chapter 5 data analysis and...

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CHAPTER 5

DATA ANALYSIS AND INTERPRETATION

This chapter discusses the results and interpretations of the statistical analysis done on the

data collected with the help of questionnaire in the research study. This chapter consists

of seven sub sections. Section 5.1 discusses the development of telecom sector in India

and customer retention problems in Indian mobile telecom sector. It also discusses the

key determinants of customer retention in mobile telecom sector and various customer

retention strategies adopted by telecom operators in India. Section 5.2 discusses the

demographic profile of the respondents. Section 5.3 provide the description of the

research instrument (questionnaire) and its testing of the reliability and validity of various

constructs used in the questionnaire in order to identify key determinants of customer

retention in mobile telecom sector in India. Section 5.4 discusses the results of

confirmatory factor analysis of all the constructs taken together. This section also

analyses the various aspects of convergent and discriminant validity of the constructs.

Section 5.5 analyses the results of different hypothesis considered in the research study,

analyzing the cause and effect relationship between different constructs related to

customer retention in mobile telecom sector, section 5.6 analyses the overall combined

SEM model and explain the relationship between various determinants of customer

retention. In the end, section 5.7 analyses the data collected from telecom company

representatives.

5.1 Developments in Indian Telecom Sector

Indian telecom sector can be characterized by its diversity. Service organizations ranging

from small telecom service providers to large corporations exist throughout the Indian

telecom business world. Competitive pressures and global economy affect services and

related businesses and cause those businesses to seek unique ways of differentiating their

services. The willingness and ability of managers in service firms to respond to dramatic

changes affecting the service economy will determine whether their own organizations

survive and prosper or suffer, where they throw their hands up in frustration giving in to

their more agile and adaptive competitors . With the tremendous changes in Indian

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telecom service sector, including an expansion and intensification of competition and

increasing customer sensitivity, the issue of customer retention has assumed significant

importance.

This study is intended for executives in telecommunication firms facing intense

competition & customer retention challenges. As the telecommunication industry has

grown and matured, it has driven service providers to fight even harder for customer

wins. Results from this study will contribute to a greater understanding of customer

retention both to practitioners and academicians. In the light of the centrality of customer

retention from all stand points, this research aims to investigate its various dimensions in

the Indian mobile telecommunication services context.

5.1.2 Key Determinants of Customer Retention

Retaining customers in highly competitive business environment is critical for any

company’s survival because a lost customer represents more than the loss of the next sale.

The company loses the future profits from that customers’ lifetime of purchases. Also,

keeping customers makes the cost of selling to existing customers lower than the cost of

selling to new customers. Therefore, acquisition should be secondary to retaining

customers and enhancing relationships with them (McCarthy, 1997). That is, because

according to Levy (2008), new customers are more difficult to find and reach, they buy 10%

less than the existing customers, and they are less engaged in the buying process and

relationship with retailers in general. Meanwhile, according to Eibenet al. (1998), existing

customers tend to buy more, which in turn generates more profit through more cash flow.

In addition, repeat customers were tested and shown to be less price-sensitive, they

provide positive word of mouth, and they generate a fall in transaction costs, all of

which increase firms’ sales and profits, leading to sales referrals (Stahl et al., 2003).

Key determinants of customer retention are as follows:

I Satisfaction and Customer Retention

Businesses in the relationship marketing sector have tended to view any future sales

opportunities as depending primarily on relationship quality and satisfaction (Crosby et al.,

1990); these are the key tools for increasing customer retention (Sweeney and Swait,

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2008).

Satisfaction is defined by Engel et al. (1995) as “a post-consumption evaluation that a

chosen alternative at least meets or exceeds expectations”, while Ranaweera and Prabhu

(2003) defined it as “an evaluation of an emotion, reflecting the degree to which the

customer believes the service provider evokes positive feelings”. Therefore, satisfaction

occurs with the enhancement of a customer’s feelings when he or she compares his/her

perception of the performance of products and services in relation to his/her desires and

expectations (Spreng et al., 1996).

II Trust and Customer Retention

Trust has many definitions in the relationship marketing literature. Moorman et al. (1993)

defined trust as “a willingness to rely on an exchange partner in whom one has confidence”.

Also, Morgan and Hunt (1994) have described trust as “the perception of confidence in the

exchange partner's reliability and integrity”. Evans et al. (2006) presented a number of

concepts that are employed to explain and describe successful relationships; one of these

concepts is trust. The author argues that trust is the basis for relationship exchange and the

glue that holds a relationship together.

One of the study examples that investigated the relationship between trust and customer

retention was conducted by Teichert and Rost (2003). The authors measured the

effects of trust and involvement on customer retention, assuming general customer

satisfaction. They found that trust serves as a strong trigger for enhancing customer

retention, and involvement is revealed to play a prominent role in explaining both trust

creation and customer retention. They also concluded that trust is a major constituent

element of relational customer retention, supported in different measures by affective and

cognitive involvements.

III Commitment and Customer Retention

Commitment is considered one of the major elements of successful relationship marketing.

Consequently, there is no successful relationship without commitment from both parties,

especially if the relationship requirements and conditions have been agreed and written

between them. This view is validated by many scholars (Too et al., 2001; Bansal et al., 2004;

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Sanchez and Iniesta, 2004; Hess and Story, 2005) who have examined the effect of

commitment on customer retention.

Commitment in the relationship marketing research field is defined by Dwyer et al. (1987) as

“an implicit or explicit pledge of relational continuity between exchange partners”.

Likewise, Moorman et al. (1992), argue that commitment is essential to customer retention

and describe it as “an enduring desire to maintain a valued relationship”. Morgan and Hunt

(1994), consider this phrase to be a relational commandment and define

commitment as:

“an exchange partner believing that ongoing relationship with another is so important as to warrant

maximum effort at maintaining it; that is , the committed party believes the relationship is worth

working on to ensure that it endures indefinitely”.

IV Service Quality and Customer Retention

Service quality has gained a great deal of attention from researchers, managers, and

practitioners during the past few decades. Many scholars have studied the effect of service

quality on customer retention (Oliver, 1980; Lehtinen and Lehtinen, 1982; Ennew and Binks,

1996; Ranaweera and Neely, 2003; Venelis and Ghauri, 2004). Their findings reveal that

there is a direct correlation between service quality and customer behavioural intentions and

retention. Service has many dimensions, definitions, and techniques which may affect

its way of production, consumption, and delivery.

V Switching Barriers and Customer Retention

The switching barrier refers to the difficulty of switching to another provider that is

encountered by a customer who is dissatisfied with the existing service, or to the

financial, social and psychological burden felt by a customer when switching to a new

carrier (Fornell, 1992). Therefore, the higher the switching barrier, the more a customer is

forced to remain with his or her existing carrier. According to previous studies, the

switching barrier is made up of switching cost, the attractiveness of alternatives, and

interpersonal relationships. These three switching barriers are summarized below:

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(A) Switching cost

The switching cost is a main factor having effect on the customer retention. As the

switching cost increases, risk and burden on consumers are increased in the customer side

and dependency on the service provider gets increased as a result [Jones et al., 2000;

Morgan & Hunt, 1994]. In other words, the more consumers recognize the switching

cost, the higher retention rate even though customers have dissatisfaction on the service.

(B) The interpersonal relationship

The long term interpersonal relationship between the company and customers offers a lot

of benefits to the customers: social benefits such as fellowship and personal recognition,

psychological benefits such as reducing anxiety and credit, economic benefits such as

discount and time-saving, and finally customization benefits such as customer

management etc [Berry, 1995; Peterson 1995]. Therefore the interpersonal relationship

between the company and the customers can be an important factor as a switching

barrier. The continuous interpersonal relationship becomes a relationship-specific asset

which acquires customer to pay cost to be out of the relationship and therefore protects

customer from being apart from the relationship with the company.

(C) The attractiveness of alternatives

When consumers does not think that they have various alternatives or the service level,

distinguished image of the alternatives is better than the current service provider, the

possibility of the customers switching the service provider is very low [Anderson &

Narus, 1990; Jones et al., 2000]. Therefore, the attractiveness of the alternatives would be

a component building the switching barrier.

5.1.3 Strategies for Customer Retention

An important distinction can be made between strategies that lock the customer in by

penalizing their exit from a relationship, and strategies that reward a customer for

remaining in a relationship. The former are generally considered negative and the latter

positive customer retention strategies. Negative customer retention strategies impose high

switching costs on customers, discouraging their defection. The customer retention

strategies which are in practice in mobile telecom sector are as follows:

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(a) Customer Delight: It is very difficult to build long-term relationships with

customers if their needs and expectations are not understood and well met. It is

a fundamental precept of modern customer management that companies should

understand customers, and then acquire and deploy resources to ensure their

satisfaction and retention. This is why CRM is grounded on detailed customer-

related knowledge. Customers that you are not able to serve well may be better

served by your competitors.

(b) Add Customer-Perceived Value: The second major positive customer retention

strategy is to add customer- perceived value. Companies can explore ways to create

additional value for customers. The idea is to add value for customers without

creating additional costs for the company. If costs are incurred then the value-adds

may be expected to recover those costs. For example, a customer club may be

expected to generate a revenue stream from its membership. There are three

common forms of value-adding programme: loyalty schemes, customer clubs and

sales promotions.

(c) Loyalty Schemes: Loyalty schemes reward customers for their patronage. Loyalty

schemes or programmes can be defined as follows:

„A loyalty programme is a scheme that offers delayed or immediate incremental

rewards to customers for their cumulative patronage‟.

The more a customer spends, the higher the reward. Loyalty schemes have a long

history. In 1844, in the UK, the Rochdale Pioneers developed a cooperative retailing

operation that distributed surpluses back to members in the form of a dividend. The

surpluses were proportionate to customer spendings. S&H Pink Stamps and Green

Shield stamps were collected in the 1950s and 1960s, and redeemed for gifts selected

from catalogues.

(d) Customer Clubs: Customer clubs have been established by many organizations.

A customer club can be defined as follows:

A customer club is a company-run membership organization that offers a range of value-

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adding benefits exclusively to members. The initial costs of establishing a club can be

quite high, but thereafter most clubs are expected to cover their operating expenses

and, preferably, return a profit. Research suggests that customer clubs are successful at

promoting customer retention.

(e) Sales Promotions: Whereas loyalty schemes and clubs are relatively durable, sales

promotions offer only temporary enhancements to customer value. Sales promotions,

Retention-oriented sales promotions encourage the customer to repeat purchase, so

the form they take is different.

(f) Bonding: The next customer retention strategy is customer bonding. B2B

researchers have identified many different forms of bond between customers and

suppliers. These include interpersonal bonds, technology bonds (as in EDI), legal

bonds and process bonds. These different forms can be split into two major

categories: social and structural.

(g) Build Customer Engagement: The final positive strategy for building customer

retention is to build customer engagement. Various studies have indicated that

customer satisfaction is not enough to ensure customer longevity. For example,

Reichheld reports that 65 to 85 per cent of recently defected customers claimed

to be satisfied with their previous suppliers. Another study reports that one in ten

customers who said they were completely satisfied, scoring ten out of ten on a

customer satisfaction scale, defected to a rival brand the following year. Having

satisfied customers is, increasingly, no more than a basic requirement of being in the

game.

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5.2 Demographic profiles of the respondents

Table 5.1: Characteristics of the respondents on the basis of gender (N=740)

The total numbers of respondents considered in the research study are 740. The

demographic profile of the respondents on the basis of age is shown in table and graph

given below. The total numbers of respondents were 740 where 260 (35.82%) respondents

were females and 475 (64.18%) respondents were males as shown in table 5.1. The table

5.1 also represents that there was a fair percentage of male respondents.

Gender Frequency Percentage

Male 475 64.18%

Female 260 35.82%

Total 720 100 740 100

Table 5.2: Characteristics of the respondents on the basis of Age groups (N=740)

Age Frequency Percentage

Less than 25

years

313 42.29

25-34 years 189 25.54

35-44 years 146 19.75

45-54 years 62 8.37

Above 55

years

30 4.05

Total 740 100.0

As shown in the table 5.2 the respondents were grouped in five categories. 313 (42.29%)

respondents were below 25 years of age, 189 (25.54%) respondents were 25-34 years old,

146 respondents (19.75%) were between 35- 44 years old, 62 (8.37%) respondents were

42.29

25.54

19.75

8.374.05

05

1015202530354045

Less than 25

years

25-34 years

35-44 years

45-54 years

Above 55

years

64.18%

35.82%

0.00%

20.00%

40.00%

60.00%

80.00%

Male Female

Gender

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between 45-54 years old and only 30 (4.05%) respondents were those above 55 years The

graph also indicates that there was a fair representation of young respondents.

