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SIT Journal of Management
Vol. 1. No. 1. June 2012. Pp. 122- 139
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Factors Affecting Brand Value of Bharti Airtel and Customers’ Perception
towards Selected Telecom Service Providers: An Empirical Study
Mrinal Kanti Das1
Abstract
India is potentially one of the most exciting GSM markets in the world with the increase in
population growing roughly at 1.7 percent a year. India's telecommunications market has
undergone steady liberalization since 1994 when the Indian government first sought private
investment in the sector. Bharti Airtel, Hutch, Idea, Aircel, Spice and MTNL are the main GSM
providers and Reliance Communications and Tata Indicom are the main CDMA providers in
India. This paper is an endeavor to study factors affecting brand value of Bharti Airtel and also to
study customers’ perception towards some selected telecom service providers. The results of the
study are based on survey of 104 retailers selected from Nadia district of West Bengal by using
convenience sampling technique. The study brings out several useful findings and the more
important among them are –customers’ perception is more favourable towards Bharti Airtel than
that of other telecom service providers in Nadia district, and factors affecting brand value of
Bharti Airtel are - Reverse Problem, Helpline Problem, Promotion Problem, Scheme Problem
and SIM Activation Problem. This study also establishes that there is significant influence of
different brands of mobile telecommunication services and different types of recharge voucher
on the sale of service product.
Keywords: Branding, Telecom Industry, Scaling, Factor Loading, Normal Distribution
1 Assistant Professor, Centre for Management Studies, JIS College of Engineering, Kalyani, India.
M: 9433710231, E-mail: [email protected]
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Introduction
India has become the fifth largest country in terms of telecommunications network in the world
and the second largest among the emerging economies of Asia. It has the fastest growing mobile
market in the stagnant global scenario. Recently, the Indian telecom industry is slated to an
estimated contribution of nearly 1% to India’s GDP. As on Apr 2007 India has 167 million
mobile phone subscribers. Out of this 125 million are GSM users and 41 million CDMA users.
BSNL, Bharti Airtel, Hutch, Idea, Aircel, Spice and MTNL are the main GSM providers in
India. Reliance Communications and Tata Indicom are the main CDMA providers in India. They
are classified in three heads:
State owned companies - BSNL and MTNL
Private Indian owned companies - Reliance Infocom and Tata Teleservices
Foreign invested companies - Vodafone-Essar, Bharti Tele-Ventures, Escotel, Idea
Cellular, BPL Mobile, and Spice Communications
Airtel is the fifth largest telecom operator in the world with over 243.336 million customers
across 20 countries as of March 2012. It is the largest cellular service provider in India, with over
183.3 million subscribers at the end of May 2012. Airtel is the third largest in-country mobile
operator by subscriber base, behind China Mobile and China Unicom. Bharti Airtel and
Vodafone top the list with 28.26% and 23.63% Market Shares in India. Vodafone grew at a
faster rate, with an increase of 1.50% compared to Airtel’s increase of 1.27%. State sector
incumbent BSNL managed to get 4th place (Source: www.pluggd.in).
This study examined factors affecting brand value of Bharti Airtel in Nadia district of West
Bengal and also developed an interval scale to ascertain customers’ attitude regarding telecom
services considered for this study. For this study, retailers’ survey was undertaken with 104
retailers of Nadia district. This study also reveals the impact of different types of recharge
voucher for different brands of mobile telecommunication services on the sale of company’s
products.
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Objectives of the Study
The following are the specific objectives of the study:
1) To identify factors affecting brand value of Bharti Airtel
2) To develop interval scale for ranking different brands of mobile telecommunication
services resorting to some statistical tools.
3) To ascertain the impact of types of recharge (Voucher Card & LSO) for different brands
of mobile telecommunication services on the sale of company’s product.
4) To know the monthly SIM activation of different brands of mobile telecommunication
services.
Methodology
For this study five telecom service providers – Airtel, Vodafone, Docomo, Reliance Smart and
Uninor have been selected. This study is basically empirical in nature. As the study is
empirical in nature, primary data have been mainly used. Secondary data have also been
collected to know the telecommunication market practice in India. This study was carried out
on 104 retailers of different areas of Nadia district. For this survey convenience sampling
technique was used. A structured questionnaire was prepared and requisite information was
collected through personal interviews of retailers of telecommunication services. Data
collection period was limited to three months, from May to July 2011. To analyze data
collected for this study, Exploratory Factor Analysis, ANOVA, Paired Comparison Scale, and
Thurstone Case V Scale was administered. ANOVA was conducted using Excel and Factor
Analysis was conducted using SPSS 16.0.
