Motivational factors, customer engagement and loyalty in the ...

Motivational factors, customer engagement and loyalty in the mobile instant

messaging environment: Moderating effect of application usage

Ronnie Kritzinger

Student number: 18254463

A research article submitted to the Gordon Institute of Business Science, University

of Pretoria, in partial fulfilment of the requirements for the degree of Master of

Business Administration.

11 November 2019

ii

ABSTRACT

Mobile instant messaging (MIM) applications have changed the communication style

in society. MIM providers have been known to bypass traditional operator networks

to deliver services, forcing operators to rethink customer trends and behaviours in

engaging with mobile services. Despite the extensive literature on the technology

adoption of MIM, few studies focus on the factors driving ongoing customer

engagement. First, this study assessed and validated specific gratifications obtained

from using MIM applications by applying the uses and gratifications theory. Second,

the research investigation attempted to understand the relationship between

motivational factors and customer engagement, in addition to the association

between customer engagement and customer loyalty for existing subscribers of

WhatsApp in South Africa. Third, the study investigated whether application usage

moderates the relationship between customer engagement and constructs of

utilitarian, hedonic and social motivation. Purposive sampling was utilised in this

quantitative study to obtain responses from an online survey. The study revealed that

utilitarian and hedonic motivation impacts customer engagement positively, which in

turn impacts loyalty. Results indicated that social motivation in using WhatsApp bore

no relationship to customer engagement. Furthermore, this study found that medium

application usage moderates the link between customer engagement and both

utilitarian and hedonic motivation.

KEYWORDS

Mobile instant messaging, motivational factors, customer engagement, customer

loyalty, application usage.

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DECLARATION

I declare that this research project is my own work. It is submitted in partial fulfilment

of the requirements for the degree of Master of Business Administration at the

Gordon Institute of Business Science, University of Pretoria. It has not been

submitted before for any degree or examination in any other University. I further

declare that I have obtained the necessary authorisation and consent to carry out

this research.

Ronnie Kritzinger

11 November 2019

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CONTENTS

ABSTRACT ............................................................................................................. ii

DECLARATION ...................................................................................................... iii

MOTIVATION OF JOURNAL CHOICE ................................................................... vi

CHAPTER 1: THEORY AND LITERATURE REVIEW ............................................ 1

1. Introduction .................................................................................................... 1

2. Mobile instant messaging ............................................................................... 1

3. Uses and gratifications theory ........................................................................ 2

4. Mobile engagement motivations .................................................................... 3

5. Customer engagement on mobile instant messaging applications ................. 5

6. Customer loyalty ............................................................................................ 7

7. Theoretical model development ..................................................................... 8

7.1 The relationship between IM motivations and customer engagement ............ 8

7.2 The relationship between customer engagement and customer loyalty ......... 9

7.3 Moderating effect of application usage on the relationship between IM----------

------- motivations and customer engagement ....................................................... 10

7.4 Theoretical model ........................................................................................ 11

8. Conclusion to literature review ..................................................................... 11

CHAPTER 2: RESEARCH METHODOLOGY AND DESIGN ............................... 13

1. Introduction .................................................................................................. 13

2. Choice of methodology ................................................................................ 13

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3. Population .................................................................................................... 14

4. Unit of analysis ............................................................................................. 15

5. Sampling method and size ........................................................................... 15

6. Measurement instrument ............................................................................. 16

7. Data gathering process ................................................................................ 18

8. Data analysis approach................................................................................ 18

9. Validity and reliability ................................................................................... 21

10. Limitations .................................................................................................... 22

11. Conclusion ................................................................................................... 24

REFERENCE LIST ............................................................................................... 25

APPENDIX 1: SURVEY ........................................................................................ 36

APPENDIX 2: MEASUREMENT SCALE .............................................................. 42

APPENDIX 3: ETHICAL CLEARANCE ................................................................ 44

APPENDIX 4: DATA COLLECTION PROCESS FOLLOWED FOR OFFICIAL

SURVEY ............................................................................................................... 45

APPENDIX 5: MODEL FIT INDICES .................................................................... 46

APPENDIX 6: SUMMARY OF RESEARCH METHODOLOGY FOR THIS STUDY

.............................................................................................................................. 47

APPENDIX 7: AUTHOR GUIDELINES OF “INTERNATIONAL JOURNAL OF

INFORMATION MANAGEMENT” AND JOURNAL ARTICLE EXAMPLES ......... 48

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Date: 2019-11-11

To whom it may concern,

MOTIVATION OF JOURNAL CHOICE

The International Journal of Information Management was chosen for this research

article which is Scopus indexed, has an impact factor of 5.063 and an Academic

Journal Guide (AJG) quality rating of 2. This journal was chosen considering the

research type, the subject area, the research topic, as well as the source of the

referenced articles in this study. The research article is focused on understanding

customer engagement interactions in the instant messaging environment in the

telecommunications industry and is similar to other research contained in articles

found in the journal and contributes to the debate in the subject area. This research

article references several journals in the customer engagement domain which

include the Journal of Interactive Marketing, Journal of Marketing Management,

Computers in Human Behavior and, of course, the International Journal of

Information Management. This study aims to understand the relationship between

motivational factors impacting customer engagement in an instant messaging

environment, in addition to the association between customer engagement and

loyalty, which resonates with the type of research published in this journal.

ADDITIONAL NOTES

Appendix 7 contains the author guidelines from the International Journal of

Information Management (IJIM), which were used as the basis for the journal article

for this study. This appendix also contains two examples used to align the style of

the journal article to that of the journal. The examples of the journal articles are:

Deng, Z., Lu, Y., Wei, K. K., & Zhang, J. (2010). Understanding customer satisfaction

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and loyalty: An empirical study of mobile instant messages in China.

International Journal of Information Management, 30(4), 289–300.

https://doi.org/10.1016/j.ijinfomgt.2009.10.001

Zhang, M., Guo, L., Hu, M., & Liu, W. (2017). Influence of customer engagement with

company social networks on stickiness: Mediating effect of customer value

creation. International Journal of Information Management, 37(3), 229–240.

https://doi.org/10.1016/j.ijimfomgt.2016.04.010

• The referencing style of the journal article was aligned to that of the American

Psychological Association (APA) as indicated in the journal guidelines. The

referencing style for the journal was imported into Mendeley, which was the

referencing management software used for this study.

• The word count of the journal article compiled for this study was 8762 words in

total (excluding references), aligned to similar journal articles submitted to the

IJIM. Despite the recommended word count for the IJIM to be between 4000 to

6000 words, the examples listed above clearly show that the journal does allow

journal articles of much longer lengths (up to 9000 words).

• The content of the journal article for this study were aligned to the style of the

second example listed above. However, tabular styles from the first example

were used within the journal article as well. Table 1 in the journal article for this

study was aligned to the format of Table 1 presented in the first example, since

it reflects the measurement scale used for this study clearly to the reader.

Unfortunately, the second example did not contain a table listing the

measurement scale, and the author thought that the added content can enhance

the presentation of research within the article. Since the second example did not

contain any moderating results, the style from Table 7 in the first example was

used as guideline to present moderating results in Table 6 of the article.

Yours sincerely,

Ronnie Kritzinger

Researcher: Ronnie Kritzinger

Email: [email protected]

Mobile: 083-200-7332

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CHAPTER 1: THEORY AND LITERATURE REVIEW

1. Introduction This chapter provides an overview of the academic literature to provide insight into

the constructs and associated relationships addressed in this research study. The

key theoretical constructs are outlined together with their definitions and the

relationships between them are explained. This chapter focusses on the associations

between motivational factors and customer engagement in a mobile instant

messaging (MIM) environment, in addition to the association between customer

loyalty and customer engagement. The chapter also discusses whether application

(app) usage can moderate the association between customer engagement and the

constructs of utilitarian, hedonic and social motivation. The literature review includes

introducing the theories underpinning the research, as well as explaining the

application thereof in the context of the study. An overview of each construct for this

research is provided, and the chapter ends with the hypotheses formulated and the

theoretical model presented.

2. Mobile instant messaging MIM is an experience that has amazed millions of users worldwide. Instant

messaging (IM) apps enable customers to send and receive text messages, videos,

photos and audio on smart devices in real-time (Larson, 2019; Oghuma, Libaque-

Saenz, Wong, & Chang, 2016). Most MIM applications (e.g. WhatsApp, WeChat,

Snapshat and Facebook Messenger) can operate on diverse operating systems (e.g.

Android, iOS, Windows) and have become a platform for entertainment and work

(Karapanos, Teixeira, & Gouveia, 2016; Marino & Lo Presti, 2018; Wu & Lu, 2013).

Previous generations of MIM apps that were popular in South Africa (SA) include

ICQ (Leung, 2001) and Mxit (Alfreds & Van Zyl, 2015; Chigona & Chigona, 2008).

MIM apps offer ease of use to customers of different ages and profiles, and Church

and de Oliveira (2013) have indicated that cost influences the frequency of use of

message platforms in the case when using WhatsApp compared to that of short

message service (SMS).

The literature thus far has predominantly focused on the technology adoption of

instant messaging applications and the value factors leading to satisfaction and

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intention to use. The expectation-confirmation model was used by Oghuma et al.

(2016) to show that perceived usability and perceived service quality significantly

influenced continuance intention to use MIM apps in addition to user satisfaction.

Zhou and Lu (2011) indicated that flow experience and network externality

significantly affect perceived satisfaction and usefulness, as well as loyalty.

Furthermore, Lu, Deng and Wang (2010) examined users’ acceptance of IM utilising

the theory of planned behaviour (TPB), flow theory, as well as the technology

acceptance model (TAM) and concluded that perceived enjoyment and usefulness

of users significantly influenced their intention and attitude to use IM apps. Moreover,

Deng, Lu, Wei and Zhang (2010) examined several antecedents of customer loyalty

and satisfaction in using MIM apps, which included trust, perceived service quality,

monetary value, social value, emotional value and functional value. Several studies

have also examined the use of instant messaging applications in the healthcare

sector (Drake, Claireaux, Khatri, & Chapman, 2016; Johnston, King, Arora, Behar,

Athanasiou, Savdalis, & Darzi, 2015) as an aid for improving customer relationship

management (Marino & Lo Presti, 2018), as well as a tool for educational activities

(Bouhnik & Deshen, 2014; Junco & Cotton, 2011). Although the literature focused on

technology adoption of IM apps and the factors leading to satisfaction and intention

to use, few attempts were been made to ascertain whether customer engagement

and its dimensions affected customer satisfaction and loyalty in an instant messaging

setting (Deng et al., 2010; Hernandez-Ortega, Aldas-Manzano, Ruiz-Mafe, & Sanz-

Blas, 2017; Marino & Lo Presti, 2018; Tsai & Men, 2018).

3. Uses and gratifications theory Understanding the motives of customers continued interaction using MIM apps can

assist IM app providers to formulate better strategies to increase customer

engagement. This research is grounded in the uses and gratifications (U&G) theory,

which has been extensively utilised in media investigations to determine the

motivational factors for employing certain media (McQuail, 1983). U&G theory can

be employed to understand why and how customers employ certain media to satisfy

needs (Klapper, 1963), with the core premise that the users are engaged and

continuously seek to fulfil needs and receive satisfaction just as needs are satisfied

(Khan, 2017). In U&G theory a clear difference is made between gratifications

obtained and those sought (Ku, Chu, & Tseng, 2013; Palmgreen, Wenner, &

Rayburn, 1980). Gratifications sought indicate fulfilment that customers indeed

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experience before they utilise the medium (e.g. technology, mobile application).

Gratifications obtained refer to the fulfillment that customers demand when using a

certain medium. Moreover, the U&G theory also states that gratifications obtained

from a medium may differ from those sought (Palmgreen et al., 1980; Quan-Haase

& Young, 2010).

McQuail (1983) mentioned four arguments for using media: social interaction,

information, entertainment and personal identity. Furthermore, Katz, Gurevitch and

Haas (1973) postulated 35 needs within the mass media domain classified across

five categories: personal integrative demands (e.g. status and credibility), social

integrative demands (e.g. interactions with family and/or friends), affective demands

(e.g. emotion and pleasure), cognitive demands (e.g. acquiring information and

insight) and tension release demands (e.g. digression and escape). The U&G theory

can be used to provide an understanding of the motives used in seeking content

within social media and IM (Dolan, Conduit, Frethey-Bentham, Fahy, & Goodman,

2019; Lo & Leung, 2009). This study follows a U&G approach to understand the

specific gratifications obtained by customers in using MIM applications. Key

motivational factors in the adoption and use of IM apps have been identified from

extant research and assessed during this study to validate the findings of

gratifications obtained in engaging with MIM apps.

4. Mobile engagement motivations In recent years, there have been several attempts to determine the specific

gratifications and motives for the use of MIM and social media: Facebook (Banerjee

& Dey, 2013; Cheung, Chiu, & Lee, 2011; Karapanos et al., 2016), Twitter (Chen,

2011; Liu, Cheung, & Lee, 2010), WhatsApp (Church & de Oliveira, 2013; Karapanos

et al., 2016; Shambare, 2014), MSN Messenger and Yahoo! Messenger (Lo &

Leung, 2009), and Youtube (Haridakis & Hanson, 2009; Khan, 2017). Bellman,

Potter, Treleaven-Hassard, Robinson and Varan (2011) provided two categories of

branded mobile apps, namely: experiential and informational. Experiential app

content provides hedonic experiences in the form of intrinsic enjoyment and

entertainment, whereas informational app content provides functional or utilitarian

experiences to achieve specific goals (Dovaliene, Piligrimiene, & Masiulyte, 2016; Y.

H. Kim, Kim, & Wachter, 2013). Hedonic motivations are related to activities that offer

enjoyment and pleasure, and utilitarian motivations are based on lifestyle decisions

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and functionality (Y. H. Kim et al., 2013). Sociability was also identified as a motive

for customer engagement in apps (Dovaliene et al., 2016; Y. H. Kim et al., 2013),

and is concerned with the passion to connect and share content with other individuals

or groups.

Malik, Suresh and Sharma (2017) indicated that hedonic applications are mainly

used by customers to take part in entertainment activities (e.g. Facebook and

WhatsApp) and utilitarian apps are used for information seeking activities (e.g. online

newspaper and mobile banking). Haridakis and Hanson (2009) investigated Youtube

motivations of customers and concluded that videos were explored for the goal of

pursuing information and distributed for social interaction and enjoyment purposes.

Park, Kee and Valenzuela (2009) examined the motivations in participating on

Facebook using four factors, namely: self-status seeking, entertainment,

socialisation and information seeking. Moreover, Leung (2001) indicated that the

motivations for fashion, relaxation, affection, entertainment, inclusion, as well as

motivations for sociability and escape were important in engaging with IM platforms.

In a similar vein, Ku et al. (2013) examined six motivational factors used in an IM

environment, namely: Information seeking, killing time, sociability, style, amusement

and relationship maintenance. In contrast, Wu and Lu (2013) classified IM and social

networking within a hedonic system-use context only, however, the authors did

indicate that systems (e.g. IM and social networking) can serve a dual purpose.

The type of app experience depends on its relevance and the context within which it

is used (Bellman et al., 2011; Chang, 2015; Khan, 2017). Calder, Malthouse and

Schaedel (2009) indicated that the experiences of a customer with a service might

not necessarily be mutually exclusive and could be a combination of multiple

experiences. For example, some mobile apps can be engaging because they are fun

and enjoyable, other apps can be appealing since they grant utilitarian experiences,

or apps can provide both types of experiences. This research used U&G theory to

develop a categorisation of MIM content, organised according to three categories,

namely: hedonic, utilitarian and sociability. In this study, the motivations impacting

engagement on MIM are aligned to the views of Dovaliene et al. (2016), Y. H. Kim et

al. (2013), as well as Marino and Lo Presti (2018).

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5. Customer engagement on mobile instant messaging applications According to the Oxford English Dictionary (2014) the word “engage” denotes a state

of being “involved,” “bound,” or to “participate,” and so on. The notion of customer

engagement has been extensively studied over diverse areas in marketing literature,

which includes psychology (Achterberg, Pot, Kerkstra, Ooms, Muller, & Ribbe, 2003;

Bryson & Hand, 2007), organisational behavior (Kahn, 1990), political science

(Resnick, 2001) and sociology (Jennings & Stoker, 2004). Based on relationship

marketing, customer engagement is generally characterised as a multifaceted

construct and has been shown to influence value, word-of-mouth, involvement, trust,

satisfaction, and loyalty (Bowden, 2009; Hollebeek, 2011; Islam & Rahman, 2016;

Leckie, Nyadzayo, & Johnson, 2016; Patterson, Yu, & De Ruyter, 2006; Van Doorn,

Lemon, Mittal, Nass, Pick, Pirner, & Verhoef, 2010; Vivek, Beatty, & Morgan, 2012).

Several attempts were made to define the notion of customer engagement.

Schaufeli, Salanova, Gonzalez-Roma and Bakker (2002) indicated that customer

engagement is a state of mind that is positive and fulfilling, however, defined by

absorption, vigour, and dedication. Furthermore, Patterson et al. (2006) viewed

customer engagement in the context of relationships customers have with a service

organisation, as different states of cognitive, emotional, and physical presence.

Customer engagement is primarily seen as a context-specific variable impacting

consumer choice in relation to organisations, brands and products. By contrast,

Bowden (2009) posited customer engagement as a mental process modelling the

structures contributing towards how loyalty amongst new customers is formed and

preserved for continued purchases of a particular brand. Customer engagement is

modelled across three facets (i.e. cognitive, behavioural and emotional) as an activity

that comprises a formulation of a situation where customers are committed,

emotionally committed towards a service brand, and greater levels of association

backed by a higher level of trust for continued purchase.

Van Doorn et al. (2010) viewed customer engagement as a behavioural

interpretation concerning an organisation after the purchase process and influenced

through motivational factors. Five facets of customer engagement were proposed

that included a form of modality, scope, valence, goals, as well as the circumstances

surrounding its influence. Van Doorn et al. (2010) postulated that engagement can

be viewed as having a positive impact on a firm if the goals of the customer and firm

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are aligned. However, customer engagement may possess more negative outcomes,

if goals of firm and customer are misaligned. Vivek et al. (2012) posited customer

engagement as the intensity of the relationship of a customer with the activities or

offerings of a company, started by the company or customer. Customer engagement

is viewed across four dimensions (i.e. cognitive, affective, social and behavioural) as

connections individuals form with a firm, established on the know-how from the firm’s

offerings and activities. Moreover, Brodie, Hollebeek, Jurić and Ilić (2011) defined

customer engagement across three facets (i.e. cognitive, behavioural, and affective)

as a mental attitude that ensues due to bilateral observations during service

relationships with an object or an agent. Customer engagement is seen as interactive

and dynamic actions within service relationships that cocreate value considering

special situations reliant on the context.

In recent years, customer engagement caused considerable debate in the paradigm

of social sciences, more specifically that of social media engagement (Brodie, Ilić,

Jurić, & Hollebeek, 2013; Carlson, Gudergan, Gelhard, & Rahman, 2019; Cheung &

Lee, 2011; Dessart, Veloutsou, & Morgan-Thomas, 2015; Dolan et al., 2019;

Dovaliene et al., 2016; E. Kim, Lin, & Sung, 2013; Thakur, 2016). Y. H. Kim et al.

(2013) posited that perceived value and satisfaction in using mobile services are a

prerequisite for the behaviour of customer engagement. The presence of linkages

between customer engagement, perceived satisfaction and value has been

confirmed by Dovaliene, Masiulyte and Piligrimiene (2015), who also found that the

customer engagement dimension of cognition has no influence on perceived value.

Customer satisfaction was posited to act as a factor influencing customer

engagement for current customers, but also as an outcome for customers who are

new (Brodie et al., 2011; Dovaliene et al., 2016). Read, Robertson, McQuilken and

Ferdous (2019) posited that brand intimacy and customer service significantly impact

consumer engagement with Twitter and that the link among co-promotion intentions

and antecedents are mediated by consumer engagement. Moreover, Islam and

Rahman (2016) deduced that increased levels of involvement towards the brand

generate stronger customer engagement on Facebook, resulting in word-of-mouth

behaviour and trust.

Few attempts have been made to examine customer engagement in the MIM

environment. Tsai and Men (2018) found that privacy perception and social

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messenger dependency adequately impact engagement via WeChat, and improve

organisation-public affiliations. Deng et al. (2010) confirmed that trust, service

quality, and perceived value contribute in generating satisfaction within MIM apps.

Moreover, Oghuma et al. (2016) concluded that the constant intent to use MIM apps

is impacted by the quality of the service and perceived usability. The customer

engagement facet of affection influenced attitude and satisfaction in continuing using

MIM apps with an organisation as confirmed by Marino and Lo Presti (2018).

Furthermore, Marino and Lo Presti (2019) concluded that MIM apps can contribute

to customer care when used as support to relationships with customers and that the

distance between customers and organisations can be decreased by creating

personalised experiences via these apps. Marino and Lo Presti (2019) further stated

that if MIM apps appease utilitarian and hedonic demands of customers in the short

term, value can be delivered to both consumers and organisations.

In conclusion, this study follows the customer engagement perspective devised by

Van Doorn et al. (2010) and is aligned to other studies (Brodie et al., 2011; Marino &

Lo Presti, 2018; Vivek et al., 2012). The views of Marino and Lo Presti (2018)

regarding customer engagement on MIM have been adopted in this study, which

defines engagement as “a behavior that goes beyond purchase and is the level of

the customer’s interactions and connections with the brand, firms or activities often

present in the MIM chat created around the brand/firm/activity” (p. 688). From the

literature, it is agreed that customer engagement may be treated as a three-

dimensional notion. The following customer engagement dimensions are considered

in this study: cognitive, emotional and behavioural (Hollebeek, Glynn, & Brodie,

2014; Y. H. Kim et al., 2013; Marino & Lo Presti, 2018). The customer engagement

dimension of cognition is a state of mind concerned with the perception an individual

has about the object (e.g. product or brand). The emotional dimension is associated

with satisfaction and a positive sensation, both related to the special feelings an

individual has towards the object. The behavioural dimension entails the expression

of an individual’s interest in an object through interaction and participation.

6. Customer loyalty The notion of customer loyalty was addressed throughout numerous studies and is

commonly viewed as both behavioural and attitudinal (Leckie et al., 2016). Jacoby

and Chestnut (1978) delineated loyalty to a brand as “the biased (i.e. nonrandom)

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behavioural response (i.e. purchase) expressed over time by some decision-making

unit with respect to one or more alternative brands out of a set of such brands and is

a function of psychological (decision-making) evaluative processes” (p. 80).

Moreover, Liu, Li, Mizerski and Soh (2012) defined loyalty as the connection strength

of a customer with a brand linked to use experience. For service organisations, brand

loyalty is impacted by customers engagement and has an impact on firm revenues

(Dwivedi, 2015). This study follows attitudinal manifestations of loyalty that entail

focusing on the customer’s brand commitment and the objective to continue

purchasing the brand (Leckie et al., 2016; Russell-Bennett, McColl-Kennedy, &

Coote, 2007; Yoo & Donthu, 2001).

In relation to engagement with customers, Vivek et al. (2012) posited that more

positive attitudes linked to the engagement towards a company, brand, or product

can be developed by an engaged customer, which may enable the customer to be

more loyal. Leckie et al. (2016) concluded that the affection and activation

dimensions of customer engagement strongly influenced brand loyalty in the cellular

communications industry. In the customer engagement framework proposed by

Bowden (2009) satisfaction, trust, involvement, delight and commitment were seen

as precursors towards achieving loyalty. Sivadass and Baker-Prewitt (2000)

indicated that the absolute aim of customer satisfaction is loyalty. In relation to MIM,

Deng et al. (2010) posited that satisfied users will use the instant messaging platform

more than those dissatisfied and expected to acquire increased levels of constant

intent. Moreover, Brodie et al. (2013) posited that the engagement of customers

leads to loyalty, satisfaction, connection, as well as emotional bonding, trust,

empowerment and commitment.

7. Theoretical model development 7.1 The relationship between IM motivations and customer engagement The relationship between antecedents (i.e. utilitarian, hedonic and sociability) and

customer engagement were confirmed in research by Y. H. Kim et al. (2013) and

Dovaliene et al. (2016). Findings by Y. H. Kim et al. (2013) indicate the motivations

for the engagement of customers across hedonic, utilitarian and social categories in

using mobile apps influence satisfaction, perceived value, and engagement intention.

Moreover, Dovaliene et al. (2016) indicated that utilitarian and sociability motivations

9

significantly influence customer engagement in mobile apps. As far as the researcher

could determine, motivational factors (i.e. utilitarian, hedonic and social) impact on

customer engagement in an instant messaging context such as WhatsApp have not

been investigated. Therefore, customer engagement and motivations in using MIM

are hypothesised to have relationships with each other. The next hypothesis is

formulated for the research study:

H1: Utilitarian, hedonic and social motivation positively affect customer engagement

in using MIM applications.

Based upon H1, the following sub-hypotheses are formulated:

H1a: Utilitarian motivation positively affects customer engagement in using MIM

applications.

H1b: Hedonic motivation positively affects customer engagement in using MIM

applications.

H1c: Social motivation positively affects customer engagement in using MIM

applications.

7.2 The relationship between customer engagement and customer loyalty Extensive research has focused on the association between customer engagement

and loyalty (Banyte & Dovaliene, 2014; Bowden, 2009; Brodie et al., 2013;

Hollebeek, 2011; Leckie et al., 2016; Thakur, 2016; Vivek et al., 2012). Vivek et al.

(2012) posited that the engagement of customers is fundamentally associated with

the loyalty individuals have with a brand. Banyte and Dovaliene (2014)

conceptualised that if the engagement of customers contributes towards value being

created, it could result in trust and satisfaction being established, whereafter

customer loyalty is attained. Santouridis and Trivellas (2010) have shown that the

satisfaction of customers and the quality of service impact loyalty in the mobile

market, with Morgan and Govender (2017) reaching the same conclusion on

customer loyalty focussing on the South African cellular industry in general. Leckie

et al. (2016) concluded that customer loyalty can be influenced by the behavioural

and emotional dimensions of engagement in the telecommunications industry.

Thakur (2016) has shown that the engagement of customers can significantly predict

loyalty in mobile apps. Deng et al. (2010) argued that values of a utilitarian and

10

affectional nature influences customer satisfaction in using IM apps, with a strong

association between loyalty and satisfaction. Moreover, Marino and Lo Presti (2018)

indicated that the cognitive (i.e. conscious attention) and emotional (i.e. enthused

participation) dimensions of customer engagement influence satisfaction in MIM

apps. Therefore, customer engagement in using MIM and customer loyalty are

hypothesised to have a relationship. The following hypothesis is formulated:

H2: Customer engagement positively affects customer loyalty in using MIM

applications.

7.3 Moderating effect of application usage on the relationship between IM

motivations and customer engagement

The impact of application usage was investigated in the context of mobile

applications (Cheri, 2016; Deng et al., 2010; Kim, Wong, Chang, & Park, 2016;

Leung, 2001; Rodgers, Negash, & Suk, 2005). Leung (2001) found that male ICQ

users spent less time on each chat session for relaxation and entertainment

purposes, and female users chatted longer and more frequently for sociability

reasons. Leung (2001) concluded that light users of ICQ were motivated by fashion,

and heavy users were motivated by sociability and affection. Rodgers (2005)

concluded that consumers with added online experience impacted the association

between loyalty and satisfaction more than consumers who had less online

experience.

Application usage was found to moderate the association between loyalty and

switching cost within a MIM setting (Deng et al., 2010). Venkatesh, Thong and Xu

(2012) indicated that the association between behavioural intention and hedonic

motivation in an information technology context was moderated by experience, age,

and gender. Furthermore, Kim et al. (2016) found that usage experience and

relationship length in the smartphone market moderated the link between loyalty and

customer satisfaction, aside from the link between loyalty and switching cost.

Moreover, the authors concluded that highly experienced users impacted the link of

customer satisfaction with loyalty more compared to low-experienced users.

Customers favour greater commitment toward a product or company if the product

is used more by the customer over time, a sentiment shared by Kim et al. (2016).