Table 5. 3: Characteristics of the respondents on the basis of location or residential

status (N=740)

The total numbers of respondents were 740, where 333 respondents were those from rural

background and 407 respondents from urban areas as shown in table 5.3. The graphical

representation is also provided herewith.

Region Frequency Percentage

Rural 333 45.00%

Urban 407 55.00%

Total 740 100.00%

Table 5.4: Characteristics of the respondents on the basis of educational qualification

(N=740)

Education Frequency Percentage

Below Secondary Level 13 1.75%

Secondary –Sr. Secondary level 45 6.08%

Bachelors Degree 324 43.78%

Master Degree (PG) 138 18.64%

Others 220 29.72%

Total 740 100

45%

55%

0%

10%

20%

30%

40%

50%

60%

Rural Urban

Location

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As shown in table 5.4, respondents were from different educational backgrounds. In t

The sample surveyed 13 (1.75%) respondents were studied up to below tenth standard, 45

(6.08%) respondents were studied up to secondary to senior secondary level.

It was also observed that out of 740 only 324 (43.78%) respondents were graduates, 138

(18.64%) respondents were post graduates and 220 (29.72%) respondents were having

other type of educational qualifications like diploma, professional qualification. The

graph also represents that there was a fair percentage of graduate respondents.

Table 5.5: Characteristics of the respondents on the basis of monthly income (N=740)

Monthly

Income

Frequency Percentage

Up to Rs 10000 263 35.54%

Rs. 10001 to

25000

84 11.35%

Rs 25001 to

40000

223 30.15%

RS. 40001 to

65000

135 18.24%

Rs. 65001 and

above

35 4.72%

Total 740 100.0

1.75%6.08%

43.78%

18.64%

29.72%

0.00%5.00%

10.00%15.00%20.00%25.00%30.00%35.00%40.00%45.00%50.00%

Below Secondary Level

Secondary level Bachelor degree Master Degree (PG)

Others

Education

35.54%

11.35%

30.15%

18.24%

4.72%

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

40.00%

Up to Rs

10000

Rs. 10001

to 25000

Rs 25001

to 40000

RS. 40001

to 65000

Rs. 65001

and above

Income

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On the basis of monthly income groups of the telecom subscribers it was observed that

263 (35.54%) respondents were those earning less than Rs.10000 monthly, 84 (11.35%)

respondents were those earning between Rs. 10001-25000, 223 (30.15%) respondents

were those earning between Rs. 25001-40000, 135 (18.24%) respondents were those

earning between 40001-65000, and only 35 (4.72%) were earning Rs.65001 and above as

shown in table 5.5.

Table 5.6: Characteristics of the respondents on the basis of their occupation (N=740)

Occupation Frequency Percent

Agriculture 72 9.72%

Self Employed-Shop 80 10.81%

Self Employed-Other 120 16.23%

Business Owner 125 16.89%

Service Professionals Pvt. 109 14.72%

Govt. Employees 108 14.59%

Student 96 12.97%

Retired 30 4.05%

Total 740 100

9.72%10.81%

16.23% 16.89%

14.72% 14.59%12.97%

4.05%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

18.00%

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As shown in table 5.6, respondents were surveyed from different occupational

backgrounds. Seventy two (9.72%) respondents were farmers, 80 (10.81%) respondents

were shop owners, 120 (16.23%) respondents were self employed, 125 (16.89%)

respondents were business owners, 109 (14.72%) respondents were serving in private

sector, 108 (14.59%) were government employees, 96 (12.97%) respondents were

students, 30 (4.05%) respondents were retired persons. The graph also represents that

there was a fair percentage of service professionals followed by government employees

in the sample size.

Table 5.7: Characteristics of the respondents on the basis of their current telecom

Service Provider (N=740)

Company Frequency Percentage

BSNL 215 29.05

AIRTEL 276 37.29

Reliance 98 13.26

VODAFONE 151 20.40

Total 740 100.0

The table 5.7 shows that in the sample surveyed 215 (29.05%) respondents were using

telecom services of BSNL, 276 (37.29%) of AIRTEL, 98 (13.26%) of Reliance and 151

(20.40%) opted the telecom services of VODAFONE. It is clear that majority of the

customers who undertook the survey use telecom services of AIRTEL and the least

preferred service provider is Reliance.

29.05%

37.29%

13.26%

20.40%

0.00%5.00%

10.00%15.00%20.00%25.00%30.00%35.00%40.00%

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Table 5.8: Characteristics of the respondents on the basis of the type of mobile

connection subscribed (N=740)

Type of

Mobile

connection

Frequency

ncy

Percent

Percen

Percent

Post paid 122 16.48%

Pre Paid 618 83.51%

Total 740 100.00%

It was also observed that out of 740 only 122 (16.48%) respondents had subscribed to

postpaid mobile services and 618 (83.51%) subscribed for pre paid mobile services. The

graph also represents that there was a high percentage of prepaid telecom subscribers.

Table 5.9: Characteristics of the respondents on the basis of their history of relationship

with current service provider (N=740)

Duration of

Relationship with

service providers


N

Less than 2 years 310 41.89

2 to less than 3 years 183 24.72

3 o less than 4 years 115 15.54

4 to less than 5 years 76 10.29

Above 5 years 56 7.56

Total 740 100.0

16.48%

83.51%

0.00%

20.00%

40.00%

60.00%

80.00%

100.00%

Post Paid Prepaid

41.89%

24.72%

15.54%10.29%

7.56%

0.00%5.00%

10.00%15.00%20.00%25.00%30.00%35.00%40.00%45.00%

Less than 2 years

2 to less

than 3 years

3 o less

than 4 years

4 to less

than 5 years

Above 5

years

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Table 5.9 shows that in the sample surveyed 310 (41.89%) respondents had maintained a

relationship with current telecom service provider since less than 2 years, 183 (24.72%)

for a period of 2 to less than 3 years, 115 (15.54%) for a period of 3 to less than 4 years,

76 (10.29%) for a period of 4 to less than 5 years and only 56 (7.56%) continued with the

same service provider for more than 5 years.

5.3 Reliability and Validity Analysis

Measurement is the assigning of numbers to observation in order to quantify

phenomenon. In customer retention many of the phenomena such as service quality,

customer satisfaction and switching barriers are abstract concepts known as theoretical

constructs. Measurement involves the operationalization of these constructs in defined

variables and the development and application of instruments or tests to quantify these

variables. This section focuses primarily on testing of reliability and validity issues

involved in the research process.

Reliability: The concepts of reliability and validity are core issues in a research process.

Together they are the core of what is accepted as scientific study.

Reliability Analysis: The idea behind reliability is that any significant result must be

more than a one of finding and be inherently repeatable. If other researchers perform the

same experiment under the same conditions, the results will be the same. Without the

replication of statistically significant results the experiment and research have not

fulfilled all of the requirements of testability. In the research study, the internal

consistency reliability is measured with the help of Cronbach alpha statistic.

Validity Analysis: Validity is defined as the extent to which the instrument measures

what it proposes to measure. There are different types of validity including content

validity, face validity, criterion validity, construct validity, etc. These different types of

validity are discussed below:

Content validity: The content validity of a construct can be defined as the degree to

which the measure spans the domain of the constructs. For the present study, the content

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validity of the instrument was ensured as customer retention dimensions and items were

identified from the literature and were thoroughly reviewed by professionals and

academicians. The best practice to ensure the content validity is to show the set of

possible variables in the construct to five academicians as well as five industry experts.

After analyzing the advice received from these experts, the constructs along with the set

of variables is finalized. In this way the issue of content validity is resolved.

Construct validity: It involves the assessment of the degree to which an

operationalization correctly measures its targeted variables. Establishing construct

validity involves the empirical assessment of uni-dimensionality, reliability and validity

(convergent and discriminant validity). In the present study, in order to check for uni-

dimensionality, a measurement model was specified for each construct and CFA is run

for all the constructs taken together. Individual items in the model are examined to see

how closely they represent the same construct. A comparative fit index (CFI) of 0.80 or

above for the model implies that there is a strong evidence of uni-dimensionality. The

CFI values obtained for all the seven dimensions in the scale are equal to or above 0.80 as

shown in the respective constructs.

Convergent validity: Convergent and Divergent validity are ways to assess the construct

validity of a measurement procedure. Convergent validity helps to establish construct

validity when the researcher used two different measurement procedures and research

methods in the research study to collect data about a construct. The discriminant validity

helps to establish construct validity by demonstrating that the construct is different from

other constructs. Convergent validity is the degree to which multiple methods of

measuring a variable provide the same results whereas discriminant validity is the degree

to which measures of different latent variables are unique. Discriminant validity is

ensured if a measure does not correlate very highly with other measures from which it is

supposed to differ.

Discriminant validity: It is the degree to which the measures of different latent

variables are unique. Discriminant validity is ensured if a measure does not correlate very

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highly with other measures from which it is supposed to differ. For assessing

discriminant validity, two chi-square comparison models were considered. The two

comparison models are referred as Model 1 and Model 2. The comparison of chi-square

statistic for Model 1 and Model 2 provides support for discriminant validity.

Criterion-related validity: It is established when a criterion, external to the

measurement instrument is correlated with the factor structure. A construct can be

defined as the latent variable which cannot or difficult to be measured directly from the

respondents. Hence a set of variables is to be included in the construct for its

measurement. Before finalizing the set of variables in the construct the content validity is

to be assured. After ascertaining the content validity the next issue was to analyze the

validity of each individual construct. The construct validity consists of convergent

validity, discriminant validity and face validity. The convergent validity can be tested

with help of factor loadings of each individual variable to the construct. The high Factor

loadings indicate convergent validity and since high factor loadings indicate that the

variable is highly explained by the construct, it will not be explained by any other

construct which indicates the presence of discriminant validity. The description of

various constructs, the set of variables in each construct and their mean, standard

deviation, composite reliability and average variance explained are shown below:-

First Construct: Tangibility

Second Construct: Reliability

Third Construct: Responsiveness

Fourth Construct: Assurance

Fifth Construct: Empathy

Sixth Construct: Network Quality

Seventh Construct: Convenience

Eight construct: Satisfaction from technical factors

Ninth construct: Satisfaction from value added services

Tenth construct: Satisfaction from convenience

Eleventh construct: Interpersonal Relationship

Twelfth construct: Switching Cost

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Thirteenth construct: Attractiveness of Alternatives

Fourteenth construct: Customer retention

5.3 Perceived Service Quality: Table 5.10: Mean, S.D., Cronbach Alpha, Average

Variance Extracted and Composite reliability of the variables in different construct

considered in the study

Construct Included Measured Variable Mean

(S.D.) Cronbach

Alpha

Average

Variance

Extracted

(AVE)

Composite

Reliability

(CR)

Tangibility Up to date Equipment 3.33 .964 .894 .682 .895

Visually appealing Physical facilities 3.28 .829

Service staff appear neat & well

dressed 3.26 .979

Physical facilities match with

telecom services 3.33 .953

Reliability Keep Promise 3.32 .754 .914 .672 .911

Sympathetic & reassuring 3.25 .855

Dependable 3.02 .944

Provide service at promised time 3.19 .807

Keep records accurately 3.17 .853

Responsiveness Exactly tell when service will be

performed 2.24 .926

.857 .616 ..863

Not realistic to expect prompt

service form staff 2.32 .872

Don’t always have to willing to help

customers 2.47 .870

Ok if staff is too busy to respond 2.37 .962

Assurance Able to trust on customer service

staff 2.67 .864

.877 .717 .910

Feel safe in my transaction 2.73 .962

Customer service staff should be

polite 2.84 .937

Should get adequate support 2.98 .955

Empathy Individual attention should not be

expected 2.06 .993

.881 .691 .916

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Can't be expected to give customer

personal attention 2.27 .962

Unrealistic to expect to know

customer needs 2.20 .953

Unrealistic to expect convenient

hours 2.03 .857

Network

Quality

Sufficient geographic coverage 2.33 .964 .922 .703 .921

pre mature termination free call 3.56 .922

Voice clarity 3.59 .983

Call connected during first attempt 3.65 .967

Able to make call at peak hours 3.50 .996

Convenience Convenient business hours 3.46 .993 .881 .940 .984

Mechanism of easy lodging of

queries/complaints 3.41 .993

Flexibility in payment of bills 3.33 .816

Simple application formalities 3.35 .877

Satisfaction: Table 5.11: Mean, S.D., Cronbach Alpha, Average Variance Extracted and

