Business Correspondents

and to facilitate large scale use of electronic banking and alternate delivery channels.

Under the new bank license policy of the Reserve Bank, new entities are expected to

enhance the banking penetration in India. These measures are also expected to provide

platforms for launching innovative processes, partnerships and products for ensuring a

deeper financial sector with a focus on sustainable financial inclusion. Enriched access to

the financial sector is expected to enable the marginal population to deal better with

uncertainties, enhance their productivity and bring them to the mainstream of the growth

process.

43


CONCLUSION

At last it is very clear that the role of financial markets in the process of a country‟s

economic development has a great importance. In countries like India, where financial

markets are yet to achieve the size and sophistication as those of the more developed

countries, perhaps there is merit in adopting a cautious approach to financial development.

The banking sector of India has done a great job so far in this regard; financial markets

have to play an increasingly important role in generating incremental funding, particularly

for expansion of the infrastructure sector. This explains the regulatory push towards the

development of the corporate bond market as an adjunct to the bank-dominated financial

system of India. Importance of the banking sector in the Indian financial system, however,

remains critical. The agenda to make the financial sector responsive yet resilient has to

include improving liquidity in the G-Sec market across the tenor, create a liquid yield

curve to provide a basis for pricing private debt, further development of the corporate

bond market, expanding the set of products and participants in the derivative market to

provide adequate hedging options for credit, interest rate and forex risks, particularly at the

long end and gradual capital account liberalization within the framework of financial and

macro-economic stability.

Surjit Das

44



DIFFICULTIES IN IMPLEMENTING IFRS: A STUDY ON PERCEPTION OF

CA STUDENTS IN KOLKATA

Surajit Das

Asst.Professor of Institute of Management Study

West Bengal University of Technology

Abstract:

ICAI had decided to converge with IFRS for the accounting period commencing on or

after 1 April, 2011.But till date it has not been possible to make it mandatory in India. In

this study, the main purpose is to find out which factors are creating difficulty for

implementing IFRS in India. Chi-square test was used to find out interdependency

between these variables. The main finding from the study is that IFRS training and

awareness ,presentation of financial statement and conversion process , book value based

accounting and faire value based accounting are all interrelated and create difficulty for

IFRS in India.

Key Words: IFRS in India, factors, difficulty, Chi-square test

Introduction:

Since the advent of internet, the world has shrunk, distances have lost their importance and

information has no more remained the differentiating factor. In this global arena one of the

foremost requirements to operate a business successfully, is to have a good financial

reporting system. If we believe in the old saying, “Accounting is the language of

business,” then the business enterprises around the globe should be speaking in same

languages to each other while exchanging and sharing financial results of their

international business activities.

With the growing economy and increasing integration among the global economies, Indian

companies are also raising their capital globally due to diversification, cross-border

mergers, investments or divestments. At this point, it is the need of the hour to have

globally set standards in all domains to avoid discrepancies and conflicts across

boundaries and have a well defined, structured policy framework throughout. International

Financial Reporting Standards (IFRS) are formulated as a common set of language for

business affairs so that company accounts are easily understandable, transferable and

comparable across international border.

45 DIFFICULTIES IN IMPLEMENTING IFRS: A STUDY ON PERCEPTION

OF CA STUDENTS IN KOLKATA

ICAI, at its 269th

meeting, has decided to converge with IFRS for the accounting period

commencing on or after 1 April, 2011for the listed companies, all the banks, insurance

companies and large-sized organizations.

Road map for IFRS adaptation in India

In India ICAI proposed the phased approach for implementing IFRS in 2011. That time

they have proposed the guide line for this event.

Phase I (opening balance sheet as at 1 April, 2011):

1. Companies that are part of BSE - Sensex 30

2. Companies that are part of NSE Nifty 50;

3. Companies whose shares or other securities are listed on a stock exchange outside

India

4. Companies whether listed or not, having net worth exceeding Rs. 1,000 crores.

Phase II (opening balance sheet as at 1 April, 2013):-

Companies not listed in phase I and having net worth exceeding INR500 crore

Phase III (opening balance sheet as at 1 April, 2014):-

Listed companies not covered in earlier phases.

But up to 2014 it is not possible to implemented IFRS in India. Some Indian Companies

have adopted IFRS voluntarily for their own interest and try to provide a path to the other

Indian firms those who will be adopting IFRS in near future. These adopted Companies

are: Wipro, Rolta India, Infosys, Great Eastern Energy Corporation, Dabour India Ltd,

Noida Toll Bridge Ltd

As per a CA institute official” Since the international accounting standards board is

expected to come up with new standards on important areas like leasing, financial

instruments, we don‟t want our corporate to face hardships. So we have suggested that

IFRS may be adopted from April 1, 2015,” (The Hindu, Business line, 2013).

As per Corporate Affairs Ministry, they have proposed a new road map for implementing

IFRS in India.

Phase I (opening balance sheet as at 1 April, 2011):

Companies having net worth of over Rs 1,000 crore

Phase II (opening balance sheet as at 1 April, 2013):-

Companies having net worth of Rs 500-1,000 crore

Phase III (opening balance sheet as at 1 April, 2014):-

Companies having all other companies (including listed ones),

Review of literature:

B. Kapoor & Jyoti Ruhela (2013) found that existing laws and regulations is the biggest

challenge in India for implementing IFRS.In this paper they found some real problems

regarding IFRS implementation in India and suggest some remedial measures. Kishore

Surjit Das

46

Kumar Shah (2014) has shown some problems and challenges regarding IFRS

convergence in Indian firm. Further the paper advises some Recommendations and

Measures for IFRS implementation in India. Srivastava Anubha et.al(2012) have state

some major problem ,challenges and opportunities ,faced by the companies regarding

IFRS. They have also focuses on the awareness and adoption of IFRS in India. Ankita

Kathiriya (2013) proposed some areas that are facing by Indian SME.She suggest that

implementation IFRS mostly effected on three aspect ; people, process and technology and

also required deep knowledge and time and initial implementation cost for SME.Shobana

Swamynathan & Sindhu (2013) unveiled how the harmonization of Indian accounting

practices with IFRS impact on cash flow predictability and persistence. They found that

harmonization of IFRS with Indian AS increase cash predictability and high cash flow

operation persistence. Titas Rudra & Dipnoan Bhattacharjee (2012) disclosed that how

IFRS influence on Earning Management. They stated that high quality accounting

standard does not necessarily means high quality financial information reporting and thus

lead low earning management. India Briefing, (2014). „The corporate affairs ministry is

likely to notify within a month all sections and rules of the new Companies Act and start

immediately thereafter the process of converging Indian accounting standards with the

International Financial Reporting Standards (IFRS), which have to be implemented from

April 2015 for companies with a net worth of more than Rs 1,000 cr. The ministry has

said, 'let the Companies Act get notified and then we will take up the convergence of IAS-

IFRS'.

On the above review of literature helps me to find out some important factors which are

affecting the process of IFRS implementation in India, These factors are:

1. Synchronization problem between Indian GAAP and US GAAP. (SYCNZTN PRB =

synchronization problem of FS)

2. Create complexity for the user for finalizing the old and new accounting data.

(COMPLEX =complex)

3. Create confusion between old and new standard.

4. In India, no separate committee for implementation, follow up and feedback process

of IFRS.