Hypotheses for the Study
This study was conducted on the following lines of hypotheses:
H01: There is no significant influence of different brands of mobile telecommunication on the
sale of service products.
Ha1: There is significant influence of different brands of mobile telecommunication on the sale of
service products.
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H02: There is no significant influence of different types of recharge on the sale of service
products.
Ha2: There is significant influence of different types of recharge on the sale of service products.
Analysis, Results and Discussion
I. Reliability Measurement
Cronbach’s Alpha Scale was used as a measure of reliability and its estimated value is 0.785 that
reveals satisfactory level of scale reliability as satisfactory value is required to be more than 0.7
for the scale to be reliable.
Table 1: Reliability Statistics
Cronbach's Alpha N of Items
0.785 16
II. Identifying Factors affecting Brand Value of Bharti Airtel
To identify factors that affect brand value of Bharti Airtel, Exploratory Factor Analysis was
administered. The data was captured in a spreadsheet and transported to a software statistical
package (SPSS 16.0). The results of the factor analysis are shown below.
KMO and Bartlett's Test: The Kaiser-Meyer-Olkin measure of sampling adequacy was
adapted to test whether the partial correlations among items are small. Bartlett's test of sphericity
examines whether the correlation matrix is an identity matrix, which would indicate that the
factor model is inappropriate. The KMO measures the sampling adequacy which should be
greater than 0.5 for a satisfactory factor analysis to proceed further. Both tests (Table 2) give
satisfactory results.
Table 2: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.644
Bartlett's Test of Sphericity Approx. Chi-Square 1136.596
df 120.000
Sig. .000
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In this study, questionnaire, designed for factor analysis, consisted of 16 statements and the
respondents were asked to indicate their degree of agreement with these on a seven point scale
(1=strongly disagree, 5=strongly agree). Table 3 reflects the statements used for factor analysis.
Table 3: Statements Related to the Factors affecting Brand Value of Bharti Airtel
Label Statements
S01 Once recharge by mistake to other number the recharged amount cannot be
reversed back which is termed as reverse problem
S02 Reverse problem leads to monetary loss to retailer
S03 Reverse problem affects profitability of Airtel heavily
S04 The sign boards and promotional instrument are not adequately distributed
S05 The brand name of Airtel is unable to occupy a distinct place in the minds of
buyers for improper visibility
S06 Promotional problem occurs after change of symbol
S07 Problems arising out of inadequate distribution of promotional instruments
affect the sale adversely
S08 Total number of activation decreases due to deactivation of SIM card after 2 to 3
days of activation of new connection
S09 The activation time is longer than that of other Telecom Services.
S10 Aged customers prefer Airtel SIM to young customers.
S11 There is no tariff cutter scheme like other connections
S12 Non availability of tariff cutter scheme heavily affects the sale to young
generation
S13 Less variety of Schemes is one of the reasons of less profitability and sale
S14 The Retailer and Customer Service Care Executives take long time to respond.
S15 The complaints of customers and retailers are not resolved as per requirement
S16 Customer care problem heavily affects the customer Service.
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Correlation Matrix: The correlation matrix was computed and examined. Correlation matrix
was constructed using primary data, which has been presented in Table 5. The correlation matrix
revealed that there is a strong positive correlation between the statements. These statements were
considered appropriate for factor analysis procedure.
Anti-Image Correlation Matrix: The anti-image correlation matrix contains the negative values
of the partial correlation coefficients. Most of the off-diagonal elements should be small in a
good factor model. Anti-image correlation matrix was developed and presented in Table 6. This
matrix shows that partial correlations among the statements are low. Similarly, most of the off
diagonal elements are small, indicating that real factors exist in the data, which is necessary for
factor analysis.