Composite reliability of the variables in different construct considered in the study


(S.D.) Cronbach

Alpha

Average

Variance

Extracted

(AVE)

Composite

Reliability

(CR)

Technical

Factors

Network Connectivity 3.9400 .99687 .892 0.681

0.894

Coverage 3.7200 .93765

Roaming Facility 3.8300 .99393

Voice Clarity 3.7000 .96922

Value Added

Services

Tariff/call rate 3.9600 .98391 .901 0.693

0.900

Value added service 3.6500 .95743

Transparency in billing 3.7600 .93333

Sales promotion offers 3.8700 .95078

Convenience Ease of availability of Recharge 3.8700 .96035 .889 0.673

0.890

Customer care service 3.4100 .94538

Advertisement 3.7500 .96719

Dealer network 3.6400 .95896

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Switching Barriers: Table 5.12: Mean, S.D., Cronbach Alpha, Average Variance Extracted and

Composite reliability of the variables in different construct considered in the study


(S.D.) Cronbach

Alpha

Average

Variance

Extracted

(AVE)

Composite

Reliability

(CR)

Interpersonal

Relationship

Bond with telecom operator 3.630 .91691 .959 .771 .959

Personal Friendship with

telecom operator 3.4700 .95867

Comfortable 3.7200 1.11083

Miss the operator if switch 3.7000 .96922

Lose a friendly & comfortable

relationship if change 3.6500 .98391

Like public image of operator 3.3800 .95743

Caring 3.4900 .93333

Switching Cost Switching is hassle 3.5900 .95078 .896 .638 .898

Cost a lots of money 3.2300 .96922

Cost of lots of time 3.2900 .88317

Lots of Efforts to switch 3.3300 .96415

Prices of other operator are

higher 3.2800 .93778

Attractiveness

of Alternatives

Don’t care about the brand 3.6300 .90626 .882 .523 .884

Trust on telecom operator 3.3600 .82612

Likely to switch 3.2500 .87887

Hate spending time in finding

new operator 3.1700

.81938

Not certain about the quality of

services other operator will

provide

3.3700

.93430

Risk in switching 3.3800 .87384

Feel uncertain 3.0700 .67632

148

5.3.1 Construct Validity

5.3.1 (a) Tangibility: The first construct defined as the “Tangibility” is shown below

in figure 5.1. This construct is designed to analyses the tangibles include the appearance

of physical facilities, equipment, personnel and communication material in mobile

telecom services. This construct consists of four measured variables defined as below:

Up to date equipment,

Physical facilities are visually appealing

Service staff appears neat & clean

Physical facilities matching with telecom services

The condition of the physical surroundings is tangible evidence of the care and attention

to details exhibited by the service provider. When a customer uses mobile phone services

of a telecom company, tangibles may affect the perception of that customer with

reference to the mobile services provided by the service provider. These attributes are

measurable in nature and express the defined construct. In order to analyze the structure

of the construct and measured variables, the construct analysis is done in the research

study. The construct “Tangibility” along with the measured variables is shown in figure

5.1. The regression weights of each measured variable are estimated and shown in table

5.13. The results indicate that all the regression weights are high (greater than 0.5) and

significant. Hence the convergent validity of the construct is ensured and can be

concluded that the construct significantly explains the variables. The standardized

regression weights as well as the multiple squared correlations of the individual variables

are shown in table. The standardized regression weights indicate comparative influence

of the construct to its variables. The high value of the standardized regression weights

indicates the higher influence of the construct to the variable. The squared multiple

correlations indicate the percentage of variance of the measured variable that can be

explained with the help of the variations in the construct.

The results as shown in table 5.13 indicate that the tangibility is highly influenced by the

variable “physical facilities matching with telecom services”. This is due to the fact that

when a customer is going to use the telecom services provided by a telecom company, he

149

may give more weight to the physical facilities provided by that company. The next most

influencing measured variable for the construct tangibility is “Service staff appears neat

& clean”. The least influence (but statistically significant) of the construct is on the

variable “Physical facilities are visually appealing”. The squared multiple correlations of

the measured variable “physical facilities matching with telecom services” indicate that

the 75.5 percent of the variance of the variable is explained by the construct.

Figure 5.1: Tangibility

Table 5.13: Tangibility

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

Up to date

equipments <---

Tangibility

.793 1.00

.628

Physical

facilities are

visually

appealing

<--- .782 .956 .115 8.290 *** .611

Service staff

appears neat

& clean

<--- .855 .999 .108 9.224 *** .730

150

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

Physical

facilities

matching

with telecom

services

<--- .869 1.07 .114 9.385 *** .755

5.3.1(b) Reliability: The second construct defined as the “Reliability” is shown below

in figure 5.2. This construct is designed to analyses the reliability of mobile telecom

services offered by selected telecom operators. This construct consists of five measured

variables defined as below:

Keep Promises,

Sympathetic & reassuring

Dependable

Provide service at promised time

Keep records accurately

Reliability is the ability to perform the promised service dependably and accurately.

Reliable service performance means that the service is accomplished on time, every time,

in the same manner, and without errors. Reliability extends into the back office, as well

as where accuracy in billing and records keeping is expected. When a customer uses

mobile phone services of a telecom company, reliability offered by that service affects

the perception of that customer with reference to the mobile services provided by the

service providers. These attributes are measurable in nature and express the defined

construct. In order to analyze the structure of the construct and measured variables, the

construct analysis is done in the research study. The construct “Reliability” along with

the measured variables is shown in the figure 5.2. The regression weights of each

measured variable are estimated and shown in table 5.14. The results indicate that all the

regression weights are high (greater than 0.5) and significant. Hence the convergent

validity of the construct is ensured and can be concluded that the construct significantly

explains the variables. The standardized regression weights as well as the multiple

151

squared correlations of the individual variables are shown in table .The standardized

regression weights indicate comparative influence of the construct to its variables. The

high value of the standardized regression weights indicates the higher influence of the

construct to the variable. The squared multiple correlations indicate the percentage of

variance of the measured variable that can be explained with the help of the variations in

the construct.

The results as shown in table 5.14 indicate that the perceived reliability is highly

influenced by the variable “Provide service at promised time”. This is due to the fact that

when a customer is going to use mobile telecom services, he/she will definitely evaluate

the reliability of that service provider whether the service is accomplished on promised

time or not. The next most influencing measured variable for the construct reliability is

“Sympathetic & reassuring”. The least influence (but statistically significant) of the

construct is on the variable “Keep records accurately”. The squared multiple correlation

of the measured variable “Provide service at promised time” indicates that the 73.1

percent of the variance of the variable is explained by the construct.

152

Figure 5.2: Reliability

Table 5.14: Reliability

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

Keep

Promises <---

Reliability

.846 1.00

.716

Sympathetic

& reassuring <--- .840 1.004 .98 10.216

*** .706

Dependable <--- .798 .854 .90 9.445 ***

.637

Provide

service at

promised

time

<--- .855 .970 .92 10.447

***

.731

Keep records

accurately <--- .783 .877 .95 9.184

*** .614

5.3.1 (c) Responsiveness: The third construct defined as the “Responsiveness” is

shown below in figure 4.2. This construct is designed to analyses responsiveness of

mobile telecom services offered by selected telecom operators. This construct consists of

five measured variables defined as below:

Exactly tell when service will be performed,

Not realistic to expect prompt service form staff

Don’t always willing to help customers

Provide service at promised time

Ok if staff is too busy to respond

Responsiveness is the willingness of the firms’ staff to help customers and provide

prompt service. These attributes are measurable in nature and express the defined

153

construct. In order to analyze the structure of the construct and measured variables, the

construct analysis is done in the research study. The construct “Responsiveness” along

with the measured variables is shown in the figure 5.3. The regression weights of each

measured variable are estimated and shown in table. The results indicate that all the

regression weights are high (greater than 0.5) and significant. Hence the convergent

validity of the construct is ensured and can be concluded that the construct significantly

explains the variables. The standardized regression weights as well as the multiple

squared correlations of the individual variables are shown in table .The standardized





the construct.

The results as shown in table 5.15 indicate that the perceived responsiveness is highly

influenced by the variable “Exactly tell when service will be performed”. This is due to

the fact that when a customer is going to use mobile telecom services, he/she would

definitely like to assure about the time of execution of service. The next most influencing

measured variable for the construct reliability is “Not realistic to expect prompt service

form staff”. The least influence (but statistically significant) of the construct is on the

variable “don't always willing to help customers”. The squared multiple correlation of the

measured variable “Exactly tells when service will be performed” indicates that the 80.1

percent of the variance of the variable is explained by the construct.

154

Figure 5.3: Responsiveness

Table 5.15: Responsiveness

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

Exactly tell

when service

will be

performed

<---

Responsiveness

.912 1.000

.801

Not realistic

to expect

prompt

service form

staff

<--- .838 .960 .095 10.132 ***

.703

don't always

willing to

help

customers

<--- .711 .661 .081 8.116 ***

.506

Ok if staff is

too busy to

respond

<-- .662 .783 .107 7.342 ***

.438

155

5.3.1 (d) Assurance: The fourth construct defined as the “Assurance” is shown below

in figure 5.4. Assurance relates to the knowledge and courtesy of employees and their

ability to convey trust and confidence. The assurance dimension includes competence to

perform the service offered, politeness and respect for the customer, effective

communication with the customer, and the general attitude that the service provider has

towards the customer’s best interest at heart.

This construct is designed to analyses the level of assurance rendered by selected telecom

operators. This construct consists of four measured variables defined as below:

Able to trust on customer service staff

Feel safe in transactions

Customer service is polite

Received adequate support

When a customer uses mobile phone services of a telecom company, assurance offered

by that service affects the perception of that customer with reference to the mobile

services provided by the service providers. These attributes are measurable in nature and

express the defined construct. In order to analyze the structure of the construct and

measured variables, the construct analysis is done in the research study. The construct

“Assurance” along with the measured variables is shown in the figure 5.4. The regression

weights of each measured variable are estimated and shown in table. The results indicate

that all the regression weights are high (greater than 0.5) and significant. Hence the

convergent validity of the construct is ensured and can be concluded that the construct

significantly explains the variables. The standardized regression weights as well as the

multiple squared correlations of the individual variables are shown in table .The

standardized regression weights indicate comparative influence of the construct to its

variables. The high value of the standardized regression weights indicates the higher

influence of the construct to the variable. The squared multiple correlations indicate the

percentage of variance of the measured variable that can be explained with the help of the

variations in the construct.

156

The results as shown in table 5.16 indicate that the assurance is highly influenced by the

variable “Customer service is polite”. This is due to the fact that when a customer is

going to use mobile telecom services, he/she will definitely evaluate whether the service

is politely rendered to him. The next most influencing measured variable for the construct

reliability is “Feel safe in transactions”. The least influence (but statistically significant)

of the construct is on the variable “Able to trust on customer service staff”. The squared

multiple correlation of the measured variable “Provide service at promised time”

indicates that the 70.4 percent of the variance of the variable is explained by the

construct.

Figure 5.4: Assurance

Table 5.16: Assurance

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardize

d Regression

Estimate

S.E

.

C.R

. P

Squared

Multiple

Correlation

Able to trust on

customer service

staff

<--- Assurance

.725 1.000

.526

157

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardize

d Regression

Estimate

S.E

.

C.R

. P

Squared

Multiple

Correlation

Feel safe in

transactions <--- .827 1.041 .135 7.723 ***

.685

Customer service

is polite <--- .839 1.229 .157 7.811 ***

.704

Received

adequate support <-- .820 1.219 .159 7.659 ***

.672

5.3.1 (e) Empathy: The fifth construct defined as the “Empathy” is shown below in

figure 5.5 This construct is designed to analyses the level of empathy perceived by

mobile telecom customers with reference to the service provided by selected telecom

operators. Empathy is the provision of caring, individualized attention to customers.

