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Investigating the consumer switch to online banking


Electronic Commerce Research and Applications 10 (2011) 115–125

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Electronic Commerce Research and Applications
journal homepage: www.elsevier.com/locate/ecra

From marketplace to marketspace: Investigating the consumer switch to online banking
Kuo-Wei Lee a,?, Ming-Ten Tsai b, Maria Corazon L. Lanting c
a

Department of Business Administration, National Tai-Chung Institute of Technology, Taiwan Department of Business Administration, National Cheng Kung University, Taiwan c Department of Business Administration, Southern Taiwan University, Taiwan
b

a r t i c l e

i n f o

a b s t r a c t
Even though scholars have placed considerable focus on studying the attitudes and intentions towards using the virtual market (marketspace), there are still few studies that examine the potential effect of the physical market (marketplace) on the virtual market. The physical and virtual markets have some substitution effects; as users utilize the virtual market more frequently, they use the physical market less regularly. Under this premise, factors relating to the physical market may have a potential effect on the user’s acceptance of the virtual market. The primary goal of this study was to explore the factors that affect the attitude and intention towards switching from the physical to the virtual market in the context of online banking. In total, 400 questionnaires were sent out and 250 effective questionnaires were returned, for an effective recovery rate of 62.5%. Factor analysis and regression analysis were used to examine the hypotheses. The results showed that perceived usefulness, perceived ease of use and of?ine trust have positive effects on attitude towards switching. Additionally, of?ine loyalty and switching costs had negative signi?cant in?uence on attitude towards switching. Moreover, attitude towards switching had a positive effect on the behavior intention to switch. Finally, computer self-ef?cacy moderates the effect of attitudes and behavior intention towards switching to online banking. ? 2010 Elsevier B.V. All rights reserved.

Article history: Received 25 February 2010 Received in revised form 27 August 2010 Accepted 29 August 2010 Available online 7 September 2010 Keywords: Attitude towards switching Behavior intention to switch Online Banking

1. Introduction Rayport and Sviokla (1994a,b) indicated that in the future, businesses will compete not only in the physical market (marketplace), but also in the virtual market (marketspace). That is, more and more companies will switch their business models from the physical to the virtual market (online technology). At present, the perspective of Rayport and Sviokla has been con?rmed, as the physical distribution of goods through stores, banks, bookstores, mail and newspapers, among others, is gradually moving to the virtual market (see Table 1). To a large extent, most consumer goods and services are now available through virtual markets (marketspace), hence the ubiquity of online business models (Wu and Chen 2005, Alam et al. 2009, Sejin and Leslie 2009). Yet consumers can also secure these goods and services via traditional markets (marketplace). This phenomenon prompts the research question: How does the marketplace affect consumers’ decisions to switch from the physical to the virtual market? This question is investigated in the context of online banking. Physical banking allows personal interactions
? Corresponding author. Address: 13F.-11, No. 539, Chin Ping Rd., Anping District, Tainan 708, Taiwan. Tel.: +886 6 2980922; fax: +886 6 2422420. E-mail address: leealan1022@yahoo.com.tw (K.-W. Lee).
1567-4223/$ - see front matter ? 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.elerap.2010.08.005

when conducting business transactions, and this gives consumers a sense of security. However, if these transactions are done online, how do of?ine banking factors interplay with the factors related to using online technologies? What factors in?uence consumers when switching to online banking? These questions will be examined further in the following sections. This research underscores the potential effects of the marketplace on the virtual market and on the adoption and use of new technologies. The technology acceptance model (TAM) is utilized in this context to investigate the attitude and behavior towards switching to online banking (i.e., the virtual market). Physical market variables are then integrated into the framework. According to TAM, users’ perceived usefulness (PU) and perceived ease of use (PEOU) in?uence their attitude and behavior intention towards using new technologies (Davis 1989, Davis et al. 1989). TAM has been extensively veri?ed using factors that affect consumers’ intentions toward using virtual markets (Tan and Teo 2000, Kim and Prabhakar 2000, Cheng et al. 2006). For instance, some researchers have discussed behavior intentions to use e-mail, while others focused on user acceptance of the worldwide web (WWW) (Adams et al. 1992, Gefen and Straub 1997, Fenech 1998). Follow-up studies have included other constructs to derive a more comprehensive interpretation of consumers’ intentions to use online technologies. Agarwal and Prasad (1999) probed this