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Table 4: Total Variance Explained
Component Initial Eigen values Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative % Total % of Variance Cumulative %
1 4.141 25.882 25.882 4.141 25.882 25.882 2.815 17.593 17.593
2 2.700 16.875 42.757 2.700 16.875 42.757 2.708 16.924 34.517
3 2.506 15.661 58.418 2.506 15.661 58.418 2.631 16.445 50.962
4 1.661 10.383 68.801 1.661 10.383 68.801 2.153 13.457 64.419
5 1.219 7.616 76.417 1.219 7.616 76.417 1.920 11.998 76.417
6 .850 5.315 81.732
7 .672 4.202 85.933
8 .623 3.892 89.825
9 .447 2.793 92.619
10 .365 2.283 94.902
11 .243 1.519 96.421
12 .192 1.198 97.619
13 .162 1.015 98.634
14 .105 .659 99.293
15 .069 .434 99.726
16 .044 .274 100.000
Extraction Method: Principal Component Analysis.
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Table 5: Correlation Matrix
Correlation S01 S02 S03 S04 S05 S06 S07 S08 S09 S10 S11 S12 S13 S14 S15 S16
S01 1.000 .841 .939 .038 -.017 .124 .217 -.047 -.071 .159 .115 .070 .064 .118 .200 .257
S02 .841 1.000 .870 -.017 .021 .074 .096 -.044 -.023 -.032 .115 .110 .057 .158 .216 .273
S03 .939 .870 1.000 .030 -.031 .089 .144 -.015 -.060 .091 .125 .080 .076 .084 .176 .239
S04 .038 -.017 .030 1.000 .556 .668 .510 .143 .083 -.020 -.022 .000 .039 .334 .275 .257
S05 -.017 .021 -.031 .556 1.000 .401 .415 .176 .136 .172 .023 .021 .119 .332 .284 .290
S06 .124 .074 .089 .668 .401 1.000 .560 .195 .162 .191 .085 .226 .196 .292 .286 .278
S07 .217 .096 .144 .510 .415 .560 1.000 .179 .072 .110 .109 .017 .186 .249 .319 .304
S08 -.047 -.044 -.015 .143 .176 .195 .179 1.000 .825 -.070 .389 .149 .215 .203 .179 .150
S09 -.071 -.023 -.060 .083 .136 .162 .072 .825 1.000 -.007 .407 .175 .318 .171 .146 .151
S10 .159 -.032 .091 -.020 .172 .191 .110 -.070 -.007 1.000 .097 .140 .192 .107 .093 .091
S11 .115 .115 .125 -.022 .023 .085 .109 .389 .407 .097 1.000 .503 .606 -.120 -.053 .087
S12 .070 .110 .080 .000 .021 .226 .017 .149 .175 .140 .503 1.000 .405 -.126 -.119 -.027
S13 .064 .057 .076 .039 .119 .196 .186 .215 .318 .192 .606 .405 1.000 -.031 .050 .091
S14 .118 .158 .084 .334 .332 .292 .249 .203 .171 .107 -.120 -.126 -.031 1.000 .909 .815
S15 .200 .216 .176 .275 .284 .286 .319 .179 .146 .093 -.053 -.119 .050 .909 1.000 .802
S16 .257 .273 .239 .257 .290 .278 .304 .150 .151 .091 .087 -.027 .091 .815 .802 1.000
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Table 6: Anti-image Correlation Matrix
Anti-
image
Correlatio
n
S01 S02 S03 S04 S05 S06 S07 S08 S09 S10 S11 S12 S13 S14 S15 S16
S01 .713a -.182 -.728 .031 .108 .002 -.274 .109 -.061 -.223 -.059 .031 .097 -.062 .036 .028
S02 -.182 .667a -.469 .293 -.267 -.140 .040 .286 -.210 .418 -.101 -.106 .061 -.226 .088 .107
S03 -.728 -.469 .614a -.223 .109 .087 .196 -.289 .214 -.101 .102 .025 -.112 .258 -.136 -.161
S04 .031 .293 -.223 .573a -.438 -.555 -.156 .148 -.058 .349 -.120 .087 .082 -.283 .175 .148
S05 .108 -.267 .109 -.438 .684a .133 -.170 -.128 .053 -.299 .089 -.002 -.084 .015 .016 -.102
S06 .002 -.140 .087 -.555 .133 .687a -.283 -.074 -.026 -.254 .172 -.282 -.081 .075 -.061 -.067
S07 -.274 .040 .196 -.156 -.170 -.283 .716a -.201 .190 .022 -.009 .126 -.115 .230 -.209 -.124
S08 .109 .286 -.289 .148 -.128 -.074 -.201 .493a -.796 .233 -.261 -.023 .214 -.221 .053 .213
S09 -.061 -.210 .214 -.058 .053 -.026 .190 -.796 .567a -.114 .001 .061 -.229 .059 -.001 -.105
S10 -.223 .418 -.101 .349 -.299 -.254 .022 .233 -.114 .439a -.096 -.058 -.071 -.227 .106 .137
S11 -.059 -.101 .102 -.120 .089 .172 -.009 -.261 .001 -.096 .626a -.336 -.449 .251 -.036 -.292
S12 .031 -.106 .025 .087 -.002 -.282 .126 -.023 .061 -.058 -.336 .694a -.127 -.035 .094 .017
S13 .097 .061 -.112 .082 -.084 -.081 -.115 .214 -.229 -.071 -.449 -.127 .673a .061 -.125 .055
S14 -.062 -.226 .258 -.283 .015 .075 .230 -.221 .059 -.227 .251 -.035 .061 .627a -.740 -.471
S15 .036 .088 -.136 .175 .016 -.061 -.209 .053 -.001 .106 -.036 .094 -.125 -.740 .740a -.106
S16 .028 .107 -.161 .148 -.102 -.067 -.124 .213 -.105 .137 -.292 .017 .055 -.471 -.106 .795a
a. Measure of Sampling Adequacy (MSA)