Empathy includes approachability, sense of security, and the efforts to understand the

customers’ needs. This construct consists of five measured variables defined as below:

Get individual attention,

Personal attention to customer

Know customer needs

Customer benefit from heart

Convenient business hours

When a customer uses mobile telecom services of a telecom company, empathy

perceived by customer with reference to the mobile services provided by the service

providers will play a key role in determining the perceived level of service quality. These

attributes are measurable in nature and express the defined construct. In order to analyze

the structure of the construct and measured variables, the construct analysis is done in the

research study. The construct “Empathy” along with the measured variables is shown in

the figure 5.5. The regression weights of each measured variable are estimated and shown

in table. The results indicate that all the regression weights are high (greater than 0.5) and



158


are shown in table .The standardized regression weights indicate comparative influence





The results as shown in table 5.17 indicate that the empathy is highly influenced by the

variable “Know customer needs”. This is due to the fact that when a customer is going to

use mobile telecom services, he/she will definitely evaluate whether the service is

designed according to his needs. The next most influencing measured variable for the

construct reliability is “Customer benefit from heart”. The least influence (but statistically

significant) of the construct is on the variable “Convenient hours”. The squared multiple

correlations of the measured variable “Know customer needs” indicate that the 75 percent

of the variance of the variable is explained by the construct.

Figure 5.5: Empathy

159

Table 5.17: Empathy

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

Get individual

attention <---

Empathy

.757 1.000

.573

Personal

attention to

customer

<--- .725 .929 .129 7.199 ***

.526

Know

customer

needs

<--- .866 1.099 .127 8.676

*** .750

Customer

benefit from

heart

<--- .833 1.120 .134 8.364

*** .695

Convenient

hours <--- .695 .985 .143 6.873 ***

.484

5.3.1(f) Network Quality: The sixth construct defined as the “Network quality” is

shown below in figure 5.6. This construct is designed to analyses the level of network

quality perceived by mobile telecommunication customers. This construct consists of


Sufficient geographical Coverage,

Provides termination free calls

Voice clarity

Call connected in first attempt

Able to make call at peak hours

Network quality is an indicator of mobile network performance in terms of voice quality,

call drip rate, network coverage and network congestion. In the context of cellular

mobile, communication network quality is a very important dimension. It is the capability

of a mobile network to provide services and to fulfill user’s expectations. These attributes

of network quality are measurable in nature and express the defined construct. In order to

160

analyze the structure of the construct and measured variables, the construct analysis is

done in the research study. The construct “network quality” along with the measured

variables is shown in the figure 5.6. The regression weights of each measured variable are

estimated and shown in table 5.18. The results indicate that all the regression weights are

high (greater than 0.5) and significant. Hence the convergent validity of the construct is

ensured and can be concluded that the construct significantly explains the variables. The

standardized regression weights as well as the multiple squared correlations of the

individual variables are shown in table .The standardized regression weights indicate

comparative influence of the construct to its variables. The high value of the standardized

regression weights indicates the higher influence of the construct to the variable. The

squared multiple correlations indicate the percentage of variance of the measured variable

that can be explained with the help of the variations in the construct.

The results as shown in table 5.18 indicate that the network quality is highly influenced

by the variable “voice clarity”. This is due to the fact that when a customer makes a call

on mobile then voice clarity is of immense importance for him, if some disturbance is

there during conversation on mobile phone that means network quality is poor.

Customers always give more weight-age to network quality provided by their service

provider. The next most influencing measured variable for the construct network quality

is “provides termination free calls”. This is natural as the termination free calls by the

telecom service provider provide satisfaction to the customers. The next influencing

measured variable for the construct network quality is “Sufficient geographic Coverage”

The least influence (but statistically significant) of the construct is on the variable “Able

to make call at peak hours”.

161

Figure 5.6: Network Quality

Table 5.18: Network Quality

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

Sufficient

geographical

Coverage

<---

Network

Quality

.895 1.000

.498

Provides

termination free

calls

<--- .908 1.243 .142 8.728 ***

.825

Voice clarity

.952 1.283 .141 9.084 *** .906

call connected in

first attempt <--- .858 1.189 .144 8.263 ***

.736

Able to make call

at peak hours <-- .776 1.016 .136 7.481 ***

.602

162

5.3.1 (g) Convenience: The seventh construct defined as the “convenience” is shown

below in figure 5.7. This construct is designed to analyses the flexible and comfortable

facilities to suit the customers’ needs. This construct consists of four measured variables

defined as below:

Has convenient business hours,

Easy mechanism of queries and complaint lodging

Has flexibility in bills payment

Application formalities are simple

In mobile telecom services the convenience construct may depend upon various

attributes. These attributes are measurable in nature and express the defined construct. In

order to analyze the structure of the construct and measured variables, the construct

analysis is done in the research study. The construct “convenience” along with the

measured variables is shown in the figure 5.7. The regression weights of each measured

variable are estimated and shown in table 5.19. The results indicate that all the regression

weights are high (greater than 0.5) and significant. Hence the convergent validity of the

construct is ensured and can be concluded that the construct significantly explains the

variables. The standardized regression weights as well as the multiple squared

correlations of the individual variables are shown in table 5.19 .The standardized





the construct.

The results as shown in table 5.19 indicate that the convenience is highly influenced by

the variable “Has convenient business hours”. This is due to the fact that when a

customer is going to use mobile telecom services, he may have some expectation about

convenient business hours. The next most influencing measured variable for the construct

convenience is “Easy mechanism of queries and complaint lodging”. This is natural as

the customer always wants the service provider to listen to his complaints and queries

effectively and efficiently. The next influencing measured variable for the construct

163

convenience is “Application formalities are simple”. The customer always expect to have

simple formalities with reference to application and if wants to make some change in

tariff plans etc. The least influence (but statistically significant) of the construct is on the

variable “Has flexibility in bills payment”. The squared multiple correlation of the

measured variable “Has convenient business hours” indicate that the 81.5 percent of the

variance of the variable is explained by the construct.

Figure 5.7: Convenience

Table 5.19: Convenience

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

Has convenient

business hours <---

Convenience

.903 1.000

.815

Easy

mechanism of

queries and

complaint

lodging

<--- .846 .859 .081 10.565 ***

.716

Has flexibility

in bills <--- .714 .674 .082 8.250 ***

.511

164

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

payment

Application

formalities are

simple

<-- .748 .748 .085 8.836 ***

.560

5.3.1 (h) Interpersonal Relationship: The eighth construct defined as the “Interpersonal

Relationship” is shown below in figure 5.8. This construct is designed to analyses the

level of interpersonal relationship between customer and telecom service provider that

works as barrier in customer switching. This construct consists of seven measured


Bond with telecom operator,

Personal Friendship with telecom operator

Comfortable

Miss the operator if switch

Lose a friendly & comfortable relationship if change

Like public image of operator

My telecom operator is Caring

Interpersonal relationship means a psychological and social relationship that manifests

itself as care, trust, intimacy and communication (Gremler, 1995). The interpersonal

relationship built through recurrent interactions between a telecom operator and a

customer can strengthen the bond between them and finally lead to a long-term

relationship. Telecom companies are not alone in desiring a sustained relationship. These

attributes are measurable in nature and express the defined construct. In order to analyze

the structure of the construct and measured variables, the construct analysis is done in the

research study. The construct “Interpersonal relationship” along with the measured

variables is shown in figure 5.8. The regression weights of each measured variable are

estimated and shown in table 5.20. The results indicate that all the regression weights are

high (greater than 0.5) and significant. Hence the convergent validity of the construct is

165



individual variables are shown in table .The standardized regression weights indicate





The results as shown in table 5.20 indicate that the interpersonal relationship is highly

influenced by the variable “Bond with telecom operator”. This is due to the fact that if

there is bond between customer and telecom operator, it will lead to sustained

relationship. The next most influencing measured variable for the construct Perceived

cost is “Miss the operator if switch”. This is natural as the sustained relationships offers a

lot of benefits to the customers, such as social benefits (reducing anxiety), economic

benefits (discount, time saving) and customization that commit themselves to establishing

relationships with a telecom operator that provide superior value benefits and create a

panic in the customer’s mind to miss the same if switch. The next influencing measured

variable for the construct interpersonal relationship is “ Comfortable”. When

customers are comfortable with service provider it will lead to building interpersonal

relationship. The next influencing measured variable for the construct interpersonal

relationship is “Personal Friendship with telecom operator”. With the passage of time if

customer is comfortable with the telecom service provider then personal friendship may

get developed between customer & telecom operator. The next influencing measured

variable for the construct interpersonal relationship is “Lose a friendly & comfortable

relationship if change”. Customer may have panic in his mind to lose a friendly &

comfortable relationship if switch to another telecom operator. The least influence (but

statistically significant) of the construct is on the variable “My telecom operator is

Caring”. The squared multiple correlation of the measured variable “Bond with telecom

operator” indicates that the 94.6 percent of the variance of the variable is explained by

the construct.

166

Figure 5.8: Interpersonal Relationship

Table 5.20: Interpersonal Relationship

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

Bond with

telecom operator

Interpersonal

Relationship

.972 1.000

.946

Personal

Friendship with

telecom operator

.884 .901 .054 16.727 ***

.782

Comfortable

<- .905 .884 .048 18.365 ***

.819

Miss the operator if

switch <--- .915 .912 .047 19.268 ***

.837

Lose a friendly &

comfortable

relationship if

change

<--- .868 .857 .055 15.677 ***

.753

Like public image <--- .818 .829 .063 13.123 ***

.669

167

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

of operator

My telecom

operator is Caring <-- .767 .695 .062 11.225 ***

.588

5.3.1 (i) Switching Cost: The ninth construct defined as the “switching cost” is

shown below in figure 5.9. This construct is designed to analyses the perceptions of

telecom customers associated with changing service providers. This construct consists of


Switching is hassle,

Cost a lot of money

Costs lots of time

Lots of Efforts to switch

Prices of other operator are higher

Switching costs are customers’ perceptions of the time, money, and effort associated with

changing telecom service providers. The total economic and psychic cost associated with

changing from one alternative to another. Previous researches indicate that switching

costs have an important impact on firms’ performance in terms of customer retention in

the mobile telecommunication sector.

In order to analyze the structure of the construct and measured variables, the construct

analysis is done in the research study. The construct “switching cost” along with the

measured variables is shown in figure 5.9. The regression weights of each measured

variable are estimated and shown in table. The results indicate that all the regression

weights are high (greater than 0.5) and significant. Hence the convergent validity of the

construct is ensured and can be concluded that the construct significantly explains the

variables. The standardized regression weights as well as the multiple squared

correlations of the individual variables are shown in table .The standardized regression

weights indicate comparative influence of the construct to its variables. The high value of

168

the standardized regression weights indicates the higher influence of the construct to the

variable. The squared multiple correlations indicate the percentage of variance of the

measured variable that can be explained with the help of the variations in the construct.

The results as shown in table 5.21 indicate that the switching cost is highly influenced by

the variable “Costs lots of time”. This is due to the fact that when a customer is planning

to move to another telecom service provider may think that it will cost a lot of time to

him. The next most influencing measured variable for the construct switching cost is

“Switching is hassle”. Customers may feel that switching from one telecom service

provider to another is a hassle. The next influencing measured variable for the construct

switching cost is “Lots of Efforts to switch”. Customer may perceive that switching for

one service provider to another will require lots of effort and due to this they may cancel

to postpone the plan of switching.

The least influence (but statistically significant) of the construct is on the variable “Cost a

lot of money”. Monetary cost associated with switching has been considered by the

consumer least important in case of telecom services. The squared multiple correlation of

the measured variable “Cost of lot of time” indicates that the 70.3 percent of the variance

of the variable is explained by the construct.

