E-Lifestyle, Customer Satisfaction and Loyalty among · PDF fileE-Lifestyle, Customer...

Knowledge Management International Conference (KMICe) 2014, 12 – 15 August 2014, Malaysia

http://www.kmice.cms.net.my/ 506

E-Lifestyle, Customer Satisfaction and Loyalty among the

Generation Y Mobile Users

Siti Hasnah Hassan1, Ramayah Thurasamy

2, Osman Mohamed

3and Marhana Mohamed

Anuar4

1Universiti Sains Malaysia, [email protected] 2Universiti Sains Malaysia, [email protected]

3Multimedia University, Cyberjaya Campus, [email protected] 4Universiti Malaysia Terengganu, [email protected]

ABSTRACT

Technology advancement is gaining a great deal of

attention among young individual. Technology has

significantly impacted and changed the context

and the way young people live in recent years

particularly in developing countries across Asian

regions. Telecommunication companies have

noticed the importance of e-lifestyle factors which

largely will influence on their identity. This

research determines the impact of e-lifestyle on

customer satisfaction and loyalty from mobile

consumer users in emerging county. The data

collected using the survey form among 197

respondents from Generation Y that aged between

18 to 37 years old. The data was analyzed using

Smart-PLS and the results show that two

predictors under the e-lifestyle which are e-

activities and e-opinion influence consumers’

satisfaction that is strongly affect consumer

loyalty for four main service providers in

Malaysia.

Keyword: E-lifestyle, Customer Satisfaction,

Customer Loyalty, Mobile user, Generation Y.

I. I-TRODUCTIO-

Mobile services and applications are emerged due

to the convergence of Internet, media, IT and

advanced telecommunication technologies has

stimulated phenomenal influence of information

and communication technology (ICT). There are

numerous different mobile services and

applications available to consumers in the

telecommunication market. Individual acceptance

and adoption of mobile services have not been

full-fledged (Bouwman, López-Nicolás, Molina-

Castillo, & Van Hattum, 2012; Karnowski &

Jandura, 2014) or shown an asynchronous

pattern(Carlsson, Hyvonen, Repo, & Walden,

2005).This is due to the exponential growth in the

mobile telecommunications andthe dynamic

nature of the market. For this reason, many service

providers’ think that the mobile service market has

not yet reached its optimal level and there are still

huge opportunities to improve it services.

Malaysia has also witnessed advancement and

keeping appropriate pace with global technology

advancements, especially the mobile

telecommunication market. The major Telco

companies in Malaysia are TM Berhad, Celcom,

Maxis, DiGi and U Mobile. Others are the 4G

providers such as YES 4G and P1 WiMax which

are fully Digital WiMAX Operators. The growth

rate in the use of telecommunication facilities has

increased dramatically, especially in the rank of

increasing number of mobile service subscribers.

There are many reasons why consumers choose a

particular type of mobile service provider. One of

the factors effecting the selection of service

provider is e-lifestyle. Technology has

significantly impacted and changed the context

and the way people live in recent years (Yu,

2011).

The aim of this study is to understand consumer e-

lifestyle and how this factor influences consumer

satisfaction and affect customer loyalty. The

understanding of this relationship is important in

tailoring and delivering appropriate services to

Generation Y and service related to mobile

technologies.

II. LITERATURE REVIEW

The main objectives of marketing activities are

often the development, maintenance, or

enhancement of customer loyalty (Dick & Basu,

1994).Customer loyalty has significant positive

impact on the profitability of business. Loyal

customers, who continuously use the same service

provider and ignore the competitor’s will offer a

long-term revenue for a company (Lam, Shankar,

Erramilli, & Murthy, 2004). In mobile service

industry context, loyal customers are more likely

to focus on long-term benefits and engage in

cooperative actions beneficial to both partners.

However, most mobile service providers face



considerable challenges due to the dynamic nature

of technological convergence and rapid evolution

of internet (Wong, Chan, Ngai, & Oswald, 2009).

Creating a loyal customer in mobile service

provider is not only about maintaining customers

overtime but also nurturing relationships with

customer for future purchase (Rauyruen & Miller,

2007). There are many factors to determine

consumer brand loyalty and understanding these

factors will enable companies to main their

customers.

