Predicting the adoption of mobile financial services: The impacts of perceived mobility and personal habit

Computers in Human Behavior 65 (2016) 31e42

Contents lists available at ScienceDirect

Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh

Predicting the adoption of mobile financial services: The impacts of perceived mobility and personal habit Yung-Shen Yen a, *, Feng-Shang Wu b a b

Department of Computer Science and Information Management, Providence University, Taichung, Taiwan Graduate Institute of Technology, Innovation & Intellectual Property Management, National Chengchi University, Taipei, Taiwan

a r t i c l e i n f o

a b s t r a c t

Article history: Received 14 August 2015 Received in revised form 15 June 2016 Accepted 11 August 2016

Mobile financial services (MFS) have become a critical issue in the financial sectors. An expanding application of mobile commerce, MFS play an important role in managing customer relationships. Thus, we proposed a model that incorporates three external variablesdperceived enjoyment, perceived mobility, and personal habitdinto the technology acceptance model (TAM) to assess the antecedents that influence continued usage intention in MFS. In addition, we examined the moderating effect of gender on customer relationships. Structural equation modeling was used, and 368 MFS users were investigated. The findings revealed that perceived mobility, personal habit, perceived usefulness, and perceived ease of use are the major antecedents that influence continued usage intention in MFS. However, perceived enjoyment is not significantly associated with intention. Moreover, gender moderates the relationships between the variables in the proposed model. Perceived mobility affecting usage intention will be stronger for men than for women, whereas personal habit affecting usage intention will be stronger for women than for men. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Mobile financial services Technology acceptance model Perceived mobility Personal habit

1. Introduction Mobile commerce has changed our lives. Mobile communication technology has been widely applied to existing services because of the rapid development of apps and smartphones. According to The Statista Portal (2016), the number of smartphone users is forecast to reach 2.08 billion in 2016, and the number of users worldwide is expected to pass the five billion mark by 2019. Moreover, various industries have engaged in the intense development of mobile customer services for smartphones. Mobile financial services (MFS) are a well-known example of this phenomenon. MFS is a service provided by a financial institution (e.g., a bank or securities provider) that enables customers to conduct various financial transactions remotely using a mobile device (e.g., a smartphone or tablet) and mobile software (e.g., apps programs). Thus, mobile banking is typically available 24 h per day, enabling users to access account balances, pay bills, and transfer funds through their mobile devices instead of visiting banks and using computer-based Internet banking. In despite of the widespread

* Corresponding author. 200, Sec. 7, Taiwan Boulevard, Shalu Dist, Taichung 43301, Taiwan. E-mail addresses: [email protected] (Y.-S. Yen), [email protected] (F.-S. Wu). http://dx.doi.org/10.1016/j.chb.2016.08.017 0747-5632/© 2016 Elsevier Ltd. All rights reserved.

adoption of mobile devices, the adoption rate of MFS is relatively low (Malaquias & Hwang, 2016; Zhou, Lu, & Wang, 2010). Accordingly, the crucial antecedents that affect MFS adoption by customers must be explored. In addition, from the perspective of relationship management, MFS cannot be ignored because they can facilitate user adoption and retain bank customers (Lu, Tzeng, Cheng, & Hsu, 2014). By incorporating a new system (i.e., mobile banking) into existing systems (e.g., Internet banks, local banks), banks not only can retain existing customers but also have an opportunity to convert potential customers (i.e., smartphone users). Therefore, integrating mobile technology into financial services is an inevitable trend that helps banks both acquire new customers and retain old customers. The adoption of new technologies has gained considerable attention in the literature, and many studies use the technology acceptance model (TAM) to explore the determinants that influence the use of technology (Davis, 1989; Davis, Bagozzi, & Warshaw, 1989). Indeed, TAM is an information systems theory that models how users come to accept and use a technology. In this model, perceived ease of use and perceived usefulness are two critical predictors that influence the adoption of new technologies. Perceived ease of use refers to the degree to which a person believes that using a particular system will be free from effort,

32

Y.-S. Yen, F.-S. Wu / Computers in Human Behavior 65 (2016) 31e42

whereas perceived usefulness refers to the degree to which a person believes that using a particular system will enhance his or her job performance (Taylor & Todd, 1995). Although the TAM has been widely adopted, researchers have suggested that it will further address the specific impacts of technological and individual factors to reflect the acceptance of new technology (Venkatesh & Brown, 2001). Thus, the extended TAMde.g., TAM2 (Venkatesh & Davis, 2000), the unified theory of acceptance and use of technology (UTAUT) (Venkatesh, Morris, Davis, & Davis, 2003), and TAM3 (Venkatesh & Bala, 2008)dhave been proposed to complement the original TAM by adding external variables (e.g., social influence, job relevance, facilitating conditions, experience, etc.) to the original model. In addition, numerous studies have incorporated behavioral beliefs and individual factors into the TAM to better understand their impact on the adoption of a particular system (e.g., Daud, Kassim, Said, & Noor, 2011; Hanafizadeh, Behboudi, Koshksaray, & Tabar, 2014; Legris, Ingham, & Collerette, 2003). For example, Luarn and Lin (2005) incorporated perceived credibility, perceived self-efficacy, and perceived financial cost into the TAM to examine their impact on the behavioral intention to use mobile banking. Gu, Lee, and Suh (2009) incorporated trust into the TAM to evaluate the factors that influence user intention in mobile banking. Furthermore, Shaikh and Karjaluoto (2015) have reviewed the literature on mobile banking adoption from January 2005 to March 2014, finding that 23 (42%) of 55 studies used the TAM as their theoretical framework. As mentioned above, TAM seems to be a more popular and robust model for examining the intention to adopt a new technology. Thus, this study proposed an extension of the TAM by adding adequate variables to predict the adoption of MFS. From the motivation perspective, the hedonic value of perceived enjoyment is important to users of mobile services (Lin & Wang, 2006; Van der Heijden, 2004). Indeed, MFS are specific, professional and connected to the financial businesses that manage customers' assets. However, MFS have attributes that are similar to websites with respect to providing online financial services. Van der Heijden (2004) argued that a hedonic system can affect the degree to which the user experiences fun when using the system. Thus, we may assume that perceived enjoyment could be an important predictor influencing the adoption of MFS if users recognize MFS as a hedonic system. Moreover, from the system perspective, perceived mobility is an indispensable factor for mo€o € rni (2009) bile service users. Mattat, Rossi, Tunnainen, and O argued that the mobility function of mobile ticketing allows customers to access information, communication, and services independent of time and place. Therefore, we can assume that perceived mobility is a key antecedent influencing the adoption of MFS. Finally, from the individual perspective, personal habit is a critical factor influencing users to continuously use mobile services. Ye and Potter (2011) noted that personal habit can suppress other beliefs' impact on specific services. In other words, when users have habitualized the use of a particular technology product, they will be € heinger less likely to intend to use an alternative. Barnes and Bo (2011) found that habit is a strong determinant influencing continued usage intention for Twitter users. Thus, we can assume that personal habit is a critical antecedent influencing the adoption of MFS. For that reason, we added three external variablesdperceived enjoyment, perceived mobility, and personal habitdto the original TAM as this study's proposed model. In addition, previous studies have suggested that men and women differ in new-technology adoption (e.g., Gefen & Straub, 1997). Women are more empathic and likely to be influenced by other people's emotions than are men (Timmers, Fischer, & Manstead, 1998). Because gender has been a significant variable in consumer behavior (Yang & Lee, 2010), gender differences in the use of MFS are expected. Thus, we can assume that relationships

