The Analysis of Antecedents of Customer Loyalty in the Australian

Mobile Telecommunication Market

Hassan Shakil Bhatti*, Ahmad Abareshi and Siddhi Pittayachawan

School of Business IT and Logistics, RMIT University, 445 Swanston Street, Melbourne, Australia

Keywords: Customer Loyalty, SEM, Australia, Marketing Mix Factors.

Abstract: Marketing mix factors such as price product value, facilitating conditions, and social influence; and factors

such as customer satisfaction, customer experience, habit, hedonic motivation, performance expectancy, effort

expectancy are the major antecedents of customer loyalty. As, loyal customers may buy more services and

have a positive word-of-mouth effect. Moreover, it is evident from previous literature that the cost of selling

service to new customers is much higher than the cost of retaining existing customers. Furthermore, it is

evident fact from telecommunication industry ombudsman Australia reports that companies are still losing

customers at a formidable rate. In this context, the main aim of this paper is to examine the relationships

between these factors and customer loyalty, and the relationships among these factors in the Australian mobile

service scenario. Data for this study was obtained from 1,985 mobile phone users in Australia via online

questionnaire using a marketing company Research Now. The data was analysed by structural equation

modelling (SEM) to test all the relationships between variables in the model. The findings of this research

focus on the measurement model fit part only. The analysis results showed that perceived customer experience

is a necessary but not sufficient condition for customer loyalty. In order to generalize the findings from this

research, this research model should be used in different service industries with different geographic samples.

The contribution of this research is to model all the relationships between customer loyalty and its antecedents,

and to test these relationships simultaneously. In this paper, the effects of all the factors on customer loyalty

are tested simultaneously via structural equation modelling (SEM) and this paper only focuses on model fit.

1 INTRODUCTION

There is a significant importance of customer loyalty

in service industries (Caruana, 2002; Rai and

Srivastava, 2012). Customer loyalty has been viewed

as the strength of the relationship between an

individual’s relative attitude and repeat patronage.

The technology has been evolved with the passage of

time and service providers are doing network

upgrades. There is a furious competition among

service providers for more competitiveness and

customers’ acquirement. According to

Telecommunication industry reports TIO (2014),

customers are making complaints about network

service, quality, contract, customer service and billing

issues. The service quality has an impact on customer

satisfaction and customer loyalty (Bhatti, 2016).

Customer satisfaction is measured by a tool called

Net Promoter Score (NPS) in service industry. TIO

reports that NPS which is used to measure the

customer satisfaction for many service providers has

been dropped dramatically down, because of it there

is substantial increase in customer service complaints.

This study evaluates the antecedents which can

influence the customer loyalty in mobile

telecommunication sector. The theoretical framework

for this study is based on unified theory of acceptance

and use of technology (UTAUT 2), marketing mix

theory and expectation confirmation theory (ECT).

The identified factors such as customer experience,

customer satisfaction, effort expectancy, performance

expectancy, facilitating conditions, social influence,

price value, habit and hedonic motivation are derived

from literature review and previous studies (Brown

and Venkatesh, 2005; Escobar-Rodríguez and

Carvajal-Trujillo, 2013, 2014; Venkatesh et al., 2003;

Venkatesh et al., 2012; Wirtz et al., 2006). The

research instrument has been developed for this study

with 65 items. The online survey is conducted with a

364 respondent. The analysis for this study has been

Bhatti, H., Abareshi, A. and Pittayachawan, S.

The Analysis of Antecedents of Customer Loyalty in the Australian Mobile Telecommunication Market.

DOI: 10.5220/0006419100910099

In Proceedings of the 14th International Joint Conference on e-Business and Telecommunications (ICETE 2017) - Volume 2: ICE-B, pages 91-99

ISBN: 978-989-758-257-8

done in R-studio programming tool using Lavaan

package partial for structural equation modelling

analysis. This study will give future directions to

businesses and researchers. This study will help in

formation of a theoretical model which can be

implemented in service industries.

