Global e-Business Association
[ Article ]
The e-Business Studies - Vol. 17, No. 5, pp.89-105
ISSN: 1229-9936 (Print) 2466-1716 (Online)
Print publication date Oct 2016
Final publication date 30 Oct 2016
Received 27 Sep 2016 Revised 19 Oct 2016 Accepted 27 Oct 2016

A Preferring Analysis on the Specific Mobile Application in Using the Smart Phone

Kook Yong Lee*
*Associate Professor, Dept. of Business Administration, Kunsan National University
스마트폰을 이용한 특정 모바일 앱 선호분석
*군산대학교 사회과학대학 경영학부 부교수


The purpose of this study is to examine the role of Negative Emotions(Regret and Disappointment), Switching Cost, Familiarity, Usability, Habit, Innovativeness in decision making of selecting the mobile application via smart-phone or tablet pc. Particularly i wished to confirm the mediating effect of Switching Cost in purchasing or download freely mobile applications. To this end, the secondary data were collected and itheoretically arranged step by step. Using the theoretical proposed model to explain the relationships between the constructs, i identify the operational definitions, 10 Hypotheses were established. There was executed the survey of 149 mobile application users.

Using the collected data, previous performances to confirm the construct validity and internal consistency by Cronbach’s a and Partial Least Square Analysis was executed. As the result of test that make the relations of used variables clear, i can get the conclusion as followings; First, Regret has the effect negatively to Switching Cost, the effects of between Familiarity, Usability and Switching Cost was statistically identified. Second, it was significantly tested relations between Familiarity and Habit. Third, relations between Switching Cost, Usability, Habit, Innovativeness and Switching Intention was confirmed positive effects in statistics.


최근에는 하루가 멀다하고 새로운 모바일 앱이 시장에 출시되고 있으며, 많은 모바일 개발자들은 자신이 개발한 모바일 앱을 홍보하고 판매하기 위해 많은 노력을 기울이고 있다. 모바일 마켓에 많은 앱이 출시되었다고 하더라도, 대부분의 사용자들은 새로 출시된 모바일 앱을 사용하기 보다는 기존에 사용하고 있는 모바일 앱을 더 선호하는 경향이 강하게 된다. 본 연구는 이러한 모바일 앱 이용자들이 특정한 모바일 앱 사용에 집착하는 이유를 찾아내고, 새로운 모바일 앱으로의 전환을 억제하는 요인이 무엇인가를 규명하고자 한다. 이를 통해 새로운 모바일 앱 개발자 및 마케터들에게 있어 특정한 모바일 앱을 선호(집착)하는 이유를 규명함으로써, 향후 새로운 모바일 앱이 갖추어야 할 조건 또는 전략적 선택에 도움을 주고자 하였다. 이를 위해 선행연구로부터 7개의 영향요인을 도출하였으며, 총 10개의 연구가설을 설정하였다. 실증분석을 위해 K대학교 대학생 149명을 대상으로 하는 설문조사를 실시, SPSS V.21과 smart-PLS 2.0을 통한 실증분석을 수행하였다. 그 결과 실망이 전환비용에 미치는 영향력(가설 2)과 친숙성이 전환의도에 미치는 영향력(가설 5)을 제외한 총 8개의 연구가설이 유의적이라는 점을 확인하였다. 이를 통해 특정한 모바일 앱을 지속적으로 이용하려는 이유와 부정적 감정의 영향력 관계를 확인하였다는 의미를 지닌다. 이러한 본 연구의 결과는 모바일 앱을 개발하는 기업에게 실무적인 시사점을 줄 것으로 판단된다.