5. Lack of proper training and guidance program in India (LCKOFTRNG =lack of

training)

6. New system considers value for money.

7. IFRS create difficulty on taxation system in India

8. Lack of uniformity between IFRS and individual set of rule for the whole world.

9. IFRS is a set of principal. There are no detail rules to follow up. (NODTLRL = no

details rule)

10. IFRS mainly focus on presentation of financial statement rather users of accounting

standard. . (LSFCSUSRACTNG =less focus on accenting standard)

11. IFRS create uniformity for each country‟s business and reduce the competition from

the market. (MNOPLY=monopoly)

http://economictimes.indiatimes.com/topic/net%20worth



12. Lack of awareness creates difficulty for implementing IFRS in India. (LKOFAWRNS

=lack of awareness)

13. IFRS uses fair value and IGAAP uses book value for valuation of assets. So this

conversation process creates difficulty. (CNVRSNVLU =conversion value)

14. Lack of data (primary and secondary) impact on implementation of IFRS process.

(LKOFDTA = lack of data)

15. IFRS need to convert historical data into market value for comparison purpose. So it

creates difficulty.

16. Coordination for IFRS implementation is a challenging task. (CHLNGTSK =

challenging task)

17. Strict rules affect hedge accounting process in India.

Objectives:

1. To find out whether there is any relationship between training and awareness for

implementing IFRS in India.

2. To find out whether there is any relationship between presentation of financial

statement and conversion process from IAS to IFRS for implementing IFRS in India.

3. To find out whether fair value based accounting creates any difficulty for book value

based accounting.

RESEARCH METHODOLOGY & HYPOTHESIS FORMULATION:

DATA SOURCES

Primary and secondary data were used for this study. Primary data were collected through

the questionnaire & structured Interviews. Secondary data were collected from, texts,

journals, Companies annual report, announced document by the ICAI (Institute of

chartered Account of India).

RESEARCH DESIGN

The priority in this design was given to the quantitative method, because in this study the

quantitative research was used to answer the research question as “Difficulties in

implementing IFRS: a study on perception of ca students in Kolkata.”

SAMPLING FRAME WORK

In this study, the survey instrument in the form of close-ended questionnaire was

developed for the purpose of collecting the main data for the study. The target population

of this research is the Final phase students of CA in Kolkata. Therefore, Stratified

proportionate sampling method was adopted to select respondents. Researcher has issued

one hundred (100) questionnaires for selecting the respondent. Out of one hundred and

three (100) questionnaires, seventy four (74) was returned; the response rate was 74%.

Surjit Das

48

INSTRUMENT DEVELOPMENT

The questionnaire predominantly made and used of five point Likert Scale to determine

Difficulties in implementing IFRS. Close – ended questions were used for this research.

RELIABILITY

The internal consistency of the research instrument should be tested by reliability analysis

(Ndubisi, 2006). Nunnally (as cited in Ahsan et al., 2009) suggested that the minimum

alpha of 0.6 sufficed for early stage of research. The cronbach‟s alpha in this study was

0.806 (higher than 0.6), the constructs were therefore deemed to have adequate reliability.

Reliability Statistics

Cronbach's Alpha N of Items

.806 17

HYPOTHESIS

H01: There is no relationship between training and awareness for implementing IFRS in

India.

H02: There is no relationship between presentation of financial statement and conversion

process from IAS to IFRS for implementing IFRS in India.

H03: There is no relationship between fair value based accounting and book value base

accounting.

Analysis & Findings:

The above descriptive statistics shows that the values of mean, standard deviation,

skewness and Kurtosis of all the factors are more or less the same. The mean value of all

the factors varies from 2.8 to 3.8 and the deviation varies from 0.9 to 1.3, whereas

skewness and Kurtosis values vary in between -1 and -2 respectively. So it can be

concluded that all 17 factors, which create difficulty for implementing of IFRS in India,

are equally important.

H01: There is no relationship between training and awareness for implementing IFR

India.

The above Chi- square test between training and awareness shows that the calculated chi –

square value is 41.980 (df= 16), which is significant at 0.001 alpha level and the p value

(Likelihood Ratio) is 0.001 which is less than 0.05. Thus, null hypothesis is rejected and it



Descriptive Statistics

N Minim

um

Maxi

mum

Mean Std.

Deviation

Skewness Kurtosis

Statist

ic

Statisti

c

Statist

ic

Statist

ic

Statistic Statis

tic

Std.

Error

Statist

ic

Std.

Error

SYCNZTN

PRB 74 1 5 3.20 1.193 -.255 .279 -1.028 .552

COMPLEX 74 1 5 3.31 1.006 -.167 .279 -.969 .552

CONFSING 74 1 5 3.45 1.062 -.419 .279 -.746 .552

NSPRTCMT 74 1 5 3.54 1.284 -.551 .279 -.984 .552

LCKOFTRN

G 74 1 5 3.72 1.067 -.727 .279 -.216 .552

VLUFRMN

Y 74 1 5 3.43 1.048 -.587 .279 -.162 .552

TXATNSYS

TM 74 1 5 3.43 .877 -.727 .279 .307 .552

DIFFRNTR

UL 74 1 5 3.32 1.112 -.065 .279 -1.254 .552

NODTLRL 74 1 5 3.00 .965 -.094 .279 -.111 .552

LSFCSUSR

ACTNG 74 1 5 2.81 1.069 .459 .279 -.502 .552

MNOPLY 74 1 5 3.18 1.186 -.249 .279 -.704 .552

LKOFAWR

NS 74 1 5 3.55 1.009 -.481 .279 -.271 .552

CNVRSNVL

U 74 1 5 3.42 1.147 -.216 .279 -.889 .552

LKOFDTA 74 2 5 3.68 .893 -.135 .279 -.705 .552

MRSBJCTV 74 1 5 3.26 1.061 -.112 .279 -.683 .552

CHLNGTSK 74 1 5 3.41 1.122 -.385 .279 -.853 .552

HDGACING 74 1 5 3.58 .828 -

1.011 .279 2.191 .552

Surjit Das

50

can be concluded that that there is a significant relationship between training and

awareness for implementing IFRS in India.

Chi-Square Tests

Value df Asymp. Sig. (2-sided)

Pearson Chi-Square 41.980a 16 .000

Likelihood Ratio 40.928 16 .001

Linear-by-Linear Association 5.539 1 .019

N of Valid Cases 74

a. 21 cells (84.0%) have expected count less than 5. The minimum expected count is .05.

H02: There is no relationship between presentation of financial statement and

conversion process from IAS to IFRS for implementing IFRS in India.

The above Chi- square test between training and awareness shows that the calculated chi –

square value is 63.591 (df= 16), which is significant at 0.001 alpha level and the p value

Chi-Square Tests




Linear-by-Linear Association 3.139 1 .076

N of Valid Cases 74


(Likelihood Ratio) is 0.001 which is less than 0.05. So the null hypothesis is being

rejected ,means there is a significant relationship between presentation of financial

statement and conversion process from IAS to IFRS for implementing IFRS in India.

Chi-Square Tests




Linear-by-Linear Association .058 1 .010

N of Valid Cases 74




H03: Fair value based accounting does not create any difficulty for book value base

accounting. So there is no relationship between fair value based accounting and book

value base accounting.

The above Chi- square test fair value based accounting and book value base accounting

shows that the calculated chi –square value is 61.216 (df= 16), which is significant at

0.001 alpha level and the p value (Likelihood Ratio) is 0.001 which is less than 0.05. So

the null hypothesis is being rejected and it concludes that there is a significant relationship

between fair value based accounting and book value base accounting in India.