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Table 7: Rotated Component Matrix
a
Statements Component Commonalities
1 2 3 4 5
S01 .948 .929
S02 .932 .886
S03 .965 .944
S04 .875 .792
S05 .685 .534
S06 .815 .720
S07 .748 .607
S08 .881 .845
S09 .851 .828
S10 .540 .549
S11 .745 .731
S12 .715 .551
S13 .777 .644
S14 .929 .932
S15 .916 .902
S16 .869 .832
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 7 iterations.
The Principal Components Analysis (PCA) method was selected after determining the
appropriateness of the data set. In factor analysis, only factors having eigen values greater
than 1.0 are retained, the other factors are not included in the model. It is observed from
the table 4 that eigen value greater than 1.0 result in five factors being extracted. From
the cumulative percentage of variance, it is evident that five factors accounted for
76.417% of the total variance. This is a pretty good bargain, and thus, five factors appear
to be reasonable in this situation. Table 4 also shows commonalities which provide
relevant information after the desired number of factors have been extracted.
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An important output from factor analysis is the rotated factor matrix. The factor matrix
contains the coefficients used to express the standardized variables in terms of the factors.
These coefficients, the factor loadings, represent the correlations between the factors and
the variables. A coefficient with large absolute value indicates that the factor and the
variables are closely related. The coefficients of the factor matrix can be used to interpret
the factors. Here varimax procedure is adopted for rotation which is an orthogonal
rotation method that minimizes the number of variables with high loadings on a factor,
thereby enhancing the interpretability of factors.
Table 8: Naming of the Factors affecting Brand Value of Bharti Airtel
Factor
No.
Name of
Factors Label Statements
Factor
Loading
1 Reverse
Problem
S1
Once recharge by mistake to
other number the recharged
amount cannot be reversed back .948
S2 Reverse problem leads to
monetary loss to retailer .932
S3 These problem affects
profitability of Airtel heavily .965
2 Helpline
Problem
S14 The Retailer and Customer
Service Care Executives take
long time to respond. .929
S15
The complaints of customers and
retailers are not resolved as per
requirement .916
S16 Customer care problem heavily
affects the customer Service. .869
3 Promotion S4 The sign boards and promotional
instruments are not adequately .875
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Problem distributed
S5
The brand name of Airtel is
unable to create a distinct place
in the minds of buyers for
improper visibility
.685
S6 Promotional problem occurs
after change of symbol .815
S7
Problems arising out of
inadequate distribution of
promotional instruments affect
the sale adversely
.748
4 Scheme
Problem
S10 Aged customers prefer Airtel
SIM to young customers. .540
S11 There is no tariff cutter scheme
like other connections .745
S12 Non availability of tariff cutter
scheme heavily affects the sale to
young generation .715
S13 Less variety of Schemes is one of
the reasons of less profitability
and sale .777
5
SIM
Activation
Problem
S8
Total number of activation
decreases due to deactivation of
SIM card after 2 to 3 day of
activation of new connection
.881
S9
The activation time is longer
than that of other Telecom
Services. .851
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(i) Factor 1: Reverse Problem: The three variables identified under Factor 1 are Once
recharge by mistake to other number the recharged amount cannot be reversed
back (factor loading = 0.948), reverse problem leads to monetary loss to retailer
(0.932), and reverse problem affects profitability of Airtel heavily (0.965). These
three variables reflect reverse problem; hence, this factor is collectively referred to
as “Reverse Problem”. These three variables together explain 17.593% of the total
variance through Factor 1, which is the highest among all the factors.
