1777_forecasting customer switching intention in mobile service an exploratory study of predictive...

8/8/2019 1777_Forecasting Customer Switching Intention in Mobile Service an Exploratory Study of Predictive Factors in Mobi

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Forecasting customer switching intention in mobile service:

An exploratory study of predictive factors in mobile

number portability

Dong-Hee Shin a,, Won-Yong Kim b

a College of Information Sciences and Technology, Penn State University, Tulpehocken road, P.O. Box 7009,

Luerssen Building, Reading, PA 19610-6009, USAb Ewha Womans University, Seoul, Korea 11-1 Daehyun-dong, Seodaemun-gu, Seoul, 120-750, Korea

Received 21 March 2007; received in revised form 6 May 2007; accepted 7 May 2007

Abstract

This study investigates switching barriers under the mobile number portability (MNP) in the U.S. mobile

market. The structural equation modeling analysis is used to evaluate the causal model, and confirmatory factor

analysis is performed to examine the reliability and validity of the measurement model. The logistic regression isused to investigate the effect of demographics on switching decision. The findings indicate that customer

satisfactions, switching barriers, and demographics significantly affect subscribers' intent to switch. Among them,

switching barriers had the most significant influence, which raises a question of the effectiveness of MNP. The

MNP in the U.S. mobile market is intended to play an important role in lowering switching costs which can

increase the level competition among providers. The findings, however, imply that subscribers still perceive

switching barrier high, discouraging them from switching carriers.

2007 Elsevier Inc. All rights reserved.

Keywords: Mobile number portability; Switching barrier; Switching behavior; Structural equation modeling

Available online at www.sciencedirect.com

Technological Forecasting & Social Change 75 (2008) 854874

Corresponding author. Tel.: +1 610 396 6135; fax: +1 610 396 6024.

E-mail addresses: [email protected] (D.-H. Shin), [email protected] (W.-Y. Kim).

0040-1625/$ - see front matter 2007 Elsevier Inc. All rights reserved.

doi:10.1016/j.techfore.2007.05.001
mailto:[email protected]:[email protected]://dx.doi.org/10.1016/j.techfore.2007.05.001http://dx.doi.org/10.1016/j.techfore.2007.05.001mailto:[email protected]:[email protected]


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1. Introduction

Mobile number portability (MNP) requires mobile carriers to allow customers to keep their telephonenumbers when switching from one carrier to another. MNP was adopted in the U.S. in 2004 to address the

perceived switching costs of changing one's telephone number when one changes service providers. The

literature on switching cost shows that consumers will not switch their service providers if they are

required to change their mobile numbers because it is both inconvenient and financially burdensome. The

extent to which consumers invest in their telephone numbers, both personally and financially, outweighs

the benefits that may be realized by switching service providers. By removing a barrier to customer

switching in those markets where customers value their telephone numbers, number portability is

intended to foster competition among service providers.

Yet, as mobile carriers increase termination charges and introduce various switching barriers in

response to MNP, it has been questioned whether a switching barrier is effectively lowered with the

presence of MNP. An emergent question with MNP is whether subscribers are able to switch carrierswithout significant switching barriers. Although a growing body of research has examined users'

intentions to adopt and use mobile services [1,2], very little research has been conducted on subscribers'

actual switching behaviors in the context of MNP. There is a growing need to predict how switching

barriers affect customer satisfaction and switching intentions. The theoretical framework built up around

customer satisfaction and intentions must incorporate switching barriers [3] to redefine them in the

context of MNP and answer such questions as Who is switching and who is not? and How do

switching barriers interact with satisfaction and deter switching intentions in the MNP regulation?

These questions are important because policymakers have asked whether MNP has produced positive

benefits. Ex-ante evaluations of MNP carried out in several countries have produced detailed estimates

of expected costs and direct benefits (e.g., strengthened competition and reduced prices). Whileresearchers have suggested that MNP should have a range of potentially important effects [49], such as

lower switching costs and barriers, few attempts have been made to quantify current switching intentions

to predict future switching. In this light, this study investigates the role of switching barriers in the

behavior of switching carriers and further explores the structural relationship among customer

satisfaction, demographics, and switching intentions under the MNP policy. Previous studies [4,5] found

a link between service quality and satisfaction, between satisfaction and customer loyalty, and between

customer loyalty and retention. Other studies [6,1] show that when a switching barrier exists, customers

tend not to switch even when customers are not satisfied with services. Given these empirically

demonstrated relationships, it is worthwhile to test switching intention under the MNP policy by

analyzing the effects of customer satisfaction and switching barriers on switching intention and the

structural relationship among these factors on U.S. mobile service customers. The objectives of thisstudy are (1) to identify variables that contribute to customers' switching in the mobile market; (2) to

conduct an empirical analysis of the relative effects of MNP on customers' switching in order to predict

the extent to which the switching intention is influenced by the level of perceived switching costs and

whether switching costs moderate the satisfactionretention linkages, as suggested by previous research

[7]; and (3) to investigate the impact of demographics on switching intentions. The framework of this

study allows for more facile forecasting in a market with environmental volatility. More accurate

forecasting can enable both better technology policy analysis and more effective industry response.

Thus, the results of this study contribute to the body of work surrounding subscribers in mobile markets

and the regulation imposed to induce and increase competition.

855D.-H. Shin, W.-Y. Kim / Technological Forecasting & Social Change 75 (2008) 854874


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2. U.S. mobile number portability

Number portability dates back to 1995 when the Federal Communication Commission (FCC) enforcedthis policy in local telephone service. The FCC later enforced local number portability between wireline

service providers, which has been occurring since 1998. MNP was first introduced in November 2003 in

100 metropolitan cities and expanded nationwide in May 2004. The main regulatory objectives of MNP

have been the benefits for consumers and the increased competition among carriers, which would lower

prices. The availability of MNP has been thought to bring substantial benefits to subscribers: lower price,

greater choice, higher quality, and a greater range of services. In particular, MNP would allow subscribers

to take full advantage of the choices that become available in a more competitive telecommunication

market. Subscribers will also be able to choose the provider that best meets their needs without incurring

switching costs by changing their phone numbers.