Regret, Disappointment, Switching cost, Familiarity, Usability, Habit, Innovativeness


후회, 실망, 전환비용, 친숙성, 사용성, 습관, 혁신성


Ⅰ. Introduction

The outcomes of decisions often give rise to the experience of positive or negative emotions. Sometimes we experience positive emotions when a decision turns out favorably and we experience negative emotions when a decision turns out unfavorably(van Dijk, Zeelenberg and van der Pligt, 2003). Two of the emotions that attracted most attention from decision researchers are regret and disappointment(see for overviews, Gilovich and Medvec, 1995; Zeelenberg, van Dijk, Manstead and van der Pligt, 2000). Many studies recognize that emotions play a major role in customer(dis)satisfaction and their subsequent judgments(Erevelles and Leavitt, 1992; Mano and Oliver, 1993), and recent studies have also examined that the emotions—not just cognition—influence judgment, decision-making, and even post-purchase behaviors(Lerner and Keltner, 2000; Oliver, 1997). There is a consensus regarding the positive relationship between regret/disappointment and customer complaint behavior such as behavioral intention to switch. More particularly, previous studies have shown that regret and disappointment play an important role in customers’ decision-making processes(e.g., Cooke, Meyvis and Schwartz, 2001; McCollough, Berry and Yadav, 2000). Moreover, recent research emphasized the role of regret and disappointment in switching another or satisfaction-related behaviors.

However, consumers’ regret and disappointment always not result in switching behavior, sometimes customers persist in service continuance even if they encounters regret and disappointment in previous usage. Also, despite of regret and disappointment, many people make decision not to switch the better service of system easily. Why do users hesitate to switch a better service or system ? Perhaps the economic/non-economic costs or even act in accordance with the complaint or regret and disappointment because they can not keep readily transferred to the new service or system is in transition to a new service from existing services to existing services or systems that would be expected to. In this study, switching costs and the impact on the transition to the familiarity of these new services to existing services(system) and is expected to have. Therefore, the purposes of this study are three-fold: 1) to investigate the relative effect of regret and disappointment on behavioral intention to switch; 2) to examine the effect of familiarity, usability on behavioral decision making process; and 3) to examine the effect of switching cost and habit, innovativeness of users on switching intention.

Ⅱ. Literature Review

1. Switching intention to another mobile application

Switching refers to the termination of a relationship with the service provider(Zeelenberg and Pieters, 2004). This termination may either be followed by initiating a relationship with another service provider, by performing the service yourself, or refraining from the service altogether. Ample research has shown that dissatisfied consumers are more likely to switch than satisfied or disappointed customers, i expect a positive relation between regret, disappoint and switching behavior. Regarding the importance of discontinuance behavior, research has started to focus on customer switching behavior(Fan and Suh, 2014). Users’ behavior of IT switching in this paper is defined as the behavior of replacing an incumbent IT with a disruptive one. In contrast to a large body of research on IT usage, few studies have focused on IT switching(Bhattacherjee, Limayem and Cheung, 2012).

Customers’ switching behavior has been studied in marketing literature which analyzes brand or service switching(Anton, Camarero, Carreroet, 2007), Rather than developing a general model of switching behavior, antecedents of switching behavior in the context of service switching were identified, several studies have been conducted on switching behavior in marketing, few have addressed users’ switching behavior in ITs.

Service switching, switching intention, customer loyalty, customer retention, and repurchase intention are all associated (Bansal and Taylor, 1999; Han, Kim and Hyun., 2011). While customer loyalty, retention, and repurchase intentions indicate favorable outcomes for the provider, service switching and switching intention imply unfavorable outcomes(Bansal and Taylor, 1999; Han et al., 2011). In particular, the term “behavioral intention” includes both switching and rebuy intention(Keaveney, 1995). Whereas intention to switch refers to negative consequences or results, intention to repurchase(rebuy) indicates positive consequences(Han et al., 2011) such as satisfaction, habit, switching cost and so on.

2. Negative Emotions(Regret and Disappointment)

The purpose of this study attempts to understand the switching behavior from old to new system(IT). Especially, my research focuses on the roles of two specific emotions, regret and disappointment based by a service failure experience. Previous many research suggest that these two negative emotions(regret and disappointment) are associated with service failures and are most directly related to decision-making(Inman, Dyer and Jia, 1997) such as continuance or switching other service. As i argued earlier, regret stems from ‘‘wrong decisions,’’ implying that there would have been a better alternative. If that is the case, it is likely that customers opt for this better alternative when they are again confronted with a similar situation(Zeelenberg and Pieters, 2004).

