The effects of customer satisfaction and switching barrier on users’ intention to switch in using mobile social network sites

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intention to switch in using mobile social network sites

Yu-Lung Wu1, Yu-Hui Tao2, Ching-Pu Li3, Sheng-Yuan Wang1

Department of Information Management, I-Shou University, Taiwan1

Department of Information Management, National University of Kaohsiung, Taiwan2 Department of Information Engineering, I-Shou University, Taiwan3

wuyulung@isu.edu.tw1 ytao@nuk.edu.tw2 isu9803005d@isu.edu.tw3 s750905kimo@hotmail.com1

Corresponding Author: isu9803005d@isu.edu.tw

Abstract

With the rise of Web 2.0 and the popularity of mobile devices, Social Network Sites (SNSs) could be described as the most representative and useful Web 2.0 application. Under the intense SNS market competition, and as the number of mobile device users is increasing continuously, users are increasingly frequent to switch between different SNSs. This study takes service quality and switching cost as the antecedent factors to discuss their impacts on user’s satisfaction with SNSs and their switching behaviors. The results shows that service quality and switching cost indirectly influence users’switching intentionthrough satisfaction and switching barrier. The suggestions for SNS providers and further researches are also discussed.

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1. Introduction

With the development of the Internet, the emergence of Web 2.0 enables a variety of network applications, of which the social network sites (SNSs) makes the most prominent performance; for example, the well-known Facebook, MySpace and Twitter are a trend sweeping the world. In addition, as users of smart phones and mobile browsing devices (Tablet PC) grow continuously, the combination of SNSs and mobile devices just coincides with the concept of community media to bring each other close (FIND, 2010a). Therefore, using mobile device to surf the SNSs on mobile Internet seems to be one of the latest network applications and becomes a driving force to encourage people to surf the mobile Internet.

According to eMarketer’s latest survey in 2009, the users of global mobile Internet who connected SNSs through a mobile device are estimated to exceed 607.5 million in 2013, accounting for 43% of global mobile Internet users (FIND, 2009). The survey data of Foundation of Taiwan Network Information Center also showed that up to 27.2% smart phone or 3.5G mobile phone card users in Taiwan browse SNSs through mobile phone. FIND (2010b) report also pointed out that in Q1 2010, the number of users who enlightened the mobile surfing function of mobile phone grew continuously, and has reached 1.855 million (FIND, 2010a). In view that the frequency and influence of using mobile devices to browse SNSs rapidly increased, the major SNS player thought that mobile phone users have been in the early stage to develop the social behavior (FIND, 2010b). Therefore, it has become a topic worthy of attention and discussion as for how to make existing users continuously use SNSs through mobile device and how to expand new users.

At present, all related research on SNSs are focusing on its applications and development; for example, Boyd & Ellison (2007) thought that most of the previous researches focused in a few areas, including image management and friendship performance, networks and network management, and online and offline connectivity and privacy. Meanwhile, many studies have focused on the impact of Facebook on school education (Hewitt & Forte, 2006; Mazer et al., 2007; Charnigo & Barnett-Ellis, 2007; Ellison et al., 2007; Kirschner & Karpinski, 2010; Roblyer et al., 2010). Up to now, there is no formal literature discussing the behavior of switching SNSs from personal computer to mobile device. Based on the above existing research issues about SNS platform switch, and with the strategic development of SNS globalization and mobility management, the key elements regarding how SNS service providers can succeed also highlight the importance and necessity of understanding the prior factors influencing SNS platform switch.

2. Literature Review

2.1 Social Network Sites

In an early study, community is defined as a group of members communicating with each other on an electronic platform (Creulo, Ruane & Cayko, 1992). Tredinnick (2006)

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believes that a SNS is a website driven by the user’s participation and the content generated by users. Boyd & Ellison (2007) more clearly defined that a SNS is an Internet-based service, allowing individuals to (1) establish a public or semi-public personal data system with restricted access, (2) have a list of other users and share connections with them, and (3) browse within this system and link to other users via the user list. Therefore, SNS is a web-based service and is a community established on Internet mainly by a group of people having the common interest, activity preferences, and experience or by professionals in a certain field, to provide an interactive platform for the members to make various social communications, contact each other emotionally, and exchange information.