between variables will differ by gender. Although the adoption of a new technology or service has been examined by TAM and its extensions, few studies have sought to understand the determinants that influence continued usage intention and the moderating effect of gender in the context of MFS. Therefore, this study aims both to explore how antecedents influence continued usage intention of MFS and to examine the moderating effect of gender on this relationship. The field has not previously provided a direct investigation of these issues. To fill the research gap, we investigated the behaviors of MFS users in Taiwan to obtain a better understanding of the antecedents influencing continued usage intention and the moderating role of gender in MFS. Thus, it contributes the following significant results: extending our previous understanding of the TAM in the context of MFS and formulating a research framework to explain how antecedents influence continued usage intention of MFS. 2. Theoretical background The TAM, which was introduced by Davis (1989), is widely used to examine the acceptance of new technology in the information system. This model was adapted from the theory of reasoned action and identified the causal relationship among perceived ease of use, perceived usefulness, attitudes, and behavioral intentions toward the use of the technology (Fishbein & Ajzen, 1975). The TAM posits that user acceptance can be explained by two beliefs: perceived usefulness and perceived ease of use. This model has been examined in the fields of information systems, marketing, and electronic commerce (Chau & Lai, 2003; Chen, Gillenson, & Sherrell, 2002; Igbaria, Zinatelli, Cragg, & Cavaye, 1997; O'Cass & Fenech, 2003). Although the TAM provides a quick and inexpensive way to gather information about an individual's perceptions of a system, scholars believe that this model's inclusion of perceived usefulness and perceived ease of use only is insufficient to explain an individual's technology acceptance (Mathieson, 1991). To explain users' acceptance in more detail, the TAM has been extended. Davis (1989) suggested that external variables can enhance the TAM's ability to predict the acceptance of information technology. In other words, the TAM's constructs need to be extended by incorporating additional factors. Moon and Kim (2001) argued that choosing additional factors for the TAM depends on the target technology, the main users and the context. Prior studies regarding motivations, system characteristics, and individual differences as external constructs of TAM has suggested strong relationships between these characteristics and the TAM's theoretical constructs (e.g., Kim, Mirusmonov, & Lee, 2010; Van der Heijden, 2004; Venkatesh & Davis, 1996; Wang, Wang, Lin, & Tang, 2003). Therefore, to understand the antecedents that influence the adoption of MFS, this study incorporated three external variablesdnamely perceived enjoyment, perceived mobility, and personal habitdinto the original TAM model. Perceived enjoyment is an intrinsic motivation that specifies the extent to which fun can be derived from using a system (Van der Heijden, 2004). According to the motivational theory (Deci & Ryan, 1975), user acceptance is determined by two fundamental types of motivation: extrinsic and intrinsic. An extrinsically motivated user is driven by the expectation of some reward or benefit that is external to the system, whereas an intrinsically motivated user is driven by benefits derived from the interaction with the system per se (Brief & Aldag, 1977). Thus, for utilitarian systems, extrinsic motivation is expected to be the major predictor of intention to use. Similarly, for hedonic systems, intrinsic motivation is expected to be the major predictor of intention to use. Teo and Lim (1997) argued that individuals engage in activities because these activities can lead to enjoyment and pleasure. In this regard,

Y.-S. Yen, F.-S. Wu / Computers in Human Behavior 65 (2016) 31e42

we assumed that MFS serve both utilitarian and hedonic purposes. Therefore, we added perceived enjoyment to the original TAM as an antecedent influencing the adoption of MFS. Perceived mobility is an important antecedent in the study of mobile services (Huang, Lin, & Chuang, 2007). In the literature, mobility includes three elements: convenience, expediency and €la € & Alama €ki, 2003). In other words, mobility can immediacy (Seppa permit users to gain access to services or information via mobile devices at any time while on the move. Consequently, MFS can support users' efforts to easily manage their financial assets in a mobile environment when and where necessary. Prior studies have demonstrated both that users valued efficiency and availability as the primary advantages of mobile services and that these advantages result from the mobility of mobile devices (Hill & Roldan, 2005). MFS offer a new way of accessing financial services via mobile devices and therefore facilitate a new dimension of bank and customer interaction. Thus, MFS are valuable because of their mobility. Accordingly, we added perceived mobility into the original TAM as an antecedent influencing the adoption of MFS. The concept of habit was introduced in the field of psychology (e.g., Hull, 1943; James, 1890). Verplanken and Aarts (1999) defined habit as “learned sequences of acts that have become automatic responses to specific cues and are functional in obtaining certain goals or end states” (p. 104). Given this definition, habits are goaldirected, learned activities. In other words, habit can be viewed as an automatic behavioral response triggered by a situational stimulus without a cognitive analysis process because of the learned association between usage behavior and satisfactory results. Thus, habit development requires a certain amount of repetition or practice (Aarts, Verplanken, & Knippenberg, 1998). Once a habit is created, behavior is performed automatically (Orbell, Blair, Sherlock, & Conner, 2001). However, the use of habit in research models varies. Whereas some studies assert that habit has a moderating effect on the relationship between intention and its determinants (e.g., Agag & El-Masry, 2016; Chiu, Hsu, Lai, & Chang, 2012), others assert that habit has a direct effect on intention to use €heinger, 2011; Lankton, Wilson, & Mao, online (e.g., Barnes & Bo 2010). Indeed, MFS can improve banks' value to customers because those customers can engage in transactions at their convenience (Laukkanen, 2007). This fosters consumers' habit to use MFS, increasing continued usage intention (Chiu et al., 2012). Lin and Wang (2006) also supported a strong relationship between habit and loyalty in mobile commerce contexts. Thus, our focus is habit's direct effect on intention in the use of MFS. We added personal habit to the original TAM as an antecedent that influences the adoption of MFS. 3. Research model and hypotheses development 3.1. Perceived mobility affects continued usage intention Perceived mobility in this study refers to how MFS are perceived as providing pervasive and timely connections. As previously noted, mobility enables customers to receive and transmit information anytime, anywhere (Hill & Roldan, 2005). Anckar and D'incau (2002) argued that the mobility associated with time-related needs can encourage customers to adopt mobile technology. Compared with Internet banking, MFS are free of temporal and spatial constraints: mobility helps banks improve their financial service quality and reduce their customers' transaction costs (Luarn & Lin, 2005). Thus, the mobility utility fosters stronger relationships between banks and customers (Riquelme & Rios, 2010). We assumed that perceived mobility positively affects continued usage intention in MFS. Accordingly, the first hypothesis (H1) of this study is as follows:

33

H1. Perceived mobility is positively associated with continued usage intention in MFS. 3.2. Perceived mobility affects perceived usefulness Personal usefulness in this study refers to the extent to which MFS are perceived as providing benefits in performing certain financial activities. In the context of MFS, the benefits of such services may include immediacy, convenience, and affordability (Lin, 2011). These advantages permit consumers to access MFS more easily than other services (e.g., Internet financial services). Thus, mobility is used to express the benefits of time and place, service access, and use. In other words, perceived mobility encourages customers to perceive the substantial benefits (or relative advantage in the diffusion of innovation theory) of services (Rogers, 1995). Consumes who perceive the value of mobility understand the uniqueness of mobile services and have a strong perception of €o € rni, 2009). Thus, we usefulness (Mallat, Rossi, Tuunainen, & O assumed that perceived mobility can increase perceived usefulness for MFS customers. Accordingly, the second hypothesis (H2) of this study is as follows: H2. Perceived mobility is positively associated with perceived usefulness in MFS. 3.3. Perceived mobility affects perceived ease of use In this study, perceived ease of use refers to the extent to which a user perceives MFS as easy to understand and use. Perceived ease of use is similar to computer self-efficacy (Venkatesh & Davis, 1996). MFS allow customers to access financial services anytime and anyplace, whereas other electronic services are bound to a fixed location (Püschel, Afonso Mazzon, & Hernandez, 2010). Through their mobile devices (e.g., smartphones or tablets), customers can easily manipulate the medium interface to access financial services for asset management. Thus, we assumed that perceived mobility will positively affect perceived ease of use in MFS. Accordingly, the third hypothesis (H3) of this study is as follows: H3. Perceived mobility is positively associated with perceived ease of use in MFS. 3.4. Perceived mobility affects perceived enjoyment Perceived enjoyment in this study refers to the extent to which MFS are perceived to be personally enjoyable in their own right for reasons other than their functional benefit. Babin, Darden, and Griffin (1994) noted that perceived enjoyment can be viewed as self-cognitive. Through mobility, customers can enjoy the atmosphere and access amusement through their interaction with financial services (Hill & Roldan, 2005). Similar to connecting with friends on social networking sites, customers can interact with financial services anytime and anyplace (Mallat et al., 2009). Thus, we assumed that perceived mobility can create customer enjoyment while using MFS. Accordingly, the fourth hypothesis (H4) of this study is as follows: H4. Perceived mobility is positively associated with perceived enjoyment in MFS. 3.5. Personal habit affects continued usage intention In this study, personal habit refers to an action that has been