(ii) Factor 2: Helpline Problem: Factor 5 is a combination of three variables i.e., the
Retailer and Customer Service Care Executives take long time to respond (0.929),
the complaints of customers and retailers are not resolved as per requirement
(0.916), and Customer care problem heavily affects the customer Service (0.869).
These three variables are highly correlated with each other and together explain
16.924% of the variation. As these three variables are problems of helpline; hence,
the factor comprising of these four variables is named as “Helpline Problem”.
(iii) Factor 3: Promotion Problem: Factor 2 is a combination of four variables i.e., the
sign boards and promotional instrument are not adequately distributed (0.875), the
brand name of Airtel is unable to create a distinct place in the mind of buyer for
improper visibility (0.685), promotional problem occurs after change of symbol
(0.815) and problems arising out of inadequate distribution of promotional
instruments affect the sale adversely (0.748). These four variables are highly
correlated with each other and together explain 16.445% of the variation. As these
four variables reflect problems regarding promotion, the factor comprising these
four variables is named as “Promotion Problem”.
(iv) Factor 4: Scheme Problem: Four variables are included under Factor 4 - Aged
customers prefer Airtel SIM to young customers (0.540), there is no tariff cutter
scheme like other connections (0.745), non availability of tariff cutter scheme
heavily affects the sale to young generation (0.715) and less variety of Schemes is
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one of the reasons of less profitably and sale (0.777). This factor explains 13.457%
of the variation and is labeled as “Scheme Problem”.
(v) Factor 5: SIM Activation Problem: This factor explains 11.998% of the total
variance. Total number of activation decreases due to deactivation of SIM card
after 2 to 3 day of activation of new connection (0.881), and the activation time is
longer than that of other Telecom Services (0.851) are highly correlated with each
other. These two variables show problem of SIM activation; hence, this factor is
named as “SIM activation Problem”.
III. Construction of Interval Scale (Thurstone Case V Scale) to rank of Different
Brands of Telecommunication Services
Table 9: Observed proportions preferring Brand X (Column of the table) to Brand Y
(Row of table)
Brands
(Y)
Preferred Brands (Brand X)
Airtel Vodafone Docomo Smart Urinor
Airtel - 0.13 0.27 0.39 0.03
Vodafone 0.87 - 0.4 0.63 0.07
Docomo 0.73 0.6 - 0.66 0.03
Smart 0.61 0.37 0.34 - 0.04
Urinor 0.97 0.93 0.97 0.96 -
(Source: Primary Data)
For developing above paired-comparison scale (Table 9), two brands were chosen at a
time and the respondents were forced to answer one alternative in each of the ten cases as
the maximum number of paired comparison was ten for this study. Finally, the responses
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were aggregated. These observed proportions will tend to imply the distance between
different brands.
To determine if the data set is normal, Kolmogorov-Smirnov Z test was performed and
results show the data are normally distributed. Under the assumption of normality, these
proportions can be interpreted as the distance between those two brands. For the basic
Case V, the frequency dominance matrix is translated into proportions and interfaced
with the standard scores. The scale is then obtained as a left-adjusted column marginal
average of this standard score matrix (Thurstone, 1927b). Using these proportions as
probabilities, ordinates for each proportion were obtained from any normal distribution
table. These ordinates are symbolically represented as Z values. In general, Z values have
a symmetric pattern around zero. If the proportion is less than 0.5, the corresponding Z
value has negative sign, and conversely, if the proportion is greater than 0.5, the Z value
is positive. In the next table (Table 10) Z values have been summarized.