E-lifestyle

Traditional segmentation strategies are based on

individual socio-demographic, attitudinal, or

psychographic characteristics (Penz, 2006). The

term psychographic puts together 'psychology and

'demographics' to add richness of both social and

behavioral sciences to demographics in order to

improve understanding of consumer

behaviour(Demby, 1974). The first spectrum of

psychographic studies was originally rooted in

personality profiles with the most frequently used

scale for measuring general aspects of personality

traits. However, these researches being plagued

with inconsistent correlations with consumer

behavior (Vyncke, 2002). In the second spectrum

of psychographic research, concept of personality

has been replaced by concept of 'lifestyle' which

was introduced by Lazer (1963). To date, the term

lifestyle has become prevalent amongst scholar in

the field of consumer behavior, and hence, the

term is used in this research.

Lifestyle is commonly referred to patterns in

which people live and spend their time and money

(Kaynak & Kara, 2001). Lifestyle can also be

defined as patterns of action which differentiate

people in order to help to understand what people

do and why they do it (Chaney, 1996).

Accordingly, the term lifestyle has become

central, while the personality concept has become

marginal to psychographic studies and the latter is

currently replaced by lifestyle concept (Vyncke,

2002). The term lifestyle is more comprehensive

than that of socioeconomics and demographic

characteristics (Blackwell, Miniard, & Engel,

2001). More importantly, individual lifestyles

seem to be stronger predictors of consumer

behavior including use and disposition of products

and services (Murry, Lastovicka, & Austin, 1997).

Thereby, decision makers will be able to

communicate with their consumers more

effectively by recognizing the lifestyle factors of

potential consumers (Lee, Lim, Jolly, & Lee,

2009).

Rapid convergence of the internet and mobile

usage, particularly among the youth, has

dramatically impacted and changed the way

people live since last decade (Yu, 2011).

Therefore, understanding consumer lifestyles has

been considered useful in delivering suitable

products and/or services to particular target

segments of information and communication

technology and hence, concept of e-lifestyle has

been introduced that could help marketers to

decide precisely within this target segment (Chen

& He, 2006). Furthermore, Yu (2011) develops

and validate an e-lifestyle construct that could

provide marketers some insights of what triggers

people's e-lifestyles. E-lifestyle, in this research,

conceives as patterns in which people live and

spend their time and money through internet and

electronic, which this definition is consistent with

that of Kaynak and Kara (2001). Lifestyle theories

agreed that consumer behaviors can be predicted

by a function of sociological and psychological

variables. Consistently, consumer e-lifestyle is

also predictable and assessable by psychological

and sociological constructs (Yu, 2011).

Numerous researches have assessed the lifestyle

construct, but among various lifestyle scales, two

conceptualizations are popular and broadly used.

The first one is lifestyle's construct

conceptualizing by three dimensions of activities,

interests, and opinions (AIO), originally developed

by Wells and Tigert (1971). The second construct

includes value, attitude, and lifestyles (VALS)

rating scale, which developed by Mitchell (1983).

Wells and Tigert (1971) defined activities as

actual observable behaviors, interests as the

continuous paying of attention to certain objects,

and opinions as responses to specific events. Since

then, lifestyle has been conceptualized based on

AIO approach extensively to help marketers

tailoring particular service/product to various

target segments (Bates, Cooper, & Wachs, 2001;

Green, Cordell, & DiStefano, 2006; Hur, Kim, &

Park, 2010).

Mitchell (1983) developed VAL’s instrument by

observing the relations among individual values,

lives, beliefs, and actions. He explained that

mixture of personal life and perceived value

determine consumer behavior, while a perceived

value is a synthesis of individual beliefs, attitudes,

hopes and demands. Therefore, many scholars



argue that value is one of the necessary constructs,

beside activities, interests, and opinions, to assess

consumer lifestyle (Lin, 2003). Interestingly, in

parallel with that, Yu (2011) operationalized e-

lifestyle employing four constructs of e-activities,

e-opinions, e-interests, and e-values. Furthermore,

based on theory of lifestyle, lifestyle is a set of

behaviors reflecting individual psychological

concerns (internal beliefs) and sociological

consequences (external stimuli). This research

adopts from seminal work of Yu (2011) to

conceptualize e-lifestyle construct.