In previous Literature, loyalty has been discussed

in different ways, there are two prime approaches:

behavioural and attitudinal approaches (Dekimpe et

al., 1997; Dick and Basu, 1994). Below mentioned

are the definitions from past studies. Another study

Oh (1995) from past explores the three broad

categories of customer loyalty: the behavioural

approach, the attitudinal approach and the integrated

approach. The integrated approach contains both

attitudinal and behavioural approaches. In this study,

the focus of the research is only at attitudinal

approach. The following tables presents some key

definition from previous literature.

Table 1: Loyalty Definitions in Literature.

Customer Loyalty References

Those who rebought a brand,

considered only that brand, and did no

brand-related information seeking.

(Newman and

Werbel, 1973);

Oliver (1999,p.34)

It is defined as “repeat purchase

frequency” or “relative volume of same

brand purchasing”.

(Telli,s 1988)

Loyalty has been defined from

attitudinal perspective as well.

Attitudinal loyalty includes cognitive,

affective, and conative aspects.

(Oliver,

1997,p.392);

Oliver, (1999,p.34)

Oliver (1997, p. 392) proceeds to

describe the consumer who "fervently

desires to rebuy a product or service and

will have no other."

(Oliver,

1997,p.392)

Customer loyalty is a buyer’s affection

or deep association to a product, facility,

image, or company.

(Oliver, 1999)

Oliver (1999, p.36) discussed four phases of

loyalty development.

• First, loyalty in the cognitive phase is entirely

based on either prior knowledge or experience-

based information about a brand.

• Second, loyalty in the affective phase is a liking

or attitude toward a brand. Moreover, loyalty is

defined as cumulatively satisfying usage

occasions, it implies feelings or association

towards a brand.

• Thirdly, conative loyalty is defined as customer’s

behavioural intention to keep on purchasing a

product in the future.

• The final stage of loyalty phases in which

motivated intention is changed into readiness to

act. This approach of action loyalty is linked with

behavioural approach.

For this study, the definition of customer loyalty is “A

deeply held commitment to rebuy or re-patronise a

preferred product or service consistently in the future,

despite situational influences and marketing efforts

having the potential to cause switching behaviour”

(Oliver, 1997,p.392). The service has been studied

intensively worldwide in different areas and the

unique characteristics required for customer

orientation are very well investigated in previous

studies (Harris and Goode, 2004; Oliver, 1997, 1999;

Oliver and Swan, 1989; Rai and Srivastava, 2012).

The major concepts for customer loyalty, customer

satisfaction, service quality, and customer complaints

have been highlighted in many past studies (Harris

and Goode, 2004; Mano and Oliver, 1993; Oliver,

1997, 1999; Oliver et al., 1988; Oliver and Richard,

1981; Oliver and Richard, 1993; Oliver, 1977, 1980;

Oliver, RL and Bearden 1985; Oliver and DeSarbo,

1988; Oliver and Swan, 1989; Taylor and Baker,

1994). The role of customer loyalty gains more

popularity when it is applied in customer service

context due to the direct human involvement. The

characteristics of service durability, reliability,

service delivery, and customer service etc. enhance

the integrity between service and human relationship.

Customer loyalty is very critical for any business or

service. Due to the continues evolving of services,

and marketing competitiveness, companies are now

focusing on the significance of human involvement in

form of customer loyalty and retention. Customer

loyalty has been including as the strategic objective

in decision making process due to the competition

with other companies (Rai and Srivastava, 2012).

Customer loyalty has been seen as a source of

competitive advantage (Bharadwaj et al., 1993).

Moreover, this study focuses on the customer relative

attitudes and attitudinal approach as defined by Dick

and Basu (1994) while defining different approaches

towards loyalty. In this study, the determinants of

customer loyalty as highlighted and derived from

behavioural intention are discussed. The relationship

needs to be tested in mobile telecommunication

service context.

