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Whydosatisfiedcustomersdefect?Acloserlookatthesimultaneouseffectsofswitchingbarriersandswitchinginducements...

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Journal of Service Theory and PracticeWhy do satisfied customers defect? A closer look at the simultaneous effects of switching barriers andinducements on customer loyaltyStephanie Hui-Wen Chuah Philipp A. Rauschnabel Malliga Marimuthu Ramayah T. Bang Nguyen

Article information:To cite this document:Stephanie Hui-Wen Chuah Philipp A. Rauschnabel Malliga Marimuthu Ramayah T. Bang Nguyen , (2017)," Why do satisfiedcustomers defect? A closer look at the simultaneous effects of switching barriers and inducements on customer loyalty ",Journal of Service Theory and Practice, Vol. 27 Iss 3 pp. -Permanent link to this document:http://dx.doi.org/10.1108/JSTP-05-2016-0107

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Why do satisfied customers defect? A closer look at the simultaneous

effects of switching barriers and inducements on customer loyalty

Abstract

Purpose - The purpose of this paper is to go beyond satisfaction as an indicator of

customer loyalty and proposes a holistic model of service switching in a mobile

Internet setting. The model, which reflects both barriers and inducements of

switching, is developed based on the “mooring” and “pull” concepts in the migration

literature.

Design/methodology/approach - Focusing on Generation Y mobile Internet

subscribers, the study analyzed a total of 417 usable questionnaire responses. Partial

least squares structural equation modeling was used to test the research model.

Findings - The results show that first, satisfaction and switching barriers (i.e., a focal

firm’s marketing innovation initiatives, switching costs, inertia, and local network

effects) are positively related to customer loyalty; second, switching barriers has a

stronger influence on customer loyalty compared with satisfaction; third, switching

inducements (i.e., competitors’ marketing innovation initiatives, alternative

attractiveness, variety-seeking tendencies, and consumers’ susceptibility to social

reference group influence) is negatively related to customer loyalty and the

relationship is weaker when perceived switching barriers is high.

Originality/value - This study empirically validates multidimensional scales of

switching barriers and inducements from a more nuanced perspective, and specifies

them as reflective-formative type II models. This study is among the first to use

opposing dimensions to measure switching barriers and its counterpart. Hence, it

illustrates how the two contrasting mechanisms can coexist in the minds of mobile

Internet subscribers.

Keywords Customer satisfaction, Switching barriers, Switching inducements,

Customer loyalty, Generation Y, Mobile Internet

Paper type Research paper

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1. Introduction

Customer satisfaction has long been regarded as a key determinant of customer

loyalty and repurchase intentions (Buoye, 2016; Hallowell, 1996; Kandampully and

Suhartanto, 2000). However, this conventional belief has increasingly been

challenged as recent empirical studies have shown that satisfaction does not always

translate into loyalty and dissatisfaction does not result in switching (Li, 2015;

Sánchez‐García et al., 2012; Wu, 2011). For example, Reichheld et al. (2000) found

that 60 – 80% of customers who defect stated that they were satisfied or very satisfied

with their former suppliers. Bennett and Rundle-Thiele (2004) reported that among

the customers of Australia’s four largest banks an estimated 70% intended to

repurchase from their current bank, despite being dissatisfied. Furthermore, meta-

analysis of customer satisfaction research revealed that satisfaction explains less than

25% of the variance in repurchase behavior (Szymanski and Henard, 2001). Recent

research (e.g., Haumann et al., 2014; Kumar et al., 2013), however, shows that merely

satisfying customers is not adequate to ensure both sustainable and profitable

customer relationships in today’s turbulent and competitive global marketplace.

A growing number of empirical studies have investigated the role of switching

barriers as a complement construct for customer satisfaction in an attempt to explain

customer loyalty (e.g., Ghazali et al., 2016; Kim et al., 2004; Qiu et al., 2015).

Switching barriers (e.g., psychological, physical, and economic) make it difficult or

costly for customers to change providers (Jones et al., 2000; Patterson and Smith,

2003). Despite its importance, few studies have simultaneously examined switching

barriers and inducements (the counterpart of switching barriers) and their effects on

customer loyalty. This gap is especially surprising considering that a holistic

conceptualization of service switching requires a greater understanding of factors that

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support the switching action and those that undermine it (Zikiene and Bakanauskas,

2009). According to Colgate and Lang (2001), customer switching is a complex

phenomenon; customers have to undergo a cognitive process (the so-called switching

dilemma) that requires them to determine whether they should stay with or leave a

service provider. Rooted in this notion, we argue that switching barriers and

inducements can coexist in the minds of consumers, and that the absence of one may

result in a biased estimation of consumer behavior, resulting in erroneous conclusions

and ineffective managerial decisions.

To provide a more comprehensive view of customer switching behavior, and

to fill the foregoing gaps, this study proposes a dual model of service switching to

explain customer loyalty in the context of mobile Internet service. The dual model,

which includes barriers and inducements of switching, is based on the “mooring” and

“pull” concepts in migration literature. Specifically, the push-pull-mooring (PPM)

migration model underscores the importance of the pull variables (positive factors at

the destination that attract people), such as the attractiveness of alternatives, and the

mooring variables (personal and social factors that hamper the migration decision),

which are based on attitudes towards switching, subjective norms, switching costs,

prior switching behavior, and variety-seeking, in influencing consumers’ switching

behavior (Bansal et al., 2005).

Hence, this study advances our current understanding of mobile Internet

subscriber switching behavior by explicating the components of switching barriers

and inducements and by linking them to a customer loyalty framework. The study

findings may also shed light on how the two contrasting factors simultaneously

determine customer loyalty towards mobile service providers. In addition, this study

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seeks to understand the interrelationships of these factors by examining the

moderating effect of switching barriers.

This study is conducted in the context of mobile Internet services. This

industry segment, one of the fastest-growing in mobile telecommunications, is

forecast to reach 70% penetration by 2020 (GSMA, 2015). The spectacular growth in

mobile Internet services has been driven not only by continuous advancement in

mobile networks but also by competition among mobile service providers who offer

increasingly affordable smartphone and data plans. In addition, new mobile

applications and mobile plans, especially “Friends and Family Plans” are changing

consumer behavior. That is, innovation and local network effects play a more

important role in consumers’ decision making than ever before. Hence, induced by

constant technology and service innovations from rival companies, consumers

continuously seek variety and tend to switch service providers frequently (Han et al.,

2015). According to Keaveney and Parthasarathy (2001), customer churn can be

particularly damaging for subscription-based service firms, such as mobile

telecommunications, where customers commit to ongoing relationships and

continuous service delivery. The impact of a high customer churn rate on a service

provider is a decrease in profit levels, and a loss of price premiums, future revenue

streams, referrals from existing customers, and market share (Ahn et al., 2006). For

example, an annual churn rate of 15 – 30% can cost mobile service providers up to a

$10 billion in revenue (Ascarza et al., 2016). Clearly, mobile Internet service

providers have a lot to gain by minimizing customer churn and maximizing customer

loyalty, which makes this segment an excellent context for the present study.

This paper is organized as follows. First, we review the relevant literature and

develop the research model. This model is empirically tested using a sample of

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Generation Y mobile Internet subscribers and structural equation modelling. From the

results, we derive implications for theory and practice, and then discuss avenues for

further research.