Customer Satisfaction

The relationship between satisfaction and loyalty

seems to be nearly intuitive. Satisfaction is used as

a common marketing benchmark to measure a

product’s performance in the market. Previous

research has identified customer satisfaction is

considered as a key to achieving customers’

retention and loyalty and therefore helps in

realizing economic goals such as turnover and

revenue(Dick & Basu, 1994).Satisfaction is an

"overall customer attitude towards a service

provider" (Levesque & McDougall, 1996, p. 14),

or an emotional reaction to the differences

between customers expectation and what they

receive(Zineldin, 2000). There are at least two

different conceptualizing of customer satisfaction

construct – at the first time post purchase

evaluation (Oliver, 1977) or the cumulative

satisfaction to overall evaluation after usage for a

period of time(Fornell, 1992). For this study the

cumulative construct of satisfaction is more

relevant to telecommunication service sector,

where customer satisfaction can be measured as

the overall evaluation of the service provided by

the specific mobile service providers in Malaysia.

The relationship between satisfaction and loyalty

is assumed to be positive (Zeithaml, Berry, &

Parasuraman, 1996). This is widely recognized in

previous empirical studies.

Based on the above literature review, the research

framework and hypothesis are proposed as below:

H1: Consumer e-activities have positive impact on

customer satisfaction.

H2: Consumer e-interests have positive impact on


H3: Consumer e-opinions have positive impact on


H4: Consumer e-values have positive impact on


H5: Customer satisfaction has positive significant

impact on loyalty to the service provider.

Figure 1: Proposed Research Framework

III. RESEARCH METHODOLOGY

A self-administered questionnaire was distributed

and collected from subscribers of mobile service

providers in Malaysia. The unit of analysis in this

study is subscribers of mobile service providers

(i.e., Celcom, Digi, Maxis, and Umobile) within

some areas in Malaysia including Kedah, Perlis,

Perak, Penang, Selangor, Kelantan, Melaka, and

Negeri Sembilan. Based on rule of thumb, the

minimum number of respondents is five-to-one

ratio of the number of latent variables to be tested.

Since we were not able to obtain list of total

population in the suggested area, thereby a non-

probability purposive sampling approach is

employed whereby only mobile subscribers of the

five particular mobile providers in Malaysia were

chosen and the rest were excluded from the data

set.

The questionnaire consists of three major sections.

First section included four sub-constructs

underlying consumer e-lifestyle. The four sub-

constructs include e-activities, e-opinions, e-

interests, and e-values adapted from previous

research (Lee et al., 2009; Mitchell, 1983; Yu,

2011). The second part includes the customer

satisfaction and loyalty and the last section gathers

demographic information of the respondents such

as gender, age, race, and education level.

IV. RESULTS A-D DISCUSSIO-

Purposive sampling method was used as the list of

population was not available. About 600 self-

administered questionnaires were distributed.

However only a total 197 usable questionnaire

were finally used for data analysis with32.8%

response rate. SmartPLS 2.0 software (Ringle,

Wende, & Will, 2005) was used to evaluate the

relationships among the constructs of the research

model by conducting partial least squares (PLS)

analysis.

Customer

Loyalty

Customer

satisfaction

Customer

E-lifestyle

• E-interest

• E-activities

• E-opinion

• E-values



Demographic Profiles of Respondents

Table 1 depicts the demographic profile of 197

respondents in Malaysia. The demographic profile

includes the respondent's gender, age, race, highest

education level, monthly salary, monthly mobile

subscription and their most recent subscription

Table 1: Demographic Profile

Variable Categories Frequency %

Gender Male

Female

61

134

31.3

68.7 Age 18-22

23-27 28-32

33-37

128

25 19

11

69.9

13.6 10.3

5.8

Race Malay Chinese

Indian

Others

56 114

3

23

28.6 58.2

1.5

11.7 Highest

educational level

High school (SPM)