One may also expect a relation between disappointment and switching, since one way to cope with this disappointment is to get away from the situation(refrain from the service altogether) or try to do better next time(initiate a relationship with another service provider or performing the service yourself). Disappointment is experienced when the chosen option turns out to be worse than expected. Whereas regret is experienced when the chosen option ends up being worse than the rejected options. In other words, disappointment stems from disconfirmed expectancies, whereas regret stems from bad decisions(van Dijk et al. 2003).

Research has shown that both emotions have a negative impact on the utility that is derived from decision outcomes(e.g., Mellers, 2000) and previous research suggests that these two emotions are most directly related to decision-making(Inman et al, 1997). Although other negative emotions, such as anger, shame, disgust, embarrassment, and sadness, might be experienced during or following service encounters(Oliver, 1997), these other emotions are not directly linked to the decision-making process (Zeelenberg and Pieters, 1999).

Regret and disappointment are related emotions, but they differ in the context of decision-making. Regret and disappointment share in common the fact that they are experienced when the outcome of a decision is unfavorable: They both concern “what might have been,” had things occurred differently. However, previous studies on emotions (Inman et al, 1997; Zeelenberg and Pieters, 1999) regarded the differences between these emotions as significantly important, arguing that they differ with respect to the conditions under which they are felt, and how they affect decision-making. In my research, i want to find out that both emotions have a qualitatively different phenomenology, and both have different impact on behavior (e.g., Zeelenberg et al., 2000). In the present study i focus on the effects of the anticipation of negative emotion on future’s decision making. In accordance with the Regret and Disappointment related studies, i suggest the following hypotheses regarding the perceived switching cost of mobile system users:

H1: The degree of regret positively affects the perceived switching cost.
H2: The degree of disappointment positively affects the perceived switching cost.

3. Familiarity

.Familiarity is the people knowledge of a product or service, based on their experience and previous contacts(Luhmann, 1988). Accordingly, familiarity is defined as ‘‘the number of experience related to a product that has been accumulated by the consumer’. Moreover, several studies which conducted individuals’ purchasing behavior reported that familiarity can have notable influence on consumers’ decision-making processes(Flavian, Guinaliu and Gurrea., 2006). According to Paswan and Ganesh(2003) familiarity got little attention in the area of services because the measurements of familiarity in service context are relatively difficult. I might consider particularly relevant those studies conducted around individuals’ switching decision making, due to the notable influence that familiarity can have on consumers’ decision-making processes(Gefen and Straub, 2004).

Familiarity is an understanding, often based on previous interactions, experiences, and learning of what, why, where and when others do what they do(Gefen, 2000). Familiarity refers to the extent to which individuals know each other, and it can be built up through interactions(Zhao, Lu, Wang, Chau and Zhang, 2012).

Gefen(2000) indicate that customer’s familiarity significantly has a positive effect intent to purchase in e-commerce. Celeste, S-P. Ng.(2013) reveal that the familiarity is a critical predictor affecting positively intention to purchase in social commerce, conducting the research in cross-regional study. Casalo´´ et al.(2008) reveal the moderating role of consumer familiarity on the website loyalty formation process. The familiarity may be defined as the number of experiences related to a product that have been accumulated by the consumer, authors such as Gefen(2000) defend the idea that familiarity manages to reduce uncertainty and simplify relationships with others by generating a knowledge structure. Prior research has shown that people are prone to trusting those that they are familiar with irrespective of either in a physical or virtual environment(Lu, Zhao and Wang, 2010; Wu and Chang, 2005) as familiarity reduces uncertainty. Accordingly, because a higher familiarity indicates more accumulated knowledge based on previous successful interactions, familiarity may lead to more participation environments. and i guess familiarity affect intention to switch another service(system).

H3: The degree of system users’ familiarity positively affects the perceived switching cost.
H4: The degree of system users’ familiarity positively affects system usage habit.
H5: The degree of system users’ familiarity negatively affects system switching intention.