2. Literature review, conceptual background and research hypotheses

2.1 Customer satisfaction and customer loyalty

Because of their impact on financial performance (Sun and Kim, 2013),

customer satisfaction and loyalty are crucially important to company management.

From a cognitive psychology view, customer satisfaction arises from consumers’

subjective perceptions of post-consumption performance against their prior

expectations of performance (Kim et al., 2015). The expectation disconfirmation

paradigm (Oliver, 1981) proposes that customer satisfaction arises in situations where

expectations are met, or even exceeded (positively disconfirming/disconfirming)

(Qian et al., 2015). Because expectations differ among consumers, customer

satisfaction is a highly subjective concept, and is the result of cumulative service

evaluations (Kaura et al., 2015). Following this stream of research, we define

customer satisfaction as a customer’s overall assessment of his or her mobile service

provider to date (Keiningham et al., 2014).

As a fundamental concept of marketing, customer satisfaction is widely

recognized as a key intangible asset, and one of the best indicators for future profits of

a firm as it is positively associated with customer loyalty (Kim et al., 2015; Luo et al.,

2010; Ryding, 2010). Customer loyalty can be described as “the strength of a

customer’s dispositional attachment to a brand (or a service provider) and his/her

intent to rebuy the brand (or repatronize the service provider) consistently in the

future” (Pan et al., 2012, p.151). Besides driving higher repurchase intentions, loyal

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customers are more likely to pay premium prices, make additional purchases, and

bring referrals through favorable word-of-mouth (Haumann et al., 2014; Ryding,

2010; Qiu et al., 2015). In the context of mobile services, empirical studies show that

customer satisfaction leads to favorable post-purchase behaviors, such as increased

customer loyalty, decreased customer complaints and lower switching intentions

(Calvo-Porral et al., 2015; Morgeson III et al., 2015). Replicating the established

findings that customer satisfaction leads to customer loyalty, we hypothesize:

H1. Customer satisfaction is positively related to customer loyalty.

2.2 Switching barriers and customer loyalty

Switching barriers refers to the difficulties encountered by (dissatisfied)

customers to make future purchases with another company. Prior research suggests a

variety of switching barriers: financial (e.g., early termination fees); social (e.g., loss

of personal bond or friendship with an existing provider); and psychological (e.g.,

perceived risk and uncertainty about a new provider) (Blut et al., 2015; Jones et al.,

2007; Kim et al., 2004). As a strategic tool for managing customer retention,

switching barriers have received considerable scholarly and managerial attention.

While it is an established finding that switching barriers are an effective tool

for marketers, the literature still lacks consensus in terms of its dimensions and

measurements (Vázquez‐Casielles et al., 2009). For example, some researchers argue

that switching barriers represent a multidimensional construct (Han et al., 2011; Li et

al., 2015), but others see it as unidimensional (Kim et al., 2015; Liu et al., 2011).

Although the types of switching barriers might vary across industries (Han et al.,

2011; Qiu et al., 2015), researchers generally agree that switching barriers can be

classified into two categories, namely positive and negative. Positive switching

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barriers are affirmative reasons for consumers to retain an existing relationship, and

are created primarily through service providers’ investment in the relationships with

their customers (Han and Hyun, 2012; Qiu et al., 2015). Examples of this type of

barrier include the existence of an affective bond with a service provider, special

discounts and unique benefits (Vázquez‐Casielles et al., 2009). In contrast, negative

switching barriers represent the negative reasons that compel customers to retain a

relationship. This kind of barrier encompasses the monetary (e.g., setup costs) and

non-monetary (e.g., evaluation or learning costs) sacrifices incurred when customers

switch service providers (Han and Hyun, 2012; Qiu et al., 2015; Vázquez‐Casielles et

al., 2009).

As switching barriers are impacted by the study context, unified models

provide only limited explanatory power in particular contexts. In addition, knowing

that switching barriers reduce the likelihood of switching does not provide specific

managerial guidance. Therefore, in this research, we conceptualize switching barriers

as multi-dimensional in a reflective first order-formative second order type II model

(see Jarvis et al., 2003, for a discussion). Building on a comprehensive literature

review, we identified four factors, namely (1) a focal firm’s marketing innovative

initiatives, (2) switching costs, (3) inertia, and (4) local network effects.

A focal firm’s marketing innovative initiative (FFMII) is defined here as

customers’ perceptions about the capability of a focal service provider to engage in

marketing innovation initiatives; that is, the introduction of new products, the use of

new pricing strategies, and the adoption of new methods for promoting and selling the

firm’s products (adapted from Hult et al., 2004). In the context of mobile services,

perceived innovativeness of a service provider and its marketing activities have been

shown to significantly reduce customers’ propensity to switch (Malhotra and

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Malhotra, 2013; Wirtz et al., 2015). Thus, customers’ perceptions of FFMII could

form formidable barriers to defection.

Switching costs refer to the monetary (e.g., fees to break a contract, loss of

reward points) and non-monetary costs (e.g., time, effort, and uncertainty in using a

new service provider) customers face when switching service providers (adapted from

Nagengast et al., 2014). For example, Shin et al. (2010) found that perceived

switching costs could reduce the number of customers that switch from mobile

network operators to mobile virtual network operators.

Inertia has been described as a non-conscious process in which a customer

repeatedly purchases the same brand, passively and without much contemplation

(Huang and Yu, 1999). Inert-driven customers make repeat purchases not because

they are emotionally attached to a brand, but rather because of the time saved, the

perceived indifference to choice, the familiarity with the brand, and the reduction of

perceived risk (Bloemer and Kasper, 1995). Inertia exists in a continuous purchasing

setting (e.g., mobile services) and it produces a behavioral lock-in effect that prevents

customers from switching to other service providers (D’Alessandro et al., 2012).

Local network effects refer to the situation where a large number of consumers’

social subset (e.g., family, friends, and colleagues) use the same mobile service

provider (adapted from Birke and Swann, 2010). The benefits stemming from local

network effects are typically pecuniary in nature (e.g., free or cheaper calls and SMSs)

(Corrocher and Zirulia, 2009). Research shows that local network effects create soft

lock-in and reduce customers’ inclination to switch to another mobile service provider

(e.g., Czajkowski and Sobolewski, 2015; Malhotra and Malhotra, 2013).

While the conceptualization of switching barriers in this research is novel,

prior research linked different conceptualizations of switching barriers to loyalty in a

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variety of service settings, such as airlines (Chang and Chen, 2007), hospitality (Qiu

et al., 2015), mobile (Liu et al., 2011), and online portals (Kim et al., 2015).

Replicating these findings with a context-specific, multi-dimensional

conceptualization, we hypothesize that:

H2. Switching barriers are positively related to customer loyalty.

2.3 Switching inducements and customer loyalty

Switching inducements refer to any factors that stimulate consumers’ desire to

switch from one service provider to another (Goode and Harris, 2007). However, the

extant service switching literature provides limited insights into switching

inducements due to its focus on general alternative attractiveness (e.g., better service

quality, lower prices, more choices, and quicker delivery) (Bansal et al., 2005; Goode

and Harris, 2007). However, other factors such as perceived innovativeness of

alternatives (Lee et al., 2015), variety-seeking tendencies (Jung and Yoon, 2012), and

reference group influence (Lee and Murphy, 2005) might also precipitate the

switching action. Thus, to capture the conceptual richness of the construct, the present

study conceptualizes switching inducements as a higher-order formative construct

made up of four first-order dimensions that refer to (1) competitors’ marketing

innovation initiatives, (2) the attractiveness of alternatives, (3) variety-seeking

tendencies, and (4) consumers’ susceptibility to social reference group influence.