169

Figure 5.9: Switching Cost

Table 5.21: Switching Cost

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

Switching is

hassle <---

Switching

Cost

.824 1.000

.678

Cost a lot of

money <--- .736 1.012 .126 8.015 ***

.541

Costs lots of

time <--- .839 1.262 .132 9.542 ***

.703

Lots of

Efforts to

switch

<--- .818 1.108 .120 9.234 ***

.669

Prices of

other

operator are

higher

<-- .773 1.118 .131 8.550 ***

.597

170

5.3.1 (j) Attractiveness of alternatives: The tenth construct defined as the

“Attractiveness of alternatives” is shown below in figure 5.10. This construct is designed

to analyze the perceptions of telecom customers regarding the extent to which viable

competing alternatives are available in the market. Several researches have shown that

when viable alternatives are lacking, the probability of terminating an existing

relationship decreases (Jones et al., 2008).This construct consists of seven measured


Don’t care about the brand,

Trust on telecom operator

Likely to switch

Hate spending time in finding new operator

Uncertain about the quality of services if switch

Risk in switching

Feel uncertain

Attractiveness of alternatives means the reputation, image and service quality of the

replacing telecom operator, which are expected to be superior or more suitable than those

of the existing telecom operator. These attributes are measurable in nature and express

the defined construct. In order to analyze the structure of the construct and measured

variables, the construct analysis is done in the research study. The construct

“Attractiveness of alternatives” along with the measured variables is shown in the figure






are shown in table .The standardized regression weights indicate comparative influence





171

The results as shown in table 5.22 indicate that the attractiveness of alternatives is highly

influenced by the variable “Hate spending time in finding new operator”. Telecom

customers may hate to spend time to search for new telecom operator this practice will

become a switching barrier. The next most influencing measured variable for the

construct attractiveness of alternatives is “Likely to switch”. This is natural because as

and when telecom customers feel strong attractiveness of other alternatives, they may be

like to switch. The customer is having the habit of comparing the cost of the service with

the value derived from the service.

The next influencing measured variable for the construct attractiveness of alternatives is

“Feel uncertain”. When customers feel uncertain about remaining with the same telecom

service provider, it may be due to attractiveness of alternatives. The least influence (but

statistically significant) of the construct is on the variable “Risk in switching”. The

squared multiple correlation of the measured variable “Hate spending time in finding new

operator” indicates that the 64.3 percent of the variance of the variable is explained by

the construct.

Figure 5.10: Attractiveness of Alternatives

172

Table 5.22: Attractiveness of Alternatives

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

Don’t care

about the

brand

<---

Attractiveness

of alternatives

.716 1.000

.512

Trust on

telecom

operator

<--- .703 1.218 .186 6.551 ***

.495

Likely to

switch <--- .723 1.190 .177 6.732 ***

.523

Hate spending

time in

finding new

operator

<--- .802 1.384 .186 7.423 ***

.643

Uncertain

about the

quality of

services if

switch

<--- .742 1.287 .187 6.902 ***

.551

Risk in

switching <--- .648 1.203 .199 6.045 ***

.420

Feel uncertain <--- .718 1.190 .178 6.682 *** .515

5.3.1 (k) Customer Satisfaction from technical factors : The eleventh construct

defined as the “Customer satisfaction with technical factors” is shown below in figure 5.11. This

construct is designed to analyze the satisfaction level of telecom customers about the technical

factors included in the telecom services. Four items were used to measure customer satisfaction

with technical factors. This construct consists of four measured variables defined as below:

Network Connectivity,

Coverage

Roaming Facility

Voice Clarity

173

Customer satisfaction constitutes a cardinal indicator of assessing the success of any business

organization. Satisfied customers are assets that ensure a regular cash flow for the business

organization in future. Customer satisfaction from technical factors is an experience-based

assessment made by the customer of how far his own expectations about the individual

characteristics or the overall technical functionality of the service obtained from the provider has

been fulfilled. These attributes are measurable in nature and express the defined construct. In

order to analyze the structure of the construct and measured variables, the construct analysis is

done in the research study. The construct “Customer satisfaction with technical factors” along

with the measured variables is shown in figure 5.11. The regression weights of each measured

variable are estimated and shown in table 5.23. The results indicate that all the regression weights

are high (greater than 0.5) and significant. Hence the convergent validity of the construct is


standardized regression weights as well as the multiple squared correlations of the individual

variables are shown in table .The standardized regression weights indicate comparative influence

of the construct to its variables. The high value of the standardized regression weights indicates

the higher influence of the construct to the variable. The squared multiple correlations indicate

the percentage of variance of the measured variable that can be explained with the help of the


The results as shown in table 5.23 indicate that the customer satisfaction from technical

factors is highly influenced by the variable “Network Connectivity”. This is due to the

fact that in telecom services, network connectivity is a major concern of consumers.

Network connectivity is a technical aspect of telecom service & if it is good, customers

may feel satisfied.

The next most influencing measured variable for the construct customer satisfaction from

technical factors is “Coverage”. In telecom service coverage has always been an important

consideration by consumers. Telecom customers generally prefer those telecom services which

provide wide coverage and it is also a matter of deciding the satisfaction level of the customers.

The next influencing measured variable for the construct customer satisfaction from technical

factors is “Roaming Facility”. For those customers who travel a lot, roaming facility is always

an important concern. The least influence (but statistically significant) of the construct is on the

variable “Voice Clarity”. The squared multiple correlation of the measured variable “Network

174

Connectivity” indicates that the 84.4 percent of the variance of the variable is explained by the

construct.

Figure 5.11: Satisfaction from technical factors

Table 5.23: Satisfaction from technical factors

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

Network

Connectivity <---

Satisfaction

From

technical

factors

.919 1.000

.844

Coverage <--- .857 .904 .078 11.643 *** .735

Roaming

Facility <--- .816 .890 .083 10.714 ***

.666

Voice Clarity <--- .690 .656 .081 8.119 *** .476

5.3.1 (l) Customer Satisfaction from Price &Value added services: The twelfth

construct defined as the “Customer Satisfaction from price Value added services” is

shown below in figure 5.12. This construct is designed to analyses the satisfaction level of

telecom customers about the price & value added services included in the telecom services.

This construct consists of four measured variables defined as below:

Tariff/call rate,

175

Value added service

Transparency in billing

Sales promotion offers

Customer satisfaction from price & value added services is an experience-based assessment made

by the customer of how far his own expectations about the individual characteristics or the overall

price & value added of the service obtained from the provider have been fulfilled.

The attributes like price and value added services are measurable in nature and express


variables, the construct analysis is done in the research study. The construct “Customer

Satisfaction from price & value added services” along with the measured variables is

shown in figure 5.12. The regression weights of each measured variable are estimated

and shown in table 5.24. The results indicate that all the regression weights are high

(greater than 0.5) and significant. Hence the convergent validity of the construct is



individual variables are shown in table 5.24 .The standardized regression weights indicate





The results as shown in table 5.24 indicate that the customer satisfaction from price &

value added services are highly influenced by the variable “Sales promotion offers”. This

is due to the fact that when a customer is going to buy a telecom service, he may have

some influence from the sales promotion offers. The next most influencing measured

variable for the construct is “Tariff/call rate”. Tariff/call rate plays a very important role

in deciding the satisfaction level of customers with reference to the price and value added

service delivered by the telecom service. The next influencing measured variable for the

construct customer satisfaction from price & value added service is “Value added

service”. The customer is having the habit of comparing the cost of the service with the

value added service delivered by the service. The least influence (but statistically

176

significant) of the construct is on the variable “Transparency in billing”. Transparency in

billing may be less important in case of prepaid mobile telecom customers and more

important in case of post paid telecom customers. The squared multiple correlation of the

measured variable “Sales promotion offers” indicates that the 77.6 percent of the variance

of the variable is explained by the construct.

Figure 5.12: Price and value added services

Table 5.24 Customer Satisfaction with price & value added services

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

Tariff/call rate <---

Value

added

services

.860 1.000

.740

Value added

service <--- .826 .934 .093 10.040 ***

.682

Transparency in

billing <--- .768 .847 .094 8.987 ***

.590

Sales promotion

offers .881 1.094 .099 11.006 ***

.776

177

5.3.1 (m) Customer Satisfactionfrom convenience factor: The thirteenth construct

defined as the “customer satisfaction from convenience” is shown below in figure 5.13.

This construct is designed to analyses the customer satisfaction level from the

convenience factor in telecom services. This construct consists of four measured


Ease of availability of Recharge,

Customer care service

Advertisement

Dealer network

Customer satisfaction from convenience is an experience-based assessment made by the customer

of how far his own expectations about the individual characteristics or the overall convenience &

customer care etc. of the service obtained from the provider have been fulfilled.

The attributes like convenience & customer care etc. are measurable in nature and express



Satisfaction from convenience” along with the measured variables is shown in figure






are shown in table 5.25.The standardized regression weights indicate comparative

influence of the construct to its variables. The high value of the standardized regression

weights indicates the higher influence of the construct to the variable. The squared

multiple correlations indicate the percentage of variance of the measured variable that can

be explained with the help of the variations in the construct.

The results as shown in table 5.25 indicate that the customer satisfaction from

convenience is highly influenced by the variable “Ease of availability of Recharge”. This

is due to the fact that in telecom services a customer has to recharge frequently and for

the same easy availability of recharge facility is of high concern always. The next most

178

influencing measured variable for the construct is “Dealer network”. The next

influencing measured variable for the construct customer satisfaction from convenience is

“Advertisement”. Now a day’s customers are dependent on advertisement for information

about any product or service. So if customers are getting relevant information about

telecom services via advertisement, it is a matter of convenience for customers. The least

influence (but statistically significant) of the construct is on the variable “Customer care

service”. Transparency in billing may be less important in case of prepaid mobile telecom

customers and more important in case of post paid telecom customers. The squared

multiple correlation of the measured variable “Ease of availability of Recharge” indicate

that the 87.6 percent of the variance of the variable is explained by the construct.

Figure 5.13: Customer Satisfaction from convenience

179

Table 5.25: Customer Satisfaction from convenience

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

Ease of

availability of

Recharge

<---

Convenience

.936 1.000

.876

Customer care

service <--- .718 .757 .085 8.853 ***

.516

Advertisement <--- .719 .773 .087 8.866 *** .517

Dealer network .888 .859 .067 12.865 *** .789

5.3.1 (n) Customer Retention: The fourteenth construct defined as the “Customer

retention” is shown below in figure 5.14. This construct is designed to analyses the

retention decision taken by the customer. This construct consists of eight measured


My operator would be my first choice

Plan to continue relationship

Recommend the operator

Encourage friends & relatives

Loyal to my operator

Said positive things about my operator

Relationship is important for me

My operator is first choice

Customer retention is the future propensity of the customers to stay with their service

provider Customer retention reflect from various behavior of customer like when

customer encourage friends and relatives to do business with the same operator to which

the customer is loyal.

The attributes like plan to continue relationship, recommend the operator, operator is

first choice and encourage friends and relatives are measurable in nature and express the

180

defined construct. In order to analyze the structure of the construct and measured


Retention” along with the measured variables is shown in the figure 5.14. The regression

weights of each measured variable are estimated and shown in table 5.26. The results

indicate that all the regression weights are high (greater than 0.5) and significant. Hence

the convergent validity of the construct is ensured and can be concluded that the construct

significantly explains the variables. The standardized regression weights as well as the

multiple squared correlations of the individual variables are shown in table 5.26. The

standardized regression weights indicate comparative influence of the construct to its

variables. The high value of the standardized regression weights indicates the higher

influence of the construct to the variable. The squared multiple correlations indicate the

percentage of variance of the measured variable that can be explained with the help of the


The results as shown in table 5.26 indicate that the customer retention is highly

influenced by the variable “My operator would be my first choice”. This is due to the fact

that in telecom services when a customer considers his telecom operator his first choice

that shows his loyalty towards the service provider. The next most influencing measured

variable for the construct is “Said positive things about my operator”. The next

influencing measured variable for the construct customer retention is “Plan to continue

relationship”. If a customer decides to continue with the same service provider that

means retention is there and telecom companies’ retention strategies are successful. The

least influence (but statistically significant) of the construct is on the variable

“Relationship is important for me”.. The squared multiple correlation of the measured

variable “My operator would be my first choice” indicates that the 98.9 percent of the

variance of the variable is explained by the construct.