Table 10: Thurstone Case V Scale
Brands (Y)
Preferred Brands (Brand X)
Airtel Vodafone Docomo Smart Uninor
Airtel - -1.13 -0.61 -0.28 -1.89
Vodafone 1.13 - -0.25 0.33 -1.48
Docomo 0.61 0.25 - 0.41 -1.88
Smart 0.28 -0.33 -0.41 - -1.75
Urinor 1.89 1.48 1.88 1.75 -
Total 4.11 0.27 0.61 2.21 -7
MEAN (Z) 0.82 0.05 0.12 0.44 -1.4
R*
(Case-V Scale
Value)
2.22 1.45 1.52 1.84 0
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RANK 1 4 3 2 5
Table 10 reflects that Airtel has been ranked first among five popular brands of mobile
telecommunication services considered for this study in Nadia district of West Bengal.
IV. Influence of different Brands of Mobile Telecommunication Services and
different types of Recharge Voucher on the sale of service product
Table 11: ANOVA Table
Source of
Variation SS DF MS F P-value F crit
Types of
recharge
4.28987E+11 1 4.28987E+11 18.82922 0.01226 7.708647
Brands 6.39092E+11 4 1.59773E+11 7.012805 0.042838 6.388233
Error 91132186000 4 22783046500
Total 1.15921E+12 9
As the critical value of F (4, 1) with = 0.05 is 7.7086 which is lesser than the calculated
value of F= 18.8292, we reject the null hypothesis that implies significant influence of
different types of recharge on the sale of service products.
On the other hand, it is found from the above table (Table 11) that the critical value of F
(4, 4) with = 0.05 is 6.3882 which is also lesser than the calculated value of F =
7.0128. So we have to reject the null hypothesis that indicates there is significant
influence of different brands of mobile telecommunication on the sale of service
products.
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V. Graphical Presentation of the Number of SIM activation per month
This study also presents number of SIM activation per month of different brands of
mobile telecommunication services (Figure 1). Figure 1 shows Airtel has got second
position and Reliance Smart has got first position in Nadia district. But customers’
perception shows different results where Airtel managed to get first position. This is due
to some attractive short term offers offered by Reliance Smart.
Figure 1: Number of SIM Activation per month
Conclusion
This study provides empirical evidence and has come up with more proof that perception
of the customers of Nadia district is more favourable towards Bharti Airtel than that of
other telecom service providers. The finding of this study has a significant relevance and
importance to management of the companies as well as academicians. This study has
proven that there is significant influence of different brands of mobile telecommunication
services and different types of recharge voucher on the sale of service products. This
study also discovered five factors that affect brand value of Bharti Airtel and these factors
are: Reverse Problem, Helpline Problem, Promotion Problem, Scheme Problem and SIM
Activation Problem. Thus, to sustain its position in the long run and to experience a
flourishing growth Bharti Airtel must consider factors affecting its brand value.
0
1000
2000
3000
4000
Number of SIM Activation
Airtel Vodafone Docomo Smart Uninor
Different Brands of Mobile Telecommunication
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Limitations of the Study
(a) The study covers a limited time period of three months starting from May, 2011 to
July, 2011. The results of this study are valid for this particular period and prediction
may not be reliable beyond this time period.
(b) This study is based on 104 retailers which may not represent the population and the
sampling technique used is convenience sampling, which has its own limitations.
(c) There may be errors due to biasness of the respondents.
(d) Lack of co-operation and support on the part of retailers may have been a major
constraint.
(e) The study was conducted in Nadia district, so the result of the study may not be
applicable to other areas.
Reference:
Thurstone, L. L. (1927b) “The method of paired comparisons for social values”, Journal
of Abnormal and Social Psychology, 21, 384-400.
Nargundkar, R. (2008), Marketing Research, Tata McGraw-Hill Education Pvt. Ltd.,
New Delhi, 3rd
Edn.
Bharti Airtel adds 2m mobile users in April'. The Times of India. 25 May 2012. Retrieved
25 May 2012.
http://www.airtel.in/QuarterlyResult/Quarterly_report_Q3_11 - accessed on 12/06/2012
http://www.apho2012india.org/about-the-hosts/about-india/communication - accessed on
14/06/2012