Competitors’ marketing innovation initiatives (CMII) is defined as customers’

perceptions about the capability of alternative mobile service providers to engage in

marketing innovation initiatives, that is, the introduction of new products, the use of

new pricing strategies, the adoption of new methods for promoting, and selling the

firm’s products (adapted from Hult et al., 2004). In today’s fiercely competitive

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marketplace, competitors tend to advertise the advantages and strengths of their new

services to prospective customers (Prins and Verhoef, 2007). Exposure to this kind of

advertising may induce customers to change service providers as they will be aware

of the potential benefits associated with switching (Polo and Sesé, 2009). Empirical

evidence shows that perceived distinctiveness of competitors’ marketing mix

strategies decrease customers’ preference towards their existing service providers, and

hence persuade them to switch to another service provider (Shum, 2004; Woodside

and Wilson, 1994).

Alternative attractiveness refers to customers’ perceptions that they have a

viable alternative to their existing service provider (Jones et al., 2000). Ghazali et al.

(2016) argued that customers’ evaluation of alternative attractiveness is affected by

the existence of alternatives, the degree of difference among alternatives, and the

switching costs between alternatives. Furthermore, Lee et al. (2008) observed that

when market competition increases, the possibility of emerging alternatives can be

high and customers are more likely to defect.

Variety-seeking is defined as “the tendency of individuals to seek diversity in

their choices of services or goods” (Kahn, 1995, p. 139), and considered a key

determinant in service switching (Rajendran, 2014). Kahn (1995) developed a unified

framework exploring why consumers seek variety, consisting of three categories: (1)

satiation or stimulation, (2) external situations (e.g., price changes, the launch of new

products, and marketing mix elements), and (3) future preference uncertainty.

Previous empirical research has shown that variety-seeking negatively affects

customer loyalty (e.g., repurchase intention, revisit intention, share of visits) in a

variety of contexts, including automotive services (Shirin and Puth, 2011), restaurants

(Kim et al., 2010), and tourism services (Bigné et al., 2009).

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Consumers’ susceptibility to social reference group influence (CSSRGI) refers

to the willingness of individuals to accept the expectations or suggestions of reference

group members (e.g., family, friends, and colleagues) with regard to switching

decisions (adapted from Bearden et al., 1989). For example, Liang et al. (2013)

discovered that, in a collective society such as China, because mobile subscribers

tended to follow group norms, their switching behavior was likely to be influenced by

their friends and family. Also, Rauschnabel et al. (2015) showed that normative

expectations increase the likelihood that consumers intend to adopt future

technologies. Likewise, Anacom (2006) reported that one-third of mobile subscribers

in Portugal switched because most of their friends and family members subscribed to

a new service provider.

Based on the foregoing discussion, we hypothesize that:

H3. Switching inducements are negatively related to customer loyalty.

In H1 through H3, we propose three direct effects for customer satisfaction,

switching barriers, and switching inducements on customer loyalty. In H4 and H5, we

propose that the role of switching barriers goes beyond its direct effect. That is, we

propose that switching barriers also impact the effect from customer satisfaction and

switching inducements on customer loyalty. In other words, we propose in H4 and H5

that switching barriers, besides their direct effect, also moderate the effect of customer

satisfaction (H4) and switching inducements (H5).

2.4 Moderating effect of switching barriers on the relationship between customer

satisfaction and customer loyalty

Although customer satisfaction has long been considered as a prerequisite for

customer loyalty (Mittal and Lassar, 1998), Abdullah et al. (2000) argued that

customer satisfaction is not a surrogate for customer loyalty. In particular, prior

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research has suggested several boundary conditions in which the link to loyalty is

particularly strong or weak (e.g., Anderson and Swaminathan, 2011; Homburg and

Giering, 2001; Walsh et al., 2008).

Loyal customers are not necessarily satisfied, even though most satisfied

customers tend to be loyal (Castañeda, 2011). In order to explain the increasingly

complex phenomenon of customer retention, Fornell (1992) added switching barriers

to customer satisfaction-loyalty function. Mobile service providers have long

recognized that switching barriers are a powerful defensive marketing tool for

maintaining customer retention when service issues might result in defection (Chebat

et al., 2011; Jones et al., 2000). Therefore, apart from functioning as a complement to

customer satisfaction, switching barriers also play an adjustment role in the

satisfaction-loyalty link (Kim et al., 2004). Research by Han et al. (2011) confirmed

that high switching barriers may lock dissatisfied customers into a relationship, even

after a poor service experience.

Previous studies have also confirmed that the positive relationship between

satisfaction and loyalty is contingent on switching barriers and that the relationship is

weaker under the condition of high switching barriers (e.g., Han et al., 2009; Kim et

al., 2015; Jones et al., 2000; Qiu et al., 2015). In the mobile services setting, Lee et al.

(2001) revealed that high switching costs significantly moderate the satisfaction-

loyalty association for the economy and standard customer groups. That is, although

customers are not fully satisfied, they will not switch to another service provider if the

perceived switching barriers is high. Thus, we propose that:

H4. The relationship between customer satisfaction and customer loyalty is moderated by switching barriers, such that the higher the switching barriers, the weaker the positive effect.

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2.5 Moderating effect of switching barriers on the relationship between switching

inducements and customer loyalty

While the moderating effect of switching barriers on the relationship between

customer satisfaction and customer loyalty has been empirically validated in previous

studies, few studies have examined the moderating effect of switching barriers on the

link between switching inducements and customer loyalty. Switching barriers may not

only act as an ‘insurance-like’ policy against customer defection when dissatisfaction

occurs (Xie et al., 2015), but may also serve as a buffer against the negative impact of

switching inducements on customer loyalty (Ghazali et al., 2016). For example,

various switching inducements (e.g., CMII, alternative attractiveness, variety-seeking

tendencies, and CSSRGI) may entice mobile subscribers to switch service providers.

However, the likelihood that customers will switch should diminish if the perceived

switching barriers is high. Thus, we hypothesize that:

H5. The relationship between switching inducements and customer loyalty is moderated by switching barriers, such that the higher the switching barriers, the weaker the negative effect.

3. Research methodology

3.1 Setting and sample

We tested the research model with a sample of Gen Y post-paid mobile

Internet subscribers in Malaysia. The competition in the Malaysian mobile

telecommunications industry is intense and mobile service providers suffer from a

high churn rate (Hrin, 2015). A recent study by Frost & Sullivan indicated that 83%

of mobile customers in Malaysia intended to switch to another service provider

(Digital News Asia, 2015).