In telecommunication industry, the main

barometer for customer satisfaction measurement is

Net Promoter Score (NPS) (Ozlem, 2009; Prajogo et

al., 2014; Reichheld et al., 1989; Reichheld and Teal,

1996). The main theme of NPS is based on feedback

from customers include the time it took to complete

requests, network reliability and coverage and

customer service issues. Moreover, the feedback on

customers’ activation episodes reflect issues with

mobile companies prepaid mobile service and the

increase in cycle time to activate. This increase

ICE-B 2017 - 14th International Conference on e-Business

coincides with a number of changes to IT systems

with flow-on impacts to the way that mobile

companies inform customers via Keep Me Informed

(KMIs) (Ramli, 2015). Moreover, in prepaid mobile

services, the key areas of focus are to improve the

digital user experience, increase the number of

Interactive voice response (IVR) self-help options for

customers and the improvement of activation cycle

times and system stability. Clearly, mobile services

network stability can help in improving the customer

experience (TIO, 2014).

Customer acquisition which is acquiring a new

customer is most costly when compared to retaining

existing customers (Edward and Sahadev, 2011). The

consequent increase in business revenue is directly

related to the customer behavioural intention to stay

loyal and customer repurchase behaviour (Edward

and Sahadev, 2011; Santouridis and Trivellas, 2010).

Every research has several research boundaries,

hence for this study, the theoretical framework

focusses on attitudinal perspective of behavioural

intention which is customer loyalty. Technology is

evolving and changes in network along with service

upgrade can impact services (Bhatti, 2016).

Moreover, it is evident empirically in previous studies

(Islam and Rima, 2013; Kuo and Yen, 2009)that

technology change affects the customer loyalty. This

research will investigate the lack of customer loyalty

issue in the mobile service market in Australia.

According to previous studies (Aydin and Ozer,

2005; Aydin et al., 2005), it is empirically tested that

customers that are not loyal will spread bad word of

mouth and company will be in loss.

2 THEORETICAL FRAMEWORK

The term customer loyalty is a relatively a common

phenomenon used in marketing and business studies.

The extant body of literature available in field of

IT/IS adoption extensively used empirical work of

Theory of reasoned action (TRA) (Ajzen and

Fishbein, 1977; Fishbein and Ajzen, 1975; Warshaw

and Davis, 1985), Technology acceptance model

TAM (Davis 1989), Theory of planned behaviour

TPB (Ajzen, 1991), Innovation diffusion theory IDT

(Roger, 1995) and Technology adoption model 2

(TAM2) (Venkatesh and Davis, 2000). Moreover,

these studies have been extensively used to

understand the technology adoption process in

various information system, information technology

and business studies (Benlian et al., 2011; Brown and

Venkatesh, 2005; Hanafizadeh et al., 2014; Hong and

Tam, 2006; Hsu et al., 2007; Pagani, 2004).

Although, there are many factors such as

demographics, research area, socio-economic profile

of the participants which can impact analysis, validity

of robustness and predictive ability of these models.

Similarly, unified theory of acceptance and use of

technology (UTAUT) Venkatesh et al., (2003)

presents the four factors such as performance

expectancy, effort expectancy, social influence and

facilitating conditions effecting the behavioural

intention and user actual behaviour. The UTAUT

model is combination of eight theories mentioned

above. Furthermore, a revised extension UTAUT 2

model is proposed by researcher Venkatesh et al.,

(2012) which imputes the pricing, habit and hedonic

motivation factors in the UTAUT model. The basic

motivation of this study is to propose a synthesized

single framework which improves parsimony,

descriptive power, and predictive ability and to

overcome the basic limitations of the existing models.

In the current model UTAUT 2 is only focusing on

user behaviour and this model doesn’t discuss the

customer satisfaction and customer experience

impact on customer behavioural intention to stay

loyal. It is therefore, imperative to understand the

customer loyalty of mobile telecommunication

service in an integrated framework in the back drop

of an extension of established IT/IS adoption theories

and specific dimensions required in view of inherent

characteristics of mobile services.

In previous studies (Benlian et al., 2011; Liu,

2012; San Martín and Herrero, 2012; Shin, 2009;

Turel et al., 2007; Zhou and Lu, 2011), major theories

such as theory of planned behaviour, technology

acceptance model, unified theory of acceptance and

use of technology, SERVQUAL model, and social

influence theory are used to address user adoption,

behavioural intention to stay issues in service

industries. In this study, the focal point of study is the

attitudinal perspective of behavioural intention. The

relationships between customer experience and

customer loyalty; and customer satisfaction and

customer loyalty are conceptualised by using the

theoretical underpinning of customer expectation

confirmation theory. As, a customer’s experience can

be defined as the expectation gap between the level of

customer experience that the customer thinks they

should be getting (built up by marketing promises and

other experiences) and the level they actually receive

(Millard, 2006). Moreover, the relationship among

marketing factors such as product price value,

physical conditions and customer loyalty are

conceptualised with the support of unified theory of

acceptance and use of technology. The constructs

such as product price value, facilitating conditions,

The Analysis of Antecedents of Customer Loyalty in the Australian Mobile Telecommunication Market

social influence and hedonic motivation are

contextualised in the area of the mobile service.