It was initially expected that 30 million subscribers would switch within the first year of MNP's

introduction. In the three years since MNP was expanded, however, only 10 million subscribers haveswitched from one carrier to another according to data released by the FCC [8]. Furthermore, small

mobile carriers have not added subscribers significantly, whereas the top five big carriers have all

added subscribers with MNP [9]. It may be inferred that churn did not increase significantly with the

introduction of MNP because there was a positive impact (positive switching barrier) for consumers as

operators engaged in aggressive customer retention strategies, including better deals on upgrade

handsets, incentives for longer contracts, better customer service, and increased network spending. The

question, however, is raised whether switching barriers have been effectively lifted and thus have

subscribers benefited from MNP. While many researchers are questioning the intended effects of

competition, an emergent and the most fundamental question with MNP is whether subscribers are

able to switch carriers without significant switching barriers. Since the primary goal of MNP lies inconsumer benefit, a fundamental and priori question needs to address the consumers' actual

responses.

3. Theoretical concepts of mobile telecommunication markets

Many previous studies have investigated the relationship between customer satisfaction and customer

loyalty [10,11], the relationship between customer satisfaction and call qualities [1214], and the

relationship between switching demand and MNP [15]. However, the relationship between the factors and

actual customers' switching intentions has not been extensively discovered yet. The hypotheses in this

study are based on the relationships among the factors. The perception of switching barriers is key in the

hypotheses, which lead this study to explore customers' switching intentions.

3.1. Service quality

Service quality in the telecom industry is an important indicator to assess a firm's performance. Due to

inherent intangibility, inseparability, heterogeneity, and perishability of characters, service quality can be

defined as a consumer's overall impression of the relative efficiency of the organization and its services.

The dominant conceptualization and measurement of service quality has been the SERVQUAL

instrument developed by [16]. SERVQUAL identifies determinants of perceived quality and indicates the

arithmetic differences between customer expectations and perceptions across 22 measurement items.

856 D.-H. Shin, W.-Y. Kim / Technological Forecasting & Social Change 75 (2008) 854874


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Using factor analysis, SERVQUAL further is condensed into tangible, reliability, assurance, and empathy

dimensions, which are generic across service contexts.

A survey conducted by [17] found that mobile subscribers who perceived that operators differed inservice levels were more likely to switch than those subscribers who did not see any difference among

networks. Nevertheless, the analysis did not go beyond the descriptive results to further postulate or

explore what differences exist between the network operators and how the perceived difference affected

switching intention. Thus, the following is hypothesized:

H1. Higher levels of service quality are associated with higher levels of customer satisfaction.

3.2. Customer satisfaction

Overall, satisfaction refers to the customers' rating of the brand, based on all encounters and

experiences [10]. Satisfaction can be viewed as a function of all previous transaction-specificsatisfaction [18]. Oliver [19] considered that customer satisfaction means customer reaction to the state

of fulfillment and customer judgment of the fulfilled state, and introduced the expectancy-

disconfirmation model for studies of customer satisfaction in the retail and service industries. The

main factor determining customer satisfaction is the customers' own perceptions of service quality [20].

Oliver [19] defines satisfaction as a pleasant past-purchasing experience from a product or service

given the anti-purchasing expectancy of the customer. In the context of mobile services, service quality

has been measured by call quality, pricing structure, mobile devices, value-added services, convenience

in procedures, and customer support [14].

H2. Higher levels of perceived price are associated with higher levels of customer satisfaction.

H3. Higher levels of customer satisfaction are associated with lower levels of switching intention.

3.3. Switching cost and perceived switching cost

Customer switching refers to migration of customers from one provider to another. Switching cost means

the cost incurred when switching, including time, money, and psychological cost [21], and is defined as

perceived risk, insofar as there are potential losses perceived by subscribers when switching carriers, such as

loss of a financial, performance-related, social, psychological, and safety-related nature [22]. Switching costs

exist whenever consumers face changeover costs in a market when switching from a purchased product to

one of its substitutes. According to Chang [23], initial costs are assumed to be the same across all carriers,

when the subscriber makes a purchase decision. These first period sales create the second period, oraftermarket, switching costs. Switching cost can be explained in three categories [24]: learning costs occur if

knowledge between brands is not transferable; transaction costs occur when changing providers; and

contractual costs, or purely pecuniary costs, occur when a firm develops particular schemes, such as loyalty

benefits or withdrawal penalties, to encourage retention of existing subscribers. While the learning costs and

transaction costs represent the social cost of brand switching, the contractual cost occurs with a firm's

strategy, which punishes subscribers who switch by creating intentional barriers [24]. These different

switching costs are collectively perceived by subscribers, the so-called perceived switching cost.

Perceived switching cost is the degree to which an individual believes that switching service providers

would incur certain cost to him or her[25]. One way to investigate switching costs will be simply to ask



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consumers at what price differentials they would switch. The problem, however, is that consumers often

cannot estimate their non-financial switching costs correctly; therefore, their answers will reflect their

perceived switching costs, which may be different from actual switching costs. Perceived switching costrather than actual switching cost explains customer switching intention and affects the market outcome. In

addition, perceived switching costs constructed by carriers can be used strategically to retain customers,

even when customers are less than satisfied with the provider [15]. Because these costs may vary with

customer characteristics and the nature of the product, these costs can be used as attributes for market

segmentation and targeting. Fornell [26] distinguishes search, transaction, and learning and emotional

costs, as well as loyal customer discounts, customer habit, cognitive effort and financial, social, and

psychological risks for the consumer as switching costs. For services, customers may be less inclined to

switch when financial, search, and psychological costs are involved [27].

H4. Higher levels of switching cost are associated with higher levels of the switching barrier.

3.4. Switching barrier

Similar to switching cost, the notion of switching barriers has been spotlighted in recent marketing

research due to its importance in customer retention and profits to service providers [6]. Kim et al. [2]

identify the positive role of switching barriers in customer retention in the Korean mobile phone industry.

Patterson and Smith [28] also argue that switching barriers capture a substantial amount of the explained

variance in propensity to stay with focal service provider (p. 26). It is logically understood that the

switching barrier makes it difficult to switch service providers (H5).