Diploma/Matriculation/

STPM Degree

Master

PhD

1

22 129

31

13

0.5

11.2 65.8

15.8

6.6 Monthly salary Less than RM1000

RM1000-2000

RM2001-3000 RM3001-4000

RM4001-5000

More than RM5000

131

15

9 6

5

3

77.5

8.9

5.3 3.6

3.0

1.8 Monthly

subscription

Less than RM50

RM50-100

RM101-150 RM151-200

More than RM200

78

75

29 6

3

40.8

39.3

15.2 3.1

1.6

Current subscription to

mobile operator

Celcom Digi

Maxis

U-mobile Others

51 63

57

16 14

21.1 27.4

24.2

2.6 1.6

Table 1 shows the respondent’s profile. The

respondents who are female are more than double

that of the number of male respondents with rate

of 68.7% female versus 31.3% male. Age of

respondents in this category varies differently. The

results show that the majority of respondents are

in the range of generation Y (i.e., birth date years

between 1980s and 2000s). More than half of

respondents are Chinese (58.2%), while 28.6 are

Malay and only 1.5% of respondents are Indians.

About 40.8% of mobile users as respondents

spend less than RM50 monthly on mobile

subscription. Similarly, about the equal numbers

of respondents spend monthly between RM50 and

RM100 on subscription, while only 1.6% spends

over RM200 on mobile subscription. In general,

mobile users in Malaysia closely use different

mobile network operators. It shows high

competition on telecommunication sector. For

example, Digi is number one in terms of

subscription among the respondents by 27.4%

followed by Maxis second after Digi with 24.2%,

and Celcom (21.1%) respectively. Umobile is a

newly introduced brand to the market and has yet

to be recognized among the mobile users.

Measurement Model The measurement model with reflective indicator

was modelled using SmartPLS. The measurement

model is evaluated by examining individual item

reliability, internal consistency or construct

reliability, average variance extracted analysis,

and discriminant validity.

A measurement model has satisfactory internal

consistency reliability when the composite

reliability (CR) of each construct exceeds the

threshold value of 0.7. Table 2 shows that the CR

of each construct for this study ranges from 0.8 to

0.9 and this is above the recommended threshold

value of 0.7. Thus, the results indicate that the

items used to represent the constructs have

satisfactory internal consistency reliability.

Indicator reliability of the measurement model is

measured by examining the items loadings. A

measurement model is said to have satisfactory

indicator reliability when each item’s loading is at

least 0.7 and is significant at least at the level of

0.05. Based on the analysis, all items in the

measurement model exhibited loadings exceeding

0.65 ranging from a lower bound of 0.649 to an

upper bound of 0. 936. All items are significant at

the 0.01 level. Table 2 shows the loading for each

item. Based on the results, all items used for this

study have demonstrated satisfactory indicator

reliability

Table 2: Overview Validity and Reliability of the Model

Loading AVE CR α Communality

E-activities

EA2 0.805

0.626 0.834 0.709 0.626 EA3 0.782

EA6 0.786

E-interest

EI1 0.688

0.573 0.843 0.755 0.573 EI4 0.757

EI5 0.776

EI6 0.802

E-opinions

EO1 0.802

0.593 0.853 0.771 0.593 EO2 0.687

EO3 0.852

EO4 0.728

E-values

EV1 0.649

0.562 0.885 0.845 0.562

EV2 0.769

EV3 0.784

EV4 0.752

EV6 0.740

EV7 0.796

Satisfaction CS1 0.936

0.877 0.935 0.860 0.877 CS2 0.937

Loyalty

L1 0.919

0.794 0.939 0.912 0.784 L2 0.935

L3 0.917

L4 0.786



In this study the measurement model’s

discriminant validity is assessed by using the

Fornell and Larcker’s (1981) criterion. The results

in Table 3 show that a measurement model has

discriminant validity when the square root of the

AVE exceeds the correlations between the

measure and all other measures, and the

indicators’ loadings are higher against their

respective construct compared to other constructs.

Hence, the result confirmed that the Fornell and

Larcker’s criterion is met.

