CHAPTER VI ANALYSIS OF MEASUREMENT OF...
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CHAPTER VI
ANALYSIS OF MEASUREMENT OF SERVICE QUALITY AND
SATISFACTION
6.1 INTRODUCTION
This chapter presents the analysis of data pertaining to the measurement of
service quality among the customers, to meet the various objectives and hypothesis
set for the study. Cross-tabulation of responses towards service quality perception
between firms is presented first. Influence of service quality dimensions on
satisfaction and gap between perception and expectation on service quality are
analysed. Further, a comparison of satisfaction levels among the firms, a SWOT
analysis of the firms under study as well as analysis of major problems faced by
customers are made. This meets the objectives two to six of the study.
The major objectives for this study are to find the influence of service quality
dimensions on satisfaction, perception expectation gap analysis and comparison
between the firms with respect to SWOT and satisfaction levels. To achieve these,
data analysis on the service quality dimensions to establish the various objectives are
presented here. Relation between various service quality dimensions and its
influence on Perception and Satisfaction of customers is analysed. Influence of
demographic factors of customers on service quality dimensions is also analysed. The
level of satisfaction among various firms is compared. A SWOT analysis of the firms
under study is carried out from the customer perception. . Consumer behaviour
towards the purchase of telecom service is analysed from their response to
behavioural and opinion questions. Various problems faced by the customers is also
analysed.
6.2 ANALYSIS OF SERVICE QUALITY PERCEPTION BETWEEN FIRMS
In this study, customer perception is measured using twenty- two attribute
scales under five service dimensions of Tangibility, Reliability, Assurance,
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Responsiveness and Empathy. Customer satisfaction is considered as an antecedent to
Service quality. Based on the perception measurement, Influence of service
dimensions is analysed using a regression model, correlation between service quality
dimensions is analysed using Karl Pearson correlation matrix, difference in
Perception towards the service dimensions between genders is analysed, influence of
demographic variables on service quality perception as well as satisfaction is
analysed, perception- expectation gap for the five service dimensions is analysed,
relative importance or ranking given by customers for the five service dimensions is
analysed, overall satisfaction is compared with an assumed value of 4, and satisfaction
level between the four firms are compared.
TABLE 6.1
RESPONDENTS’ PERCEPTION TOWARDS TANGIBILITY ATTRIBUTES
(HD- Highly Disagree, D- Disagree, N- No opinion, A- Agree, HA- Highly agree)
Source: Computed data
From Table 6.1, it can be seen that majority of respondents have neutral or
positive perception towards tangibility attributes. 14% of the respondents are Idea
customers with neutral perception and 10% are Idea customers with positive
Name ofFirm
HD D N A HA Total
BSNL 6
(1.38%)
11
(2.53%)
27
(6.21%)
22
(5.06%)
4
(0.92%)
70
(16.09%)
IDEA 20
(4.60%)
35
(8.05%)
59
(13.56%)
42
(9.66%)
11
(2.53%)
167
(38.39%)
VODAFONE 7
(1.61%)
16
(3.68%)
42
(9.66%)
34
(7.82%)
16
(6.68%)
115
(26.44%)
AIRTEL 6
(1.38%)
15
(3.45%)
29
(6.67%)
23
(5.29%)
10
(2.30%)
83
(19.08%)
Total 39
(8.97%)
77
(17.70%)
157
(36.09%)
121
(27.82%)
41
(9.43%)
435
(100%)
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perception. Further it can be seen that another 10% respondents are Vodafone
customers with neutral perception. For BSNL, 1% has very high perception towards
service quality, whereas 1.4% has very low perception. Overall, 37% of customers
show positive perception towards service quality and 27% are showing low
perception. 36% are neutral in their opinion. For the quality dimension Tangibility,
the attributes considered here are: a) Mobile company uses modern technology and
offers latest features, b) Physical facilities like Towers, offices, dealers etc are
visually appealing, c) their materials, showroom displays etc are good and staff are
very pleasing and neat in appearance.
TABLE 6.2
RESPONDENTS’ PERCEPTION TOWARDS RELIABILITY ATTRIBUTES
Source: Computed data.
From table 6.2 it is seen that respondents have high or very high perception
towards Reliability attributes. 19% of Idea customers are having very high perception
about reliability. In the case of Vodafone and Airtel, it is 115 and 95 respectively.
1.3% of Airtel customers and 1.6% of Vodafone customers show very low perception
towards reliability. Roughly 60% of all respondents have higher perception towards
reliability attribute. For the service quality dimension Reliability, the attributes
Name ofFirm
HD D N A HA Total
BSNL 6(1.38%)
13(2.99%)
7(1.61%)
11(2.53%)
33(7.59%)
70(16.09%)
IDEA 20(4.60%)
21(4.83%)
13(2.99%)
29(6.67%)
84(19.31%)
167(38.39%)
VODAFONE 7(1.61%)
29(6.67%)
10(2.30%)
23(5.29%)
46(10.57%)
115(26.44%)
AIRTEL 6(1.38%)
17(3.91%)
8(1.84%)
13(2.99%)
39(8.97%)
83(19.08%)
Total 39(8.97%)
80(18.39%)
38(8.74%)
76(17.47%)
202(46.44%)
435(100%)
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considered are: a) whether they always deliver what they promise to you, b) they
always provide the right service at the first time itself, c) they always maintain error-
free records of transactions, d) the company deals promptly with customer
complaints, and e) they deliver services at the time promised.
TABLE. 6.3
RESPONDENTS’ PERCEPTION TOWARDS RESPONSIVENESSATTRIBUTES
Source: computed data
From table 6.3, it is seen that 10% of respondents are Idea customers with high
perception towards responsiveness. 9% of respondents are Idea customers with very
low perception towards responsiveness. 8% of respondents are Vodafone customers
with high perception towards responsiveness. 1.8% of respondents are BSNL
customers with very low perception towards responsiveness and 2.5% of Airtel
customers also have very low perception towards responsiveness. The service quality
attributes considered are: a) whether they always inform when the service will be
Name ofFirm
HD D N A HA Total
BSNL 8
(1.84%)
15
(3.45%)
16
(3.68%)
17
(3.91%)
14
(3.22%)
70
(16.09%)
IDEA 38
(8.74%)
28
(6.44%)
30
(6.90%)
43
(9.89%)
28
(6.44%)
167
(38.39%)
VODAFONE 20
(4.60%)
17
(3.91%)
22
(5.06%)
35
(8.05%)
21
(4.83%)
115
(26.44%)
AIRTEL 11
(2.53%)
19
(4.37%)
13
(2.99%)
24
(5.52%)
16
(3.68%)
83
(19.08%)
Total 77
(17.70%)
79
(18.16%)
81
(18.62%)
119
(27.36%)
79
(18.16%)
435
(100%)
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provided, b) they provide prompt service, c) their staff are willing to help customers,
and d) whether they are prompt in responding to customer requests.
TABLE .6.4
RESPONDENTS PERCEPTION ABOUT ASSURANCE ATTRIBUTES
Source: computed data
From table 6.4, it is seen that 14% of respondents are Idea customers who are
neutral with respect to assurance attribute and 8% of respondents are Airtel customers
with neutral opinion towards assurance. 8% of respondents are Idea customers with
very high perception towards assurance perception. It is also seen that 1.6% of BSNL
customers have very high perception towards assurance. 1.8% of respondents are
Airtel customers with low assurance perception. The attributes considered here are: a)
if their staff are very polite in behavior, b) their staff gives confidence to customers,
c)mif you feel secure and safe to deal with the company, and d) their staff are
knowledgeable to answer your questions.
Name of
FirmHD D N A HA Total
BSNL 10
(2.30%)
8
(1.84%)
28
(6.44%)
17
(3.91%)
7
(1.61%)
70
(16.09%)
IDEA 31
(7.13%)
15
(3.45%)
60
(13.79%)
26
(5.98%)
35
(8.05%)
167
(38.39%)
VODAFONE 23
(5.29%)
10
(2.30%)
36
(8.28%)
22
(5.06%)
24
(5.52%)
115
(26.44%)
AIRTEL 16
(3.68%)
8
(1.84%)
33
(7.59%)
13
(2.99%)
13
(2.99%)
83
(19.08%)
Total 80
(18.39%)
41
(9.43%)
157
(36.09%)
78
(17.93%)
79
(18.16%)
435
(100%)
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TABLE. 6.5
RESPONDENTS’ PERCEPTION TOWARDS EMPATHY ATTRIBUTES
Source: Computed data
From table 6.5 it is seen that 12% of respondents are idea customers with very
high perception towards empathy attribute, 11.5% of Idea customers are neutral in
their opinion towards empathy perception. 10% of respondents are Vodafone
customers with very high perception of empathy. 1% of respondents are Airtel
customers with high perception towards empathy dimension and 1.6% of respondents
are BSNL customers having low perception towards empathy dimension. The
empathy attributes considered are: a) whether customers get individual attention
when you contact the company, b) if they understand and know your details and
needs, c) if they always have your best interest in mind, d) they take care and spend
time to solve your problems and, e) the company offers convenient operating hours.
Name of
FirmHD D N A HA Total
BSNL 14
(3.32%)
7
(1.61%)
15
(3.45%)
8
(1.84%)
26
(5.98%)
70
(16.09%)
IDEA 35
(8.05%)
13
(2.99%)
50
(11.49%)
15
(3.45%)
54
(12.41%)
167
(38.39%)
VODAFONE 17
(53.91%)
10
(2.30%)
32
(7.36%)
12
(2.76%)
44
(10.11%)
115
(26.44%)
AIRTEL 14
(3.22%)
8
(1.84%)
19
(4.37%)
5
(1.15%)
37
(8.51%)
83
(19.08%)
Total 80
(18.39%)
38
(9.43%)
116
(26.67%)
40
(9.20%)
161
(37.01%)
435
(100%)
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6.3 ANALYSIS OF SERVICE QUALITY PERCEPTION AND EXPECTATIONGAPS
This study tries to measure customer satisfaction as an antecedent of the gap
between customer perception and expectation on various service- related attributes
according to the SERVQUAL model. In order to answer the second research question
as to which service attributes the customers are satisfied and which they are not
satisfied and the third research question as to which service dimensions customers
accord more priority, the difference between customer perception and expectation is
measured for various service attributes. Hence the second objective of this study is
met through this analysis.