Previous studies found that switching cost reduces customers' sensitivity to price and satisfaction level

and that they perceive functionally homogenous brands as differentiated heterogeneous brands [29,26].That is, in the presence of switching cost, customers who might be expected to select from a number of

functionally identical brands display brand loyalty. In due course, ex-ante homogenous products may be

ex post differentiated by switching cost after they have been bought [24]. In addition, when customers are

sensitive to product attributes such as quality, uncertainty will decrease price sensitivity, and customers

will behave as if brand-loyal. For these reasons, switching cost is the factor that most directly influences

customers' sensitivity to price level and so influences customer loyalty [29]. Therefore, it is reasonable to

hypothesize that perceived switching barriers have a moderator effect on the relationship between

customer satisfaction and switching intention (H6).

H5. Higher levels of perceived switching barriers are associated with higher levels of switching behavior.

H6. Higher levels of perceived switching barriers moderate the relationship between customersatisfaction and switching intention.

3.5. Subscriber lock-in

Firms keep developing various strategies to gain control over access to subscribers in an attempt to

achieve customer lock-in, which is usually referred to loyalty [30]. While subscriber lock-in is often

regarded as a concept similar to switching barrier, the distinction is clear: subscriber lock-in is a supply-

side variable describing providers' efforts to create switching barriers, whereas the perceived switching

barrier is a demand-side variable describing consumer perception.



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As an effort at subscriber capture, subscriber lock-in can take many forms, including contractual

commitments, bundling of services, product-specific learning costs, search costs, and loyalty programs.

However, each of these industry tactics represents various embodiments of switching costs and aftermarketmonopolization. The higher the cost of switching carriers within a particular market, the more the

subscriber is captured, or locked into, the original purchase decision (H7). In this situation, the provider

may be able to increase the service price without a significant loss of subscribers, providing the service

price increase does not exceed the cost of switching providers. This approach forces subscribers to balance

switching costs with the benefit of saving money in a competitor's aftermarket [31]. Switching costs thus

generates consumer lock-in, allowing firms to earn above-competitive, monopoly profits. In any market, a

provider competes for both existing and potential consumers. While markets with high switching costs

serve to retain existing subscribers, industry claims suggest that potential subscribers provide the

competitive discipline to resist overpriced products and services. This supports the reputation effects. If it

becomes known that providers will charge excess prices in the aftermarket, consumers may avoid such

costs by purchasing from a different provider during the first period. While the tactics employed by variousindustries to seduce potential subscribers and retain existing subscribers will vary extensively across

markets, it has become an economic axiom that lower switching costs force competition for initial

subscribers and liberate second period subscribers from a particular aftermarket.

H7. Higher levels of customer lock-in are associated with higher levels of the switching barrier.

3.6. User demographics and switching intention

Studies of the adoption of new technologies have focused on an individual's socioeconomic

characteristics, the perceived attributes of innovations, technology clusters, situational factors, and thecharacteristics of innovations that influence adoption [32,33]. Past adoption studies also show that early

adopters of new technologies tend to be young, well-educated, and richer than non-adopters; males are

more likely than females to be adopters of new technologies [34].

Although some research has highlighted the influence of demographics, the impact of demographics on

switching intention has received relatively little attention. Few studies to date have identified which, if

any, customer characteristics might be effective in predicting customer switching intention. This study

examines whether customers' switching intentions differ with respect to their demographics (age, gender,

and education). Carroll et al. [35] found that young mobile users use mobile services to satisfy their social

and leisure needs, reinforce group identity, and add value to their lifestyles. The researchers also found

that more educated people view mobile devices as lifestyle-related tools as well as task-oriented

technologies. This attitudinal shift might influence people's switching intentions as well. Thus, it isreasonable to hypothesize that young and more educated subscribers tend to show higher intention to

switch than older subscribers (H8 and H10). In addition, applying the gender difference of mobile use to

switching intention, it can be hypothesized that male subscribers tend to switch more often than female.

Several studies have showed that female customers tend to experience higher levels of anxiety than males

in using technologies [36]. Gilbert et al. [37] found that females tend to show more techno-phobia and

anxiety toward mobile technologies. The anxiety is likely to discourage females from switching from one

carrier to another. The most recent study by Ranganathan et al. [38] confirms the previous findings of

demographics: males are more prone to switching carriers and age is negatively linked to switching (H8

and H9).



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H8. Age is negatively related to switching intention.

H9. Male subscribers tend to show more intention to switch than female subscribers.

H10. Education has a positive effect on switching intention.

4. Survey design and variables

Data for the present study were collected by a private market-research firm, which conducted a

standardized telephone survey among customers of mobile services in the U.S.

The telephone questionnaire was developed based on a previous study on MNP [5,14,15]. The survey

questionnaire asked about subscribers' behaviors and reasons for switching carriers such as satisfaction,

service, switching cost, lock-in, and prices. A pilot survey was administered to revise and complement the

survey questions. Five hundred and twelve valid survey responses were obtained by phone. Therespondents' ages ranged between 19 and 72, with a mean of 37.12 years (S=12.99; n =520). SPSS 10.0

was used for descriptive statistical analysis, factor analysis, and reliability analysis. The SEM tool was

used for the path analysis of H1 through H7. Logistic regression was used for H8, H9, and H10. A logit

model helps to understand the extent to which such factors influence a customer's choice of product. This

model evaluates the influence of independent variables on an event, either switch or not switch. In this

analysis of the independent variables (continuous and dichotomous) in this study, each of the categories is

assessed for its impact on the dependent variable (switching intention). Respondents were questioned as

to whether or not they would switch (switch 1 = yes, 0 = no).

4.1. Measurement

All variables in the hypotheses were assessed with multi-item scales (Appendix A). Customer

satisfaction was measured with a 3-item scale designed to capture the overall satisfaction [39]. The study

adopted an approach to the measurement of customer satisfaction that is common in the literature of

existing approaches to measurement of customer satisfaction. The measurement scales of switching cost

were adopted from Jones et al. [6]. Three questions were used to measure switching barriers and customer

lock-in, which were adopted from Chen and Hitt [40]. The measurements of switching barrier and

switching intention were both adopted from Kim et al. [10]. Perceived price was measured with a 3-item

scale. Similar items appear in Brynjolfsson and Smith [41]. Finally, service quality was assessed with a 3-

item scale that replicates the commitment scale developed by Cheong and Park[42]. Since LISREL 8.51

was used as an analytical tool, all of the constructs were subject to confirmatory factor analysis.