4. Usability

Usability stands on the user-system axis, focuses on the effective, efficient and satisfactory task accomplishment and aims to support a normal and uninterrupted interaction between the user and the system(Tsakonas and Papatheodorou, 2008). Easiness of use is considered as a crucial attribute of interactiona, especially in advanced systems, like aggregated search interfaces(Park, 2000). Previous usability studies (McMullen, 2001) have shown that terminology raises important barriers in user’s understanding of principal functions and contribute to negative changes in their affective state. Additionally the aesthetic appearance and layout has a crucial role to the overall satisfaction rate. Van van House, Butler, Ogle and Schiff (1996) suggested a simplified interface that would reduce users’ efforts and recent usability studies have concentrated on the effect of inappropriate visual layout to user interaction(Allen, 2002). Shackle(1991) describes usability as “a technology's capability to be used easily and effectively by the specified range of users, given specified training and user support, to fulfill the specified range of tasks, within the specified range of environmental scenarios”. There is an extensive set of usability evaluation methods that can be used, with a variety of performance measures(Morris and Turner, 2001).

It is reasonable to believe that the acquisition of higher abilities will not only depend on the time dedicated by the consumer to managing the information technologies, but also on the facilities that the IT Application offers its users(Casalo’ Flavian and Guinaliu, 2008). Thus, the level of usability may help the individual to acquire a degree of ability that will ensure that the IT Application occupies a favoured position compared to the others belonging to the same category, so enhancing the level of lock-in and loyalty or switching cos and switching intention to another application. In this research, i gauge usability in terms of how well subjects perform a series of Mobil Applications with a particular selection, this performance is measured by speed and task correctness.

H6: The degree of system users’ usability positively affects the perceived switching cost.
H7: The degree of system users’ usability negatively affects system switching intention.

5. Switching Cost

A number of researchers have adopted the term “switching costs” to investigate the aspects of losing existing benefits or incurred extra efforts when accepting a new product or service(Burnham, Frels and Mahajanet, 2003). Intrinsically, switching costs arise as a result of prior commitments to the incumbent supplier in terms of specific physical, informational, artificially created, or psychological investments. It appears that consumer acceptance of an innovation involves various levels of switching behaviors. A rigorous definition of switching (full switching or attrition) means that consumers totally abandon their habitual behaviors, terminate a relationship with a service provider, and adopt the new behaviors(Huang and Hsieh, 2012). Regarding the importance of discontinuance behavior, research has started to focus on customer switching behavior. Users’ behavior of IT switching in this paper is defined as the behavior of replacing an incumbent IT with a disruptive one. In contrast to a large body of research on IT usage, few studies have focused on IT switching(Bhattacherjee et al., 2012).

Switching costs, such as the investment of time, money, and effort, in customers’ perception make it difficult to change providers(Ranaweera and Prabhu, 2003). The theory of planned behavior suggests that the degree to which individuals can control behavior is a factor influencing behavioral intentions(Ajzen and Schifter, 1985). In other words, when an individual perceives some negative factor as impeding the execution of a behavior, that individual will reduce his/her intentions for that behavior(Chuang, 2012). Though several studies have been conducted on switching behavior in marketing, few have addressed users’ switching behavior in ITs. Adapted from switching literature in marketing, this paper aims to develop a general model for explaining users’ switching intention to a disruptive IT(Fan and Suh, 2014). The present study, however, adopts the definition of switching costs, as Burnham et al.(2003) propose, which refers to the one time costs that customers associate with the process of adopting a new service(Huang and Hsieh, 2012).

H8: The degree of perceived switching cost negatively affects system switching intention.