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Table 1 Switching of business models from physical to virtual market. Market Physical market (marketplace) Virtual market (marketspace) Business model Store Online store Bank Online banking Bookstore Online bookstore Mail E-mail Newspaper E-paper Book E-book

issue from the perspective of a professional background, and Bhattacherjee (2001) included personal creativity in his study. In addition, others extended the model by adding playfulness (Van der Heijden 2003) or convenience (Yoon and Kim 2007) as a factor. Although TAM has been extensively veri?ed with factors that affect users’ intentions toward virtual markets, prior research has mostly focused on the concept of attitude towards using and behavioral intention to use (Davis et al. 1989, Adams et al. 1992, Agarwal and Prasad 1999, Tan and Teo 2000, Kim and Prabhakar 2000, Chan and Lu 2004, Porter and Donthu 2006). In this study, we argue that when business models switch from a physical to a virtual market, the constructs of attitude and behavioral intention to use cannot fully re?ect users’ acceptance of the virtual market because user acceptance of the virtual market implies that they will reduce use of the physical market. In other words, the physical and virtual markets are substitutes. As consumers use virtual markets to conduct transactions, they will use physical markets less often. To a certain extent, the use of the virtual market infers a behavioral switch away from the physical market. Before a consumer decides to accept this new technology, the behavioral intention to switch must take precedence. When consumers make a choice between physical and virtual markets, the viewpoints of ‘‘attitude towards use’’ and ‘‘intention to use’’ (Featherman and Fuller 2003, Lin 2006, Porter and Donthu 2006, Lee 2009) are not as applicable as ‘‘attitude towards switching’’ and ‘‘intention towards switching’’ in re?ecting the substitution effect between physical and virtual markets. Following the points above, the ?rst objective of this study was to investigate the concept of attitude and intention towards switching from physical to virtual markets, instead of the traditional viewpoint of attitude and intention towards use of the virtual market. Second, regarding the factors that affect users’ attitudes and intention to use the virtual market, previous research has mostly focused on the features of the new technology itself, such as its PU, PEOU and characteristics of the users (personal creativity, professional background and online experience), or on the social perspectives, such as trust and subjective norms (Bhattacherjee 2001, Chau and Hu 2001, Featherman and Fuller 2003, Gefen et al. 2003). Because physical and virtual markets have some substitution effect, the factors relating to the physical market, like the satisfaction of consumers using the physical market, may also have a potential effect on the user’s acceptance of the virtual market. Little research has been conducted that considers the impact of the physical market on consumers’ intention toward switching. Therefore, the second objective of this study was to integrate a technology acceptance model and the factors relating to the physical market to understand their reciprocal effects on attitude and intention towards switching. Fig. 1 identi?es the gap of this research. 2. Literature review 2.1. Technology acceptance model Scholars and researchers have continuously expressed an interest in studying the acceptance and use of technology (see Lee and Lee 2001, Chau and Lai 2003, Featherman and Fuller 2003, Porter

Physical Market
(2) This study focus on the switching behavior from physical to virtual market

Virtual Market
(Online technology)
(1) TAM mainly focus on the acceptance of new technology (virtual market)

Fig. 1. The gap of this research.