The difference between the two is an indicator of service quality gap or Perception
– Expectation. If service quality perception is less than expectation, this will give a
negative value. If company has exceeded the expectation for any attribute, this will
return a positive value. Analysis for each attribute of service quality is done below
and suitable strategies suggested to improve the customer perception about the
attributes.
i) Analysis of Tangibility Dimension
Tangibility in service refers to the physical aspects of service which the
customer can see, touch, feel, smell, taste etc. For mobile phone service these include
modern equipments, offices of company and dealers, appearance of staff, other
promotion materials etc. The customer forms a quality perception about the company
based on these physical aspects. From this analysis it is seen that neat appearance of
staff and physical facilities are the more negative factors influencing the perception.
The tangible factors and its influence on customers have undergone dramatic
changes due to introduction of pre-paid connection, distribution of recharge coupons
through common retail outlets like provision stores, convenience shops etc. The
customer does not get a chance to experience the company offices, staff and
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equipments as the pre-paid card distribution is completely outsourced to channel
members. For the customer, the tangibles may mean the tangibles of the outlet from
which he purchases the recharge coupon, which is far inferior to that of the company.
Sharing of physical facilities like towers and equipments among the different service
firms has added another dimension to it. Those who are using roaming may get
exposed to the roaming partner company’s tangibles which may be good or bad
compared to the basic service company. This is yet another dimension to the problem
of assessing impact of tangibles clearly.TABLE 6.6
PERCEPTION- EXPECTATION GAP FOR TANGIBILITY ATTRIBUTES
TANGIBILITYATTRIBUTES
Perceptionscore
(Mean)
Std.deviation
Expectationscore(Mean)
Std.Deviation
ServiceQuality(P-E)
Modernequipment
2.30 0. 45 4.46 0.86 -2.16
Physical facilities 2.01 0.48 4.31 0.91 -2.30
Visuallyappealingmaterials
2.62 0.64 4.46 0.78 -1.84
Neat appearanceof staff
2.01 0.42 4.53 0.98 -2.52
Source: Computed data
As seen from the Table 6.6, out of the Tangibility attributes, the Perception-
Expectation gap is 2.52 for neat appearance of staff which shows dissatisfaction.
Physical facilities are next with a gap of 2.30. Modern equipment and materials are
showing lesser gaps of 2.16 and 1.84 showing relatively better perception towards
these attributes.
An analysis of the individual tangibility attributes is appropriate now to
formulate individual strategies.
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a) Perception regarding use of modern equipments
In India, the latest GSM technologies are used by the service providers. For
this they have to use modern equipments. The customer perceives quality of service
delivery as a result of these modern equipments. As indicated in Table 6.18, the mean
score of customer perception about use of modern equipment is 2.30 which shows a
lesser satisfaction. This is influenced by the visibility of towers, equipments, modern
office rooms etc of the service provider. However, due to technology changes many
mobile service providers are sharing towers and exchange equipments.
b) Perception towards Physical facilities
In marketing of services, physical evidence is an important factor. Since
quality of service cannot be explained or demonstrated as in the case of a product,
customers form their perception about the service by looking at the physical evidences
like office ambience, equipments used, behaviour of staff, cleanliness of offices etc.
In the case of mobile phone services, as most of the customers are using pre-paid
facility, they do not get opportunity to see the company office or staff normally. Their
main contact point with service is dealers of the service provider. So it is possible that
the physical evidence they see are the dealers properties. Based on this they form
perception about physical facilities of the service provider.
c) Perception on visually appealing materials
Customers form their opinion about service quality based on different
materials about the firm which reaches the customers as part of communication and
promotion strategies. These can be printed materials like brochures, pamphlets, direct
mailers, packaging, store interiors, posters, danglers, demonstration units, Point of
Purchase materials, towers, advertisements, hoardings, electronic displays, SIM cards
etc. These are tangible attributes which convey a quality impression to the consumer
about the service.
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d) Perception on Neat appearance of staff
Mobile phone service sim cards and recharge coupons have become a low
involvement product today. Majority of customers are using pre-paid connection and
they frequently visit the dealer or retailer points for this purchase. So practically when
staff is referred, it is the dearler and retailer staff who interact with the customer. Very
few customers need to directly interact with company staff. Generally customers
interact with dealer staff only and percieve them as the company staff or
representative. This is an issue in many service industries today, especially those who
use out- sourcing like call centres.
ii) Analysis of Reliability Dimension
This dimension shows one of the best performances by the service providers
as the mean values are less negative. Perception about a) companies deliver what they
promised, and b) company is prompt in dealing with customer complaints are
relatively better. For maintaining error- free records and deliver service at right time
first, especially for new features, needs more attention.
Due to vast improvement in technology in mobile phone services, the
customer is able to enjoy the latest features; very soon they become available in the
world. Countries like India which started mobile phone services relatively late, one
blessing in disguise was that companies started with GSM and digital technologies
from the beginning itself. Also they kept updating to international standards.
Moreover mergers with leading global companies provided access for less known
local companies to get latest technologies and business practices. Introduction of dual
SIM, number portability, 2G, 3G etc has happened in India quickly. Because of this,
the companies are able to promise and deliver new features, technical as well as
commercial to the customers.
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TABLE 6.7
PERCEPTION- EXPECTATION GAP FOR RELIABILITY
ATTRIBUTES
Sl
No
RELIABILITY
ATTRIBUTES
Perception
score
(mean)
Std
Deviation
Expectation
score
(mean)
Std
Deviation
Service
quality(P-
E)
1 Deliver what is
promised
2.80 0.68 4.26 1.02 -1.46
2 Right service
at first time
2.44 0.72 4.35 0.98 -1.91
3 Error-free
records
2.75 0.54 4.47 0.98 -1.72
4 Prompt dealing
of customers
2.74 0.65 4.28 0.88 -1.54
5 Services at
time promised
2.66 0.72 4.32 1.12 -1.66
Source: Computed data.
As seen from the above table, service quality gap 1.46 is least for the attribute,
deliver what is promised. Prompt dealing of customer is next with a gap of 1.54.
Services at promised time and error free records are next with service quality gaps of
1.66 and 1.72. It is inferred that companies are able to deliver their promises in this
high technology field and satisfy customers. Perception towards keeping error- free
records are less and to be improved.
An analysis of individual Reliability attributes is presented here;
a) Whether firms deliver what is promised
Due to heavy competition in the field, firms have to match the offers made
by other firms. This makes it necessary to frequently announce new features,
schemes, tariffs and other conveniences to attract the customers. Announcing new
offers without gearing up for delivery of that offer or convenience can land the firm
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in trouble with the customers as well as legal tangles. Many a time offers are made
without proper follow-up on implementation and this leads to customer
dissatisfaction. Trust towards the brand is a performance indicator in markets today
and this trust is built by delivering what is promised. Mobile services is a high
technology and information- oriented industry. New applications and features are
introduced through information technology capabilities which are highly reliable and
quick. Hence firms are able to promise the new features and deliver them in practice.
Customers are satisfied about this and a mean score of 2.8 indicates this. However
there is scope for improvement on this attribute.
b) Perception on Right service at first time
In service delivery, a failure is percieved by a customer in a different way
compared to a product delivery. The service recovery time is an important factor
here. The customer may not complain or carry the bad experience and impression in
their mind if the service recovery is effected quickly before the customer starts
worrying too much about the failure. Today due to advancement in technology, many
of the hardware have redundancy which can make instant replacement of failed
resources in a system even before customer notices and feels bad about a failure. Even
if there is a noticeable failure, the customer will accept a suitable compensation from
the firm towards the damages suffered which will make him forget the bad experience
soon.
c) Perception towards error- free records
In the early stages of introduction, majority of customers were using post-
paid connections where call charges are paid against a monthly bill from the service
provider. Since detailed bills also required additional payment from customers, many
of them did not opt for detailed bills. At that time, it was common to have
clarifications and arguments between the form and customers over various types of
billing errors. Customer satisfaction was greatly affected by such clarifications and
arguments over the billing amount. Computer billing was available but still many of
the billing procedure was not clear to the consumers and hence such arguments arose.
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Hence it was very essential that the firm kept error- free records of call details so that
any customer query related to billing could be given immediately and satisfy the
customer.
d) Prompt dealing of customers
The customer is the king today and he expects prompt dealing at all contact
points with the firm. Hence customer care is an important function, especially for a
service firm. Customer Relations Management ( CRM) tools and softwares are used
by all mobile service firms to enhance customer handling level. Increased
consumerism level and legal rights granted by various customer- oriented laws make
it mandatory to provide information, educate the customers, redressal of complaints
promptly and protection from hazards. The mean score of 2.74 shows above average
satisfaction on this attribute. Obviously, the firm has to improve complaints handling.
This can be done through conducting customer satisfaction surveys, meetings or
adalaths for redressal of pending complaints, complaint- free week etc. They should
act proactively by giving the right information at the right time so that many
complaints can be solved even before they actually crop up.
e) Services at time promised
Waiting time or interruptions in delivery of service may irritate the customers
and cause dissatisfaction. The company may promise billing on 25th day, SIM
activation in 24 hours, customer care 24x7 etc which has to be strictly adhered to. The
dealers may make promises regarding online recharge time, scheme availability
duration etc. An attribute score of 2.66 shows just above average and hence there is
scope for further improvement here by both the company and the dealers.
iii) Analysis of Responsiveness Dimension
Analysis shows relatively better perception on the responsiveness attributes.
But there is scope for improvement in all attributes like willingness to help customers,
prompt response to customers, timely service etc.
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Availability of excellent technology, learning curve and customer education is
helping firms to listen to customers better and be prompt in service. Experienced,
loyal employees are with companies who take customer service seriously.
TABLE 6.8
PERCEPTION- EXPECTATION GAP FOR RESPONSIVENESS
ATTRIBUTES
RESPONSIV-
ENESS
Perceptio
n score
P(mean)
Std
Devia
tion
Expectation
score
E(mean)
Std
Deviation
Service
quality-P-
E(mean)
Inform when
service provided2.60 0.54 4.20 0.97 -1.60
Prompt service 2.94 0.61 4.30 1.04 -1.36
Willing to help 2.61 0.60 4.22 1.01 -1.61
Prompt response to
customers2.66
0.564.23 0.96 -1.57
Source: Computed data.
From the above table, service quality gap is the least, (1.36) for the attribute
‘Prompt service’. It is 1.61 and 1.60 for “willingness to help” and “providing
information about service” which are relatively high. Prompt response to customers
also shows a gap of 1.57.