4.2. Pretest and pilot survey

To enhance the validity of the proposed model's measurement item, a pretest and a pilot survey were

conducted before the main survey. To assure content validity in the MNP context, faculty members of a

marketing department reviewed a set of questionnaire items that were based on relevant previous research.

The content validity of the questionnaire items was verified through personal interviews with experts in

the mobile industry. A pilot survey was conducted using the initial questionnaire items. Forty-nine

customers with experience in mobile switching participated in the pilot survey through the web survey.



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4.3. Reliability and validity

Cronbach's alpha is used to test the internal reliability of each of the composite constructs. Internal

consistency measures estimate how consistently individuals respond to the items within a scale. Allindependent variables show acceptable values (above 0.7) in Cronbach's alpha coefficients, which

indicate the reliable measures of their respective constructs [43]. The reliability analysis of each factor is

produced in Table 1.

4.4. Confirmatory analysis: LISREL measurement model

In the data from the studies, confirmatory factor analysis (CFA) and coefficient alpha were used to

assess the reliability and unidimensionality of the scales in order to determine whether it was appropriate

to operationalize each of the constructs as an index. Drawing from service literature and qualitative

interviews, switching cost is conceptualized to encompass three dimensions. Based on scale refining,

eight of the initial 21 switching cost items were found to have acceptable psychometric properties. Ameasurement model was specified constraining the eight items to load on three factors as theorized

(x2 =729.5, df=242, p =0.000, CFI=0.96, GFI=0.93, AGFI=0.813, RMSEA=0.05). All items were

found to load strongly on the intended latent dimension. Scale reliabilities were above 0.75 for all scales,

Using factor reduction, 13 service quality items are condensed to three dimensions.

5. SEM and structural paths

To test the structural relationships, the hypothesized causal paths were estimated with SEM of LISREL.

The results with SEM show that six hypotheses were supported and one hypothesis was rejected at

Table 1

Convergent validity and internal consistency reliability

Items Factor loadings Cronbach's alpha

Customer satisfaction CS1 0.873 0.8894

CS2 0.832

CS3 0.833

Switching cost SW1 0.859 0.8941

SW2 0.829

SW3 0.831

Subscriber lock-in LO1 0.738 0.8389

LO2 0.768

LO3 0.809

Perceived price PP1 0.711 0.9467

PP2 0.941

PP3 0.914

Switching barrier SB1 0.719 0.7339

SB2 0.801

SB3 0.721

Subscriber lock-in SL1 0.703 0.743

SL2 0.811

SL3 0.792



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p =0.05 (Table 2). The model explains 68% of the variance in customer switching, 41% of the variance in

customer satisfaction, and 53% of the variance in switching barriers. There is a high level of explanatorypower.

The findings indicate that both customer satisfaction and switching barriers exhibited strong impacts on

customers' attitudes toward and behaviors in switching mobile carriers. The test of hypothesis 1 showed

that service quality is significantly related to customer satisfaction, implying an indirect effect on

switching intention. Interestingly, however, the test of hypothesis 1 reveals that perceived price is not a

significant factor affecting customer satisfaction, which implies the insignificant effect of price on

switching intention. According to the test of hypothesis 3, customer satisfaction has a direct influence on

switching intention, suggesting that satisfied customers will more than likely not switch carriers. Together

with hypotheses 1, 2, and 3, it can be said that if customers think that they are getting a high value from the

service they receive in relation to price, they are more likely to be satisfied and more likely to stay with thecurrent carrier. It can be said that the perceived price paid by subscribers is diluted by increased service

quality and customer satisfaction. These linkages confirm previous studies' findings that service quality

and price directly influence customer satisfaction.

The results of hypotheses 4, 5, and 7 suggest that the switching barrier is influenced by customer lock-

in and the increased switching cost, which negatively influence customer switching intention. The path

coefficient of the switching barrier and switching intention show that the switching barrier is a key factor

in subscribers' switching intention.

Hypothesis 6 reflected the moderating effect of the perceived switching barrier of customer satisfaction

and switching intention. The result of hypothesis 6 suggests that the switching barrier has a moderator

effect on customer satisfaction and switching intention. It suggests that customers having high switching

costs tend not to switch even if they are not satisfied with the services delivered. This further implies themoderator role of switching barrier on the relationships between call quality and customer satisfaction,

and between price and customer satisfaction. It can be inferred that the perceived switching barrier

reduces subscribers' sensitivity to the level of subscriber satisfaction.

6. Logistic regression and demographics

For the test of 8, H9, and H10, logistic regression is used to denote whether demographics affect

customers' switching intention. Logistic regression is used as a tool to determine which of the factors

identified in the switching intention were significant with regard to predicting switching intention. Using

Table 2

The results of hypothesis tests

Hypotheses Estimates t-value S.E. Result

H1 0.281 2.701 0.001 Accept

H2 0.175 2.261 0.021 Reject

H3 0.109 0.248 0.681 Accept

H4 0.489 4.238 0.000 Accept

H5 0.197 2.121 0.292 Accept

H6 0.192 4.301 0.103 Accept

H7 0.193 3.193 0.001 Accept

pb0.05; pb0.001.



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binary data, the logistic regression method gives estimates of model coefficients that can be used to

quantify the probability of subsidence. The independent variables in this study are age, gender, and

education. Dichotomous dependent variables are used in this study whether the customers switch carriers

or stay with the current carrier. Although there are other appropriate methods (discriminant analysis, binomial regression, etc.), logistic regression analysis is an appropriate type of analysis since the

dependent variable has only two values (yes=1, no=0), and the variable regards future behaviors.