There are several reasons for choosing Gen Y in the post-paid segment. First,

compared with their predecessors (Generation X and Baby Boomers), Gen Ys (18 –

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34 years) are more technologically savvy (Bruwer et al., 2011; Nusair et al., 2013;

Parment, 2011), are often early adopters of new technologies, and are extensive users

of the Internet and mobile services (Kumar and Lim, 2008). Despite being in the main

stream of mobile Internet subscribers (Kumar and Lim, 2008), Gen Ys are more

unpredictable and less brand-loyal than their older counterparts (Generation X and

Baby Boomers), making it difficult for marketers to retain them as customers (Mitsis

and Leckie, 2016.). Second, the average revenue per user (ARPU) of post-paid users

is three times that of the prepaid category (Oxford Business Group, 2012). Hence, the

consequences of losing post-paid customers are more severe than prepaid customers.

From this, the Malaysian Gen Y post-paid segment offered an appropriate context for

studying customer switching and loyalty behavior.

3.2 Data collection

We collected data through paper-based, self-administered questionnaires.

Participants were recruited through purposive sampling with potential respondents

approached by trained surveyors in shopping malls, colleges, and universities located

in the cities of Kuala Lumpur, Penang, and Johor Bahru. Potential respondents were

first qualified to ensure they subscribed to post-paid mobile Internet plans and were in

the age group of 18 – 34 years (Gen Y). Potential respondents who then agreed to

participate were given the questionnaires to fill in. A stationery gift set was given to

participants as a token of appreciation. Over a four-month data collection period, 470

questionnaires were distributed and 452 responses were received, generating a

response rate of 96.2%. After removing the cases of excessive missing values and

straight lining, a total of 417 usable responses were available for data analysis.

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Our sample consists of a wide range of Gen Y respondents with an average

age of 26 (SD = 4.659) years. About half of them (50.8%) were females; 70.3% had

earned a bachelor’s degree or higher; 48.2% were professionals, managers, executives,

or businesspersons; and 43.4% earned an annual household income of MYR 36,000

($8,863.61 USD) or above. Most, 71.2%, of the sample had been using the a mobile

Internet service for less than three years, and 50.4% reported they spent, on average,

more than 10 hours per week on mobile Internet. More than half (56.4%) of the

respondents had been customers of their present mobile service providers for more

than three years. Approximately 73% of the respondents reported having a monthly

mobile phone bill of less than MYR 150 ($36.93 USD). In addition, 80.1% of the

respondents had a principal line and 70.5% paid their own mobile phone bills.

3.3 Measures

Scale measures used in this study were all adapted from previous studies,

making only minor changes in the wording to suit the target context. The

measurement content were validated with the help of industry and academic experts.

Switching barriers was operationalized with four dimensions, consisting of (1)

FFMI, which was measured with three items adapted from Ouellet (2006); (2)

switching costs, which was operationalized using six items adapted from Aydin and

Özer (2005) and Burnham et al. (2003); (3) inertia, which was measured with three

items adapted from Wu (2011) and Yanamandram and White (2010); and (4) local

network effects, which used four items adapted from Malhotra and Malhotra (2013)

and Wang et al. (2008).

Switching inducements was conceptualized with four dimensions: (1) CMII

was operationalized using three items adapted from Ouellet (2006); (2) alternative

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attractiveness was measured with three items adapted from Wu (2011); (3) variety-

seeking tendencies was measured with five items adapted from Baumgartner and

Steenkamp (1996) and Steenkamp and Baumgartner (1995); and (4) CSSRGI, was

measured with three items adapted from Tarus and Rabach (2013) and Wangenheim

and Bayon (2004).

Customer satisfaction was measured with four items adapted from Hennig-

Thurau (2004) and customer loyalty was measured using five items adapted from

Aydin and Özer (2005).

As the data of this study were collected from a single source (mobile Internet

subscribers) via self-reported questionnaires, different scale endpoints were used to

assess the predictor and criterion variables to minimize common method bias

(Podsakoff et al., 2003). We employed a 5-point semantic differential scale (ranging

from 1 = not at all unique/creative/trendy to 5 = extremely unique/creative/trendy) to

assess customer perceptions of FFMII and CMII; and a 5-point Likert scale (ranging

from 1 = strongly disagree to 5 = strongly agree) to assess their level/perceptions of

satisfaction, switching costs, inertia, local network effects, alternative attractiveness,

variety-seeking tendencies, and susceptibility of social reference group influence.

Finally, we adopted a 7-point Likert scale (ranging from 1 = strongly disagree to 7 =

strongly agree) to assess customer loyalty. In addition, we guaranteed anonymity,

highlighted the importance of honest answers, and the academic background of this

research. These steps are often discussed as reducing the risk of common method bias

in surveys (Chang et al., 2010; MacKenzie and Podsakoff, 2012).

4. Data analysis and results

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To analyze the research model, we employed partial least squares structural

equation modeling (PLS-SEM) using SmartPLS 3.0 software (Ringle et al., 2015).

PLS is preferred to the covariance-based SEM (CB-SEM) approach for this study

because PLS can handle both reflective and formative constructs, compared with CB-

SEM, which requires indicators and constructs to be modeled reflectively (Urbach and

Ahlemann, 2010). Furthermore, compared with CB-SEM, which is more confirmed-

oriented, PLS is a prediction-oriented variance-based approach that focuses on

endogenous targets in the model and aims to maximize their explained variance (i.e.,

their R2 value) (Hair et al, 2012). Given the prediction-oriented nature of this study,

that is, to assess how well the endogenous variable (customer loyalty) can be

predicted by those exogenous variables (customer satisfaction, switching barriers, and

switching inducements), PLS was chosen. We tested our proposed research model

using a two-step approach in which the measurement model was examined first,

followed by the structural model (Anderson and Gerbing, 1988).

4.1 Common method bias (CMB)

Although we included several ex-ante procedures to reduce the risk of CMB,

we also applied a series of tests to assess the threat of substantial common method

bias. First, we conducted the Harman’s single factor test to determine the existence of

CMB. Podsakoff et al. (2003, p. 889) pointed out that if there is a critical level of

CMB, “(a) a single factor will emerge from the factor analysis (b) one general factor

will account for the majority of the covariance among the measures.” With a result of

29.2%, the first factor did not account for a substantial amount of common method

variance. Second, we assessed the principal constructs inter-correlations in the

correlation matrix. CMB is evidenced by substantially high correlations (r > 0.90)

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(Bagozzi et al., 1991). As Table II shows, the highest inter-construct correlation is

0.669. Third, in situation with substantial common method variance, correlations

between unrelated variables are high. Also in Table II, correlations between

theoretically unrelated variables (e.g., FFMII and CSSRGI) are insignificant and

negligible (-.004). In data with substantial common method variance, these

correlations would be higher. In sum, the above tests provided evidence that CMB is

not a major issue in this study.

4.2 Measurement model analysis: First-order constructs level

The measurement model for the first-order constructs was assessed using

convergent validity, discriminant validity, and reliability. As illustrated in Table I, all

the first-order constructs’ loadings surpassed the minimum required cut-off value of

0.40 (Hair et al., 2013), and the average variance extracted (AVE) of all exceeded the

threshold value of 0.50 (Fornell and Larcker, 1981), denoting a sufficient level of

convergent validity. Further, the composite reliability of all constructs was well above

the suggested threshold of 0.708 (Hair et al., 2013), providing supportive evidence for

construct reliability. In addition, the discriminant validity of the measured constructs

was examined by comparing the square root of the AVE constructs with the inter-

construct correlations (Fornell and Larcker, 1981). As depicted in Table II, the square

root of the AVE for each construct was greater than its correlations with other

constructs, indicating discriminant validity had been achieved.