Therefore, marketing concepts help in supporting the

argument and shaping the theoretical underpinning

for this research framework. Each theory in this

framework is covering one phenomenon of each

aspect i.e. marketing mix and product association,

behavioural aspect covered by UTAUT 2, and

psychological aspect such as customer experience

and customer satisfaction are underpinned by

customer expectation confirmation theory (ECT).

This research project aims to explore the factors

that affect customer loyalty in the telecommunication

sector of Australia. The main objective of this study

is to develop a framework to investigate the factors

affecting customer loyalty in the telecommunication

service industry. To achieve this objective, several

questions have been formulated, as follows:

1- What are the factors that influence the customer

loyalty in the Australian telecommunication sector?

2- To what extent do these factors affect customer

loyalty?

3- What is the influence of age, gender and length of

experience as a moderating factor between

antecedents and dependent variable in the extended

Unified Theory of Acceptance and Use of

Technology model?

In order to address the research questions, the

following specific objectives are formulated.

Aims

• This study will explore the factors which can

affect customer loyalty.

• This study framework will be used by

telecommunication business managers and

business units in formulating their business plans

for sustainable revenue.

• This study framework will help the companies in

improving their customer base.

• This study framework will significantly

contribute by empirically testing a new model for

customer repurchase behaviour.

Objectives

• To determine the significance of these factors on

customer loyalty.

• To investigate the determinants of repurchase

behaviour in the Australian telecommunication

sector.

2.1 Research Framework

The model is amended to suit the context of this stu-

dy. The UTAUT 2 model also fulfils the requirement

of marketing mix factors such as product, price, plan,

place, process and promotion for this study. This

model also supports the influence of social influence

through social networking using marketing and

advertising tools for the promotion of product and

services. The revised model will also consider the

influence of moderators that are thought would

influence the three direct determinants: gender, age

and the length of experience.

Figure 1: Loyalty Framework (UTAUT 2 Extension).

The above discussion would lead to the following

hypothesis:

H1: Facilitating conditions has a positive impact on

customer loyalty.

H2: Effort expectancy has a positive impact on

customer loyalty.

H3: Performance expectancy has a positive impact on

customer loyalty.

H4: Social influence has a positive impact on

customer loyalty.

H5: Price value has a positive impact on customer

loyalty.

H6: Habit has a positive impact on customer loyalty.

H7: Hedonic Motivation has a positive impact on

customer loyalty.

H8: Customer satisfaction has a positive impact on

customer loyalty.

H9: Customer experience has a positive impact on

customer loyalty.

H10: Customer experience has a positive impact on

customer satisfaction.

H11: Facilitating conditions has a positive impact on

customer loyalty.

H12-H21: The moderating impact of age, gender and

length of experience on above mentioned

relationships between independent and dependent

variables.

ICE-B 2017 - 14th International Conference on e-Business

3 RESEARCH METHODOLOGY

This research is based on positivist paradigm with

quantitative method. It is based on hypothetico-

deductive quantitative approach. The positivist

methodology is based on the experiments, hypothesis

testing, validity, verifications and quantitative

methods of study. The interpretive approach

considers reality as a relative term; it focuses on

subjectivity of social issue with interpretative

dialectical method (Bryman and Bell, 2011). This

study investigates the relationship between customer

loyalty, customer satisfaction, customer experience

and its measure, hedonic motivation, habit, marketing

mix factors such as product price value, facilitating

conditions in the context of customer loyalty in

mobile telecommunication industry using UTAUT.