Table3: Discriminant Analysis

AVE EA EI EO EV LY CS

E-activities 0.626 0.792

E-interest 0.573 0.264 0.757

E-opinions 0.593 0.266 0.571 0.770

E-values 0.562 0.142 0.532 0.685 0.750

Loyalty 0.794 0.205 0.363 0.421 0.361 0.891

Satisfaction 0.877 0.257 0.367 0.442 0.389 0.778 0.937

Note: Values in the diagonal (bolded) are square root of the AVE while the off-diagonals are the inter-construct correlations.

Path Coefficients and Hypotheses testing A major emphasis in PLS analysis is on variance

explained as well as establishing the significance

of all path estimates. Specifically, the predictive

power of the structural model is assessed by the R2

values of the endogenous constructs. Figure 2 and

Table 4 present the results of the model and the

path analysis to test the hypotheses. Based on the

path coefficient and t-test results shows out of five

hypotheses, only three hypotheses are supported

and another 2 of the hypotheses related to e-

lifestyle are not supported. The results show that

e-activities and e-opinion positively influence the

satisfaction of four mobile service providers at

significant level of p<0.05. The final e-activities

predictors that been identified in the model is the

three items that related the usages of internet to

EA2:shop for products/services, EA3:do the

banking transactions or finances and EA6:arrange

trips by booking flight/bus tickets or

accommodation. These are the main activities that

consumer do online. The positive results of this

factor indicate that customers are satisfied with the

current service provider that they are using. On

the other hand, for e-opinion, the four items

related are the continued development of ICT that

give positive impact forEO1: society, EO2:

culture, EO3: education system and EO4:

economy. The two constructs under e-lifestyle,

namely e-interest and e-values do not have any

significant impact on customer satisfaction.

Based on the both e-activities and e-opinion,

consumers are satisfied with brand and the quality

of the service provider by the four mobile

companies which are Celcom, Digi, Maxis and U-

mobile. Consistent with prior literature,

satisfaction has the strongest relationship with

loyalty. The results of R2

value is 0.241 and 0.605

suggesting that 24.1% of the variance in customer

satisfaction can be explained by the e-lifestyle

factors while customer satisfaction can explain

60.5% of the variance in loyalty. Customers are

willing to L1: loyal to the current brand, L2:

continue to the subscription of the brand, L3:

recommend the brand to their friends and L4: not

switching to the other service providers.

Table 4: Path coefficient

Hypothesis β Std. Beta Std. Error t-value

H1: E-activities --> Satisfaction

0.142 0.153 0.063 2.234*

H2: E-interest -->

Satisfaction

0.117 0.129 0.085 1.383

H3: E-opinions -->

Satisfaction

0.241 0.239 0.101 2.399**

H4: E-values -->

Satisfaction

0.141 0.142 0.095 1.491

H5: Satisfaction --> Loyalty

0.778 0.778 0.031 25.008**

Note: if the t-value is greater than 1.645 (*p< 0.05), 2.33 (**p< 0.01)

Figure 2: Measurement model

V. CO-CLUSIO-

In today’s dynamic global environment, it is

important to understand how e-lifestyle factors

influence customer satisfaction-loyalty

relationship toward the mobile service providers.

The competition among the mobile service

providers in Malaysia is more intense now than

ever before as the market has not yet reached its

optimal level and there are still huge opportunities

to improve their services. Four major service

providers are evaluated and the results show that

e-lifestyle directly influence the customer



satisfaction and indirectly impact the customer

loyalty. Although, e-lifestyle was validated

previously by Yu (2011), this study used different

validity assessment tools and approach in the

Malaysian context. Understanding consumer

lifestyle has long been conceived beneficial in

tailoring and delivering appropriate product and

services to particular target segments, ample

opportunity exists in conceptualizing an e-lifestyle

instrument. By doing so, marketing management

researchers could be able to build a better strategic

planning based on understanding their potential

consumer’s lifestyle towards mobile

communication services.

ACK-OWLEDGME-T The authors would like to thank UniversitiSains

Malaysia for funding this research under the Research

University grant1001/PMGT/816203.

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