34

Y.-S. Yen, F.-S. Wu / Computers in Human Behavior 65 (2016) 31e42

performed many times and become automatic in MFS. In general, habit influences behavior only when the behavior has been habitualized (Lankton et al., 2010). Ye and Potter (2011) indicated that habit formation requires the repeated, frequent performance of a particular action. Therefore, habit is most likely to play a role in daily routines. Behaviors performed at longer intervals, such as paying rent or celebrating anniversaries, do not usually become habitual despite their repetitive nature. Thus, the habit of using MFS may seem mature for the customers who automatically repeat their use of such services. Once an MFS habit is established, customers are unlikely to switch to alternatives, despite the small size of the device screen is small or the need to pay an extra communication fee. Although Limayem, Hirt, and Cheung (2007) asserted that stronger habit leads to intention's diminished predictive power of continued IT usage, some scholars argue that habit not only competes with intention in determining behavior but also directly influences intention (e.g., Honkanen, Olsen, & Verplanken, 2005; Mahon, Cowan, & McCarthy, 2006). Gefen (2003) also noted that habit alone can explain a large proportion of the variance in the use of a website. Thus, we assumed that personal habit will be a critical antecedent influencing continued usage intention for MFS. Accordingly, the fifth hypothesis (H5) of this study is as follows: H5. Personal habit is positively associated with continued usage intention in MFS.

3.6. Personal habit affects perceived usefulness Habits require learning (Verplanken, Aarts, van Knippenberg, & Moonen, 1998). Thus, through the prior use of a specific information technology and the knowledge that is gained by doing so, people may learn more about the technology, including how to operate it and how to gain more advantage (Ye & Potter, 2011). Such increased understanding will result in a customer perception of potential usefulness (Gefen, 2003). Karahanna, Straub, and Chervany (1999) found that experienced users can perceive more usefulness of a technology than can users with limited experience. Liao, Palvia, and Lin (2006) argued that habit positively influences perceived usefulness when customers use a B2C web site. Thus, we assumed that personal habit positively affects continued usage intention in MFS. Accordingly, the sixth hypothesis (H6) of this study is as follows: H6. Personal habit is positively associated with perceived usefulness in MFS.

3.7. Personal habit affects perceived ease of use In general, satisfactory experiences enhance the tendency to repeat a behavior. The transformation of a behavior into a habit implies that the behavior is well practiced (Liao et al., 2006). Gefen (2003) argued that through the habitual use of a website, users can increase their understanding of it. Ouellette and Wood (1998) noted that habitual responses are automatic in the sense that they can be performed quickly in parallel with other activities. Thus, we can infer that habits make it easier for consumers to perceive services. Customers using MFS usually use their smartphones or tablets as mobile devices to arrange their financial assets. Once the habit is created, they may perceive MFS manipulation as easy. Thus, we assumed that personal habit positively affects perceived ease of use in MFS. Accordingly, the seventh hypothesis (H7) of this study is as follows: H7. Personal habit is positively associated with perceived ease of use in MFS.

3.8. Personal habit affects perceived enjoyment In some situations, habits may increase perceived enjoyment. For example, people play games as a habit when they are lonely (Ng & Wiemer-Hastings, 2005). The creation of habit requires a stable context conducive to its formation through repetition or practice (Orbell et al., 2001). Thus, if people tend to repeatedly interact with specific entities, the perception of enjoyment related to such interactions will be sustained (Klimmt, Hartmann, & Schramm, 2006). Accordingly, we assumed that personal habit will positively affect perceived enjoyment in MFS. Accordingly, the eighth hypothesis (H8) of this study is as follows: H8. Personal habit is positively associated with perceived enjoyment in MFS.

3.9. The relationship of perceived enjoyment and the TAM model According to Van der Heijden (2004), perceived ease of use has an indirect impact on behavioral intention through both perceived usefulness and perceived enjoyment. The perception that a technology is easier to use facilitates its acceptance and use (Kim, Choi, & Han, 2009). In addition, Luarn and Lin (2005) stated that people tend to use mobile service systems because they find such systems useful for their banking transactions. Yang (2009) showed that perceived usefulness encourages the adoption of mobile banking. Although MFS have some restrictions, such as low computing power and small screen size, people may perceive both usefulness and enjoyment in such services because manipulation is easy and interaction is clear. Thus, in the context of MFS, we assumed that perceived ease of use affects continued usage intention directly and perceived usefulness and perceived enjoyment affect continued usage intention indirectly, as proposed in Van der Heijden's (2004) model. Accordingly, the ninth to thirteenth hypotheses (H9eH13) of this study are as follows: H9. Perceived ease of use is positively associated with perceived usefulness in MFS. H10. Perceived ease of use is positively associated with perceived enjoyment in MFS. H11. Perceived ease of use is positively associated with continued usage intention in MFS. H12. Perceived usefulness is positively associated with continued usage intention in MFS. H13. Perceived enjoyment is positively associated with continued usage intention in MFS.

3.10. The moderating effect of gender Researchers often develop their theories by using gender as a moderator to segment consumers (Herring & Paolillo, 2006; Stowers, 1995). Previous studies indicated that independence, rationality, and focus on individual goals are considered male traits, whereas sensitivity, intuition, passion, and focus on communal goals are associated with females (Cross & Markus, 1993; Palan, 2001). Thus, men and women may differ in their coping strategies when confronted by different situations. For example, Chou and Tsai (2007) found that men prefer playing sport games and car-race games, whereas women prefer adventure games, puzzles or card games, reflecting their instructive attributes. McDonald and Korabik (1991) found that men tend to use the avoidance or withdrawal strategies for stress release, whereas women are more likely to talk to others

Y.-S. Yen, F.-S. Wu / Computers in Human Behavior 65 (2016) 31e42

and seek social support. Gefen and Straub (2000) found that women tend to use electronic communication for building relationships, but men tend to use it for news. Moreover, Herring (1996) found that it is more common for women than for men to request and provide electronic messages. Thus, it is speculated that men's behavior tends to be more task-oriented and women's behavior tends to be more person-oriented (Lohan, 1997). In the context of mobile commerce, Mante and Piris (2002) found that women use mobile services more frequently for communication about personal and emotional matters, whereas men use such services to accomplish tasks. As previously noted, perceived mobility benefits consumers in that they can efficiently use MFS (i.e., related to task-oriented behavior), whereas personal habit is a learned sequences of acts for consumers using MFS (i.e., related to person-oriented behavior). Thus, we assumed that perceived mobility affecting usage intention will be stronger for men than for women, whereas personal habit affecting usage intention will be stronger for women than for men. In other words, for MFS customers, gender plays a moderator role influencing continued usage intention. Accordingly, the fourteenth hypothesis (H14) of this study is as follows: H14. Gender moderates the relationships between variables in the proposed model in MFS. Perceived mobility affecting usage intention will be stronger for men than for women, whereas personal habit affecting usage intention will be stronger for women than for men. 4. Research method 4.1. Framework of the research Fig. 1 depicts the research framework of this study in terms of the previous literature's proposals. 4.2. Measuring instruments

in

The research method design was adopted from previous results the literature of shopping behavior, with appropriate

35

modifications for MFS customers (e.g., terminology). The scale of perceived mobility was revised from Huang et al. (2007), which includes four items. The scale of personal habit was revised from Limayem et al. (2007), which includes three items. The scale of perceived usefulness was revised from Davis (1989), which includes four items. The scale of perceived ease of use was revised from Parthasarathy and Bhattacherjee (1998), which includes four items. The scale of perceived enjoyment was revised from Davis, Bagozzi, and Warashaw (1992), which includes three items. The scale of continued usage intention was revised from Bhattacherjee (2001), which includes three items. All of the items were measured on a 7-point Likert-type scale in which possible answers ranged from strongly disagree (1) to strongly agree (7). To increase face validity, the instrument was modified and analyzed by six marketing experts and scholars to identify ambiguities in terms and meanings. The adjusted instrument is shown in Appendix A. 4.3. Subjects This research was conducted at the consumer level. According to statistics released by Taiwan's National Communications Commission, Taiwan's mobile phone penetration rate has surpassed the 100% mark (Yen, 2012). In other words, almost every Taiwanese citizen owns a mobile phone. Thus, we conducted a convenience sampling similar to that of Sigala (2006), inviting visitors to two department stores near Taipei's main station to participate in a survey. Taipei is Taiwan's largest commercial and financial center, and the main station area is both a business center and important traffic transfer hub. Visitors to this area could include residents from all parts of the city. To assist in data collection, we recruited four graduate students from a renowned university in Taipei. The investigation took place from Monday to Saturday during the mornings and the evenings, thus ensuring a more representative and diverse sample. This study was conducted using a selfadministered questionnaire. All of the respondents were asked to confirm the use of MFS over the past six months. Respondents who completed the questionnaires were rewarded with a small gift. The sample size considered for this study is ten times higher than the

H1(+) Perceived usefulness H2(+) Perceived mobility

Personal habit

H3(+)

H12(+) H9(+) H9(+) H5(+) H9(+)

H4(+)

Perceived

H6(+)

ease of use

H7(+)

H11(+) H11(+)

Continued usage intention

H10(+) H10(+) H13(+)

H8(+) Perceived enjoyment H5(+)

H14 Moderator: Gender Fig. 1. This study's research model.