=== PLACE TABLE I HERE ===

=== PLACE TABLE II HERE ===

4.3 Measurement model analysis: Second-order construct level

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We modeled switching barriers and switching inducement as formative

second-order constructs that consist of four first-order reflective constructs,

respectively. Switching barriers are expected to be caused by customers’ perceptions

of FFMII, switching costs, inertia, and local network effects. In contrast, switching

inducements are expected to be caused by customers’ perceptions of CMII, alternative

attractiveness, variety-seeking tendencies, and their susceptibility to social reference

group influence.

First, we performed a collinearity test on the constructs underlying switching

barriers and inducements. As shown in Table III, the variance inflation factor (VIF)

values for each of the underlying constructs are lower than the threshold of 3.3

(Diamantopoulous and Siguaw, 2006). This implies that each of the switching-related

constituents is independent from one another, distinctly forming customers’

perceptions of switching barriers and inducements.

Next, we assessed the weight of each of the first-order constructs on the

designated second-order constructs by using the repeated indicator approach. The

advantage of the repeated indicator approach lies in its ability to estimate all the

indicators in the lower- and higher-order constructs simultaneously, thus avoiding

interpretational confounding (Becker et al., 2012). Furthermore, a bootstrapping

procedure with 5,000 resamples (Hair et al., 2013) was applied to assess the

significance of the weight for each of the constructs underlying switching barriers and

inducements. The bootstrapping results showed that all the first-order constructs were

significantly related to corresponding second-order constructs (as illustrated in Table

III). Thus, we can conclude that both switching barriers and switching inducements

are a reflective-formative type II model.

=== PLACE TABLE III HERE ===

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4.4 Assessment of the structural model

Upon establishing the measurement model, the analysis then shifted to the

structural model evaluation. Prior to assessing the structural model, a collinearity test

assessed the presence of highly correlated constructs. The results showed that the VIF

values of all constructs ranged from 1.108 to 2.393, which is well below the suggested

threshold of 3.3 (Diamantopoulous and Siguaw, 2006), indicating the absence of

substantial amounts of multicollinearity.

4.5 Hypothesis Testing

To assess the hypothesized relationships between the constructs, we applied a

bootstrapping sample of 5,000. We first examined the direct effects, followed by

moderating effects. In line with our theorizing, customer satisfaction (β = 0.301, t =

7.919, p < 0.001) and switching barriers (β = 0.390, t = 9.926, p < 0.001) positively

influenced customer loyalty. In contrast, switching inducements (β = -0.288, t = 8.738,

p < 0.001) was found to be negatively related to customer loyalty. Thus, H1, H2, and

H3 were supported. The results further revealed that these three exogenous constructs

collectively explained 67.5% of the variance in the endogenous construct (i.e.,

customer loyalty). In order to determine whether these three exogenous constructs

have substantial impact on the endogenous construct, we tested their respective effect

size (f2) (Hair et al., 2013). In determining the magnitude of the effect size, we

employed the Cohen’s (1988) guidelines, in which f2 values of 0.02, 0.15, and 0.35

represent small, medium, and large effects, respectively. The results indicated that

customer satisfaction (f2 = 0.172), switching barriers (f2 = 0.253), and switching

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inducements (f2 = 0.166) had medium effects on customer loyalty. In particular, the

effect size of switching inducements was situated between medium to large.

After testing the direct effects, we examined the moderation hypotheses using

the two-stage approach recommended by Henseler and Fassott (2010). Contrary to our

prediction, the moderating effect of switching barriers on the relationship between

customer satisfaction and customer loyalty did not reach significance (β = -0.036, t =

1.587, p > 0.05). In support of H5, the moderating effect of switching barriers on the

relationship between switching inducements and customer loyalty was significant

with a small effect size (β = 0.064, t = 2.835, p < 0.01, f2 of 0.02)1. However, a small

f2 does not necessarily signify an unimportant effect (Limayem et al., 2001). As Chin

et al. (2003, p. 211) stated, “even a small interaction effect can be meaningful under

extreme moderating conditions, if the resulting beta changes are meaningful, then it is

important to take these conditions into account.”

In the case of this study, the results in Figure 1a give approximately equal

standardized beta for customer satisfaction (0.301), switching barriers (0.390), and

switching inducements (-0.288) with an R2 of 0.675 for customer loyalty. The

inclusion of the interacting effect (Figure 1b) depicts a beta of 0.064 increasing the R2

for customer loyalty to 0.680. Thus, these results imply that one standard deviation

increase in switching barriers would not only impact customer loyalty directly by

0.369, but it would also decrease the impact of switching inducements from -0.273 to

practically zero. To further elaborate the moderating phenomenon of switching

barriers, the pattern of the relationship between switching inducements and customer

loyalty was plotted at both high and low switching barriers (see Figure 2). The slope

1 As shown the moderation effect in this case is only marginally significant and the effect is not very large. While the results still confirm the hypothesis, this should be considered in the interpretation of the findings.

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for high switching barriers is flatter compared to low switching barriers, suggesting

high switching barriers mitigate the negative impact of switching inducements on

customer loyalty.

=== PLACE FIGURE 1 HERE ===

=== PLACE FIGURE 2 HERE ===

Finally, we examined the predictive capacity of the model by checking Stone-

Geisser’s Q-square value. The Q2 value can be obtained by applying the blindfolding

procedure for omission distance, preferably between 5 and 10 (Hair et al., 2013). By

using an omission distance of seven, we found that customer loyalty had a Q2 value of

0.495, which was greater than zero as propagated by Fornell and Cha (1994). Thus,

we can conclude that the model has predictive relevance.

Figure 3 graphically depicts the results of hypotheses testing.

=== PLACE FIGURE 3 HERE ===

5. Discussion

The results of our study are congruent with some previous studies, which

found a positive association between customer satisfaction and customer loyalty (e.g.,

Kim et al., 2015; Martínez and Del Bosque, 2013), particularly in the context of

mobile services (e.g., Calvo-Porral et al., 2015; Morgeson III et al., 2015). While

customer satisfaction remains as a significant predictor of loyalty, we found that

switching barriers exerted an even stronger influence on customer loyalty. This

finding is in line with prior studies (e.g., Burnham et al., 2003; Ghazali et al., 2016,

Yang, 2015) but is in conflict with Kim et al. (2004). Their study of Korean mobile

telecommunications services reported that customer loyalty is less influenced by

switching barriers compared with satisfaction. A possible explanation for this

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variation is that as intense competition creates more alternatives for consumers to

choose from, consumers tend to be more demanding, more difficult to satisfy, and less

loyal than ever before. Without the glue of switching barriers, even a well-designed

customer satisfaction program will fail to achieve its retention goals. Since

satisfaction appears to be a necessary but insufficient condition for retaining

customers, it is deemed as a “competitive necessity” rather than a “competitive

weapon” for business success and survival. As Zhang et al. (2014) noted, when the

level of customer satisfaction is analogous, customer loyalty is significantly

dependent on the magnitude of the switching barriers.