An analysis of individual Responsiveness attributes is presented here;
a) Inform when service provided
Regular information on all services and any change in schedule is expected
by customers, especially since technologies like sms, internet make communication
instant and cheap. So, firms should take special care to inform about change in
schedules, operating time of various services, discontinuation of any service etc.
Other customers may waste time and energy without having proper information from
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the service provider. Attribute score of 2.6 shows low satisfaction and the firm has to
improve and streamline communication with the consumers.
b) Perception towards Prompt service
In any service industry, the customer expects the firm to deliver prompt
service always. Breakdowns, outages, faults etc. create a negative perception. The
customer expects prompt dealing at all contact points with the firm. Hence customer
care is an important function, especially for a service firm. Customer Relations
Management ( CRM) tools and softwares are used by all mobile service firms to
enhance customer handling level. Increased consumerism level and legal rights
granted by various customer-oriented laws make it mandatory to provide information,
educate the customers, redressal of complaints promptly and protection from hazards.
The mean score of 2.74 shows above average satisfaction on this attribute. Obviously
the firm has to improve complaints handling.
c) Willingness of firm to help
Modern marketing emphasise on customer focus and customer retention as
the key to continued profits. Customers expect certain amount of hand holding from
the company, especially in service industries. They want their problems to be
patiently heard, and solved promptly. The legal frame- work for consumer protection
also mandates certain rights to the consumer with respect to getting information,
choice of products, protection from hazardous products, redressal on time etc.
The customer expects the firm to help through service personnel and
information from company side. If help is delayed or disinterested, it will create a
negative perception about service quality in the minds of customers. The way of
talking to customers, going out of the way to help and sincereity shown to help, all
these matters to the customers. A perception score of 2.61 shows just average
satisfaction on this attribute. The firms can improve this by training the personnel
handling customer care, use of relevant CRM technology and prompt communication
with the customers.
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d) Prompt Response to customers
The customers expect prompt response regarding technical information,
billing clarification, availability of service etc from the firm. Modern marketing
emphasises on customer focus and customer retention as the key to continued profits.
The customers expect certain amount of hand holding from the company, especially
in service industries. They want their problems to be patiently heard, and solved
promptly. The legal frame- work for consumer protection also mandates certain rights
to the consumer with respect to getting information, choice of products, protection
from hazardous products, redressal on time, etc.
iv) Analysis of Assurance Dimension
Confidence given by staff (company staff or dealer staff) is low. Knowledge
level of staff is also not up to the mark. Politeness of staff and safety to deal with
company are relatively better. These are important observations as many customers
are using mobile phones for bill payments, money transfer etc. It is difficult to expect
dealers staff to be knowledgeable on latest features and technologies as they are not
specialists in mobile phone service market.
The results show that companies fail to instill confidence in the minds of
customers. Recent corruption scams, financial misappropriations reported from major
players and political news have eroded the confidence of customers. All companies
are involved in such scams and customers have no choice. They are even afraid which
company will sustain in the long run. Serious public relation efforts are required from
companies to dispel such apprehensions from the customers’ mind. Since the financial
risk for dealing with a company is very small, the customers still continue to patronise
them.
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TABLE 6.9
PERCEPTION- EXPECTATION GAP FOR ASSURANCE ATTRIBUTES
ASSURANCEATTRIBUTES
Perceptionscore
P(mean)
Standard
DeviationExpectation
scoreE(mean)
Standard
Deviation
Servicequality-
P-E(mean)
Polite staff 2.72 0.45 4.30 0.96 -1.58
Givesconfidence 2.66 0.52 4.48 1.23 -1.82
Feel safe todeal 3.12 0.67 4.43 1.02 -1.31
Staff areknowledgeable 2.63 0.45 4.36 0.98 -1.73
Source: Computed data
As seen from table 6.21, from the various assurance attributes, “company
gives confidence” shows the highest perception- expectation gap of 1.85. It is 1.73 for
the attribute “staff are knowledge” and 1.58 for “politeness of staff”. The gap is least
for the attribute “feel safe to deal with the company”.
Analysis of Individual Assurance attributes are presented here.
a) Perception towards polite staff
Attribute score of 2.72 shows there is scope for improvement in this area. The
company has to train the staff in exhibiting good manners in telephone or personal
meetings. People are the key element in the 7 Ps of service marketing. The concept
of inseparability of service delivery and customers presence highlights the need for
good, pleasing, skilled people to provide service and satisfy customers. Mobile
service user profile is quite diverse in demographics and it is a real challenge to
behave appropriately to all segments and leave a good impression. The customers
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expects thecompany personnel to be polite and courteous in their behaviour. They will
be more open and confident to share information with such polite staff.
b) Perception on staff giving confidence
Attribute score of 2.66 is low and this has to be addressed by proper training to
staff and retention strategies for trained staff. Technically competent and
behaviourally motivated staff can handle customers with confidence. This confidence
will be imparted to customers as well. When new features or plans are introduced,
many customers need an asuurance from the company executives to go ahead. They
look for right advice and empathy from the company executives. When there is
frequent change of staff and they take time to clarify doubts or provide information,
the customers lose confidence.
c) Perception towards customers’ confidence in dealing with the company
A relatively good score of 3.12 indicates the customer confidence in dealing
with the company. It may be using good technology and software with security to
protect the interests of the customer. Because of the technological advancements and
interconnecting features, the customers are using mobile phone service to deal
payments, bank transactions etc. This has created a security issue in the minds of
many customers. They may not feel confident to key in bank account numbers,
passwords, customer identification details etc in to the phone as they suspect some
chance of interfering and misusing by unauthorized sources. This is one reason why
electronic commerce is not picking up in this country. Many cyber frauds are being
reported daily which erodes the customer confidence further.
d) Perception on knowledge level of staff
A low score of 2.63 shows there is scope for improvement on this aspect. This
is a factor which can be influenced by the earlier discussed attribute of giving
confidence. Mobile service is a technology-based business and the personnel handling
the service are expected to be experts to advise the customers on any related matter.
143
Giving incomplete or wrong information will reduce the confidence of the customers.
Training the customer care personnel on technological aspects, retaining these trained
staff and proper communication with the customer will help. The dealer staff also
have to be trained to further boost the image.
v) Analysis of Empathy Dimension
Scores on Attributes like “personal attention to customers”, “keeping their best
interests in mind” and “spending time to solve customer problem” are not at all good.
Improvement is required on working hours and knowing individual details while
talking to customers as well. The customer should feel he is special in treatment and
the company has time to spare for him. Working hours may not be an issue now as
most contacts are through phone or mail and most companies offer 24x7 service
through toll numbers or BPOs.
Customers, especially the new ones will expect some amount of hand holding
from the company in the initial stages. Otherwise they will feel neglected. Moreover,
if the customers need some assistance, they expect the company to patiently take
some time to solve their problems. Quality perception regarding convenient timings
and understanding customer details are better because most of the firms now have
24x7 hours operation and they also use extensive CRM softwares to capture customer
data and use it while interacting with customers.
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TABLE 6.10
PERCEPTION- EXPECTATION GAP FOR EMPATHY ATTRIBUTES
EMPATHYATTRIBUTES
Perceptionscore(mean)
Standard
deviationExpectationscore(mean)
Standard
deviationP-E
Individualattention
2.14 0.45 4.38 0.89 -2.24
Understand yourdetails
2.47 0.56 4.44 1.03 -1.97
Your best interestin mind
2.40 0.55 4.45 1.10 -2.05
Spend time tosolve problems
2.25 0.54 4.48 1.21 -2.23
Convenientoperating hours
2.60 0.63 4.37 0.98 -1.77
Source: Computed data
From the results of table 6.22, the perception- expectation gap is the highest
for the attribute “ Individual attention”. Next is 2.23 for the attribute “company
spends time to solve customer problems”. “Company keeps the customer’s best
interest in mind” shows a gap of 2.05. This is followed by “understanding customer
details” and “convenient working hours” with perception- expectation gaps of 1.97
and 1.77 respectively.
6.3.1 Overall analysis of scores for service Quality attributes, irrespective of
company
Table 6.11 shows summary of the Perception-Expectation scores all
dimensions. The Perception-Expectation gap is negative for all the 22 attributes.
Mean score for expectation is 4.12 which means the customer expectation from the
companies are high, whereas the perception about service received has a mean value
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of 2.55 which is below average. The service quality delivery gap is 1.5 on a 5- point
scale, which is considerably large. Maximum value for perception is 3.1 and
minimum value for expectation is 4.2. This means customer satisfaction is low on all
attributes. It shows a similar analysis Dimension-wise also.
TABLE 6.11
Summary Of The Perception- Expectation Scores for All DimensionsData analysis I- mean Scores of Perception(P), Expectation(E) and
Service Quality (SQ=P-E) for 22 Features of Service Quality/Customer
Satisfaction
Sl
NoFeature
Percepti
on score
Expectation
score
Service
Quality(P-
E)
TANGIBILITY ATTRIBUTES
1 Modern equipment 2.30 4.46 -2.16
2 Physical facilities 2.01 4.31 -2.30
3 Visually appealing materials 2.62 4.46 -1.84
4 Neat appearance of staff 2.01 4.53 -2.52
RELIABILITY ATTRIBUTES
5 Deliver what is promised 2.80 4.26 -1.46
6 Right service at first time 2.44 4.35 -1.91
7 Error- free records 2.75 4.47 -1.72
8 Prompt dealing of customers 2.74 4.28 -1.54
9 Services at time promised 2.66 4.32 -1.66
RESPONSIVENESS
10 Inform when service provided 2.60 4.20 -1.60
11 Prompt service 2.94 4.30 -1.36
12 Willing to help 2.61 4.22 -1.61
13 Prompt response to customers 2.66 4.23 -1.57
146
Sl
NoFeature
Percepti
on score
Expectation
score
Service
Quality(P-
E)
ASSURANCE ATTRIBUTES
14 Polite staff 2.72 4.30 -1.58
15 Gives confidence 2.66 4.48 -1.82
16 Feel safe to deal 3.12 4.43 -1.31
17 Staff are knowledgeable 2.63 4.36 -1.73
EMPATHY ATTRIBUTES
18 Individual attention 2.14 4.38 -2.24
19 Understand your details 2.47 4.44 -1.97
20 Your best interest in mind 2.40 4.45 -2.05
21 Spend time to solve problems 2.25 4.48 -2.23
22 Convenient operating hours 2.60 4.37 -1.77
AVERAGE 2.55 4.37 -1.82
MAXIMUM 3.12 4.53 -1.31
MINIMUM 2.01 4.2 -2.52
STD DEV 0.31 0.10 0.35
Source: Computed data.