Conclusion:

The above findings lead to the conclusion that India is lacking in the required number of

IFRS professionals. For this reason it creates difficulty for the conversion process. The

ICAI should therefore take initiatives to arrange training programmes for increasing

awareness among its members and other parties for IFRS. Although book value based

accounting and fair value based accounting are inter related, but without training and

expert knowledge in this area, it is difficult to convert book value based accounting to fair

value based accounting.

References:

Journal and Research paper

1. Ankita Kathiriya. Challenges In Ifrs Convergence in SMEs in India. abhinav

national monthly refereed journal of research in commerce & management. ISSN

2277-1166 1. Volume No.2, issue no.5.

2. B. Kapoor and Jyoti Ruhela.2013. “IFRS Implementation“Issues And Challenges

For India”. VSRD International Journal of Business and Management. P-ISSN:

2319-2194, Vol. 3 No. 2.

3. Dr. A. Vinayagamoorthy. Jan-March, 2014 Opportunitiesand Challenges In

Adoptingifrs In India. International Journal of Business and Administration Research. E- ISSN -2347-856XISSN -2348-0653. Vol.2, Issue.3.

4. Kishore Kumar Shah. 2014. IFRS And Indian: Opportunities And Challenges.

Global Journal of Multidisciplinary Studies. ISSN: 2348-0459, Vol 3, No 9.

5. Shobana Swanynathan, Sindhu., May 2013. Harmonization of Indian Accounting

Practices with IFRS: Analysis in terms of Cash flow Predictability and

Persistence, IJCEM International Journal of Computational Engineering & Management, ISSN (Online): 2230-7893, Vol. 16 Issue 3.

Surjit Das

52

6. Srivastava, Anubha et.al. April 2012.IFRS in India: Challenges and

Opportunities. IUP Journal of Accounting Research & Audit Practices. Vol. 11

Issue 2.

7. Titas Rudra and Dipanjan Bhattacharjee., 2012. Does IFRs Influence Earnings

Management? Evidence from India, Journal of Management Research,ISSN

1941-899X, Vol. 4, No. 1: E7

Regulatory body

1. Ind AS (IFRS) Implementation Committee | ICAI - Institute of Chartered

Accountants of India.www.icai.org

2. Implementation of Indian

Accounting Standards converged with IFRS.,

4nov2010.Ministry of Corporate Affairs.

www.mca.gov.in

Print Media

1. India Briefing.,April 10, 2014 IFRS Convergence: Audit Season in India.

2. Nabeel Ahmed., October 27, 2013. Will India align with IFRS? The Hindu.

http://www.google.co.in/url?q=http://ssrn.com/abstract%3D2160631&sa=U&ei=nyVXVIeUO42dugSCl4HoAQ&ved=0CBMQFjAA&usg=AFQjCNEhBHRZvymKNxPlDTrRld0SYhgMVg

http://www.google.co.in/url?q=http://ssrn.com/abstract%3D2160631&sa=U&ei=nyVXVIeUO42dugSCl4HoAQ&ved=0CBMQFjAA&usg=AFQjCNEhBHRZvymKNxPlDTrRld0SYhgMVg

http://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&cad=rja&uact=8&ved=0CEsQFjAE&url=http%3A%2F%2Fwww.icai.org%2Fnew_category.html%3Fc_id%3D330&ei=mTBrU6C9LdC3uATwr4L4Cw&usg=AFQjCNFgP8OZvM8OVG4LceSZLCflxsIQoQ&bvm=bv.66330100,d.c2E

http://www.google.co.in/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&cad=rja&uact=8&ved=0CEsQFjAE&url=http%3A%2F%2Fwww.icai.org%2Fnew_category.html%3Fc_id%3D330&ei=mTBrU6C9LdC3uATwr4L4Cw&usg=AFQjCNFgP8OZvM8OVG4LceSZLCflxsIQoQ&bvm=bv.66330100,d.c2E

http://www.india-briefing.com/news/author/india-briefing/

53 FINITELY GENERATED FREE ABELIAN GROUPS AND

THEIR APPLICATIONS



FINITELY GENERATED FREE ABELIAN GROUPS

AND THEIR APPLICATIONS Simon Eze Ejiofor

Grundtvig International Secondary School, Oba, Anambra State, Nigeria

ABSTRACT

In this paper we established a connection between A, an arbitrary finitely

generated Abelian group, and F, the finitely generated free Abelian group. An

explicit decomposition (up to isomorphism) of all finite Abelian groups of any

order into their respective p-groups is given. Given a map ƒ: F → A, defined by

ƒ(a1u1 + a2u2 + … anun) = a1s1 + a2s2 + … + ansn where ai (1, 2, …, n) are

integers, and R, the kernel of ƒ, the elements a1u1 + a2u2 + … + anun∈ R are in a

one-to-one correspondence with all relations satisfied by the generators of A.

Keywords: Finitely generated free Abelian group, p-groups, Free generators,

Direct sums, Quotient groups

1. INTRODUCTION

Just as the concept of basis is important in the study of real vector spaces in

linear algebra, it is equally useful to consider Abelian groups which possess a

“basis”. However of course in an Abelian group A, there is generally no concept

of scalar multiplication with an arbitrary real number. However as nx is defined

for 𝑥 ∈ 𝐴, 𝑛 ∈ 𝑍, we have the concept of multiplication with an integer. This

motivates the following definition.

2. Finitely Generated Free Abelian Group

A finitely generated Abelian group F = <u1, u2, …, un> in which the

generators u1, u2, …, un satisfy no non-trivial relations is called a free Abelian

group. That is, in a free Abelian group F we make the hypothesis that

c1u1 + c2u2 + … + cnun = 0

always implies that the integers c1 = c2 = … = cn = 0.

The following theorem expresses a free Abelian group precisely as a

projective object.

Simon Eze Ejiofor

54

Theorem 2.1The direct sum F = 𝑅𝑖𝑖∈𝐼 satisfies the following condition:

for every mapping θ: Ri → A, A any Abelian group, there exists a homomorphism

θ*: F → A such that θ*|R = θ.

This yields the following important result that every Abelian group A is

the homomorphic image of some free Abelian group F, and it follows that to

describe the structure of all finitely generated Abelian groups; we need only

classify the subgroups R of F and their corresponding quotients groups.

THEOREM 2.2Let F be a finitely generated free Abelian group of rank

n, and let H be a non-zero subgroup of F. Then H is a finitely generated free

Abelian group of rank m ≤ n. It is possible to choose a set of free generators v1,

v2… vn for F in such a way that

H = <h1v1, h2v2… hmvm>,

Where h1, h2… hm are positive integers satisfying the relations hi\hi+1 (i = 1, 2…

m-1).

Proof: (i). suppose that F is originally given in terms of the free

generators u1, u2… un. With every non-zero element x = a1u1 + a2u2 + … + anun of

F we associate the HCF of its coefficients relative to this set of generators, say

δ(x) = (a1, a2, …, an).

This number is, however, independent of the choice of generators. For if u′1, u′2…

u′n is another set of generators of F we have that ui = 𝑝𝑖𝑗𝑚𝑗=1 𝑢′𝑗 (i = 1, 2… n),

where (pij) is a unimodular matrix. Then x = a′1u′1 + a′2u′2 + … + a′nu′n, where a′j

= 𝑎𝑖𝑛𝑖=1 𝑝𝑖𝑗 (j = 1, 2… m). Hence any common factor of the ai must divide all

the a′j, whence

(a′1, a′2… a′n) ≥ (a1, a2… an).