The present study has empirically validated that switching barriers are a

formative second-order construct that consists of four first-order reflective constructs,

namely FFMII, switching costs, inertia, and local network effects in the context of

mobile Internet service. Our results show that switching costs are the most salient

contributor to switching barriers index with the highest weight, followed by FFMII,

inertia, and local network effects. This information should help mobile service

providers build more solid exit barriers in their ongoing efforts to ensure customer

loyalty. These retention efforts will ultimately lead to increased customer lifetime and

profitability.

However, while satisfaction and switching barriers were found to have a

positive impact on customer loyalty, we also found that switching inducements

exerted a negative impact on customer loyalty. This finding suggests that to

accurately predict the loyalty of mobile Internet subscribers, all the affective-

(satisfaction), constraint- (switching barriers), and temptation-based components

(switching inducements) must be taken into consideration. The result is somewhat

consistent with previous studies, which reported that switching inducement attributes

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such as alternative attractiveness (Zhang et al., 2014) and variety-seeking tendencies

(Sánchez‐García et al., 2012) had negative impacts on customer loyalty. However, our

study differs from prior research in that it validated switching inducements as a

formative second-order construct that consists of four first-order reflective constructs,

namely CMII, alternative attractiveness, variety-seeking tendencies, and CSSRGI. It

is also worth noting that variety-seeking tendencies is the most significant contributor

to switching inducement index with the highest weight. This is followed by

alternative attractiveness, CMII, and CSSRGI. These findings imply that

operationalizing switching inducements, as a single factor of alternative attractiveness,

is too simplistic and unable to capture the holistic attributes of the construct. An

oversimplification of switching inducements may lead to the implementation of less

than effective strategies to manage customer churn.

Interestingly, we found that switching barriers has a significant moderating

effect on the switching inducements-loyalty link but not on the satisfaction-loyalty

link. These findings suggest that switching barriers plays a “buffer” role in offsetting

the adverse impact of high switching inducements on loyalty, rather than a “protective”

role. It reduces the sensitivity of (dis)loyalty to (dis)satisfaction when negative

incidents occur. This can be explained by both cost-benefit and prospect theories,

which posit that customers would employ net utility (perceived benefits vis-à-vis

perceived costs of switching) when making a loyalty/switching decision. When the

level of satisfaction is low, the possibility of obtaining more satisfactory services from

another provider is likely to be high. In the competitive mobile services market,

consumers continually receive incentives to switch providers (Malhotra and Malhotra,

2013), thus increasing the net utility of the change. In fact, a recent study by Verizon

(2014) revealed that Gen Y consumers have a low tolerance for problems with service

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quality. As such, dissatisfied customers, especially Gen Ys, would rather pay “one-

time” switching costs to achieve a better deal than continue to pay for a mobile

Internet service they are not satisfied with (Lee et al., 2001). On the contrary, when

the levels of satisfaction and switching barriers are high, the chance of getting better

services from alternative providers are not likely to be high. Because they perceive a

low net utility from switching, customers resist the temptation and are more likely to

stay with their existing providers.

5.1 Theoretical implications

The current study has several significant theoretical implications. First, this

study has illustrated how some of the “mooring” and “pull” elements (i.e., switching

costs, variety-seeking tendencies, and alternative attractiveness) in the push-pull-

mooring model (PPM) can be applied in the mobile Internet service context, thereby

contributing to the literature on loyalty behavior.

Second, this study contributes to a growing body of literature on service

switching by empirically validating multidimensional measure scales of switching

barriers and inducements in a more nuanced manner. Zhang et al. (2014) noted that

too few researchers studied the factors that influence customer loyalty and switching

through the lens of competing service providers, personal, and social factors in the

mobile telecommunications service industry. The present study attempts to fill this

gap and provides a more holistic and delineate investigation into the determinants of

service switching by considering the marketplace (FFMII, CMII, switching costs,

alternative attractiveness), customers (inertia and variety-seeking tendencies), and

social-related variables (local network effects and CSSRGI). Additionally, the study

highlights the emerging importance of marketing innovation initiatives (from a focal

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service provider and competitors), local network effects, and CSSRGI in determining

the loyalty of mobile Internet subscribers. These attributes impact switching process

and costs, but have received scant attention in the service switching literature.

Third, the present study provides new insights into the conceptualization of

switching barriers and inducements by specifying and estimating them as reflective-

formative type II models. In previous service switching models (e.g., PPM model),

both switching barriers (mooring factors) and switching inducements (pull factors)

were operationalized as reflective-reflective type I models. In the reflective-formative

type II model, the lower-order constructs that define the characteristics of the higher-

order construct are expected to be distinguished from each other and not

interchangeable. In contrast, the reflective-reflective type I model posits that lower-

order constructs are manifestations of the higher-order construct, and they should be

conceptually interchangeable and highly correlated (Becker et al., 2012). The

collinearity test indicated low covariation among the constituents of switching barriers

and inducements, meaning that changes in one may not cause proportional changes in

the other constituents. For example, perceived innovativeness of a focal service

provider’s marketing initiatives may not necessarily lead to an increase in customer

inertia. These findings lend empirical support to the proposition that switching

barriers and inducements are both higher-order mental constructs constituted by four

distinct lower-order constructs.

Fourth, this study, to the best of our knowledge, is the first to use opposing

dimensions (e.g., FFMII vs. CMII; switching costs vs. alternative attractiveness) to

measure switching barriers and its counterpart (switching inducements). The existing

service switching model, which is grounded in the PPM model, uses unparalleled

dimensions when conceptualizing switching barriers (mooring factors) and

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inducements (pull factors) (see e.g., Lee et al., 2015; Zhang et al., 2014). However,

the mental accounting theory postulates that customers evaluate perceived incentives

(inducements) against perceived disincentives (barriers) when making a switching

decision (Kim and Gupta, 2009; Thaler, 1985). Thus, merely focusing on one-side of

service switching attributes may ignore the potential threats of another. The present

study has empirically verified that switching barriers and inducements can

simultaneously be present in consumers’ minds as positive and negative valence, thus

offering novel insights into understanding the service switching phenomenon in the

mobile Internet setting.

5.2 Managerial implications

From a managerial point of view, this study highlights several important

implications that can be a valuable guide for mobile service providers in developing

better customer retention and churn management strategies. First, mobile service

providers should simultaneously establish switching barriers and manage customer

satisfaction in order to strategically retain their customers. Although switching

barriers appears to be effective in generating customer loyalty, it is not a powerful

deterrent to switching when dissatisfaction occurs. Therefore, while constructing exit

barriers to secure their customer base, mobile service providers must also closely

monitor changes in customer satisfaction.

Second, the findings suggest that switching costs and FFMII constitute the two

most prominent types of switching barriers. This is followed by inertia and local

network effects. Hence to cultivate higher switching barriers/costs, mobile service

providers should be more innovative in their marketing initiatives, which include

developing products and services that increase the benefits to subscribers. For

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example, through co-creation activities, mobile service providers may develop more

personalized plans that best suit customers’ needs, and also increase their innovation

capacities. More importantly, the perceived benefits derived from customized

offerings should substantially increase customers’ perceptions of the costs of

switching, thereby increasing retention. Liu (2006) further pointed out that customers

may feel guilty at searching for alternatives when service providers have successfully

co-created desired products and services with them. In addition, formal (contract

commitments) and informal lock-in (free or cheaper on-network calls and SMSs, data

sharing with friends and family on the same network) can be used to foster greater

customer retention.