6. Habit

Habits are routine behaviors that repeat regularly and tend to occur subconsciously. They can be automatic responses to specific situations (Ouellette and Wood 1998, Limayem and Hirt, 2003) and reflect automatic behaviour tendencies(Ouellette and Wood 1998; Limayem, Hirt and Cheung, 2007). Many researchers in different disciplines such as social psychology, marketing, consumer behavior, and online users’ behavior have examined the role habits(Liyaman and Hirt; 2003; Khalifa and Liu, 2007; Limayem and Cheung, 2008; Wood and Neal, 2009; Lanton, Wilson and Mao, 2010; Barnes, 2011; Guo and Barnes, 2011; Chiu, Hsu, Lai and Chang, 2012; Vance, Siponen and Pahnila, 2012), research reported in the extant literature emphasize that the formation of a habit is derived from a certain amount of repetition or practice in a given stable environment (Ouellette and Wood, 1998). Prior information systems studies related to habitual behavior mainly explore and verify the positive effects of habit on behavioral intention or the continual usage of an existing information system(Chiu et al., 2012; Vance et al., 2012). Ouellette and Wood(1998) support that habit can increase the continuance of existing behaviors, Gefen(2003) finds that habit is a major factor in explaining the variance of continued use of a Web site, showing the positive effect of habit on the continued use of the same technology. Limayem et al.(2007) also show that habit has a significantly positive effect on continuance in the context of WWW usage and various literature explain the role of habit/habitual behavior predicting the continuance or usage. Most studies insist that the role of habits is not only an automatic behaviour to specific situations, but also an antecedent of behavioral intentions to increase the continuance intention of existing behavior. Besides the negative effect of perceived risk in the online channel on the intention to transfer usage, another negative effect on the intention to transfer usage is offline habit related to the offline channel. There have been few information systems studies related to habit focusing on the negative effects of deep-seated habitual behavior toward an existing system on the intention to use a new system or new technology, particularly, Lu, Cao, Wang and Yang (2011) show that offline habit has a negative effect on the intention to conduct e-commerce transactions.

H9: Systemusage habit negatively affect systemswitching intention

7. Innovativeness

From Rogers(1995), innovation diffusion theory lends additional support by suggesting that users’ personality differences can potentially influence how users form their intentions to perform behaviors, Various forms of consumer innovativeness are said to exist including consumer innate innovativeness(CII), domain specific innovativeness(DSI), and vicarious innovativeness(VI). Nevertheless, in the study of the measurement of consumer innovativeness, Hauser, Tellis and Griffin (2006) note that the results of different consumer innovativeness scales indicate a lack of consensus, and the strength of the relationship between measures of consumer innovativeness and product adoption behavior have been mixed(Chao, Reid and Mavondo, 2012). Similar to the positive effect of relative benefit on the intention to transfer usage, innovativeness has been found to be an impetus for the adoption of new products or services (Im, Mason and Houston, 2003). According to Rogers(1995), innovativeness represents the extent to which an individual will adopt an innovation before other members of the new system. It reflects the degree of adoption of new products or ideas in the individual’s experience. Many studies in consumer behavior, marketing, and innovation diffusion examined innovativeness and results show that different personal innovativeness can lead to different intentions to adopt an innovative technology(Lu et al., 2011).

Some studies have examined domain-specific innovativeness’ influence on the adoption of new shopping procedures or new service models. Aldas-Manzano, Lassala-Navarre, Ruiz-Mafe and Sanz-Blas(2009) also show that domain-specific innovativeness has a positive impact on the use of online banking. Jackson, Yi and Park (2013) note that the most important predictors of user acceptance of an e-commerce system for streamlining the process and procedures of hospitals. Personal innovativeness had a significant effect on each of these predictors, showing that involving highly innovative people throughout the adoption process is one of the most important success factors in the implementation of a technology. Prior research suggests that the relationship between consumer innate innovativeness, in particular, it is argued that domain specific innovativeness and vicarious innovativeness may play an effective mediating role between consumer innate innovativeness and the adoption of really new products(Im et al., 2007). To date no academic research to date actually considers consumer innate innovativeness, domain specific innovativeness and vicarious innovativeness together. This research aims to provide much needed evidence and insight by examining the relationship between these measures of consumer innovativeness and switching intention with the switching or transferring to new mobile application.

H10: The degree of system users’ innovativeness positively affects system switching intention.

Ⅲ. Research Model and Methods

1. Research Model

This research addresses two research objectives. Firstly to develop a theoretically derived conceptual framework as outlined in Fig. 1 to investigate the role of negative emotions(regret/disappointment) in switching system behavior, Secondly, the research seeks to examine the effects of switching cost and familiarity, usability, included habits, innovativeness to choose the new system(application).

[Figure 1]

Research Model

2. Measurement development

The unit of analysis of this study is the individual users to switch from offline to use online channel. Measurement items for the focal constructs were derived from prior research and adjusted for our study. After measurement item development, items were translated into a Korean version of the survey. Questionnaire survey approach is employed in this research. Totally, eight major variables(seventeen items) are included in this study. Measurements were modified and employed from prior studies. This study uses five point Likert scale to measure each variable. Each of these items are ranging from ‘‘very disagree’’ to ‘‘very agree’’ and coding from 1 to 5 respectively.