If we interchange the roles of the two sets of generators by inverting the matrix

(pij), we can establish the opposite inequality. Hence (a′1, a′2… a′n) = (a1, a2…

an), which proves the invariance of δ(x).


THEIR APPLICATIONS

(ii). among the non-zero elements of H let

y1 = b1u1 + b2u2 + … + bnun

be such that δ attains it least value, say δ(y1) = h1 ≥ 1. We can then write y1 =

h1(c1u1 + c2u2 + … + cnun = h1v1, where v1 = c1u1 + c2u2 + … + cnun is an element

of F with the property that (c1, c2, …, cn) = 1. By Theorem 3.6.1 there exist

elements v′2, v′3… v′n such that

F = <v1, v′2, v′3… v′n>.

Using this set of generators let y = d1v1 + d2v′2 + d3v′3 + … + dnv′n be an arbitrary

element of H. we know that

y1 = h1v1∈H,

and we now claim that h1\d1. For if not, we could find integers q and r such that d1

= qh1 + r, where 0 <r<h1. Thus y – qy1 = rv1 + d2v′2 + … + dnv′n would be an

element of H such that δ(y – qy1) = (r, d2… dn) ≤ r<h1, contradicting the

minimality of h1. Hence we conclude that r = 0, that is

y – qy1 = d2v′2 + … + dnv′n. (*)

(iii). the proof proceeds by induction on n. When n = 1, we have already

reached our goal. For in that case, the right-hand side of (*) must be replaced by

zero, and y = qy1 = qh1v1. This amounts to the statement that F = <v1>, H =

<h1v1>, as asserted by the theorem when n = m = 1. Now suppose that n> 1 and

put

F1 = <v′2, v′3… v′m>, H1 = H∩F1.

Note that the right-hand side of (*) belongs to F1 whilst the left-hand side lies in

H. Hence (*) represents an element of H1. Two cases have to be considered: first,

if H1 = {0}, we have that y = qy1 = qh1v1 and, as before H = <h1v1>. This

establishes the theorem, when n is arbitrary and m = 1. Next, when H1 is a non-

zero subgroup of F1 we apply the inductive hypothesis to F1 and H1. Thus we can

find elements v2, v3… vn of F1 such that

F1 = <v2, v3… vn>, H1 = <h2v2, h3v3… hmvm>,

Simon Eze Ejiofor

56

where m is a certain integer satisfying 2 ≤ m ≤ n and hi| hi+1 (i = 1, 2, …, m-1).

The two sets of free generators for F1 are linked by equations

vi = 𝑝𝑖𝑗𝑚𝑗=1 𝑣′𝑗 = 𝑞𝑖𝑗

𝑚𝑗=1 𝑣𝑗 (i = 1, 2, …, n).

We claim that

F = <v1, v2… vn>.

For on expressing the v‟s in terms of the v′‟s in F = <v1, v′2… v′n> we see that v1,

v2… vn certainly generates F. Moreover, these elements are free generators; for

suppose that we have non-trivial relation

c1v1 + c2v2 + … + cnvn = 0.

We observe that c1 ≠ 0; for if not, we should have a relation between v2, v3… vn in

contradiction to the above. If we now substitute for v2, v3… vn in terms v′2, v′3…

v′n, we should get a relation between v1, v′2, …, v′n in which the coefficients of v1

is c1. Hence we find that the elements

h1v1(= y1), h2v2, …, hmvm

generate H. In fact, they are free generators, because any non-trivial relation

between them would also be a relation between v1, v2… vm… vn. Thus

H = <h1v1, h2v2… hmvm>.

To conclude the proof we have still to show that h1| h2. Now y0 = h1v1 + h2v2 is an

element of H. Hence by the minimality of h1, δ(y0) = (h1, h2) ≥ h1. From the

definition of HCF, (h1, h2) ≤ h1. Therefore (h1, h2) = h1, that is, h1| h2.

3. Finitely generated Abelian groups

We establish the fundamental theorem of Abelian groups.

THEOREM 3.1Every finitely generated Abelian group A is the direct sum of

cyclic groups, involving r (≥ 0) infinite and k (≥ 0) finite cyclic groups; thus

A = <t1> + … + <tr> + <w1> + … + <wk>,

Where tρ (ρ = 1, 2… r) is of infinite order, whilst wκ (κ = 1, 2… k) is of finite

order eκ (≥ 2). Moreover,


THEIR APPLICATIONS

eκ+1| eκ (κ = 1, 2, …, k).

Proof: Let R ≠ {0} ≤ F. Let v1, v2… vn be free generators for F such that

F = <v1, v2… vn>, R = <h1v1, h2v2, …, hmvm>

(m ≤ n), and hi| hi+1 (i = 1, 2, …, m-1).

By way of preparation, let us consider the case in which n = 1. Three

cases have to be distinguished:

i. F = <v>, R = {0}. Then F/R≅F, the infinite cyclic group generated by v.

ii. F = <v>, R = <hv>, where h ≥ 2. Then F/R≅Ch the cyclic group of order h.

iii. F = <v>, R = <v> (h = 0). Then F/R≅ {0}, because F = R.

In the general situation the same features will appear, and it is desirable to

adapt the notation to the three types of generators. If r = n – m> 0, there are r

generators in F which do not occur in R; these will be denoted by x1, x2… xr. If h1

= h2 = … = hl = 1, the corresponding generators, say z1, z2… zl will occur both in

F and in R. If n = r + l + k, the remaining k generators correspond to the values of

h which are greater than one, and it is convenient to rearrange them in decreasing

order of magnitude, say e1, e2, …, ek. Thus we shall write

F = <x1, x2… xr, y1, y2… yk, z1, z2… zl>,

R = <e1y1, e2y2… ekyk, z1, z2… zl>,

Where eκ+1| eκ (κ = 1, 2… k), n = r + k + l, m = k + l, with the obvious

modifications when one or the other type does not occur.

If x∈F, let 𝑋 = x + R be the image of x under the natural epimorphism F

→ F/R. in particular, considering the generators of F in turn, we find that (i) xρ (ρ

= 1, 2, …,r) is an element of infinite order because no multiple (≠ 0) of xρ lies in

R; (ii) yκ is of order eκ (κ = 1, 2, …, k); (iii) zλ (λ = 1, 2, …, l) is the zero element 0

of F/R because zλ∈R. Now the general element of F can be expressed in the form

Simon Eze Ejiofor

58

x = 𝑎𝜌𝑟𝜌=1 𝑥𝜌+ 𝑏𝜅𝑦𝜅

𝑘𝜅=1 + 𝑐𝜆𝑧𝜆

𝑙𝜆=1

when a typical element of F/R becomes

x = 𝑎𝜌𝑟𝜌=1 𝑥𝜌+ 𝑏𝜅𝑦𝜅

𝑘𝜅=1 (*)

thus F/R is generated by x1, …, xr, y1, …, yκ. We claim, however, that in fact

F/R = <x1> + … + <xr> + <y1> + … + <yκ>, (**)

That is, we assert that the right-hand side of (*) can vanish only if each term is

zero. Suppose that

𝑎𝜌𝑟𝜌=1 𝑥𝜌+ 𝑏𝜅𝑦𝜅

𝑘𝜅=1 = 0.

This means that

𝑎𝜌𝑟𝜌=1 𝑥𝜌+ 𝑏𝜅𝑦𝜅

𝑘𝜅=1 ∈R.

This implies that aρ = 0 (ρ = 1, 2… r) because xρ does not occur in R.