Most SNSs provide a variety of services such as online chat (MSN, Yahoo instant messaging), e-mail, video and file sharing (Youtube), discussion groups and blogs, etc., to allow participants to communicate with each other and share information. SNSs often have millions of registered users, and they have become a necessary condition of daily life for users to use such service. At present, there are many SNSs in the world, and the well-known SNSs include Facebook, Plurk and Twitter and so on. According to the InsightXplorer database survey in May 2010, the overall SNSs flow in Taiwan reached the second place, ranking only after the portals. In addition, as the expansion of wireless network construction, recently the phenomenon of using mobile internet to browse SNSs shows a rising trend; as the global mobile Internet users connected SNSs through mobile device, Facebook, MySpace and Twitter are still the most widely used and are the most frequently visited SNSs by U.S. mobile Internet users, estimated to reach 5.62 million in 2013 (FIND, 2009).

Summary of the above survey data shows that since the prevalence of SNSs, they have been a routine of daily life for many people to log in SNSs to update their status, take a look at the status, photos, etc. of friends. As surfing Internet through mobile device becomes increasingly popular, it’s more and more difficult for users to separate SNSs. Therefore, how to understand the user satisfaction with mobile SNSs and their switching willingness is also a key point this study would focus on.

2.2 E-Service Quality (E-SQ)

With the phenomenal growth of e-services, a stream of research has been developing that aims to understand the dimensions of e-service quality (e-SQ) and their relationship with overall performance. Zeithaml et al. (2002) proposed a model for understanding and improving e-service quality, relating the design and operation of the website to certain customer perspectives. Although related studies on e-SQ do not fully concur with the dimensions and statements explored, Hernona and Calvert (2005) identified the following guidelines: (1) e-SQ is multifaceted, not unidimensional; (2) most of the personal service issues are part of recovery service, which involves dimensions different from the core service; (3) e-SQ affects satisfaction, purchase intention, and purchase; and (4) technology readiness,

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a customer-specific construct, is related to the perceptions of e-SQ.

Based on the evolving literature, Parasuraman et al. (2005) combined various concepts of online service quality (Loiacono, et al., 2002; Wolfinbarger and Gilly, 2002, 2003; Yoo and Donthu, 2001) and proposed the most comprehensive work on e-service quality. They used an empirical test and a multiple item scale (E-S-QUAL) to assess the service quality of online shopping providers and divided service quality into two categories: the core web service quality (E-S-QUAL) and the E-Recovery Service Quality (E-RecS-QUAL).

2.3 Switching cost

From literatures in economics and marketing, it is found that switching cost is considered as an important strategy element. Its definition is very broad, first proposed by Porter (1980) as the cost rising when the buyer switches the supplier. Subsequent research shows that from different angles of economics, psychology, or marketing or because of different industrial categories, switching cost may face different types of costs. Jones et al. (2000) defined switching cost as possible cost rising when consumer switches the service provider, such as time, money, and effort. Dick & Basu (1994) thought that the switching cost is the cost rising when switch occurs, including time, money and psychological costs; however, from the angle of perceived risk, it refers to any possible loss of operators when the customer perception switches, including financial loss, performance loss, as well as social, psychological and security level (Murray, 1991).

Jones et al. (2002) discussed the switching cost from the angle of service in six essential sub-types of services, including: lost performance costs, uncertainty costs, pre-switching search and evaluation costs, post-switching behavioral and cognitive costs, setup costs and sunk costs, and summarized them into three types through empirical analysis: continuity costs, learning costs and sunk costs, which has been empirically verified by in banking industry and hair service industry. Burnham et al. (2003) listed out eight different types of switching costs, and concluded three major categories: (1) procedural switching costs: economic risk costs, evaluation costs, learning costs, set-up costs; (2) financial switching costs: benefit loss costs, monetary loss costs; (3) relational switching costs: personal relationship loss costs, brand relationship loss costs.

Summing up the above studies, common scholars believe that increase of switching cost willreducethe consumer’sswitching behavior. However, Lam et al. (2004) pointed out in his study that switching cost has a positive impact on customers. The constructs of switching costs are generally used as an indicator to measure customer loyalty. As synthesis of these scholars’researches, different switching costs in different service industries have different constructs.