181

Figure 5.14: Customer Retention

Table 5.26: Customer Retention

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

My operator would

be

my first choice

<---

Customer

Retention

.994 1.000

.989

Plan to continue

relationship <--- .935 .886 .035 24.974 ***

.874

Recommend the

operator <--- .908 .833 .047 17.633 ***

.767

Encourage friends

& relatives <--- .913 .868 .040 21.481 ***

.833

182

Measured

Variables Construct

Standardized

Regression

Estimate

Unstandardized

Regression

Estimate

S.E. C.R. P

Squared

Multiple

Correlation

Loyal to my

operator <--- .926 .877 .038 23.370 ***

.857

Said positive

things

about my operator

<--- .940 .886 .034 26.065 ***

.884

Relationship is

important for me <--- .876 .842 .040 20.910 ***

.825

My operator is first

choice <--- .923 .920 .040 23.014 ***

.853

5.4 Confirmatory factor analysis (CFA)

Confirmatory factor analysis (CFA) provides enhanced control for assessing unidimensionality

(i.e., the extent to which items on a factor measure one single construct) than exploratory factor

analysis (EFA) and is more in line with the overall process of construct validation. In this

research study, confirmatory factor analysis model is run through AMOS software. Confirmatory

Factor Analysis is a statistical technique used to verify the factor structure of a set of observed

variables. Confirmatory Factor Analysis (CFA) allows the researcher to test the hypothesis that a

relationship between observed variable and the underlying latent construct exists. The researcher

uses the knowledge of the theory, empirical research or both, postulates the relationship pattern a

priori and then tests the hypothesis statistically.

Confirmatory Factor Analysis could occur with the development of measurement

instruments such as satisfaction scales, attitude or customer service questionnaires. In this

research a blueprint is developed, questions written, appropriate scales were determined.

The research instrument was used after conducting spade work and pilot survey, data

collected and Confirmatory Factor Analysis completed. Confirmatory Factor Analysis

allows the researcher to test the hypothesis that a relationship between the observed

variables and their underlying latent construct (s) exists.

183

5.4.1 Perceived service quality: Perceived service quality is an overall evaluation of a

specific service firm that results from comparing the firms’ performance with the

customers’ general expectations of how the firm should perform in that industry. In this

research study seven dimensions of service quality (Tangibility, Reliability, Assurance,

Responsiveness, Empathy, Network Quality, and Convenience) has been included.

CFA is applied on various dimensions of perceived service quality in order to test the

construct validity [convergent and discriminant validity]. Results of the CFA are shown

below with the help of table and diagram.

184

Figure 5.15: CFA of Perceived Service Quality

185

Table 5.27: CFA of Perceived Service Quality

Table 5.28 Output of CFA, Perceived Service Quality

Dimensions Composite Reliability (CR) Average Variance Extracted (AVE) MSV ASV

Tangibility

0.895 0.682 0.581 0.126

Reliability

0.920 0.697 0.581 0.150

Responsiveness

0.863 0.616 0.205 0.038

Assurance

0.910 0.718 0.176 0.085

Empathy

0.916 0.690 0.205 0.043

Network

Quality

0.921 0.704 0.110 0.048

Convenience

0.895 0.940 0.171 0.039

All constructs of perceived service quality taken together are analyzed with the help of

confirmatory factor analysis (CFA). The purpose of applying CFA model is to check the

convergent & discriminant validity of the constructs as well as to identify correlation

between different constructs.

The conditions of convergent validity include:

(a) Composite reliability (CR) must be greater than average variance explained

(AVE).

CR AVE MSV ASV Convenience Preliability Presp Passurance Empathy NetQuality Tangibility

Convenience 0.984 0.940 0.171 0.039 0.970

Preliability 0.920 0.697 0.581 0.150 0.179 0.835

Presp 0.863 0.616 0.205 0.038 -0.043 -0.055 0.785

Passurance 0.910 0.718 0.176 0.085 0.413 0.419 0.021 0.847

Empathy 0.916 0.690 0.205 0.043 -0.021 -0.032 0.453 0.194 0.831

NetQuality 0.921 0.704 0.110 0.048 0.174 0.332 0.126 0.167 0.113 0.839

Tangibility 0.895 0.682 0.581 0.130 0.040 0.762 -0.059 0.317 -0.008 0.303 0.826

186

(b) Individual Composite reliability (CR) of the constructs should be greater than .5.

(c) The individual average variance explained (AVE) should be greater than .5.

The results of CFA as shown in table 5.27 & 5.28 reveal that all above mentioned

conditions of convergent validity are fulfilled. Hence, it can be concluded that the

constructs are valid in terms of convergent validity.

In addition to this the different conditions of discriminant validity are as follows:

(a) Average variance explained (AVE) should be greater than MSV.

(b) AVE should be greater than ASV.

(c) AVE should be greater than .5

The results of CFA as shown in table 5.27 reveal that all above mentioned conditions of

discriminant validity are fulfilled. Hence, it can be concluded that the constructs are valid

in terms of convergent validity.

5.4.2 Customer Satisfaction

Customer satisfaction refers to the assessment of all interactions with product or service

from a provider, relative to the expectations. It seems logical that a highly satisfied

customer would be retained customer. In this research study the efforts are made to

analyze the impact of customer satisfaction in mobile telecom sector on customer

retention. CFA is applied on the various dimensions of customer satisfaction in order to

test the construct validity [convergent and discriminant validity]. Results of the CFA are

shown below with the help of table and diagram.

187

Figure 5.16: CFA of Customer Satisfaction

All constructs of customer satisfaction taken together analyzed with the help of





(d) Composite reliability (CR) must be greater than average variance explained

(AVE).

(e) Individual Composite reliability (CR) of the constructs should be greater than .5.

(f) The individual average variance explained (AVE) should be greater than .5.

188

5.4.3 Switching barrier: The switching barriers refers to the difficulty of switching to

another provider that is encountered by a customer who is dissatisfied with the existing

service, or to the financial, social and psychological burden felt by a customer when

switching to a new service provider. The switching barriers in mobile telecom sector are

supposed to have positive impact on customer retention. In this research study the efforts

are made to analyze the impact of switching barriers in mobile telecom sector on

customer retention. CFA is applied on various dimensions of switching barriers in order

to test the construct validity [convergent and discriminant validity]. Results of the CFA

are shown below with the help of table and diagram.

Figure 5.17 CFA of switching barrier

189

Table 5.29: Output of CFA, switching barriers

CR AVE MSV ASV

Interpersonal

Relationship Switching cost AOA

Interpersonal

Relationship 0.959 0.771 0.196 0.102 0.878

Switching cost 0.898 0.638 0.007 0.004 0.083 0.799

AOA 0.884 0.523 0.196 0.099 0.443 0.044 0.723

All constructs of switching barriers taken together are analyzed with the help of





(g) Composite reliability (CR) must be greater than average variance explained

(AVE).

(h) Individual Composite reliability (CR) of the constructs should be greater than .5.

(i) The individual average variance explained (AVE) should be greater than .5.


convergent validity are fulfilled. Hence, it can be concluded that the constructs are valid


In addition to this the different conditions of discriminant validity are as follows:

(a) Average variance explained (AVE) should be greater than MSV.

(b) AVE should be greater than ASV.

(c) AVE should be greater than .5


discriminant validity is fulfilled. Hence, it can be concluded that the constructs are valid


190

5.5 Hypothesis Testing

5.5.1: Individual cause and effect relationship between various determinants on

customer retention.

(a) Impact of perceived service quality on customer retention.

According to Parasuraman, Berry and Zethaml (1988), Perceived service quality is the

result of the customers’ comparison of expected service quality with the service received.

The perceived service quality in mobile telecom sector is supposed to have positive

impact on customer retention. In this research study the efforts are made to analyze the

impact of perceived service quality in mobile telecom sector on customer retention. The

following hypothesis is tested with the help of structural equation modeling (SEM).

Figure 5.17 represents the theoretical hypothesis to be tested.

Figure 5.18: Service quality and customer retention

191

Table 5.30: Relationship between Service quality and customer retention

Exogenous

Construct

Endogenous

Construct

Standardized

Regression

Coefficient

Unstandardized

Regression

Coefficient

CR P Value Squared

multiple

correlation

Service

Quality

Customer

Retention

.849 3.444 5.033 .000

.721

Table 5.31 Model fit index Service quality and customer retention

Fitness of Model Index CFI NFI RFI RMSEA LO 90 Hi 90

Value .969 .920 .904 .076 .051 .100

H1: The perceived service quality in mobile telecom sector has a significant positive

impact on customer retention.

The results of the above mentioned hypothesis is shown in table 5.31. The results indicate

that the structured regression rate of the relationship between perceived service quality

and customer retention is .849 and is found to be significant (p=.000) . Hence, with the

95% confidence level the null hypothesis of no cause and effect relationship cannot be

accepted. Hence, it can be concluded that the perceived quality of services in mobile

telecom sector have a positive significant impact on customer retention. Kangis and

Zhang (2000) explored the link between service quality and customer retention in

banking. Their findings showed that service quality had an effect on customer retention

through doing business with the bank.

The goodness of fit indicators such as CFI (.969 ), GFI (.845), NFI (.920), AGFI (.788),

RMSEA (.076) indicate that the tested structural equation model is have a significant fit.

192

(b) The customer satisfaction in mobile telecom sector has a significant

positive impact on customer retention.

Customer satisfaction refers to the assessment of all interactions with product or service

from a provider, relative to expectations. It seems logical that a highly satisfied customer

would be a retained customer. The customer satisfaction in mobile telecom sector is

supposed to have positive impact on customer retention. In this research study the efforts

made analyze the impact of customer satisfaction in mobile telecom sector on customer

retention. The following hypothesis is tested with the help of structural equation

modeling (SEM). The figure 5.18 represented the theoretical hypothesis to be tested.

H2: The customer satisfaction in mobile telecom sector has a significant positive impact

on customer retention.

Figure 5.19: Customer satisfaction and customer retention

193

Table 5.32 Relationship between customer satisfaction and customer retention

Exogenous

Construct

Endogenous

Construct

Standardized

Regression

Coefficient

Unstandardized

Regression

Coefficient

CR P Value Squared

multiple

correlation

Customer

Satisfaction

Customer

Retention

.865 1.842 14.440 .000

.748

Table 5.33: Model fit relationship between customer satisfaction and customer

retention


Value .998 .976 .969 .031 .037 .077


that the structured regression rate of the relationship between customer satisfaction and

customer retention is .865 and is found to be significant (p=.000) . Hence, with the 95%

confidence level the null hypothesis of no cause and effect relationship cannot be

accepted. Hence, it can be concluded that the customer satisfaction in mobile telecom

sector have a positive significant impact on customer retention. Johnson et al (2001) also

found same in his study concerned with mobile telecommunication services that satisfied

customer are retained customers.



(C) Impact of switching barriers on customer retention.

The switching barriers refers to the difficulty of switching to another provider that is

encountered by a customer who is dissatisfied with the existing service, or to the

financial, social and psychological burden felt by a customer when switching to a new

194

service provider. The switching barriers in mobile telecom sector are supposed to have

positive impact on customer retention. In this research study the efforts are made to

analyze the impact of switching barriers in mobile telecom sector on customer retention.

The following hypothesis is tested with the help of structural equation modeling (SEM).

The figure 5.19 represented the theoretical hypothesis to be tested.

H3: The switching barrier in mobile telecom sector has a significant positive impact on

customer retention.


that the structured regression rate of the relationship between switching barrier and

customer retention is .704 and is found to be significant (p=.000) . Hence, with the 95%

confidence level the null hypothesis of no cause and effect relationship cannot be

accepted. Hence, it can be concluded that the switching barriers in mobile telecom sector

have a positive significant impact on customer retention.

Figure 5.20: Switching barriers and customer retention

195

Table 5.34 Relationship between switching barriers and customer retention

Exogenous

Construct

Endogenous

Construct

Standardized

Regression

Coefficient

Unstandardized

Regression

Coefficient

CR P Value Squared

multiple

correlation

Switching

barriers

Customer

Retention

.704 1.451 6.556 .000

.495

Table 5.35: Model fit of relationship between switching barriers and customer

retention


Value .985 .956 .944 .071 .030 .105

5.5.2: Difference between service quality expectations and perceived service quality

levels in mobile telecommunications services.

In Indian mobile telecom sector the companies offer different type of services to the

customers. The customers using telecom services get the knowledge about services

through advertisements, friends and relatives and other sources. When a customer is

going to buy mobile telecom services he or she is having certain expectations about the

services. After buying the services, the customer evaluates the actual performance of the

telecom service. The comparison of expected and perceived performance of the telecom

service providers will results into the level of satisfaction and the attitude for staying with

the service. In this research study an effort is made to analyze the perception of the

customers with respect to the expected quality of service as well as the perception about

service quality of the telecom services. Independent sample T-test is applied to analyze

the difference between expected and perceived service quality. The Independent sample

T-test is used to test the null hypothesis, “There is no significant difference between

expected & perceived service quality”. The various determinants of service quality were

196

included in the questionnaire and respondents were asked to rate these statements in the

scale of 1 to 5, where 1 means strongly agree and 5 means strongly disagree.

