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Introduction
Indian mobile phone industry has witnessed a
remarkable growth, in the recent years. Cheap mobile
handsets, availability of services at affordable rates, low
initial cost and reasonable recurring charges have motivated
several million people to become the customers of leading
service providers. Let it be business people, professionals,
employed, housewives, senior citizens, youth population
including the students, or any other category, everyone
considers cell-phone to be an integral part of their day to day
life.
In this context, it would be necessary and
interesting too, to analyze whether the mobile service
providers are successful in managing and retaining the
overwhelming customer strength, what are the strategies
they adopt to retain the existing customers and to attract the
new customers, and how do their CRM strategies work out.
Key words: CRM, Customer-in-absentia, Attitude,
Grievance-handling, Accessibility, Alpha-testing,
Customer relationship management (CRM)
CRM is a widely-implemented strategy for
managing a company’s interactions with customers, clients
and sales prospects. It involves using technology to
organize, automate, and synchronize business processes—
principally sales activities, but also those for marketing,
customer service, and technical support.
The relationship between customers and the
company authorities is peculiar in the mobile service
industry. In case of prepaid customers, the company people
and the clients do not mostly meet each other. But, still the
CRM concept exists in this relationship too. The efforts of
the cell-phone service providers to retain and attract these
customers-in-absentia are interesting. This paper attempts to
study the CRM measures adopted by the leading GSM
service providers like Aircel, Airtel and BSNL. It also
attempts to analyze the attitude of the customers towards the
CRM measures.
Objectives of the study
1. To analyze various CRM measures adopted by the
leading GSM service providers.
2. To analyze the attitude of the customers towards
the CRM measures.
Methodology
This is an empirical study which is based on the
fresh/first-hand data collected from the customers of the
GSM service providers, at Pollachi.
Sampling technique
A combination of ‘Quota sampling’ and ‘purposive
sampling’ was adopted for the purpose of data collection.
The population was divided into the strata like ‘College
Students’, ‘employed’ and ‘others’. A tool, designed and
standardized for the purpose of this study was used for data
collection.
Sampling unit and sample size
The sampling unit is consisting of two colleges in
Pollachi and the general public. The sample size is limited to
120 respondents.
Conceptual framework
The organizations can motivate and retain the
existing customers by various retention strategies and
selling techniques. One of those strategies is Customer
Relationship Management. The attitude of the customer is
formed, based on the experience of the customer that he has
had from various aspects of the company’s operations. The
attitude may be positive or negative. The direction and the
degree of attitude of the customers towards the CRM are
normally determined by the quality of CRM measures
adopted by the companies. Here, the researcher has taken
some variables to measures the CRM and also the attitude of
the customers.
Variables of the study
Two sets of variables ‘independent’ and ‘dependent’ are
taken up for the purpose of this study. The following
diagram shows the variables in detail.
Customer Attitude towards the CRM-Measures of
Cell-phone service providers (GSM)
– An empirical study at Pollachi Dr. C.K. Kotravel Bharathi Professor of Management & Principal,
Sri Kandhan College of Arts and Science, ERODE – 638 008 TAMILNADU
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Tools of Analysis
The following statistical tools have been used for
analyzing the data collected for the purpose of this study.
1. Cronbach’s Alpha testing (Test for internal-consistency
and reliability of data)
2. Correlation analysis
Implications of the Study
This study has brought out some interesting facts
about the CRM concept that is practiced by the GSM service
providers. Though the study has been carried on only in
Pollachi town, some of the findings can be generalized to
other areas also. This study would also give greater scope
for the similar research studies in the same line in future.
Cronbach's Alpha Testing
Cronbach's alpha will generally increase when the
correlations between the items increase. For this reason the
coefficient is also called the internal consistency or the
internal consistency reliability of the test. Cronbach's alpha
measures how well a set of items (or variables) measures a
single uni-dimensional latent construct. When data have a
multidimensional structure, Cronbach's alpha will usually be
low. Technically speaking, Cronbach's alpha is not a
statistical test - it is a coefficient of reliability (or
consistency). Cronbach's alpha can be written as a function
of the number of test items and the average inter-correlation
among the items.