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Based on extant research, there is proof to show that application usage is likely to

impact the association between motivational factors, customer engagement, and

loyalty. Therefore, it is hypothesised that MIM application usage moderates the

association between motivational factors and customer engagement. The next

hypothesis is formulated for the study:

H3: Application usage moderates the relationships between motivational factors

(utilitarian, hedonic and social) and customer engagement in using MIM applications.

Based upon H3, the following sub-hypotheses are formulated:

H3a: Application usage moderates the relationship between utilitarian motivation and

customer engagement in using MIM applications.

H3b: Application usage moderates the relationship between hedonic motivation and


H3c: Application usage moderates the relationship between social motivation and


7.4 Theoretical model

Figure 1 illustrates the theoretical model developed. This study aimed to assess

whether customer engagement should be considered as important in the MIM

environment since several instant messaging providers are investing significantly to

attract and maintain users engaged, or if motivations to use MIM apps are enough in

affecting customer engagement and loyalty.

8. Conclusion to literature review This chapter provided the theoretical background of U&G theory grounding the study,

customer engagement in the instant messaging and social media environments,

together with the different classes of motivation considered when customers engage

with mobile apps. A theoretical model was produced considering the

interrelationships between motivational factors (i.e. utilitarian, hedonic and social),

customer engagement and loyalty.

12

Figure 1: Proposed theoretical model

Source: Researcher’s own construct

Motivations

Utilitarian

Hedonic

Social

Customer Engagement

Cognitive

Emotional

Behavioural

Customer Loyalty

Application Usage

H2

H1a

H1b

H1c

H3c

H3a

H3b

13

CHAPTER 2: RESEARCH METHODOLOGY AND DESIGN

1. Introduction A review of the literature was provided in the previous chapter related to mobile

instant messaging, together with the marketing constructs of motivation, customer

engagement and customer loyalty. A theoretical model was presented in the previous

chapter and evaluated in this study. In this chapter, the research methodology is

highlighted, in addition to discussions held on the population targeted, the unit of

analysis, sampling method and size. This chapter also highlights the measurement

scale used for this study, the data gathering process followed, together with outlining

the data analysis approach and the limitations of this study.

2. Choice of methodology Following a positivist research philosophy, the research addressed the customer’s

role of engagement with MIM and to discover the association between existing

customer engagement and loyalty, in addition to the impact of motivational factors

upon customer engagement. The positivist approach focuses on discovering

measurable and observable facts, but also tests and confirms hypotheses developed

from existing theory in part or whole, which could possibly lead to further research

(Guba & Lincoln, 2005; Muijs, 2004; Saunders & Lewis, 2017). The positivist

research philosophy has been utilised quite considerably in the relationship

marketing domain (Algesheimer, Dholakia, & Hermann, 2005; Hollebeek et al., 2014;

Y. H. Kim et al., 2013; Thompson & Sinha, 2008).

A deductive research approach is undertaken when theory is tested or verified by

examining hypotheses derived from it (Cohen, Manion, & Morrison, 2007; Creswell,

2014; Kothari, 2004). Theoretical propositions and hypotheses are evaluated using

research strategies designed to collect data for testing purposes (Creswell, 2014;

Muijs, 2004). This research study investigated the association between customer

engagement (including its sub-dimensions) and loyalty, together with the impact of

antecedents on customer engagement. The research investigation consisted of

formulating hypotheses to evaluate relationships, as well as the collection of data to

assess the hypotheses developed. The research approach was deductive in nature

and hence appropriate for this study. For this study, a quantitative research design

14

was utilised within the positivist philosophical paradigm (Creswell, 2014; Muijs,

2004). A quantitative research design approach is commonly used to test theories

by exploring the relationship between key variables (Creswell, 2014) and analysing

those relationships using statistical techniques. In this study the data was collected

by only using an online survey only – a mono method quantitative study.

Descripto-explanatory research was the appropriate research design for this study

since this approach focusses on studying a situation or problem and seeks to explain

a relationship between variables (Creswell, 2014; Kothari, 2004). Applying the lens

of U&G theory, this study aimed to determine whether the motivational factors

identified have a strong association with customer engagement. Moreover, the study

also assessed whether the engagement of customers had a strong association with

customer loyalty. A moderator (i.e. application usage) was considered to determine

whether it impacts the relationship between motivational factors and customer

engagement.

Cohen et al. (2007) indicated that survey techniques are commonly utilised to gather

data at a specific timeframe to assess relationships existing between key events, or

to depict the nature of current situations. Oates (2006) indicated that positivists

commonly use quantitative data and often use surveys for data collection, whereby

data collected from measurements are used to test hypotheses. The research

strategy entailed using an online self-administered survey for data collection

(Creswell, 2014; Muijs, 2004) since it results in less risk of bias and requires less

time and effort to manage (Morgan & Govender, 2017). The survey followed a

structured approach to ensure that each respondent was presented with an identical

set of standardised closed-ended questions and statements in the same order

(Cohen et al., 2007). This study collected data from respondents at one specific time

period. Therefore, a cross-sectional research design was employed, since it presents

a view of a sample population at a specific period in time (Cohen et al., 2007).

3. Population In this study, the target population was delineated to be all mobile subscribers (pre-

paid and post-paid) of smartphones, tablets or laptops in South Africa. The study

considered these devices to have the same functionality since they tend to use

popular operating systems (e.g. iOS, macOS, Android, and Windows) and apps

15

downloaded are most often obtained from the same markets (Harris, Brookshire and

Chin, 2016). For the purposes of this study, WhatsApp was chosen as the MIM

platform under investigation, since the majority of mobile app users in SA use this

social messaging platform (Statista, 2019a). The focus was placed on adult

subscribers over the age of 18 years (Statista, 2019b), however, subscribers who

owned more than one device could also participate. A further restriction was that

smart device subscribers used at least one MIM application to communicate (with

friends, family and/or colleagues).

4. Unit of analysis Zikmund, Babin, Carr and Griffen (2013) indicated that for a particular study the unit

of analysis relate to who or what produces the suitable data at a certain aggregation

level. For this study, the unit of analysis was an individual using a smart device (e.g.

smartphones, tablets or other devices) to interface with a MIM application.

5. Sampling method and size According to McKane (2018), the SA cellular industry has more than 90 million active

subscribers. It has also been reported in the third quarter of 2017 that approximately

32% of the SA population were actively using social media, with WhatsApp the most

used application at a 49% penetration rate (Statista, 2019a). Due to the difficulty and

impracticality in obtaining access to the entire target population during the survey

(i.e. chances are not known of respondents of the target population selected for the

sample), a non-probability sampling method was utilised (Cohen et al., 2007; Muijs,

2004). For this study, purposive sampling was applied by sending requests to

participate in an online survey via emails that included screening questions with the

aim of reaching individuals within South Africa who used MIM apps via smart devices

and which excluded respondents who did not use WhatsApp.

The Consulta Panel was selected to be the sampling frame. Consulta is a well-known

market research provider in South Africa and permits consumers to participate in

industry surveys. This company has access to a panel of consumers in SA who share

information and characteristics about themselves. Similar sampling techniques in this

research field have been utilised by Singh (2017), Dovaliene et al. (2016), Harris et

al. (2016) and Morgan and Govender (2017). The central limit theorem, fundamental

16

to inferential statistics, states that if the sample size retrieved from the target

population is large enough (usually more than 30 samples), then the sampling

distribution of the sample statistic is normally distributed (Creswell, 2014; Wegner,

2017). Gorard (2003) indicated that sample size will increase if a phenomenon

contains potential variability, and Borg and Gall (1979) posited that large sample

sizes are necessary when there are many variables. Moreover, Cody and Smith

(2006) indicated that at a minimum of ten times the number of survey respondents

for every variable is required for data to be analysed.

For this research, the theoretical model comprised of seven constructs and one

moderator. The number of independent variables used will be dependent on the

measurement scale chosen, however, for this study the researcher limited the

number of independent variables (i.e. items) to 26, to avoid the survey becoming too

long. The nature of measurement scales employed does exert an influence on

sample size (Oppenheim, 1992). Structural equation modelling (SEM) was

considered for the analysis of data and according to Jackson (2003) the optimal

number of responses to aim for was 260 based on the p:v hypothesis (where v

represents independent variables and p the sample size) and 26 independent

variables examined during this study. The size of the sample chosen for this research

is aligned with similar studies and is of sufficient quantity to test hypotheses using

statistical techniques. The study eventually produced 282 usable responses from an

online survey (after data screening techniques were performed).

6. Measurement instrument Surveys are useful in providing a quantitative characterisation of opinions, beliefs,

experiences, trends, behaviours and attitudes of a target population with the aim of

making generalisations from a sample to a target population (Cohen et al., 2007;

Fowler, 2009; Weisberg, Krosnick, & Bowen, 1996). Cohen et al. (2007) listed a 14-

stage process of planning a survey, and Rosier (1997) mentioned that survey

planning needs to include clarification of 11 critical items.

In this study, an anonymous, structured, self-administered online survey was used

as a measurement instrument to investigate the relationship between customer

engagement and loyalty, in addition to the impact of antecedents on the engagement

of customers. By exploring the effect of several motivation factors influencing

17

customer engagement in MIM applications, a better understanding can be obtained

of the influence of those antecedents on the three customer engagement dimensions

identified, together with the association between customer engagement and loyalty.

The study can provide insights into the reasons existing subscribers continue to use

MIM apps on SA cellular networks (e.g. voice calling performed on WhatsApp rather

than the legacy platform), instead of using other apps with similar functions. The

survey comprised of two screening questions and five sections in total as depicted

in Appendix 1.

Screening questions – This section of the survey contained two screening

questions to determine if the respondent met the minimum criteria to take part in the

survey. The criteria required respondents to be users of WhatsApp and resident in

South Africa.

Section 1 – This part of the survey was dedicated to the demographics of MIM

subscribers in SA, such as age, education, gender, home language and employment

status.

Section 2 – This section requested information related to patronage habits of

WhatsApp respondents, such as the duration and time spent using the application,

as well as the device used to interact with the application.

Sections 3 to 5 – These sections of the survey consisted of items to measure the

motivational factors impacting customer engagement, in addition to the questions

regarding customer engagement and loyalty. The items in these sections were

mandatory. The measurement scale for this study is listed in Appendix 2 and was

obtained from prior research investigations that proved the validity and reliability of

the sub-scales. The individual items have been adapted for this research. Items listed

in the survey were measured on 7-point unlabelled Likert-type scales, ranging from

1 (strongly disagree / not at all important) to 7 (strongly agree / extremely important).

Likert scales that have five to ten values are regarded as acceptable for survey

respondents to complete (Kline, 2011) and this type of scale is recommended for

self-administered surveys (Hair, Bush, & Ortinau, 2006).

18

7. Data gathering process

The Consulta Panel was used during the pilot and official survey to retrieve the data

from the respondents. Ethical clearance for this research study was granted by the

university on 24 June 2019 (Appendix 3). Before the official survey was distributed,

a pilot was conducted amongst 61 respondents (from Consulta Panel) to evaluate

the flow and contents of the survey and to address design issues within the survey

in preparation for the research study. Basic data screening tests were applied using

pilot results, which included addressing lost data and anomalies (in the form of

outliers), together with investigating the normality of the dataset (Tabachnick & Fidell,

2013).

Accordingly, only 40 valid responses remained after data screening tests were

performed. Results from the pilot survey were analysed to determine the reliability of

responses gathered, and if the measurement scale had to be updated to ensure it

would assist in providing answers to the research questions. The pilot study

produced sufficient results confirming that no changes to the measurement scale

were necessary, and the results subsequently formed part of the dataset for the

remainder of this study. Once the pilot test was completed, the requests to take part

in the official online self-administered survey were performed via email by Consulta,

with the aim of reaching members that use MIM apps via smart devices. The data

collection process followed during the official survey is summarised in Appendix 4.

Respondents were given a deadline for responding to the online survey to improve

response rates. The email was officially distributed on the 20th August 2019 and the

survey was closed on 27 August 2019. The official survey was distributed to 17480

panel members. Considering the responses from both the pilot and official surveys,

a total number (n) of 282 usable responses for this study were obtained.

8. Data analysis approach Covariance-based SEM was employed during this study since it was shown to be an

effective multivariate statistical analysis technique to investigate multiple

relationships of independent and dependent variables (Hair, Black, Babin, &

Anderson, 2014; Kline, 2011; Muijs, 2004). SEM is a useful tool to model complex

realities of research better than conventional techniques, but most often requires

large samples to avoid inaccurate statistical estimates (Kline, 2011; Muijs, 2004).

19

Moreover, SEM was used during this study, since reflective instead of formative

measures were employed to assess constructs (Singh, 2017), and this technique

reduces common errors due to simultaneous parameter estimations in a particular

model (Iacobucci, Saldanha, & Deng, 2007). In this study, the SEM process

consisted of two parts (Hair et al., 2014): (1) Developing the measurement model

that shows the relationships between independent and dependent latent variables,

and evaluated by calculating construct validity, composite reliability and the model fit

indices; (2) Developing the structural model that shows the causal associations

between latent variables aligned to hypotheses formulated, and evaluated by

calculating the model fit indices.

The main assumptions of SEM were assessed before continuing to analyse the data

and included evaluating the acceptable size of the sample and the normal distribution

of the data set (Hair et al., 2014; Tabachnick & Fidell, 2013). The sample size was

deemed adequate since it aligned with the suggestions made by Jackson (2003) for

the 26 variables under investigation in this study.

Data screening tests were applied to the whole sample set, which included

addressing homoscedasticity and multicollinearity, in addition to investigating

missing data, outliers and normality (Tabachnick & Fidell, 2013). The distribution

shape, skewness and kurtosis of every variable in the theoretical model were

analysed to assess normality of the data. Considering the guidelines provided by

Kline (2011), the skewness and kurtosis values obtained were assessed to check if

they were between -2 and +2, which would indicate that normality of distribution was

obtained with reference to every variable item. Multivariate outliers were addressed

by comparing the Mahalanobis distance of independent variables to a Chi-square

(χ2) distribution with the same degrees of freedom (Tabachnick & Fidell, 2013), which

resulted in 52 survey responses being removed.

Homoscedasticity was assessed by performing a scatter plot of each variable under

investigation on the y-axis and the variable’s residual on the x-axis, and to determine

if consistent patterns occurred near the linear fit line (Hair et al., 2014).

Multicollinearity was evaluated in calculating the variable inflation factor (VIF) of each

independent variable after executing a multivariate regression analysis (O’Brien,

2007; Steven, 2001; Tabachnick & Fidell, 2013). VIF values lower than 3 were

20

generally considered as acceptable, and values higher than 5 indicated that it was

very likely that there were multicollinearity problems.

The data analysis process consisted of six steps: (1) Preparation of data (includes

data validation, editing, coding and tabulation); (2) Calculate descriptive statistics;

(3) Evaluate construct validity utilising confirmatory factor analysis (CFA); (4) Assess

data reliability by calculating composite reliability (CR) and Cronbach’s alpha

coefficients; (5) Assess the fitness of the measurement model by calculating key

model fit indices employing CFA; (6) Utilise SEM to evaluate the hypotheses

formulated and to test the structural model by calculating model fit indices.

Since an online survey was utilised for data collection, common method bias (CMB)

was assessed utilising Harman’s one-factor test (with an unrotated factor solution)

(Podsakoff & Organ, 1986) to determine external bias that might have influenced

responses (Leckie et al., 2016). Measurement model invariance was also assessed

during CFA to validate that the factor loadings and structure were sufficiently

equivalent across male and female gender groups. Configural invariance was

investigated by calculating model fit measures when estimating the two gender

groups freely without constraints (Vandenberg & Lance, 2000). Metric invariance was

evaluated in performing a Chi-square difference test between the unconstrained and

full-constrained models. Lastly, scalar invariance was assessed by analysing the

significance of the p-value retrieved from a multi-group analysis (considering male

and female gender groups).

Measurement and structural model fitness were assessed by calculating the indices

of common model fit (Islam & Rahman, 2016; Leckie et al., 2016) as listed in

Appendix 5. The thresholds recommended by Hair et al. (2014) and Hu and Bentler

(1999) were confirmed from research data to asses model fit. IBM’s SPSS (including

AMOS module) was employed to gain insights from data collected and test

hypotheses on complex variable relationships. The moderating effect of app usage

within the theoretical model was assessed employing the Hayes PROCESS analysis

module within SPSS, which is a regression-based technique to statistical testing

(Hayes, 2013; Read, Robertson, McQuilken, & Ferdous, 2019). Table 1 lists the

hypotheses as well as the independent and dependent variables for this study.

21

9. Validity and reliability Validity is usually concerned with whether useful and meaningful inferences can be

drawn from item scores captured from measurement scales (Creswell, 2014; Muijs,

2004). To guarantee the adequate validity level of the data captured in this study,

CFA was used to evaluate construct validity (Hair et al., 2014; Leckie et al., 2016;

Tabachnick & Fidell, 2013). Construct validity was determined using the following

measures, namely: convergent validity, variance extracted, and discriminant validity

(Hair et al., 2006; Leckie et al., 2016).

Muijs (2004) indicated that construct validity is the extent items collect data about

intended measurements. Moreover, convergent validity was determined by

computing the average variance extracted (AVE) for every variable, which should

generally be above 0.50 (Hair et al., 2014; Leckie et al., 2016). Discriminant validity

was evaluated by comparing the AVE values of all the constructs with their

corresponding maximum shared variances (MSVs), assessing whether correlations

between constructs are considerably lower than 1 (at p = 0.001), and that the

correlation coefficients for the constructs were less than the square root of the AVE

(Leckie et al., 2016; Petzer & Van Tonder, 2018).

Table 1: Variables of this research study

Hypotheses Independent

variable

Dependent

variable

H1: Utilitarian, hedonic, and social motivation

positively affect customer engagement in

using MIM applications.

Utilitarian,

hedonic, and

social motivation

Customer

engagement

H1a: Utilitarian motivation positively affects

customer engagement in using MIM

applications.

Utilitarian

motivation

Customer

engagement

H1b: Hedonic motivation positively affects


applications.

Hedonic

motivation

Customer

engagement

H1c: Social motivation positively affects


applications.

Social

motivation

Customer

engagement

22

H2: Customer engagement positively affects

customer loyalty in using MIM applications.

Customer

engagement

Customer

loyalty

H3: Application usage moderates the

relationships between motivational factors

(utilitarian, hedonic and social) and customer

engagement in using MIM applications.

Utilitarian,

hedonic, and

social motivation

Customer

engagement

H3a: Application usage moderates the

relationship between utilitarian motivation and


applications.

Utilitarian

motivation

Customer

engagement

H3b: Application usage moderates the

relationship between hedonic motivation and


applications.

Hedonic

motivation

Customer

engagement

H3c: Application usage moderates the

relationship between social motivation and


applications.

Social

motivation

Customer

engagement

To test the reliability and consistency of the measurement scale, the Cronbach’s

alpha coefficients were calculated (Kline, 2011; Zikmund et al., 2013), and have been

used in similar studies (Deng et al., 2010; Hollebeek et al., 2014; Y. H. Kim et al.,

2013). During the assessment of the measurement model, the CR was also

calculated to determine the reliability of the measurement scale, where the

recommended threshold level is commonly set at 0.7 (Bagozzi & Yi, 1988; Leckie et

al., 2016). Fornell and Larcker (1981) indicated that CR can be utilised to evaluate

the internal consistency of constructs, with the benchmark level for a successful

reliability outcome achieved when a result of 0.7 and higher is obtained (Deng et al.,

2010; Hair et al., 2014). Therefore, construct reliability was obtained when the

Cronbach’s alpha and CR estimates exceed the recommended thresholds.

10. Limitations There are a few limitations that have been identified that pose opportunities for future

studies. First, the time period for data collection in this study was cross-sectional in

nature, therefore, it is expected that the data will provide only a snapshot of customer

23

engagement with MIM apps at a specific moment in time. Customers’ intentions and

perceptions change based on their experience and interactions with MIM apps. A

longitudinal study approach could be undertaken to overcome this limitation.

Second, this study presented a theoretical model used to examine the association

amongst motivational factors, customer engagement and loyalty in the South African

MIM environment. Therefore, caution should be exercised by researchers when

citing the findings, since there might be differences between South Africa and other

countries. Further studies could consider countries of similar culture to replicate and

validate the research findings of this and extant studies.

Third, this study focused on a limited number of motivational factors impacting

customer engagement, and one moderator to investigate the influence on the

association between motivational factors and customer engagement. Future studies

could consider more complex relationships with customer engagement in using MIM

apps. Additional motivational factors that can be further investigated can include

switching cost and other user gratifications that might impact engagement with MIM

apps. The study also focused on the amount of time spent by MIM customers

interacting with WhatsApp, but did not consider the behaviour of customers in the

amount of time spent making voice and video calls, sending text messages, and

uploading pictures. The customers’ behaviour towards using an MIM app can impact

the customer engagement level towards using IM platforms, however, for this study

the assumption is made that customers on average spent the same time in sending

text messages, making voice/video calls and uploading pictures.

Fourth, the type of smart device can also impact the engagement level of a customer

communicating using an MIM app. For example, smart feature phones that have MIM

apps loaded, but do not have touch-screen capabilities, can cause difficulty for

customers to communicate with the mobile application and can lead to lower

customer engagement levels. Since this study does not consider the smartphone

type or quality used when interacting with MIM apps, consequent studies could

consider the impact these aspects have on customer engagement.

Finally, since no clear agreement regarding the concept of customer engagement in

mobile apps has yet been reached, a widely used measurement scale has not yet

24

been found for this study. The research data might not necessarily depict that of the

entire population since purposive sampling was performed using an online survey. A

larger sample size used during multivariate statistical analysis might lead to more

accurate results.

11. Conclusion This chapter discussed the elements of the research methodology process selected

to meet the study objectives identified. The limitations of this study have been

provided and highlight the potential biases in the research design. Appendix 6 lists a

summary of the research methodology and associated design elements for this

research study.

25

REFERENCE LIST

Achterberg, W., Pot, A., Kerkstra, A., Ooms, M., Muller, M., & Ribbe, M. (2003). The

effect of depression on social engagement in newly admitted Dutch nursing

home residents. The Gerontologist, 43(2), 213–218.

Alfreds, D., & Van Zyl, G. (2015). Last throw of the dice for Mxit. Retrieved May 13,

2019, from https://www.fin24.com/Tech/News/Last-throw-of-the-dice-for-Mxit-

20151023

Algesheimer, R., Dholakia, U. M., & Hermann, A. (2005). The social influence of

brand community: Evidence from European car clubs. Journal of Marketing,

69(3), 19–34.

Bagozzi, R., & Yi, Y. (1988). On the evaluation of structural equation models. Journal

of the Academy of Marketing Science, 16(1), 74–94.

https://doi.org/10.1007/BF02723327

Banerjee, N., & Dey, A. K. (2013). Identifying the factors influencing users’ adoption

of social networking websites—A study on Facebook. International Journal of

Marketing Studies, 5(6), 109–121. https://doi.org/10.5539/ijms.v5n6p109

Banyte, J., & Dovaliene, A. (2014). Relations between customer engagement into

value creation and customer loyalty. Procedia - Social and Behavioral Sciences,

156(5), 484–489. https://doi.org/10.1016/j.sbspro.2014.11.226

Bellman, S., Potter, R. F., Treleaven-Hassard, S., Robinson, J. A., & Varan, D.

(2011). The effectiveness of branded mobile phone apps. Journal of Interactive

Marketing, 25(4), 191–200. https://doi.org/10.1016/j.intmar.2011.06.001

Borg, W. R., & Gall, M. D. (1979). Educational research: An introduction (3rd ed.).

London: Longman Publishing.

Bouhnik, D., & Deshen, M. (2014). WhatsApp goes to school: Mobile instant

messaging between teachers and students. Journal of Information Technology

Education: Research, 13(1), 217–231. https://doi.org/10.28945/2051

Bowden, J. L.-H. (2009). The process of customer engagement: A conceptual

framework. The Journal of Marketing Theory and Practice, 17(1), 63–74.

https://doi.org/10.2753/MTP1069-6679170105

Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2011). Customer engagement:

Conceptual domain, fundamental propositions, and implications for research.

Journal of Service Research, 14(3), 252–271.

https://doi.org/10.1177/1094670511411703

26

Brodie, R. J., Ilić, A., Jurić, B., & Hollebeek, L. (2013). Consumer engagement in a

virtual brand community: An exploratory analysis. Journal of Business

Research, 66(1), 105–114. https://doi.org/10.1016/j.jbusres.2011.07.029

Bryson, C., & Hand, L. (2007). The role of engagement in inspiring teaching and

learning. Innovations in Education and Teaching International, 44(4), 349–362.

Calder, B. J., Malthouse, E. C., & Schaedel, U. (2009). An experimental study of the

relationship between online engagement and advertising effectiveness. Journal

of Interactive Marketing, 23(4), 321–331.

https://doi.org/10.1016/j.intmar.2009.07.002

Carlson, J., Gudergan, S. P., Gelhard, C., & Rahman, M. M. (2019). Customer

engagement with brands in social media platforms: Configurations, equifinality

and sharing. European Journal of Marketing, 53(9), 1733–1758.

https://doi.org/10.1108/EJM-10-2017-0741

Chang, C. C. (2015). Exploring mobile application customer loyalty: The moderating

effect of use contexts. Telecommunications Policy, 39(8), 678–690.

https://doi.org/10.1016/j.telpol.2015.07.008

Chen, G. M. (2011). Tweet this: A uses and gratifications perspective on how active

Twitter use gratifies a need to connect with others. Computers in Human

Behavior, 27(2), 755–762. https://doi.org/10.1016/j.chb.2010.10.023

Cheri, B. (2016). The antecendents of consumer brand engagement: The role of

relationship quality (Master’s thesis). Auckland, NZ: Auckland University of

Technology.

Cheung, C. M. K., Chiu, P. Y., & Lee, M. K. O. (2011). Online social networks: Why

do students use facebook? Computers in Human Behavior, 27(4), 1337–1343.

https://doi.org/10.1016/j.chb.2010.07.028

Cheung, C. M. K., & Lee, M. K. O. (2011). Customer engagement in an online social

platform: A conceptual model and scale development. Thirty Second

International Conference on Information Systems, 1–8.

Chigona, A., & Chigona, W. (2008). MXit it up in the media: Media discourse analysis

on a mobile instant messaging system. The African Journal of Information and

Communication, 9(1), 42–57. https://doi.org/10.23962/10539/19778

Church, K., & de Oliveira, R. (2013). What’s up with WhatsApp? Comparing mobile

instant messaging behaviors with traditional SMS. Proceedings of the 15th

International Conference on Human-Computer Interaction with Mobile Devices

and Services, 352–361. https://doi.org/10.1145/2493190.2493225

27

Cody, R., & Smith, J. (2006). Applied statistics and the SAS programming language

(5th ed.). Upper Saddle River, New Jersey: Pearson Prentice Hall.

Cohen, L., Manion, L., & Morrison, K. (2007). Research methods in education (6th

ed.). New York: Routledge.

Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed

methods approaches (4th ed.). Thousand Oaks, California: SAGE Publications.





Dessart, L., Veloutsou, C., & Morgan-Thomas, A. (2015). Consumer engagement in

online brand communities: A social media perspective. Journal of Product and

Brand Management, 24(1), 28–42. https://doi.org/10.1108/JPBM-06-2014-0635

Dolan, R., Conduit, J., Frethey-Bentham, C., Fahy, J., & Goodman, S. (2019). Social

media engagement behavior: A framework for engaging customers through

social media content. European Journal of Marketing, 53(10), 2213–2243.

https://doi.org/10.1108/EJM-03-2017-0182

Dovaliene, A., Masiulyte, A., & Piligrimiene, Z. (2015). The relations between

customer engagement, perceived value and satisfaction: The case of mobile

applications. Prodedia - Social and Behavioral Sciences, 213, 659–664.