H04: There is no significant difference between service quality expectation levels and

customers’ service quality perception for mobile telecommunication customers.

The results of independent sample T-test are shown in table 5.37 to table 5.43.

(1) Tangibility: The results of independent sample t-test on various statements of

tangibility are shown in table 5.37.

Table 5.36: Independent Sample t Test w.r.t. expected and perceived tangibility.

Variables Group Mean

(S.D.)

t-statistic

(P-value)

Remarks

Up to date

Equipment

Expectation 3.98

(1.128) 5.039

(.000)

Null Hypothesis

rejected

Perception 3.33

(1.164)

Visually appealing

Physical facilities

Expectation 3.95

(.999) 4.995

(.000)

Null Hypothesis

rejected

Perception 3.28

(1.129)

Service staff

appear neat & well

dressed

Expectation 3.73

(1.072 3.146

(.002)

Null Hypothesis

rejected

Perception 3.26

(1.079)

Physical facilities

match with telecom

services

Expectation 3.87

(.939) 4.160

(.000)

Null Hypothesis

rejected

Perception 3.33

(1.138)

The results of the independent sample t-test (as shown above), the probability value of t-

statistic is less than 5% level of significance. Therefore, with 95% confidence level the

null hypothesis of no significant difference between expected and perceived tangibility

aspect of service quality cannot be accepted. Hence, it can be concluded that the expected

and perceived tangibility aspect of service quality in telecom sector are significantly

197

different from each other. The results also indicate that the mean score of all statements

related to tangibility in case of expectation is higher than the mean score of perception

about tangibility. Therefore, it can be stated that the expectation about service quality is

significantly higher than the perceived service quality

It is found in the research study that initially customers have higher expectation about the

services but after using the service the customers found low level of service quality

provided by mobile telecom service providers. The main reason of this may be that the

physical facilities of mobile telecom service providers may not be visually appealing.

(2) Reliability: The results of independent sample t-test on various statements of

reliability are shown in table 5.38.

Table 5.37: Independent Sample t Test w.r.t. expected and perceived reliability


(S.D.)

t-statistic

(P-value)

Remarks

Keep Promises Expectation 4.10

(.847) 5.798

(.000)

Null Hypothesis

rejected Perception 3.32

(1.154)

Sympathetic &

reassuring

Expectation 4.22

(.836) 7.192

(.000)

Null Hypothesis

rejected

Perception 3.27

(1.162)

Dependable Expectation 4.07

(.913) 6.446

(.000)

Null Hypothesis


(1.035)

Provide service at

promised time

Expectation 4.03

(.822) 5.798

(.000)

Null Hypothesis

rejected

Perception 3.30

(1.040)

Keep records

accurately

Expectation 4.13

(1.002)

6.328

(.000)

Null Hypothesis

rejected

Perception 3.17

(1.092)



198

null hypothesis of no significant difference between expected and perceived reliability


and perceived reliability aspect of service quality in telecom sector are significantly


related to tangibility in case of expectation is higher than the mean score of perception


significantly higher than the perceived service quality It is found in the research study

that initially customers have higher expectation about the reliability aspect of services but

after using the service the customers found low level of perceived reliability provided by

mobile telecom service providers. The main reason of this may be that the customers may

not consider the mobile telecom operator so much reliable.

(3) Responsiveness: The results of independent sample t-test on various

statements of responsiveness are shown in table 5.39.

Table 5.38: Independent Sample t Test w.r.t. expected and perceived responsiveness.


(S.D.)

t-statistic

(P-value)

Remarks

Exactly tell when

service will be

performed

Expectation 2.69

(1.270)

3.342

(.001)

Null Hypothesis


(1.061)

Not realistic to

expect prompt

service form staff

Expectation 2.61

(1.270) 2.085

(.040)

Null Hypothesis


(1.061)

Don’t always have

to willing to help

customers

Expectation 2.85

(1.067) 3.071

(.003)

Null Hypothesis


(.903)

Ok if staff is too

busy to respond Expectation 2.80

(1.054) 3.171

(.002)

Null Hypothesis


(1.026)



null hypothesis of no significant difference between expected and perceived

199

responsiveness aspect of service quality cannot be accepted. Hence, it can be concluded

that the expected and perceived tangibility aspect of service quality in telecom sector are

significantly different from each other. The results also indicate that the mean score of all

statements related to responsiveness in case of expectation is higher than the mean score

of perception about responsiveness. Therefore, it can be stated that the expectation about

service quality is significantly higher than the perceived service quality.




customer may not be getting prompt service from customer service staff and uncertain

about the expected help from the service staff.

(4) Assurance: The results of independent sample t-test on various statements of

assurance are shown in table 5.40.

Table 5.39: Independent Sample t Test w.r.t. expected and perceived assurance.


(S.D.)

t-statistic

(P-value)

Remarks

Able to trust on

customer service

staff

Expectation 4.12 (.795)

3.342 (.001)

Null Hypothesis


(1.195)

Feel safe in my

transaction Expectation 4.05

(.821) 2.085 (.040)

Null Hypothesis


(1.090)

Customer service staff should be

polite

Expectation 3.90 (.823) 3.071

(.003)

Null Hypothesis rejected

Perception 2.89 (1.230

Should get

adequate support Expectation 4.01

(.847) 3.171 (.002)

Null Hypothesis


(1.232)



null hypothesis of no significant difference between expected and perceived assurance


200

and perceived assurance aspect of service quality in telecom sector are significantly


related to assurance in case of expectation is higher than the mean score of perception

about assurance. Therefore, it can be stated that the expectation about service quality is

significantly higher than the perceived service quality




customer may not feel safe in transaction with service provider and getting adequate

support from the service staff.

(5) Empathy: The results of independent sample t-test on various statements of

empathy are shown in table 5.41.

Table 5.40: Independent Sample t Test w.r.t. expected and perceived empathy.


(S.D.)

t-statistic

(P-value)

Remarks

Individual attention

should not be

expected

Expectation 2.53

(.893)

3.187

(.002)

Null Hypothesis


(.962)

Can't be expected

to give customer

personal attention

Expectation 2.41

(1.120) 2.789

(.003)

Null Hypothesis


(1.090)

Unrealistic to

expect to know

customer needs

Expectation 2.51

(.833) 2.519

(.013)

Null Hypothesis


(1.002)

Unrealistic to

expect to the firm

to have its interests

at heart

Expectation 2.76

(.847) 4.785

(.000)

Null Hypothesis


(1.012)

Unrealistic to

expect convenient

hours

Expectation 2.56

(.897) 3.213

(.002)

Null Hypothesis


(1.010)

201



null hypothesis of no significant difference between expected and perceived empathy


and perceived empathy aspect of service quality in telecom sector are significantly


related to empathy in case of expectation is higher than the mean score of perception


significantly higher than the perceived service quality.




telecom service providers are not giving individual attention to customers & not offering

convenient business hours.

(6) Network Quality: The results of independent sample t-test on various

statements of network quality are shown in table 5.42.

202

Table 5.41: Independent Sample t Test w.r.t. expected and perceived network

quality.


(S.D.)

t-statistic

(P-value)

Remarks

Sufficient

geographic

coverage

Expectation 3.68

(1.043)

-.075

(.941)

Null Hypothesis

accepted

Perception 3.69

(1.134)

pre mature

termination free

call

Expectation 3.72

(.965) 1.038

(.302)

Null Hypothesis

accepted

Perception 3.59

(1.083)

Voice clarity Expectation 3.73

(.983) .674

(.502)

Null Hypothesis

accepted

Perception 3.65

(1.067)

Call connected

during first attempt

Expectation 3.73

(1.043) 1.974

(.051)

Null Hypothesis

accepted

Perception 3.48

(1.096)

Able to make call

at peak hours

Expectation 3.75

(1.086) 1.459

(.148)

Null Hypothesis

accepted

Perception 3.57

(1.018)

The results of the independent sample t-test (as shown above) indicate that the probability

value of t-statistic is more than 5 percent level of significance. Therefore, with 95 percent

confidence level the null hypothesis of no significant difference between expected and

perceived tangibility aspect of service quality can be accepted. Hence, it can be

concluded that the expected and perceived network quality aspect of service quality in

telecom sector are not significantly different from each other. The results also indicate

that the mean score of all statements related to network quality in case of expectation and

perception are almost same. Therefore, it can be stated that the expectation about network

quality is similar to the perceived network quality.

203

It is found in the research study that customers’ expectations of network quality are

fulfilled. The main reason of this may be that due to advancement of technology the

telecom service providers are able to provide good network quality to customers.

(7) Convenience: The results of independent sample t-test on various statements of

convenience are shown in table 5.43.

Table 5.42: Independent Sample t Test w.r.t. expected and perceived convenience.


(S.D.)

t-statistic

(P-value)

Remarks

Convenient business

hours

Expectation 3.66

(1.139)

1.788

(.077)

Null Hypothesis

accepted

Perception 3.39

(1.286)

Mechanism of easy

lodging of

queries/complaints

Expectation 3.68

(1.127) 1.964

(.52)

Null Hypothesis

accepted

Perception 3.38

(1.293)

Flexibility in

payment of bills

Expectation 3.56

(.988) .918

(.361)

Null Hypothesis

accepted

Perception 3.42

(1.296)

Simple application

formalities

Expectation 3.63

(.981) 1.248

(.215)

Null Hypothesis

accepted

Perception 3.44

(1.290)


statistic is more than 5% level of significance. Therefore, with 95% confidence level the

null hypothesis of no significant difference between expected and perceived tangibility

aspect of service quality can be accepted. Hence, it can be concluded that the expected

and perceived convenience aspect of service quality in telecom sector are not

significantly different from each other. The results also indicate that the mean score of all

204

statements related to convenience in case of expectation and perception are almost same.

Therefore, it can be stated that the expectation about convenience is similar to the

perceived convenience in mobile telecom services.

It is found in the research study that customers’ expectations of convenience are fulfilled.

The main reason of this may be attributed to the convenient business hours and simple

application formalities.

5.6 Determinants of customer retention in mobile telecommunication

sector

In order to retain customers more effectively, companies must understand its clients, as well

as the forces inspiring them to stay with the current provider and not to switch.

Several studies have considered the impact of customer relationship management

tools and metrics on retention rates, varying from measuring satisfaction levels to

returns on loyalty programs. The construct of customer retention focuses on repeat

patronage. It is different from, while still closely related to, purchasing behaviour and brand

loyalty. In retention the marketers is seen as having more active role in the relationship. The

trigger is some element in the relationship between the provider and the purchaser, causing

customer retention. This extends beyond satisfaction, quality, and other constructs. There

are a variety of motivators of customer retention such as customer satisfaction and

switching costs, CRM, marketing strategies and customer acquisition.

In this research study the efforts are made to understand the determinants of customer

retention in mobile telecommunication sector and impact of these determinants on

customer retention. The theoretical proposed model representing the interrelationship is

shown in figure 5.20. The proposed model is tested using structural equation modeling

(SEM) technique using the software AMOS 20. The results of the SEM analysis are

shown in table 5.37.

205

Figure 5.21: Model of Determinants of customer retention

Table 5.43: Determinants of customer retention

Endogenous Exogenous

Construct

Standardized

Regression

Coefficient

Unstandardized

Regression

Coefficient

CR P Value Squared

multiple

correlation

Customer

retention

Perceived

Service

Quality

.362 1.453 3.062 .002

.837

.673

.594

Customer

Satisfaction

.457 .973 5.091 .000

Switching

Barriers

.291 .494 2.578 .003

206

Table 5.44: Model fit, Determinants of customer retention


Value .970 .903 .888 .063 .043 .081

The results indicate that the structured regression rate of the relationship between

perceived service quality and customer retention is .362 and is found to be significant

(p=.002) . Hence, with the 95% confidence level perceived service quality and customer

retention is related with each other. Hence, it can be concluded that the perceived service

quality in mobile telecom sector has a positive significant impact on customer retention

and can be considered an important determinant of customer retention.