Maximum negative values are for Physical facilities, appearance of staff,
individual attention, spending time to solve customer problems. Relatively more
positive attributes are, prompt service, deliver service as promised, customer feels
safe to deal with company etc. The customers are not interested in physical facilities
etc but they want to be treated well and the company should deliver quality as
promised. Any complaints should be attended promptly. However, there is scope for
improvement in all 22 attributes of service by adopting suitable strategies.
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6.4 INFLUENCE OF THE FIVE SERVICE DIMENSIONS ON
SATISFACTION
In this analysis, Customer satisfaction is considered as the dependent variable
and the set of dependent variables include the five service quality dimensions
Tangibility, Reliability, Responsiveness, Assurance, and Empathy. To answer research
objective, the relationship between Customer satisfaction, the dependent variable and
service quality dimensions, the independent variables is studied using a regression
equation.
Table 6.12 shows the output from SPSS on computer for the Regression
analysis. The beta values or coefficients indicate the influence level of various
independent variables on the dependent variable Customer satisfaction. It also indicates
to what extent the change in the dependent variables can be attributed to the changes in
the independent variables.
TABLE 6.12
INFLUENCE OF SERVICE DIMENSIONS ON SATISFACTION
ServiceQualityDimensions
β Std. error Standardised β t significance
Constant 0.482 0.320 1.507 0.133
Tangibility 0.035 0.101 0.024 0.346 0.730
Reliability 0.519 0.089 0.376 5.866 0.000
Responsiveness 0.103 0.089 0.074 1.154 0.000
Assurance 0.398 0.062 0.365 6.394 0.000
Empathy -0.147 0.055 -.131 -2.673 0.008
Source: Computed data
Note: level of significance 5%.
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From the results of table 6.12, the regression coefficients for reliability, assurance
and responsiveness are relatively high. Reliability dimension has the highest influence
indicated by the coefficient of 0.519. Assurance dimension is next with a coefficient
of 0.398. This indicates the higher influence of these factors on the overall service
perception. Coefficient for tangibility is low at 0.035 which shows a much lesser
influence. The positive coefficient for the service quality dimensions of tangibility,
reliability, responsiveness and assurance suggests that high service quality leads to
higher level of customer satisfaction. However, the empathy dimensions had negative
coefficient (-0.147), meaning decreasing level of customer satisfaction with high
empathy element of service quality. In other words, higher empathy results in
decreasing client loyalty.
Dependent variable: Customer satisfaction
R2 = 0.486
Adjusted R2 = 0.478
Interpretation
Additionally, among the variables, only reliability, responsiveness and
assurance have significant values and thus, significantly contributed to the
explanation of the dependent variable. Overall, this study concludes that mobile
network providers’ service quality elements affects customer satisfaction by
explaining 48 percent of the variance in the Dependent Variable (customer
satisfaction) which is quite respectable.
The regression equation for the relation between dependent variable and
independent variables under study can be approximated as,
Customer satisfaction = 0.482 + 0.519 x reliability + 0.398 x assurance +
0.103 x Responsiveness + 0.035 x Tangibility – 0.147 x Empathy + ε ( an unknown
value to explain the influence of other exogenous variables.)
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The equation suggests that the firms should focus on the factors Reliability,
Assurance and Responsiveness, which have more influence on the satisfaction by
maintaining the offers and services to the customers. For other factors where the
influence is low, they need not focus as much as the other dimensions. Incongruence
on these factors may not pose a big threat for over- all customer perception. 48% of
the variance in satisfaction is explained by the above regression equation. Hence there
is the presence of many exogenous variables which contribute to the unexplained
portion of the variance. This gives opportunity for further study identifying these
variables and their influence.
6.5 CORRELATION BETWEEN SERVICE QUALITY DIMENSIONS
To study the inter- relation between the service quality dimensions of
Tangibility, Reliability, Responsiveness, Assurance, and Empathy, correlation
between them are calculated. A Karl Pearson correlation test is conducted using SPSS
which gave the following output as per Table 6.2.
TABLE 6.13
CORRELATION BETWEEN SERVICE QUALITY DIMENSIONS
Tangibility Reliability Responsiveness Assurance Empathy
Tangibility 1 .804 .769 .798 .871
.000** .000** .000** .000**
Reliability .804 1 .659 .899 .837
.000** .000** .000** .000**
Responsiveness .769 .659 1 .619 .829
.000** .000** .000** .000**
Assurance .798 .899 .619 1 .840
.000** .000** .000** .000**
Empathy .871 .837 .829 .840 1
.000** .000** .000** .000**
Source: Computed data ** Correlation is significant at 0.01 level ( 2-tailed)
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As seen from Table 6.13, the correlation coefficients between all the
dimensions are positive and closer to 1 which shows a high degree of correlation
between the variables. Correlation between Tangibility factors and Empathy is 0.871
which is very high. Similarly correlation between Reliability dimension and
Assurance dimension is 0.899 which is also very high. However, correlation between
Responsiveness and Assurance dimensions is relatively low at 0.619 followed by
correlation between Responsiveness and Reliability at 0.659. Tangibility and
Empathy dimensions have high correlation with all other factors. Further, all these
correlations are seen significant at 1% confidence level as indicated in the table.
Now it is pertinent to analyse the equality of means of gender groups with
respect to the five service quality dimensions, namely Tangibility, Reliability,
Responsiveness, Assurance, and Empathy.
6.6 GENDER AND PERCEPTION TOWARDS SERVICE QUALITY
DIMENSIONS
A set of five Hypotheses are set to test the significance of difference in means among
gender groups with respect to the five service quality dimensions. To test these
hypotheses, independent sample t test is conducted in SPSS using corresponding
“perception score’ as the test variable and “gender” as the grouping variable. The
results are as follows:
i) Difference in perception about Tangibility dimension between genders
The hypothesis to test the significance of mean scores of perception with
respect to gender is set as,
H1: The difference in perception about tangibility dimension with respect to gender is
not significant.
Independent sample t test using SPSS is done to compare mean scores of
tangibility between genders which gave the following outputs.
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TABLE.6.14
PERCEPTION OF MEN AND WOMEN TOWARDS TANGIBILITY
Source: Computed data
From table 6.14, mean score of perception towards tangibility for male group
is 3.383 and that for female group is 3.353. The independent sample t test statistics
will further confirm if the difference in these means is significant or not. Test
statistics show Levene’s test significance value of 0.274 which is higher than 0.05.
Hence the test values of top row ( equal variance assumed) are to be considered in this
case. The t value in top row is 0.446 and corresponding significance value is 0.656.
This is greater than 0.05 and hence it can be inferred that the difference in means is
not significant. In other words, there is no significant difference in perception of men
and women towards Tangibility dimension. Men and women customers are
perceiving tangibility aspects like offices, displays, product packages etc in the same
way.
The Hypothesis H1 is thus accepted.
ii) Difference in perception about Reliability dimension between genders
The hypothesis to test the significance of mean scores of perception with
respect to gender is set as,
Gender N mean Std.
deviation
t value significance
Male 253 3.383 0.676 .446 0.656
Female 182 3.353 0.734
Total 435
152
H2: The difference in mean scores of perception about reliability dimension with
respect to gender is not significant.
Independent sample ‘t’ test using SPSS gave the following outputs.
TABLE.6.15
Perception of Men and Women towards Reliability
Gender N mean Std.
deviation
t value significance
Male 253 3.134 0.6691.293 0.197
Female 182 3.049 0.692
Total 435
Source: Computed data
From the above table, mean score of perception towards Reliability for male
group is 3.134 and that for female group is 3.049. The independent sample t test
statistics will further confirm if the difference in these means is significant or not.
Test statistics show Levene’s test significance value of 0.644 which is higher
than 0.05. Hence the test values of top row ( equal variance assumed) are to be
considered in this case. The t value in top row is 1.293 and corresponding significance
value is 0.197. This is greater than 0.05 and hence it can be inferred that the
difference in means is not significant. In other words, there is no significant difference
in perception of men and women towards Reliability dimension. Male and female
customers are perceiving reliability aspects like delivering promises, error-free
records etc in a similar fashion.
The Hypothesis H2 is thus accepted.
153
iii) Difference in perception about Responsiveness dimension between genders.
The hypothesis to test the significance of mean scores of perception with
respect to gender is set as,
H3: The difference in mean scores of perception about Responsiveness
dimension with respect to gender is not significant.
Independent sample ‘t’ test using SPSS gave the following outputs.
TABLE.6.16
PERCEPTION OF MEN AND WOMEN TOWARDS RESPONSIVENESS
Gender N mean Std.
deviation
t value significance
Male 253 3.404 0.6691.880 0.061
Female 182 3.221 0.692
Total 435
Source: Computed data.
From the above table, mean score of perception towards Responsiveness for
male group is 3.134 and that for female group is 3.049. The independent sample t test
statistics will further confirm if the difference in these means is significant or not. The
test output is given below.
Test statistics show Levene’s test significance value of 0.241 which is higher
than 0.05. Hence the test values of top row ( equal variance assumed) are to be
considered in this case. The t value in top row is 1.880 and corresponding significance
value is 0.061. This is greater than 0.05 and hence it can be inferred that the
difference in means is not significant. In other words, there is no significant difference
154
in perception of male and females towards Responsiveness dimension. Men and
women customers are perceiving responsiveness aspects like informing about service,
staff willing to help etc in identical terms.
The Hypothesis H3 is thus accepted.
iv) Difference in perception about Assurance dimension between genders
The hypothesis to test the significance of mean scores of perception with
respect to gender is set as,
H4: The difference in mean scores of perception about assurance dimension
with respect to gender is not significant.
Independent sample ‘t’ test using SPSS gave the following outputs.
TABLE 6.17
PERCEPTION OF MEN AND WOMEN TOWARDS ASSURANCE
Gender N meanStd.
deviationt value significance
Male 253 3.369 0.669.827 0.409
Female 182 3.302 0.692
Total 435
Source: Computed data
From the above table, mean score of perception towards Assurance for male
group is 3.369 and that for female group is 3.302. The independent sample t test
statistics will further confirm if the difference in these means is significant or not. The
test output is given below.