Dovaliene, A., Piligrimiene, Z., & Masiulyte, A. (2016). Factors influencing customer

engagement in mobile applications. Engineering Economics, 27(2), 205–212.

https://doi.org/10.5755/j01.ee.27.2.14030

Drake, T. M., Claireaux, H. A., Khatri, C., & Chapman, S. J. (2016). WhatsApp with

patient data transmitted via instant messaging? The American Journal of

Surgery, 211(1), 300–301. https://doi.org/10.1016/j.amjsurg.2015.04.004

Dwivedi, A. (2015). A higher-order model of consumer brand engagement and its

impact on loyalty intensions. Journal of Retailing and Consumer Services, 24,

100–109. https://doi.org/10.1016/j.jretconser.2015.02.007

Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with

unobservable variables and measurement error. Journal of Marketing

Research, 18(1), 39–50. https://doi.org/10.2307/3151312

Fowler, F. J. (2009). Survey research methods (4th ed.). Thousand Oaks, California:

SAGE Publications.

Gorard, S. (2003). Quantitative methods in social science research (1st ed.). London:

28

Continuum Books.

Guba, E. G., & Lincoln, Y. S. (2005). Paradigmatic controversies, contradictions, and

emerging influences (3rd ed.). Thousand Oaks, California: The Sage Handbook

of Qualitative Reserarch, N. K. Denzin and Y. S. Lincoln, Eds.

Hair, J., Bush, R., & Ortinau, D. (2006). Marketing research within a changing

environment (3rd ed.). New York: McGraw-Hill/Irvin.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data

analysis (7th ed.). Harlow, Essex: Pearson Education.

Haridakis, P., & Hanson, G. (2009). Social interaction and co-viewing with YouTube:

Blending mass communication reception and social connection. Journal of

Broadcasting and Electronic Media, 53(2), 317–335.

https://doi.org/10.1080/08838150902908270

Harris, M. A., Brookshire, R., & Chin, A. G. (2016). Identifying factors influencing

consumers’ intent to install mobile applications. International Journal of

Information Management, 36(3), 441–450.


Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process

analysis: A regression-based approach. New York, NY: The Guilford Press.

Hernandez-Ortega, B., Aldas-Manzano, J., Ruiz-Mafe, C., & Sanz-Blas, S. (2017).

Perceived value of advanced mobile messaging services. Information

Technology & People, 30(2), 324–355. https://doi.org/10.1108/itp-01-2014-

0017

Hollebeek, L. D. (2011). Demystifying customer brand engagement: Exploring the

loyalty nexus. Journal of Marketing Management, 27(7–8), 785–807.

https://doi.org/10.1080/0267257X.2010.500132

Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2014). Consumer brand engagement

in social media: Conceptualization, scale development and validation. Journal

of Interactive Marketing, 28(2), 149–165.

https://doi.org/10.1016/j.intmar.2013.12.002

Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure

analysis: Conventional criteria versus new alternatives. Structural Equation

Modelling: A Multidisciplinary Journal, 6(1), 1–55.

https://doi.org/10.1080/10705519909540118

Iacobucci, D., Saldanha, N., & Deng, X. (2007). A meditation on mediation: Evidence

that structural equations models perform better than regressions. Journal of

29

Consumer Psychology, 17(2), 139–153.

https://doi.org/10.1016/S1057-7408(07)70020-7

Islam, J. U., & Rahman, Z. (2016). Linking customer engagement to trust and word-

of-mouth on Facebook brand communities: An empirical study. Journal of

Internet Commerce, 15(1), 40–58.

https://doi.org/10.1080/15332861.2015.1124008

Jackson, D. L. (2003). Revisiting sample size and number of parameter estimates:

Some support for the n:q hypothesis. Structural Equation Modelling: A

Multidisciplinary Journal, 10(1), 128–141.

https://doi.org/10.1207/S15328007SEM1001_6

Jacoby, J., & Chestnut, R. (1978). Brand loyalty: measurement and management.

New York: John Wiley & Sons.

Jennings, M. K., & Stoker, L. (2004). Social trust and civic engagement across time

and generations. Acta Politica, 39(4), 342–379.

Johnston, M. J., King, D., Arora, S., Behar, N., Athanasiou, T., Sevdalis, N., & Darzi,

A. (2015). Smartphones let surgeons know WhatsApp: an analysis of

communication in emergency surgical teams. The American Journal of Surgery,

209(1), 45–51.

Junco, R., & Cotton, S. R. (2011). Perceived academic effects of instant messaging

use. Computers & Education, 56(2), 370–378.

https://doi.org/10.1016/j.compedu.2010.08.020

Kahn, W. A. (1990). Psychological conditions of personal engagement and

disengagement at work. Academy of Management Journal, 33(4), 692–724.

https://doi.org/10.5465/256287

Karapanos, E., Teixeira, P., & Gouveia, R. (2016). Need fulfillment and experiences

on social media: A case on Facebook and WhatsApp. Computers in Human

Behavior, 55(B), 888–897. https://doi.org/10.1016/j.chb.2015.10.015

Katz, E., Gurevitch, M., & Haas, H. (1973). On the use of the mass media for

important things. American Sociological Review, 38(2), 164–181.

Khan, M. L. (2017). Social media engagement: What motivates user participation

and consumption on YouTube? Computers in Human Behavior, 66, 236–247.

https://doi.org/10.1016/j.chb.2016.09.024

Kim, E., Lin, J.-S., & Sung, Y. (2013). To app or not to app: Engaging consumers via

branded mobile apps. Journal of Interactive Advertising, 13(1), 53–65.

https://doi.org/10.1080/15252019.2013.782780

30

Kim, M. K., Wong, S. F., Chang, Y., & Park, J. H. (2016). Determinants of customer

loyalty in the Korean smartphone market: Moderating effects of usage

characteristics. Telematics and Informatics, 33(4), 936–949.

https://doi.org/10.1016/j.tele.2016.02.006

Kim, Y. H., Kim, D. J., & Wachter, K. (2013). A study of mobile user engagement

(MoEN): Engagement motivations, perceived value, satisfaction, and continued

engagement intention. Decision Support Systems, 56(1), 361–370.

https://doi.org/10.1016/j.dss.2013.07.002

Klapper, J. T. (1963). Mass communication research: An old road surveyed. The

Public Opinion Quarterly, 27(4), 515–527.

Kline, R. B. (2011). Methodology in the social sciences: Principles and practice of

structural equation modelling (3rd ed.). New York: Guilford Press.

Kothari, C. R. (2004). Research methodology: Methods and techniques (2nd ed.).

New Delhi: New Age International Publishers.

Ku, Y. C., Chu, T. H., & Tseng, C. H. (2013). Gratifications for using CMC

technologies: A comparison among SNS, IM, and e-mail. Computers in Human


Larson, G. W. (2019). Encyclopeadia Brittanica: Instant messaging. Retrieved March

14, 2019, from https://www.britannica.com/topic/instant-messaging

Leckie, C., Nyadzayo, M. W., & Johnson, L. W. (2016). Antecedents of consumer

brand engagement and brand loyalty. Journal of Marketing Management, 32(5–

6), 558–578. https://doi.org/10.1080/0267257X.2015.1131735

Leung, L. (2001). College student motives for chatting on ICQ. New Media & Society,

3(4), 483–500. Retrieved from http://dx.doi.org/10.1177/14614440122226209

Liu, F., Li, J., Mizerski, D., & Soh, H. (2012). Self-congruity, brand attitude, and brand

loyalty: A study on luxury brands. European Journal of Marketing, 46(7), 922–

937. https://doi.org/10.1108/03090561211230098

Liu, I. L. B., Cheung, C. M. K., & Lee, M. K. O. (2010). Understanding Twitter usage:

What drives people continue to tweet. Proceedings of the Pacific Asia

Conference on Information Systems, Paper 92, 927–939. Retrieved from

http://aisel.aisnet.org/pacis2010/92

Lo, O. W. Y., & Leung, L. (2009). Effects of gratification-opportunities and

gratifications-obtained on preferences of instant messaging and e-mail among

college students. Telematics and Informatics, 26(2), 156–166.


31

Lu, Y., Deng, Z., & Wang, B. (2010). Exploring factors affecting Chinese consumers’

usage of short message service for personal communication. Information

Systems Journal, 20(2), 183–208.

https://doi.org/10.1111/j.1365-2575.2008.00312.x

Malik, A., Suresh, S., & Sharma, S. (2017). Factors influencing consumers’ attitude

towards adoption and continuous use of mobile applications: A conceptual

model. Procedia Computer Science, 122, 106–113.

https://doi.org/10.1016/j.procs.2017.11.348

Marino, V., & Lo Presti, L. (2018). Engagement, satisfaction and customer behavior-

based CRM performance An empirical study of mobile instant messaging.

Journal of Service Theory and Practice, 28(5), 682–707.

https://doi.org/10.1108/JSTP-11-2017-0222

Marino, V., & Lo Presti, L. (2019). Disruptive marketing communication for customer

engagement. The new frontiers of mobile instant messaging. JMM International

Journal on Media Management, 21(1), 3–23.

https://doi.org/10.1080/14241277.2019.1590837

McKane, J. (2018). Mobile subscribers in South Africa – Vodacom vs MTN vs Cell C

vs Telkom. Retrieved January 19, 2019, from MyBroadband website:

https://mybroadband.co.za/news/cellular/272691-mobile-subscribers-in-south-

africa-vodacom-vs-mtn-vs-cell-c-vs-telkom-2.html

McQuail, D. (1983). Mass communication theory: An introduction. London: Sage

Publications.

Morgan, S., & Govender, K. (2017). Exploring customer loyalty in the South African

mobile telecommunications sector. Cogent Business and Management, 4(1).

https://doi.org/10.1080/23311975.2016.1273816

Muijs, D. (2004). Doing quantitative research in education with SPSS (1st ed.).

London: Sage Publications.

O’Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation

factors. Quality & Quantity, 41(5), 673–690. https://doi.org/10.1007/s11135-

006-9018-6

Oates, B. J. (2006). Researching information systems and computing (1st ed.).

Thousand Oaks, California: SAGE Publications.

Oghuma, A. P., Libaque-Saenz, C. F., Wong, S. F., & Chang, Y. (2016). An

expectation-confirmation model of continuance intention to use mobile instant

messaging. Telematics and Informatics, 33(1), 34–47.

32


Oppenheim, A. N. (1992). Questionnaire design, interviewing and attitute

measurement (1st ed.). New York: Pinter Publishers.

Oxford English Dictionary. (2014). “Engage.” Oxford: Oxford University Press.

Palmgreen, P., Wenner, L. A., & Rayburn, J. D. (1980). Relations between

gratifications sought and obtained: A study of television news. Communication

Research, 7(2), 161–192. https://doi.org/10.1177/009365028000700202

Park, N., Kee, K. F., & Valenzuela, S. (2009). Being immersed in social networking

environment: Facebook groups, uses and gratifications, and social outcomes.

Cyberpsychology & Behavior, 12(6), 729–733.

Patterson, P., Yu, T., & De Ruyter, K. (2006). Understanding customer engagement

in services. Advancing Theory, Maintaining Relevance, Proceedings of

ANZMAC 2006 Conference. Brisbane.

Petzer, D. J., & Van Tonder, E. (2018). Loyalty intentions and selected relationship

quality constructs: The mediating effect of customer engagement. International

Journal of Quality & Reliability Management, 36(4), 601–619.

Podsakoff, P. M., & Organ, D. W. (1986). Self-reports in organizational research:

Problems and prospects. Journal of Management, 12(4), 531–544.

Quan-Haase, A., & Young, A. L. (2010). Uses and gratifications of social media: A

comparison of Facebook and instant messaging. Bulletin of Science,

Technology & Society, 30(5), 350–361.

https://doi.org/10.1177/0270467610380009

Read, W., Robertson, N., McQuilken, L., & Ferdous, A. S. (2019). Consumer

engagement on Twitter: Perceptions of the brand matter. European Journal of

Marketing, 53(9), 1905–1933. https://doi.org/10.1108/EJM-10-2017-0772

Resnick, E. (2001). Defining engagement. Journal of International Affairs, 54(2),

551–566.

Rodgers, W., Negash, S., & Suk, K. (2005). The moderating effect of on-line

experience on the antecedents and consequences of on-line satisfaction.

Psychology and Marketing, 22(4), 313–331. https://doi.org/10.1002/mar.20061

Rosier, M. J. (1997). Survey research methods (2nd ed.). Oxford: Elsevier Science.

Russell-Bennett, R., McColl-Kennedy, J. R., & Coote, L. V. (2007). Involvement,

satisfaction, and brand loyalty in a small business services setting. Journal of

Business Research, 60(12), 1253–1260.

https://doi.org/10.1016/j.jbusres.2007.05.001

33

Santouridis, I., & Trivellas, P. (2010). Investigating the impact of service quality and

customer satisfaction on customer loyalty in mobile telephony in Greece. TQM

Journal, 22(3), 330–343. https://doi.org/10.1108/17542731011035550

Saunders, M., & Lewis, P. (2017). Doing research in business and management: An

essential guide to planning your project (2nd ed.). London: Pearson Education.

Schaufeli, W., Salanova, M., Gonzalez-Roma, V., & Bakker, A. B. (2002). The

measurement of engagement and burnout: A two sample confirmatory factor

analytic approach. Journal of Happiness Studies, 3(1), 71–92.

https://doi.org/10.1023/A:1015630930326

Shambare, R. (2014). The adoption of WhatsApp: Breaking the vicious cycle of

technological poverty in South Africa. Journal of Economics and Behavioral

Studies., 6(7), 542–550.

Singh, I. (2017). The interrelationships between selected relationship marketing

constructs, customer engagement and behavioural intentions in the life

insurrance industry (Master’s thesis). Illovo, Sandton: Gordon Institute of

Business Science.

Sivadass, E., & Baker-Prewitt, J. L. (2000). An examination of the relationship

between service quality, customer satistfaction, and store loyalty. International

Journal of Retail & Distribution Management, 28(2), 73–82.

Statista. (2019a). Penetration of leading social networks in South Africa as of 3rd

quarter 2017. Retrieved January 19, 2019, from

https://www.statista.com/statistics/284468/south-africa-social-network-

penetration/

Statista. (2019b). Percentage of U.S. Internet users who use WhatsApp as of

January 2018, by age group. Retrieved January 19, 2019, from

https://www.statista.com/statistics/814649/whatsapp-users-in-the-united-

states-by-age/

Steven, J. (2001). Applied mutivariate statistics for the social sciences (4th ed.).

Hillsdale, NJ: Lawrence Erlbaum Associates.

Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Upper

Saddle River, New Jersey: Pearson Education.

Thakur, R. (2016). Understanding customer engagement and loyalty: A case of

mobile devices for shopping. Journal of Retailing and Consumer Services, 32,

151–163. https://doi.org/10.1016/j.jretconser.2016.06.004

Thompson, S. A., & Sinha, R. K. (2008). Brand communities and new product

34

adoption: The influence and limits of oppositional loyalty. Journal of Marketing,

72(6), 65–80.

Tsai, W. H. S., & Men, R. L. (2018). Social messengers as the new frontier of

organization-public engagement: A WeChat study. Public Relations Review,

44(3), 419–429. https://doi.org/10.1016/j.pubrev.2018.04.004

Van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P.

C. (2010). Customer engagement behavior: Theoretical foundations and

research directions. Journal of Service Research, 13(3), 253–266.

https://doi.org/10.1177/1094670510375599

Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the

measurement invariance literature: Suggestions, practices, and

recommendations for organizational research. Organizational Research

Methods, 3(1), 4–70. Retrieved from https://doi.org/10.1177/109442810031002

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of

information technology: Extending the unified theory of acceptance and use of

technology. MIS Quarterly, 36(1), 157–178.

Vivek, S. D., Beatty, S. E., & Morgan, R. M. (2012). Customer engagement: Exploring

customer relationships beyond purchase. The Journal of Marketing Theory and

Practice, 20(2), 122–146. https://doi.org/10.2753/MTP1069-6679200201

Wegner, T. (2017). Applied business statistics: Methods and Excel-based

applications (4th ed.). Cape Town: Juta and Company Ltd.

Weisberg, H. F., Krosnick, J. A., & Bowen, B. D. (1996). An introduction to survey

research, polling, and data analysis (3rd ed.). Thousand Oaks, California: SAGE

Publications.

Wu, J., & Lu, X. (2013). Effects of extrinsic and intrinsic motivators on using utilitarian,

hedonic, and dual-purposed information systems: A meta-analysis. Journal of

the Association for Information Systems, 14(3), 153–191.

https://doi.org/10.17705/1jais.00325

Yoo, B., & Donthu, N. (2001). Developing and validating a multidimensional

consumer-based brand equity scale. Journal of Business Research, 52(1), 1–

14. https://doi.org/10.1016/S0148-2963(99)00098-3

Zhou, T., & Lu, Y. (2011). Examining mobile instant messaging user loyalty from the

perspectives of network externalities and flow experience. Computers in Human


Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffen, M. (2013). Business research

35

methods (9th ed.). Canada: Cengage Learning.

36

APPENDIX 1: SURVEY

Customer Engagement and Instant Messaging

I am Ronnie Kritzinger, a final year student at the Gordon Institute of Business

Science, University of Pretoria, South Africa. I am conducting research as part of my

MBA studies in the area of consumer engagement towards using mobile instant

messaging applications. This survey is designed to obtain feedback regarding your

motivations and loyalty when engaging with WhatsApp.

Taking part in this survey is completely voluntary and anonymous and you may

withdraw at any time without penalty, however your valuable input is appreciated.

The survey consists of five sections. The survey should take no more than 10

minutes of your time. When evaluating a statement, please answer the statement

from your own perspective. By completing the survey, you indicate that you

voluntarily participate in this research. For any concerns or questions, please contact

me or my supervisor using details provided below.

Researcher: Dr. Ronnie Kritzinger Supervisor: Prof. Danie Petzer

Email: [email protected] Email: [email protected]

Screening questions

Do you currently use WhatsApp?

Yes

No

Do you live in South Africa?

Yes

No

37

If your answer is “Yes‟ to both questions, please complete the survey.

If your answer is “No‟ to either of the questions, you do not need to complete the

survey.

SECTION 1 – DEMOGRAPHIC INFORMATION

What year were you born?

19_____

What is your highest level of education?

Some high school

Matric / Grade 12 completed

Diploma

University degree (B-degree or Honours)

Postgraduate degree (Masters or

Doctorate)

What is your gender?

Male

Female

Prefer not to say

What is your home language?

Afrikaans

English

Sepedi

Sesotho

Setswana

isiSwati

Tshivenda

isiNdebele

38

isiXhosa

isiZulu

isiTsonga

Other, please specify:

Which ONE of the following options describes your employment status the best?

Self-employed

Full-time employed by an organisation

Part-time employed by an organisation

Full-time student

Housewife or Househusband

Retired

Unemployed

Other, please specify:

SECTION 2 – PATRONAGE HABITS

How much time do you spend on WhatsApp per day on average?

hours and

minutes

How long have you been using WhatsApp?

years and

months

Which device do you most often use to interact with WhatsApp?

Smartphone

Tablet

Laptop

39

SECTION 3 – MOTIVATIONAL FACTORS

On a scale of 1 to 7 where 1 is “not at all important‟ and 7 is “extremely important‟,

please rate the importance of the following statements when you are engaging with

WhatsApp.

Statements

Not at all

important

Extremely

important

1 2 3 4 5 6 7

Utilitarian motivation

To keep me informed and updated using

WhatsApp. 1 2 3 4 5 6 7

To increase my skills and knowledge

using WhatsApp. 1 2 3 4 5 6 7

To keep me organised (e.g. checking

messages and voice notes) 1 2 3 4 5 6 7

WhatsApp offers a variety of ways to

communicate with others (e.g. texting,

voice & video calling)

1 2 3 4 5 6 7

Hedonic motivation

To get rest and relaxation using

WhatsApp. 1 2 3 4 5 6 7

To enjoy the variety of contents (e.g.

texting, voice & video calling, group chats,

voice notes, sharing pictures & videos)

that WhatsApp offers.

1 2 3 4 5 6 7

To enjoy what I like about using

technology. 1 2 3 4 5 6 7

Social motivation

To keep in touch/share events with friends

and family. 1 2 3 4 5 6 7

To be connected and meet other people

with similar interests. 1 2 3 4 5 6 7

To tell my friends and family about what I

learned/read/heard using WhatsApp. 1 2 3 4 5 6 7

40

Adapted from Y. H. Kim et al. (2013).

SECTION 4 – CUSTOMER ENGAGEMENT

On a scale of 1 to 7 where 1 is “strongly disagree‟ and 7 is “strongly agree‟, please

rate the importance of the following statements when you are engaging with

WhatsApp.

Statements

Strongly

disagree

Strongly

agree

1 2 3 4 5 6 7

Cognitive engagement

Using WhatsApp is so absorbing that I

forget about everything else. 1 2 3 4 5 6 7

I am rarely distracted when using

WhatsApp. 1 2 3 4 5 6 7

My mind is focused when using

WhatsApp. 1 2 3 4 5 6 7

I pay a lot of attention to WhatsApp. 1 2 3 4 5 6 7

Emotional engagement

I am enthusiastic about using WhatsApp. 1 2 3 4 5 6 7

I am excited when using WhatsApp. 1 2 3 4 5 6 7

I am interested in using WhatsApp. 1 2 3 4 5 6 7

I am proud in using WhatsApp. 1 2 3 4 5 6 7

Behavioral engagement

I feel strong and vigorous when I am


I feel very resilient, mentally, as far as

WhatsApp is concerned. 1 2 3 4 5 6 7

I devote a lot of energy to WhatsApp. 1 2 3 4 5 6 7

I try my hardest to perform well when


Adapted from Cheung and Lee (2011).

41

SECTION 5 – CUSTOMER LOYALTY

On a scale of 1 to 7 where 1 is “strongly disagree‟ and 7 is “strongly agree‟, please

rate the importance of the following statements when you are engaging with

WhatsApp.

Statements

Strongly

disagree

Strongly

agree

1 2 3 4 5 6 7

Customer loyalty

In the future, I will be loyal to WhatsApp. 1 2 3 4 5 6 7

I will use WhatsApp again. 1 2 3 4 5 6 7

WhatsApp will be my first choice in the

future. 1 2 3 4 5 6 7

I will not use other brands if WhatsApp is

available. 1 2 3 4 5 6 7

Adapted from Leckie et al. (2016).

Thank you for your time in completing this survey!

42

APPENDIX 2: MEASUREMENT SCALE

Table A2-1: Measurement scale used in the research study

Construct Item Source

Utilitarian

(UT)

To keep me informed and updated using

WhatsApp.

Adapted

from Y. H.

Kim et al.

(2013)

To increase my skills and knowledge using

WhatsApp.

To keep me organised (e.g. checking messages

and voice notes).

WhatsApp offers a variety of ways to communicate

with others (e.g. texting, voice and video calling).

Hedonic

(HE)

To get rest and relaxation using WhatsApp.

To enjoy the variety of contents (e.g. texting, voice

and video calling, group chats, voice notes, sharing

pictures and videos) that WhatsApp offers.

To enjoy what I like about using technology.

Social (SO)

To keep in touch/share events with friends and

family.

To be connected and meet other people with similar

interests.

To tell my friends and family about what I

learned/read/heard using WhatsApp.

Cognitive

(CO)

Using WhatsApp is so absorbing that I forget about

everything else.

Adapted

from Cheung

and Lee

(2011)

I am rarely distracted when using WhatsApp.

My mind is focused when using WhatsApp.

I pay a lot of attention to WhatsApp.

Emotional

(EM)

I am enthusiastic about using WhatsApp.

I am excited when using WhatsApp.

I am interested in using WhatsApp.

I am proud in using WhatsApp.

Behavioural I feel strong and vigorous when I am using

43

(BH) WhatsApp.

I feel very resilient, mentally, as far as WhatsApp is

concerned.

I devote a lot of energy to WhatsApp.

I try my hardest to perform well when using

WhatsApp.

Customer

Loyalty

(CL)

In the future, I will be loyal to WhatsApp. Adapted

from Leckie

et al. (2016)

I will use WhatsApp again.

WhatsApp will be my first choice in the future.

I will not use other brands if WhatsApp is available.

44

APPENDIX 3: ETHICAL CLEARANCE

45

APPENDIX 4: DATA COLLECTION PROCESS FOLLOWED FOR OFFICIAL

SURVEY

The data collection process can be summarised as follows:

a) Consulta distributed an email to individuals that form part of the company’s

panel to take part in the online survey;

b) The email distributed by Consulta to respective panel members provided the

description and purpose of the research study and also indicated the type of

respondent required for the study. Respondents were informed that

participation is voluntary, and that all data collected will be reported

anonymously;

c) The respondent can click on the link listed in the email when choosing to

participate in the online survey;

d) When the respondents click on the link in the email, the respondents were

redirected to the online survey and asked to complete two screening

questions. The screening questions were used to confirm if respondents

taking part in the survey complied with the target population requirements for

the research study. If an answer of no was provided to any of the screening

questions by the respondents, they were not allowed to proceed with the

survey. If respondents answered yes to all the screening questions, they

proceeded to complete the next section of the survey; and

e) Respondents were thanked for their participation in the online survey once

completed and anonymity of their responses was assured.

46

APPENDIX 5: MODEL FIT INDICES

Table A5-1: Summary of indices used to assess model fit

Model fit measure Threshold

Chi-square / degree of freedom (cmin/df) < 3 good; < 5 acceptable

CFI (Comparative Fit Index) > .80 acceptable; > .90 good

GFI (Goodness of fit Index) > .80 acceptable; > .90 good

AGFI (Adjusted Goodness of fit Index) > .80 acceptable; > .90 good

TLI (Tucker Lewis Index) > .80 acceptable; > .90 good

NFI (Normed Fit Index) > .80 acceptable; > .90 good

Standardised Root Mean Square Residual

(SRMR)

< .09

RMSEA (Root Mean Square Error of

Approximation)

< .05 good; .05 to .1 moderate;

> .1 bad

Source: (Hair et al., 2014; Hu & Bentler, 1999)

47

APPENDIX 6: SUMMARY OF RESEARCH METHODOLOGY FOR THIS STUDY

Table A6-1: Summary of research methodology and associated design

elements

Design elements Research Methodology

Research design Deductive, Descripto-Explanatory

Scope National, South Africa

Population All mobile subscribers (pre-paid and post-paid) of

smartphones, tablets or laptops within South Africa

Research strategy Self-administered electronic surveys

Sampling frame Consulta Panel

Unit of analysis

Individual customer using a smart device (e.g.

smartphones, tablets or laptops) to interface with a

mobile instant messaging application

Sampling size 282

Sampling method Census

Time frame Cross-sectional study (during July and August 2019)

Statistical method Co-variance Based Structural Equation Modelling

(CB-SEM)

Statistical tools IBM SPSS and AMOS (Version 25)

48

APPENDIX 7: AUTHOR GUIDELINES OF “INTERNATIONAL JOURNAL OF

INFORMATION MANAGEMENT” AND JOURNAL ARTICLE EXAMPLES

This appendix contains the author guidelines from the International Journal of

Information Management (IJIM), which were used as the basis for the journal article

for this study. This appendix also contains two examples used to align the style of

the journal article to that of the journal. The examples of the journal articles are:





Zhang, M., Guo, L., Hu, M., & Liu, W. (2017). Influence of customer engagement with

company social networks on stickiness: Mediating effect of customer value

creation. International Journal of Information Management, 37(3), 229–240.

https://doi.org/10.1016/j.ijimfomgt.2016.04.010

AUTHOR INFORMATION PACK 13 Jul 2019 www.elsevier.com/locate/ijinfomgt 1

INTERNATIONAL JOURNAL OF INFORMATIONMANAGEMENTThe Journal for Information Professionals See also Elsevier Library and Information Sciences programme home

AUTHOR INFORMATION PACK

TABLE OF CONTENTS.

XXX.

• Description• Audience• Impact Factor• Abstracting and Indexing• Editorial Board• Guide for Authors

p.1p.2p.2p.2p.2p.4

ISSN: 0268-4012

DESCRIPTION.

The International Journal of Information Management (IJIM) is an international, peer-reviewed journalwhich aims to bring its readers the very best analysis and discussion in the developing field ofinformation management.

The journal:• Keeps the reader briefed with major papers, reports and reviews• Is topical: Viewpoint articles and other regular features including Research Notes, Case Studies anda Reviews section help keep the reader up to date with current issues.• Focusses on high quality papers that address contemporary issues for all those involved ininformation management and which make a contribution to advancing information managementtheory and practice.