Table 3 presents the results of the logistic regression analyses. The overall model significance can be

evaluated by the model X2 value, which is significant (pb0.001). The H-L Goodness of Fit Test also

indicates a significant fit (X2 =60.22, pb0.001). Further, the odds ratio helps understand the relative

importance of the independent variables. These results provide considerable support for H8 and H10,

which show that age and education have a direct impact on switching intention: younger subscribers and

more educated subscribers are more prone to switching carriers because they appear to be better aware of

switching provisions. However, it was found that gender may not affect subscribers' decision on

switching, which is a contrast with the finding of Ranganathan et al. [38]. These demographic findingscall for further investigation, such as other socio-economic factors that may affect the perceived ease of

switching and the perceived importance of price in the subscribers' choice of mobile carrier.

7. Forecasting scenarios: switchers versus continuers

In order to conduct dynamic analysis on the subscribers' behavior, this study performed additional

statistical analyses on the collected samples by dividing them into several groups. Based on the responses

from the survey, a 22 matrix was made (Fig. 1). Two customer groups were divided into four groups

(Fig. 1). The first group (n =141) is a customer group representing switching. The second group (n =121)

is those who would remain without further switching. The third group (n =116) is from the continuer

(unswitching) group that intends to switch in the future. The fourth group ( n =124) represents a customergroup that remains consistently with the current provider and will not switch.

Table 3

The results of logistic regression

B S.E. Wald X2 Odds ratio

H8: Age 0.012 0.001 482.06 1.910

H9: Gender 0.171 0.031 39.690 1.297

H10: Education 0.189 0.029 49.913 2.330

Model X2 6790.22

H-L test X2 60.19

pb0.1, pb0.05, pb0.01.

Fig. 1. Customer type regarding switching intention.



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For the comparison of the switched group (Groups 1 and 2) versus the continuer group (Groups 3 and

4), ANOVA is used. Then, for the investigation of future intention (Group 1 and 3), logistic regression is

used. For the groups without future switching intention (Group 2 and 4), multiple regression is used to

investigate the factors blocking their switching intention.

7.1. Switching group versus continuer group

The two most determining factors for the switching group to change carriers shown in Table 6 are priceand the switching cost. Of those who switched, 44% responded they left for a better price on monthly

service, and 12% switched because of a promotion of sale. Twenty-two percent switched because of

coverage or service quality, the two factors that had been considered major reasons that would lead to

widespread switching. Service factors show a generally higher influence than switching barriers. Unlike

the commonly accepted notion, switching barriers were not a significant factor. MNP was not by itself a

reason for subscribers to switch carriers, but it did lessen the hassles of switching.

Among switching barriers, subscriber lock-in is the most significant factor discouraging subscribers

from switching. Analyses show that subscribers who switched generally are satisfied with their new

service and carriers. In general, the switched subscribers are well informed about MNP. Approximately

82% of subscribers were very or somewhat satisfied with their porting experience. In addition,

approximately 81% of subscribers indicated that length of porting met or exceeded expectations.On the other hand, most continuing subscribers felt the switching barriers higher than the switching

group. Even after the MNP enforcement, switching barriers have influenced subscribers' decisions. In

addition, subscriber lock-in is the second important factor making subscribers stay with current carriers.

Continuing subscribers have been locked in with long-term contracts, fees, and memberships. These

continuing subscribers received a promotional discount when they selected the current carrier as a

condition of a 2-year contract. Furthermore, the continuing subscribers worried about hidden costs that

included early termination fees (if subscribers switch carriers during the contract, subscribers should pay an

early termination fee). Fourteen percent of respondents said that they stopped pursuing switching after they

became aware of the early termination fee. The continuing groups explained another hidden cost of fees to

Table 4

ANOVA on switching

Variable Switcher group Continuer group t-value p-value

Mean S.D. Mean S.D.

Usage time/week 4.390 0.113 1.813 0.172 4.221 0.020

Call frequency/week 68.11 0.231 25.12 0.180 4.391 0.027

Services in use 6.610 0.119 2.323 0.231 2.196 0.015

Switching cost 1.331 0.440 5.241 0.451 3.275 0.002

Subscriber lock-in 3.321 0.320 4.141 0.272 2.2543 0.001

Price 4.822 0.205 2.132 0.243 3.283 0.003

Opportunity cost 1.811 0.218 4.810 0.408 2.8123 0.071

Call quality 6.531 0.274 3.812 0.341 3.1481 0.0012

Subscriber service 4.233 0.234 1.123 0.121 2.1129 0.002

Value-added service 4.938 0.441 2.169 0.404 .84780 0.130

pb0.1, pb0.05, pb0.01.



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keep the current number after switching carriers. With MNP, the FCC allowed carriers to charge a fee to

recover number porting costs. Carriers charge this fee to subscribers or new carriers. In addition, the

continuing group worried about a requirement to purchase a new handset compatible with the new carrier'snetwork. These subscriber lock-in factors have heavily discouraged subscribers from changing carriers.

Interestingly, continuing subscribers rarely see mobile service as important. They have had little real

experience with various value-added services. They tend not to differentiate or discriminate different

service quality other than voice. They are not highly satisfied with current services nor think the service

will be improved with other carriers. Interestingly, 2% of continuing subscribers are completely unaware

of the existence of MNP. Subscribers' awareness of MNP was not high enough that they lacked a detailed

understanding of the process, timing, and cost of porting. Most continuing subscribers had the simplistic

notion that MNP would be free, immediate, and hassle-less. Approximately 3% of continuing subscribers

responded that they would wait for a while until switching costs are lowered (Table 4).

7.2. People with future intention (Group 1 and 3)

For Group 1 and Group 3, logistic regression is used. Logistic regression, in general, supports the

ANOVA analyses (Table 6). Subscribers from the continuer group are most likely to switch when

switching barriers become low. In particular, subscriber lock-in is shown to be the biggest deterrent that

keeps subscribers from switching. Subscribers in the continuer group perceive switching barriers to be

higher than the switching group does, as shown in the subscriber lock-in. In addition, the continuer group

in general is less intrigued or prompted by lower prices. These subscribers are interested in lower price

(monthly fees), but they think the lower price would be offset by a high switching cost and a move-in cost.