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3. Research Method

3.1 Research Model and Hypotheses

The purpose of this study is to discuss the user’s intention to switch SNSs, so this study first integrated related literature on SNSs to make analysis. Accordingly, the e-SQ (Parasuraman et al., 2005) measurement scales will be a good candidate to assess the quality of any web-based services, including the SNS service. Kim et al. (2004) proposed that switching barrier is one of the influencing factors. When verifying the relationship between the switching cost and the switching intention, Burnham et al. (2003) pointed out that switching cost includes three potential constructs: at the higher switching cost in procedural, financial and relational construct, the customer is less willing to switch to other services suppliers. On the other hand, customer satisfaction is highly related to performance; satisfaction and switching cost are considered as antecedents of sustained use or switching intention (Bateson and Hoffman, 1999). Finally, we combined these conceptions and established the following hypotheses, and put forward the conceptual framework, as shown in Fig I.

H1: Greater SNS Service Quality will be associated with higher Satisfaction. H2: Greater Switching Cost will be associated with higher Switching Barrier. H3: Greater Satisfaction will be associated with lower Intention to Switch. H4: Greater Switching Barrier will be associated with lower Intention to Switch.

Fig I. Research Model

3.2 Measurement Scales and Data Collection

This study summarized the literature, and initially accessed the factors that affect the SNS switching, in order to know whether these factors are representative. Then this study interviewed mobile SNS users and general SNS users for their view on the influence on the switching of SNS provider and platform. The purpose is to confirm the appropriateness of these factors and check whether there are other important factors not included into the research model.

The target population of this study is the SNS users with mobile device. Through sending instant messages to certain communities in SNS for inquiry and assistance, this study recruited qualified interviewees. For the sake of preciseness, this study arranged 6 senior SNS users to make pretest and invited 10 participants to take the pilot test, and then modify the questionnaire content according to the interviewees’feedback.

Switching Cost Satisfaction Switching Barrier Intension to Switch H1 H2 H4 SNSs Service Quality H3

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As for the collection of empirical data, this study took online survey as a research tool. The questionnaire is divided into four parts, including network use behavior, measurement of factors influencing SNS switching, satisfaction measurement, and basic personal data. The questions in the questionnaire apply Likert seven-point scale, with 1 representing “strongly disagree”and 7 representing “strongly agree”. The questionnaire was setup in an html format on a Web server with a fixed IP address. By using instant message or email, we invited users of major SNSs in Taiwan to fill up the questionnaire. Volunteers can fill up the questionnaire by clicking the URL link in the instant message or email. Questionnaire data was saved in the database server setup for future analysis.

Due to the Internet characteristics of anonymity, we could not know whether the respondents are qualified subjects. Therefore, this study judged the qualification of each respondent according to their answer to the question of “whether have experience in switching SNS platform or using SNSs through mobile device”. Unqualified respondents were discarded. There were also reverse questions in the questionnaire, used to filter out invalid questionnaires which were falsely filled. There were totally 274 questionnaires recorded in the database, after deducting 121 invalid or nonconforming ones, 153 valid records were retained for the following analyses. In the data set, the male to female ratio was 46.4% to 53.6%, and the 21 to 30 age group accounted for 66.0%, followed by the 31 to 40 age group, which accounted for 26.8%. Students were the largest group, which accounted for 42.5%, followed by service industry and technology industry with 17.0% and 11.1%, respectively. The time spent on SNSs was almost in 10 hrs/ week, which accounted for 49.7%, and the ratio of 20 hrs/ week was 27.5%.

4. Data Analysis and Results

Scales for the SNS service quality, switching costs, satisfaction, switching barrier, and intentions to switch were first refined using exploratory factor analysis (EFA) and Cronbach’s αreliability analysis. Then confirmatory factor analyses (CFAs) was conducted for further model and hypothesis verification (Anderson and Gerbing 1988; Churchill 1979). Data analysis methods in the EFA phase used principal component analysis (PCA) to extract the eigenvalue, and Varimax orthogonal rotation to analyze dimensions of factors, and both methods were implemented in SPSS 15. In the CFA phase, Partial Least Square (PLS), as implemented in SmartPLS 2.0 M3, was used to process the hypothesis model fitting and testing.