The result also indicate that structured regression rate of the relationship between

customer satisfaction and customer retention is .457 and is found to be significant

(p=.000). Hence, with the 95% confidence level customer satisfaction and customer

retention are related to each other. In addition it can be concluded that the customer

satisfaction in mobile telecom sector has a positive significant impact on customer

retention and can be considered an important determinant of customer retention.

The results also indicate that structured regression rate of the relationship between

switching barriers and customer retention is .291 and is found to be significant (p=.003).

Hence, with the 95% confidence level switching barriers and customer retention are

related to each other. In addition it can be concluded that the customer satisfaction in

mobile telecom sector has a positive significant impact on customer retention and can be

considered an important determinant of customer retention.



207

ANALYSIS OF DATA COLLECTED FROM TELECOM

SERVICE PROVIDERS

5.7: Analysis of data collected from telecom service providers

The data of telecom service providers has been collected from the executives at the touch

points, officials dealing with customer care services, relationship managers, sales

personnel and collection executives etc. Data was collected from the head office, web

world offices, touch points and sales offices of selected telecom operators by personally

& by post administering questionnaire to the executives.

5.7.1: Demographic profiles of the respondents (Telecom Operators)

Table 5.45: Characteristics of the respondents on the basis of Telecom Company they

represent

Company Frequency Percentage

BSNL 26 32.50%

AIRTEL 18 22.50%

Reliance 20 25%

VODAFONE 16 20.00%

Total 80 100.0

The total numbers of respondents (company executives) considered in the research study

are 80. The demographic profile of the respondents on the basis of company they

represent is shown in table and graph shown above. The total numbers of respondents

were 80 where 26 (32.50%) respondents were representatives of BSNL, 18 (22.50%)

respondents were representing AIRTEL, 20 (25%) respondents were Reliance and 16

(20%) were representing VODAFONE as shown in table 5.46.

32.50% 22.50

%25% 20.00

%

0.00%5.00%

10.00%15.00%20.00%25.00%30.00%35.00%

208

Table 5.46: Characteristics of the respondents on the basis of their primary job

function

Job

Function


IT 8 10%

Sales &

marketing 28 35%

Operations 10 12.50%

Finance 6 7.50%

HRM 12 15%

Customer

Care 16 20%

Total 80 100

As shown in the table 5.47, 8 (10%) respondents were performing IT related

responsibilities, 28 (35%) respondents were in sales and marketing department, 10

(12.50%) respondents were in operations, 6 (7.50%) respondents in finance area, 12

(15%) were in HRM, and 16 (20%) were attached with customer care division.

Table 5.47: Characteristics of the respondents on the basis of their primary job level

Job Function Frequency Percentage

CEO 8 10

Deptt. Head 22 27.5

Team

Leader 16 20

manager 8 10

Technical

Management 8 10

Customer

Care 18 22.5

Total 80 100

10%

35%

12.50%7.50%

15%20%

0%5%

10%15%20%25%30%35%40%

10%

27.50%

20%

10% 10%

22.50%

0%

5%

10%

15%

20%

25%

30%

209

As shown in the table 5.48, 8 (10%) respondents were working as CEO, 22 (27.5%)

respondents as department head, 16 (20%) respondents were team leaders, 8 (10%) were

managers, 8 (10%) were in technical management and 18 (22.5%) were customer care

executives.

Table 5.48: Organizations have an explicit, documented customer retention plan

Retention

Plan

Frequency Percent

Yes 65 81%

No 11 13.75%

Don't

Know 4 5.00%

Total 80 100

As shown in the table 5.49, 65 (81%) respondents reported that their company have an

explicit, documented customer retention plan, 11 (13.75%) said no and 4 (5%) expressed

their ignorance about their organization have an explicit, documented customer retention

plan.

Table 5.49 Plan specify a budget for customer retention activities

Budget Frequency Percent

Yes 67 84%

No 10 12.50%

Don't

Know 3 3.75%

Total 80 100

As shown in the table 5.50, 67 (84%) respondents reported that their company’s plan

specify a budget for customer retention activities, 10 (12.50%) said no and 3 (3.75%)

81%

13.75%5.00%

0%

20%

40%

60%

80%

100%

Yes No Don't Know

84%

12.50%3.75%

0%

20%

40%

60%

80%

100%

Yes No Don't Know

210

expressed their ignorance about their organization’s plan specified a budget for customer

retention activities.

Table 5.50: Nomination of person or group to be responsible for customer retention

Nominated

Person

Frequency Percent

Yes 69 86%

No 7 9%

Don't Know 4 5%

Total 80 100

As shown in the table 5.51, 69 (86%) respondents reported that their company nominated

a particular person or group to be responsible for customer retention, 7 (9%) said no and

4 (5%) expressed their ignorance about their organization’s plan specified a budget for

customer retention activities.

Table 5.51: Formal model to identify customers who might take some or all of their

business elsewhere in the future

Model to

identify

Frequency Percent

Yes 60 75%

No 13 16%

Don't Know 7 9%

Total 80 100

60 (75%) respondents reported that their company used a formal model to identify

customers who might take some or all of their business elsewhere in the future, 13 (16%)

said no and 7 (9%) expressed their ignorance about their organization’s used any forma l

86%

9% 5%

0%

20%

40%

60%

80%

100%

Yes No Don't Know

75%

16%9%

0%

20%

40%

60%

80%

Yes No Don't Know

211

model to identify customers who might take some or all of their business elsewhere in the

future.

Table 5.52: Clues or signals which indicate customers might be likely to take some or

all of their business elsewhere in the future

Signals for

shifting

Frequency Percent

Yes 63 79%

No 10 13%

Don't Know 7 9%

Total 80 100

63 (79%) respondents reported that their company look for clues or signals which

indicate customers might be likely to take some or all of their business elsewhere in the

future, 10 (13%) said no and 7 (9%) expressed their ignorance about their organization

look for clues or signals which indicate customers might be likely to take some or all of

their business elsewhere in the future.

Table 5.53: Organizations have a documented process for handling customer

complaints

complaint

handling

Frequency Percent

Yes 65 81%

No 7 9%

Don't Know 8 10%

Total 80 100

79%

13% 9%

0%

20%

40%

60%

80%

100%

Yes No Don't Know

81%

9% 10%

0%10%20%30%40%50%60%70%80%90%

Yes No Don't Know

212

As shown in the table 5.53, 65 (81%) respondents reported that their company have a

documented process for handling customer complaints, 7 (9%) said no and 8 (10%)

expressed their ignorance about their organization have a documented process for

handling customer complaints.

5.5 Hypothesis Testing

5.5.1: Individual cause and effect relationship between various activities of customer

retention and customer retention expectation achieved.

(a) Impact of having an explicit, documented customer retention plan on customer

retention.

Hypotheses 5: Telecom operators those excel at customer retention have an

explicit, documented customer retention plan.

This study investigates the impact of having explicit, documented customer retention plan

on customer retention. There are indications that well designed and implemented

customer retention plan can have a positive effect on customer retention. In this study an

effort is made to find out if the presence of an explicit, documented customer retention

plan was a factor, with a greater or lesser impact on customer retention outcomes than

other customer retention strategies. Kendall’s tau was used to measure the hypothesized

relationship. Below table 5.55 shows the result of the application of Kendall’s tau

correlation.

213

Table 5.54: Documented customer retention plan & extent to customer retained

Correlations

Documented

Retention Plan

Extent to

customer

retained

Kendall's tau_b

Documented Retention

Plan

Correlation

Coefficient 1.000 .136

Sig. (2-tailed) . .187

N 80 80

Extent to customer

retained

Correlation

Coefficient 136 1.000

Sig. (2-tailed) .187 .

N 80 80

Spearman's rho

Documented Retention

Plan

Correlation

Coefficient 1.000 .147


N 80 80

Extent to customer

retained

Correlation

Coefficient .147 1.000


N 80 80

**. Correlation is significant at the 0.01 level (2-tailed).

The results of the Kendall’s tau correlation indicate that there is no statistically

significant relationship correlation between having a retention plan and exceeding

customer retention expectations (p> 0.05). Hypothesis 5 is therefore cannot be accepted.

It is found in the research study that in mobile telecom sector presence of an explicit,

documented customer retention plan was not a factor, with a greater or lesser impact on

customer retention outcomes.

(b) Impact of having a budget dedicated to customer retention activities on customer

retention.

Hypotheses 6: Telecom operators those excel at customer retention have a budget

dedicated to customer retention activities.

214

This study investigates the impact of have a budget dedicated to customer retention

activities on customer retention. It is expected that dedicating a budget for customer

retention activities may increase outcome of customer retention. In this study an effort is

made to find out that to have a budget for customer retention activities was a factor, with

a greater or lesser impact on customer retention outcomes. Kendall’s tau was used to

measure the hypothesized relationship. Below table 5.56 shows the result of the

application of Kendall’s tau correlation:

Table 5.55: Budget dedicated to retention extent to customer retained

Correlations

budget for

Retention

Extent to

customer

retained

Kendall's tau_b

budget for Retention

Correlation Coefficient 1.000 .136


N 80 80

Extent to customer

retained

Correlation Coefficient .136 1.000


N 80 80

Spearman's rho

budget for Retention



N 80 80

Extent to customer

retained



N 80 80



significant relationship correlation between having a budget and exceeding customer

retention expectations (p> 0.05). Hypothesis 6 is therefore cannot be accepted.

(C) Impact of nominated a particular person or group to be responsible for customer

retention on outcome of customer retention.

215

Hypotheses 7: Telecom operators those excel at customer retention have nominated a

particular person or group to be responsible for customer retention.

This study investigates the impact of nominated a particular person or group to be

responsible for customer retention on the outcome of customer retention activities. It is

expected that nominated a particular person or group responsible for customer retention

may increase outcome of customer retention. In this study an effort is made to find out to

nominate a particular person or group to be responsible for customer retention activities

was a factor, with a greater or lesser impact on customer retention outcomes. Kendall’s

tau was used to measure the hypothesized relationship. Below table 5.57 shows the result

of the application of Kendall’s tau correlation:

Table 5.56: Nominated a person responsible for retention & extent to customer retained

Correlations

person of

gropup

responsible for

retention

Extent to

customer

retained

Kendall's tau_b

Person or group

responsible for retention



N 80 80

Extent to customer

retained



N 80 80

Spearman's rho

Person or group

responsible for retention



N 80 80

Extent to customer

retained



N 80 80


216


significant relationship exists between nominated a particular person or group to be

responsible for customer retention and exceeding customer retention expectations (p>

0.05). Hypothesis 7 is therefore cannot be accepted.

(D) Impact of having a documented process for handling customer complaints on

customer retention.

This study investigates the impact of documented complaint-handling processes on

customer retention. There are indications that well designed and implemented complaint

–handling processes can have a positive effect on customer retention. Indeed, customers

who complain and are well recovered can be more satisfied and less likely to switch than

customers who had no cause for complaint at all. In this study an effort is made to find

out if the presence of a documented complaint –handling process was a factor, with a

greater or lesser impact on customer retention outcomes than other customer retention

strategies.

Kendall’s tau was used to measure the hypothesized relationship. Below table 5.57 shows

the result of the application of Kendall’s tau correlation

Hypotheses 8: Telecom operators that excel at customer retention have a documented

process for handling customer complaints.

217

Table 5.57: Documented process for handling customer complaints & extent to customer

retained

Correlations

Documented

Process for

complaint

Handling

Extent to

customer

retained

Kendall's tau_b

Documented Process for

complaint Handling

Correlation Coefficient 1.000 .560**


N 80 80

Extent to customer

retained

Correlation Coefficient .560**

1.000


N 80 80

Spearman's rho

Documented Process for

complaint Handling

Correlation Coefficient 1.000 .608**


N 80 80

Extent to customer

retained

Correlation Coefficient .608**

1.000


N 80 80


The results of the Kendall’s tau correlation indicate that the probability value (p=.000) is

less than 5% level of significance. Therefore, with 95% confidence level the alternate

hypothesis of a positive correlation between exceeding customer retention expectations

and the presence of a documented complaint handling process is supported. Therefore, it

can be stated telecom operators that excel at customer retention have a documented

process for handling customer complaints.

It is found in the research study that telecom organizations can improve customer

retention by having a documented complaint handling process. The main reason of this

may be that when the customer’s complaints are well taken will have a head-start in

identifying systemic or repetitive problems that affect the bottom line and, and therefore,

have an advantage in developing solutions to those problems.

chapter 5 data analysis and...

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