155
Test statistics show Levene’s test significance value of 0.316 which is higher
than 0.05. Hence the test values of top row ( equal variance assumed) are to be
considered in this case. The t value in top row is 0.827 and corresponding significance
value is 0.409. This is greater than 0.05 and hence it can be inferred that the
difference in means is not significant. In other words, there is no significant difference
in perception of men and women towards Assurance dimension. Male and female
customers are perceiving Assurance aspects like polite behaviour, safe to deal etc in
the same way.
The Hypothesis H4 is thus accepted.
v) Difference in perception about Empathy dimension between genders
The hypothesis to test the significance of mean scores of perception with
respect to gender is set as,
H5: The difference in mean scores of perception about empathy dimension
with respect to gender is not significant.
Independent sample t test using SPSS gave the following outputs.
TABLE 6.18
PERCEPTION OF MEN AND WOMENTOWARDS EMPATHY
Gender N meanStd.
deviationt value significance
Male 253 3.430 0.632 1.658 0.098
Female 182 3.330 0.647
Total 435
Source: Computed data
156
From the above table, mean score of perception towards Assurance for male
group is 3.430 and that for female group is 3.330. The independent sample t test
statistics will further confirm if the difference in these means is significant or not. The
test output is given below.
Test statistics show Levene’s test significance value of 0.841which is higher
than 0.05. Hence the test values of top row ( equal variance assumed) are to be
considered in this case. The t value in top row is 1.658 and corresponding significance
value is 0.098. This is greater than 0.05 and hence it can be inferred that the
difference in means is not significant. In other words, there is no significant difference
in perception of men and women towards Empathy dimension. Male and female
customers are perceiving Empathy aspects like personal attention, best interest in
mind, convenient working hours etc in a similar fashion.
The Hypothesis H5 is thus accepted.
TABLE 6.19
SUMMARY OF‘t’ TEST BETWEEN GENDER AND SERVICE DIMENSIONS
Service Dimension Levene’s test
F value
t d.f Significance (2tailed)
Tangibles 1.198 .446 433 0.656
Reliability .614 1.293 433 0.197
Responsiveness 1.378 1.880 433 0.061
Assurance 1.006 0.827 433 0.409
Empathy 0.040 1.658 433 0.098
Source: Computed data
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From Table 6.19 it is seen that the significance values of all service
dimensions are above the critical level 0.05 and it can be inferred that the difference
in means between gender for all the service dimensions are not significant. In other
words men and women perceive the service quality dimensions in the same manner.
6.7 INFLUENCE OF DEMOGRAPHIC VARIABLES ON SERVICE
PERCEPTION AND SATISFACTION
It is now pertinent to look in to the influence of Demographic characteristics
of respondents on service quality as well as customer satisfaction.
H6: Demographic characteristics of the respondents in the study area affect
service quality perception.
H7: Demographic characteristics of customers influence their satisfaction with
the mobile phone service.
For testing this hypothesis, a multiple regression model is proposed as
follows-
= + + + + + + ++ + + + +∈where,
SQ - service quality, GEN – Gender, AGE – Age
INC – Income, OCCU – Occupation, DUR – Duration
EDU - Education level, TRAVEL - Travel pattern
HANDSET -Handset type, USAGE -Usage rate
LOC- Location , COMPANY is the name of network provider
a0 and ε are constants.
For testing the relation between demographic variables and Satisfaction, the
regression model is,
158
= + + + + + + ++ + + + +∈where,
CS- Customer satisfaction, GEN – Gender, AGE – Age
INC – Income, OCCU – Occupation, DUR – Duration
EDU - Education level, TRAVEL - Travel pattern
HANDSET -Handset type, USAGE -Usage rate
LOC- Location , COMPANY is the name of network provider.
a0 and ε are constants.
Regression analysis is performed with all 11 demographic characteristics as
the independent variables and service quality perception as the dependent variable. It
leads to the following analysis.
TABLE 6.20
INFLUENCE OF DEMOGRAPHIC VARIABLES ON SERVICE QUALITY
Demographic
Variable
Constant R R2 Standardised
Beta coefficients
t value
USAGE 3.312 0.182 0.033 0.143 2.89*
DUR 0.132 2.64*
Source: Computed data
Note: * significant at 1% level.
a. As seen from table 6.20, only two demographic variables – Usage in years
(USAGE) and Duration of use ( DUR) are found to have influence on service
quality perception.
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b. The R value 0.182 shows a weak but positive correlation between the
influencing variables and service quality perception.
c. The beta values found from Table 6.20 are 0.143 and 0.132 for Income and
Duration of use respectively. They indicate the relative influence on the
service quality.
It is inferred from the results that out of 11 demographic variables, only two
have significant effect over the service quality perception when the effect of all eleven
variables are considered simultaneously. It shows that when all the 11 variables enter
the regression equation, 9 out of 11 lose their predictive validity. Only two variables,
Usage and Duration of use, have influence on service quality perception.
The above result indicates that to improve the service quality perception, the
mobile service firms should segment the customers based on Usage and Duration of
use and allocate their resources proportionately among the marketing programmes
designed for various desired groups.
The Hypothesis H6 is thus accepted.
Let us now look in to the influence of demographic variables on satisfaction.
H7: Demographic characteristics of customers influence their satisfaction with
the mobile phone service.
The regression model for this is proposed as follows:
CS = ao + b1 GEN + b2 AGE + b3 INC + b4 OCCU + b5 DUR + b6 EDU + b7
TRAVEL + b8 HANDSET + b9 USAGE + b10 LOC + b11 COMPANY + e
Regression analysis with satisfaction as Dependent variable, and the 11
demographic variables as independent variables gave the following results.
160
TABLE 6.21
INFLUENCE OF DEMOGRAPHIC VARIABLES ON SATISFACTION
Demographic
Variable
Constant R R2 Standardised
Beta coefficients
t value
USAGE 3.376 0.202 0.041 0.163 3.29*
DUR 0.151 2.24*
Source: Computed data
Note: * significant at 1% level.
a. As seen from table 6.21, only two demographic variables – USAGE and
Duration of use ( DUR) are found to have influence on satisfaction.
b. The R value 0.202 shows a weak but positive correlation between the
influencing variables and satisfaction.
c. The beta values found from Table 6.21 are 0.163 and 0.151 for Usage and
Duration of use respectively. They indicate the relative influence on
satisfaction.
It is observed from the results that out of 11 demographic variables, only two
have significant effect over satisfaction when the effect of all eleven variables is
considered simultaneously. It shows that when all the 11 variables enter the regression
equation, 9 out of 11 lose their predictive validity. Only two variables, Income and
Duration of use, have influence on satisfaction.
It is implied that if the service providers in the study area want to generate
favourable satisfaction feeling among the customers, they have to manage the two
variables which show greater influence. Suitable measures to satisfy these groups and
monitoring them will result in improved satisfaction.
The Hypothesis H7 is therefore accepted.
161
It is now justified to look into the differences that exist among the various
customer groups within each of the demographic characteristics.
H8: Service quality perceptions are different for different customer groups
along with the various demographic characteristics.
By comparing the means of service quality perception of customers with the
demographic characteristics, namely Gender, Age, Income, Occupation, Duration,
Education, Travel habits, Handset type, Usage, Location, and Company name , the F
values generated is found significant only for 3 variables, i.e. Duration of relationship,
Handset type, and Company brand name. This indicates that various customer groups
in only these three variables have varying influence on the service quality perceptions
of Mobile phone services in Kerala.
The analysis of comparison means of the variable “ Duration of relationship”
using ANOVA test generated the F value of 2.640 at significance level of 5%. The
results are as follows.
TABLE 6.22
COMPARISON OF SERVICE QUALITY ACROSS
“DURATION OF RELATIONSHIP”
Duration of relationship Mean values for Service quality
Less than 2 years 3.85
2-3 years 3.72
3-4 years 3.54
4-5 years 3.56
More than 5 years 3.58
Source: Computed data
162
The report as per table 6.22 shows that the customers who are associated with
the mobile service firms for a period of less than 2 years have the highest service
quality perception followed by those who are associated for 2year to 3 years, 3 to 4
years, 4 to 5years and the lowest is for customers who are associated for more than 5
years.
The results of the analysis indicate that relatively new customers have more
positive opinion about the service quality of mobile services. This may be due to the
good and attractive physical facilities and other tangibles which create good
impression for new customers.
The analysis of comparison means of the variable “Handset type” using
ANOVA test generated the F value of 2.230 at significance level of 10%. The results
are as follows:
TABLE 6.23
COMPARISON OF SERVICE QUALITY ACROSS “ HANDSET TYPE”
Source: Computed data
As per the report in table 6.23, the customers using higher- featured handset
types are having more positive perception, with a mean value of 3.72. Followed by
those who use basic model handsets whose mean is 3.65 and perception is lowest for
those who use top- end handsets with 2G/3G facility (mean of 3.54). Due to the
lifestyle factors and travel habits, many customers find the high features very
attractive and useful. Their perception increases when they are able to use such
Handset type Mean values for Perception
Basic Model 3.65
Higher features 3.72
Top-end features 3.54
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features. However, top end services like 2G/3G etc are recently introduced and
popular applications for these features are not developed. This is the reason many
customers find the top models underutilised and hence their perception is low.
It is important for the firms to provide all needed features to customers in the
three segments and manage their perception well.
The analysis of comparison means of the variable “ Company brand” using ANOVA
test generated the F value of 2.410 at significance level of 5%. The results are as
follows:
TABLE 6.24
COMPARISON OF SERVICE QUALITY ACROSS “ COMPANY BRAND”
Company brand Mean values forPerception
Rank order(1 ishighest)
BSNL 3.95 1
IDEA 3.72 2
VODAFONE 3.54 3
AIRTEL 3.48 4
Source: Computed data.
From table 6.24 it is evident that the firms differ in customer perceived service
quality. The firm BSNL has the highest rank among the four firms under study with a
mean score of 3.95, followed by the firm IDEA and then the firm VODAFONE. The
firm AIRTEL with a mean perception score of 3.48 is the lowest among the four.
These perception scores are the combined rating of all the service dimensions
and attributes used in this study. Hence each firm should try to work on improving the
scores on each of the service dimensions, namely Tangibility, Reliability,
Responsiveness, Assurance, and Empathy. The customers along the different
demographic segments differ in their perceptions towards service quality. New
customers will have better perception on quality based on tangibility attributes,
164
whereas experienced customers may base their perception more on additional features
and cost savings. All these characteristics of various segments are to be considered
while efforts are taken to improve service quality perception of customers.
The Hypothesis H8 is thus accepted.