H1(+)

36

Y.-S. Yen, F.-S. Wu / Computers in Human Behavior 65 (2016) 31e42

number of variables (21 items) (Hatcher, 1994). Thus, we need at least 210 samples for this study. Data were collected over a period of two months. Overall, 375 responses were received, 7 of which were deleted because of incomplete data. There were 368 valid subjects. The demography of the subjects is shown in Table 1. Females (53.8%) outnumber males (46.2%). The large age groups are 15e24 years (36.7%) and 25e34 years (42.1%), the largest educational group is the one with an undergraduate education (54.1%), and the largest occupational group is that of office workers (63.3%). This result is consistent with the distribution of mobile phone users in Taiwan, as reported by FIND (2012). Thus, the study sample is moderately representative of Taiwan's population. Most people have 3e4 years of experience using smartphones (43.2%) and a monthly income of NT $30,001e40,000 (28.2%). 4.4. Reliability and validity test Reliability and validity were tested by evaluating internal consistency, convergent validity and discriminate validity. This study employed Cronbach's alpha (a) for examining the internal consistency of the constructs (Nunnally, 1978; Robert & Wortzel, 1979). The a in Table 2 indicates the reliability of the measurement constructs: perceived mobility is 0.91, personal habit is 0.86, perceived usefulness is 0.92, perceived ease of use is 0.83, perceived enjoyment is 0.92, and continued usage intention is 0.85. These numbers satisfied the general requirements in the field, suggesting a reliability coefficient above 0.7 (Nunnally, 1978). Therefore, this study had good reliability. Confirmation factor analysis (CFA) was performed for scale validity assessment (Anderson & Gerbing, 1988). Convergent validity can be measured by composite reliability (CR), which shall be greater than average variance extracted (AVE), and AVE should be greater than 0.5 (Fornell & Larcker, 1981). As shown in Table 2, both of the criteria were satisfied. Thus, this study possessed adequate convergent validity. Discriminate validity was tested by the threshold that the AVE square root of each research variable is larger than the related

Table 2 Model of research construct. Construct and observable variable PM PM1 PM2 PM3 PM4 PH PH1 PH2 PH3 PU PU1 PU2 PU3 PU4 PEOU PEOU1 PEOU2 PEOU3 PEOU4 PE PE1 PE2 PE3 CUI CUI1 CUI2 CUI3

Mean (SD) 5.24 5.28 5.39 5.50

SFL

(1.39) (1.35) (1.28) (1.32)

0.85 0.89 0.84 0.82

4.81 (1.45) 4.86 (1.57) 4.96 (1.36)

0.82 0.85 0.78

5.27 5.29 5.09 5.36

(1.43) (1.40) (1.36) (1.32)

0.88 0.88 0.90 0.82

4.89 4.87 4.90 5.05

(1.60) (1.64) (1.43) (1.47)

0.82 0.71 0.87 0.90

4.61 (1.37) 4.39 (1.31) 4.41 (1.25)

0.86 0.97 0.87

5.18 (1.40) 4.84 (1.46) 5.11 (1.34)

0.83 0.78 0.82

CR

AVE

a

0.91

0.72

0.91

0.86

0.67

0.86

0.93

0.76

0.92

0.90

0.69

0.83

0.93

0.81

0.92

0.85

0.66

0.85

Note: PM ¼ perceived mobility; PH ¼ personal habit; PU ¼ perceived usefulness; PEOU ¼ perceived ease of use; PE ¼ perceived enjoyment; CUI ¼ continued usage intention.

coefficients of the variables (Fornell & Larcker, 1981). As shown in Table 3, all of the variables were satisfied. Thus, this study had good discriminate validity. Because self-reported data from a single source were used, we employed a Harman's single factor test to assess possible common method variance (CMV). The assumption of this method is that CMV exists either when all indicators fall into a single construct or

Table 1 Subjects' demographics and use behavior. Variables

Items

N

Percent (%)

Gender

Male Female Below 15 15e24 25e34 35e44 45e54 Over 54 Senior high school Undergraduate Graduate Student Office worker Self-employed Homemaker Below 1 1e2 3e4 5e6 Over 6 Under 20,001 20,001e30,000 30,001e40,000 40,001e50,000 50,001e60,000 Over 60,000

170 198 21 135 155 36 18 3 54 199 115 82 233 41 12 38 133 159 25 13 23 93 104 79 23 46

46.2 53.8 5.7 36.7 42.1 9.8 4.9 0.8 14.7 54.1 31.2 22.3 63.3 11.1 3.3 10.3 36.2 43.2 6.8 3.5 6.3 25.2 28.2 21.5 6.3 12.5

Age (year)

Education

Occupation

Experience of using smartphones (year)

Monthly income (NT dollars)

Note: valid samples ¼ 368.

Y.-S. Yen, F.-S. Wu / Computers in Human Behavior 65 (2016) 31e42

when the first construct can explain most of the variance (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Thus, this study performed factor analysis using the principal components with the varimax rotation method, and the result showed that the first component only accounted for 36.381% of the overall variance in the items, thereby indicating no serious CMV problem. 5. Analysis of empirical results 5.1. The test of the structural paths Structural equation modeling using AMOS 20.0 was conducted to test the postulated hypotheses. Fig. 2 presents the estimation results. From the model fitness indexes, c2 ¼ 501.900, df ¼ 175, c2/ df ¼ 2.868, GFI ¼ 0.952, AGFI ¼ 0.872, CFI ¼ 0.957, and RMSEA ¼ 0.069, showing that the collected data fit the postulated model. The estimated structural coefficients were used to test each hypothesis. The results shown in Table 4 reveal that continued usage intention explains 80.8% of the variance in the model, and 69.3%, 50.6%, 37.3% of the variance in perceived usefulness, perceived ease of use, and perceived enjoyment, respectively. Furthermore, perceived mobility is significantly associated with perceived usefulness, perceived ease of use, perceived enjoyment and continued usage intention, respectively (Estimate ¼ 0.436, SE ¼ 0.123, CR ¼ 3.079, p < 0.001; Estimate ¼ 0.274, SE ¼ 0.190, CR ¼ 2.291, p ¼ 0.046; Estimate ¼ 0.324, SE ¼ 0.155, CR ¼ 3.447, p < 0.001; Estimate ¼ 0.301, SE ¼ 0.139, CR ¼ 2.172, p ¼ 0.030). Thus, H1, H2, H3 and H4 are supported. Similarly, personal habit is positively associated with perceived usefulness, perceived ease of use, perceived enjoyment, and continued usage intention, respectively (Estimate ¼ 0.569, SE ¼ 0.160, CR ¼ 3.562, p < 0.001; Estimate ¼ 0.782, SE ¼ 0.201, CR ¼ 3.893, p < 0.001; Estimate ¼ 0.740, SE ¼ 0.200, CR ¼ 3.706, p < 0.001; Estimate ¼ 0.821, SE ¼ 0.233, CR ¼ 3.527, p < 0.001). Thus, H5, H6, H7, and H8 are supported. However, perceived ease of use is positively associated with perceived usefulness and continued usage intention (Estimate ¼ 0.198, SE ¼ 0.096, CR ¼ 2.070, p ¼ 0.038; Estimate ¼ 0.240, SE ¼ 0.110, CR ¼ 2.183, p ¼ 0.029), but does not significantly influence perceived enjoyment (Estimate ¼ 0.025, SE ¼ 0.106, CR ¼ 0.233, p ¼ 0.815). Thus, H9 and H11 are supported, but H10 is rejected. Moreover, perceived usefulness is positively associated with continued usage intention (Estimate ¼ 0.326, SE ¼ 0.128, CR ¼ 3.488, p < 0.001), but perceived enjoyment is not associated with continued usage intention (Estimate ¼ 0.097, SE ¼ 0.088, CR ¼ 1.100, p ¼ 0.271). Thus, H12 is supported, but H13 is rejected. 5.2. The test of the moderating effect of gender Multiple group structural equation modeling was conducted to test the differences of the causal relationships across two groups.