α can take values between negative infinity and 1
(although only positive values make sense). Some
professionals, as a rule of thumb, require a reliability of 0.70
or higher (obtained on a substantial sample) before they will
use an instrument.
Case Processing Summary
N %
Cases Valid 120 100
Excluded 0 0
Total 120 100
Reliability Statistics
Cronbach's Alpha N of Items
0.7 14
As shown above, 14 variables leading to CRM
were taken for the Cronbach’s Alpha Testing. If the
Cronbach’s alpha value is 0.7 and above, it will indicate a
good amount of internal consistency and reliability of data.
Here, the alpha is exactly showing the highly positive and
encouraging value of “0.7”. It shows that the internal
consistency and reliability of data are good. If some of the
items are reduced, the alpha value may further go higher.
Results and Discussion
The cross-tabulation of Gender and Age shows that
the larger sector of the respondents is formed by the male
below 25 years of age (33.33%). Refer Table-1
It is to be noted that 76 respondents out of 120 are
degree holders. Thus it is assumed that majority of the
respondents are capable of well understanding the requisites
for a better CRM and they have given most appropriate
response. Refer Table 2
It was earlier decided to have quota sampling
consisting of three strata viz. the college students (50%),
employed (25%) and others (25%) which should have been
leading to collect data from 60, 30 and 30 respondents
respectively. But, during the field work, the researcher
could collect data only from 59 and 27 respondents
respectively from the first two categories. So, the response
was collected from 34 members of the third category. Refer
Table 3
As the researcher wanted to test whether there was
any impact of level of income on the perception of the
respondents regarding CRM measures, it was included in
the list of back-ground variables. Exactly two-third of the
respondents are in the ‘below 10000’ income category.
Refer Table 4
The longevity of the use of mobile phones by the
respondents was also considered to be a significant factor in
deciding the satisfaction over the CRM measure. To test
this aspect, it was added as one of the variables. More than
60 percent of the users have been using mobiles for more
than 3 years. This period is adequate to understand and rate
the CRM measures taken-up for the study. Refer Table 5
There are three leading GSM operators considered
for the study. Two-third of the respondents taken up for this
study, use AIRCEL. 25 percent of respondents use
AIRTEL, whereas BSNL mobile network is used by 10 out
of 120 respondents. Refer Table 6
The longevity of the use of network by the
respondents was also considered to be a significant factor in
deciding the satisfaction over the CRM measure. To test
this aspect, it was added as one of the variables. More than
60 percent of the users have been using mobiles for more
than 3 years. This period is adequate to understand and rate
the CRM measures of the network. Refer Table 7
The researcher had a hypothetical question whether
the type of network connection would have an impact on the
perception of CRM measures. So, it was added in the list of
Independent Variables
(CRM components)
Schemes
Rationality in tariff
Offers
Response to complaints
Grievance handling
Value-added services
Updating services
Reachability
Accessibility
Technology
Dependent Variable
Attitude towards CRM
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variables. It is interesting to note that 90.8 percent of the
respondents use pre-paid connections. If more number of
business men had been taken for the study, the percentage of
the post-paid connection would have been suppose
increased. Refer Table 8
Frequency of recharging is depending upon the
important factors like usage, tariff, etc. As the researcher
wanted to test its relationship with the company’s interest in
the customer relationship, this was added to the variables-
list. The table shows that nearly half of the respondents
recharge very frequently whenever required. Refer Table 9
The Table 10 shows that the network serves the
purpose for which it was chosen. This is endorsed by 98.4%
of the respondents.