Furthermore, bκ must be divisible by eκ (κ = 1, 2… k), say bκ = dκeκ. Hence bκyκ =

dκeκyκ = 0, because eκyκ = 0. This proves (**). Since A≅F/R, the proof is

complete.

4. p-groups

The next step consists in breaking up an arbitrary finite Abelian group into p-

groups. The adaptation of theorem 3.1 to p-groups therefore amounts to the

following statement.

THEOREM 4.1Let A be a finite Abelian p-group. Assume that

A = +< 𝑢𝜅 >𝑘𝜅=1 = +< 𝑣𝜆 >𝑙

𝜆=1 , (**)

Where |uκ| = 𝑝δκ (κ = 1, 2… k), |vλ| = 𝑝ελ (λ = 1, 2… l) and δ1 ≥ δ2 ≥ … ≥ δκ, ε1 ≥

ε2 ≥ … ≥ εl. Then k = l and δκ = εκ (κ = 1, 2… k).

Proof: If |A| = pm, then on comparing the orders in (**),

m = 𝛿𝜅𝑘𝜅=1 = 휀𝜆

𝑙𝜆=1

When m = 1, the theorem is trivial. We may therefore proceed by induction on m.


THEIR APPLICATIONS

Let Ap be the set of elements satisfying px = 0. Since p(x – y) = px – py, it follows

that Ap is a subgroup of A (possibly equal to A). It is easy to determine the order

of Ap. For let x∈Ap. Using the basis u1, u2… uk for A we have that

x = 𝑎𝑖𝑘𝑖=1 𝑢𝑖

where it may assumed that 0 ≤ ai<𝑝δi , because |ui| = 𝑝δi . Now if px = 0, then paiui

= 0 for each i and hence 𝑝δi | pai. Thus ai = bi𝑝δi−1 , where bi must satisfy 0 ≤ bi<p.

Hence for a fixed i, there are precisely p possible values for bi and hence for ai

such that px = 0. This shows that |Ap| = pk. Using the second basis in (**) we find

analogously that |Ap| = pl. But Ap is independent of the basis. Therefore k = l, as

claimed.

Next, we define the set Ap consisting of all those elements x of A which

are the pth multiple of some other element (the additive analogue of being a p

th

power). It is easily verified that Ap is in fact a group; for if x = px′, y = py′, then x

– y = p(x′ - y′). We obtain a direct decomposition of Ap into cycles if we simply

multiply each basis element of A by p. But it must be observed that this operation

„kills‟ all elements of order p. Thus let

δ1 ≥ δ2 ≥ … ≥ δK> 1, δK+1 = δK+2 = … = δk = 1,

where K is a certain integer satisfying 0 ≤ K ≤ k. Then

Ap = +< 𝑝𝑢𝑖 >𝑘𝑖=1 and |pui| = 𝑝δi−1 .

Similarly, if ε1 ≥ ε2 ≥ … ≥ εL> 1, εL+1 = εL+2 = … = εk = 1, we can write

Ap = +< 𝑝𝑣𝑗 >𝑙𝑗=1 ,

Where |pvj| = 𝑝εj−1 . When K = 0, all elements of A are of order p, whence Ap =

{0}. In that case also L = 0, because Ap is independent of the basis. Henceforth,

we shall assume that K> 0. Evidently, |Ap| < |A|, and we may apply the inductive

hypothesis to Ap. Thus we conclude that K = L and δi–1 = εi –1, that is δi = εi (i = 1,

2… K). Since the remaining δ‟s and ε‟s are equal to one, the proof of the theorem

is complete.

The invariants 𝑝δ1 , 𝑝δ2 , …, 𝑝δk of an Abelian p-group A are called the

elementary divisorsof A. Thus two Abelian p-groups are isomorphic if and only if

Simon Eze Ejiofor

60

they have the same elementary divisors, arranged in some order. When the value

of p is understood, it suffices to name the exponents, and we say that A is of type

(δ1, δ2… δk). In particular, A is called an elementary Abelian p-group if it is of

type (1, 1… 1).

THEOREM 4.2Let w be an element of order mn, where (m, n) = 1. Then

<w> = <nw> + <mw>

Proof: The elements u = nw and v = mw are of orders m and n

respectively. Put W = <w>, U = <u>, V = <v>. We claim that

W = U + V.

Since (m, n) = 1, there exist integers a and b such that an + bm = 1. Hence

w = (an + bm)w = a(nw) + b(mw) = au + bv.

This shows that w∈U + V. But w generates W, whence W U + V. Since U W

and V W, we have, conversely, that U + V W and therefore W = U + V. In

order to prove that the sum is direct, we observe that U ∩ V = {0}, since U and V

are groups of coprime orders.

The hypothesis can be generalized to more than two terms. In particular,

let

m = 𝑝1α1𝑝2

α2…𝑝𝑡αt ,

where p1, p2, …, pt, are distinct primes. Then

<w> = +< 𝑤𝜏 >𝑡𝜏=1 ,

Where wτ = (m/𝑝𝑡αt )w is of order 𝑝𝑡

αt . The formula above can still be used when

some of the α‟s are zero. The corresponding summand then reduces to the zero

groups and can be omitted.


THEIR APPLICATIONS

Let p be a prime and let P be the set of those elements of A whose order is a

power of p, that is which satisfy an equation of the form pμx = 0 (μ ≥ 0).

Evidently, P is a subgroup; for if pμx = p

νy = 0, then p

μ+ν(x – y) = 0. If p does not

divide |A|, then P = {0}. We call p the p-primary componentof A. Next, we show

that when |A| is divisible by more than one prime, the primary components furnish

a decomposition of A.

THEOREM 4.3Let |A| = 𝑝1v1𝑝2

v2…𝑝𝑛vn , and let Pi be the pi-primary

component of A. Then

A = P1 + P2 + … + Pn. (*)

Proof: If w is any element of A, then w∈P1 + P2 + … + Pn and therefore

A P1 + P2 + … + Pn. Conversely, each Pi A, whence A = P1 + P2 + … + Pn.

Moreover, the sum is direct because the terms have mutually coprime orders. The

decomposition is unique in the following sense: let

A = P1* + P2* + … + Pn*,

Where Pi* is an Abelian pi-group (i = 1, 2… n). Then Pi* = Pi. For let |Pi*| = 𝑝𝑖μi ;

computing the order of the group on each side of (*) we find that |A| = 𝑝𝑖

μ𝑖𝑛𝑖=1 ,

whence, by the unique factorization of |A| into prime factors, it follows that μi = νi.

Thus |Pi*| = |Pi|. Now by the definition of Pi each element of Pi* lies in Pi, that is

Pi* Pi. Since these two groups have the same order, we conclude that Pi* = Pi.

At last, we return to the proof of Theorem 4.1. We are given that

A = +< 𝑢𝜅 >𝑛𝜅=1 , |uκ| = dκ, dκ+1| dκ.

The idea of the proof is to break up each term into its primary components and

thence to obtain the elementary divisors of P1, P2… Pn, whose uniqueness has

been established in Theorem 4.3. Let

dκ = 𝑝𝑖

𝛿𝜅𝑖𝑛𝑖=1 (κ = 1, 2, …, k),

Simon Eze Ejiofor

62

where δκ𝑖 ≥ 0 and δκ𝑖+1 ≤ δκ𝑖

. Then

<uκ> = +< 𝑢𝜅𝑖>𝑛

𝜅=1 ,

Where |uκ𝑖| = 𝑝

𝑖

𝛿𝜅𝑖 . Thus we can express A as a double sum of p-groups, namely

A = +< 𝑢𝜅𝑖>𝑛

𝑖=1𝑘𝜅=1

For fixed i, we find that

Pi = +< 𝑢𝜅𝑖>𝑘

𝜅=1 .