6.8 PRIORITISING OF SERVQUAL DIMENSIONS
The customers were asked to distribute a total of 100 points on the five service
quality dimensions, i.e. Tangibles, Reliability, Responsiveness, Assurance, and
Empathy. The attributes studied under each dimension are given below.
Tangibles- Modern technologies, Latest features, Physical facilities( towers,
offices, dealers), ability of staff, communications
Reliability- Delivering promised service, Perform right first time itself,
quality of network and range
Responsiveness- deal effectively with customer complaints, keep error- free
records
Assurance- knowledge, experience, courtesy, maintain client confidence,
credibility, security, willingness to help, never too busy
Empathy- individual attention, understanding/knowing the customer, has
customer’s best interest in mind.
Reliability dimension stands for quality of network, range, few interruptions,
delivering promised service etc. As the market has stabilized in the last 15 years,
customers want the service to be trouble- free without any need to contact the service
company. This means that the customers can enjoy all new features without
interruption, wherever they are. Many customers view mobile phone as a gadget for
their personal safety. Others use it as a personal planner and time/activity scheduler.
High end applications like e- mail, messaging etc also are critical for customers.
Hence uninterrupted service with quality signals is preferred by most customers.
Today’s customers need recognition and good behaviour by staff. Moreover,
while using a high technology product, they need assurance from every corner. They
165
value personal relations and enjoy when someone calls them by first name. For this
reason, respondents have rated Assurance as the second important dimension.
Tangibility is not much of a concern for customers now as their contacts with
company and offices, staff is less. Most of the communications with company are
through SMS, mail etc as that they do not come across tangible materials. Customer’s
judgment of service quality has changed from tangible-based experiences in the
beginning years to quality and reliability- based evaluation of service now. Weighted
measure of Service quality is the average of the five service dimension scores, which
is computed as under.
Based on this the respondents have distributed a total of 100 points among
these five dimensions. The mean scores of distribution against each service
dimensions are given in Table 6.25. These scores are used as a weightage to compute
the weighted satisfaction measure.
TABLE 6.25
RELATIVE IMPORTANCE OF SERVICE QUALITY DIMENSIONS
Sl No Service quality Dimensions Ave. score out of
100
Rank
1 Tangibles 14 5
2 Reliability 26 1
3 Responsiveness 20.5 3
4 Assurance 21 2
5 Empathy 18.5 4
Total 100
Source: Computed data
From Table 6.25, Reliability dimension was given maximum importance
weightage of 26 by customers, followed by Assurance (21) and Responsiveness
(20.5). Empathy and Tangibles were rated least important as shown in the mean
scores.
166
ℎ = + + ++ ℎ ÷ 5ℎ = (3.37 + 3.10 + 3.33 + 3.34 + 3.49) ÷ 5 = 3.31
Weighted score of Service quality after applying weights as per table 6.20 is
computed as follows.
ℎ = ( × 14 + × 26 + × 20.5+ × 21 + ℎ × 18.5) ÷ 100ℎ = (3.37 × 14 + 3.10 × 26 + 3.33 × 20.5 + 3.34 × 21 + 3.49× 18.5) ÷ 100 = 3.29
The unweighted service quality value is 3.31 and the weighted service quality
is 3.29. The difference is that in the weighted service quality measure, the customers
are given the choice to assign the importance of each service dimension according to
their view, instead of the companies assuming equal or any other order of weightage
for the service dimension. On a scale of 5, the weighted score of service quality is
Only 3.29, which is slightly above average. There is enough room for improving the
service quality perception for all firms by working on all the service dimensions.
6.9 ANALYSIS OF OVERALL CUSTOMER SATISFACTION
In order to meet the research objective, overall satisfaction of customers
towards mobile phone services is estimated. This is done irrespective of the service
167
providing company. This meets the first part of the fourth objective . The following
hypothesis is tested here.
H9: Customer satisfaction is different from assumed value 4
One Sample t-test is used to test overall satisfaction without respect to mobile
telecom network. A significance level of 0.05 will be chosen with a specified
constant or cut-off value of four (4) for overall satisfaction scale. ( with
satisfaction scale, a rating of 4 or 5 indicates satisfaction; a rating of 1 to 3
indicates dissatisfaction or satisfaction below the required level.). t test calculates
the t statistic as per the formula given below.= ( − ) ÷Where
x is the sample mean,
m is the assumed population mean ( 4 in this case)
and standard error is s÷ √ n where s is the sample standard deviation and n is
the sample size.
The overall satisfaction was measured using a 5-point Likert scale with
extreme values of Highly dissatisfied (numeric value 1) to Highly satisfied (numeric
value 5) with average or neutral as 3. It yielded an average value of 3.243, with
standard Deviation of 1.05. This is below the expected or hypothesized level of 4 and
above for satisfaction. Hence it is concluded that overall satisfaction is lower than
accepted limits. The service quality delivery showed negative gaps for all 22 service
attributes studied. Hence overall satisfaction is also below expectation. There is scope
for improving it through suitable strategies.
168
TABLE 6.26
Comparison of Overall Satisfaction with Assumed Mean of 4.00
Measure t value Significance2 tailed
Meandifference t
95%confidencelevel -upper
lower Degreeoffreedom
Decisionon NullHypothesis
Overall
satisfacti-
on
-21.93 0.000 -0.76 -.82 -.69 434 Reject
Source: Computed data
Table 6.26 gives the summary of the t test. It indicates that with a cut-off value
of four (4), the mean difference in satisfaction using overall customer satisfaction
measure (-0.76) with p-value of 0.000 imply that the mean is significantly less
than the cut-off value (4) providing strong evidence to reject the null
hypothesis, i.e. customers are at least satisfied. Therefore, it is concluded with 95%
confidence that, using overall satisfaction measure, customers are not satisfied.
Overall satisfaction with respect to each company also showed almost the
same results which indicate that with a cut-off value of four (4), the mean
differences in satisfaction using overall satisfaction measure for Companies A, B,
C, D (-.953, -.342, -.425 and -.455 respectively) with p-values of 0.000 imply
that each of the mean satisfaction is significantly less than the cut-off value
(4), and all the confidence intervals have negative values. These provide strong
evidence to reject the null hypothesis.
The Hypothesis H9 is thus rejected. Satisfaction level is significantly lower
than assumed value of 4.0.
An analysis of the interdependence of various demographic attributes of
respondents on customer satisfaction is presented next.
169
6.10 COMPARISON OF CUSTOMER SATISFACTION IN KERALA’S FOUR
MOBILE PHONE NETWORKS
As indicated in the second part of the third objective , it will be interesting to
know if there is a significant difference between customers of various mobile service
firms in terms of customer satisfaction. This learning can prompt the firms to review
their marketing and customer strategies to remain more competitive among the firms.
The Null and Alternative hypothesis to test the difference in customer satisfaction
among the firms is given below.
H10: There is no difference between the overall customer satisfaction among
the four networks.
One- way ANOVA test is conducted to test this Hypothesis for which the
Hypothesis can be restated in terms of the Population mean for the four companies as
under.
µ A = µ B= µ C = µ D where µ is the common assumed mean for all the four
populations of Company A,B,C,D.
Then the Alternative Hypothesis condition becomes µ A ≠ µ B ≠ µ C ≠ µ D
Before performing a One-Way ANOVA test it is important first to
ensure that the assumption of equality of groups’ variances was valid. To
ensure this, the Levene statistic is used which is very robust in testing for
equality of groups’ variances at significance level 0.05. It tests the null
hypothesis that the group variances are significantly equal.
Levene’s statistic for testing for equality of groups’ variances at significance
level 0.05 is shown in Table 6.27.
170
TABLE 6.27
HOMOGENEITY OF VARIANCE FOR SATISFACTION SCORES
Levene statistic Degree of freedom
1
Degree of freedom
2significance
1.671 3 434 0.172
Source: Computed data.
As the significance value 0.172 is more than the assumed significance level of
0.05, the homogeneity of samples is established. In other words there is no reason to
reject the Null hypothesis that group variances are significantly equal. With equality
of the group variances established, the ANOVA test was conducted with a
significance level of 0.05 and the results are summarised in Table 6.28.
TABLE 6.28
COMPARISON OF SATISFACTION BETWEEN FIRMSSum of
squares
Degree of
freedom
Mean
square
F ratio Cut-off value
Between
groups
35.8 3 11.93 11.47 2.9(F ratio)
p-value .000
Within
groups
456.2 434 1.04
total 492 437
Source: Computed data.
Table 6.28 indicates that the p-value (0.00) is less than the significance
level (0.05) providing strong support for rejecting the null hypothesis that the means
are equal. Therefore, we can safely conclude with 95% confidence level that overall
satisfaction or dissatisfaction among the networks is not the same or equal. Customer
satisfaction levels are different for different firms.
The Hypothesis H10 is thus rejected.
171
6.11 SWOT ANALYSIS OF THE FOUR FIRMS
In accordance with the research objective, customer opinion on the strengths,
weakness, opportunities and threats of the firms is collected and analysed. Customer
perception about the strength and weakness of the firms under study gives an
indication how customers evaluate the various direct and indirect communications
from the companies in to the market. A company’s attempts to position itself in the
minds of a customer based on its strengths or competitive advantages may succeed
sometimes or it may result in a confused image or no particular image. In other words,
companies try to project one image but customers perceive another image. This is a
dangerous situation and the firms have to urgently reorient its positioning and
communication efforts. So a good firm should identify its strengths correctly and if
there are any unique strengths for the firm, brand image has to be strengthened around
that unique strength. This unique strength is called a competitive advantage.
The competitive advantage or unique strength of a firm may be in designing,
production, finance or marketing area. While selecting future strategies for growth, or
new product/location decisions, the firm should keep its competitive advantage in
mind. Strengths and weaknesses, though internal to the firm, may change over a long
period of time. Hence it is important to do such SWOT analysis periodically to take
note of such changes and act accordingly.
To understand the Strength, Weakness, Opportunities and Threats (SWOT)
analysis of four firms and the competitive position of the four firms under study, a
SWOT list was given to respondents. Each aspect of SWOT was measured on a five-
point Likert scale with a mid -point as neutral. Numeric values 1 to 5 were allotted to
High Disagreement to High Agreement. Table 6.29 gives tabulation of mean scores of
SWOT attributes.