Research questions are derived from a literature

review. This research focuses on all the impacts,

whether technical or behavioural, which can

influence customer loyalty. Quantitative method of

study is adopted for this research. In this study, in

order to achieve the research objective, a quantitative

method is employed by utilizing an online

questionnaire. In this study, the participants are

recruited from all the states in Australia by using the

marketing company’s Research Now panel. It

strengthens the result that can be obtained from a

certain population. Thus, the costs of distributing,

collecting and capturing questionnaires can be small

(Sanchez-Acenjo, 2014). Brand (2003) explained that

sometimes this method can be costly and time

consuming. This method can provide a narrative

which offers better and meaningful understanding of

behaviours (VanderStoep and Johnson, 2008).

However, the results of analysis can be interpretative

and can introduce potential bias while data gathering

or during data analysis (Brand, 2003). The survey is

done in three phases as mentioned below.

Survey 1: Pre-test

The primary aim of the pre-test is to test and validate

the various scales that are used for the final test of

model. Survey 1 is conducted from participation of

five academics and five content experts. Item

screening is also conducted in this step using the

interrater reliability.

Survey 2: Pilot Test

The primary aim of the pilot test is to examine the

responses of complete questionnaire and validate

various scales that are used for the final test of model.

This test also helps to test that participant can

understand wording, grammar and context of the

study. At this stage, participant’s feedback can be

incorporated in the actual study. Survey 2 is

conducted from a participation of 10-15 mobile

service users in Australia.

Survey 3: Main Study

In the main study people living in Australia are

invited to participate by using a marketing company

Research Now. The target population for the main

study is approximately 2000, with an expected

response 364 participants (Uma and Roger, 2003).

For data analysis envisaged in this study, a minimum

sample size of 320 is desirable. Data is analysed using

Structural Equation Modelling with R- Studio. The

finalised item for the research instruments for each

construct are as follow in Table 2.

Table 2: Research Instrument.

Variables No. item Source

Facilitating

Conditions

(Boontarig et al., 2012; Venkatesh et

al., 2012)

Performance

Expectancy

(Boontarig et al.. 2012; Shao and

Siponen, 2011; Venkatesh et al..

2012)

Effort

Expectancy

(Hung and Chang. 2005; San Martín

and Herrero. 2012; Venkatesh et al..

2012)

Social

Influence

(Venkatesh et al.. 2003; Venkatesh

et al.. 2012)

Hedonic

Motivation

(Chang et al.. 2009; Venkatesh et al..

2012)

Habit 6

(Lin and Wang. 2006; Venkatesh et

al.. 2012)

Price Value 5

(Baker et al.. 2000; Venkatesh et al.,

2012)

Customer

Satisfaction

(Wang et al., 2004; Zhou and Lu,

2011)

Customer

Experience

(Wang et al., 2004; Wangpipatwong

et al., 2008)

Customer

Loyalty

(Chang et al., 2009; Kuo and Yen,

2009; Ray and Chiagouris, 2009;

Zhou and Lu, 2011)

Total 64

4 DATA ANALYSIS

There were 364 respondents to questionnaire survey

after data cleaning i.e. removal of unengaged

responses. There are 184 (50.5 percent) of them male

and 180 (49.5 percent) female in this study.

The respondents are in age range of 18-25 (15.1

percent), 26-35(18.1 percent), 36-45(17.6 percent),

46-55(17 percent), 56-64(16.2 percent) and >65 (15.9

percent). The majority of respondents are with annual

income $18200-$37000 (20 percent) and minority of

respondents with the annual income $180000 and

over in the above-mentioned figure.

The Analysis of Antecedents of Customer Loyalty in the Australian Mobile Telecommunication Market

The respondent’s employment status is described

in this survey as self-employed (7.1 percent),

employed (54.9 percent), unemployed (7.4 percent),

volunteer (1.1 percent), student (6.3 percent), retired

(20.1 percent) and unable to work (3 percent). The

majority of respondents are holders of bachelor

degree, diploma or associate diploma and high school

graduate. The minority of respondents are holders of

certificate IV or below (14 percent) and 0.8 percent

never attended school.