and Donthu 2006). The factors that are signi?cant in using and accepting technology have been scrutinized across wide-ranging user technology and application platforms (see Lu et al. 2003, Yi and Hwang 2003, Shih 2004). Therefore, it is rational to say that the foundation for user technology acceptance research has been deeply entrenched. Of all the models proposed, TAM conceptualized by Davis (1989) has been widely received and adopted by IS researchers. Brie?y, TAM hypothesizes that two variables, namely perceived usefulness and perceived ease of use, are the foremost elements in establishing the attitudes and behaviors toward IT adoption, the intention to use the technology and the actual usage. These two constructs have generally been validated in many empirical studies to be important factors affecting system usage. The high explanatory power and generalizability of TAM have also been examined and supported in many studies (see Fenech 1998, Jones and Hubona 2006, Lee 2009). In this paper, TAM provides a template for analyzing behavior intention towards switching to online banking, which includes the constructs related to of?ine banking (marketplace variables). Even though the model is robust enough, it still needs to be expanded and adjusted to ?t computing technologies in a typical business environment. For example, Featherman and Fuller (2003) applied TAM to e-services adoption. Im et al. (2008) included two variables, perceived risk and technology type, to assess user acceptance of technologies. 2.2. Online banking and the technology acceptance model Huge advancements in information technology have tremendously affected the ?nancial services industry, as is evident in the development of online banking in recent years. Online banking remained largely unnoticed in Taiwan until 2003, when web-based automated teller machines (ATMs) were introduced (Wang et al. 2003). Due to the great cost bene?ts of online banking, Taiwanese banks began to develop their online banking platforms in hopes of luring customers to use the Internet to conduct their business transactions. Internet banking adoption has been the focus of several previous research studies, such as those by Kim and Prabhakar (2000), Tan and Teo (2000) and Cheng et al. (2006), which focused on Internet banking adoption and acceptance, and utilized TAM in conceptualizing a framework. TAM has been continuously and vigorously extended for use with some models, including trust (Mukherjee and Nath 2003), perceived risk (Verhagen et al. 2004), computer self-ef?cacy (Wang et al. 2003) and gender (Lai

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and Li 2005). To fully analyze behaviors towards use, different factors have been added to extend TAM to adapt to different circumstances. The above-mentioned papers contributed to our study concept by citing extensions of TAM, yet failed to address the antecedents of this usage behavior. Once the substitution effect between conventional and new technologies exists, the switching behavior bridges the adoption and usage constructs. This, in turn, points to behavior towards switching, which comprises of?ine and online channels for banking. 2.3. Switching attitude and intention from of?ine to online banking In examining the acceptance and usage of Internet banking, the authors argue that research on TAM often fails to consider the potential effects of a physical bank. TAM offers a rudimentary framework in this case because the Internet banking environment is not the default system being used in the ?rst place. To a certain extent, it substitutes and replaces the physical platform for banking. This raises the issue of the difference between using and switching. In the context of technology acceptance, usage pertains to the utilization of technology to perform a certain task (Autzen 2007), while switching refers to the tendency or intention to exchange or shift from one method to the other. Thus, there is a need to understand users’ switching attitude and intention from physical to virtual markets. The main purpose of this study was to explore the behavioral intention to switch to online banking in light of the technology acceptance model. In this paper, attitude and behavior are transformed into ‘‘attitude towards switch’’ and ‘‘behavioral intention to switch,’’ rather than ‘‘attitude towards use’’ and ‘‘behavior towards use.’’ This change is done to facilitate measuring the switching behavior, the precursor of usage behavior. Furthermore, this research will shed light on understanding attitudes preceding a switch. Some factors related to physical banking, including of?ine trust (trust of a physical bank), of?ine loyalty (loyalty toward a physical bank) and switching costs are discussed in this study. Trust, loyalty and switching costs have been examined in prior research, and the empirical evidence shows that these factors will affect a user’s purchasing intention and behavior (Jones et al. 2000, Srinivasan et al. 2002, Shankar et al. 2003, Wu and Chen 2005, Floh and Treiblmaier 2006). However, previous work used a holistic online perspective and did not specifically apply it to online banking or to the adoption of user technologies. Although the constructs of trust and loyalty have been discussed in the above research, no study has ever attempted to discuss how these factors related to physical banking affect the switching attitude towards moving from physical to virtual banking. This paper’s research tackles both of these issues in lieu of the online banking environment. Finally, this study also examines the role of perceived risk and computer self-ef?cacy involved in the research. These factors are user-related, which means they consider each user’s ability and perspective on online banking. Perceived risk and computer selfef?cacy have been replicated in various IS research (Mitchell 1999, Miyazaki and Fernandez 2000, Kim and Prabhakar 2000, Wang et al. 2003, Verhagen et al. 2004, Torkzadeh et al. 2006) and have been proven to affect users’ attitudes and behavior in an online environment. 2.4. Extending the technology acceptance model TAM is extended in this study to include of?ine banking constructs, namely of?ine loyalty, of?ine trust and switching costs, as well as user-related constructs, such as perceived risk and computer self-ef?cacy. Thus far, the extension of TAM to include the attitude and behavioral intention to switch has not yet been explored, and is therefore worthy of investigation.