Consistency in charging tariff is one of the
significant factors which help the networks to retain their
customers. 71.7 % of the respondents are happy with the
consistency in tariff. But 20.8 % of the respondents are not
happy with this. 20.8 % is considerably a big amount of
disagreement which the networks are to be really concerned
about. Refer Table 11
‘Offers’ that are availed from the network by a
customer after he has got the connection is one of the factors
that influence him in deciding whether to stick with the
same network or to switch over. Thus offers to certain
extent retain the customers and enable the network to
maintain a good relation with them. Here, as per the data,
89.2 % of the respondents are satisfied with the offers
provided by their network. Refer Table 12
As well as the validity and attractiveness of the
offers, how effectively they are communicated is very much
important. It shows to what extent the network is interested
to maintain the relationship with the customers and retain
them. Here, as per the data, 85% of the respondents are
happy with the way the offers are communicated. Refer
Table 13
In maintaining good relations with the customers,
the role of customer care officials is vital. For ensuring the
effective contribution of these officials, an effective
mechanism for having a prompt access to them is must. So,
it has been added as a significant variable in this study.
Exactly 70% of the respondents endorse that they are able to
have access to the customer-care officials when ever needed.
Refer Table 14
Queries from the customers, on various aspects of
the network services at any time are quite common in the
telecom industry. The satisfaction of the customers in this
aspect mainly depends on whether the queries are answered
in time. Nearly 65% of the respondents are satisfied in this
regard. But, 18.3 % of the respondents are not happy with
this. 18.3 % is considerably a big amount of disagreement
which the networks are to be really concerned about. Refer
Table 15
In the industries like telecommunications where
several millions of customers and hundreds of schemes are
involved, the issues and complaints relating to various
aspects are common. They are inevitable too, to certain
extent. But the question here is whether the companies have
made proper arrangements for the filing of such complaints.
Here, 78.3% of the respondents agree that there are adequate
provisions for the filing of complaints in time. Refer Table
16
Immediately after the complaint-filing mechanism,
the next question that arises in our minds is whether there is
a well evolved mechanism to sort and resolve the
complaints. According to 73.4% of the respondents, there
are adequate mechanisms evolved and maintained by the
networks to resolve the complaints in time. Refer Table 17
In spite of having the mechanisms for filing the
complaints and resolving the same, the networks may fail to
gain the confidence of the customers, if those complaints are
not resolved in time. This part of the issue is mainly
depending upon two things – number one: provision in the
system to monitor and ensure the complaint-resolution in
time and number two: the work-force that deals with the
process. 74.2% of the respondents endorse the view that the
complaints are resolved in time, where as another 15% do
not agree with this. Refer Table 18
Unlike other factors, the number of people having
negative attitude towards the networks are more (56.7%) in
case of “suffering from grievances”. Only 30.8% of the
respondents have denied the room for sufferings. This
shows that grievances are common phenomena in the
telecommunications irrespective of the colourful offers,
advanced technologies or other factors. But it is a serious
threat to the CRM efforts of the companies. So, it is
inevitable for the networks to systematically diagnose the
causes of grievances and resolve them suitably. Refer Table
19
Another important factor that may help the
networks to retain the customers and to improve the
relations with them is the value added services provided to
the customers. These services differ from network to
network. 80% of the respondents agree that the value
added services provided by the networks make them feel
comfortable and pleasant. Refer Table 20
Updating of services according to the change in the
trend and technology is an essential need and challenge for
the networks today. Any service-provider that fails to
update the technology and services lose the customers and
the market. But 80% of the respondents of this study say
that networks what they have chosen update the service.
This perception may be generalized to the entire population
too. Refer Table 21
One of the most significant factors that influence
the satisfaction and retention of the customers is the
coverage of the network. Here, 80% of the respondents are
satisfied with the coverage. But 14% of the dissatisfaction
should also be taken into consideration. Refer Table 22
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Table 23 shows that 85% of the respondents are
satisfied with the overall services of their respective
network. It is assumed that this includes the satisfaction
over the CRM efforts of the network also.
CORRELATION ANALYSIS
In statistics, correlation indicates the strength and
direction of a linear relationship between two random
variables. That is in contrast with the usage of the term in
colloquial speech, which denotes any relationship, not
necessarily linear. In general statistical usage, correlation or
co-relation refers to the departure of two random variables
from independence. Here, the researcher has applied
correlation technique to test the association among some
selected variables.