4.1 Evaluation of the Measurement

Model

The measurement model (confirmation factor

analysis or CFA) specifies the relationships that

explain how measured variables represents a

construct (Hair, 2010). It was assessed with CFA

using R-studio programming tool to empirical test the

convergent and discriminant validity. In the

confirmation factor analysis, the convergent and

discriminant validity depend upon three indicators

such as the item reliability of each measure, reliability

of the construct, and average variance extracted

(AVE). According to Hair (2010), constructs have

convergent validity when the composite reliability

(CR) exceeds the threshold of 0.70 and the average

variance extracted (AVE) is above the threshold value

of 0.5. The below mentioned table 3 shows that all

items have item loading higher than 0.7 except few

items such as one item from facilitating condition FC-

3, one item from habit HT4, three items from

customer service and one item from customer

satisfaction (CS-6). These items are dropped. All the

AVE’s after removal of these items were above 0.5

and all the CRs were above 0.7. Hence, the results

support the convergent validity of the scales (Straub

et al., 2004). Below mentioned table 3 shows the CR

and AVE values.

Table 3: Construct Validity and Discriminant Validity.

Constructs CR

AVE

Customer Satisfaction (CS) 0.939 0.721

Performance Expectancy (PE) 0.944 0.737

Habit (HT) 0.793 0.491

Customer Experience (CE) 0.909 0.626

Price Value (PV) 0.916 0.732

Social Influence (SI) 0.939 0.721

Customer Loyalty (CL) 0.874 0.582

Facilitating Conditions (FC) 0.879 0.721

Hedonic Motivation (HM) 0.901 0.605

Effort Expectancy (EE) 0.91 0.593

For the model, measurement model fits

parameters which helps to assess the models’

goodness of fit index and other parameters such as

Chi-square (χ

CFI, TLI, RMSEA, and SRMR. As

per below mentioned references, the overall model fit

well. Therefore Table 3 shows the measurement

model parameters and m in the below mentioned table

4 represents the number of items.

Table 4: Measurement Model Parameters.

Fit Indices Value Reference Range m>=30

GOF

Chi-Square

1.69 (Hair et al., 2010) P<3.

RMSEA 0.02 (Hair et al., 2010) Values<0.07

CFI 0.979 (Hair et al., 2010) Above 0.90

TLI 0.967 (Hair et al., 2010) Above 0.90

SRMR 0.037 (Hair et al., 2010) 0.08 or less

The next step after confirmatory factor analysis is

the assessment of the structural path model and then

hypothesis testing. This paper is only focusing on the

measurement model fit. So, the next part of the

analysis will be hypothesis testing.

5 CONCLUSIONS

The purpose of this study was to investigate the

factors affecting the customer loyalty and the use of

mobile services in Australia. The findings of this

study have explored interesting findings. First, it

confirms that model is reliable and valid for further

analysis. Second, with respect to the extended version

of UTAUT 2 model, the model fit relatively well as

all parameters are in adequate acceptable range. The

results of measurement models show that model fits

quite well. The next step is to test the structural model

followed by hypothesis testing. This research helps in

the designing of the theoretical framework with the

underpinned theories and concepts. This research

model is the extension of user behaviour and adoption

theories. In this research, model is based on different

assumptions which are discussed in theory building

process and how these assumptions help in the

research framework development. This model

contributes theoretically in better understanding

behavioural intention and customer loyalty

framework in telecommunication context.

ICE-B 2017 - 14th International Conference on e-Business

6 FUTURE STUDIES AND

LIMITATION

This model can be tested in other service industries

such as airline, energy and cloud computing research

etc. This model can be tested with some other

geographical samples in other countries. Moreover,

this study is based on the quantitative data analysis as

data was collected from 1,985 respondents with 364

valid responses, so this work could be extended using

qualitative data analysis technique. Companies can be

approached after ethics approval and interview could

be conducted in future studies. There was a limitation

in this study that company database access was not

available for net promoter score (NPS) quarterly

analysis for each mobile service. In future research,

companies’ customer database can be used for further

analysis which can lead to the development of some

mathematical and predictive model for big data

analytics. Thus, future research can be built on this

model of the UTAUT 2 extended model in different

countries and different service areas.

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The Analysis of Antecedents of Customer Loyalty in the Australian Mobile Telecommunication Market