Table 3 Correlation between constructs.

PM PH PU PEOU PE CUI

PM

PH

PU

PEOU

PE

CUI

0.84 0.74 0.71 0.60 0.62 0.78

0.82 0.80 0.71 0.59 0.76

0.87 0.60 0.47 0.67

0.83 0.43 0.48

0.90 0.44

0.81

Note: Diagonal elements in boldface represent the square root of AVE. PM ¼ perceived mobility; PH ¼ personal habit; PU ¼ perceived usefulness; PEOU ¼ perceived ease of use; PE ¼ perceived enjoyment; CUI ¼ continued usage intention.

37

Significant differences in two groups were determined by using a c2 difference test (Yang & Lee, 2010). Thus, Table 5 shows that the moderating effect of gender is significant for the full constrained model with thirteen limited path coefficients between female and male (c2 (13) ¼ 24.383, p ¼ 0.028). Seven paths (the path between perceived mobility and continued usage intention, the path between perceived mobility and perceived usefulness, the path between perceived mobility and perceived ease of use, the path between personal habit and perceived usefulness, the path between perceived ease of use and perceived usefulness, the path between perceived ease of use and continued usage intention, and the path between perceived usefulness and continued usage intention) are significant in the model (c2(1) ¼ 4.107, p ¼ 0.044; c2(1) ¼ 4.116, p ¼ 0.042; c2(1) ¼ 3.864, p ¼ 0.049; c2(1) ¼ 4.253, p ¼ 0.039; c2(1) ¼ 3.881, p ¼ 0.046; c2(1) ¼ 5.166, p ¼ 0.023; c2(1) ¼ 3.884, p ¼ 0.047). Thus, H14 is supported. The structural models between female and male are shown in Figs. 3 and 4, respectively. The evidence shown in Table 6 reveals that only four paths are not significant for females, namely, the path between perceived mobility and perceived enjoyment (Estimate ¼ 0.047, SE ¼ 0.494, CR ¼ 0.702, p ¼ 0.483), the path between perceived mobility and continued usage intention (Estimate ¼ 0.026, SE ¼ 0.600, CR ¼ 0.887, p ¼ 0.152), the path between perceived ease of use and perceived enjoyment (Estimate ¼ 0.294, SE ¼ 0.189, CR ¼ 1.561, p ¼ 0.119) and the path between perceived enjoyment and continued usage intention (Estimate ¼ 0.028, SE ¼ 1.181, CR ¼ 0.915, p ¼ 0.527). Similarly, four paths are not significant for males, namely, the path between perceived ease of use and perceived usefulness (Estimate ¼ 0.173, SE ¼ 0.096, CR ¼ 1.808, p ¼ 0.071), the path between perceived ease of use and perceived enjoyment (Estimate ¼ 0.028, SE ¼ 0.084, CR ¼ 0.329, p ¼ 0.742), the path between perceived ease of use and continued usage intention (Estimate ¼ 0.093, SE ¼ 0.077, CR ¼ 1.204, p ¼ 0.229), and the path between perceived enjoyment and continued usage intention (Estimate ¼ 0.042, SE ¼ 0.095, CR ¼ 0.444, p ¼ 0.657). 5.3. Discussion This study has yielded several important findings. First, this study incorporated external variables, including perceived enjoyment, perceived mobility, and personal habit into the TAM as the proposed model. The evidence reveals that perceived mobility, personal habit, perceived usefulness, and perceived ease of use are four major determinants influencing continued usage intention for customers, excluding perceived enjoyment, while using MFS. MFS are notably different from traditional local banks. Customers likely use MFS instead of local banks for reasons of mobility. Moreover, mobility enhances customers' ability to perceive usefulness and ease of use advantages related to bank services. Thus, perceived mobility directly affects continued usage intention in MFS and indirectly affects that intention via perceived usefulness and perceived ease of use. In addition, personal habit also directly and indirectly affects continued usage intention in MFS via both perceived usefulness and perceived ease of use. Using mobile devices, MFS enables customers to easily manipulate personal data and obtain desired financial information. Once a habit has been created, continued usage intention in MFS will be reinforced. Habit will also increase perceptions of MFS usefulness and ease of use, which in turn enhance intention. However, perceived enjoyment is not a significant determinant to predict continued usage intention. The possible reason for this is that existing MFS services in MFS may not be amusing for users. In other words, MFS seems to be viewed as a utilitarian system, not a hedonic system. This result can explain why current MFS users are rare: MFS remain limited. Indeed, most

38

Y.-S. Yen, F.-S. Wu / Computers in Human Behavior 65 (2016) 31e42

(R2=0.693) 0.301*

Perceived usefulness 0.326***

0.436*** 0.198* Perceived mobility

0.274*

(R2=0.506) Perceived ease of use

0.324*** 0.569*** Personal habit

(R2=0.808) 0.240*

Continued usage intention

0.025

0.782***

(R2=0.373) 0.740***

0.097

Perceived enjoyment

0.821*** Non-significant path Significant path Fig. 2. Results of structural modeling analysis. Note:

Table 4 Results of estimated structural coefficients. Relationship

Estimate

SE

CR

P

Result

H1: PM / CUI H2: PM / PU H3: PM / PEOU H4: PM / PE H5: PH / CUI H6: PH / PU H7: PH / PEOU H8: PH / PE H9: PEOU / PU H10: PEOU / PE H11: PEOU / CUI H12: PU / CUI H13: PE / CUI

0.301 0.436 0.274 0.324 0.821 0.569 0.782 0.740 0.198 0.025 0.240 0.326 0.097

0.139 0.123 0.190 0.155 0.233 0.160 0.201 0.200 0.096 0.106 0.110 0.128 0.088

2.172 3.079 2.291 3.447 3.527 3.562 3.893 3.706 2.070 0.233 2.183 3.488 1.100

0.030

Support Support Support Support Support Support Support Support Support Reject Support Support Reject

Note: Estimate is unstandardized;

*

p < 0.05,

***

***

0.046 *** *** *** *** ***

0.038 0.815 0.029 ***

0.271

p < 0.001.

Table 5 The moderating effect of gender. Path

c2

DF

P

Full constrained model H1: PM / CUI H2: PM / PU H3: PM / PEOU H4: PM / PE H5: PH / CUI H6: PH / PU H7: PH / PEOU H8: PH / PE H9: PEOU / PU H10: PEOU / PE H11: PEOU / CUI H12: PU / CUI H13: PE / CUI

24.383 4.107 4.116 3.864 0.639 0.539 4.253 1.657 0.456 3.881 3.354 5.166 3.884 1.627

13 1 1 1 1 1 1 1 1 1 1 1 1 1

0.028* 0.044* 0.042* 0.049* 0.424 0.475 0.039* 0.198 0.500 0.046* 0.067 0.023* 0.047* 0.202

Note:

*

p < 0.05.