In fact, it is consistent from a series of analyses that the continuer group is less concerned with levels of

service quality and is less responsive to price change.As for services, subscribers from the continuer group are least likely affected by the service factor. In

general, the subscribers in the continuer group are causal users who tend not to care about their numbers.

They just need to make a call when they need to. This observation is consistent with the following variable

Table 5

Logistic regression analysis on independent variable of switching

Factors Independent

variables

Switching

(Group 1)

Continuer group to intend

(Group 3)

B S.E. p-value B S.E. p-value

Switching barriers Switching cost 0.0593 2.432 0.833 0.401 9.238 0.001

Subscriber lock-in 0.294 1.541 0.092 0.471 1.421 0.005

Opportunity cost 0.014 0.344 1.042 0.842 0.511 0.0273

Services Call quality 4.681 0.723 1.441 0.431 3.313 0.621

Customer service 4.001 1.143 0.312 1.019 1.923 0.805

Value-added service 4.773 0.942 0.354 1.070 0.0152 0.332

Price 5.193 0.253 0.012 2.124 0.012 0.038

Constant 3.820 3.844 1.263 0.000 5.107 1.444

Log likelihood 173.02 56.422

Chi-square 149.54 69.742

pb0.1, pb0.05, pb0.01.



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Table 6

Multiple regression of factors affecting switching barriers

Switching Switching

cost

Subscriber

lock-in

Opportunity

cost

Call

quality

Subscriber

service

Price

structure

Subscription

fee

Addit

servic

Switching 1

Switching cost 0.149 1

Subscriber lock-in 0.036 0.555 1

Opportunity cost 0.052 0.192 0.126 1

Call quality 0.012 0.045 0.311 0.051 1

Subscriber service 0.016 0.032 0.234 0.455 0.213 1

Price structure 0.043 0.140 0.032 0.223 0.593 0.724 1 Subscription fee 0.008 0.023 0.311 0.036 0.191 0.842 0.399 1

Additional service fee 0.007 0.023 0.020 0.049 0.022 0.097 0.183 0.133 1

Intercept = 2.545 R =0.214 R2 =0.046

ANOVA summary table

Sum of Squares df Mean square F

Regression 406.442 10 235.840 4.741

Residual 8411.313 289 27.542

Total 7314.803 291

pb0.1, pb0.05, pb0.01.


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that the continuer group tends to see the value-added service as the least affecting factor. Call quality is the

most vital factor for the continuer group to switch in the future, but this is not a significant factor.

On the other hand, subscribers with switching experience are likely to switch again if they find lowerprices. These subscribers are also are tempted to switch carriers with better value-added services. They

tend to see mobile service as an additional means of communication and look for richer functionality and

greater versatility.

While price is found to be the important factor, switching barriers are the least important factor for these

subscribers. As they have experience in switching and thus they know how to better deal with switching

barriers, the switching group now sees services as a more important consideration than the switching

barriers. The switching group tends to more easily change current carriers if service quality falls short of

these subscribers' expectations. Also, subscribers in the switching group are willing to switch again if

they find lower prices through promotions or special sales offers (Table 5).

7.3. Subscribers without future switching intention

For the group without future switching intention (Groups 2 and 4), multiple regression is used to

investigate the switching barriers discouraging subscribers' switching intention. Among the identified

switching barriers, move-in cost and loss cost have a direct effect, which implies that MNP does not have

a direct effect on the subscribers' switching decisions; rather, MNP affected customer benefit indirectly by

lowering prices, by increasing customer services, and through carriers' strategies such as promotions.

Service is also a significant factor, but not as significant as switching cost. This suggests that

subscribers are likely to remain with their current carriers even when they experience only a low level of

service satisfaction. It is inferred that service qualities are almost identical among carriers because almost

every mobile carrier is striving to provide the best customer service possible in order to reduce customerchurn. Service qualities from different carriers become similar enough for customers not to differentiate

service itself when choosing carriers. In addition, although mobile carriers are aggressively introducing

value-added services (i.e., location-based services, streaming media, game-on-demand, VPN access, and

m-commerce), they do not matter to customers as far as switching decisions are concerned (Table 6).

8. Discussion

The switching barrier is a major factor directly affecting subscribers' decisions about switching.

Among the identified switching barriers, move-in cost and loss cost have a direct effect, which implies

that MNP does not have a significant effect on subscribers' switching decisions; rather, MNP affects

customer benefit indirectly by lowering prices, by increasing customer services, and by increasing promotions through carriers' strategies. Through MNP, carriers have increased subscriber lock-in by

making subscribers sign long-term contracts, by increasing termination charges, and by imposing the

burden of hidden costs. This raises important implications for the effectiveness of MNP. Despite the

regulators' aspirations, there seems to be little effect of MNP at the individual subscriber level. The

regulators' assumption was that MNP would enhance competition due to reduced switching costs.

However, because subscribers still feel the high level of switching barriers after the introduction of MNP,

there has been little effect on the competition in the mobile market. Although there are no data available to

compare pre-MNP and post-MNP, the validated relationships of the factors in SEM imply that switching

barriers are not a unidimensional concept, but there are different types of switching barrierswith unique



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effects on customer satisfaction, service quality, and perceived price in the MNP context. Such examples

include increased service quality and customer satisfaction, which can be called positive switching

barriers, along with negative switching barriers such as customer lock-in [6]. It can be inferred that mobilecarriers have improved service quality since MNP went into effect to minimize subscriber churn rates. It

can be also expected that customers stay with their current carriers because of such positive relationships,

which is called customer loyalty. The finding that service quality is not as significant a factor as switching

cost suggests that subscribers are likely to remain with their current carriers even when they experience

only a low level of service satisfaction. This implication can be explained in two ways: (1) service

qualities become almost identical among carriers (to the extent that customers do not switch just because

of service qualities) because almost every mobile carrier is striving to provide the best customer service

possible in order to reduce customer churn; and (2) therefore the issues of service quality and subscribers'

satisfaction are offset by the increased switching barrier. Subscribers may see the increased switching cost

and barrier as some form of commitment by the current carrier.