This shows that the elementary divisors of Pi are the non-units among the

monotonic decreasing sequence

𝑃δ1𝑖 , 𝑃δ2𝑖 , …, 𝑃δk 𝑖 .

Now suppose we replace the d‟s by a rival set of e‟s, where

eλ = 𝑝𝑖

ελ𝑖𝑛𝑖=1 , (λ = 1, 2, …, l).

Previous Theorem ensures that (δκ𝑖) and (ελ𝑖

) are identical, and hence k = l. This

concludes the proof of the Theorem.

The foregoing argument shows that the invariants and elementary

divisors determine each other. Both set completely describes the structure of A,

and all finitely generated Abelian groups are obtained up to isomorphism by

prescribing either the invariants or the elementary divisors.

REFERENCES

Baumslag, B and Chandler, B. (1964),“Schaum’s outline of Theory and problems

of Group Theory”, Schaum‟s outline series, McGraw Hill Book Company, New

York, U.S.A.

Crissman, C. (2009),“The Structure of Abelian Groups”. Journal of Pure and

Applied Algebra, Vol. 182, No. 1, pp. 65-78.


THEIR APPLICATIONS

Downey, R and Montalban, A. (2008), “The Isomorphism problem for torsion-

free Abelian groups is analytic complete”, Journal of Algebra, Vol. 320, No.

2008, pp. 2291-2300.

Dummit, D.S. and Foote, R.M. (1999),“Abstract Algebra, Second Edition”,

Prentice Hall, Upper Saddle River, New Jersey, U.S.A.

Gilbert, W.J. and Nicholson, W.K. (2004),“Modern Algebra with Applications,

Second Edition”, John Wiley & Sons Inc., Hoboken, New Jersey, U.S.A.

Gopalakrishnan, N.S. (1986),“University Algebra, Second Edition”, Wiley

Eastern Limited, New Age International Limited, New Delhi, India.

Ledermann, W. (1977),“Introduction to Group Theory”, Longman Group

Limited, New York, U.S.A.

Pakianathan, J. (2003),“Finitely Generated Abelian Groups”, Journal of Pure and

Applied Algebra, Vol. 321, No. 12, pp. 3704-3713.

Simon, J. (2003),“Free Abelian Groups, Direct Products, Free groups, Free

Products”, Journal of Pure and Applied Algebra, Vol. 22, pp. 201-203.

Sims, B.T. (1976),“Fundamentals of Topology”, Macmillan Publishing Co., Inc.

New York, U.S.A.

Mrs. Sheela Reddy

64



Software of the Mind: Culture-Strategy Fit a Trump card for

Multinational Corporations- A study

Mrs. Sheela Reddy Research Scholar,

Dept. of Commerce,

Bharathiar University

Abstract.

This conceptual paper seeks to review and examine the literature on National Culture

Dimensions, Organizational Culture Dimensions, Culture- strategy alignment, examine the

trilateral interactive relations among national culture, subsidiary strategic context and

Culture-Strategy among headquarters and its Subsidiaries. The discussion of this paper is

based on Hofstede‟s cultural dimensions. We propose a conceptual model for

understanding the interaction between Headquarters and Subsidiary strategic context in the

process of Cultural integration of MNCs, and ultimately the impact on the performance of

subsidiaries. The findings of this paper revealed that elements contained in national

cultures can transcend into organizational concerns. Moreover, not only are national

cultures the main determinants of the success or failure of multinational businesses, but

also organisational cultures. Drawing foundational support from this new model, we

explore implications for future research.

Key words: Geert Hofstede, MNCs, National Culture Dimensions, Organisational Culture

Dimensions, Culture-Strategy Fit.

Software of the mind

Software of the mind is the collective programming of the Mind- an Individual‟s mental

software backed by his own Culture. The development of this software emanates from

one's social environments and life experiences. Every individual is born and raised within

a country, learns not only the way of speaking but also thinking of his country, goes to

65 Software of the Mind: Culture-Strategy Fit a Trump card for Multinational

Corporations- A study

school within his country, plays with friends from his country, reads or watches the media

of his country, participates in the organizations of his country, will be rewarded according

to the norms of his country, gets married and multiplies in his country. How can his ideas

and behaviour escape the National influence? This could be superhuman.

Human culture is the result of hundreds of thousands of years of evolution. During most of

this time, competition between bands of gatherer-hunters was a powerful evolutionary

pressure. As a result our social and intellectual skills have become ever bigger. But we did

not lose the elements of our behaviour that identify us as social mammals. Fights for

dominance, competition for partners, a wish to belong and to know who does not belong -

all of these basic drives are alive in us. No wonder that culture revolves around basic

issues that have to do with group membership, authority, gender roles, morality, anxiety,

emotions and drives. Culture affects our love lives, our professional lives, our wars and

our dreams.

An individual human being acquires most of her or his programming during childhood,

before puberty. In this phase of our lives we have an incredible capacity for absorbing

information and following examples from our social environment: our parents and other

elders, our siblings and playmates. But all of this is constrained by our physical

environment: its wealth or poverty, its threats or safety, its level of technology. All human

groups, from the nuclear family to society, develop cultures as they go. Culture is what

enables a group to function smoothly.

CULTURE

Culture in the narrow sense refers to dance, drama and music. In the broader sense, culture

refers to the customs, traditions, languages, usages, law, behaviour, attitudes, ethos,

taboos, belief system, value system, colours, symbols, habit pattern, the way of thinking,

way of feelings and the way of performance. Thus, culture is a social heritage of a group

(organized community or society). It is a pattern of responses discovered, developed, or

Mrs. Sheela Reddy

66

invented during the group's history of handling problems which arise from interactions

among its members, and between them and their environment. Culture therefore, is moral,

intellectual and spiritual discipline for advancement, in accordance with the norms and

values based on accumulated heritage. It is imbibing and making our own, the life style

and social pattern of the group one belongs to. Culture is a system of learned behaviour

shared by and transmitted among the members of the group.

National Culture

National Culture encompasses the collective way of behaving, performing and getting

things done. It has the roots founded by forefathers in the yore and practiced perennially

down the line by the inheritors. One should appreciate the famous invention by Prof. Geert

Hofstede, who had given six cultural dimensions to national cultures. According to him,

countries can be segregated on the basis of Low or High Power Distance, Individualistic

and Collectivistic, Low Uncertainity Avoidance and High Uncertainity Avoidance,

Mascualinity and Femininity, Short-Term and Long-Term, Indulgence and Restraint.

Power distance is the extent to which the less powerful members of organizations and

institutions (like the family) accept and expect that power is distributed unequally.

Uncertainty avoidance deals with a society's tolerance for uncertainty and ambiguity.

Individualism on the one side versus its opposite, collectivism, is the degree to which

individuals are integrated into groups. Masculinity versus its opposite, femininity, refers to

the distribution of emotional roles between the genders which is another fundamental issue

for any society to which a range of solutions are found. Long- term oriented societies

foster pragmatic virtues oriented towards future rewards, in particular saving, persistence,

and adapting to changing circumstances. Short-term oriented societies foster virtues

related to the past and present such as national pride, respect for tradition, preservation of

"face", and fulfilling social obligations.