Measurement development

Demographic Characteristics of Respondents

3. Samples and Data Collection

A total of 149 valid survey data were collected. The characteristics of the sample are very similar to those of the target population; there is a balance in gender(51.7 percent female and 48.3 percent male) average age of respondents is about 23.63. In addition, the app store engagement ratio of the sample is Play Store95(63.8%) v.s. Apple store 54(36.2%).

Ⅳ. Data Analysis

1. Measurement Model

Assessment of the research model was conducted using PLS (Partial Least Square). PLS is a structured equation modeling technique that can analyze structural equation models(SEMs) involving multiple-item constructs, with direct and indirect paths. PLS works by extracting successive linear combinations of the predictors and is effective in explaining both response and predictor variation(Chin, 1998).

PLS is a powerful tool for analyzing models because of the minimal demands on measurement scales, sample size, and residual distributions. In addition, PLS avoids two serious problems, inadmissible solutions and factor indeterminacy. SEM approaches, such as LISREL and AMOS, are not able to deal with non-normal distributions, and they can yield non-unique or otherwise improper solutions in some cases. PLS is not as susceptible to these limitations. The emphasis of PLS is on predicting the responses as well as in understanding the underlying relationship between the variables. 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.

A PLS analysis involves two stages: (1) the assessment of the measurement model, including the reliability and discriminant validity of the measures, and (2) the assessment of the structural model Individual item loadings and internal consistency were examined as a test of reliability. Individual item loadings that are greater than 0.7 are considered to be adequate. As shown in Table 3, loadings for all measurement items are above 0.7, indicating that there is sound internal reliability. In addition, all the weights are statistically significant at p <'0.01. The almost uniformly distributed weights show each item contributes to each construct equivalently. In addition, we also investigated Cronbach's alpha for internal consistency.

Table 3 shows the inter-correlations of the constructs and variance shared between the latent variables and their indicators. The diagonal elements in Table 4 are the square root of the AVE. This showed that the square roots of each AVE value were greater than the off-diagonal elements. The measurement model, thus, had a reasonable degree of discriminant validity among all of the constructs. The results of the measurement analysis also indicated that all the constructs and measures have acceptable discriminant validity.

Construct Results of CFA

Correlation of latent variables

The Average Variance Extracted (AVE) was also calculated. This shows the variance that a construct captures from its indicators, relative to the variance contained in measurement error. This statistic is generally interpreted as a measure of reliability for the construct and as a means of evaluating discriminant validity. All AVEs for the constructs in our study were greater than 0.6. This indicated that 60% of the variance of the indicators could be accounted for by the latent variables. Also, if all composite reliability values are higher than 0.8, it can be concluded that measurements have both internal consistency and convergent validity. According to the results shown in Table 3, all result values in this study are higher than 0.8, which means that the measurement model of this study has suitable composite reliability. The AVE is also used to assess discriminant validity. The square root of AVE should be greater than the correlations among the constructs; that is, the amount of variance shared between a latent variable and its block of indicators should be greater than the shared variance between the latent variables. The discriminant validity of the measures was satisfied. Thus, with acceptable reliability, convergent validity, and discriminant validity, i proceeded to test the causal model and the research hypotheses.

2. Structural Model

After determining that the measurement model was satisfactory, i assessed the structural model. Prior to examining the structural model, i estimated the interaction terms for regret, disappointment, switching cost, familiarity, habit, usability, innovativeness and switching intention. At first, to avoid computational errors by lowering the correlations between the product indicators and their individual components, i standardized the indicators of each construct (Chin, Marcolin and Newsted, 2003).

The model testing results, including path loadings, corresponding t-values (in parentheses), and R-squares, were shown in Table 5. I next assessed the structural model. Table 5 and Fig. 2 presents the test results. I used the bootstrapping procedure to test the significance of all paths. As shown in Fig. 2, eight proposed hypotheses in the research model were found to be supported except two hypotheses. More specifically, the paths between regret, familiarity, switching cost and habit were found to be significant as hypothesized, supporting H1, H3, H4 but H2 was not supported. The hypothesized paths between familiarity, usability, switching cost, habit, innovativeness and switching intention were all significant at the p < 0.001 level, supporting H6, H7, H8, H9 and H.10 except of H.5.