***

p < 0.001.

habit, perceived usefulness, and perceived ease of usedin the context of MFS. Second, the evidence reveals that personal habit has a stronger influence on continued usage intention (the direct effect is 0.821, the indirect effect is 0.373) than perceived mobility (the direct effect is 0.301, the indirect effect is 0.208). This finding implies that personal habit dominates continued usage intention for MFS customers. Beatty and Smith (1987) found that through force of habit, approximately 40%e60% of customers purchase from a single store. People would like to purchase from the store because of the habit that makes it easy for them to buy, not because of some evaluation of the perceived benefits and costs. Therefore, we demonstrate the assumption that personal habit is a major determinant that influences continued usage intention of MFS. Third, this study finds that relationships between perceived mobility and continued usage intention differ by gender. For female customers, personal habit has a stronger impact on continued usage intention (the total effect ¼ 2.979) than perceived mobility (the total effect ¼ 0.330). In other words, for female customers, personal habit is directly and indirectly associated with intention via perceived usefulness and perceived ease of use. However, perceived mobility is not directly associated with continued usage intention; instead, the association is indirect via perceived usefulness and perceived ease of use. In contrast, for male customers, perceived mobility has a stronger influence on intention (the total effect ¼ 0.618) than does personal habit (the total effect ¼ 0.529). Perceived mobility and personal habit both directly and indirectly affect continued usage intention via perceived usefulness, but with different influential impacts for male customers. Therefore, we affirm the assumption that whereas perceived mobility has greater influence on continued usage intention for men than for women, personal habit affecting such an intention is more significant for women than for men. 6. Conclusion and suggestions 6.1. Conclusion

MFS are the same as Internet banking, except that they are mobile. Therefore, this study finds that continued usage intention is predicted by four determinantsdnamely, perceived mobility, personal

This study aims to explore the determinants influencing continued usage intention in MFS. The contributions of this study

Y.-S. Yen, F.-S. Wu / Computers in Human Behavior 65 (2016) 31e42

39

(R2=0.491) 0.026

Perceived usefulness 0.313***

0.395*** 0.315***

Perceived mobility

0.321***

(R2=0.688) Perceived ease of use

0.047 0.496*** Personal habit

(R2=0.738) 0.562**

Continued usage intention

0.294

1.501*

(R2=0.462)

0.028

0.716*** Perceived enjoyment 1.940*** Non-significant path Significant path Fig. 3. Results of structural modeling analysis for females. Note:

**

p < 0.01;

***

p < 0.001.

(R2=0.696) 0.492*** Perceived usefulness 0.345**

0.364** 0.173 Perceived mobility

0.429**

(R2=0.323) Perceived ease of use

0.239* 0.456** Personal habit

(R2=0.866) 0.093

Continued usage intention

0.028 0.305** (R2=0.374) 0.845***

-0.042

Perceived enjoyment

0.372** Non-significant path Significant path Fig. 4. Results of structural modeling analysis for males. Note:

start with a conceptual formulation of the TAM model incorporating three external variables from the motivation, system, and individual perspectives. On this basis, this study examined the relationships between variables in the proposed model and further tested the moderating effect of gender on those relationships. An empirical study with 368 MFS users in Taiwan affirmed the hypotheses and clarified our ideas. The results show that personal habit is a major determinant that influences continued usage intention for MFS users, followed by perceived mobility, perceived usefulness, and perceived ease of use. Moreover, the causal relationships in the proposed model differ between men and women. Perceived mobility affecting usage intention will be stronger for men than for women, whereas personal habit affecting usage

**

p < 0.01;

***

p < 0.001.

intention will be stronger for women than for men. The findings of this study could help researchers and practitioners understand the importance of perceived mobility and personal habit to increase continued usage intention in the context of MFS. 6.2. Theoretical implications This study's findings have three theoretical implications. First, this study affirmed the assumption that perceived mobility, personal habit, perceived usefulness and perceived ease of use are four important determinants of MFS adoption. This result is consistent with previous studies regarding TAM (e.g., Davis, 1989; Gefen, 2003; Huang et al. 2007; Limayem et al., 2007). This finding helps

40

Y.-S. Yen, F.-S. Wu / Computers in Human Behavior 65 (2016) 31e42

Table 6 Results of estimated structural coefficients between females and males. Relationship Female (N ¼ 198) H1: PM / CUI H2: PM / PU H3: PM / PEOU H4: PM / PE H5: PH / CUI H6: PH / PU H7: PH / PEOU H8: PH / PE H9: PEOU / PU H10: PEOU / PE H11: PEOU / CUI H12: PU / CUI H13: PE / CUI Male (N ¼ 170) H1: PM / CUI H2: PM / PU H3: PM / PEOU H4: PM / PE H5: PH / CUI H6: PH / PU H7: PH / PEOU H8: PH / PE H9: PEOU / PU H10: PEOU / PE H11: PEOU / CUI H12: PU / CUI H13: PE / CUI

Estimate

SE

CR

P

0.026 0.313 0.321 0.047 1.940 0.496 1.501 0.716 0.315 0.294 0.562 0.395 0.028

0.600 0.266 0.191 0.494 0.648 0.233 0.587 0.267 0.228 0.189 0.163 0.183 1.181

0.887 2.043 2.193 0.702 2.177 2.269 2.558 2.034 2.065 1.561 2.549 2.677 0.915

0.152

0.492 0.364 0.429 0.239 0.372 0.456 0.305 0.845 0.173 0.028 0.093 0.345 0.042

0.121 0.115 0.171 0.139 0.211 0.163 0.145 0.221 0.096 0.084 0.077 0.140 0.095

4.071 3.161 2.511 2.999 2.186 2.792 2.654 3.814 1.808 0.329 1.204 2.319 0.444

Note: Estimate is unstandardized;

*

p < 0.05;

**

p < 0.01;

***

*** ***

0.483 *** *** *** *** ***

0.119 0.002** ***

0.527 ***

0.008** 0.005** 0.038* 0.004** 0.003** 0.006** ***

0.071 0.742 0.229 0.007** 0.657

p < 0.001.

us understand the antecedents influencing continued usage intention in the context of MFS. Moreover, perceived mobility and personal habit both have an indirect effect on intention via perceived usefulness and perceived ease of use. This result can explain why MFS users are likely to use services because of mobility and personal habit. This is a novel finding. Thus, this study extends the TAM by adding perceived mobility and personal habit to the original model. Second, this study affirmed the assumption that personal habit as a major determinant influences continued usage intention in MFS. This result supports Triandis' (1971) argument that behavioral intentions are the product of attitude, social norms, and affect caused by habit. Moreover, Manero, Torrente, Freire, and ndez-Manjo n (2016) noted that gaming preferences and Ferna habits can determine which type of game might be more appropriate for its intended audience. Therefore, it is speculated that customers primarily engage in MFS because of personal habit. Third, our evidence reveals that gender acts as a significant moderator influencing the relationships between variables in the proposed model. In more detail, intention is predicted by personal habit, perceived usefulness, and perceived ease of use for female customers. However, for male customers, intention is predicted by perceived mobility, personal habit, and perceived usefulness. This result is consistent with the study of Yang and Lee (2010), which indicates that men are more likely to use the goal-oriented utilitarian (e.g., perceived usefulness) aspect of mobile data services and focus on the functions of mobile data services, whereas women are more likely to use the communication features (e.g., perceived ease of use) of mobile data services and are less likely than men to use various functions of mobile data services. Therefore, this finding confirmed the assumption that gender moderates the relationships between variables in the context of MFS. 6.3. Managerial implications From a practical perspective, this study makes three practical

contributions. First, this study finds that perceived mobility, personal habit, perceived usefulness, and perceived ease of use are four key variables influencing continued usage intention in MFS. Thus, service providers need to increase users' perceptions of mobility, usefulness, and ease of use. For example, to expand their customer base, service providers can highlight the value of MFS against Internet banking, emphasize functional advantages, and extend MFS such as mobile payments. In addition, the strong impact of personal habit on intention demonstrates that adopting MFS can be treated as a responsive action from a behavioral habit. As Ouellette and Wood (1998) argued, once a behavior has become a habit or a well-practiced behavior, it becomes automatic and is carried out without conscious decision. Thus, service providers may have to provide more favorable incentives and higher value than competitors, encouraging consumers to reuse their services. Second, and surprisingly, our evidence reveals that perceived enjoyment is not significantly associated with intention. This result is inconsistent with Van der Heijden (2004), implying that current customers view MFS as a non-hedonic system. However, the impact of perceived enjoyment on intention cannot be overlooked because scholars believe that perceived enjoyment is an intrinsic motivation for users to use or accept technology (Venkatesh & Brown, 2001). If users reject a utilitarian system, hedonic features may invoke the other motivation to achieve user acceptance (Van der Heijden, 2004). Thus, service providers need to develop more amusing services (e.g., music, adventure and simulation games) that can increase the hedonic motivation to use MFS. Third, gender differences in the relationships between variables will be of special interest for service providers hoping to maintain a long-term relationship with consumers. Our evidence shows that MFS customers should not be considered as a homogenous group. Thus, service providers should position MFS with individual consumers' perceptions and habits. 6.4. Limitations and future research Along with the important findings, this study contains some limitations. First, this study surveyed MFS users in Taipei only. Because Taipei is the financial center of Taiwan, MFS users in Taipei may not be representative of the total population of MFS in Taiwan. Thus, a sample bias may exist. Second, this study incorporated perceived mobility and personal habit into the TAM as our proposed model. However, service contexts might also be important to customers in MFS use (Luarn & Lin, 2005). Lee, Park, Chung, and Blakeney (2012) suggested that identifying and promoting appropriate tasks for MFS is critical. For example, customers would like to receive instant investment information directly from bank consultants. Thus, constructing an interaction interface to link customers and consultants in a secure virtual network will be essential; examples include a proprietary LINE for customer/consultant communications. Subsequent studies can explore the effect of service contexts on usage intention in MFS. Third, perceived risk may be considered a critical factor influencing the use of MFS. Chen (2013) found that perceived risk negatively and significantly affects attitudes towards adopting mobile banking services and the intention to use. Thus, security is the priority issue when people pay bills online via mobile devices. However, we might expect customers with high levels of perceived risk to have greater intentions to deter or diminish the use of MFS than do customers with low levels of perceived risk. Thus, subsequent studies can further explore the moderating role of perceived risk in the study's proposed model. Fourth, as we know, most mobile technology-based services evolved from either Internet technology or other traditional technology-based services (Yang, Lu, Gupta, Cao, & Zhang, 2012).