The variable “offers satisfying the telecom needs”
is positively correlating with another variable
“communication of offers” (.311**
). This correlation
indicates that offers communicated in time serve the purpose
and satisfy the customers. It also correlates with “answering
queries in time” (.475**
). It indicates that the queries
relating to various offers are answered in time and thus the
customers are enabled to avail the offers in time. It further
correlates with “satisfaction with coverage” (.343**
). It
shows that the perfect coverage of network is essential for
availing and enjoying any offer and it happens in case of the
three networks taken up for this study. Refer Table 24
The variable “satisfaction with coverage”
positively correlates with another variable “queries
answered in time” (.345**
). It shows that better coverage
enables the answering of queries in time. It also correlates
with the “communication of offers” (.488**
). It indicates that
the prompt communication of offer needs better coverage
and this happens in case of all the three networks taken up
for this study.
There is a positive correlation between the
“updating of network services” and “satisfaction with
overall services” (.337**
). It shows that the updating of
services leads to avail a greater amount of benefits and
results in the overall satisfaction with the network. Refer
Table 25
The variable “access to the customer care officials”
is positively correlating with another variable “resolving
complaints in time” (.237**
). This correlation indicates that
the customers are able to access the customer care officials
in time, lodge the complaints and getting it resolved. It also
correlates with “answering queries in time” (.556**
). It
indicates that the customers are enabled to access to the
customer care officials in time and thus the queries relating
to various offers and issues are answered in time. It further
correlates with “satisfaction with overall services” (.363**
).
It shows that the perfect contribution and service of the
customer care officials are one among the important reasons
for the overall satisfaction with the network. Refer Table 26
In Table 27 correlation establishes that the
“network-coverage” is one among the few significant factors
that lead to the “overall all satisfaction” of the customers
with the network.
HYPOTHESIS TESTING
The following hypotheses were also formulated for
the purpose of the study. One way ANOVA was used to
test the hypothesis.
1. Ho = There is no significant relationship between
“Network chosen” and
“Access to customer care officials”.
2. Ho = There is no significant relationship between the
“Network chosen” and
“Satisfaction with the coverage”.
3. Ho = There is no significant relationship between the
“Network chosen” and
“Satisfaction with overall services”.
The Table-28 shows that the calculated value of the
F-test in each case is higher than the table value of F. The
level of significance in each case also is below 0.05. So, all
the three null-hypotheses-1, 2 & 3 are rejected. So there is a
significant relationship between the variables shown in each
of the hypotheses taken up for the study.
4. Ho = There is no significant relationship between the
“Income-level” and
“Satisfaction with overall services”.
Refer Table 29. The table value for the degree of
freedom ‘3’ at 95% confidence level is 3.95 and the
calculated value is 1.881. As the table value is higher than
the calculated value, the null-hypothesis-4 is accepted. So,
there is no significant relationship between the “Income-
level” and “Satisfaction with overall services”.
Conclusion
From all the above analysis, the researcher
concludes that, in general, the customers have a positive
attitude towards various ‘Customer Relationship
Management’ measures practiced by the GSM service-
providers. Except a very-few respondents, almost all feel
that the purpose for which they chose the particular network,
has been served. Some of the areas like “satisfying the
customers through offers”, “communicating the offers
promptly”, “providing value added services”, “updating of
services”, etc., are found well executed. These measures are
really helpful to the efforts made in retaining the customers
and in enhancing the customer-relationship.
But still lot of improvement is needed in some
other areas like “grievance handling”, “consistency in
charging tariff”, “access to customer care officials”,
“resolving the complaints in time”, etc. Further, a little bit
improvement is required in some areas like “answering
queries in time”, “provision for filing complaints”,
“mechanism to resolve complaints”, etc.
Scope for further study
The mobile-using population has been rapidly
increasing from time to time. This will further increase in
www.theinternationaljournal.org > RJCBS: Volume: 01, Number: 06, April-2012 Page 33
the years to come. The future researchers may study the
impact of CRM measures on the retention and expanding of
the market exclusively for every individual GSM service
provider. Similar studies can be conducted with reference to
the service-providers who use CDMA-Technology also.