Hypotheses Testing

[Figure 2]

Structural Model Results

Ⅵ. Conclusion and Implications

1. Conclusion

These results would provide a better understanding of consumers’ switching behavior and offer suggestions to providers in boosting their consumers’ use of new service or new mobile application, and explain the reasons why do people stick old mobile application rather than use the better system(mobile applications) in spite of regret and disappointment to use the old system. My Research focus on finding out the answers “Why do people hesitate to switch the new system in spite of negative emotions(regret and disappointment) resulted from old system ?”. To Accomplish this objectives, collecting research data from literature and field survey and analyzing the relationships between predictors and intention to switching intention. I would identify the motivators and inhibitors that influence switching intention to new system(mobile app) perspectively. From a theoretical perspective, the contribution of this research will help understand and identify the factors that influence switching intention such as regret, disappointment, switching cost, familiarity, habit, innovativeness, including hindering constraints and promoting factors originated from two different contexts. From a practical perspective, the research will provide the suggestions to system providers on how they can encourage more users to switch a new system, and strategies to foment consumers from the old to new system(mobile app).

From a new perspective focusing on the inhibitors and motivators to facilitate the switching intention from old to new mobile application, I explores the factors that impact users’ switching intention to transfer their usage to others’ mobile application. My findings are based on data collected from respondents using smart phone services as the research context. Regret was also tested as motivator that affect positively switching cost, but disappoint s not related to switching cost statistically, contradicting results from previous research(van Dijk et al. 2003; Zeelenberg et al., 2000). Corroborating results from previous research (Zhao et al., 2012; Casalo´ et al., 2008; Lu et al., 2010; Wu and Chang, 2005), my research demonstrates that familiarity and usability has a positive effect on the switching cost and habit to continue the specific mobile app but the relation between familiarity and switching intention was not tested in statistics. Thus higher familiarity and usability from cultivated a specific mobile app affects positively higher degree of switching cost and habit, i confirmed that these constructs play the important role of facilitating to stick a specific mobile app.

From test result, i reveal that negative relationship exists between switching cost and switch intention(Huang and Hsieh, 2012) in the context of mobile application usage.

In previous research on habit, the focus is on the effect of habit in a single channel and its effect on continued intention, and results from such research validate that habit has a negative effect on switching intention like the result of Liao et al.(2006).

Innovativeness in new mobile application is found to be the most powerful antecedent of the intention to switch another mobile application resulted from structural modelling output. Therefore, marketers that prepare the new mobile application, can leverage consumers’ innovativeness and identify early adopters of new SW. For one thing, early adopters constitute the initial consumer groups of new SW; for another, the groups that adopt new mobile application tend to expand more quickly via word-of-mouth recommendation. With regards to the impetus factors, my study examines the effects of both switching intention and innovativeness in new technology on the intention to transfer others mobile application usage. Corroborating results from previous research (Lu et al., 2011; Im et al., 2007; Chao et al., 2012), our research demonstrates that innovativeness has a positive effect on the intention to transfer others mobile application usage.

2. Implications

Managerially, academics and practitioners who want to promote new mobile application can draw several insights from the present study. First, mobile applications designers and mobile app providers are encouraged to develop or facilitate the familiarity, usability for the old system and offer customized services, for example, related with the higher habit and innovativeness.

This will help to enhance the users’ sense of switching cost over conventional using not to occur the switching intention.

Second, mobile application users are apt to maintain their current application usage behaviors not to transfer to another application in fully satisfied without negative emotions(regret and disappointment). This inertia encourages mobile application designers to develop products to satisfy the needs of customers, such as providing functions and services what they wanted.