Indeed, it can be argued that the switching barriers that have been strategically set by carriers affect notonly the subscribers' switching decision directly, but also other independent variables indirectly. The

influence of customer satisfaction on their switching intention can be lowered in the presence of a

perceived switching barrier. The switching intention is likely to be reduced if customers feel a high level of

perceived switching barriers, and customers with high switching barriers become insensitive to prices and

service quality, which then leads to neutralized customer satisfaction. While this argument is partially

consistent with the findings by Ayndin and Ozer[29], who report that switching cost has a moderator effect

on the antecedent of customer satisfaction and customer loyalty, it also shows the opposite direction of

causality from the previous findings, which show that customer satisfaction reduces sensitivity to price by

lessening price elasticity [44] and minimizes customer switching from fluctuations in service quality [26].

Fig. 2. Structural path results.



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From the finding that the perceived price was found to be less significant than call quality and customer

service, it can be inferred that customers can bear a certain degree of price difference as long as they

perceive high-quality service rendered by their current carriers. While the low level of price effect can beexplained as customers becoming less sensitive to the price level as compared to service quality, it can

also be seen that mobile service costs become more and more flat or neutralized as mobile carriers try to

counter-offer to beat competitors' prices. In the long run, however, the use of pricing tactics to deter

subscribers' switching may not be effective because competitors can easily offer new pricing schemes to

beat the temporary advantages in price level or price structure [14].

Of the demographic variables, age has a stronger association with switching compared to education.

The odds ratio for age indicates that when holding all the other variables constant, younger subscribers are

1.911 times more likely to switch mobile carriers than older subscribers. The findings also indicate that

education level is positively linked to switching: subscribers with higher levels of education are relatively

more prone to switching mobile carriers. However, it is found that gender may not affect subscribers'

decisions on switching. These demographic findings call for further investigation, such as how othersocio-economic factors may affect the perceived ease of switching and the perceived benefits of switching

(Fig. 2).

9. Contributions and implication: generalizability to other communications services

Investigating customer switching and loyalty behavior in services has gained considerable attention in

recent years [25,32,37], although it must be pointed out that the mechanisms of customer switching are

not completely understood [21]. An important limitation of the abovementioned studies is that they

investigated switching or retention and reported only the single effects of a mobile carrier's efforts on

customer behavior. These studies mostly ignored the overall context of mobile services, such as policy,regulations, and customers' behavior. One of the main characteristics of the mobile telecommunications

sector is that the mobile services offered are complicated in terms of regulations and market situation to

the extent of technological advancement.

In this unpredictable new technology environment, it is important to better understand customers'

behavior and intention. By incorporating the influence of government telecom policy and firms' strategy,

this study provides an integrated framework to analyze the antecedents and consequences of switching

intention in MNP. Meanwhile, with the rapid growth of the mobile phone market worldwide, consumers'

switching costs should be understood so providers in the market can create comprehensive marketing

strategies. This study produces referential findings from research into the mobile market in terms of

antecedents and consequences of switching costs in the U.S. This study thus enhances researchers and

managers' understanding of switching costs. At the same time, this study contributes to the scholarlyliterature in a number of ways: (1) this research investigates the important work of identifying factors that

affect customers' switching under the MNP policy; (2) it examines the actual switching intention of

subscribers'' mobile numbers, not just their self-reported switching intentions; (3) it goes beyond

satisfaction and intentions to explore heretofore unexamined attitudinal, behavioral, and demographic

characteristics of customers as possible predictors of switching intention; and (4) it focuses on customers

of mobile services, which are a growing industry, and one seemingly plagued by churn.

The presented empirical study, however, has some limitations, which limit its generalizability findings

as well as open up suggestions for future research. The approach in this study produced country-specific

results. As the study was limited to a single country, the research cannot exclude the impact of country-



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specific factors, such as legislation, maturity of the market, and regulators' roles. This could be solved

with a multi-country study, which would help to control for the abovementioned effects; however, this

might prove hard as legislation was not being introduced simultaneously and, given the rapiddevelopment of the industry, might hinder the validity of results. In addition, the results of this study are

generated from a single industry; additional studies in other industries may strengthen the generalizability

of the proposed constructs and framework. In the case of multiple sectors, longitudinal studies

investigating the role of customer switching intentions may overcome the constraints of multiple cross-

sectional studies.

10. Conclusion

There is a lack of empirical research into the extent of customer switching or demand-side switching

determinants in mobile markets, particularly in the MNP context. Many studies in marketing and retailing

have focused on customer loyalty as a supply side component, and essentially assume that switching costplays a role in protecting firms' existing customer base and gaining a competitive advantage [24]. This

view is somewhat pro-industry, focusing on how to build switching barriers and how to increase customer

lock-in at the expense of subscribers' interests. The present study has approached MNP from a customer

perspective, focusing on how and why customers switch carriers and how MNP enables or facilitates

customers to switch without the significant burden of switching cost. The results of this study show that

customer satisfaction and switching barriers are each found to have a direct effect on subscribers'

intentions to switch. The findings also show the moderating effects of perceived switching barriers on the

relationships between customer satisfaction and switching intentions. It was found that due to the

moderating role of switching barriers, switching intention, in the context of MNP, is not a unified

construct but rather one with at least two distinct dimensions: positive and negative switching barriers.According to Julander and Sderlund [50], a positive switching barrier is that customers stay with their

current providers because of a perception that the provider is superior in services and products. A negative

switching barrier is that customers stay with their current providers because it is too expensive to leave the

provider and there is a monopoly on the market or the provider is powerful.

The results of this study confirm the existence of these two kinds of switching barriers in MNP: that

customers stay with current providers because the subscribers are tolerant of small price differences or

because they are happy with their current service.

The results also provide empirical support for linkages between satisfaction/switching and their sub-

components. Particularly, the degrees of perceived service quality are found to be a more significant factor

than the perceived price affecting a customer's satisfaction, which influences the extent of intention to

switch. Therefore, highly satisfied customers tend to show a high likelihood of staying and highertolerance to price increases by providers or price decreases by competitors.