Information is critical for the survival and growth of organisations and people. The challenge forInformation management is now less about managing activities that collect, store and disseminateinformation. Rather, there is greater focus on managing activities that make changes in patterns ofbehaviour of customers, people, and organizations, and information that leads to changes in the waypeople use information to engage in knowledge focussed activities.

Information management covers a wide field and we encourage submissions from diverse areas ofpractice and settings including business, health, education and government.

Topics covered include:

Aspects of information management in learning organisations, health care (patients as well healthworkers and managers), business intelligence, security in organizations, social interactions andcommunity development, knowledge management, information design and delivery, information forhealth care, Information for knowledge creation, legal and regulatory issues, IS-enabled innovationsin information, content and knowledge management, philosophical and methodological approaches toinformation management research, new and emerging agendas for information research and reflectiveaccounts of professional practice.

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In 2002, Elsevier launched Library Connect, a new initiative bringing together many of Elsevier'slibrary-focused efforts. For more information about this initiative and to read or subscribe to thecomplimentary Library Connect Newsletter, please visit Library Connect

AUDIENCE.

Senior managers in a variety of business and industrial organizations; information managers; publicsector managers and administrators; management consultants; information scientists and systemanalysts; teachers and trainers in management; public administration and related fields; researchersin business; information management and information science.

IMPACT FACTOR.

2018: 5.063 © Clarivate Analytics Journal Citation Reports 2019

ABSTRACTING AND INDEXING.

Research into Higher Education AbstractsLibrary and Information Science AbstractsCommunication AbstractsContents Pages in ManagementCurrent Contents - Social & Behavioral SciencesInternational Political Science AbstractsPAIS BulletinSociological AbstractsComputer & Control AbstractsCurrent Technology IndexComputer Literature IndexScopusSocial Sciences Citation IndexCMCIdblp - Computer Science Bibliography

EDITORIAL BOARD.

Editor

Yogesh Dwivedi, Bay Campus, School of Management, Swansea University, Fabian Way, SA1 8EN, Swansea,Wales, UK

Associate Editors

Yanqing Duan, University of Bedfordshire, Luton, England, UKRameshwar Dubey, Montpellier Business School, Montpellier, FranceMatthew K.O. LeeHuang QianNripendra Rana, Swansea University, Swansea, Wales, UKM. N. Ravishankar, Loughborough University, UK

International Editorial Board

Paul Beynon-Davies, Cardiff University, Cardiff, Wales, UKChun Wei Choo, University of Toronto, Toronto, Ontario, CanadaBrian Detlor, McMaster University, Hamilton, Ontario, CanadaKevin Grant, University of Kent, Canterbury, England, UKJosef Herget, Donau-Universitat Krems, Krems, AustriaNimal Jayaratna, Manchester Metropolitan University, Manchester, UKTommi Laukkanen, University of Eastern Finland, Joensuu, FinlandFeng Li, Cass Business School, London, UKMiguel Baptista Nunes, University of Sheffield, Sheffield, UKLinda D. Peters, Nottingham University Business School, Nottingham, England, UKYannis Pollalis, University of Piraeus, Piraeus, GreeceAles Popovic, University of Ljubljana, Ljubljana, Slovenia

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Mark Stansfield, University of the West of Scotland, Paisley, Scotland, UKColin Theakston, Durham University, Durham, UKAdam Vrechopoulos, Athens University of Economics & Business, Athens, GreeceDavid Wainwright, Northumbria University, Newcastle Upon Tyne, England, UKMartin White, Intranet Focus Ltd, Horsham, UKSue Williams, Universität Koblenz-Landau, Koblenz, Germany


GUIDE FOR AUTHORS.

INTRODUCTIONThe International Journal of Information Management (IJIM) is an international, peer-reviewed journalwhich aims to bring its readers the very best analysis and discussion in the developing field ofinformation management.

For further information please visit https://www.elsevier.com/locate/ijinfomgt

Types of PaperInformation management covers a wide field and we encourage submissions from diverse areas ofpractice and settings including business, health, education and government. Papers should normallybe 4,000 - 6,000 words long. The journal also welcomes Research Notes, which are intended todraw attention to research carried out the field of information studies. Research Notes cover researchin information studies, information management, information systems, library studies, archives andrecords management.

Research Notes can include:1) Departmental Research News;2) Research Group Activities;3) Research Projects;4) Research Grants;5) Reports on Conferences;6) Forthcoming Conferences;7) Individual Research Achievements.

Research Notes should typically be in the region of 500-2500 words though larger contributions wouldbe welcome. Items for consideration for publication in the New Series of Research Notes should bee-mailed to David Ellis Research Notes Editor [email protected]

Submission checklistYou can use this list to carry out a final check of your submission before you send it to the journal forreview. Please check the relevant section in this Guide for Authors for more details.

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Material and methodsProvide sufficient details to allow the work to be reproduced by an independent researcher. Methodsthat are already published should be summarized, and indicated by a reference. If quoting directlyfrom a previously published method, use quotation marks and also cite the source. Any modificationsto existing methods should also be described.

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Funding: This work was supported by the National Institutes of Health [grant numbers xxxx, yyyy];the Bill & Melinda Gates Foundation, Seattle, WA [grant number zzzz]; and the United States Institutesof Peace [grant number aaaa].

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Figure captionsEnsure that each illustration has a caption. Supply captions separately, not attached to the figure. Acaption should comprise a brief title (not on the figure itself) and a description of the illustration. Keeptext in the illustrations themselves to a minimum but explain all symbols and abbreviations used.

TablesPlease submit tables as editable text and not as images. Tables can be placed either next to therelevant text in the article, or on separate page(s) at the end. Number tables consecutively inaccordance with their appearance in the text and place any table notes below the table body. Besparing in the use of tables and ensure that the data presented in them do not duplicate resultsdescribed elsewhere in the article. Please avoid using vertical rules and shading in table cells.

ReferencesCitation in textPlease ensure that every reference cited in the text is also present in the reference list (and viceversa). Any references cited in the abstract must be given in full. Unpublished results and personalcommunications are not recommended in the reference list, but may be mentioned in the text. If thesereferences are included in the reference list they should follow the standard reference style of thejournal and should include a substitution of the publication date with either 'Unpublished results' or'Personal communication'. Citation of a reference as 'in press' implies that the item has been acceptedfor publication.

Web referencesAs a minimum, the full URL should be given and the date when the reference was last accessed. Anyfurther information, if known (DOI, author names, dates, reference to a source publication, etc.),should also be given. Web references can be listed separately (e.g., after the reference list) under adifferent heading if desired, or can be included in the reference list.

Data referencesThis journal encourages you to cite underlying or relevant datasets in your manuscript by citing themin your text and including a data reference in your Reference List. Data references should include thefollowing elements: author name(s), dataset title, data repository, version (where available), year,and global persistent identifier. Add [dataset] immediately before the reference so we can properlyidentify it as a data reference. The [dataset] identifier will not appear in your published article.

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International Journal of Information Management 30 (2010) 289–300

Contents lists available at ScienceDirect

International Journal of Information Management

journa l homepage: www.e lsev ier .com/ locate / i j in fomgt

nderstanding customer satisfaction and loyalty: An empirical study of mobilenstant messages in China

haohua Denga,∗, Yaobin Lua, Kwok Kee Weib, Jinlong Zhanga

School of Management, Huazhong University of Science and Technology, 1037 Luoyu Road, Hongshan District, Wuhan, ChinaDepartment of Information Systems, City University of Hong Kong, Kowloon, Hong Kong

r t i c l e i n f o

rticle history:

a b s t r a c t

With the rapid development of mobile technology and large usage rates of mobile phones, mobile instantmessage (MIM) services have been widely adopted in China. Although previous studies on the adoptionof mobile services are quite extensive, few focus on customer satisfaction and loyalty to MIM in China.

eywords:obile instant message

rusterceived customer valueerceived service qualityustomer satisfaction

In this study, we examine the determinants of customer satisfaction and loyalty. The findings confirmthat trust, perceived service quality, perceived customer value, including functional value and emotionalvalue, contribute to generating customer satisfaction with MIM. The results also show that trust, customersatisfaction and switching cost directly enhance customer loyalty. Additionally, this study finds that age,gender, and usage time have moderating effects. Finally, implications for the marketing of MIM are

witching costustomer loyalty

discussed.

. Introduction

With the development of wireless telecommunication technolo-ies, many customer services that are used in the computer-basednternet have also appeared in mobile phones (Barnes, 2002; Xu,003); mobile instant message (MIM) is a typical example. MIMnables consumers, whether sitting at the computer or on theoad, to connect instant message (IM) with existing communitiesnd across the mobile Internet. MIM brings tremendous conve-iences for customers, and is widely adopted by young people.hort message service (SMS) is another popular handheld-basedommunication tool. The differences between these two messageervices used on mobile handsets are that MIM provides moreser-friendly features, such as various user portraits, emoticonspictures expressing emotions, such as for happy), and conve-ient voice and video chatting, while SMS only offers simple textessage (Gibbs, 2008). Further, the presence information of MIM

llows users to know the status of their friends, whether theyre online or offline, free or busy, which helps them to conducteal-time conversation, thus stimulating communication. How-ver, users can send a much greater number of messages using
MS. According to the survey results of TNS Global (2008), 8% ofobile phone users worldwide have adopted MIM. While SMS is
sed by 55% of mobile phone users daily, MIM is used by 61% ofhem. More widely used than SMS, MIM is becoming the “primary

∗ Corresponding author. Tel.: +86 27 15926318828.E-mail address: [email protected] (Z. Deng).

268-4012/$ – see front matter © 2009 Elsevier Ltd. All rights reserved.oi:10.1016/j.ijinfomgt.2009.10.001

© 2009 Elsevier Ltd. All rights reserved.

non-voice method of interacting – with potentially dramatic conse-quences for service and network providers’ revenue” (TNS Global,2008).

With a large number of mobile phone users, 624 million (MIIT,2008), as well as high adoption rates of desktop IM users (CNNIC,2009), China’s MIM has gained great opportunity. According to areport by iResearch (2008a), MIM usage has the biggest percentof mobile phone users at 72%. There are various MIM productsin China. The biggest IM service provider, Tencent, offers mobileQQ, which is extended from desktop to mobile phone. Because ofthe huge loyal IM user base, mobile QQ makes Tencent the topMIM service provider in China (iResearch, 2008b). China Mobile,a main MNO (mobile network operator) in China, also enters theMIM market with Fetion. There are other MIM services, such asMicrosoft’s mobile MSN, Pica, China Unicom’s UMS, China Net-com’s MXIM, etc. For an MNO, deploying MIM can consolidate afirm’s position in the mobile commerce value chain with increas-ing ARPU (average revenue per user). For an IM service provider,developing MIM undoubtedly expands the channels of desktop IM.In such a competitive MIM market, MIM service providers are allmaking efforts to attract more users and gain more market shares.Thus, the ability to provide a high degree of customer satisfac-tion services is crucial to providers in differentiating themselvesfrom their competitors. Specifically, in increasingly competitive
markets, building strong relationships with customers, that is,developing the loyalty of consumers is seen as the key factor inwinning market share and developing a sustainable competitiveadvantage (Luarn & Lin, 2003; Nasir, 2005). Loyal customers arecrucial to business survival (Semejin, Van Riel Allard, Van Birgelen,
http://www.sciencedirect.com/science/journal/02684012

http://www.elsevier.com/locate/ijinfomgt

mailto:[email protected]

dx.doi.org/10.1016/j.ijinfomgt.2009.10.001

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Streukens, 2005) because attracting new customers is consid-rably more expensive than retaining old customers (Reichheld &chefter, 2000). Therefore, enterprises strive to increase their mar-et share by maximizing customer retention (Tsoukatos & Rand,006). “[T]he potential and opportunity value of customers gainedver a long period of time” is another advantage to maintainingxisting customers (Seo, Ranganathan, & Babad, 2008). Further,ith the aid of information technology, customers are becomingore and more open to understanding the brand; thus, satisfac-

ion alone may not be adequate to retain a long-term relationshipKassim & Abdullah, 2008). Accordingly, it is important for MIMervice marketers to understand what factors impact these users’atisfaction and loyalty, and then to take measures to retain theirustomers.

Several studies have been conducted to attempt to understandustomer satisfaction or loyalty of mobile services customers (forxample, Gerpott, Rams, & Schindler, 2001; Kim, Park, & Jeong,004; Lai, 2004; Lin & Wang, 2006; Turel & Serenko, 2006; Wang &iao, 2007). Most of these studies emphasize that customer loyaltynd analysis of factors affecting it are important for the successf mobile services firms. Furthermore, they agree that customeratisfaction is the main important mediate goal for mobile serviceroviders on their way to obtaining economic success. Neverthe-

ess, the aforementioned studies were conducted in countries otherhan China, and studied mobile services other than MIM. There is aearth of literature on China’s MIM context. Since foreign marketsave different levels of market development and distinct consumerehavior, those previous studies may provide limited applicationo China’s MIM market. As a big developing country, China’s mobile

arket has some uniqueness (Lu, Dong, & Wang, 2007; Xu, 2003).or example, MNO plays a dominant role in the mobile commercealue chain, and mobile phone users depend much on MNO. Inhe early phase of mobile commerce, government regulations on

NO are few; even if the service quality is low, customers haveo bear it. Moreover, the MIM users are mainly young people,specially college students, who are usually early adopters of newechnologies. Thus, a user’s perception of satisfaction and loyaltyn China may differ from that found in other studies. Therefore,here is a need to develop a model to explore factors influencingustomer satisfaction and loyalty of MIM in China.

Perceived service quality and customer value are supported asrivers of customer satisfaction (Lim, Widdows, & Park, 2006). Weerceive that the relationships may also be significant in China’sIM context. Moreover, MIM users often select the providers they

rust to transact with, which develops their satisfaction. Trust canlso be seen as a critical factor for customers to build and main-ain relationships with providers (Semejin et al., 2005). Satisfactionas always been viewed as the main input for customer loyalty.owever, satisfied users may switch to another brand for the low

witching costs (Lam, Shankar, Erramilli, & Murthy, 2004), such asower provider costs or ease in notifying other friends and adaptingo another new MIM tool. As a result, we suggest customer satis-action and switching cost are important predictors of customeroyalty of MIM. Other influences may depend on the moderatingffects of customer characteristics, such as age, gender, and usageime. Understanding the moderating effects of customer charac-eristics, providers can tailor MIM to preferences in segments, thusncreasing the likelihood that the service will be satisfactory andontinually used. Thus, we also view age, gender, and usage experi-nce as moderators. Considering several major factors which affecthe perceptions of MIM users, this research builds a customer
atisfaction and loyalty model. The model is then applied to a popu-ation of MIM users in China. Using a structural equation modeling
ethod (SEM), we get some results. It is believed that the resultsan provide recommendations for practitioners and offer valuablensights for future mobile services research.

ation Management 30 (2010) 289–300

Section 2 provides the study’s theoretical background andhypothesis development. In Section 3, we present the methodologyand offer the results, explain our research model and develop theresearch hypothesis. Section 4 then provides the discussion. Sec-tion 5 summarizes the implications of our study for both researchand practice. Finally we give conclusions and limitations of thisresearch.

2. Theoretical backgrounds and hypothesis development

In this section, we first discuss the roles of the three main pre-dictors of customer satisfaction from the literature. This is followedby a description of how customer satisfaction, trust and switchingcost affect customer loyalty.

2.1. Customer satisfaction and loyalty

Customer satisfaction, which refers to “the summary psycholog-ical state resulting when the emotion surrounding disconfirmedexpectations is coupled with the consumer’s prior feelings aboutthe consumption experience” (Oliver, 1981), is often considered asan important determinant of repurchase intention (Liao, Palvia, &Chen, 2009) and customer loyalty (Eggert & Ulaga, 2002). It is amost important research topic in the information system area (Au,Ngai, & Cheng, 2008). If the customer has good experiences of usingMIM over time, then he will have cumulative customer satisfaction.Previous literature theorized that customer satisfaction can be clas-sified into two types: transaction-specific satisfaction and generaloverall satisfaction (Yi, 1991). Transaction-specific customer sat-isfaction refers to the assessment customers make after a specificpurchase experience, and overall satisfaction means the customers’rating of the brand based on their experiences (Johnson & Fornell,1991). From these descriptions, we can view overall satisfaction as acombination of all previous transaction-specific satisfactions (Jones& Suh, 2000). As MIM is a communication tool, it may involve non-transactional satisfaction. Fournier and Mick (1999) argued thatonly transaction-specific research of satisfaction will narrow theconceptual boundaries, and they called for the research on non-transactional satisfaction, as well as other researchers (Anderson,Fornell, & Lehmann, 1994). Since customer satisfaction reflects thedegree of a customer’s positive feeling for a service provider in amobile commerce context, it is important for service providers tounderstand the customer’s vision of their services. On the otherhand, a high level of customer satisfaction may have a positiveimpact on customer loyalty (Mittal, Ross, & Baldasare, 1998).

Brand loyalty is defined as “a deeply held commitment torebuy or repatronize a preferred product/service consistently inthe future, thereby causing repetitive same-brand or same brand-set purchasing, despite situational influences and marketing effortshaving the potential to cause switching behavior” (Oliver, 1999).According to Sivadass and Baker-Prewitt (2000), customer loyaltyis the ultimate objective of customer satisfaction measurement. Itis found to be a key determinant of a brand’s long-term viabil-ity (Krishnamurthi & Raj, 1991). Moreover, compared with loyalcustomers, non-loyal customers are much more influenced by neg-ative information about the products or services (Donio, Massari,& Passiante, 2006). Therefore, retaining existing customers andstrengthening customer loyalty appear to be very crucial for mobileservice providers to gain competitive advantage. In this study, wemeasure customer loyalty as customers’ behavioral intention to
continuously use mobile instant messages with their present ser-vice providers, as well as their inclinations to recommend this MIMtool to other persons.
Satisfied users will have a higher usage level of MIM service thanthose who are not satisfied, and they are more likely to possess a

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tronger continuous intention and to recommend the MIM tools toheir friends or relatives (Zeithaml, Berry, & Parasuraman, 1996).f a service provider can satisfy the needs of the customer betterhan its competitors, it is easier to create loyalty (Oliver, 1999).ornell (1992) stated that high customer loyalty is mainly causedy high customer satisfaction. Clarke (2001) proposed that effec-ive satisfaction must be able to create loyalty amongst customers.revious studies have demonstrated that customer satisfactionositively affects customer loyalty (Choi, Seol, Lee, Cho, & Park,008) or negatively affects switching intention (Walsh, Dinnie, &iedmann, 2006). As mentioned earlier, there are several MIM ser-

ice providers in China. Once a customer feels dissatisfied with theervice provider because of low service quality or other factors,hen he/she will be much more likely to change to another. A fewissatisfied customers may complain after a poor service experi-nce, but will not switch. However, many dissatisfied customersill not complain but will switch silently and create negative word

f mouth (Dube & Maute, 1996). We can hypothesize that this rela-ionship between customer satisfaction and customer loyalty wille applicable in MIM. Thus, we propose the following hypothesis:

1. Customer satisfaction has a positive effect on customer loy-lty.

.2. The importance of trust

Trust has often been studied in the electronic commerce context.ccording to McKnight and Chervany (2002), trust can be vieweds trusting belief and trusting intention. Trusting belief refers tohe users’ perceptions of attributes of service providers, includinghe ability, integrity, and benevolence of the providers; trustingntention describes the truster’s willingness or intention to dependn the trustee. Therefore, trusting intentions include a one-time orontinuous usage of MIM services. In both the electronic commercend mobile commerce context, customers cannot fully regulate theusiness agreement; thus it is necessary for them to rely on theervice providers not to engage in unfair and opportunistic behaviorGefen, 2002). Seen as a considerably important factor for buildingnd maintaining relationships, trust is viewed as a main part ofhe success of electronic commerce (Lee & Turban, 2001), as wells of mobile commerce (Siau & Shen, 2003). In the MIM context,obile phone users must provide personal information, such as

heir phone numbers, in order to become subscribers. They willlso send messages to their friends from both computer-based IMnd MIM. In the experience of usage, if customers perceive no risksr unexpected conditions that will impair their communicationsrom the service or the service provider, trust will be built.

When a customer trusts a service provider, he or she will expecto increase satisfaction and loyalty towards the vendor (Kassim

Abdullah, 2008). In general, if a consumer does not trust therovider based on past experience, he or she will probably be dis-atisfied with that provider. Researchers found that trust will affectatisfaction in the long term (Kim, Ferrin, & Rao, 2009). When austomer’s feeling of faith in the provider is satisfied, his satisfac-ion will be enhanced over time (Chiou & Droge, 2006). On thether hand, earning customer trust is a main contributor to cus-omer loyalty. Since trust can reduce risk in the process of creatingxchange relationships, customers are inclined to be very “coop-rative” with this trustworthy service provider by demonstratingehavioral evidence of their loyalty (Morgan & Hunt, 1994). That

s, when customers trust the service provider, they will continu-
lly use the service and even recommend the service to others.esearchers found that trust positively influenced customer atti-ude or behavior intention in mobile commerce context (Lee, 2005;
ang, Lin, & Luarn, 2006). In MIM service, trusting beliefs can beefined as “consumers’ perceptions of particular attributes of MIM

ation Management 30 (2010) 289–300 291

service providers, including the ability, integrity and benevolenceof the vendors”, while trusting intentions exist when “the trusterfeels secure and is willing to depend, or intends to depend, onthe trustee” (Lin et al., 2006). When customers perceive the MIMservice provider is reliable and generally trustworthy, customerswill be satisfied with their services, and will be more likely tohave repeat usage behavior of mobile instant message services. Thestatement that trusting beliefs will directly affect trusting inten-tions was supported by previous studies (Mayer & Davis, 1999).Thus, we propose that customers’ perception of trusting beliefs ofa specific MIM service provider will lead to their attitude (customersatisfaction), which in turn will lead to behavior intention of contin-ual usage of MIM (customer loyalty). Because trust also can directlyand positively affect customer loyalty (Chiou, 2004; Lin et al., 2006),we expect these relationships can be applicable to MIM. Thus, wehave the following hypotheses:

H2. Trust has a positive effect on customer loyalty.

H3. Trust has a positive effect on customer satisfaction.

2.3. Perceived service quality

Providing a high level of service quality is very importantfor service providers to compete with other competitors (Bharati& Berg, 2005; Kemp, 2005; Yoo & Park, 2007). Zeithaml et al.(1996) described service quality as “the extent of discrepancybetween the customers’ expectations and perceptions”. Dabholkar,Shepherd, and Thorpe (2000) stated that since service quality hassub-dimensions of reliability and responsiveness, it will lead tocustomer satisfaction. According to Parasuraman, Zeithaml, andBerry (1988), service quality includes five dimensions: reliabil-ity, tangibles, responsiveness, assurance, and empathy. They andmany other researchers demonstrated the validity and reliabilityof those measures for perceived service quality (Cronin & Taylor,1992; Soteriou & Chase, 1998). The literature on the relationshipbetween customer satisfaction and service quality is ambiguous(Chong, Kennedy, Riquire, & Rungie, 1997). There are three com-peting theories about the linkages of service quality and customersatisfaction: satisfaction is an antecedent of service quality, servicequality is the predictor of satisfaction, and the two constructs areinterchangeable (Kassim & Abdullah, 2008). Despite the disagree-ment, the claim that customers might take attitudes or actions afterusing the services has been supported by many studies (Kassim &Abdullah, 2008). Moreover, Shin and Kim (2008) suggested servicequality is a consumer’s overall impression of the relative efficiencyof the service provider, and they found that service quality is signifi-cantly related to customer satisfaction. Our view on the relationshipbetween these two constructs is based on the claim that perceivedservice quality is a predictor of customer satisfaction.

Researchers maintain that perceived service quality is cognitiveand thus followed by satisfaction (Oliver, 1999). Several empiricalstudies confirmed that a higher level of service quality was relatedto a higher level of customer satisfaction (Brady & Robertson, 2001;Cronin, Brady, & Hult, 2000; Dabholkar et al., 2000; Yang, Wu, &Wang, 2009). Zeithaml et al. (1996) also stated the customer’s per-ception of service quality was the main factor predicting customersatisfaction. High service quality could attract new customers,retain existing customers, and even lure customers away from com-petitors whose service quality is perceived to be lower (Babakus,Bienstock, & Scotter, 2004). As in the MIM context, when customersperceive that the service quality of an MIM service provider is
higher, they will have increased satisfaction, which will in turn leadto a higher customer loyalty. Thus, this study proposes that:
H4. Perceived service quality has a positive effect on customersatisfaction.

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.4. Customer value

Customer value is considered a concept that includes many het-rogeneous components (Sweeny et al., 2001). Sheth, Newman,nd Gross (1991) argued that a customer’s purchase choice wasnfluenced by a multiple consumption value dimension, and theyeveloped a framework of five dimensions of value: functionalalue, conditional value, social value, emotional value, and epis-emic value. Different dimensions have different roles in the user’secision. For example, functional value and social value determinehether to use this service or another, and emotional value is the

ey to using the selected service. Perceived value occurs throughouthe purchasing process of customer, one-time purchase or repur-hase (Woodruff, 1997). Perceived value is different from customeratisfaction, but is related to it (Sweeny et al., 2001).

We explore four aspects of customer value to assess mobilenstant message services, including functional value, emotionalalue, social value, and monetary value. Functional value refers tohe practical or technical benefits that users can obtain when using

IM. Because of the various functions of MIM, such as sending mes-ages, voice chatting, and browsing news, people use it frequently.

hen a user wants to communicate with a friend anytime andnywhere, he will satisfy his functional value by using MIM. Emo-ional value means users’ mental or psychological needs for mobilenstant messages. When using MIM, customers will send interest-ng pictures or jokes and then have fun. Thus, users’ emotional valuean be satisfied through MIM services. Social value is defined as theenefits users can feel when they are connected to others by usingIM. Since MIM is mainly used for communication, customers’

eelings of belonging to a certain group may enhance perceivedalue. Monetary value means how much the MIM service is satis-actory considering the cost, time or effort spent in using the MIM.his factor cannot be ignored because economic considerations areften regarded as an important aspect for customers’ usage of infor-ation systems. In China, in order to enlarge market share, MIM

ervice providers make competitive pricing strategies. For exam-le, China Mobile’s Fetion only charges for the GPRS network trafficee. Mobile QQ charges from 5 to 10 Yuan more per month. If cus-omers perceive these charges are reasonable and acceptable, theyill feel the monetary value of this MIM is satisfied. As a result, theyill be more likely to be satisfied with the service. Customer satis-

action can be predicted from consumer value. The four dimensionsf consumer value are hypothesized to have an effect on customeratisfaction. Thus, we have the following hypotheses:

5a. Functional value has a positive effect on customer satisfac-ion.

5b. Emotional value has a positive effect on customer satisfac-ion.

5c. Social value has a positive effect on customer satisfaction.

5d. Monetary value has a positive effect on customer satisfac-ion.

.5. Customer loyalty and switching cost

Switching cost is “the costs that the consumer incurs by chang-ng one service provider to another” (Lee, Lee, & Feick, 2001),ncluding the costs that can be measured in monetary terms, thesychological aspect of facing a new firm, and the time and effort

nvolved in using a new service or product (Kim, Kliger, & Vale,
003). Since it pertains to time and psychological effort involved
n facing the uncertainty of dealing with a new service provider,witching cost can be a barrier to changing service providers. Thust is a mechanism for improving customer loyalty (Dick & Basu,994). According to Burnham, Frels, and Mahajan (2003), all vari-


eties of switching costs can be simplified as three types: procedural,financial and relational switching costs. Procedural switching costsmainly include economic risk costs, evaluation costs, setup costs,and learning costs; financial switching costs involve benefit losscosts and monetary loss costs; relational switching costs containpersonal relationship loss costs and brand relationship loss costs(Burnham et al., 2003). Having a direct effect on customer loy-alty, switching cost offers many advantages for service providers.For example, it weakens customers’ sensitivity to price and sat-isfaction of the product brand (Fornell, 1992), and they will viewthe brands with similar functions as different brands (Klemperer,1987). Specifically, increasing a customer’s perceptions of the risksin switching to other providers, the trouble in building a new con-tact relationship, and the difficulty in using an alternative service,will increase the likelihood that he/she keeps the relationship withthe current service provider.