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[Figure 1]

[Figure 1]
Research Model

[Figure 2]

[Figure 2]
Structural Model Results


Ⅰ. Introduction
Ⅱ. Literature Review and Hypotheses
Ⅲ. Research model and Methods
Ⅳ. Data Analysis
Ⅴ. Conclusion and Implications

<Table 1>

Measurement development

Construct No. of Items Sources of Questions
(1) regret 3 Tsiros and Mittal(2000), Lee and Cotte(2009)
(2) disappointment 2 Zeelenberg and Pieters(2004)
(3) familiarity 3 Gefen(2000), Paswan and Ganesh(2003), Casalo’ et al.(2008)
(4) usability 5 Tsakonas and Papatheodorou(2008), Casalo’ et al.(2008)
(5) habit 5 Liyaman and Hirt(2003), Liao et al.(2006), Lin and Wang(2006), Khalifa and Liu(2007)
(6) switching cost 5 Burnham et al.(2003), Jones et al.(2002)
(7) innovativeness 3 Chao et al.(2012), Lu et al.(2011)
(8) switching intention 3 Zeelenberg and Pieters(2004), Mattila and Ro(2008)

<Table 2>

Demographic Characteristics of Respondents

Measure Frequency Percentage
Measure Frequency Percentage
Sex Male 72 48.3% Female 77 51.7%
Total 149 100.0%
Age about 23.63(average)
SKT 67 45.0% KT 20 13.4%
LGT 62 41.6% 149(100.0%)
Play Store
95 63.8% App Store(Apple) 54 36.2%
Total 149 100.0%
of Mobile
Experienced for
right price
51 34.2% Only Experienced
for free
98 65.7%
Total 149 100.0%

<Table 3>

Construct Results of CFA

loading t-value AVE Composite
Regret 0.914 98.907 0.863 0.950 0.921
0.941 166.240
0.931 154.730
Disappointment 0.980 253.978 0.942 0.970 0.940
0.961 115.461
Familiarity 0.811 33.944 0.714 0.882 0.804
0.895 51.847
0.826 22.567
Habit 0.701 28.921 0.548 0.858 0.168 0.795
0.716 32.011
0.718 41.139
0.708 31.784
0.849 92.836
Usability 0.825 55.623 0.609 0.885 0.847
0.725 28.265
0.648 14.926
0.854 99.260
0.832 50.670
Innovativeness 0.741 6.727 0.764 0.906 0.854
0.949 20.101
0.918 14.308
Switching Cost 0.682 17.394 0.602 0.882 0.132 0.832
0.674 17.853
0.856 80.609
0.879 76.125
0.765 27.467
0.761 38.907 0.674 0.861 0.384 0.756
0.901 120.534
0.795 56.043

<Table 4>

Correlation of latent variables

Square Root of AVE Usability Habits Disappointment Switching Cost Switching Intention Familiarity Innovativ eness Regret
Usability 0.784 1.000
Habits 0.740 0.436 1.000
Disappointment 0.971 -0.251 -0.076 1.000
Switching Cost 0.776 0.145 0.170 0.131 1.000
Switching Intention 0.821 -0.378 -0.504 0.041 -0.317 1.000
Familiarity 0.845 0.319 0.409 -0.425 0.079 -0.239 1.000
Innovativeness 0.874 0.191 0.017 -0.044 -0.240 0.135 -0.089 1.000
Regret 0.929 -0.317 -0.155 0.718 0.270 0.305 -0.414 0.001 1.000

<Table 5>

Hypotheses Testing

Hypotheses path Coefficient T-value H. Test
H1(-) Regret → Switching Cost 0.385 8.687 Supported
H2(-) Disappointment → Switching Cost -0.070 -1.800 Not Supported
H3(+) Familiarity→ Switching Cost 0.247 6.577 Supported
H4(+) Familiarity→ Habit 0.409 18.103 Supported
H5(-) Familiarity → Switching Intention 0.032 0.943 Not Supported
H6(+) Usability → Switching Cost 0.120 2.476 Supported
H7(-) Usability → Switching Intention -0.305 -15.172 Supported
H8(-) Switching Cost → Switching Intention -0.273 -6.603 Supported
H9(-) Habit → Switching Intention -0.340 -11.330 Supported
H10(+) Innovativeness → Switching Intention 0.136 4.809 Supported
Redundancy Index : Habit(0.081), Switching Cost(0.011), Switching Intention(0.091)
R Square : Habits(0.168), Switching Cost(0.132), Switching Intention(0.384)
R Square Mean : 0.228, Communality Mean : 0.714, GOF Index : 0.403