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Volume 1 Number 2, April-Jun,2015
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
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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
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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.
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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
7 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN
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
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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.
9 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN
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.
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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
11 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN
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
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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
13 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN
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
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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
15 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN
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
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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
17 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN
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
19 ESTIMATION OF PRODUCTIVITY AND EFFICIENCY OF COTTON FARMERS IN PAKISTAN: A CASE STUDY OF DISTRICT DERA GHAZI KHAN
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
ISSN NO. 2394- 8965 GJMMS
Vol. – 1, Issue – 2, April- June-2015
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: smajumdar2004@gmail.com
Dr. Sukanta Chandra Swain
Asst. Dean
ICFAI University Jharkhand, Ranchi,
India
E-mail: sukanta_swain@yahoo.com
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
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
23 Identification of factors influencing Preferences for Green Products: A study in and
around Kolkata (India)
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.
25 Identification of factors influencing Preferences for Green Products: A study in and
around Kolkata (India)
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
Variables/Factors(used in the study) contributing for the popularity of Green products
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
Variables/Factors(used in the study) contributing for the popularity of Green products
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
around Kolkata (India)
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
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
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
Rotated Component Matrixa
Component
1 2 3
v4 .855
v6 .823
v2 .704
v1 .650
v5 .812
v3 .440 .667
Variable Description Components
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
Rotated Component Matrixa
Component
1 2
v1 .868
v2 .803
v3 .399 .386
v4 .935
29 Identification of factors influencing Preferences for Green Products: A study in and
around Kolkata (India)
Variable Description Components
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:
Rotated Component Matrixa
Component
1 2
v1 .868
v4 .803
v2 .399 .435
v3 .935
Variable Description Components
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
Rotated Component Matrixa
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.
Variable Description Components
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.
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.
31 Identification of factors influencing Preferences for Green Products: A study in and
around Kolkata (India)
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
Rotated Component Matrixa
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
Variable Description Components
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.
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.
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
Variable Description Components
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
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
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
33 Identification of factors influencing Preferences for Green Products: A study in and
around Kolkata (India)
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.
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13) Moisander, J. Motivational complexity of green consumerism International Journal of
Consumer Studies, 2007, Vol. 31, pp. 404-409
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4.139.58.2/ejournalver2/.../PavanMishraandMs.PayalSharma.pdf
35
Performance of financial markets in Indian Economy
ISSN NO. 2394- 8965 GJMMS
Vol. – 1, Issue – 2, April- June-2105
Performance of financial markets in Indian Economy Dr.A.K.Jha
Director, CMAT, Gr. Noida
&
Virinder 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
Performance of financial markets in Indian Economy
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
Performance of financial markets in Indian Economy
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
Performance of financial markets in Indian Economy
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
Performance of financial markets in Indian Economy
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
ISSN NO. 2394- 8965 GJMMS
Vol. – 1, Issue – 2, April- June-2015
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)
47 DIFFICULTIES IN IMPLEMENTING IFRS: A STUDY ON PERCEPTION
OF CA STUDENTS IN KOLKATA
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
49 DIFFICULTIES IN IMPLEMENTING IFRS: A STUDY ON PERCEPTION
OF CA STUDENTS IN KOLKATA
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
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 63.591a 16 .000
Likelihood Ratio 59.108 16 .000
Linear-by-Linear Association 3.139 1 .076
N of Valid Cases 74
a. 20 cells (80.0%) have expected count less than 5. The minimum expected count is .24.
(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
Value df Asymp. Sig. (2-sided)
Pearson Chi-Square 61.216a 16 .000
Likelihood Ratio 53.082 16 .000
Linear-by-Linear Association .058 1 .010
N of Valid Cases 74
a. 19 cells (76.0%) have expected count less than 5. The minimum expected count is .04.
51 DIFFICULTIES IN IMPLEMENTING IFRS: A STUDY ON PERCEPTION
OF CA STUDENTS IN KOLKATA
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.
53 FINITELY GENERATED FREE ABELIAN GROUPS AND
THEIR APPLICATIONS
ISSN NO. 2394- 8965 GJMMS
Vol. – 1, Issue – 2, April- June-2015
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).
55 FINITELY GENERATED FREE ABELIAN GROUPS AND
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,
57 FINITELY GENERATED FREE ABELIAN GROUPS AND
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.
59 FINITELY GENERATED FREE ABELIAN GROUPS AND
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.
61 FINITELY GENERATED FREE ABELIAN GROUPS AND
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.
63 FINITELY GENERATED FREE ABELIAN GROUPS AND
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
ISSN NO. 2394- 8965 GJMMS
Vol. – 1, Issue – 2, April- June-2015
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
67 Software of the Mind: Culture-Strategy Fit a Trump card for Multinational
Corporations- A study
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.
69 Software of the Mind: Culture-Strategy Fit a Trump card for Multinational
Corporations- A study
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.
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Volume 1 Number 2, April-Jun,2015