172
TABLE 6.29
STRENGTH ATTRIBUTE SCORES FOR THE FOUR FIRMS
Sl.No SWOT Attributes MeanscorefirmAirtel
MeanscorefirmVodafone
MeanScorefirmIdea
MeanscorefirmBSNL
STRENGTH Attributes
1 Positive Image compared to othercompetitors
3.60 3.56 3.48 3.30
2 Innovation in introducing new featuresfirst
3.00 3.80 3.67 3.20
3 A very large customer base in the state 3.80 3.78 3.45 3.75
4 Image of a large Multinationalcompany Brand
3.21 3.41 3.62 2.98
5 Focus and specialization in Telecombusiness
3.45 3.36 3.61 3.72
Source: Computed data
From Table 6.29, it can be seen that the major strength of firm Airtel is its
large customer base with a mean value of 3.8, followed by Positive image compared
to competitors with a mean of 3.60. Further, a mean of 3.21 indicates that its image as
a large MNC is not very good. But it has reasonable specialization in telecom field as
shown by a mean score of 3.45. For firm Vodafone, the major strength is innovation
in introducing new features as indicated by a mean of 3.8. followed by mean value
3.78 which again indicates a large customer base. Firm Idea is also strong in
innovation as indicated by mean of 3.67. Firm BSNL is strong in customer base as
well as focus in telecom as indicated by mean scores of 3.75 and 3.72.
173
TABLE 6.30
WEAKNESSES ATTRIBUTE SCORES FOR THE FOUR FIRMS
Source: Computed data.
As seen from Table 6.30, major weaknesses of firm Airtel are image of a low
quality operator and their entry in to so many unrelated areas as indicated by mean
values of 1.23 and 1.65. Firm Vodafone is weak in low quality image as well as not
aggressive in marketing as seen from mean values 1.36 and 1.37. Weaknesses of firm
Idea are low quality image and poor marketing strategies. For firm BSNL, mean
scores of 1.25 and 1.59 indicates weaknesses in low coverage and entering many
unrelated businesses.
Sl.No SWOT Attributes MeanscorefirmAirtel
MeanscorefirmVodafone
MeanScorefirmIdea
MeanscorefirmBSNL
WEAKNESSES Attributes
1 Not very aggressive in marketing likeother competitors
3.25 1.37 1.73 3.67
2 They are in to many unrelatedbusinesses
1.65 2.89 1.79 1.59
3 Not well equipped to cover entire state 3.10 2.35 3.00 1.25
4 Image of a low quality operator 1.23 1.36 1.35 3.45
5 Not enough advertising and Publicitylocally
1.89 1.78 1.88 1.76
174
TABLE 6.31
OPPORTUNITIES ATTRIBUTE SCORES FOR THE FOUR FIRMS
Source: Computed data.
From Table 6.31, it is seen that the major opportunities available in the market
for firm Airtel are its political influence and capacity to enter other business areas as
indicated by mean values of 3.50 and 3.21. For firm Vodafone, mean values of 3.64
and 3.57 indicate available opportunities in entering other businesses and political
influence. In the case of firm Idea, it has good opportunities in merging with other
small firms and expand in global markets. Mean values of 3.08 and 3.01 indicate
opportunities in political influence and expanding in to global areas.
Sl.No SWOT Attributes MeanscorefirmAirtel
MeanscorefirmVodafone
MeanScorefirmIdea
MeanscorefirmBSNL
BUSINESS OPPORTUNITIES
1 Can enter other related businesses intelecom
3.21 3.64 3.11 2.87
2 Financial position can be used toexpand business
3.12 3.23 3.45 3.00
3 Can expand in to global markets 3.10 3.45 3.66 3.01
4 Can merge or buy other smallcompetitors
3.50 3.19 3.69 2.45
5 Political influence can be used foradvantages
3.12 3.57 3.02 3.98
175
TABLE 6.32
THREAT ATTRIBUTE SCORES FOR THE FOUR FIRMS
Sl.No SWOT Attributes MeanscorefirmAirtel
MeanscorefirmVodafone
MeanScorefirmIdea
MeanscorefirmBSNL
THREATS TO BUSINESS
1 Many new companies may enter themarket
3.33 3.45 3.54 3.01
2 Continuous rate cutting will affectprofits
3.22 3.41 3.23 3.00
3 Saturation in the market 3.56 3.21 3.13 3.48
4 Government policies not encouraging 3.34 3.23 3.61 3.02
5 Technology changes are very costly 3.51 3.33 3.12 3.54
Source; Computed data
As far as weakness of firms are concerned, Table 6.32 indicates mean values
of 3.56 and 3.51 for firm Airtel which shows that the firm is facing threats from
saturation in the market and technology changes. Firm Vodafone has threats from new
competitors entering the market, and continuous rate cutting as indicated by mean
values of 3.45 and 3.41. For firm Idea, mean values of 3.61 and 3.54 indicate major
threats from changing government policies and new entrants in the market. From the
mean values of 3.54 and 3.48 for firm BSNL, it is seen that their major threats are
technology changes as well as saturation in the market.
176
6.12 ANALYSIS OF BEHAVIOURAL, TECHNOLOGY USE, PRICE AND
MARKET MIX- RELATED ATTRIBUTES
The availability of latest features and technologies, ever- dropping call tariffs,
number portability, dual SIM phones, all India roaming facility, convergence of many
gadgets in to mobile phone, statutory laws etc have changed the behaviour, method of
usage and perception of customers in the recent past. To study this change, their
activities, values and opinions were asked using a set of questions related to behaviour
exhibited in using mobile phones, advantage/disadvantages of technology features,
price perception and other market mix response.
The analysis of this feedback is expected to help firms in framing contact
strategies, promotional offers and improving customer satisfaction. 5 point Likert
scales were used to measure their Agreement- Disagreement to each attribute which
was later converted in to numeric values for analysis.
6.12.1 Behaviour related aspects
Widespread use and availability of latest features in mobile phones are changing how
customers are using it today. Behavioural attributes are studied and their mean scores
are given in Table 6.33.
177
TABLE 6.33
MEAN SCORES AND RANKS OF BEHAVIOUR ATTRIBUTES
SlNo
Attributes Totalscore
Meanscores SD Rank
1Use mobile phone for calls, music,calculator and camera
1722.6 3.96 .48 2
2Blue- tooth facility is very useful to you 1200.6 2.76 .45 6
3Recommends your company to friends,relatives
1170.15 2.69 .58 7
4Responds to many SMS offers andcontests on phone generally
1065.75 2.45 .54 8
5Recharges several times for smallamounts
1683.45 3.87 .39 3
6Post- paid connection is not attractive orconvenient now
1931.4 4.44 .54 1
7Cell phone should be banned in schoolsand colleges
1396.35 3.21 .54 5
8BSNL landline at home is not very usefulnow
1587.75 3.65 .48 4
Source: Computed data
As seen from Table 6.33, most of the customers find that post-paid
connections are not attractive today as indicated by the mean score of 4.44. A score of
3.87 for recharge habits show that customers have the habit of recharging several
times for small denominations. Multiple use for mobile phones is very popular as the
mean score of 3.96 indicates. Majority of them find BSNL landlines at home are not
very useful now as a score of 3.65 indicates. Bluetooth facility is not very useful now
as the score of 2.76 shows. Response rate for promotional SMS and recommendation
level to friends and relatives are also low as indicated by low scores of 2.45 and 2.69
respectively.
178
6.12.2 Technology- related attributes
TABLE 6.34
MEAN SCORES AND RANKS OF TECHNOLOGY ATTRIBUTES
SlNo
Attributes Totalscore
Meanscore
SD Rank
1Your service provider uses latesttechnologies
1748.70 4.02 .49 1
2Using mobile phone for long durationswill create health hazards
1552.95 3.57 .52 3
3Staying near mobile towers will createhealth hazards
1313.70 3.02 .35 4
4Government should restrict thenumber of companies and towers dueto health hazards
1296.30 2.98 .48 5
5Foreign brands of Mobile phones arebetter than Indian makes
1687.80 3.88 .44 2
Source: Computed data
From table 6.34, it is seen that a majority of customers feel they are getting the
latest technology features from their service provider as the mean score of 4.02
indicates. They also feel foreign brands of mobile phones are better than local brands
(score 3.88). A score of 2.98 and 3.02 for the perception on health hazards show that
customers are not much worried about the hazards from towers. However they feel
using mobile phones for long duration can cause health hazards as the score of 3.57
indicates.
179
6.12.3 Price- related aspects
TABLE 6.35
MEAN SCORES AND RANKS OF PRICE ATTRIBUTES
SlNo
Attributes Totalscore
Meanscores SD Rank
1Mobile phone call rates are veryaffordable now
1357.20 3.12 .45 2
2Price of handsets are veryaffordable now
1291.95 2.97 .34 3
3Total spending of a family inmobile phones is generally morethan the BSNL land phonecharges
1792.20 4.12 .55 1
4Mobile companies are running ata loss due to low tariffs andcompetition
1165.80 2.68 .34 4
Source: Computed data
As seen from table 6.35, a large majority of respondents confirm that total
mobile phone bills for a family are more than their landline bill as indicated by the
score of 4.12. On affordability of call rates and handset prices, the response is almost
neutral having mean scores of 2.97 and 3.12 respectively. They may be expecting call
charges and handset prices to drop from current levels. Very few believe that service
provider companies are making losses in operations as indicated by a score of 2.68.
Though the actual facts show most companies are operating in loss, they still engage
in heavy advertisement and promotion which may be creating such an impression.
180
6.12.4 Marketing- Mix attributes
TABLE 6.36
MEAN SCORES AND RANKS OF MARKET- RELATED ATTRIBUTES
SlNo
Attributes Totalscore
Meanscore
SD Rank
1Advertisements influence yourselection of company
1291.95 2.97 .46 3
2You talk to a company/ dealerexecutive before selection
1318.05 3.03 .56 2
3You are influenced by friends orrelatives
1248.45 2.87 .52 4
4You are influenced bypromotional offers
1452.90 3.34 .45 1
5You are influenced by NumberPortability to change company
630.75 1.45 .45 5
Source; Computed data.
The most influencing factor for consumer is the frequent promotion schemes
and special offers as indicated by the score of 3.34. Influence of number portability
feature and friends/relatives is relatively low with a score of 1.45 and
2.87respectively. Advertisements in the media have a lower influence with a score of
2.97 and on the question whether the customer talks to a company or dealer executive
before selection of a company, the answer is almost neutral as indicated by the score
of 3.03.
6.13 FACTOR ANALYSIS TO IDENTIFY THE MAJOR UNDERLYING
DIMENSIONS FOR THE 22 BEHAVIOURAL ATTRIBUTES
Factor analysis is a statistical method used to study the dimensionality of a set
of variables. In factor analysis, latent variables represent unobserved constructs and
are referred to as factors or dimensions.