Previous studies tested the relationship between switching costand customer loyalty, and their findings indicated that switch-ing cost was an important factor in predicting customer loyalty(Albert, 2002; Aydin, Özer, & Arasil, 2005). When people use amobile instant message service provided by one particular serviceprovider and perceive the switching cost for changing to a new MIMservice provider is high (for example, telling many old friends thenew address and learning the new services), they will have highercustomer loyalty. Thus, we have the following hypothesis:

H6. Perceived switching cost has a positive effect on customerloyalty.

2.6. Moderating effects

Moderating effects on the relationship between the indepen-dent and dependent variables have attracted many researchers’interest. Researchers argue that the contribution to marketing the-ory development will be larger if moderating variables are includedin the research model (Dabholkar & Bagozzi, 2002; Nysveen,Pedersen, & Thorbjørnsen, 2005). Age, gender, and usage experi-ence are found as key modifiers of an individual’s perception andactivity (Venkatesh, Brown, Maruping, & Bala, 2008; Venkatesh &Davis, 2000). Several studies also show these results (Chang & Chen,2008; Ha, Yoon, & Choi, 2007; Hong & Tam, 2006; Lu et al., 2009;Nysveen et al., 2005; Sanchez-Franco, Ramos, & Velicia, 2009).Because customers with longer MIM usage time have more expe-rience with the operations, they should be better able to exploitcommunication effectiveness than new users would be. We focuson age, gender, and usage time as moderating variables in orderto understand more about the different perceptions of various cus-tomer segments for MIM satisfaction and loyalty. Thus, we have thefollowing hypothesis.

H7a-i. Gender has moderating effects on the relationship betweencustomer satisfaction and its antecedents, customer loyalty and itsantecedents.

H8a-i. Age has moderating effects on the relationship betweencustomer satisfaction and its antecedents, customer loyalty and itsantecedents.

H9a-i. Usage time has moderating effects on the relationshipbetween customer satisfaction and its antecedents, customer loy-alty and its antecedents.

2.7. Research model

Based on the theoretical background discussed above, this studyestablishes a research model which suggests 10 primary links andthree pairs of moderating links between the constructs involved incustomer satisfaction and loyalty in MIM, as shown in Fig. 1.

nformation Management 30 (2010) 289–300 293

oefctsih

3

3

umfmvaatstCanruta

Table 2Descriptive statistics of respondent characteristics.

Variable Count %

Gender Male 256 47.3Female 285 52.7

Age <24 (young customers) 256 47.3>24 (old customers) 285 52.725–30 191 35.331–35 63 11.636–40 18 3.3>40 13 2.4

Education level High school 28 5.2Associate degree 77 14.2Bachelor’s degree 264 48.8Master’s degree or above 172 31.8

Monthly income <1000 Yuan 153 28.31000–2000 Yuan 130 24.02000–3000 Yuan 117 21.63000–4000 Yuan 94 17.4>4000 Yuan 47 8.7

Years using MIM <1 year (new customers) 296 54.7Long usage time customers 245 45.31–2 years 155 28.62–3 years 73 13.5

TC


The first link (H1) suggests the effect of customer satisfactionn customer loyalty. The second and third links propose separateffects of trust on customer satisfaction and customer loyalty. Theourth link is that service quality is considered as a predictor ofustomer satisfaction. The fifth to eighth links (H5a–d) suggest thathe four sub-constructs of customer value are related to customeratisfaction. The ninth link (H6) hypothesizes that switching costs an antecedent of customer loyalty. The last three links (H7–9)ypothesize moderation effects.

. Methodology

.1. Measure development

A questionnaire survey was used to collect data on mobile phonesers’ perceptions of mobile instant message. Most of the instru-ents used to measure the constructs in this study are adapted

rom previous studies in order to ensure content validity. Itemseasuring customer value, including functional value, emotional

alue, social value, and monetary value, are adapted from Sweenynd Soutar (2001). Perceived service quality is measured by itemsdapted from Shin and Kim (2008). The items measuring trust areaken from Gefen, Karahanna, and Straub (2003). Items measuringwitching cost are adapted from Gefen (2002). Customer satisfac-ion is measured by three items adapted from Croinet et al. (2000).ustomer loyalty is measured by three items adapted from Linnd Wang (2006). After we developed the preliminary question-
aire, we conducted two pretests using MIM users and e-commerceesearchers and practitioners. In the first pretest, we asked MIMsers for their feedback on the questionnaire and revised the ques-ions they identified as ambiguous. Next, we interviewed twocademic e-commerce researchers and two m-commerce busi-
able 1onstruct measuring.

Factor Item

Functional value

MIM is reliable. FV1MIM has good functions. FV2MIM fulfills my needs well. FV3MIM is well provided. FV4 (delete)

Emotional value

I feel good when I use MIM. EV1Using MIM is enjoyable. EV2MIM gives me pleasure. EV3Using MIM is interesting. EV4

Social value

MIM helps me to feel acceptable. SV1MIM makes a good impression other people.Using MIM gives me a sense of belongings toMIM improves the way I am perceived. SV4

Monetary value

MIM is reasonable priced. MV1The price of using MIM is economic. MV2MIM offers the value for money. MV3MIM is good for the current price level. MV4

Perceived service qualityMIM service provider always delivers excelleThe offerings of the service provider are of hiThe MIM service provider delivers superior s

TrustBased on my experience, I know this MIM serBased on my experience, I know this MIM serBased on my experience, I know this mobile iopportunistic. TR3

Switching costSwitching to other MIM service would causeSwitching to other MIM service would be tooSwitching to other MIM service would requir

Customer satisfactionMy choice to this MIM service is a wise one. CI think I did the right thing when I subscribedOverall, my feeling to this MIM service is sati

Customer loyaltyI will continue to use this MIM if any. CL1I will recommend others to use this MIM. CL2Even if close friends recommended another Mwould not change. CL3

3 years or more 17 3.2

ness practitioners. We asked them for feedback on our survey andrevised the questions based on their suggestions. Detailed informa-
tion about the constructs and the sources are shown in Table 1. Allthe items are measured on seven-point Likert scales, with anchorsranging from “strongly disagree” to “strongly agree”.
Source

Sweeny and Soutar(2001)

SV2others users. SV3

(delete)

nt overall service. SQ1Shin and Kim (2008)gh quality. SQ2

ervice in every way. SQ3

vice provider is honest. TR1Gefen et al. (2003)vice provider cares about customers. TR2

nstant message service provider is not

too many problems. SC1Gefen (2002)expensive. SC2

e too much learning. SC3

S1Croinet et al. (2000)to this MIM service. CS2

sfactory. CS3

Lin and Wang (2006)IM service, my preference for this MIM

294 Z. Deng et al. / International Journal of Inform

3

riewtaabMr

A structure equation model approach is used in this study. We

TI

Fig. 1. A conceptual model of customer satisfaction and loyalty of MIM.

.2. Data collection procedure

In the summer of 2008, we put the information about ouresearch objective on the Campus BBS (Bulletin Board System) tonvite respondents. We sent the final questionnaires to many IDmail addresses. In the email, we declared that if the respondentsere willing to finish this questionnaire, they would have a chance

o win in lotteries. After 2 weeks, we collected 350 responses. Welso collected data beside several mobile network operators’ oper-
ting offices and asked people who came to conduct mobile phoneased businesses to respond. The respondents who had not usedIM were not included in our survey. One week later, a total of 622
esponses were gathered. After eliminating insincere and incom-

able 3tem loadings and validities.

Constructs Item Standard loadings

Trust TR1 0.86TR2 0.89TR3 0.84

Perceived service quality SQ1 0.75SQ2 0.85SQ3 0.79

Functional value FV1 0.75FV2 0.73FV3 0.77

Emotional value EV1 0.84EV2 0.83EV3 0.81EV4 0.83

Monetary value MV1 0.72MV2 0.79MV3 0.77

Social value SV1 0.78SV2 0.78SV3 0.82SV4 0.83

Switching cost SC1 0.77SC2 0.76SC3 0.88

Customer satisfaction CS1 0.80CS2 0.72CS3 0.78

Customer loyalty CL1 0.82CL2 0.78CL3 0.84


plete responses through data filtering, we got a total number of541usable responses.

The descriptive statistics of the sample are listed in Table 2. Ofthe 541 participants, 47.3% are males, 52.7% are females, and 256are below 24 years old. Most of them are young people. Nearly 80%of the respondents have a bachelor’s degree or higher educationlevel. Among the 541 respondents, about half of them have usedMIM for less than 1 year.

To investigate the moderating effects, we conducted sample seg-mentation based on gender, age, and usage time. We segmented thesample into two groups based on age below or not below 24 yearsold. Ha et al. (2007) defined old users as older than 25 years. Mostof the MIM users are young people. In China, undergraduates andgraduate students are often younger than 24 years. We can viewthem as young customers. People not below 24 often have jobs andhave social experiences. Thus, we see them as old customers whoseperception of MIM may be different from those who are younger.Then, we divided the sample into new customers and long-timeusage customers. Respondents with less than 1 year of use are newcustomers.

We conducted independent-sample t-tests to compare themeans of the same construct between respondents from campusBBS and volunteers from MNO’s operating offices. The results indi-cate no significant differences between the groups; thus we canpool data from these two groups together.

4. Results

conducted a confirmatory factor analysis to test the validity of theconstructs, including item loading, construct reliability, and aver-age variance extracted (AVE), as shown in Table 3. All the itemloadings are greater than 0.5 on their expected factor and less

AVE CR Cranbach alpha

0.746 0.898 0.804

0.636 0.839 0.809

0.563 0.794 0.802

0.685 0.897 0.822

0.578 0.805 0.867

0.645 0.879 0.837

0.648 0.846 0.842

0.589 0.811 0.794

0.662 0.854 0.779

Z. Deng et al. / International Journal of Information Management 30 (2010) 289–300 295

Table 4Correlation coefficient matrix ands roots of the AVEs (shown as diagonal elements).

TRU PSQ FV EV MV SV SC CS CL

TRU 0.863PSQ 0.36 0.797FV 0.42 0.09 0.750EV 0.45 0.04 0.13 0.828MV 0.07 0.04 0.10 0.12 0.760SV 0.33 0.29 0.39 0.47 0.04 0.803SC 0.24 0.20 0.29 0.28 0.07 0.42 0.805CS 0.35 0.21 0.48 0.61 0.21 0.37 0.20 0.767CL 0.31 0.12 0.21 0.09 0.26 0.23 0.41 0.63 0.813

Table 5Summary of fit indices.

t(tumah0l

irts

wmcvtav(fifi

mipTf

Fit indices �2/df RMSEA GFI

Recommended value <3 <0.08 >0.90Value in this study 2.63 0.055 0.87

han 0.4 on other factors; thus the construct validity is acceptableCheung, Chang, & Lai, 2000). AVE is used to measure the varianceo the measurement error captured by the indicators. All the val-es of AVEs are greater than the cutoff value 0.5. Additionally, weeasured the reliability of each construct using the composite reli-

bility (CR) and Cranbach alpha. The results show that all constructsave higher scores than that of the acceptable level of CR and alpha.7. Every scale item is statistically significant at the significance

evel of 0.05. Thus, our data have good convergent validity.We also calculated the square root of each factor’s AVE and

ts correlation coefficients with other factors, and summarize theesults in Table 4. The square root of each factor’s AVE is largerhan its corresponding correlation coefficients with other factors,howing good discriminant validity.

For the hypothetic SEM model, we used Lisrel 8.72 to testhether the empirical data conformed to the proposed model. Theodel includes 29 items describing 9 latent constructs: trust, per-

eived service quality, functional value, emotional value, monetaryalue, social value, switching cost, customer satisfaction, and cus-omer loyalty. We examined the model fit of our research model,s shown in Table 5. The common criteria in the SEM were pre-iously suggested by Hair et al. (1998). Although the value of GFI0.87) is slightly less than the recommended value (0.90), all othert indices are acceptable. Thus the results indicate adequate modelt between our research model and the empirical data.

To test the significance of each hypothesis path in the research
odel, Lisrel reports raw and standardized estimates for all spec-
fied paths, as well as standard errors and test statistics for eachath. The result of the structure equation model is shown in Fig. 2.he effects of perceived social value and monetary value on satis-action are not supported; however, other paths are significant at

Fig. 2. Results of the structure model analysis.

AGFI CFI NFI NNFI IFI

>0.80 >0.90 >0.90 >0.9 >0.900.85 0.97 0.95 0.97 0.97

the 0.05 level. Variances in customer satisfaction and loyalty are54% and 64%, respectively.

Among the factors shown in Table 6, trust, perceived servicequality, functional value, and emotional value have positive effectson customer satisfaction; and trust, customer satisfaction, andswitching cost significantly affect customer loyalty.

Next we tested the moderator effects of age, gender, and usagetime. We categorized the sample into two groups to compare thecoefficients of each member of a pair, respectively. Calculating Tvalue for cross-multiply of moderator and dependent variable, wecan find the significance of the moderating effects (Chin, 1988;Chin, Marcolin, & Newsted, 2003). The results of path coefficientscomparisons are shown in Table 7.

The path coefficients from trust and emotional value to cus-tomer satisfaction for females are significantly larger than thosefor males (H7c and H7f). However, gender has no significant mod-erating effect on other paths. Age has a negative moderating effecton the relationship between emotional value and customer satis-faction (H8f). The influence of trust on customer satisfaction differssignificantly between young users and old users (H8c). That is, theolder the users, the more influence of trust on their perceived sat-isfaction of the MIM. Usage time has positive moderating effectson the relationship between service quality and customer loyalty(H9i). In other words, the longer usage time of MIM, the strongeris the effect of customer satisfaction on loyalty. There are no sig-nificant differences between new customers and long-time usagecustomers.

Some researchers suggested that perceived service quality andcustomer value also have direct effects on customer loyalty (Lai,Griffin, & Babin, 2009). And we have found that trust positivelyaffect customer loyalty. Thus, we conducted mediation effects ofcustomer satisfaction between trust, perceived service quality,

Table 6Results of hypotheses test.

Hypothesis Path Coefficients S.E. T value Remarks

H1 CS-CL 0.57** 0.067 11.87 OH2 TR-CL 0.21** 0.042 6.91 OH3 TR-CS 0.16* 0.039 2.42 OH4 SQ-CS 0.54** 0.026 7.37 OH5a FV-CS 0.23** 0.078 5.34 OH5b EV-CS 0.14* 0.045 2.26 OH5c SV-CS 0.09 0.059 1.31 XH5d MV-CS 0.03 0.037 0.78 XH6 SC-CL 0.18** 0.064 3.43 O

O: support; X: not support.* p < 0.05.

** p < 0.01.

296 Z. Deng et al. / International Journal of Inform

Tab

le7

Res

ult

sof

mod

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ing

effe

cts.

Path

HG

end

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Rem

arks

HA

geT

Rem

arks

HU

sage

tim

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Rem

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Mal

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mal

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ldN

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ng

CS-

CL

H7a

0.56

**0.

52**

0.61

2X

H8a

0.57

**0.

48**

−1.1

17X

H9a

0.61

**0.

46**

1.94

7X

TR-C

LH

7b0.

19**

0.23

**0.

879

XH

8b0.

17**

0.25

**1.

023

XH

9b0.

29**

0.16

*1.

337

XTR

-CS

H7c

0.15

*0.

36**

2.54

2*O

H8c

0.14

*0.

26**

1.98

2*O

H9c

0.17

**0.

140.

992

XSQ

-CS

H7d

0.51

**0.

55**

−0.5

49X

H8d

0.64

**0.

50**

1.68

7X

H9d

0.55

*0.

47*

1.13

1X

FV-C

SH

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31**

0.22

**−0

.983

XH

8e0.

29**

0.21

**−1

.101

XH

9e0.

25**

0.19

**0.

894

XEV

-CS

H7f

0.33

**0.

113.

433**

OH

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28**

0.13

2.01

3*O

H9f

0.30

**0.

101.

216

XSV

-CS

H7g

0.07

0.11

−0.8

77X

H8g

0.08

0.10

0.34

4X

H9g

0.10

0.05

0.63

5X

MV

-CS

H7h

0.01

0.08

0.92

1X

H8h

0.10

0.02

1.01

2X

H9h

0.09

0.01

0.53

2X

SC-C

LH

7i0.

16*

0.25

**1.

332

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0.17

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107

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130.

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functional value, and emotional and customer loyalty based onthe three-step method proposed by Baron and Kenney (1986). AsTable 8 shows, all of the links between independent variable andmoderator are significant so are the links between independentvariable and dependent variable. Thus, the first and second condi-tions for mediating effect are satisfied. Further, the links betweencustomer loyalty and both trust and customer satisfaction are sig-nificant, and the link of customer loyalty and trust is smaller thanthat of customer satisfaction and trust, as such, customer satisfac-tion partially mediates the effect of trust on customer loyalty. Thesame is for perceived service quality. In contrast, the coefficientsof functional value and emotional value in the regression equa-tion that contains two independent variables are not significant,which means that customer satisfaction fully mediates the effectsof functional value and emotional value on customer loyalty.

5. Discussion

This study attempts to investigate the factors affecting customersatisfaction and loyalty of MIM in China. We studied the effects oftrust, perceived service quality, and customer value on customersatisfaction, and also the effects of trust, customer satisfaction, andswitching cost on customer loyalty. In addition, we focused on iden-tifying moderating effects of gender and age. We believe that thisresearch allows us to gain insights into China’s MIM service mar-keting strategies. There are several findings as follows.

First, as we hypothesized, trust, service quality, and perceivedvalue significantly affect customer satisfaction of MIM. Specifically,perceived service quality is found to have the greatest effect oncustomer satisfaction. This implies that Chinese MIM customer sat-isfaction will be most significantly influenced by the high servicequality of providers. When users find an MIM service quality to behigh, they will form a high degree of customer satisfaction towardthe service. Trust and perceived value are also important determi-nants of customer satisfaction.

Second, trust, satisfaction, and switching cost positively influ-ence customer loyalty of MIM. On the magnitude of significance,customer satisfaction has the greatest effect, and the path coeffi-cient is 0.57. Trust has less effect than customer satisfaction forbuilding customer loyalty, which confirms Ribbink, Van Riel Allard,Liljander, and Streukens’ (2004) statement. The effects of switch-ing cost (0.18) are smaller than the two above. The results meanthat satisfaction is much related to customer loyalty; thus, increas-ing the degree of customer satisfaction through improved servicequality and customer value is an effective tool to maintain cus-tomer loyalty. Furthermore, the effect of trust on customer loyaltyis supported in our study. This result corroborates that of otherstudies (Lin et al., 2006). Switching cost has a significant effecton customer loyalty, which is in accord with prior customer loy-alty antecedent research (Gefen, 2002). Our results imply that thehigher the switching cost, the greater likelihood it will drive con-sumers to stay with their current provider, and encourage othersto use the provider’s service.

Third, among the four dimensions of perceived customer value,our findings show that functional value and emotional value havesignificant effect on customer satisfaction, while social value andmonetary value are found to have no significant effects. It meansthat the two variables, functional value and emotional value, areimportant customer value factors for customer satisfaction. That’sto say, when users’ functional value and emotional value are sat-
isfied, they will experience more satisfaction toward the services.MIM users perceive functional value more highly than other valuesmainly because MIM can be used anytime and anywhere, provid-ing more convenience to users compared to IM. Social value andmonetary value have direct effects on customer satisfaction, but

Z. Deng et al. / International Journal of Information Management 30 (2010) 289–300 297

Table 8Results of mediating effects.

IV M DV IV–DV IV–M (IV + M)–DV

IV M

Trust Satisfaction Loyalty 0.209* 0.181** 0.098* 0.630**

Service quality Satisfaction Loyalty 0.457** 0.536** 0.131** 0.623**

Functional value Satisfaction Loyalty 0.234** 0.221** −0.016 0.642**

Emotional value Satisfaction Loyalty 0.187** 0.239** −0.005 0.647**

I

tmtpoeps

mtvff(i2biUof&acjMmabgalteutitwTorsolcisutwt

V: independent variable; M: mediator; DV: dependent variable.* p < 0.05.

** p < 0.01.

he effects are not significant. The probable reason is that the vastajority of MIM users have desktop IM accounts, and the groups

hey often communicate with are familiar to some extent; thus, theerceptions of social value have no significant differences. On thether hand, the price of mobile instant message is very low. Forxample, using Fetion only costs network traffic, and many userserceive this price as acceptable. Is this case, monetary value cannotignificantly predict customer satisfaction.

Fourth, our results show that gender and age have significantoderating effects on the relationship between trust and cus-

omer satisfaction and on the relationship between emotionalalue and customer satisfaction. Trust is a more important factoror females in obtaining satisfaction with MIM. Earlier studiesound that women have less trust in Internet shopping than menRodgers & Harris, 2003), and trust plays a more important rolen a mobile environment than in the Internet (Cho, Kwon, & Lee,007). Female MIM users may have more psychological barriers touilding customer trust than males have. Therefore, trust has more

mpact on building customer satisfaction for women than for men.ser enjoyment and interesting experience are stronger driversf satisfaction for males. This finding does not fit the claim thatemales are more influenced by emotion than men are (Rodgers

Harris, 2003). We might explain this effect by the fact that menre more engaged in studying information technology, and theyan be more easily satisfied by finding and forwarding interestingokes or pictures to others. In contrast, women may just use

IM for communication purposes. As customer satisfaction hasediating effects on the relationship between trust and customer

nd satisfaction, thus, gender will indirectly affect the relationshipetween trust and customer loyalty. The moderating effects ofender on other links are not supported, which implies that femalend male’s perceptions are not significantly different for otherinks in our study. Age also significantly moderates the effects ofrust and emotional value on customer satisfaction. That is, theffect of trust on customer satisfaction is more significant for oldersers than younger ones. The probable reason lies in the fact thathe younger generations are more willing to trust the availablenformation or services (Rouibah, Khalil, & Hassanien, 2008). Andhey often indulge themselves in MIM to obtain fun experiences,hile older people usually use MIM primarily to conduct business.

hus, the users’ perceptions are different. Except H8c and H8f,ther moderating hypotheses of age are insignificant. The maineason is that the majority of our sample’s ages is below 40, and isomewhat not old enough. Thus, their perceptions of the influencesf trust, satisfaction, and perceived switching cost on customeroyalty, and the impacts of service quality, functional value onustomer satisfaction are not significantly different. The moderat-ng effects testing results also show that the relationship between
witching cost and customer loyalty is stronger for longer-timesage customers than new customers of MIM. It seems reasonablehat the longer customers use MIM, the more they are familiarith the current service interface and function, as well as with
heir buddy list. Thus, the cost to learning a new service and to

fitting a new buddy interface will be higher. Other moderatingeffects of usage time are not significant. The probable reason is thatwhen customers feel satisfied with MIM and building loyalty withit, they will continually use MIM. Thus, their usage time will notsignificantly change the relationship between trust, service quality,customer satisfaction. So do customer loyalty and its determinants.

Fifth, the mediating testing results show that customer satisfac-tion has significant mediation effects for relationships from trust,perceived quality, functional value and emotional value on loy-alty. The former two are partially mediated and the latter two arefully mediated. Thus, trust and perceived service quality have bothdirect and indirect effects on customer loyalty, while the effectsof functional value and emotional value on customer loyalty areindirect. Our results demonstrate that satisfaction has great medi-ating power between its determinants and customer loyalty, whichis probably because that customers who feels highly satisfied withsuccessful usage experiences may overemphasize the impact of thefactors that are closely related to their satisfaction on loyalty (Lai etal., 2009). This result is consistent with previous studies’ claim thatcustomer satisfaction can significantly mediate the effects of otherfactors on customer loyalty (Caruana, 2002; Heung & Ngai, 2008).

6. Implications and limitations

6.1. Implications for research

Results of this study offer several implications for marketingresearchers and mobile commerce researchers.

For marketing researchers, it empirically tested the significanteffect of satisfaction on loyalty in MIM context, which enrichesthe research on the relationship between satisfaction and loyalty.We also studied the factors’ impact on customer satisfaction andloyalty. To test the moderating effect, segmentation of the sampleis used in our study. The results of this work demonstrate that age,gender, and usage time of MIM will modify the effect of severalindependent variables on the their related dependent variables,which will shed light on future research on marketing strategy ofMIM.

For mobile commerce researchers, this study examines thefactors’ influence on customer satisfaction and loyalty of mobileinstant messages, which is the first study conducted in China’smobile instant message context. The results of this study highlightthe significant effect of trust, service quality, and customer val-ues on customer satisfaction, which is overlooked in the previousstudies in mobile instant messages. Furthermore, we demonstratethat younger male customers’ emotional value has more effecton the formation of customer satisfaction. Thus mobile commerceresearchers can realize that age and gender can modify some rela-
tionships. Trust is always an important determinant of customerbehavior in electronic commerce and other mobile service researchas mentioned earlier. Our results demonstrate trust’s significanteffect on both satisfaction and loyalty in the MIM context. Switch-ing cost is found have more impact on the development of customer

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oyalty for customers who have used MIM longer, which providesricher understanding of the prediction of a customer’s continualsage behavior of MIM.

.2. Implications for practice

Our work has important implications for practice as well. Onef the challenging tasks that MIM managers face is how to enhanceustomer satisfaction and loyalty. As suggested by our model, cus-omer loyalty will develop if the formation of trust, customeratisfaction, and switching cost is well managed. Therefore, MIMervice marketing strategies may be more fruitful through focus-ng on these psychological processes. Customer satisfaction is thereatest impact among these three factors. Thus, it is important forn MIM service provider to be a satisfying brand to increase cus-omer loyalty. MIM service providers must be concerned about theuality of their service and highlight customer value. Particularly,hey satisfy customers’ value through providing good and reliableunctions, giving a more pleasant interface.

Trust appears to be important for both customer satisfaction andoyalty formation, which implies that, in order to attract more cur-ent customers to repurchase MIM, the service providers must try tostablish an impression that they are honest to their customers andare about customers’ needs, which can then enhance the degreef customers’ perceptions of trust. The research results indicatehat perceived switching cost significantly affects customer loyalty.ccordingly, increasing switching cost is an important marketing

ool to maintain customers. As mentioned earlier, switching costs incurred by the customer’s switching behavior involving threeypes of costs. Thus, to effectively increase switching costs, serviceroviders should focus on the various types of costs that consumerserceive. Moreover, they can decrease the costs of staying with theurrent provider, increase the quality of the service to enhance theustomers’ bonds with them, and improve the customers’ value ofheir service.

Younger and male people are more influenced by trust andmotional value, and switching cost has a greater influence onustomers with long-time usage. Therefore, in order to meet thesychological demands of different types of customers, MIM ser-ice providers should exercise caution in improving pleasure andrust for younger women, and in enhancing the switching barrieror more experienced people.

.3. Limitations

Even though the rigorous validation procedure allows us toevelop a research model for exploring customer satisfaction and

oyalty with MIM, this work has some limitations. First, we devel-ped a research model to examine the factors influencing customeratisfaction and loyalty of MIM. It was tested in China, but sincehere may be differences between China and other countries,esearchers should use some caution when citing the results.

Second, the mediating effects testing result show that customeratisfaction partially mediate the effect of perceived service qual-ty on customer loyalty. But the structure equation model does notest the direct link between perceived service quality and customeroyalty. Since perceived service quality always related to customeratisfaction as mentioned earlier, and the coefficient of customeratisfaction and loyalty is as high as 0.57, as such we can believehat the effect of perceived service quality on customer loyalty ishrough the effect on customer satisfaction. Finally, our study tests
he impact of several factors on customer satisfaction and loyalty,nd the variance explained by the model is 54%. Thus, there arether important factors to consider. Accordingly, market practi-ioners should pay attention to factors other than those mentionedn our study.