The EFA and Cronbach’s α results can be seen in TABLE I. Each variable’s factor loading exceeds 0.5, a standardized value suggested by Hair et al. (1998), and each constructs’reliability (theCronbach’sα)isover0.6,exceedsthe acceptablelevelsuggested by Murphy and Davidshofer (1988). The composite reliability (C.R.) values show that the reliability of each constructs is higher than the suggested value of 0.7, as Nunnally (1994)

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recommended, which indicates that the model has good construct reliability. The average variation extracted (AVE) values for all constructs were greater than 0.5, which also indicates a model with convergent validity (Hair et al., 1998).

In TABLE II we can find that no pair of measures had correlations exceeding the criterion of 0.9 as suggested by Hair et al. (1998), which implies that no multicolinearity existed among these constructs. We also employed the opinion of Kline (2005), discriminant validity can be established when an inter-factor correlation is below .85. Fornell and Larcker (1981) suggested a more robust way of measuring discriminant validity in which a correlation between two constructs should be lower than the squared root of AVE value for any one of the two constructs. According to these suggestions, all the constructs have discriminant validity.

TABLE I. Analysis results of Measurement Model

SNS Service Quality Switching Cost

Loading Cronbachs α AVE C.R. R 2 Loading Cronbachs α AVE C.R. R 2 Efficiency 0.89 0.82 0.93 0.38 Risk 0.84 0.67 0.89 0.80 Ef1 0.66 Risk1 0.71 Ef2 0.72 Risk2 0.68 Ef3 0.70 Risk3 0.76 Fulfillment 0.91 0.80 0.94 0.67 Risk4 0.71 Ful1 0.53 Learning 0.79 0.70 0.88 0.69 Ful2 0.67 Learn1 0.71 Ful3 0.71 Learn2 0.64 Ful4 0.69 Learn3 0.66 Privacy 0.92 0.87 0.95 0.73 Evaluation 0.70 0.77 0.87 0.55 Priv1 0.60 Evl1 0.73 Priv2 0.56 Evl2 0.67

Priv3 0.57 Benefit Loss 0.69 0.61 0.83 0.57

System Availability 0.66 0.62 0.82 0.50 BnfL1 0.56

SA1 0.50 BnfL2 0.65

SA2 0.55 BnfL3 0.69

SA3 0.50 Brand Relationship Loss 0.76 0.67 0.86 0.36

Compensation 0.92 0.86 0.95 0.66 BRL1 0.72

Comp1 0.61 BRL2 0.69

Comp2 0.60 BRL3 0.7

Comp3 0.58 Personal Relationship Loss 0.85 0.87 0.93 0.29

Contact 0.88 0.81 0.93 0.51 PRL1 0.79

Cont1 0.55 PRL2 0.87

Cont2 0.53 Other Constructs

Cont3 0.57 Satisfaction 0.93 0.88 0.96 0.32

Responsiveness 0.92 0.80 0.94 0.84 Sat1 0.92

Resp1 0.63 Sat2 0.96

Resp2 0.62 Sat3 0.93

Resp3 0.6 Switching Barrier 0.84 0.61 0.89 0.36

Resp4 0.62 SB1 0.70 SB2 0.77 SB3 0.76 SB4 0.83 SB5 0.84 Intentions to Switch 0.97 0.93 0.98 0.10 SI1 0.96 SI2 0.98 SI3 0.96

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TABLE II. Correlations and Discriminant Validity of all Constructs

Ef Ful Priv SA Comp Cont Resp Risk Learn Evl BnfL BRL PRL Sat SB SI

Ef 0.90 Ful 0.56 0.89 Priv 0.43 0.60 0.93 SA 0.53 0.65 0.51 0.79 Comp 0.29 0.54 0.64 0.43 0.93 Cont 0.26 0.44 0.56 0.32 0.70 0.90 Resp 0.45 0.65 0.81 0.58 0.75 0.64 0.89 Risk 0.06 0.24 0.15 0.28 0.15 0.23 0.19 0.82 Learn 0.08 0.19 0.12 0.28 0.13 0.23 0.13 0.74 0.84 Evl 0.02 0.14 0.13 0.18 0.09 0.10 0.13 0.63 0.68 0.87 BnfL 0.09 0.21 0.05 0.19 0.04 0.20 0.09 0.60 0.52 0.45 0.78 BRL 0.29 0.41 0.26 0.25 0.18 0.23 0.22 0.38 0.28 0.26 0.40 0.82 PRL 0.28 0.21 0.18 0.13 0.03 0.08 0.07 0.34 0.25 0.23 0.32 0.52 0.93 Sat 0.58 0.47 0.46 0.40 0.37 0.33 0.48 0.11 0.11 0.11 0.11 0.49 0.34 0.94 SB 0.00 0.26 0.15 0.29 0.18 0.20 0.17 0.52 0.43 0.39 0.51 0.45 0.33 0.21 0.78 SI -0.11 -0.01 0.03 -0.04 0.09 0.07 0.06 0.15 0.22 0.12 0.08 0.10 -0.03 -0.12 0.27 0.97