Some of the disagreements on the part of the customers like
“suffering from grievances” that have been found as an
outcome of this study may be further tested by the
researchers in their future studies.
References
Jahangir Karimi, Toni M. Somers and Yash P.
Gupta, “Impact of Information Technology
Management Practices on Customer Service”,
Journal of Management Information Systems, Vol.
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Eugene W. Anderson, “Customer Satisfaction and
Price Tolerance”, Marketing Letters, Vol. 7, No. 3
(Jul., 1996), pp. 265-274
Nicholas C. Romano, Jr. and Jerry Fjermestad,
“Electronic Commerce Customer Relationship
Management: An Assessment of Research”,
International Journal of Electronic Commerce,
Vol. 6, No. 2 (Winter, 2001/2002), pp. 61-113
Sunil Mithas, M. S. Krishnan and Claes Fornell,
“Why Do Customer Relationship Management
Applications Affect Customer Satisfaction?”, The
Journal of Marketing, Vol. 69, No. 4 (Oct., 2005),
pp. 201-209
M. Tolga Akçura and Kannan Srinivasan,
“Research Note: Customer Intimacy and Cross-
Selling Strategy”, Management Science, Vol. 51,
No. 6 (Jun., 2005), pp. 1007-1012
John R. Hauser, Duncan I. Simester and Birger
Wernerfelt, “Customer Satisfaction Incentives”,
Marketing Science, Vol. 13, No. 4 (Autumn, 1994),
pp. 327-350
Frederick Hong-Kit Yim, Rolph E. Anderson and
Srinivasan Swaminathan, “Customer Relationship
Management: Its Dimensions and Effect on
Customer Outcomes”, The Journal of Personal
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Table-1
Gender * Age Cross-tabulation
Age Total
Below 25 25 to 35 35 to 45 45 to 60
Gender Male Count 40 5 4 34 83
Expected Count 45.6 4.2 9.7 23.5 83.0
Female Count 26 1 10 0 37
Expected Count 20.4 1.8 4.3 10.5 37.0
Total Count 66 6 14 34 120
Expected Count 66.0 6.0 14.0 34.0 120.0
Source: Primary Data
Table-2
Educational Qualification
Frequency Percent Valid Percent Cumulative Percent
Valid Below SSLC 32 26.7 26.7 26.7
SSLC 2 1.7 1.7 28.3
PUC/H.Sc./Diploma 10 8.3 8.3 36.7
Degree 58 48.3 48.3 85.0
PG 2 1.7 1.7 86.7
Professional Degree 16 13.3 13.3 100.0
Total 120 100.0 100.0
Source: Primary Data
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Table-3
Present Status
Frequency Percent Valid Percent Cumulative Percent
Valid Government Employee 4 3.3 3.3 3.3
Private Employee 23 19.2 19.2 22.5
Business 20 16.7 16.7 39.2
College Student 59 49.2 49.2 88.3
Retired Govt employee 2 1.7 1.7 90.0
House-Wife 12 10.0 10.0 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-4
Monthly Income
Frequency Percent Valid Percent Cumulative Percent
Valid Below 10000 80 66.7 66.7 66.7
10000 to 15000 27 22.5 22.5 89.2
15000 to 20000 4 3.3 3.3 92.5
30000 to 50000 9 7.5 7.5 100.0
Total 120 100.0 100.0 Source: Primary Data
Source: Primary Data
Table-5
Longevity of using mobile phone
Frequency Percent Valid Percent Cumulative Percent
Valid 1 to 3 years 47 39.2 39.2 39.2
3 to 5 years 34 28.3 28.3 67.5
More than 5 years 39 32.5 32.5 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-6
Net work used
Frequency Percent Valid Percent Cumulative Percent
Valid AIRCEL 80 66.7 66.7 66.7
AIRTEL 30 25.0 25.0 91.7
BSNL 10 8.3 8.3 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-7
Longevity of using network
Frequency Percent Valid Percent Cumulative Percent
Valid 1 to 3 years 47 39.2 39.2 39.2
3 to 5 years 56 46.7 46.7 85.8
More than 5 years 17 14.2 14.2 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-8