7. Conclusions

This work studied the determinants of customer satisfaction andloyalty of MIM in China. Our research has the following contribu-tions. First, we explore customers’ perceptions of MIM in China,which is seldom concerned by other researchers yet. Thus, ourresearch fills the gap in understanding this application, which isundergoing a process of rapid development. Second, we developand validate a more comprehensive customer satisfaction and loy-alty model in China’s MIM context than previous studies (Chang &Chen, 2008; Kassim & Abdullah, 2008; Lin et al., 2006). It sheds somelight on the nomological relationships among perceived value,perceived service quality, trust, customer satisfaction, switchingcost, and customer loyalty. Third, besides testing the structureequation modeling of the proposed model, we examine the mod-erating effects of gender, age, and usage time on the relationshipbetween each pair of links, which provides useful managementinsights for better segmentation marketing strategies to improvecustomer satisfaction and to strengthen customer loyalty of MIMin China.

Acknowledgements

This work was partially supported by a grant from China Post-doctoral Science Foundation (no. 20090450924), a grant from theNational Natural Science Foundation of China (no. 7073100, no.170971049), and a grant from the Program for New Century Excel-lent Talents by the Ministry of Education (no. NCET-08-0233).

References

Albert, C. (2002). Service loyalty the effects of service quality and the mediating roleof customer satisfaction. European Journal of Marketing, 36(7–8), 811–828.

Anderson, E. W., Fornell, C., & Lehmann, D. R. (1994). Customer satisfaction, mar-ket share, and profitability: Findings from Sweden. Journal of Marketing, 58(3),53–66.

Au, N, Ngai, E. W. T., & Cheng, T. C. E. (2008). Extending the understanding of enduser information systems satisfaction formation: An equitable needs fulfillmentmodel approach. MIS Quarterly, 32(1), 43–66.

Aydin, S., Özer, G., & Arasil, Ö. (2005). Customer loyalty and the effect of switch-ing costs as a moderator variable: A case in the Turkish mobile phone market.Marketing Intelligence & Planning, 23(1), 89–103.

Babakus, E., Bienstock, C., & Scotter, J. (2004). Linking perceived quality and cus-tomer satisfaction to store traffic and revenue growth. Decision Sciences, 35(4),713–737.

Barnes, S. J. (2002). The mobile commerce value chain: Analysis and future devel-opments. International Journal of Information Management, 22(2), 91–108.

Baron, R. M., & Kenney, D. A. (1986). The moderator-mediator variable distinctionin social psychological research: Conceptual, strategic, and statistical consider-ations. Journal of Personality and Social Psychology, 51(6), 1173–1182.

Bharati, P, & Berg, D. (2005). Service quality from the other side: Information systemsmanagement at Duquesne light. International Journal of Information Manage-ment, 25(4), 367–380.

Brady, M., & Robertson, C. (2001). Searching for consensus on the antecedent role ofservice quality and satisfaction: An exploratory cross-national study. Journal ofBusiness Research, 51(1), 53–60.

Burnham, T. A., Frels, J. K., & Mahajan, V. (2003). Consumer switching costs: A typol-ogy, antecedents, and consequences. Journal of the Academy of Marketing Science,31(2), 109–126.

Caruana, A. (2002). Service loyalty: The effects of service quality and the mediatingrole of customer satisfaction. European Journal of Marketing, 37(7–8), 811–828.

Chang, H. H., & Chen, S. W. (2008). The impact of customer interface quality, sat-isfaction and switching costs on e-loyalty: Internet experience as a moderator.Computers in Human Behavior, 24(6), 2927–2944.

Cheung, W., Chang, M., & Lai, V. (2000). Prediction of Internet and world wide webusage at work: A test of an extended triandis model. Decision Support Systems,30(1), 83–100.

Chin, W. W. (1988). The partial lease squares approach for structural equationmodeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp.295–336). Lawrence Erlbaum Associates.

Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latentvariable modeling approach for measuring interaction effects: Results from aMonte Carlo simulation study and an electronic-mail emotion/adoption study.Information Systems Research, 14(2), 189–217.

Chiou, J.-S. (2004). The antecedents of consumers’ loyalty toward Internet serviceproviders. Information & Management, 41(6), 685–695.

nform

C

C

C

C

C

C

C

C

D

D

D

D

D

E

F

F

G

G

G

GH

H

H

i

i

J

J

K

K

K

K

K

K

K


hiou, J.-S., & Droge, C. (2006). Service quality, trust, specific asset investment, andexpertise: Direct and indirect effects in a satisfaction-loyalty framework. Journalof the Academy of Marketing Science, 34(4), 613–627.

ho, D.-Y., Kwon, H. J., & Lee, H.-Y. (2007). Analysis of trust in Internet and mobilecommerce adoption. In Proceedings of the 40th Hawaii international conferenceon system sciences. Hawaii: IEEE.

hoi, J., Seol, H., Lee, S., Cho, H., & Park, Y. (2008). Customer satisfaction factors ofmobile commerce in Korea. Internet Research, 18(3), 313–335.

hong, E., Kennedy, R., Riquire, C., & Rungie, C. (1997). The difference between sat-isfaction and service quality. New and evolving paradigm: The emerging futureof marketing. In The American Marketing Association’s 1997 special conference.

larke, K. (2001). What price on loyalty when a brand switch is just a click away?Qualitative Market Research: An International Journal, 4(3), 160–168.

NNIC. (2009). 23rd statistical survey report on the Internet development in China.From http://www.cnnic.cn/uploadfiles/pdf/2009/1/13/92458.pdf.

ronin, J., Brady, M., & Hult, G. (2000). Assessing the effects of quality, value, andcustomer satisfaction on behavior intentions in service environments. Journal ofRetailing, 76(2), 193–218.

ronin, J., & Taylor, S. A. (1992). Measuring service quality: A reexamination andextension. Journal of Marketing Research, 56(July), 55–68.

abholkar, P. A., & Bagozzi, R. P. (2002). An attitudinal model of technology-basedself-service: Moderating effects of consumer traits and situational factors. Jour-nal of the Academy of Marketing Science, 30(3), 184–201.

abholkar, P., Shepherd, C., & Thorpe, D. (2000). A comprehensive framework forservice quality: An investigation of critical conceptual and measurement issuesthrough a longitudinal study. Journal of Retailing, 76(2), 139–173.

ick, A. S., & Basu, K. (1994). Customer loyalty: Toward an integrated conceptualframework. Journal of the Academy of Marketing Science, 22, 99–113.

onio, J., Massari, P., & Passiante, G. (2006). Customer satisfaction and loyalty ina digital environment: An empirical test. Journal of Consumer Marketing, 23(7),445–457.

ube, L., & Maute, M. (1996). The antecedents of brand switching, brand loyalty andverbal responses to service failure. Advances in Services Marketing and Manage-ment, 5, 127–151.

ggert, A., & Ulaga, W. (2002). Customer perceived value: A substitute for satisfactionin business markets. Journal of Business & Industrial Marketing, 17(2–3), 107–118.

ornell, C. (1992). A national customer satisfaction barometer: The Swedish experi-ence. Journal of Marketing, 56(1), 6–12.

ournier, S., & Mick, D. G. (1999). Rediscovering satisfaction. Journal of Marketing,63, 5–23.

efen, D. (2002). Customer loyalty in e-commerce. Journal of the Association forInformation Systems, 3(1), 27–51.

efen, D., Karahanna, E., & Straub, D. W. (2003). Trust and tam in online shopping:An integrated model. MIS Quarterly, 27(1), 51–90.

erpott, T. J., Rams, W., & Schindler, A. (2001). Customer retention, loyalty, andsatisfaction in the German mobile cellular telecommunications market. Telecom-munications Policy, 25(4), 249–269.

ibbs, C. (2008). SMS vs. MIM. RCR Wireless News, 27(13), 1–8.a, I., Yoon, Y., & Choi, M. (2007). Determinants of adoption of mobile games under

mobile broadband wireless access environment. Information & Management,44(3), 276–286.

eung, V. C. S., & Ngai, E. W. T. (2008). The mediating effects of perceivedvalue and customer satisfaction on customer loyalty in the Chinese restau-rant setting. Journal of Quality Assurance in Hospitality & Tourism, 9(2), 85–107.

ong, S.-J., & Tam, K. Y. (2006). Understanding the adoption of multipurposeinformation appliances: The case of mobile data services. Information SystemsResearch, 17(2), 162–179.

Research. (2008a). China IM research report in 2007–2008. From http://www.iresearch.com.cn/Consulting/instant messenger/Free.asp?classid=&id=1175.

Research. (2008b). China mobile netizen Internet phone acts research report. Fromhttp://down.iresearch.cn/Reports/Free/1184.html.

ohnson, M. D., & Fornell, C. (1991). A framework for comparing customer satisfactionacross individuals and product categories. Journal of Economic Psychology, 12(2),267–286.

ones, M. A., & Suh, J. (2000). Transaction-specific satisfaction and overall satisfac-tion: An empirical analysis. Journal of Services Marketing, 14(2), 147–159.

assim, N. M., & Abdullah, N. A. (2008). Customer loyalty in e-commerce settings:An empirical study. Electronic Markets, 18(3), 275–290.

emp, A. H. (2005). Getting what you paid for: Quality of service and wireless con-nection to the Internet. International Journal of Information Management, 25(2),107–115.

im, D. J., Ferrin, D. L., & Rao, H. R. (2009). Trust and satisfaction, two stepping stonesfor successful e-commerce relationships: A longitudinal exploration. Informa-tion Systems Research, 20(2), 237–257.

im, M., Kliger, D., & Vale, B. (2003). Estimating switching costs: The case of banking.Journal of Financial Intermediation, 12(1), 25–56.

im, M. K., Park, M. C., & Jeong, D. H. (2004). The effects of customer satisfactionand switching barrier on customer loyalty in Korean mobile telecommunication
services. Telecommunication Policy, 28(2), 145–159.
lemperer, P. (1987). Markets with consumer switching costs. The Quarterly Journalof Economics, 102, 376–394.

rishnamurthi, L., & Raj, S. P. (1991). An empirical analysis of the relationshipbetween brand loyalty and consumer price elasticity. Marketing Science, 10(2),172–183.


Lai, T. L. (2004). Service quality and perceived value’s impact on satisfaction, inten-tion and usage of short message service (SMS). Information Systems Frontiers,6(4), 353–368.

fLai, F., Griffin, M., & Babin, B. J. (2009). How quality, value, image, and satisfaction

create loyalty at a Chinese telecom. Journal of Business Research, 62(10), 980–986.Lam, S. Y., Shankar, V., Erramilli, M. K., & Murthy, B. (2004). Customer value, satis-

faction, loyalty, and switching costs: An illustration from a business-to-businessservice context. Journal of Marketing Science, 32(3), 293–311.

Lee, T. (2005). The impact of perceptions of interactivity on customer trust andtransactions in mobile commerce. Journal of Electronic Commerce Research, 6(3),165–180.

Lee, J., Lee, J., & Feick, L. (2001). The impact of switching costs on the customersatisfaction-loyalty link: Mobile phone service in France. Journal of Service Mar-keting, 15(1), 35–48.

Lee, M. K. O., & Turban, E. (2001). A trust model for consumer Internet shopping.International Journal of Electronic Commerce, 6(1), 75–91.

Liao, C., Palvia, P., & Chen, J.-L. (2009). Information technology adoption behaviorlife cycle: Toward a technology continuance theory (TCT). International Journalof Information Management, 29(4), 309–320.

Lim, H., Widdows, R., & Park, J. (2006). M-loyalty: Winning strategies for mobilecarriers. Journal of Consumer Marketing, 23(4), 208–218.

Lin, H.-H., & Wang, Y.-S. (2006). An examination of the determinants of customerloyalty in mobile commerce contexts. Information & Management, 43(3), 271–282.

Lu, Y., Deng, Z., & Wang, B. (2009). Exploring factors affecting Chinese consumers’usage of short message service for personal communication. Information SystemsJournal, doi:10.1111/j.1365-2575.2008.00312.x.

Lu, Y., Dong, Y., & Wang, B. (2007). The mobile business value chain in China: A casestudy. International Journal of Electronic Business, 5(5), 460–477.

Luarn, P., & Lin, H.-H. (2003). A customer loyalty model for e-service context. Journalof Electronic Commerce Research, 4(4), 156–167.

Mayer, R. C., & Davis, J. H. (1999). The effect of the performance appraisal system ontrust for management: A field quasi-experiment. Journal of Applied Psychology,84(1), 123–136.

McKnight, D. H., & Chervany, N. L. (2002). What trust means in e-commerce customerrelationships: An interdisplinary conceptual typology. International journal ofelectronic commerce, 6(2), 35–59.

MIIT. (2008). China telecommunications industry statistics report (Septem-ber of 2008). Retrieved November 12, 2008, from http://www.miit.gov.cn/n11293472/n11295057/n11298508/11711487.html.

Mittal, V., Ross, W., & Baldasare, P. (1998). The asymmetric impact of negativeand positive attribute-level performance on overall satisfaction and repurchaseintentions. Journal of Marketing, 61(1), 33–47.

Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationshipmarketing. Journal of Marketing, 58(3), 20–38.

Nasir, S. (2005). The development, change, and transformation of managementinformation systems (MIS): A content analysis of articles published in businessand marketing journals. International Journal of Information Management, 25(5),442–457.

Nysveen, H., Pedersen, P. E., & Thorbjørnsen, H. (2005). Explaining intention to usemobile chat services: Moderating effects of gender. Journal of Consumer Market-ing, 22(5), 247–256.

Oliver, R. L. (1981). Measurement and evaluation of satisfaction processes in retailsettings. Journal of Retailing, 57(3), 25–48.

Oliver, R. L. (1999). Whence consumer loyalty? Journal of Marketing, 63(4), 33–44.Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Servqual: A multiple item scale

for measuring consumer perceptions of service quality. Journal of Retailing, 64(1),12–40.

Reichheld, F. F., & Schefter, P. (2000). E-loyalty: Your secret weapon on the web.Harvard Business Review, 78(4), 105–113.

Ribbink, D., Van Riel Allard, C. R., Liljander, V., & Streukens, S. (2004). Comfort youronline customer: Quality, trust, and loyalty on the Internet. Managing ServiceQuality, 14(6), 446–456.

Rodgers, S., & Harris, M. A. (2003). Gender and e-commerce: An exploratory study.Journal of Advertising Research, 43(3), 322–329.

Rouibah, K., Khalil, O. E. M., & Hassanien, A. E. (2008). Emerging markets and e-commerce in developing economies. Idea Group Inc.

Sanchez-Franco, M. J., Ramos, A. F. V., & Velicia, F. A. M. (2009). The moderating effectof gender on relationship quality and loyalty toward Internet service providers.Information & Management, 46(3), 196–202.

Semejin, J., Van Riel Allard, C. R., Van Birgelen, M. J. H., & Streukens, S. (2005).E-services and offline fulfillment: How e-loyalty is created. Managing ServiceQuality, 15(2), 182–195.

Seo, D., Ranganathan, C., & Babad, Y. (2008). Two-level model of customer retentionin the US mobile telecommunications service market. Telecommunications Policy,32(3–4), 182–196.

Sheth, J. N., Newman, B. I., & Gross, B. I. (1991). Consumption values and market choice.
Cincinnati, OH: South Western Publishing.
Shin, D.-H., & Kim, W.-Y. (2008). Forecasting customer switching intention in mobileservice: An exploratory study of predictive factors in mobile number portability.Technological Forecasting & Social Change, 75(6), 854–874.

Siau, K., & Shen, Z. (2003). Building customer trust in mobile commerce. Communi-cations of the ACM, 46(4), 91–95.

http://www.cnnic.cn/uploadfiles/pdf/2009/1/13/92458.pdf

http://www.iresearch.com.cn/Consulting/instant_messenger/Free.asp?classid=&id=1175

http://down.iresearch.cn/Reports/Free/1184.html

http://dx.doi.org/10.1111/j.1365-2575.2008.00312.x

http://www.miit.gov.cn/n11293472/n11295057/n11298508/11711487.html

3 nform

S

S

S

T

T

T

V

V

W

W

W

W

X

Y

Y

Y

Jinlong Zhang is professor of Information Management at the School of Man-agement, Huazhong University of Science and Technology, China. His research


ivadass, E., & Baker-Prewitt, J. L. (2000). An examination of the relationship betweenservice quality, customer satisfaction, and store loyalty. International Journal ofRetail & Distribution Management, 28(2), 73–82.

oteriou, A., & Chase, R. (1998). Linking the customer contact model to service qual-ity. Journal of Operations Management, 16(4), 495–508.

weeny, J. C., & Soutar, G. N. (2001). Consumers perceived value: The developmentof a multiple item scale. Journal of Retailing, 77(2), 203–220.

NS Global. (2008). Instant messaging via mobile set to challenge the status quo of non-voice communication. Retrieved January 30, 2009, from http://www.tnsglobal.com/news/news-13B3916A7F4A40E694C47912EC09EB8A.aspx.

soukatos, E., & Rand, G. K. (2006). Path analysis of perceived service quality, sat-isfaction and loyalty in Greek insurance. Managing Service Quality, 16(5), 501–519.

urel, O., & Serenko, A. (2006). Satisfaction with mobile services in Canada: Anempirical investigation. Telecommunications Policy, 30(5–6), 314–331.

enkatesh, V., Brown, S. A., Maruping, L. M., & Bala, H. (2008). Predicting differentconceptualizations of system use: The competing roles of behavioral inten-tion, facilitating conditions, and behavioral expectation. MIS Quarterly, 32(3),483–502.

enkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology accep-tance model: Four longitudinal field study. Management Science, 45(2), 186–204.

alsh, G., Dinnie, K., & Wiedmann, K.-P. (2006). How do corporate reputation andcustomer satisfaction impact customer defection? A study of private energycustomers in Germany. Journal of Services Marketing, 20(6), 412–420.

ang, Y.-S., & Liao, Y.-W. (2007). The conceptualization and measurement of m-commerce user satisfaction. Computers in Human Behavior, 23(1), 381–398.

ang, Y.-S., Lin, H.-H., & Luarn, P. (2006). Predicting consumer intention to usemobile service. Information Systems Journal, 16(2), 157–179.

oodruff, R. A. (1997). Customer value: The next source for competitive advantage.Journal of the Academy of Marketing Science, 25(2), 139–153.

u, Y. (2003). Mobile data communications in China. Communications of the ACM,46(12), 81–85.

ang, H.-E., Wu, C.-C., & Wang, K.-C. (2009). An empirical analysis of online game ser-
vice satisfaction and loyalty. Expert Systems with Applications, 36(2), 1816–1825.
i, Y. (1991). A critical review of consumer satisfaction. In V. A. Zeithaml (Ed.), Reviewof marketing 1990 (pp. 68–123). Chicago, IL: American Marketing Association.

oo, D. K., & Park, J. A. (2007). Perceived service quality: Analyzing relationshipsamong employees, customers, and financial performance. International Journalof Quality & Reliability Management, 24(9), 908–926.


Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequencesof service quality. Journal of Marketing Research, 60(2), 31–46.

Zhaohua Deng is postdoctoral researcher of Information Management at theSchool of Management, Huazhong University of Science and Technology, China. Herresearch focuses on electronic commerce and mobile commerce. Her research hasappeared in the Electronic Markets, Information Systems Journal, International Journalof Services, Economics and Management, International journal of Services Technologyand Management, and International journal of Information Technology and Manage-ment.

Yaobin Lu is professor of Information Management at the School of Management,Huazhong University of Science and Technology, China. He was also a visiting scholarat Michigan State University and the University of Minnesota. His research focuseson business models, online trust, and electronic markets. His research has appearedin the Electronic Markets, Journal of Research and Practice in Information Technology,the Chinese Economy, International Journal of Electronic Business, and InternationalJournal of Services, Economics and Management, Information Systems Journal, ElectronicCommerce Research and Application.

Kwok Kee Wei is Chair Professor in the Department of Information Systems at theCity University of Hong Kong. He obtained his Ph.D. from the University of York andB.S. from Nanyang University. His research focuses on human–computer interac-tion, innovation adoption and management, electronic commerce, and knowledgemanagement. Dr. Wei has published widely in the information systems fieldwith articles appearing in Management Science, Journal of Management Informa-tion Systems, MIS Quarterly, Journal of the American Society for Information Scienceand Technology, ACM Transactions on Information Systems, ACM Transactions onComputer–Human Interaction, IEEE Transactions on Systems, Man, and Cybernetics,Decision Support Systems, IEEE Transactions on Professional Communication, Inter-national Journal of Human–Computer Studies, and European Journal of InformationSystems.

focuses on business information management and mobile commerce. His researchhas appeared in the Journal of Global Information Management, International Jour-nal of Product Management, Expert Systems with Applications, International Journal ofApproximate Reasoning, and Information Systems Journal.

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International Journal of Information Management 37 (2017) 229–240

Contents lists available at ScienceDirect

International Journal of Information Management

jou rna l h om epage: www.elsev ier .com/ locate / i j in fomgt

nfluence of customer engagement with company social networks ontickiness: Mediating effect of customer value creation

ingli Zhanga, Lingyun Guob,∗, Mu Huc, Wenhua Liud

School of Economics and Management, Beihang University, Beijing, ChinaSchool of Economics and Management, Beihang University, Beijing, ChinaSchool of Economics and Management, Beihang University, Beijing, ChinaSchool of Economics and Management, Beihang University, Beijing, China

r t i c l e i n f o

rticle history:eceived 19 January 2015eceived in revised form 19 January 2016ccepted 11 April 2016vailable online 12 May 2016

a b s t r a c t

Company social networks have become an important means for the socialized marketing of a company,forming a new challenge to companies on how to attract customers. Based on such theories as customerengagement, value co-creation, and relationship marketing, this paper presents a model of the influence ofcustomer engagement on stickiness. Data collected from 260 valid questionnaires from Sina’s enterprisemicroblog users were analyzed by structural equation modeling. Empirical results show that customer

eywords:ompany social networks (CSNs)ustomer engagementustomer value creationtickinessord of mouth (WOM)

engagement has a direct and positive influence on customer stickiness as well as an indirect influencethrough customer value creation. This study enriches previous researches on existing theories of customerengagement, value co-creation, and stickiness, and gives practical guidance for companies to encouragecustomer engagement and enhance the stickiness of company social networks.

© 2016 Elsevier Ltd. All rights reserved.

. Introduction

Social media has changed the way people communicate andompanies have set up their own websites based on social net-ork sites (CSNs) so that they can have direct interaction and

onnection with consumers (Martins and Patrício, 2013; Hajli, Sims,eatherman, & Love, 2014a; Hajli, Lin, Featherman, & Wang, 2014b).n the one hand, social media has a powerful social function to

urn customers’ offline social networks into online ones; on thether hand, it also has great timeliness and influence because ofts viral spread of information (Kaplan & Haenlein, 2010). There-ore, it can serve as an effective platform for companies to makese of the customers’ social networks and spread the informa-ion instantly to a large number of potential customers. In thehina alone, 69.4 percent of microblog consumers focus on Sinaicroblog (China Internet Network Information Center, 2015), and

ver 80 percent of microblog users are connected to enterpriseicroblog. For instance, DELL builds fan page on twitter; Xiaomi

stablishes enterprise microblog attracting more than ten mil-

∗ Corresponding author at: Lingyun Guo, School of Economics and Management,eihang University, No. 37 XueYuan Road, Haidian District, Beijing, China.

E-mail address: [email protected] (L. Guo).

ttp://dx.doi.org/10.1016/j.ijinfomgt.2016.04.010268-4012/© 2016 Elsevier Ltd. All rights reserved.

lion followers. However, social network platforms are open andindependent. Customers can easily turn to other websites forsimilar contents, products, or services (a customer may followseveral enterprise websites) and that means it is no longer aneasy task to get customers “stuck” to you (i.e. stickiness; Lu& Lee, 2010). Meanwhile, with the continuous development ofsocial media, the role of consumers has evolved from tradition-ally passive information “receivers” to information co-creators(Jahn & Kunz, 2012). Moreover, they expect more than promot-ing information from supermarket posters. They have a higherdemand for company social networks—getting more valuableand meaningful information about the company and its prod-ucts through interaction. For example, UC, a browser, is oftenused by customers with smart phone. UC customers on themicroblog often discuss how to save traffic, improve efficiencyand select browse mode, other than focusing on the productitself only. This significant change requires companies to caterto their needs and provide them with better services (Sashi,2012).

With the changes in both CSNs and customers, numerousresearchers and practitioners currently hold the view that cus-
tomer engagement can be interpreted as “the repeated interactionsbetween consumers and brand that strengthen the emotional, psy-chological or physical investment a customer has in that brand”

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30 M. Zhang et al. / International Journal of

Brodie, Hollebeek, Juric, & Ilic, 2011; Van Doorn et al., 2010;aakkola & Alexander, 2014; Sashi, 2012). Corporate performancemprovement can be realized through increasing sales growthate, gaining competitive advantage, and enhancing profitabilitySedley, 2008). Market research company Aggregate Knowledge2013), for example, found that among Fortune 500 companies

arketing strategies that included social networks increased salesy 24% over strategies that did not include the social-media plat-orm (Boehmer & Lacy, 2014). The theoretical basis of these claimss that the highly engaged customers on the social platform playn important role in generating contents, co-creating customerxperience and value, and referring products, services, and brandso other customers (Jaakkola & Alexander, 2014; Hajli, 2014). Inhis paper, engaged customers seem particularly important. Unlikerand community, customers of CSN may or may not be branddmirers (Martins and Patrício, 2013). That is, quite a part of peo-le just simply follow a CSN, rather than connecting closely withhe CSN. They may just use or browse the news and informa-ion posed by CSNs. Highly engaged customers intend to activelyarticipate in sharing messages and recommending them to poten-ials. Therefore, most CSNs managers agree that highly engagedustomers determine the sustainability of their CSNs (Zhou, Wu,hang, & Xu, 2013). Because of the importance of customer engage-ent with CSNs to the business activities and the increasing

nterest of academic world, this study answers the Marketing Sci-nce Institute (MSI, 2008) call that customer engagement is onef the priority research subjects in the present-day marketingeld.

To our knowledge, academic researches on customer engage-ent mostly stay in definition of the concept and development

f the scale (e.g. Brodie, Ilic, Juric, & Hollebeek, 2013; Hollebeek,013; Jaakkola & Alexander, 2014; Hollebeek, Glynn, & Brodie,014). Few studies empirically analyze that customer engage-ent, a new relationship marketing paradigm, is the core of good

elationship maintenance with the CSNs. However, the problems that these studies lack sufficient understanding on how cus-omer engagement helps forming a good relationship betweenustomer and CSNs. And this paper answers several calls foronsiderable interest in the potential to engage customers andustomer communities in co-production or co-creation value tonhance business performance or customer well-beings. This kindf value can be viewed as benefits/values from both consumernd company contribution (Brodie et al., 2013; Hollebeek, 2013;aakkola & Alexander, 2014). Based on this view, regarding CSNss our study object, this paper tries to build a model about thenfluence of customer engagement on customer value creationnd stickiness of websites. Theories of customer engagement,alue co-creation along with relationship marketing are adopted.eanwhile we also intend to explore the influence mechanism

f the different aspects of customer engagement on customeralue co-creation and stickiness, as well as the mediating effectf customer value co-creation in the process. It is expected thathis study can enrich previous research in customer engage-

ent, customer value co-creation, and stickiness. At the sameime, we hope to provide practical guidance for enterprises toncourage customer engagement, and enhance the stickiness ofSNs.

The paper begins with an overview of the previous studies onustomer engagement, customer value creation, stickiness, andord of mouth (WOM) marketing. The subsequent section dis-

usses the research framework and hypotheses. Research methodsre described and results of analysis are presented. Finally, man-
gerial implications, limitations of this study, and suggestions forurther research are discussed.