Y.-S. Yen, F.-S. Wu / Computers in Human Behavior 65 (2016) 31e42

Thus, we expect most MFS consumers to upgrade from Internet banking or local bank services. Consequently, consumers' prior experiences with traditional services can affect their perceptions and beliefs related to MFS. Subsequent studies can examine MFS adoption from a dynamic cross-environment perspective. Acknowledgements This research is partially supported by the Ministry of Science and Technology, Taiwan, R.O.C. (MOST 103-2627-E-004-001) Appendix A. The study instrument Perceived mobility (adapted from Huang et al. (2007)). PM1: I know that mobile devices are the mediums for MFS. PM2: It is convenient to access MFS anytime, anyplace. PM3: Mobility makes it possible to get the real-time data. PM4: Mobility is an outstanding advantage of MFS. Personal habit (adapted from Limayem et al. (2007)). PH1: Using MFS has become automatic to me. PH2: Using MFS is natural to me. PH3: When I need to arrange the things in the financial institution, using MFS is an obvious choice for me. Perceived usefulness (adapted from Davis (1989)). PU1: Using MFS helps me accomplish task more quickly. PU2: Using MFS enhances my tasks effectiveness. PU3: Using MFS makes it easier to do my tasks. PU4: Overall, using MFS is useful. Perceived ease of use (adapted from Parthasarathy and Bhattacherjee (1998)) PEOU1: Learning how to use MFS is difficult for me (reversed). PEOU2: It is difficult for me to become skillful at using MFS (reversed). PEOU3: My interaction with MFS is unclear (reversed). PEOU4: Overall, using MFS is easy for me. Perceived enjoyment (adapted from Davis et al. (1992)). PE1: Using MFS is pleasurable. PE2: Using MFS provides me with enjoyment. PE3: Overall, using MFS is interesting. Continued usage intention (adapted from Bhattacherjee (2001)). CUI1: I intend to continue the use of MFS in the future. CUI2: I intend to increase the use of MFS in the future. CUI3: I would keep using MFS as regularly as I do now. References Aarts, H., Verplanken, B., & Knippenberg, A. (1998). Predicting behavior from actions in the past: Repeated decision making or a matter of habit? Journal of Applied Social Psychology, 28(15), 1355e1374. Agag, G., & El-Masry, A. A. (2016). Understanding the determinants of hotel booking intentions and moderating role of habit. International Journal of Hospitality Management, 54, 52e67. Anckar, B., & D'incau, D. (2002). Value creation in mobile commerce: Findings from a consumer survey. JITTA: Journal of Information Technology Theory and Application, 4(1), 43. Anderson, J., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411e423. Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: Measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20(4), 644e656. €heinger, M. (2011). Modeling use continuance behavior in Barnes, S. J., & Bo microblogging services: The case of Twitter. Journal of Computer Information Systems, 51(4), 1e10. Beatty, S. E., & Smith, S. M. (1987). External search effort: An investigation across several product categories. Journal of Consumer Research, 14(1), 83e95. Bhattacherjee, A. (2001). Understanding information systems continuance. An expectation-confirmation model. MIS Quarterly, 25(3), 351e370. Brief, A. P., & Aldag, R. J. (1977). The intrinsic-extrinsic Dichotomy: Toward conceptual clarity. Academy of Management Review, 2(3), 496e500. Chau, P. Y. K., & Lai, V. S. K. (2003). An empirical investigation of the determinants of

41

user acceptance of internet banking. Journal of Organizational Computing & Electronic Commerce, 13(2), 123e146. Chen, C. (2013). Perceived risk, usage frequency of mobile banking services. Managing Service Quality, 23(5), 410e436. Chen, L.-D., Gillenson, D. L., & Sherrell, M. (2002). Enticing on-line consumers: An extended technology acceptance perspective. Information & Management, 39(8), 705e719. Chiu, C. M., Hsu, M. H., Lai, H., & Chang, C. M. (2012). Re-examining the influence of trust on online repeat purchase intention: The moderating role of habit and its antecedents. Decision Support Systems, 53(4), 835e845. Chou, C., & Tsai, M.-J. (2007). Gender differences in Taiwan high school students' computer game playing. Computers in Human Behavior, 23(1), 812e824. Cross, S. E., & Markus, H. R. (1993). Gender in thought, belief, and action: A cognitive approach. In A. E. Beall, & R. J. Sternberg (Eds.), The psychology of gender (pp. 55e98). New York, NY: Guilford Press. Daud, N. M., Kassim, N. E. M., Said, W. S. R. M., & Noor, M. M. (2011). Determining critical success factors of mobile banking adoption in Malaysia. Australian Journal of Basic and Applied Sciences, 5(9), 252e265. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319e340. Davis, F. D., Bagozzi, R. P., & Warashaw, P. R. (1992). Extrinsic and intrinsic motivation to user computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111e1132. Davis, L. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982e1003. Deci, E. L., & Ryan, R. M. (1975). Intrinsic motivation. John Wiley & Sons, Inc. FIND. (2012). The investigation of App application users. Digital knowledge, 26, 1e6. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and Behavior: An introduction to theory and research. Reading, MA: Addison-Wesley. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equations with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39e50. Gefen, D. (2003). TAM or just plain habit: A look at experienced online shoppers. Journal of End User Computing, 15(3), 1e13. Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of Email: An extension to the technology acceptance model. MIS Quarterly, 21(4), 389e400. Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in is adoption: A study of e-commerce adoption. Journal of Association of Information Systems, 1(8), 1e28. Gu, J. C., Lee, S. C., & Suh, Y. H. (2009). Determinants of behavioral intention to mobile banking. Expert Systems with Applications, 36(9), 11605e11616. Hanafizadeh, P., Behboudi, M., Koshksaray, A. A., & Tabar, M. J. S. (2014). Mobilebanking adoption by Iranian bank clients. Telematics and Informatics, 31(1), 62e78. Hatcher, L. (1994). A step-by-step approach to using the SAS system for factor analysis and structural equation modelling. Cary, NC: SAS Institute. Herring, S. C. (1996). Two variants of an electronic message schema. In S. Herring (Ed.), Computer-mediated Communication: Linguistic, social, and cross-cultural (pp. 81e106). Amsterdam/Philadelphia, PA: John Benjamins Publishing. Herring, S. C., & Paolillo, J. C. (2006). Gender and genre variation in weblogs. Journal of Sociolinguistics, 10(4), 439e459. Hill, T. R., & Roldan, M. (2005). Toward third generation threaded discussions for mobile learning: Opportunities and challenges for ubiquitous collaborative environments. Information Systems Frontiers, 7(1), 55e70. Honkanen, P., Olsen, S. O., & Verplanken, B. (2005). Intention to consume seafood The importance of habit. Appetite, 45(2), 161e168. Huang, J. H., Lin, Y. R., & Chuang, S. T. (2007). Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. The Electronic Library, 25(5), 585e598. Hull, C. L. (1943). Principles of Behavior: An introduction to behavior theory. New York, NY: Appleton-Century-Crofts. Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A. L. M. (1997). Personal computing acceptance factors: A structural equation model. MIS Quarterly, 21(3), 279e302. James, W. (1890). The principles of psychology. New York: Henry Holt & Co. Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and postadoption beliefs. MIS Quarterly, 23(2), 183e213. Kim, B., Choi, M., & Han, I. (2009). User behaviors toward mobile data services: The role of perceived fee and prior experience. Expert Systems with Applications, 36(4), 8528e8536. Kim, C., Mirusmonov, M., & Lee, I. (2010). An empirical examination of factors influencing the intention to use mobile payment. Computers in Human Behavior, 26(3), 310e322. Klimmt, C., Hartmann, T., & Schramm, H. (2006). Parasocial interactions and relationships. Psychology of entertainment, 291e313. Lankton, N. K., Wilson, E. V., & Mao, E. (2010). Antecedents and determinants of information technology habit. Information & Management, 47(5), 300e307. Laukkanen, T. (2007). Internet vs mobile banking: Comparing customer value perceptions. Business Process Management Journal, 13(6), 788e797. Lee, Y.-K., Park, J.-H., Chung, N., & Blakeney, A. (2012). A unified perspective on the factors influencing usage intention toward mobile financial services. Journal of Business Research, 65(11), 1590e1599. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information