Type of connection
Frequency Percent Valid Percent Cumulative Percent
Valid Pre-paid 109 90.8 90.8 90.8
Post-paid 11 9.2 9.2 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-9
Frequency of recharging
Frequency Percent Valid Percent Cumulative Percent
Valid Twice in a month 29 24.2 24.2 24.2
Once in a month 29 24.2 24.2 48.3
Once in three months 4 3.3 3.3 51.7
Very frequently 58 48.3 48.3 100.0
Total 120 100.0 100.0
Source: Primary Data
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Table-10
Purpose is served
Frequency Percent Valid Percent Cumulative Percent
Valid Disagree 2 1.7 1.7 1.7
Agree 59 49.2 49.2 50.8
Strongly Agree 59 49.2 49.2 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-11
Consistency in charging tariff
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly Disagree 6 5.0 5.0 5.0
Disagree 19 15.8 15.8 20.8
Neither Agree Nor Disagree 9 7.5 7.5 28.3
Agree 62 51.7 51.7 80.0
Strongly Agree 24 20.0 20.0 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-12
Offers satisfy the telecom needs
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly Disagree 2 1.7 1.7 1.7
Disagree 1 .8 .8 2.5
Neither Agree Nor Disagree 10 8.3 8.3 10.8
Agree 74 61.7 61.7 72.5
Strongly Agree 33 27.5 27.5 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-13
Offers are properly communicated
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly Disagree 2 1.7 1.7 1.7
Disagree 5 4.2 4.2 5.8
Neither Agree Nor Disagree 11 9.2 9.2 15.0
Agree 41 34.2 34.2 49.2
Strongly Agree 61 50.8 50.8 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-14
Access to Customer care officials
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly Disagree 3 2.5 2.5 2.5
Disagree 11 9.2 9.2 11.7
Neither Agree Nor Disagree 22 18.3 18.3 30.0
Agree 49 40.8 40.8 70.8
Strongly Agree 35 29.2 29.2 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-15
Queries are answered in time
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly Disagree 3 2.5 2.5 2.5
Disagree 19 15.8 15.8 18.3
Neither Agree Nor Disagree 21 17.5 17.5 35.8
Agree 50 41.7 41.7 77.5
Strongly Agree 27 22.5 22.5 100.0
Total 120 100.0 100.0
Source: Primary Data
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Table-16
Provision to file complaints
Frequency Percent Valid Percent Cumulative Percent
Valid Disagree 11 9.2 9.2 9.2
Neither Agree Nor Disagree 15 12.5 12.5 21.7
Agree 45 37.5 37.5 59.2
Strongly Agree 49 40.8 40.8 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-17
Mechanism to resolve complaints
Frequency Percent Valid Percent Cumulative Percent
Valid Disagree 13 10.8 10.8 10.8
Neither Agree Nor Disagree 19 15.8 15.8 26.7
Agree 53 44.2 44.2 70.8
Strongly Agree 35 29.2 29.2 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-18
Complaints are resolved in time
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly Disagree 3 2.5 2.5 2.5
Disagree 15 12.5 12.5 15.0
Neither Agree Nor Disagree 13 10.8 10.8 25.8
Agree 60 50.0 50.0 75.8
Strongly Agree 29 24.2 24.2 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-19
Suffering from grievances
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly Disagree 4 3.3 3.3 3.3
Disagree 33 27.5 27.5 30.8
Neither Agree Nor Disagree 15 12.5 12.5 43.3
Agree 44 36.7 36.7 80.0
Strongly Agree 24 20.0 20.0 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-20
Network provides value added services
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly Disagree 2 1.7 1.7 1.7