Factor analysis of the 22 attributes on behavioural aspects of customers were
conducted to extract the principal factors which underlie the behaviour. SPSS was
181
used to conduct a Principal component analysis, with Oblimin Rotation and absolute
value of coefficients above 0.3. Three components were extracted based on factor
loadings and grouped based on certain underlying factors. The results of the SPSS
output are as follows:
TABLE 6.37
KMO TEST FOR SUFFICIENCY OF SAMPLES
Kaiser-Meyer-Olkin
measure of sampling
adequacy
K-M-O test value 0.600
Chi- square 4548.60
Bartlett’s test of sphericity d.f 231
significance .000
Source: Computed data.
The sampling adequacy factor of 0.600 in the KMO test indicates that the
sample is adequate to conduct the Factor analysis. Hence it is justified to conduct the
further test on total variance explained by the various attributes under study. For this
SPSS gave the following results.
182
TABLE 6.38
VARIANCE OF ATTRIBUTES EXPLAINED BASED ON EIGEN VALUES
Component Initial Eigen values
Total % of variance Cumulative %
1 4.357 19.807 19.807
2 2.757 12.531 32.338
3 2.576 11.708 44.045
4 1.830 8.317 52.362
5 1.581 7.186 59.548
6 1.370 6.227 65.775
7 1.168 5.309 71.084
8 .925 4.206 75.290
9 .854 3.883 79.174
10 .695 3.157 82.331
11 .554 2.520 84.851
12 .531 2.412 87.263
13 .481 2.186 89.449
14 .436 1.981 91.430
15 .393 1.784 93.214
16 .347 1.579 94.793
17 .297 1.350 96.143
18 .233 1.058 97.201
19 .212 .964 98.165
20 .151 .685 98.851
21 .143 .650 99.501
22 .110 .499 100.000
Source: Computed data.
183
As evident from table 6.38, the first three attributes explain 44% of the total
variance and they have Eigen values relatively higher to 1. Now the three components
are extracted using the principal components methods. The factor loadings on the
three components are shown below.
TABLE 6.39
PRINCIPAL COMPONENT MATRIX FOR THE THREE EXTRACTED
COMPONENTS
Attributes Component1 2 3
Advertisements influence your selection of company .709Use mobile phones for calls and other apps -.662Recommends your company to others -.658 .472Staying near mobile towers will cause health hazards .654 .306Bluetooth facility is not very useful -.644 .366Your service provider uses latest technologies .636Price of handsets are very affordable .551Recharges several times in small amounts .502You are influenced by friends or relatives .482Responds to many sms offers -.704You are influenced by number portability to changecompany
.592 -.658
Total spending of family in mobile phones -.353 .581Mobile phone call rates are very affordable .575Govt should restrict the number of companies .428BSNL landline at home is not very useful -.331You talk to a company dealer before selection .684You are influenced by promotional offers -.365 .613
Cell phones should be banned in schools and colleges .366 .544
Mobile companies are running at a loss due to lowtariffs
-.316 .513
Foreign brands of mobile phones are better than Indianmakes
.396 .491
Postpaid connection is not attractive .386 .416Using mobile phone for long durations .361
Source; Computed data.
Based on the factor loadings with oblimin rotation, the three major
components of customer behaviour are classified based on underlying factors. The
184
first classification of customers based on behaviour ( component 1) can be termed as
“Active users” who make use of many features of mobile phones and susceptible to
outside influences. The second classification ( component 2) can be termed as “Cost-
conscious users” who are worried about cost aspects and are somewhat negative in
attitude. The third group ( component 3) can be described as the “ Choosy type” who
do extensive search and are less ethnocentric.
6.14 PROBLEMS FACED BY CUSTOMERS, IRRESPECTIVE OF
COMPANY
Due to the changes in technology, expansion of networks, entry of new firms in
to the market etc, new problems are introduced. Many a time, there may be customer
issues about which company has no information at all or they never expect that such a
problem can arise. The best method to find out the presence of such problems or
complaints is to periodically ask the customers about any problems or inconveniences
they face. This will answer the research question ten of this study. In order to collect
data on commonly faced problems by customers irrespective of company, a set of 10
questions using a Likert scale to indicate the degree of satisfaction from Highly
Dissatisfied to Highly satisfied were administered. Numeric value 1 represents High
dissatisfaction and 5 represents High satisfaction. In addition, one open- ended
question to list any other problems/suggestions the customer has, was also used. The
mean scores for the 10 attributes are shown in Table 6.40.
185
TABLE 6.40
MEAN SCORES AND RANKS OF SERVICE PROBLEM ATTRIBUTES
SlNo
Attribute of Service Total score Meanscore
SD Rank
1Brand reputation of Company 1774.80 4.08 .58 1
2Range or reach of signals 1313.70 3.02 .46 7
3Enough towers near your area 1444.20 3.32 .47 5
4Call charges are affordable 1483.35 3.41 .50 4
5Frequent special offers andpromotions
1396.35 3.21 .61 6
6Offering latest technology features 1726.95 3.97 .56 2
7Sufficient distribution channels 1500.75 3.45 .65 3
8Frequent communication throughmedia, advertisements
1313.70 3.02 .45 7
9After-sales support/ information 1291.95 2.97 .34 9
10Prompt Solving of problems 1309.35 3.01 .59 8
Source: Computed data.
6.14.1 Analysis of Major problems faced by customers
As seen from table 6.40, customer problems generally pertain to after- sales
support as seen from the low score of 2.97. Range or strength of signals, lack of
communication with customers and prompt solving of complaints are other areas
which need improvement as the scores are near neutral (3.01, 3.01 and 3.02
respectively). On brand reputation of companies and offering latest features,
customers are satisfied as indicated by the mean scores of 4.01 and 3.97. On
availability of enough towers the score is 3.32, on sufficiency of distribution channel
it is 3.45 and on affordability of call charges it is 3.41. This indicates more than
average satisfaction on these aspects. The open- ended question brought out certain
186
factors like shortage of small denomination recharge coupons, dealer outlets
selectively stocking certain company products, handset software getting corrupted,
difficulty in reading and doing the recharge from coupons, delay in getting the
recharge activated etc. Firms will do well if they address these issues and take
corrective actions.
It is of interest now to further analyse some of the major problems faced by
customers across the geographical dimensions. The following Hypotheses are set for
this.
H11: The perception towards brand reputation is same across the three
geographical regions ( µ A = µ B= µ C)
H12: The perception towards after- sales support is same across the three
geographical regions ( µ A = µ B= µ C)
H13: The customer perception towards having enough towers is same across
the firms
ANOVA test is carried out to find out if the means of Brand reputation and
After- sales support are the same or different across the three geographical areas used
in the study. Before running the ANOVA test, the homogeneity of variance is ensured
using the Levene statistics. The results are as follows:
From the Levene test for homogeneity of variance, the significance value is
0.475 which is higher than the cutoff 0.05 and hence it can be concluded that the
variance is homogenous to conduct an ANOVA test. SPSS output for the ANOVA
test is shown below.
187
TABLE 6.41
COMPARISON OF BRAND REPUTATION ACROSS AREAS.
Source: Computed data
From table 6.41, the F-ratio for ANOVA test is 15.048. The significance
value obtained is 0.000 which is less than the cut off .05 and hence the null hypothesis
is rejected. This means the means of the different groups are not equal. In other
words, the customer perception about Brand reputation in the three areas are not
equal. The customers perceive the four brands differently in different area with
respect to reputation.
Hypothesis H11 is thus rejected.
From the Levene test for homogeneity of variance, the significance value is
0.609 which is higher than the cutoff 0.05 and hence it can be concluded that the
variance is homogenous to conduct an ANOVA test. SPSS output for the ANOVA
test is shown below.
Variance Sum ofsquares
df Mean square F ratio Significance
Betweengroups
18.251 3 6.084 15.048 .000
Within groups 174.245 431 .404
Total 192.497 434
188
TABLE 6.42
COMPARISON OF AFTER- SALES SUPPORT ACROSS AREAS
Source: Computed data.
From table 6.42, the F- ratio for ANOVA test is 5.295. The significance value
obtained is 0.001 which is less than the cut- off .05 and hence the null hypothesis is
rejected. This means the means of the different groups are not equal. In other words,
the customer perception about After- sales support in the three areas are not equal.
Customers are perceiving the four brands differently in different area with respect to
After -sales support .
The Hypothesis H12 is thus rejected.
From the Levene test for homogeneity of variance, the significance value is
0.131 which is higher than the cutoff 0.05 and hence it can be concluded that the
variance is homogenous to conduct an ANOVA test. SPSS output for the ANOVA
test is shown below.
Variance Sum ofsquares
d.f Mean square F ratio Significance
Betweengroups
6.585 3 2.195 5.295 .001
Within groups 178.670 431 .415
Total 185.255 434
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TABLE 6.43
COMPARISON OF SUFFICIENCY OF TOWERS ACROSS FIRMSVariance Sum of
squares
df Mean square F ratio Significance
Between
groups
.399 4 .100 .243 .914
Within groups 176.070 430 .409
Total 176.469 434
Source: Computed data.
From table 6.43, the F-ratio for ANOVA test is 0.243. The significance value
obtained is 0.914 which is greater than the cut off .05 and hence the null hypothesis is
accepted. This means the means of the different groups are equal. In other words, the
customer perception about enough towers in their locality in the three areas are equal.
The customers perceive the four brands similarly in different areas with respect to
enough towers.
The Hypothesis H13 is thus accepted.
6.14.2 Strategies for solving customer problems
Companies can try to get customer feedback through surveys, interviews,
dealers’ opinions etc to assess and improve customer service. Use of up-to-date CRM
software will facilitate customer experience management and provide positive
contacts. Eliminating anticipated problems as well as quick service recovery strategies
in case any problem occurs should be a better approach. Customers expect frequent
promotion programmes as well. Companies can check their own performance towards
the problems which came out through the open -ended question as mentioned above
and take corrective actions.
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6.15 CHAPTER SUMMARY
A detailed analysis of customer perception towards 22 service quality attributes
is presented in the chapter. The overall response to the five service quality dimensions
is also done. Further, overall customer satisfaction and satisfaction across the firms is
analysed. Behavioural aspects of customers and problems faced by customers is also
analysed in the chapter. Thus, after completing the data analysis , major findings and
suggestions are presented in the next chapter.