2. Literature review

2.1. Customer engagement

Since its initial study in the working environment by Kaln(1900), the concept of engagement has attracted widespread atten-tion from the academic world. As a result, many scholars haveconducted related research in a variety of fields. Studies includework by sociologists on social engagement, psychologists on civilengagement, educationists on student engagement, and scholarsof organizational behavior on employee engagement and occupa-tional engagement (Ilic, 2008; Hollebeek, 2011). In recent years,some scholars in the marketing domain have showed interest inengagement and they have put forward the concept of customerengagement. Patterson, Yu, and De Ruyter (2006) claimed that cus-tomer engagement was the psychological, cognitive, and emotionallevels shown by customers while interacting with a certain orga-nization or brand. Bowden (2009) held the view that customerengagement is a mental process in which new customers develoployalty and old customers maintain their loyalty to a certain brand.Van Doorn et al. (2010) insisted that customer engagement is anon-transactional behavior of customers when they, out of somemotivation, show an interest in a certain enterprise or brand. Thisnon-transactional behavior mainly involves putting forward sug-gestions, spreading WOM praises, recommending the enterpriseor brand to others, helping other customers, writing blogs, and/orposting comments. Slightly different from the belief of Van Doornet al. (2010), Gummerus, Liljander, Weman, & Pihlström, 2012maintained that customer engagement involves non-transactionaland transactional behaviors. In the related studies in sociologyand management, Hollebeek (2011) considered customer-brandengagement as a psychological state generated by customers whenthey are interacting with a brand. Such a motivation-driven andbrand related psychological state involves customers’ cognitive,emotional, and behavioral aspects and changes along with theenvironment. Based on this study, the author has come up witha curve model that shows the changes of customer value withcustomer-brand engagement (Hollebeek, 2013). Reviewing therelated literatures about customer engagement in the field ofmarketing, Brodie et al. (2011) presented five basic hypotheticalpropositions and a general definition of “customer engagement” ofvirtual brand community. They concluded that customer engage-ment was a psychological state generated by customers when theyinteracted and co-created customer experience with other stake-holders in a specific service relationship, and it was a dynamic andcircular process in the service relationship of value co-creation.Brodie et al. (2013) presented a further study on the subject byadopting a netnographic method. They came up with the idea thatcustomer engagement was a multidimensional concept includingcognitive, emotional, and behavioral factors and customers mayhave different forms of engagement in different environments withdifferent stakeholders.

It can be seen from above studies that although scholars in themarketing field have not reached an agreement with the definitionof customer engagement, most of their interpretation of the termshare the following elements: 1) The connection between customerand enterprise or brand includes customers’ emotional, cognitive,and behavioral involvement and 2) their definitions are focusedon customers’ interaction and value co-creation with enterprises,brands, or other customers. On this account, based on the def-initions provided by Brodie et al. (2011) and Hollebeek (2011),this paper considers that customer engagement consists of three
dimensions—customers’ cognition, emotion, and behavior. It is apsychological state of the customers as they are co-creating inter-active experience with enterprises and other customers. The core

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f customer engagement is the interactive experience and valueo-creation.

As far as the dimensions of customer engagement, little atten-ion has been paid to it in the current academic world. Only a fewcholars have carried out some exploratory research. Algesheimer,holakia, and Herrmann (2005) have provided the concept of brandommunity engagement that refers to the customers’ active par-icipation in brand community engagement out of their intrinsic

otivation. Patterson et al. (2006) suggested that customer engage-ent involves four dimensions—vigor, dedication, absorption, and

nteraction. Calder, Malthouse, and Schaedel (2009) believed thatnline customer engagement consists of both personal and social-nteractive engagement. Kumar et al. (2010) have come up withhe concept of customer engagement value that contains cus-omer lifetime value, customer referral value, customer influencealue, and customer knowledge value. Hollebeek, Glynn, and Brodie2014) suggested that the three dimensions of brand engage-

ent are cognitive processing, affection, and activation. By using scale development procedure, Vivek (2009) has come up with thehree dimensions of “customer engagement”—enthusiasm, con-cious participation, and social interaction. In this scale, enthusiasms a reflection of the emotional element in customer engagement,onscious participation is the cognitive element, and social inter-ction is the behavioral element.

From the literature discussed above, it can be seen that althougho agreement about the dimension of customer engagement haseen reached in the marketing world, many scholars have agreedhat customer engagement involves three dimensions −- cogni-ion, emotion, and behavior. Among all the available literature, thecale for measurement engagement put forward by Vivek (2009) isost widely accepted. Regarding our study object, CSNs have twoain features. First, by setting up websites to make use of social

etworks’ sharing capability and customers’ social relationship,nterprises can facilitate the spread of ideas, news, entertainments,ctivities, and user-generated information about brands, prod-cts or services to other users when consuming (King, Racherla,

Bush, 2014; Hajli, 2014). This transmission process must relyn customers’ interpersonal interaction and communication aboutheir experience, which drive the flowing of information. Second,n terms of customers, they may or may not be brand admirersMartins and Patrício, 2013). That is, quite a part of people justimply follow a CSN, but do not engage substantially with the CSN.ost CSNs managers agree that fans and highly engaged customers

etermine the sustainability of their CSNs (Zhou et al., 2013). There-ore, highly engaged customers with strong enthusiasm can giveSN an active online environment. Customer engagement (enthu-iasm, conscious participation, and social interaction) pointed outy Vivek (2009) can reflect the characteristics of the CSNs. Based onhe above reasons, this work uses Vivek’s scale for measurement ofustomer engagement.

.2. Customer value creation

The concept of customer value has received extensive attentionn the marketing world since the 1980s. Vargo and Lusch (2008)nd Prahalad and Ramaswamy (2004) have put forward the ideaf value co-creation. They have claimed that enterprises co-createalue with consumers, which means that customer value is neither

means used by producers to please consumers nor a value createdy consumers for producers by participating in the production pro-ess. According to these scholars, customer value creation refers tohe process by which producers and consumers, as peer subjects,
o-create value for themselves and each other. In this co-creationrocess, these two subjects build personalized service experienceogether through continual dialogue and interactions (Grönroos,008). Based on this theory, some scholars have drawn a new view-

point from the perspective of consumers that customer value iscreated based on interaction between consumers and enterprises,and the interaction among them as well (Schau, Muniz Jr, & Arnould,2009).

As for value co-creation, some scholars view functional andhedonic values as two dimensions. They have extended their scopeof study to the field of social media and have implemented somerelated empirical researches. Based on their research, scholars haveconcluded that information (Foster, Francescucci, & West, 2010;Hajli, Sims, 2014a; Hajli, Lin, 2014b) and entertainment (Dholakia,Bagozzi, & Pearo, 2004) constitute the two vital benefits appealingto users of social media.

Of the two dimensions in value co-creation, functional valuemainly involves the instrumental and functional aspects of thesocial media and it is mainly related to information seeking andupdating (Lee, Yen, & Hsiao, 2014). The information-seeking modeldeveloped by Choo (2000) shows that consumers evaluate infor-mation based on their perceptions of information, namely, theusefulness and accessibility of the information. If the informationthat consumers get is what they seek, it will undoubtedly affectconsumer perceived value.

On the other hand, hedonic value mainly derives from pleas-ant experience and feelings directly relating to personal emotionsand feelings (Babin, Darden, & Griffin, 1994) and forms in theprocess when customers interact with enterprises and/or othercustomers. For instance, the so popular “VANCL style” in microblogjust originates from the style used by VANCL in its advertisements.The original intention of the advertisements is solely to stress thebrand’s uniqueness. However, the novel expression has attracteda large number of net users and posts parodying “VANCL style”appear on the web one after another. Individuals who come toread the posts cannot help laughing and name the writing style as“VANCL style” just for fun. This kind of computer-mediated com-munication is interesting and special. In the process of “parody”,consumers feel comfortable, pleased, and even psychologically con-tent.

Some other scholars deem that social elements are the coreof social media (Yu et al., 2013; Jahn & Kunz, 2012). They haveseparated social value from hedonic value and defined it as theeffectiveness of customers to enhance their social self-conception.The social function of social media lies in fully satisfying consumerneeds to have real social interaction in cyberspace. It can expandcustomers’ off-line social networks to online ones and enable themto build new social relationships at the same time. In these hugenetworks, customers with the same values and similar hobbies andinterests can gather through microblog groups. Relational networksbetween different consumers and different microblog groups canbe linked together and different forms of information can flowfreely through retransmission and information sharing. This phe-nomenon is a good manifestation of the sociability of social media(Cheng et al., 2009).

Company social networks, the focus of present study, take infor-mation and service as platforms for socialization and interaction.Therefore, this paper also regards customer value creation as athree-dimensional value, namely, functional value, hedonic value,and social value.

2.3. Stickiness and WOM

Stickiness is an important ability for enterprises to attractand retain customers (Zott, Amit, & Donlevy, 2000). Lin et al.(2010) defined stickiness as customers’ time spent on a company
social network. Li, Browne, and Wetherbe (2006) have pointed outthat, from the customers’ perspective, even if there are market-ing activities from other company social networks, the stickiness(a deep-rooted commitment) of customers to a certain company

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ocial network still ensures them of a repeated visit and use of thatompany social network in question. Lin (2007) held the view thattickiness is customers’ underlying and unconscious willingness toevisit a company social network. To summarize, all the definitionsiven by scholars involve two aspects: visit time length and useretention. Therefore, the definition of stickiness given by Kumaroy et al. (2014) is adopted in this study. The concept of stickinessomprises both customers’ visit time length in a company socialetwork and the social network’s ability to retain customers.

As for WOM, Tax, Chandrashekaran, and Christiansen, 1996efined WOM as informal interactions between customers aboutn enterprise or its products and services. According to Stern1994), WOM refers to “the exchange of ephemeral oral or spoken

essages between a contiguous source and a recipient who com-unicate directly in real life.” The above definitions are consistentith recent studies of WOM (Gruen, Osmonbekov, & Czaplewski,

006). A review of the literature reveals that customer satisfac-ion, trust, equity perceptions, perceived value, perceptions aboutervice quality, affective attitude, and commitment are frequentlyentioned and examined as antecedents to WOM in online or

ffline settings (Harrison-Walker, 2001; Kumar, Petersen, & Leone,007; De Matos & Rossi, 2008). For this study, we use “word-of-outh” to refer to online or offline behavior and define WOM as

he sender’s interactive positive experiences about the CSNs withthers. And the WOM in this study focuses on a positive one.

. Conceptual model and hypotheses

.1. Customer engagement and customer value creation

Customer engagement with CSNs can increase customers’ per-eived benefit and value. From the perspective of consumers,ustomer engagement may come from the fact that their needsre satisfied during the process of their participation, or becausehey are beneficiaries of the relationship, which they establish withthers (Gummerus et al., 2012).

According to the extant literature, customer engagementncludes three dimensions—enthusiasm, conscious participation,nd social interaction, among which conscious participation referso customers’ intentional participation in activities and they haveome cognition with the activities (Vivek, 2009). Customers’ cogni-ive and conscious participation in CSNs may enhance the perceivedalue. Because in CSNs, reason-oriented customers may want toave a quick and comprehensive understanding of useful informa-ion (such as a product’s function and usage); emotion-orientedustomers prefer to experience this process, society-oriented cus-omers would like to get together and communicate with thoseho possess the same interests, goals, or needs (Babin, Darden, &riffin, 1994). Thus, individuals with different cognition participa-

ion in CSNs may obtain such values as acquiring news and productsing skills, perceiving the relaxing and pleasant experience, asell as gaining a sense of belonging and identification (Jaakkola

Alexander, 2014).Yu et al. (2013) found that individuals intend to participate

n social networks due to perceived value as hedonic value, util-tarian value, and social value. Similarly, Jahn and Kunz (2012)nalyzed three aspects motives (content-oriented, relationship-riented, and self-oriented) for participation in brand fan pages.revious studies of social networks demonstrated a close relation-hip between participation and perceived value (Cheng et al., 2009;
ollebeek, 2013). For these reasons, the following hypotheses areroposed:
H1a: Conscious participation has a direct and positive influencen functional value;


H1b: Conscious participation has a direct and positive influenceon hedonic value;

H1c: Conscious participation has a direct and positive influenceon social value.

Enthusiasm means that customers participation with intenseexcitement or passion (Vivek, 2009). Glassman and McAfee (1990)pointed out that people with enthusiasm are inclined to take risks,which makes them willing to take the initiative to avoid uncer-tainty and reduce misunderstandings. Since social media impliesinformation explosion, enthusiastic customers incline to alleviateanxiety and uncertainty, which could increase trust of enterprisesand customers, especially information in CSNs. Based on this, inter-actions and communication building could enable customers to getthe needed information and knowledge while providing a relaxingand pleasant experience (Lanier & Hampton, 2008). This enables thecustomers to express and show themselves as they like (Kaplan &Haenlein, 2010; Gummerus et al., 2012).

Several studies have examined the effects of related affectivevariables on value. Yüksel (2007) claimed that individuals’ emo-tions are related positively to utilitarian and hedonic shoppingvalue. Hightower, Brady, and Baker (2002) concluded that positiveaffect, such as happy, satisfied, relaxed, excited etc., would promotethe production of perceived value for customers. Accordingly, thefollowing hypotheses are proposed:

H1d: Enthusiasm has a direct and positive influence on func-tional value;

H1e: Enthusiasm has a direct and positive influence on hedonicvalue;

H1f: Enthusiasm has a direct and positive influence on socialvalue.

Social interaction refers to the communication and interactionof opinions, ideas, and feelings among customers, enterprises, andothers (Vivek, 2009). CSNs with a higher level of interactivity canattract customers to discuss issues and respond quickly to ques-tions (Teeni, 2001). Therefore, customers can quickly and easily getinformation and learn related knowledge of the brands/product,which facilitate individuals to know each other and become friendseasily (Muniz & O’guinn 2001). The establishment of a closerrelationship can, on the one hand, provide a stronger feeling ofdependency and a sense of belonging. It can also offer customersa pleasant experience through this kind of harmonious interaction(Gremler, Gwinner, & Brown, 2001).

A number of social network-based studies show a causal linkbetween social interaction and value. Stewart and Pavlou (2002)deemed that interactivity could create value by building trust. Teoet al. (2003) found that interactivity could increase consumers’ per-ceptions of value for products and services. Kuo and Feng (2013)identified how the three interaction characteristics of brand com-munity affect the perceived benefits (hedonic, social, self-esteem,and learning benefits) of community members that deeply revealedthe interaction effect of value. Authors such as Vargo and Lusch(2008), Prahalad and Ramaswamy (2004) stress the importance ofinteraction and suggest that interaction is the nature of co-creation.Therefore, the following hypotheses are proposed:

H1g: Social interaction has a direct and positive influence onfunctional value;

H1h: Social interaction has a direct and positive influence onhedonic value;

H1i: Social interaction has a direct and positive influence onsocial value.

3.2. Customer value creation and stickiness

Customer value creation is an important driving factor for stick-iness (Cheng et al., 2009; Kang, Tang, & Fiore, 2014). In the contextof social media, customers and enterprises interact with each other

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nd pay attention to common topics about society, lives, and indus-ries. In this process, obtaining valuable information, customersould continuously keep an eye on the CSNs platform for more use-

ul message (Lin & Lu, 2011). Meanwhile, such content of humorousokes and anecdotes provide a pleasant emotional experience.sers tend to closely contact CSNs in the future for perceived plea-

ure and happiness (Van der Heijden, 2004). Additionally, valuesnd emotions of common topics match with those of customersnd this may strike a responsive chord in their hearts. Therefore,onsumer sense of identity could be enhanced making them moreikely to revisit and participate in CSNs (Zhou et al., 2013). Fur-hermore, previous studies have examined the perceived value asntecedents to commitment, loyalty, and attachment (Cheng et al.,009; Gruen et al., 2006; Gummerus et al., 2012; Kuo & Feng, 2013).ased on this, the following hypotheses are proposed:

H2a: Functional value has a direct and positive influence ontickiness;

H2b: Hedonic value has a direct and positive influence on stick-ness;

H2c: Social value has a direct and positive influence on sticki-ess.

.3. Stickiness and WOM

Some scholars have suggested that stickiness can be shown inhe form of revisits, repeat purchases, enhancement of relation-hip, and positive suggestions. Therefore, customers’ stickiness to

certain website is formed when the customers have adopted aositive attitude towards the contents, features, products, and ser-ices of the website and have developed such loyal behavior asttachment (Wu, Chen, & Chung, 2010). In other words, sticki-ess is a representative indicator of customer loyalty in a cyber-ontext.

Much work has been done in previous studies about the rela-ionship between customer loyalty and WOM. For instance, basedn their studies of online banking service, Yang and Peterson (2004)ointed out that loyal customers were inclined to develop a positiveOM. Besides, Jones and Reynolds (2006); Gruen, Osmonbekov,

nd Czaplewski, 2006 also believed that customer loyalty is thentecedent of WOM. Because of this, we consider that Stickinessan also bring about the WOM effect so the following hypothesis isroposed:

H3: Stickiness has a direct and positive influence on word ofouth.

.4. Mediating effects of customer value creation

Additionally, it can be seen that customer engagement withocial media can influence the co-creation of hedonic value (emo-ional experience), functional value (information), and social valueidentity) resulting in customers’ stickiness to CSNs. Customersith a higher level of engagement are likely to initiate “inter-

sting interaction” by posting extremely teasing and hilariousontents with vivid languages. This kind of interaction will bringleasant emotional experience to customers, which will in turnenerate positive attitude towards CSNs (Hollebeek, 2011) andesult in the probability of revisits. Individuals engaged in CSNsre constantly playing an active role (Bijmolt et al., 2010). With

higher level of participation in interactions, they could obtainuch values as common knowledge of life, product knowledge,nd use skills. Therefore, they are more willing to establish aonger relationship with related CSNs. Apart from this, highly
ngaged customers are more willing to expand their social net-orks through social media so that they can find those customersho share the same interests, goals, or needs and then commu-icate with them. In the above process, customers are likely to

develop a sense of belonging and identification with each other,and may develop those in CSNs at the same time. Then, a stablevisit time length and frequency will be guaranteed (Cheng et al.,2009). Thus, customer engagement can increase their time andenergy (stickiness) devoted to CSNs through the effective means ofvalue co-creation. Because of this, the following hypothesis is pro-posed:

H4: Customer value creation plays the role mediation betweencustomer engagement and stickiness.

The sub-hypotheses of this hypothesis are as follows:H4a: Functional value mediates the relation between conscious

participation and stickiness;H4b: Hedonic value mediates the relation between conscious

participation and stickiness;H4c: Social value mediates the relation between conscious par-

ticipation and stickiness;H4d: Functional value mediates the relation between enthusi-

asm and stickiness;H4e: Hedonic value mediates the relation between enthusiasm

and stickiness;H4f: Social value mediates the relation between enthusiasm and

stickiness;H4g: Functional value mediates the relation between social

interaction and stickiness;H4h: Hedonic value mediates the relation between social inter-

action and stickiness;H4i: Social interaction mediates the relation between social

interaction and stickiness.Based on the theoretical analysis discussed above, a concep-

tual model is developed, seeing Fig. 1. This model has explainedthe influence mechanism of the different dimensions of customerengagement on stickiness. That is, customer engagement not onlyhas a direct influence on stickiness, but also exerts an indirect onethrough customer value creation.

4. Research methodology

4.1. Research setting and participants

Sina, one of China’s most popular microblog platforms, is usedas the object of our research. By March 2013, the number of usersof this website topped 536.5 million, among which the enter-prise users exceeded 300,000, making it an ideal object for studyof company social networks. The enterprises that have foundedtheir microblog websites are mainly those whose business is linkedclosely with consumer life, such as catering and hospitality, soft-ware and information technology services, electronic consumerproducts, and cosmetics. One primary reason for choosing thesewebsites as our object is that they are at the top of enterprisemicroblog rankings so far that users’ interaction, participation, andattention are concerned. Another reason is that the contents ofmicroblog are updating at least once a week. With new informa-tion posted, it has created a better environment for customersto interact with other customers and the enterprise that makesit possible to generate valuable information for both customersand the enterprise. Thus, only users who have experience in theabove enterprise microblogs are eligible for participating in thesurvey.

4.2. Measures

The questionnaire in this paper is generated basedon previous measures. For the measurement of customerengagement—conscious participation, enthusiasm, and socialinteraction are developed based on the study of Vivek (2009). The

234 M. Zhang et al. / International Journal of Information Management 37 (2017) 229–240

ConsciousParticipation

Enthusiasm

Social Interaction

Functional Value

Hedonic Value

Social Value

Stickiness Word ofMouth

H1a

H1b

H1c

H1d

H1e

H1f

H1gH1h

H1i

H2a

H2b

H2c

H3

Customer Value CreationCustomer Engagement

Key Outcomes

H4a,4b,4c

H4g,4h,4i

H4d,4e,4f

Fig. 1. The conceptual model.

ConsciousParticipation

Enthusiasm

Social Interaction

Functional Value

Hedonic Value

Social Value

Stickiness Word ofMouth

0.61***

0.46***

0.71***

0.30***

0.22***

0.38***

0.010.31***

-0.28***

0.51***

0.21**

0.12

0.78***

Customer Value CreationCustomer Engagement

Key Outcomes

0.27

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Fig. 2. Results of the model. Note: Sig

easurement of functional value of customer value co-creationefers to the studies of Jahn and Kunz (2012). The measurementf hedonic value and social value of customer value creation is
esigned after consulting the studies by Kuo and Feng (2013).he measurement of stickiness combines the studies of Lu andee (2010) and Kumar Roy et al. (2014) while the measurementf WOM comes with consultation of the studies by Jahn and
t at: *p < 0.05, **p < 0.01, ***p < 0.001.

Kunz (2012). With the help of these studies, the questionnairematches the Chinese context and the characteristics of Sina’senterprise microblogs. The exploratory research subjects include
a professor of marketing, three doctoral students of marketing,and 112 customers. All items were measured using a seven-pointLikert scale from “strongly disagree” to “strongly agree” (seeAppendix A).

M. Zhang et al. / International Journal of Inform

Table 1Demographic characteristics (n = 260).

Demographic profile Frequency Percent

Age Less than 25 44 16.84%25–35 174 67.09%36–45 37 14.03%> = 45 5 2.04%

Gender Male 116 44.64%Female 144 55.36%

Education High school or below 5 1.79%Junior college or Undergraduate 216 82.91%Post graduate 39 15.30%

Time of participation Less than 3 month

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3–6 months 47 18.11%6–12 months 89 34.18%More than12 months 124 47.71%

.3. Data collection procedures

This paper has collected its data with questionnaires postedn sojump (http://www.Sojump.com) which is a popular onlineurvey platform in China. To ensure the validity of the data col-ected, the payment service for samples provided by sojump haseen used, which includes more than 2.6 million samples fromifferent cities. By asking screening questions, frequent users ofhe enterprise’s microblog are selected. Sojump grants 100 pointss a reward for those users who have offered effective answerso the questionnaire, and users can exchange the points for gifts.ur formal investigation began in mid-June of 2014 and ended inid-September of the same year. Records show that 685 users

esponded and submitted their questionnaires. After removinghose samples which were blank, with too many unanswered ques-ions, shorter than the time baseline (for instance, scanning theuestionnaire in less than 5 min), or illogical answers to tricky ques-ions (For instance, the meaning of a question is just the opposite to

previous question, but the user who answers the question with-ut noticing the trap there), altogether 260 valid questionnairesere obtained. Among the respondents, 55% were females, 67%ere between 26 and 35, and 83% had a junior college or collegeegree; demographics that match with the orientation and groupharacteristics of Sina’s microblog websites. Approximately 50%f respondents have followed the enterprise’s microblog for morehan one year, which guaranteed a certain degree of knowledge ofhe respondents about Sina’s microblogs. Therefore, the samplesollected represent the customers on this company social network.able 1 shows the demographics of the valid samples.

. Data analysis and results

Structural equation modeling (SEM) is applied to test the currentesearch hypotheses empirically. Following the two-step approachecommended by Anderson and Gerbing (1992), we first exam-ned the measurement model to verify the reliability and validity ofhe instrument and then assessed the structural model. This paper,ith SPSS 16.0, adopted descriptive analysis to find demographic

haracteristics of the sample, as well as Cronbach’s alpha to testeliability. Using LISREL8.70, confirmatory factor analysis was con-ucted to prove the validity of each instrument, while structuralquation modeling was used to test hypotheses.

.1. Tests of measurement model

The cronbach’s coefficients of each scale (Cronbach, 1990)
anged from 0.770 to 0.910, and exceeded the threshold level of.70 suggested by Nunnally, Bernstein, and Berge (1967), thus sug-esting that these data were reliable (see Table 2). In addition, theomposite reliability (CR) of latent variables was between 0.786

and 0.867, which was also higher than 0.70, the minimum criticalvalue suggested, meaning that the scale used here has good internalconsistency (Dillon & Goldstein, 1984).

Our validity analysis involved both convergent and discriminantvalidity. It can be seen in Table 2 that the standardized factor load-ings of all the elements measured were between 0.66 and 0.90,which was higher than 0.6 meaning that all have statistical sig-nificance (Hair et al., 2006). The average variance extracted (AVE)values was between 0.568 and 0.687 (as shown in Table 2), whichmeans all the variables have good convergent validity (Fornell &Larcker, 1981). Meanwhile, the square roots of AVE of the latentvariables were larger than the correlation coefficients and all werelarger than 0.5 (as shown in Table 3). This means that the scale usedhere has fairly good discriminant validity.

5.2. Tests of structural model

The structural relationships between the latent variablewere examined using covariance structure analysis. The fittingtests found the following: �2 = 710.78, df = 237, RMSEA = 0.088,GFI = 0.85, AGFI = 0.76, CFI = 0.96, NFI = 0.94, NNFI = 0.95, andIFI = 0.96. The data reached the critical values suggested, indicat-ing that the fitting level of the hypothesis model and the data wasapproximately acceptable. The results of the hypothesis testing areshown, in Fig. 2 and Table 4, that H1 g and H2c were rejected whileH1i received a negative result, while the others were verified.

5.3. Tests of mediating effects

Mediation exists if the coefficient of the direct path between theindependent variable and the dependent variable is reduced whenthe indirect path via the mediator is introduced into the model (Kuo& Feng, 2013). In this paper, 9 paths were examined.

Firstly, according to Fig. 2, social interaction did not significantlyinduce functional value. Therefore, functional value did not mediatethe relation between social interaction and stickiness. And socialvalue had limited influence on stickiness. Accordingly, social valuedid not mediate the effects of customer engagement on stickiness.

Secondly, to test mediating effects of customer value creationon the relation between customer engagement and stickinessfor the other 5 paths, we relied on the three-step mediatedregression approach that Baron and Kenny (1986) recommend(The results are shown in Tables 5 and 6). The amount of therelationship between enthusiasm and stickiness accounted forby functional value was (0.37 − 0.15) = 0.22, which represented59.46 percent of the direct effect. The amount of the relation-ship between enthusiasm and stickiness accounted for by hedonicvalue was (0.37 − 0.15) = 0.22, which also represented 59.46 per-cent of the direct effect. When functional value was in control,the path coefficient of conscious participation-stickiness was notsignificant. When hedonic value was in control, neither of thepath coefficients of conscious participation-stickiness and socialinteraction-stickiness was significant. Based on these findings,it is concluded that customer value creation serves as a par-tially mediator of five links: conscious participation-functionalvalue-stickiness, conscious participation-hedonic value-stickinessand social interaction-hedonic value-stickiness are fully mediatingpaths; enthusiasm-hedonic value − stickiness and enthusiasm-functional value − stickiness are partially mediating paths.

6. Discussion

First, customer engagement has a direct and positive influenceon customer value creation. Conscious participation, enthusiasm,and social interaction can promote the generation of functional,hedonic, and social values co-created by customers. This conclusion

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236 M. Zhang et al. / International Journal of Information Management 37 (2017) 229–240

Table 2Cronbach’s Alpha, Composite reliability, Factor loading, and average variance extracted (AVE).

Constructs Items Factor loading T-Value Cronbach’salpha Composite reliability AVE

Conscious Participation CP1 0.77 14.06 0.910 0.852 0.658CP2 0.80 14.99CP3 0.86 16.53

Enthusiasm EN1 0.66 11.26 0.845 0.849 0.586EN2 0.77 13.88EN3 0.78 14.12EN4 0.84 15.73

Social Interaction SI1 0.87 16.41 0.770 0.849 0.653SI2 0.77 13.89SI3 0.78 14.16

Functional Value FV1 0.80 14.38 0.841 0.786 0.648FV2 0.81 14.57FV3a – –FV4a – –

Hedonic Value HV1 0.83 15.48 0.837 0.831 0.622HV2 0.82 15.32HV3 0.71 12.53

Social Value SV1 0.90 17.96 0.849 0.867 0.687SV2 0.82 15.54SV3 0.76 13.81

Stickiness ST1 0.77 13.65 0.864 0.796 0.568ST2 0.78 13.92ST3 0.74 13.05

Word of mouth WOM1 0.77 14.08 0.893 0.807 0.583WOM2 0.78 15.33WOM3 0.74 13.57

a Items that are deleted in the final analysis due to high cross loadings.