Previous studies have emphasized the role of the perceived usefulness on attitudes towards use (Davis et al. 1989, Venkatesh and Morris 2000, Suh and Han 2002). Davis (1989) argued that individuals tend to undertake behaviors they believe will help them perform their job better and more ef?ciently. Regarding switching behaviors, Chen and Hitt (2002) argued that switching behavior and attrition are affected by usage behavior. When users consider online banking useful, they will hold a more positive attitude towards switching from a physical to a virtual platform. Hence, the following hypothesis is developed: H1a. Perceived usefulness will have a signi?cant effect on a user’s attitude towards switching to online banking. Similar to perceived usefulness, perceived ease of use is one of the most important determinants of the TAM model (Davis et al. 1989). It follows that a system perceived to be easy to use has a higher chance of affecting customers’ attitudes toward switching to online banking. Extensive literature has also been published regarding the importance of this antecedent on attitude towards use (Taylor and Todd 1995, Chen and Hitt 2002, Hsu 2004, Hsu et al. 2006). Consequently, if consumers perceive a service to be easier and more convenient for them to use, then it will directly in?uence their attitude towards switching, leading to the following hypothesis: H1b. Perceived ease of use will have a signi?cant effect on attitude towards switch to online banking. The concept of trust has long been an important issue in online banking because it underlies what makes an enabling online banking environment (Mukherjee and Nath 2003). Customers are concerned about the security and privacy of their information. Wang and Emurian (2005) argued that trust leads to action, which includes risk-taking behaviors. Indeed, trust is an important element to consider when studying the behavioral intention to switch to online banking. As a bank expands its business model from a physical to a virtual market, and its customers feel that their physical bank is trustworthy, they will hold a positive attitude towards switching their service to online banking. The trust in their physical bank (of?ine trust) will reduce the uncertainty in their switching behavior. Thus, the following hypothesis can be formed: H2. Of?ine trust will have a positive effect on the attitude towards switching to online banking. As was discussed in the literature review regarding loyalty, allegiance towards a physical bank has a lot to do with the attitude towards switching. Loyalty measures how likely a customer is to repurchase and engage in partnership activities (Shoemaker and Lewis 1999). When a customer’s loyalty to their physical bank (offline loyalty) is high, the intention to continuously use the physical bank will be high (Chen and Chen 2003). That is, the higher the customer’s loyalty is to physical banking, the less enthusiastic his or her attitude toward switching to online banking will be. If there is no obvious stimulus for the consumer, they will be reluctant to switch from the physical to virtual bank. A study by Alam et al. (2009) also found that this reluctance to change has a signi?cant effect on the consumer’s attitude. Following this logic, the following hypothesis is formed: H3. Of?ine loyalty will have a negative effect on attitude towards switching to online banking. When a consumer faces a decision to switch from one provider to another, he or she is faced with the costs and bene?ts of switch-

