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


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


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


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


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


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


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

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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


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


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 overall 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


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



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

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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


Male 253 3.134 0.6691.293 0.197

Female 182 3.049 0.692

Total 435


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.


153

iii) Difference in perception about Responsiveness dimension between genders.



H3: The difference in mean scores of perception about Responsiveness

dimension with respect to gender is not significant.


TABLE.6.16

PERCEPTION OF MEN AND WOMEN TOWARDS RESPONSIVENESS

Gender N mean Std.

deviation


Male 253 3.404 0.6691.880 0.061

Female 182 3.221 0.692

Total 435


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.






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.


iv) Difference in perception about Assurance dimension between genders



H4: The difference in mean scores of perception about assurance dimension

with respect to gender is not significant.


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


From the above table, mean score of perception towards Assurance for male




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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.


v) Difference in perception about Empathy dimension between genders



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


156

From the above table, mean score of perception towards Assurance for male




Test statistics show Levene’s test significance value of 0.841which is higher





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.


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


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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*


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.

159

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.


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*


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


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”


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


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.


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


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


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


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


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


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


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.



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


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.



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



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


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


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


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


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


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


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


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


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


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.


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.





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


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.





Variance Sum ofsquares

d.f Mean square F ratio Significance

Betweengroups

6.585 3 2.195 5.295 .001


Total 185.255 434

189

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


Total 176.469 434


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.


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.

190

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.

chapter vi analysis of measurement of...

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