Disagree 5 4.2 4.2 5.8
Neither Agree Nor Disagree 17 14.2 14.2 20.0
Agree 59 49.2 49.2 69.2
Strongly Agree 37 30.8 30.8 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-21
Network updates services
Frequency Percent Valid Percent Cumulative Percent
Valid Disagree 10 8.3 8.3 8.3
Neither Agree Nor Disagree 14 11.7 11.7 20.0
Agree 54 45.0 45.0 65.0
Strongly Agree 42 35.0 35.0 100.0
Total 120 100.0 100.0
Source: Primary Data
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Table-22
Satisfied with the coverage
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly Disagree 7 5.8 5.8 5.8
Disagree 10 8.3 8.3 14.2
Neither Agree Nor Disagree 7 5.8 5.8 20.0
Agree 49 40.8 40.8 60.8
Strongly Agree 47 39.2 39.2 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-23
Satisfied with the overall services
Frequency Percent Valid Percent Cumulative Percent
Valid Strongly Disagree 5 4.2 4.2 4.2
Disagree 9 7.5 7.5 11.7
Neither Agree Nor Disagree 4 3.3 3.3 15.0
Agree 57 47.5 47.5 62.5
Strongly Agree 45 37.5 37.5 100.0
Total 120 100.0 100.0
Source: Primary Data
Table-24
Correlations
Offers satisfy the
telecom needs
Offers are properly
communicated
Queries are
answered in
time
Satisfied with
the coverage
Offers satisfy
the telecom
needs
Pearson Correlation 1 .311**
.475**
.343**
Sig. (2-tailed) .001 .000 .000
N 120 120 120 120
Satisfied with
the coverage
Pearson Correlation .343**
.488**
.345**
1
Sig. (2-tailed) .000 .000 .000
N 120 120 120 120
**. Correlation is significant at the 0.01 level (2-tailed).
Table-25
Satisfied with overall services Network updates services
Satisfied with the
overall services
Pearson Correlation 1 .337**
Sig. (2-tailed) .000
N 120 120
Network updates
services
Pearson Correlation .337**
1
Sig. (2-tailed) .000
N 120 120
Table-26
Correlations
Satisfied with the
overall services
Access to
Customer care
officials
Complaints are
resolved in
time
Queries are
answered in
time
Access to
Customer care
officials
Pearson Correlation .363**
1 .237**
.556**
Sig. (2-tailed) .000 .009 .000
N 120 120 120 120
Complaints are
resolved in time
Pearson Correlation .177 .237**
1 .269**
Sig. (2-tailed) .053 .009 .003
N 120 120 120 120
Queries are
answered in time
Pearson Correlation .343**
.556**
.269**
1
Sig. (2-tailed) .000 .000 .003
N 120 120 120 120
**. Correlation is significant at the 0.01 level (2-tailed).
www.theinternationaljournal.org > RJCBS: Volume: 01, Number: 06, April-2012 Page 38
Table-27
Correlations
Satisfied with coverage Satisfied with the overall services
Satisfied with the
coverage
Pearson Correlation 1 .702**
Sig. (2-tailed) .000
N 120 120
Satisfied with the
overall services
Pearson Correlation .702**
1
Sig. (2-tailed) .000
N 120 120
**. Correlation is significant at the 0.01 level (2-tailed).
Table-28
ANOVA
Sum of Squares df Mean Square F Sig.
Satisfied with the overall services Between Groups 10.450 2 5.225 5.136 .007
Within Groups 119.017 117 1.017
Total 129.467 119
Satisfied with the coverage Between Groups 11.275 2 5.638 4.527 .013
Within Groups 145.717 117 1.245
Total 156.992 119
Access to Customer care officials Between Groups 7.046 2 3.523 3.486 .034
Within Groups 118.254 117 1.011
Total 125.300 119
Table-29
ANOVA
Satisfied with the overall services
Sum of Squares df Mean Square F Sig.
Between Groups 6.005 3 2.002 1.881 .137
Within Groups 123.462 116 1.064
Total 129.467 119
***
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