The results imply that MNP has not significantly contributed to the regulators' goal of removing

switching barriers that have been prevalent in the subscribers' perceptions. Instead, MNP has indirectly

enhanced switching barriers through increased subscriber lock-in strategies and tactics [45]. From the

finding that subscribers seem to still feel high barriers in switching carriers, it can be inferred that

subscribers' decisions about switching are not much influenced by the MNP requirement. This raises a

question about the validity of MNP and whether it is being enforced and implemented in an effective way

as it was intended and planned. At the same time, it can be inferred that carriers continue to improve

customer satisfaction by restructuring price and by increasing service quality in order to reduce customer



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churn in response to MNP. That may be the reason that subscribers are insensitive to or even not aware of

the price structure and switching condition as carriers keep changing the provisions related to switching

and pricing.The arguments of this study are partially consistent with the previous studies by Aoki and Small [46]

and Reinke [47], who argue that positive gains from portability are actually offset by the carriers' inertia

not to lose subscribers by increasing hidden costs and subscriber lock-in. Although MNP is designed to

benefit consumers, it becomes possible that the corresponding increase in the marginal cost of production

reduces consumer surplus and makes entrants and consumers worse off. As argued [46], it is also very

likely for consumers to receive fewer benefits following a reduction in the cost of switching between

carriers. There is a discrepancy between the regulatory assumption held by the FCC and actual industrial/

market phenomena. MNP is not always and everywhere socially beneficial; therefore, future studies may

further research this discrepancy. Based on the findings here, this study makes a suggestion to regulators:

in order to develop socially desirable, legally justifiable, industrially feasible, and economically viable

policy, it is advisable for regulators not just to enforce MNP: they should rather seek to reduce the perceived switching barriers held by subscribers and raise customers'' awareness of MNP to have

subscribers fully take advantage of MNP. This argument is in line with the previous research [48,49] that

investigated the effects of consumer ignorance of relevant pricing and suggested that MNP may

deteriorate customers' price information. By understanding customers' switching intentions, regulators

can ensure MNP's effective implementation, which will also make it more convenient for end users to

switch operators if their services do not meet customer expectations.

11. Limitations and future studies

Although hypotheses were tested and confirmed, this study must be considered exploratory regardingthe role of switching barriers for switching intentions as well as attitudinal motivations. Empirically, it is

rather difficult to forecast the potential magnitude of the MNP effects because of three reasons: Firstly,

MNP has only recently been introduced in most countries so the time span is rather short for an empirical

analysis. At the time of this study, MNP was not an option in most countries; thus rigorous methods were

limited such as comparative, longitudinal, or dynamic analysis. Secondly, very little data are publicly

available on consumers' actual switching intention. Thirdly, the mobile telecommunications sector is so

dynamic with growing demand, changing technologies, and changing market structures that it is difficult

to empirically isolate the effects of MNP. Therefore, this study is vulnerable to the criticism of static

analysis that this study analyzed customers' switching intention under certain conditions. Therefore, the

results of this study have a limited applicability when carriers are changing new conditions by altering the

terms of service. The SEM for switching costs in this study suggests that other antecedents exist in thestudy context. This preliminary study forms the basis for further analysis. Future studies may investigate a

longitudinal assessment of consumer satisfaction to see the dynamic aspect of the cognitive process.

Future research into the effect of MNP will benefit from the existence of time-series cross-section data

from jurisdictions where MNP has been implemented.

As the goal of this study was to test the effect of switching cost and customer satisfaction on switching

decisions, this study deliberately over-simplifies the relationships of the possible variables. This poses

weaknesses for this study that it could have investigated more complicated relationships, such as the

moderator effect of perceived switching cost on customer satisfaction andcustomer loyalty. Luckily, since this

study reveals the one moderator effect of switching barriers on the relationship between customer satisfaction



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and switching intention, future studies may further investigate various moderate effects using the moderated

regression analysis tool. Since this study sheds light on positive switching barriers, further studies should put

much more care into trying to define and operationalize positive and negative switching barriers [6].In addition, this study does not take into account customer loyalty because it is over-researched in the

marketing and retailing literature. Future study may incorporate customer satisfaction into the switching

intention to investigate a more comprehensive model. The last limitation could be that this study chose

only limited aspects of demographics and analyzed only the direct effects on switching intention. As Kim

et al. [10] suggest, future studies can include customers' demographic characteristics and their behavioral

and psychological characteristics. These can be structurally analyzed for the direct effects of

demographics on customer satisfaction as well as customer loyalty. Further, demographic factors can

be tested for moderating effects as well as adjusting effects on the overall causal relationships involved in

switching.

Appendix A. Items used to measure

Scale Scale items Alpha

Customer satisfaction I am satisfied with the current service 0.94

The current service meets all the requirements that I see reasonable

The service satisfies my need

Switching intentions I intend to switch carrier 0.69

Next time I shall need services of other carrier

I would not continue to have service from my current carrier

Switching barriers It is difficult for me to use other carrier 0.80

It would be complicated for me to change carrier

It takes a lot of time to get information about other CarrierCustomer lock-in I feel locked to this carrier 0.73

There are hassle procedures to switch service

In order to switch service, I have to breach contractual agreements

Switching cost In general, it would be a hassle changing carriers 0.84

It would take a lot of effort changing carriers

It would take a lot of time changing carriers

Service quality I think that my current carrier provides satisfying services 0.81

I think that the services I can get from my current carrier are valuable

My mobile service provides a quality of content and services that I need

Perceived price I think the price for the mobile service is reasonable 0.86

I think the monthly charge for the mobile use is reasonable

I think the additional service charge and other incurring cost for the mobile use is reasonable

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Dong-Hee Shin is an Assistant Professor in the College of Information Sciences and Technology at Penn State University. Dr. Shin is based at the

Berks Campus of the Penn State University, where he teaches courses in telecom, user interfaces, and information systems. He has been

researching on telecommunications management and policy and his publications have appeared in several international journals such as

Information Research and Government Information Quarterly.

Won-Yong Kim is a Professor at Ewha Womans University, the Division of Digital Media. Dr. Kim received his Ph.D. and M.A. from the

University of Texas, Austin. He can be reached at [email protected]. His postal address is Ewha Womans University, Seoul, Republic of

Korea 11-1 Daehyun-dong, Seodaemun-gu, Seoul, 120-750, Republic of Korea.

mailto:[email protected]:[email protected]

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