Fourth, while satisfaction and switching barriers have been shown to be

instrumental in fostering customer loyalty, switching inducements, on the other hand,

could erode customer loyalty by encouraging switching. However, the magnitude of

the effect of switching inducements on customer loyalty is contingent on the level of

switching barriers. This highlights the need to simultaneously manage switching

barriers and inducements in order to achieve higher levels of customer retention. That

being said, while attempting to increase customer satisfaction and perceived switching

barriers, mobile service providers should also monitor the level of switching

inducements and take necessary steps to alleviate these.

The results show that perceived switching inducements are most likely to be

influenced by variety-seeking tendencies, followed by alternative attractiveness, CMII,

and CSSRGI. Therefore, it is crucial for mobile service providers to identify

consumer segments that exhibit high variety-seeking tendencies. As variety-seeking

consumers are promotion focused (Kim, 2013), mobile service providers should

regularly hold special promotions (e.g., smartphone offers, gifts, and contests) to

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surprise and delight them. In addition, ‘wow’ products, introduced periodically,

reduce customers’ boredom with existing products. Mobile service providers must

also assess the extent to which customers perceive the alternative providers and their

marketing campaigns to be attractive and innovative. Most effective in combating

competitive offers are innovative products and excellent services that are inherently

distinct from those of competitors. In addition, comparative or negative advertising

that highlights the superiority of their performance and alerts customers to the risks of

switching to other service providers. These tactics intensify the perceptions of regret

or loss among the prospective switchers. Furthermore, mobile service providers

should not neglect the influence of social reference groups in customers’ switching

decisions. A powerful reference group could align members with the norms and

standards of the group, and easily change customers’ switching decisions (Gounaris

and Stathakopoulos, 2004). Thus, mobile service providers can use the power of an

influential reference group to help them in retaining customers, increasing cross-

selling, and recruiting new customers.

5.3 Limitations and future research

With any scholarly work, this study is constrained by limitations that offer

venues for future research. First, the sample of this study comprised only Gen Y who

had subscribed to post-paid mobile Internet plans. While this represents a strength in

terms of internal validity, caution must be taken when extrapolating the findings to

other consumer segments. For example, some of the proposed effects could be

stronger or weaker on consumers who are not part of Gen Y. Second, since only

selected attributes of switching barriers and inducements are examined in this study,

future research can deploy qualitative research methods to explore other relevant

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attributes. For example, in-depth interviews with mobile Internet subscribers should

provide additional insights into the factors that trigger and inhibit service switching,

thereby minimizing the unexplained variance in customer loyalty. Future research

should look at the interaction effect of each of the switching barrier dimensions with

satisfaction and with switching inducement dimensions. Such analysis could provide

more meaningful insights. For example, complexity theory and configurational

analyses have solved similar challenges in related disciplines (e.g., Wu et al., 2014).

This approach could also be used to better understand the different configurations of

switching barriers and inducements in driving loyalty behavior. Third, as this study

employs a cross-sectional design, the results can only show associations between the

constructs under investigation rather than a causal relationship. Fourth, this study

captured only the loyalty intention of mobile Internet subscribers, which may not be

an adequate proxy for actual loyalty behavior in all circumstances (De Cannière et al.,

2010). In order to provide a more comprehensive and realistic picture of switching

phenomenon, future research can extend the model by incorporating actual loyalty

behavior. In addition, understanding how switchers (customers who have switched

from other service providers) and stayers (those who have not) differ in their attitudes

and behaviors can provide crucial insights into developing effective churn reduction

strategies.

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Biographies

Stephanie Hui-Wen Chuah, earned her Bachelor’s degree in marketing and Master’s degree in Consumer Behavior from Universiti Sains Malaysia, Penang. Her research interest includes generational cohorts, mobile and wearable technologies, and

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customer switching behavior. Currently she is undertaking her PhD research in the area of technology management. She has also published her research works in international journals and presented them at international conferences. She has also received best paper award for the conference paper presented in an international conference.

Dr. Philipp A. Rauschnabel, PhD, is an Assistant Professor of Marketing at University of Michigan-Dearborn, USA. He received his PhD in Marketing (psychological branding) from University of Bamberg in Germany. Dr. Rauschnabel’s research addresses contemporary issues in brand management and the management of new media. His current research agenda addresses topics such ‘augmented reality smart glasses’ (AR Glasses) and the role of brands in social media. He has published numerous papers on these topics as well as academic journals, books, and conference proceedings. Furthermore, Professor Rauschnabel consults regularly with, and presents research findings at various companies and organizations on these topics.

Dr. Malliga Marimuthu, PhD, is a Lecturer at the School of Business, Charles Darwin University, Australia. She was conferred Doctor of Philosophy in Management from the University of Newcastle, Australia. Her major research interests are in the area of general marketing, information system/technology marketing, services marketing and consumer behaviour. Her research has been published in more than 40 peer-reviewed international journals including those SSCI journals. She has also published several book chapters. Her work has been presented at numerous international conferences and she has won several awards for papers presented in the conferences. Malliga also serves on the journal editorial advisory board.

Thurasamy Ramayah is a Professor at the School of Management in Universiti Sains Malaysia, Penang. He teaches mainly courses in research methodology and business statistics. His articles have been published in international journals such as Computers in Human Behavior, Technovation, Information & Management, Electronic Markets, Journal of Business Economics and Management, and Information Systems Management. He also serves on the editorial boards and program committees of several international journals and conferences of repute. His full profile can be accessed at www.ramayah.com.

Dr Bang Nguyen, PhD, is an Associate Professor at the ECUST School of Business in Shanghai, China. Previously, he held faculty positions at the Oxford Brookes University and RMIT University Vietnam. His research interests include customer management, customer relationship management, services marketing, consumer behavior, branding and issues of fairness and trust. Bang has extensive knowledge in service organizations (consumer products/services) and has published widely in journals such as Industrial Marketing Management, Journal of Marketing

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Management, Journal of Services Marketing, Journal of Consumer Marketing, Journal of General Management and Service Industries Journal. He has presented at various national and international conferences including EMAC and Frontiers. Bang Nguyen is an experienced consultant and advises on marketing and brand development for SMEs and start-ups.

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Notes: *** p < 0.001, ** p < 0.01, * p < 0.05, n.s. = non-significant

Figure 1a. Results of main effect model

Notes: *** p < 0.001, ** p < 0.01, * p < 0.05, n.s. = non-significant

Figure 1b. Results of interaction effect model

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Figure 2. Moderating Effect of switching barriers on the relationship between switching

inducements and customer loyalty

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Notes: *** p < 0.001, ** p < 0.01, * p < 0.05, n.s. = non-significant

Figure 3. Graphic depiction of structural relationships

Competitors’

Marketing

Innovation

Initiatives (CMII)

Alternative

Attractiveness

Variety-Seeking

Tendencies

0.334***

0.402***

0.477***

0.204****

Focal Firm’s

Marketing

Innovation

Initiatives (FFMII)

Switching

Costs

Inertia

Switching

Barriers

Customer

Loyalty

0.282***

0.523***

0.306***

0.324***

First-order construct

Second-order construct

Customer

Satisfaction

Local Network

Effects

Consumers’

Susceptibility to

Social Reference

Group Influence

(CSSRGI)

Switching

Inducements

0.390***

0.301***

-0.288***

-0.036 n.s.