* The squared root of average variance extracted (AVE) is depicted in bold italic type on the diagonal.

TABLE III. Results of Hypotheses

Hypothesis: Path Estimates SE T value Results

Hypothesis1:SNS’ServiceQuality Satisfaction 0.53 0.03 18.15*** supported Hypothesis 2: Switching Cost Switching Barrier 0.60 0.03 23.47*** supported Hypothesis 3: Satisfaction Intention to Switch -0.18 0.04 5.27*** supported Hypothesis 4: Switching Barrier Intention to Switch -0.30 0.04 8.24*** supported

***: significant value at p < 0.001, one-tailed.

The results of the structural model are shown in the TABLE III. The four hypotheses proposed by this research are all significant with p<0.001, and thus all the hypotheses are supported by these results. The final results of the research model are shown in Fig II.

Fig II. Result of Research Model

5. Discussion and Conclusions

In order to understand the influencing factors for SNS users switching to their mobile

SNSs SQ Satisfaction

Switching

Cost SwitchingBarrier

Intention to Switch

Ef Ful Priv SA Com

p Cont Resp

Risk Learn Evl BnfL BRL PRL

0.614 0.821 0.852 0.705 0.811 0.716 0.916 0.892 0.829 0.741 0.754 0.599 0.534 0.531*** 0.599*** - 0.185*** -0.304***

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device, this study included constructs of service quality, switching costs, satisfaction, and switching barrier in the framework. The empirical data analyses lead to several interesting observations. First, regarding SNS service quality, the factors mostly emphasized by users is responsiveness, indicating that the user cares whether the SNS provider can quickly deal with problems; meanwhile, privacy is still a factor attached much importance to by most users as seen in previous studies (Parasuraman et al., 2005; Wolfinbarger and Gilly, 2003); another critical concern is whether SNS providers can fulfill their promise. Second, regarding switching cost, most users are concerned about the potential risks if they switch to new SNSs or mobile device, such as uncertainty of whether the new SNSs or mobile device would be more useful than the one currently used, or would lead to unexpected problems; in addition, to switch to new SNSs or mobile device, users need to learn new SNS operations, which is one of the factors why the users hesitate to switch.

The proposed four hypotheses are all verified in empirical research, showing thatSNSs’ service quality and switching cost will indirectly affect the intention to switch respectively through satisfaction and switching barrier. These outcomes match with previous studies of service quality and switching cost, such as the greater service quality bringing higher satisfaction (Loiacono, et al., 2002; Parasuraman et al., 2005; Wolfinbarger and Gilly, 2002, 2003; Yoo and Donthu, 2001), and higher switching cost causing higher switching barrier (Kim et al., 2004). Accordingly, higher satisfaction and greater switching barrier of the present SNSs both decrease the users’intention to switch, The primary implication is that SNS providers do not only need to improve their SNS service quality in order to improve user satisfaction, it is also important whether they can provide a more unique and useful service, in order to distinguish with other competitors, and thus increase the barriers to switch to other SNSs, and reduceusers’switching intention.

Acknowledgements

The authors are grateful to National Science Council of the Republic of China for financially supporting this research under NSC98-2410-H-214-016-MY2.

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數據

TABLE I. Analysis results of Measurement Model

TABLE I.

Analysis results of Measurement Model p.7
TABLE II. Correlations and Discriminant Validity of all Constructs

TABLE II.

Correlations and Discriminant Validity of all Constructs p.8
TABLE III. Results of Hypotheses

TABLE III.

Results of Hypotheses p.8

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