Organisation Culture

Organisational Culture is defined as the way in which members of an organisation relate to

each other, their work and the outside world in comparison to other organisations. The



Dimensions enable a tangible alignment of Organisational Culture and Strategy. The

dimensions include Means-oriented vs. Goal-oriented, Internally driven vs. Externally

driven, Easygoing work discipline vs. Strict work discipline, Local vs. Professional, Open

system vs. Closed system and Employee-oriented vs. Work-oriented. The means-oriented

versus goal-oriented dimension is, among the six dimensions, most closely connected with

the effectiveness of the organisation. In a means oriented culture the key feature is the way

in which work has to be carried out; people identify with the “how”. In a goal-oriented

culture employees are primarily out to achieve specific internal goals or results, even if

these involve substantial risks; people identify with the “what”. In an internally driven

culture employees perceive their task towards the outside world as totally given, based on

the idea that business ethics and honesty matters most and that they know best what is

good for the customer and the world at large. In an externally driven culture the only

emphasis is on meeting the customer‟s requirements; results are most important and a

pragmatic rather than an ethical attitude prevails. Easy going verses strict going culture

refers to the amount of internal structuring, control and discipline. An easygoing culture

reveals loose internal structure, a lack of predictability, and little control and discipline;

there is a lot of improvisation and surprises. A strict work discipline reveals the reverse.

People are very cost-conscious, punctual and serious. In a local company, employees

identify with the boss and/or the unit in which one works. In a professional organisation

the identity of an employee is determined by his profession and/or the content of the job.

In a local culture, employees are very short-term directed, they are internally focused and

there is strong social control to be like everybody else. In a professional culture it is the

reverse. Open system verses Closed system dimension relates to the accessibility of an

organisation. In an open culture newcomers are made immediately welcome, one is open

both to insiders and outsiders, and it is believed that almost anyone would fit in the

organisation. In a closed organisation it is the reverse. Employee-oriented verses Work-

oriented aspect of culture is most related to the management philosophy per se. In an

employee-oriented organisation, the members of staff feel that personal problems are

taken into account and that the organisation takes responsibility for the welfare of its

Mrs. Sheela Reddy

68

employees, even if this is at the expense of the work. In a work-oriented organisation,

there is heavy pressure to perform the task even if this is at the expense of employees.

Objectives of the Study

This conceptual paper seeks to review the literature on National Culture Dimensions,

Organizational Culture Dimensions, International strategic management, Culture- strategy

alignment, examine the trilateral interactive relations among national culture, subsidiary

strategic context and Culture-Strategy among headquarters and its Subsidiaries.

The research issues for the present article are

1. What is the relationship between National Culture and Business Strategy of an

MNC?

2. How far the Culture-Strategy Fit of an MNC impact the business performance?

3. What are the mechanisms that are available to establish quality fit between

Culture and Strategy for an MNC operating in different countries of the world?

4. What are the success factors and the failure factors of the Culture-Strategy Fit by

MNC?

Culture-Strategy Fit a Trump card

The global companies are spreading their tentacles to all parts of the world. When such

being the case, inevitably, the MNC ought to understand and gel with host country

cultures. This is called Culture-Strategy Fit of an MNC. This also implies the alignment of

organisational culture with national cultures, apart from establishing fit with individual

value system in the host country. The Corporate Strategy Encompasses Financial Strategy,

Production Strategy, HR Strategy, Marketing Strategy, R & D Strategy and the like. The

organizations are grouped under their own cultures backed by national cultures. The

former could be proactive Process oriented vs. Results oriented, Employee Oriented vs.

Job oriented, Parochial vs. Professional, Open system vs. Closed system, Loose vs. Tight

control and Normative vs. Pragmatic.



The paper also seeks to understand the various factors to be considered for aligning the

corporate strategy with host country‟s national culture. In the current knowledge era,

global companies operating in different countries needs to understand the host country‟s

cultures and thereafter align their business strategies with them. The non-alignment will

lead to business losses and the alignment will produce rich results in terms of Firm‟s

Performance. Therefore Culture-Strategy Fit by MNC is the need of the hour and acts as a

key resource to be used at an opportunistic movement. Culture-Strategy implies the

alignment of organisational cultures with national culture, apart from establishing fit with

individual value system in the host country.

Conclusions and Implications

Cultural integration is a top management responsibility, but if top managers don't know

what culture is, the disasters are predictable. It is palpable that the multinational

corporations when transcending their business globally, they ought to gel with the host

country‟s culture and therefore align corporate strategies according to the host country

culture. The intricate issue for top management is to find out nitty-gritty factors for

aligning strategies for achieving corporate goals and objectives.

In this paper we have chosen to focus on the Subsidiary Business Units of MNC‟s and

clarify the complexities that are bound to arise in designing corporate strategies in

compliance with national culture between the headquarters and subsidiaries. Foremost

attributes are to be considered by top level management in MNC Subsidiary companies

are at the individual level by aligning individual goals and objectives according to

subsidiary culture for metamorphosing individual performance. Departmental goals and

objectives are to be in range with the Subsidiary business unit‟s national culture to

outreach departmental performance. Business unit goals and objectives need to be in

commensurate with national culture of the host country for accomplishing overall business

unit performance.

Strategic designing of Subsidiary Business Unit‟s goals and Objectives by considering the

above mentioned attributes in line with host country national culture brings burgeoning

and fascinating results in productivity, profitability, expansion, diversification,

interpersonal relations and the like. Thus, national culture is a catalyst in realizing and

Mrs. Sheela Reddy

70

fortifying Subsidiary Business unit goals and Objectives which in turn leads to attainment

of overall Corporate goals and objectives.

Conniving corporate strategies in compliance with national culture is imperative and the

need of the hour for sustaining and maintaining market position globally in the

competitive era. Hence, Culture-Strategy alignment is the crux and fulcrum in designing

Corporate Strategies which embrace Financial Strategy, Production Strategy, HR Strategy,

Marketing Strategy, R & D Strategy and the like.

Directions for future study

This literature review focused on the application of national culture theories profound by

renowned guru in the field of Management and Anthropology Geert Hofstede and found

that although there are several cultural frameworks, Hofstede‟s is by far the most widely

used. Our review of this literature suggests that there are umpteen opportunities to use

other cultural frameworks. For example, Hall‟s dimension of “high-context” versus “low

context” seems to be a good fit for research in the technology-mediated communication

area.

References

Armstrong. M. (2006). Handbook of Human Resource Management Practice (10th ed.).

London: Kogan Page.

Bae, J., & Lawler, J. J. (2000). Organisational and HRM Strategies in Korea: Impact on

Firm Performance in An Emerging Economy. Academy of Management Journal, 43(3),

502 – 517. http://dx.doi.org/10.2307/155640

Dartey-Baah, K., (2011). The impact of national cultures on corporate cultures in

organisations. Academic Leadership Journal, 9(1).

Delaney, J.T., & Huselid, M. A. (1996). The Impact of Human Resources Management

Practices on Perceptions of Organisational Performance. Academy of Management

Journal, 39(4), 949 – 969. http://dx.doi.org/10.2307/256718

Dessler, Gary. (1997). Manajemen Sumber Daya Manusia. Penerjemah Triyana

Iskandarsyah. Penerbit PT. Prenhallindo Jakarta.