42

Y.-S. Yen, F.-S. Wu / Computers in Human Behavior 65 (2016) 31e42

technology? A critical review of the technology acceptance model. Information & management, 40(3), 191e204. Liao, C., Palvia, P., & Lin, H.-N. (2006). The roles of habit and web site quality in ecommerce. International Journal of Information Management, 26(6), 469e483. Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705e737. Lin, H. (2011). An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust. International Journal of Information Management, 31(3), 252e260. Lin, H. H., & Wang, Y. S. (2006). An examination of the determinants of customer loyalty in mobile commerce contexts. Information & management, 43(3), 271e282. Lohan, M. E. (1997). Men, masculinity and the domestic telephone: A theoretical framework for studying gender and technology. Retrieved from http://www.dcu. ie/communications/iegis/Marial2.htm. Luarn, P., & Lin, H. H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873e891. Lu, M.-T., Tzeng, G.-H., Cheng, H., & Hsu, C.-C. (2014). Exploring mobile banking services for user behavior in intention adoption: Using new hybrid MADM model. Service Business. http://dx.doi.org/10.1007/s11628-014-0239-9. June, 125. Mahon, D., Cowan, C., & McCarthy, M. (2006). The role of attitudes, subjective norm, perceived control and habit in the consumption of ready meals and takeaways in Great Britain. Food Quality and Preference, 17(6), 474e481. Malaquias, R. F., & Hwang, Y. (2016). An empirical study on trust in mobile banking: A developing country perspective. Computers in Human Behavior, 54, 453e461. €o €rni, A. (2009). The impact of use context Mallat, N., Rossi, M., Tuunainen, V. K., & O on mobile services acceptance: The case of mobile ticketing. Information & management, 46(3), 190e195. ndez-Manjo n, B. (2016). An instrument to Manero, B., Torrente, J., Freire, M., & Ferna build a gamer clustering framework according to gaming preferences and habits. Computers in Human Behavior, 62, 353e363. Mante, E. A., & Piris, D. (2002). SMS use by young people in The Netherlands. Revista de Astudios de Juventud, 57, 47e58. Retrieved from http://www.mtas.es/injuve/ biblio/revistas/Pdfs/numero57ingles.pdf. Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information System Research, 84(1), 123e136. €o €rni, A. (2009). The impact of use context Mattat, N., Rossi, M., Tunnainen, V. K., & O on mobile services acceptance: The case of mobile ticketing. Information & Management, 46(3), 190e195. McDonald, L. M., & Korabik, K. (1991). Sources of stress and ways of coping among male and female managers. In P. L. Perrewe (Ed.), Handbook on job stress (pp. 185e199). New York, NY: Select Press. Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a world wide web context. Information & Management, 38(4), 217e230. Ng, B. D., & Wiemer-Hastings, P. (2005). Addiction to the internet and online gaming. Cyberpsychology & behavior, 8(2), 110e113. Nunnally, J. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill. O'Cass, A., & Fenech, T. (2003). Web retailing adoption: Exploring the nature of internet users' web retailing behavior. Journal of Retailing and Consumer Services, 10(2), 81e94. Orbell, S., Blair, C., Sherlock, K., & Conner, M. (2001). The theory of planned behavior and ecstasy use: Roles for habit and perceived control over taking versus obtaining substances. Journal of Applied Social Psychology, 31(1), 31e47. Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124(1), 54e74. Palan, K. M. (2001). Gender identify in consumer behavior research: A literature review and research agenda. Academy of Marketing Science Review, 6(2), 1e25. Parthasarathy, M., & Bhattacherjee, A. (1998). Understanding post-adoption behavior in the context of online services. Information Systems Research, 9(4), 362e379. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 8(5), 879e903.

Püschel, J., Afonso Mazzon, J., & Hernandez, J. M. C. (2010). Mobile banking: Proposition of an integrated adoption intention framework. International Journal of Bank Marketing, 28(5), 389e409. Riquelme, H. E., & Rios, R. E. (2010). The moderating effect of gender in the adoption of mobile banking. International Journal of Bank Marketing, 28(5), 328e341. Robert, M. L., & Wortzel, L. H. (1979). New life-style determinants of women's food shopping behavior. Journal of Marketing, 43(3), 28e39. Rogers, E. M. (1995). Diffusion of innovation (4th ed.). New York, NY: Free Press. €la €, P., & Alama €ki, H. (2003). Mobile learning in teacher training. Journal of Seppa computer assisted learning, 19(3), 330e335. Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129e142. Sigala, M. (2006). Mass customization implementation models and customer value in mobile phones services. Managing Service Quality, 16(4), 395e420. Stowers, G. N. (1995). Getting left behind? Gender differences in computer conferencing. Public Productivity and Management, 19(2), 143e159. Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption. International Journal of Research in Marketing, 12(2), 137e155. Teo, T. S. H., & Lim, V. K. G. (1997). Usage patterns and perceptions of the internet: The gender gap. Equal Opportunities International, 16(6/7), 1e8. The Statista Portal. (2016). Number of smartphone users worldwide from 2014 to 2019 (in millions). Retrieved from http://www.statista.com/statistics/330695/ number-of-martphone-users-worldwide/. Timmers, M., Fischer, A. H., & Manstead, A. S. R. (1998). Gender differences in motives for regulating emotions. Personality and Social Psychology Bulletin, 24(9), 974e985. Triandis, H. C. (1971). Attitude and attitude change (Vol. 8). New York: Wiley. Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695e703. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273e315. Venkatesh, V., & Brown, S. A. (2001). A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly, 25(1), 71e102. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Science, 27(3), 451e481. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186e204. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425e478. Verplanken, B., & Aarts, H. (1999). Habit, attitude, and planned behaviour: Is habit an empty construct or an interesting case of goal-directed automaticity? European Review of Social Psychology, 10, 101e134. Verplanken, B., Aarts, H., van Knippenberg, A., & Moonen, A. (1998). Habit versus planned behavior: A field experiment. British Journal of Social Psychology, 37(1), 111e128. Wang, Y. S., Wang, Y. M., Lin, H. H., & Tang, T. I. (2003). Determinants of user acceptance of internet banking: An empirical study. International Journal of Service Industry Management, 14(5), 501e519. Yang, A. S. (2009). Exploring adoption difficulties in mobile banking services. Canadian Journal of administrative sciences, 26(2), 136. Yang, K., & Lee, H.-J. (2010). Gender differences in using mobile data services: Utilitarian and hedonic value approaches. Journal of Research in Interactive Marketing, 4(2), 142e156. Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior, 28(1), 129e142. Yen, Y.-S. (2012). Exploring customer perceived value in mobile phone services. International Journal of Mobile Communications, 10(2), 213e229. Ye, C., & Potter, R. (2011). The role of habit in post-adoption switching of personal information technologies: An empirical investigation. Communications of the Association for Information Systems, 28(35), 585e610. Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26, 760e767.