Table 3Discriminant validity: Latent variable correlations with Square Root of AVE (average variance extracted) along lead diagonal.

Constructs 1.CP 2.EN 3.SI 4.FV 5.HV 6.SV 7.ST 8.WOM

1.Conscious Participation 0.812.Enthusiasm 0.40 0.773. Social Interaction 0.63 0.34 0.814. Functional Value 0.72 0.53 0.51 0.815. Hedonic Value 0.76 0.52 0.68 0.61 0.796. Social Value 0.67 0.56 0.30 0.68 0.48 0.837. Stickiness 0.53 0.52 0.35 0.75 0.56 0.59 0.76

0.56 0.68 0.46 0.73 0.76

N .

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Table 4Result of hypotheses.

Hypothesis Standardized Coefficient T-value Conclusion

H1a 0.61*** 8.58 SupportedH1b 0.46*** 6.05 SupportedH1c 0.71*** 8.45 SupportedH1d 0.30*** 4.61 SupportedH1e 0.22*** 3.78 SupportedH1f 0.38*** 6.17 SupportedH1g 0.01 0.15 Not SupportedH1h 0.31*** 4.29 SupportedH1i −0.28*** −3.63 Not SupportedH2a 0.51*** 5.42 SupportedH2b 0.21** 2.61 SupportedH2c 0.12 1.61 Not SupportedH3 0.78*** 9.46 Supported

Note: Significant at:* p < 0.05, ** p < 0.01, *** p < 0.001.

Table 5Mediation tests of functional value.

CE → FV FV → ST (CE → ST) CE → ST

CP 0.61*** 0.51*** 0.42*** NSEN 0.30*** 0.51*** 0.37*** 0.15**

8. Word of mouth 0.54 0.56 0.50

ote: Diagonal elements show the Square Root of average variance extracted (AVE)

as supplemented the theory proposed by Vivek (2009) that theingle variable of engagement can result in customer value (func-ional and hedonic values). There are two differences between ouresults and those of other scholars. Firstly, social interaction doesot have a positive influence on functional value − H1g is rejected. Aossible explanation for this rejection is that the functional value ofina’s enterprise microblog is demonstrated mainly in the informa-ion posted on the website. Therefore, this functional value, which iselated to the usefulness and timeliness of the information postedn the website, is a relatively objective existence there. In con-rast, social interaction mainly exists among customers. This kind ofnteraction mainly has an influence on the interpersonal value. Sec-ndly, social interaction has a negative influence on social value −1i receives a negative result. A possible explanation for this result

s that most users of Sina’s enterprise microblog are unfamiliar withach other. Thus, it does not match their original intention for theustomers to get “unfamiliar” social value from people of this group.or instance, some customers registered on the website with theirersonal information with the mere intention of getting the infor-ation about housing price and opening time of new buildings.owever, some businesspeople make use of customer telephoneumbers and/or email addresses obtained from this platform and
ry to sell their buildings with no regard to the other party’s time orccasion. The kind of social interaction has annoyed people. There-ore, it will not promote social value. On the contrary, it only hindersustomers from obtaining good social value.
Note: CE: customer engagement.

M. Zhang et al. / International Journal of Inform

Table 6Mediation tests of hedonic value.

CE → HV HV → ST (CE → ST) CE → ST

CP 0.46*** 0.21*** 0.42*** NS

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EN 0.22*** 0.21*** 0.37*** 0.15**

SI 0.31*** 0.21*** 0.05* NS

Secondly, customer value co-creation partially mediates theelation between customer engagement and stickiness. Based onhe validation method of mediator variables provided by Baronnd Kenny (1986), this paper conducted an analysis on the medi-ting effect of customer value creation (The results are shown inables 5 and 6). The analysis shows that when functional value is inontrol, the path coefficient of conscious participation-stickiness isot significant. This means that functional value fully mediates theelation between conscious participation and stickiness. Thus, H4as verified. Although the path coefficient of enthusiasm-stickinessecreases, the path coefficient remains significant. It indicates thatunctional value mediates the relation between enthusiasm andtickiness. Thus, H4d is verified. Meanwhile, functional value doesot mediate the relation between social interaction and stickiness.hus, H4g is not verified. The main reason for this is that, in theontext of this study, social interaction has limited influence onunctional value. When hedonic value is in control, neither of theath coefficients of conscious participation-stickiness and social

nteraction-stickiness is significant, which means that hedonicalue fully mediates the relationship between the two paths of con-cious participation-stickiness and social interaction-stickiness.hus, both H4b and H4e are verified. Even then, the path coefficientf enthusiasm-stickiness decreases, the path coefficient remainsignificant. This means that hedonic value partially mediates theelationship between enthusiasm and stickiness. Therefore, H4h iserified. Additionally, social value does not mediate the relationetween customer engagement and stickiness, so H4c, H4f, and4i are not verified. The main reason for this is that social valueas limited influence on stickiness in the context of this paper.

Finally, value creation has a direct and positive influence ontickiness, which further influences WOM. Different from othertudies on the relation between customer value and customer loy-lty, the present analysis shows that social value has no influencen stickiness. Therefore, H2c is rejected. A possible reason for thisejection is that the negative social value obtained from Sina’snterprise microblog cannot fully generate stickiness or positiveOM from customers.

. Conclusion

Based on studies on company social networks and surveyingsers of Sina’s enterprise microblog as objects, this paper hasxplored the relationship among customer engagement, customeralue creation, stickiness and WOM as well as the mediating effectf customer value creation by using the structural equation model.he conclusion of this study can be summarized in the followingay.

First, in general, conscious participation, enthusiasm, and socialnteraction − the three dimensions of customer engagement,an exert a direct and positive influence on customer value co-reation.1) Customers participate in activities with an intentionnd some cognition. Their cognition varies with their reason, emo-ion, or society orientation. The value co-created by customers alsoaries − reason-oriented customers attach more importance to the
sefulness of information; emotion-oriented customers focus moren the process of experience; and society-oriented customers areore willing to have some communication with other customersho share the same interests and develop a sense of belonging and

identity in the process. 2) People with enthusiasm are willing totake risks, get rid of anxiety and sense of uncertainty, thus enhanc-ing customer trust with other members of company social networksand information posted on these networks. The interaction builtupon this trust can directly improve customer perception of thevalue created. 3) The interactions among members of companysocial networks can provide an effective means for them to get toknow each other, establish friendship, or other close relationships.In the process, customers can develop a strong feeling of depen-dency and belonging and perceive pleasure from the interactionsbetween close customers.

Secondly, studies show that customer value creation medi-ates the relation between customer engagement and stickiness.When customers consciously participate in CSNs, functional andhedonic values completely mediate the relationship between thecognition-based engagement and stickiness. When customers getengaged in CSNs with enthusiasm, functional and hedonic val-ues partially mediate the relationship between the emotion-basedengagement and stickiness. When customers interact with othermembers of CSNs, hedonic value completely mediates with thebehavior-based engagement and stickiness. Therefore, the medi-ating effect of customer value creation has been verified. In theprocess of maintaining the stickiness between customers and CSNs,when customer cognition is engaged, the functional and hedonicvalues co-created by both customers and enterprises are the twokey factors contributing to stickiness. Moreover, customer emo-tional engagement has a direct influence on stickiness. It can alsoeffectively enhance customer stickiness to CSNs by increasing thehedonic value co-created by customers and enterprises. Only whencustomers are engaged behaviorally can hedonic value developstickiness.

Thirdly, it is manifested from the study that customer percep-tion of functional value (information) and hedonic value (pleasure)co-created by both parties really enhance stickiness. However, thedegree of influence of functional and hedonic values on stickinessis varied and is treated differently. Our finding coincides with thoseof previous studies (Cheng et al., 2009; Kang et al., 2014), which fur-ther proves that the theory of value co-creation has a wider scopeof applicability. Moreover, customer stickiness to CSNs does bringabout WOM.

8. Implications

8.1. Managerial implications

Firstly, establishment of customer engagement with CSNs is aneffective way of making CSNs into a new generation of compet-itive marketing channels. Management of enterprises should befully aware of the fact that a good mastery of CSNs − the newmarketing channel, plays an important role in the promotion of abusiness enterprise. Therefore, it is far from enough for enterprisesto build CSNs and attract some visitors. What enterprises should dois improve them and make it an active community in which cus-tomers should be completely engaged, involved, and immersed.This will enable enterprises to enhance the value co-created bycustomers and guarantee customer stickiness to CSNs and WOM.

Secondly, attaching more importance to customer value cre-ation, especially functional and hedonic values, is an importantdriving factor for CSNs to attract customers. Researches show thatcustomer value creation, especially functional and hedonic values,is keys to stickiness. If customer engagement can be viewed as a
competitive edge to enterprises, customer value co-creation will bean important factor for enterprises to turn this competitive advan-tage into their market and at the same time develops the stickinessof their social networks. In this particular age of socialized media,

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dequate, timely, and useful information on Internet and a pleasantnteractive experience are important for enterprises to succeed in

diversified competitive space.

.2. Theoretical implications

The present study has enriched the existing probes in theelds of customer engagement and customer value creation anderified the previous theoretical argument (Brodie et al., 2013;ollebeek, 2013; Jaakkola & Alexander, 2014). Firstly, the medi-ting effects of customer value creation on the path of customerngagement-stickiness were verified: 1) Functional and hedonicalues completely mediated the relationship between consciousngagement and stickiness; 2) Functional and hedonic valuesartially mediated the relationship between enthusiasm and sticki-ess; 3) hedonic value completely mediated with social interactionnd stickiness. This conclusion extended the scope of existingtudies on the relationship between customer engagement andtickiness. Previous studies point out that customer engagementan bring about relationship maintenance between customer andnterprise (e.g. Commitment, satisfaction, and loyalty; Jahn &unz, 2012), but not investigate transformation mechanism fromngagement to key outcomes. This research is one of the earli-st studies proved, from an empirical perspective, that customeralue creation serves as an important driving factor for customerngagement to generate stickiness and that customer value cre-tion mediates the relationship between them.

Secondly, our findings have enriched the existing studies byxploring deeper into the relationship between customer valuereation and stickiness in marketing, and have provided a prelim-nary explanation for the different degrees of influence of valueo-created by customers and enterprises. The empirical resultsndicated that the effects of functional value on stickiness, hedo-ic value on stickiness, and social value on stickiness were varying.nder company social networks, the biggest factor for sticki-ess is functional value and hedonic value second place; socialalue, however, has no significant effect. This finding implies thatompany social networks’ stickiness may primarily depend onndividual-related factors rather than interpersonal factors. Ournding coincides with those of previous studies, which concludedhat information (Foster, Francescucci, & West, 2010; Hajli, Sims,014a; Hajli, Lin, 2014b) and entertainment (Dholakia, Bagozzi, &earo, 2004) constituted the two vital benefits appealing to users ofocial media. Customer value creation has been researched in thistudy, which approved that customer value takes an important roleot only in a retailing context (Sweeney & Soutar, 2001) but also in

networking and social context.

.3. Limitation and future research

The major limitations of this paper are listed here. Firstly, allhe data in this study are cross-section information at the sameoint of time. Future studies can track and focus on the differenthases of same group consumer engagement on stickiness throughonsumer value creation. Secondly, the CSNs included in our sam-le were Chinese context, whose users may be distinct from onesith other cultural backgrounds. For example, Singh, Zhao, and Hu,

005 find that local websites in India, China, Japan, and the Unitedtates differ significantly in their cultural dimensions. Therefore,dditional studies should test our proposed model in other culturalontexts. In regards to other CSNs, such as brand communities, Ren-en, and WeChat, the applicability of the proposed model need to be
urther verified. Thirdly, this paper has solely studied the influencef customer engagement on customer value creation and sticki-ess, without taking into account the motivation or driving factorsf customer engagement. Future research needs to investigate the

influence of the motives on customer engagement. Fourthly, thisstudy does not consider the control variables, such as students andnon-students have different influences on engagement (Alt, 2015).And how demographic differences (gender, blog usage, blog expe-rience and student or not) would affect the antecedents of blogstickiness (Lu & Lee, 2010; Hajli & Lin, 2014). This point will bethe theme of my future research. Lastly, because the purpose ofthis research is to investigate the mediating effect of customervalue creation on the relationship between customer engagement,customer value creation, stickiness, and WOM, this study treatsstickiness as a single variable. However, stickiness is a complexphenomenon. Lu and Lee (2010), for example, have identified twodimensions of stickiness. Thus, future work should consider thisissue.

Acknowledgment

This research is supported by the National Natural Science Foun-dation of China (NSFC 70972002, 71272018).

Appendix A. . Survey measurement scales

Customer engagement

Conscious participationCP1: Anything related to X enterprise microblog grabs my atten-

tion.CP2: I like to learn more about X enterprise microblog.CP3: I pay a lot of attention to anything about X enterprise

microblog.

EnthusiasmEN1: I spend a lot of my discretionary time on X enterprise

microblog.EN2: I am heavily into X enterprise microblog.EN3: I am passionate about X enterprise microblog.EN4: My days would not be the same without X enterprise

microblog.

Social interactionSI1: I love participating in X enterprise microblog with my

friends.SI2: I enjoy taking part in X enterprise microblog more when I

am with others.SI3: Participation in X enterprise microblog is more fun when

other people around me do it too.

Customer value creation

Functional valueFV1: The content (information) of X enterprise microblog is

helpful for me.FV2: The content (information) of X enterprise microblog is use-

ful for me.FV3: The content (information) of X enterprise microblog is

functional for me.FV4: The content (information) of X enterprise microblog is

practical for me.

Hedonic valueHV1: I feel pleased and relaxed in X enterprise microblog.HV2: I gain joy and happiness in X enterprise microblog.HV3: I feel inspired in X enterprise microblog.

Inform

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A

A

B

B

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B

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B

B

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C

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C

C

D

D

D

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F

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ocial valueSV1: I can make friends with people sharing common interests

ith me in X enterprise microblog.SV2: X enterprise microblog helps strengthen my connections

ith other members.SV3: I can expand my social network through participation in X

nterprise microblog.

tickinessST1: I would stay for a long time while browsing X enterprise

icroblog.ST2: I intend to prolong my stays on X enterprise microblog.ST3: I would visit X enterprise microblog frequently.

ord of mouthWOM1: I introduce X enterprise microblog to other people.WOM2: I recommend X enterprise microblog to other people.WOM3: I say positive things about X enterprise microblog to

ther people

eferences

lgesheimer, R., Dholakia, U. M., & Herrmann, A. (2005). The social influence ofbrand community: evidence from European car clubs. Journal of Marketing,69(3), 19–34.

lt, D. (2015). College students’ academic motivation: media engagement and fearof missing out. Computers in Human Behavior, 49, 111–119.

nderson, J. C., & Gerbing, D. W. (1992). Assumptions and comparative strengths ofthe two-step approach comment on Fornell and Yi. Sociological Methods &Research, 20(3), 321–333.

abin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: measuring hedonicand utilitarian shopping value. Journal of Consumer Research, 20, 644–656.

aron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction insocial psychological research: conceptual, strategic, and statisticalconsiderations. Journal of Personality and Social Psychology, 51(6), 1173.

ijmolt, T. H., Leeflang, P. S., Block, F., Eisenbeiss, M., Hardie, B. G., Lemmens, A.,et al. (2010). Analytics for customer engagement. Journal of Service Research,13(3), 341–356.

oehmer, J., & Lacy, S. (2014). Sport news on facebook: the relationship betweeninteractivity and readers’ browsing behavior. International Journal of SportCommunication, 7(1), 1–15.

owden, J. L. H. (2009). The process of customer engagement: a conceptualframework. The Journal of Marketing Theory and Practice, 17(1), 63–74.

rodie, R. J., Hollebeek, L. D., Juric, B., & Ilic, A. (2011). Customer engagement:conceptual domain, fundamental propositions, and implications for research.Journal of Service Research, 14(3), 252–271.

rodie, R. J., Ilic, A., Juric, B., & Hollebeek, L. (2013). Consumer engagement in avirtual brand community: an exploratory analysis. Journal of Business Research,66(1), 105–114.

alder, B. J., Malthouse, E. C., & Schaedel, U. (2009). An experimental study of therelationship between online engagement and advertising effectiveness. Journalof Interactive Marketing, 23(4), 321–331.

heng, J. M. S., Wang, E. S. T., Lin, J. Y. C., & Vivek, S. D. (2009). Why do customersutilize the internet as a retailing platform? A view from consumer perceivedvalue. Asia Pacific Journal of Marketing and Logistics, 21(1), 144–160.

hina Internet Network Information Center. (2015). The statistics report of Chinainternet development. Beijing: China Internet Network Information Center., inChinese

hoo, C. W. (2000). Closing the cognitive gaps: how people process information. InD. Marchand, T. Davenport, & T. Dickson (Eds.), Mastering informationmanagement (pp. 245–253). Harlow: FT-Prentice-Hall.

ronbach, J. (1990). Essential of psychology testing. New York NY: Happer and Rowpublisher.

e Matos, C. A., & Rossi, C. A. V. (2008). Word-of-mouth communications inmarketing: a meta-analytic review of the antecedents and moderators. Journalof the Academy of Marketing Science, 36(4), 578–596.

holakia, U. M., Bagozzi, R. P., & Pearo, L. K. (2004). A social influence model ofconsumer participation in network-and small-group-based virtualcommunities. International Journal of Research in Marketing, 21(3), 241–263.

illon, W. R., & Goldstein, M. (1984). Multivariate analysis: methods andapplications. New York: John Wiley & Sons.

ornell, C., & Larcker, D. F. (1981). Evaluating structural equation models withunobservable variables and measurement error? Journal of Marketing Research,
18(1), 39–50.
oster, M. K., Francescucci, A., & West, B. C. (2010). Why users participate in onlinesocial networks. International Journal of e-Business Management, 4(1), 3.

lassman, M., & McAfee, R. B. (1990). Enthusiasm: the missing link in leadership.SAM Advanced Management Journal, 55(3), 4–6.


Grönroos, C. (2008). Service logic revisited: who creates value? And whoco-creates? European Business Review, 20(4), 298–314.

Gremler, D. D., Gwinner, K. P., & Brown, S. W. (2001). Generating positiveword-of-mouth communication through customer-employee relationships.International Journal of Service Industry Management, 12(1), 44–59.

Gruen, T. W., Osmonbekov, T., & Czaplewski, A. J. (2006). eWOM: The impact ofcustomer-to-customer online know-how exchange on customer value andloyalty. Journal of Business Research, 59(4), 449–456.

Gummerus, J., Liljander, V., Weman, E., & Pihlström, M. (2012). Customerengagement in a Facebook brand community. Management Research Review,35(9), 857–877.

Hair, J. F., Tatham, R. L., Anderson, R. E., & Black, W. (2006). . Multivariate dataanalysis (Vol. 6) Upper Saddle River, NJ: Pearson Prentice Hall.

Hajli, M. N. (2014). The role of social support on relationship quality and socialcommerce. Technological Forecasting and Social Change, 87, 17–27.

Hajli, N., & Lin, X. (2014). Exploring the security of information sharing on socialnetworking sites: the role of perceived control of information. Journal ofBusiness Ethics, 1–13.

Hajli, M. N., Sims, J., Featherman, M., & Love, P. E. (2014). Credibility of informationin online communities. Journal of Strategic Marketing, 23(3), 238–253.

Hajli, N., Lin, X., Featherman, M. S., & Wang, Y. (2014). Social word of mouth: howtrust develops in the market. International Journal of Market Research, 56(5),673–689.

Harrison-Walker, L. J. (2001). The measurement of word-of-mouth communicationand an investigation of service quality and customer commitment as potentialantecedents. Journal of Service Research, 4(1), 60–75.

Hightower, R., Brady, M. K., & Baker, T. L. (2002). Investigating the role of thephysical environment in hedonic service consumption: an exploratory study ofsporting events. Journal of Business Research, 55(9), 697–707.

Hollebeek, L. (2011). Exploring customer brand engagement: definition andthemes. Journal of Strategic Marketing, 19(7), 555–573.

Hollebeek, L. D. (2013). The customer engagement/value interface: an exploratoryinvestigation. Australasian Marketing Journal (AMJ), 21(1), 17–24.

Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2014). Consumer brand engagement insocial media: conceptualization, scale development and validation. Journal ofInteractive Marketing, 28(2), 149–165.

Ilic, A. (2008). Towards a conceptualisation of consumer engagement in onlinecommunities: a netnographic study of vibration training online community. InUnpublished master’s thesis. Auckland, New Zealand: University of Auckland(Department of Marketing).

Jaakkola, E., & Alexander, M. (2014). The role of customer engagement behavior invalue Co-Creation a service system perspective. Journal of Service Research,17(3), 247–261.

Jahn, B., & Kunz, W. (2012). How to transform consumers into fans of your brand.Journal of Service Management, 23(3), 344–361.

Jones, M. A., & Reynolds, K. E. (2006). The role of retailer interest on shoppingbehavior. Journal of Retailing, 82(2), 115–126.

Kang, J., Tang, L., & Fiore, A. M. (2014). Enhancing consumer–brand relationshipson restaurant Facebook fan pages: maximizing consumer benefits andincreasing active participation. International Journal of Hospitality Management,36, 145–155.

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges andopportunities of Social Media. Business Horizons, 53(1), 59–68.

King, R. A., Racherla, P., & Bush, V. D. (2014). What we know and don’t know aboutonline word-of-mouth: a review and synthesis of the literature. Journal ofInteractive Marketing, 28(3), 167–183.

Kumar, V., Aksoy, L., Donkers, B., Venkatesan, R., Wiesel, T., & Tillmanns, S. (2010).Undervalued or overvalued customers: capturing total customer engagementvalue. Journal of Service Research, 13(3), 297–310.

Kumar, V., Petersen, J. A., & Leone, R. P. (2007). How valuable is word of mouth?Harvard Business Review, 85(10), 139.

Kumar Roy, S., Lassar, W. M., & Butaney, G. T. (2014). The mediating impact ofstickiness and loyalty on word-of-mouth promotion of retail websites: aconsumer perspective. European Journal of Marketing, 48(9/10), 1828–1849.

Kuo, Y. F., & Feng, L. H. (2013). Relationships among community interactioncharacteristics, perceived benefits, community commitment, and oppositionalbrand loyalty in online brand communities. International Journal of InformationManagement, 33(6), 948–962.

Lanier, C., & Hampton, R. (2008). Consumer participation and experientialmarketing: understanding the relationship between co-creation and thefantasy life cycle. Advances in Consumer Research, 35(1), 44–48.

Lee, M. R., Yen, D. C., & Hsiao, C. Y. (2014). Understanding the perceived communityvalue of Facebook users. Computers in Human Behavior, 35, 350–358.

Li, D., Browne, G., & Wetherbe, J. (2006). Why do internet users stick with a specificwebsite? A relationship perspective. International Journal of ElectronicCommerce, 10(4), 105–141.

Lin, L., Hu, P. J. H., Sheng, O. R. L., & Lee, J. (2010). Is stickiness profitable forelectronic retailers? Communications of the ACM, 53(3), 132–136.

Lin, J. C. C. (2007). Online stickiness: its antecedents and effect on purchasingintention. Behaviour & Information Technology, 26(6), 507–516.

Lin, K. Y., & Lu, H. P. (2011). Why people use social networking sites: an empirical
study integrating network externalities and motivation theory. Computers inHuman Behavior, 27(3), 1152–1161.
Lu, H. P., & Lee, M. R. (2010). Demographic differences and the antecedents of blogstickiness. Online Information Review, 34(1), 21–38.

http://refhub.elsevier.com/S0268-4012(16)30219-5/sbref0005











































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































2 Inform

M

M

M

N

P

P

S

S

S

S

S

S

S


arketing Science Institute. (2008). . 2008–2010 Research Priorities (Vol. 2010)Cambridge, MA: Marketing Science Institute.

artins, C. S., & Patrício, L. (2013). Understanding participation in company socialnetworks. Journal of Service Management, 24(5), 567–587.

uniz Jr, A. M., & O’guinn, T. C. (2001). Brand community. Journal of ConsumerResearch, 27(4), 412–432.

unnally, J. C., Bernstein, I. H., & Berge, J. M. T. (1967). . Psychometric theory (Vol.226) New York: McGraw-Hill.

atterson, P., Yu, T., & De Ruyter, K. (2006). Understanding customer engagementin services. Advancing theory, maintaining relevance, proceedings of ANZMAC2006 conference, Brisbane, 4–6.

rahalad, C. K., & Ramaswamy, V. (2004). Co-creation experiences: the nextpractice in value creation. Journal of Interactive Marketing, 18(3), 5–14.

ashi, C. M. (2012). Customer engagement, buyer-seller relationships, and socialmedia. Management Decision, 50(2), 253–272.

chau, H. J., Muniz Jr, A. M., & Arnould, E. J. (2009). How brand communitypractices create value. Journal of Marketing, 73(5), 30–51.

edley, R. (2008). . Six theses on digital customer engagement in a troubled economy.Accessed February 2010 (21)[http://www.velocityreviews.com/forums/t391514-annual-online-usercustomer-engagement-survey-2006-a.html].

ingh, N., Zhao, H., & Hu, X. (2005). Analyzing the cultural content of web sites: across-national comparision of China, India, Japan, and US. InternationalMarketing Review, 22(2), 129–146.

tern, B. B. (1994). A revised communication model for advertising: multipledimensions of the source, the message, and the recipient. Journal of Advertising,23(2), 5–15.

tewart, D. W., & Pavlou, P. A. (2002). From consumer response to activeconsumer: measuring the effectiveness of interactive media. Journal of theAcademy of Marketing Science, 30(4), 376–396.

weeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: the developmentof a multiple item scale. Journal of Retailing, 77(2), 203–220.


Tax, S. S., Chandrashekaran, M., & Christiansen, T. (1993). Word-of-mouth inconsumer decision-making: an agenda for research. Journal of ConsumerSatisfaction, Dissatisfaction and Complaining Behavior, 6(10), 74–80.

Teeni, D. (2001). Review: a cognitive-affective model of organizationalcommunication for designing IT. MIS Quarterly, 25(2), 251–312.

Teo, H. H., Oh, L. B., Liu, C., & Wei, K. K. (2003). An empirical study of the effects ofinteractivity on web user attitude. International Journal of Human-ComputerStudies, 58(3), 281–305.

Van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., et al. (2010).Customer engagement behavior: theoretical foundations and researchdirections. Journal of Service Research, 13(3), 253–266.

Van der Heijden, H. (2004). User acceptance of hedonic information systems. MISQuarterly, 28(4), 695–704.

Vargo, S. L., & Lusch, R. F. (2008). Service-dominant logic: continuing the evolution.Journal of the Academy of Marketing Science, 36(1), 1–10.

Vivek, S.D. (2009). A scale of consumer engagement (Doctoral dissertation, TheUniversity of Alabama TUSCALOOSA).

Wu, J. J., Chen, Y. H., & Chung, Y. S. (2010). Trust factors influencing virtualcommunity members: a study of transaction communities. Journal of BusinessResearch, 63(9), 1025–1032.

Yüksel, A. (2007). Tourist shopping habitat: effects on emotions, shopping valueand behaviours. Tourism Management, 28(1), 58–69.

Yang, Z., & Peterson, R. T. (2004). Customer perceived value, satisfaction, andloyalty: the role of switching costs. Psychology & Marketing, 21(10), 799–822.

Yu, J., Zo, H., Choi, M. K., & Ciganek, A. P. (2013). User acceptance of location-basedsocial networking services: an extended perspective of perceived value. OnlineInformation Review, 37(5), 711–730.

Zhou, Z., Wu, J. P., Zhang, Q., & Xu, S. (2013). Transforming visitors into members inonline brand communities: evidence from China. Journal of Business Research,66(12), 2438–2443.

Zott, C., Amit, R., & Donlevy, J. (2000). Strategies for value creation in e-commerce:best practice in Europe. European Management Journal, 18(5), 463–475.














































































































































































































































































































































































































































































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