Earley, P.C. (1994). Self or Group? Cultural effects of training on self-efficacy and

performance. Administrative Science Quarterly, 39(1), 89–117.

http://dx.doi.org/10.2307/2393495

Early, P. C. (1987). Intercultural Training for Managers: A Comparison of Documentary

and Interpersonal Methods. Academy of Management Journal, 30, 685-698.

http://dx.doi.org/10.2307/256155

Ferdinand, A. (2002). Structural Equation Modeling dalam Penelitian Manajement, Edisi

2. Semarang: BP Undip.

Geringer, M. J., Frayne, A. C., & Milliman, F. J. (2002, Spring). In Search of “Best

Practices” in International Human Resource Management: Research Design and

Methodology. Human Resource Management, 41(1), 5-30.

http://dx.doi.org/10.1002/hrm.10017

Hair, F.J., Anderson E.R., Tatham L.R., & Black C. W. (1998). Multivariate Data

Analysis (5th ed.). Prentice Hall International Inc.

Harzing et al. (2011). International Human Resource Management (3rd ed.). Sage

Publications, India Pvt Ltd.

Harzing, A. (2001). An analysis of the functions of international transfer of managers in

MNCs. Employee Relations, 23(6), 581-598. http://dx.doi.org/10.1108/01425450110409248

Hofstede, G. (1980). Culture‟s Consequences. International Differences in Work-Related

Values. A Bridged Edition. Sage Publication. Newburry Park.

Hofstede, G. (1980). Motivation, Leadership, and Organisation: Do American Theories

Apply Abroad? Organisational Dynamics, Summer, AMACOM, A Division of American

Management Association.

Hofstede, G. (1990). A Reply & Comment on Joginder P Singh: Managerial Culture and

Work related Values in India. Organisation Studies, 11/1(103-106), 1-5.

Hofstede, G. (1997). Cultures and Organisations, Software of the Mind. Intercultural

Cooperation and its Importance for Survival. McGraw Hill. New York.

Hofstede, G. (1998). Identifying organisational subcultures: An empirical approach.

Journal of Management Studies, 35(1), 17–28. http://x.doi.org/10.1111/1467-6486.00081

Mrs. Sheela Reddy

72

Hofstede, G. (2001). Culture's Consequences: comparing values, behaviors, institutions,

and organisations across nations (2nd ed.). Thousand Oaks, CA: SAGE Publications.

Hofstede, G., & Hofstede, J. (2005). Cultures and organisations: software of the mind

(Revised and expanded 2nd ed.). New York: McGraw-Hill.

Hofstede, G., & M. H. Bond. (1984). Hofstede's Culture Dimensions: An Independent

Validation Using Rokeach's Value Survey. Journal of Cross-Cultural Psychology, 15(4),

417-433. http://dx.doi.org/10.1177/0022002184015004003

Hofstede, G., & M. H. Bond. (1988). The Confucius connection: From cultural roots to

economic growth. Organisational Dynamics, 16(4), 5-21. http://dx.doi.org/10.1016/0090-

2616(88)90009-5

House, R., Hanges, P, Javidan, M., Dorfman, P., & Gupta, V. (2004). Culture, Leadership,

and Organisations: The GLOBE Study of 62 Societies. Thousand Oaks, CA: Sage.

House, R., Javidan, M., Hanges, P., & Dorfman, P. (2002). Understanding cultures and

implicit leadership theories across the globe: An introduction to project GLOBE. Journal

of World Business, 37, 10. http://dx.doi.org/10.1016/S1090-9516(01)00069-4

Jaeger, A. M. (1986). Organisation Development and National Culture: Where‟s the Fit?

Academy of Management Review, 11, 178-190.

Kuada, J. (2010). Culture and leadership in Africa: a conceptual model and research

agenda. African Journal of Economic and Management Studies, 1(1), 9-24.

http://dx.doi.org/10.1108/20400701011028130 Published by Sciedu Press 44 ISSN 1923-

4007 E-ISSN 1923-4015

Laurent A. (1986). The Cross Cultural Puzzle of International Human Resources

Management. Human Resource Management, 15, 91-102. http://dx.doi.org/10.1002/hrm.3930250107

Lowe, B.K, Milliman, J, De Cieri, H., & Dowling, J. P. (2002, Spring). International

Compensation Practices: A Ten Country Comparative Analysis. Human Resource

Management, 41(1), 45-66. http://dx.doi.org/10.1002/hrm.10019



Luthans, F., D.H.Welsh, & Rosenkrantz, S.A. (1993). What do Russian Managers Really

Do? An Observational Study with Comparison to US Managers. Journal of International

Business Studies, 24(4), 741-762. http://dx.doi.org/10.1057/palgrave.jibs.8490253

Newman, L.K., & Nollen, D.S. (1996). Culture and Congruence: The Fit Between

Management Practice and National Culture. Journal of International Business Studies,

Fourth Quarter, 27(4), 753–779. http://dx.doi.org/10.1057/palgrave.jibs.8490152

Saffold, Guy. (1988). Culture Traits, Strength and Organisational Performance: Moving

Beyond Strong Culture. Academy of Management Review, 13(4), 546-558.

Schneider, S. C., & DeMeyer, A. (1991). Interpreting and Responding to Strategic Issues:

The Impact of National Culture. Strategic Management Journal, 12, 307-320.

http://dx.doi.org/10.1002/smj.4250120406

Schuler, S. R. & Jackson, S. E. (1996). Human Resource Management. New York:

Prentice Hall.

Smith, P. B. (1992). Organisational Behaviour and National Cultures. British Journal of

Management, 3, 39-51.

Membership Form

74

GLOBAL JOURNAL OF MULTIDICIPLINARY AND

MULTIDIMENSIONAL STUDIES ISSN NO 2394-8695

--------------------------------------------------------- SUBSCIPTION FORM

To, Managing And Chief Editor,

Shrutaayush Publication

C-357, Gama-1, Greater Noida Mob: +91-9250940635

(Please Fill in Block Letters)

Dear Sir, I request you kindly to enroll me as Patron /Donor/Life/ Ordinary/Member of the Global

Journal of Multidisciplinary and Multidimensional studies. The Membership Fee of Rs.

/US $ ------------------------ is sent herewith by M.O. / Demand draft No. ----------------------------- Dated -----------------------------

-------- / Cash. I am giving below my full particulars. Name in full.......................................................................................................... Qualifications......................................................................................... Designation............................................................................................ Address (With pin code) Office: .......................................................................................................... ………………………………Phone No.: ...................................................... Residence: ................................................................................................... ………………………………Phone No.: …………………………………. E Mail id.:

………………………………………………………………….

Please acknowledge the receipt of the amount.

Date.......................... Yours faithfully,

(Name and Signature)

73 Membership Form

N.B.:

1. The membership is given in two broad categories. The fees of these categories are given

as follows:

(A) For Individuals:

Particular Particulars For Indian citizens For Other than Indian

(In Indian Rupees) Citizens (In

US Dollar)

Student (Annual) 500 50 Working professional Annual 1000 100 Five yearly 4000 400 Life term 8000 800 (B) For Institutional:

Particular Particulars For Indian For Other

than Indian

(In Indian Rupees) (In US

Dollar)

Annual 2000 200 Two yearly 3500 350 Five yearly 7000 700 Ten yearly 12000 1200 The membership fee should be paid either b y M.O. or Bank Draft payable in the name

of the Shrutaayush Publication payable at Greater Noida. Accounting year of publication is from 1st April to 31st March. A member is entitled to 4 issues of Global journal of Multidisciplinary and

Multidimensional Studies.

Volume 1 Number 2, April-Jun,2015

Appril june issue of GJMMS pdf

Education

Transcript of Appril june issue of GJMMS pdf