0.064**

H1

H2

H3

H4

H5

R2 = 0.675

Q2 = 0.495

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Table I.

Validity and reliability for first-order constructs

Constructs/ Items

Scale

type

Factor

loading

CR AVE

Customer satisfaction

1. I am very satisfied with my current MSP for mobile

Internet service.

Reflective 0.907 0.938 0.790

2. My current MSP always fulfils my expectations for mobile

Internet service.

0.899

3. Until now, my current MSP has never disappointed me for

mobile Internet service.

0.833

4. Overall, my mobile Internet usage experience with my

current MSP is excellent.

0.915

Focal firm’s marketing innovation initiatives (CMII)

1. The marketing mix elements (product, price, promotion,

and distribution channel) of my current MSP are

Not at all unique … Extremely unique

Reflective 0.921 0.944 0.849

2. The marketing mix elements (product, price, promotion,

and distribution channel) of my current MSP are

Not at all creative … Extremely creative

0.933

3. The marketing mix elements (product, price, promotion,

and distribution channel) of my current MSP are

Not at all trendy … Extremely trendy

0.910

Switching costs

1. Switching to a new MSP causes monetary costs. Reflective 0.711 0.888 0.571

2. If I switched to a new MSP, the service offered by the new

MSP might not work as well as expected.

0.832

3. I am not sure the billing for a new MSP would be better for

me (e.g., involves hidden costs/charges).

0.808

4. It takes a lot of energy, time, and effort to compare all the

MSPs in the market.

0.777

5. There are a lot of formalities involved in switching to a

new MSP.

0.708

6. If I switched to a new MSP, I would lose certain monetary

benefits or membership privileges.

0.685

Inertia

1. Unless I became very dissatisfied with my current MSP,

changing to a new one would be a bother.

Reflective 0.814 0.890 0.729

2. I find it habitual to use the mobile Internet service offered

by my current MSP.

0.867

3. I am not ready to put in extra effort required to change my

MSP.

0.879

Local network effects

1. As far as I know, my current MSP has a large number of

subscribers.

Reflective 0.740 0.899 0.690

2. Most of my family members/friends/colleagues are

subscribing to the mobile services offered by my current

MSP.

0.862

3. The family members/friends/colleagues whom I call most

regularly use the same MSP as mine.

0.864

4. The family members/friends/colleagues whom I send short 0.850

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message service (SMS) most regularly use the same MSP

as mine.

Competitors’ marketing innovation initiatives (CMII)

1. The marketing mix elements (product, price, promotion,

and distribution channel) of other MSPs are

Not at all unique … Extremely unique

Reflective 0.825 0.921 0.797

2. The marketing mix elements (product, price, promotion,

and distribution channel) of other MSPs are

Not at all creative … Extremely creative

0.861

3. The marketing mix elements (product, price, promotion,

and distribution channel) of other MSPs are

Not at all trendy … Extremely trendy

0.984

Alternative attractiveness

1. If I need to change MSP, there are some good MSPs to

choose from.

Reflective 0.760 0.913 0.780

2. Compared to my current MSP, I would probably be more

satisfied with the mobile Internet plan offered by other

MSPs.

0.945

3. Compared to my current MSP, subscribing to the mobile

Internet plan offered by other MSPs would benefit me

more.

0.932

Variety-seeking tendencies

1. I would rather stick to my current MSP than switch to

other MSPs which I am not very familiar with.

Reflective 0.835 0.886 0.608

2. When a new MSP comes into the market, I will consider

giving it a try.

0.710

3. I am constantly searching for new mobile Internet plans

introduced by other MSPs in the marketplace.

0.776

4. I like changing my MSP frequently. 0.769

5. When I get bored with my current MSP, I will switch to

another MSP to obtain some new experiences.

0.804

Consumers’ susceptibility to social reference group influence (CSSRGI)

1. The suggestion and recommendation of my family

members/friends/colleagues will influence my decision to

switch to a new MSP.

Reflective 0.868 0.864 0.680

2. If my family members/friends/colleagues think that I

should switch to another MSP, I will do so.

0.849

3. The negative comments made by my family

members/friends/colleagues regarding my current MSP

will make me think about switching to another MSP.

0.753

Customer loyalty

1. I intend to continue subscribing to the mobile Internet plan

offered by my current MSP in the future.

Reflective 0.879 0.936 0.745

2. If I wished to sign up for another mobile Internet plan, I

would prefer my current MSP.

0.911

3. Even if other MSPs offer cheaper mobile Internet plans, I

will still continue subscribing the mobile Internet plan

offered by my current MSP.

0.771

4. I would recommend my current MSP to those who seek

my advice about the mobile Internet service.

0.873

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5. I would encourage my friends or relatives to use the

mobile Internet service offered by my current MSP.

0.875

Notes: AVE = Average Variance Extracted; CR = Composite Reliability; MSP = Mobile Service Provider

Table II.

Discriminant validity analysis for first-order constructs

AA CL CMII CS CSSRGI FFMII IE LNE SC VST

AA 0.883

CL -0.395 0.863

CMII 0.451 -0.062 0.893

CS -0.307 0.669 0.015 0.889

CSSRGI 0.235 -0.101 0.229 -0.055 0.825

FFMII -0.223 0.627 0.012 0.603 -0.004 0.921

IE -0.325 0.633 -0.074 0.43 -0.101 0.396 0.854

LNE 0.06 0.245 0.031 0.200 0.078 0.258 0.266 0.831

SC -0.182 0.418 0.085 0.316 -0.041 0.281 0.411 0.194 0.756

VST 0.459 -0.665 0.162 -0.478 0.201 -0.393 -0.693 -0.116 -0.385 0.780

Notes: Diagonals (in bold) represent the square root of average variance extracted (AVE); off-diagonals

represent the construct correlations. AA = Alternative attractiveness; CL = Customer loyalty; CMII = Competitors’

marketing innovation initiatives; CS = Customer satisfaction; CSSRGI = Consumers’ susceptibility to social

reference group influence; FFMII = A focal firm’s marketing innovation initiatives; IE = Inertia; LNE= Local

network effects; SC = Switching costs; VST = Variety-seeking tendencies

Table III.

Weights of the first-order constructs on the designated second-order constructs

Second-order First-order Measure Weights t-value VIF

constructs constructs Switching FFMII Formative 0.324 14.182 1.235

barriers SC Formative 0.523 15.037 1.230

IE Formative 0.306 16.802 1.370

LNE Formative 0.282 8.036 1.113

Switching CMII Formative 0.334 12.594 1.311

Inducements AA Formative 0.402 20.396 1.600

VST Formative 0.477 16.231 1.284

CSSRGI Formative 0.204 7.094 1.093

Notes: FFMII = A focal firm’s marketing innovation initiatives; SC = Switching costs; IE = Inertia;

LNE= Local network effects; CMII = Competitors’ marketing innovation initiatives; AA = Alternative attractiveness;

VST = Variety-seeking tendencies; CSSRGI = Consumers’ susceptibility to social reference group influence

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