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ing (Ganesh et al. 2000). Generally, the bene?ts should outweigh the costs incurred in shifting to another provider (Lee and Feick 2001). From the online banking perspective, customers face the costs of learning how to use the new system when conducting transactions. Another area to look into is the cost of the loss of contact with the tellers. If, for example, the customer is suddenly faced with a problem in executing a transaction, the customer cannot immediately ?le a complaint because physical contact is not possible. Clearly these factors signi?cantly affect attitude towards use, which in the model of Burnham et al. (2003), is the attitude towards staying with the current provider. Once consumers feel that the switching costs from the physical to the virtual market are too high, they will dislike the idea of switching to online banking. The following hypothesis follows this logic: H4. Switching costs will have a negative effect on attitude towards switching to online banking. Attitude towards use is an important element of TAM because it signi?cantly affects online banking service in customers’ future actions (Yeow and Yee 2008). Every study that extends TAM includes this variable because of its high explanatory power (see Wang et al. 2003, Yanga and Yoo 2004, Shih 2004). Attitude is the connecting link between the belief variables and behavior intention (Lai and Li 2005), leading to the following hypothesis: H5. Attitude towards switching has a signi?cant effect on the behavior intention to switch. Self-ef?cacy re?ects the belief of the customers regarding their performance in using the system. The moderating variables, perceived risk and computer self-ef?cacy, have been included in different research studies (see Cockrill et al. 2009, Featherman and Pavlos 2003, Verhagen et al. 2004). Computer self-ef?cacy moderates this kind of effect in the sense that people have different capabilities in executing a task, such as using the online banking platform. Perceived risk measures the effect of a user’s attitude towards switching by distinguishing the risk that an online banking transaction entails. Compeau and Higgins (1995) argued in their research that computer self-ef?cacy is associated with the individual’s attitude towards the adoption of computer technologies. Previous studies (Igbaria and Iivari 1995, Johnson 2005, Mcilroy et al. 2007) also emphasized the effect of computer self-ef?cacy on adopting new innovations. Chang and Tung (2008) also suggested that self-ef?cacy plays an important role in motivation and behavior intention. If individuals are pessimistic about their ability to use a new system, then it will not have any signi?cant effect on behavior. Thus, computer self-ef?cacy may in?uence the relationship between attitude and behavior towards switching. The following hypothesis is thus formed: H6. The interaction of computer self-ef?cacy and attitude towards switching has a positive effect on the behavioral intention to switch to online banking. Consumers face a lot of risk in conducting ?nancial transactions, especially via a medium that does not have any kind of physical contact. Users’ perceived risk will affect the adoption of Internet technology (Miyazaki and Fernandez 2000, Kim and Prabhakar 2000, Wang et al. 2003). Customer behavior varies according to how they perceive online banking risk. The higher their con?dence level, the more positive their attitude and behavioral intention to switch will be. Featherman and Fuller (2003) observed in their study of undergraduate business students that increasing levels

of perceived risk moderates the adoption behavior of technology. Thus, the following hypothesis is formed: H7. The interaction of perceived risk and attitude towards switching has a negative effect on the behavioral intention to switch to online banking.

3. Methodology 3.1. Conceptual framework The research model is illustrated in Fig. 2. As shown, the proposed model modi?es TAM and adds several external variables, which are argued to in?uence attitudes towards switching and behavioral intention to switch in the context of Internet banking acceptance and usage. 3.2. Measurement The questions used in this study were adapted from the previous TAM literature and its applications using a 7-point Likert scale ranging from (1) strongly disagree to (7) strongly agree. This study was composed of nine constructs: (a) perceived usefulness; (b) perceived ease of use; (c) of?ine trust; (d) switch cost; (e) of?ine loyalty; (f) perceived risk; (g) perceived computer self-ef?cacy; (h) attitude towards switching and (j) behavioral intention to switch. The ?rst part of the questionnaire included background questions such as age, gender, education, income and occupation. The second part measured these constructs. The Appendix in Table A.1 lists the items with references to their resources. TAM-related construct questions such as perceived usefulness, perceived ease of use, attitude toward switching and behavioral intention to switch were taken from studies by Davis et al. (1989), Venkatesh and Davis (2000) and Moon and Kim (2001). These constructs have been well researched, developed, validated and adopted in several previous TAM research projects. Although these studies also examined usage behaviors, the questions used in this study were modi?ed to suit the authors’ intentions. Items regarding trust were taken from the measurements de?ned by Dimitriadis and Kyrezis (2008). These questions were carefully synthesized to identify three widely-used items that could be adapted for the model. Trust is deemed as of?ine trust because it refers to the physical bank. Trust in the previous TAM studies, such as those of Suh and Han (2002), Verhagen et al. (2004) and Mukherjee and Nath (2003), considered trust as a quality of the consumer, and not of the bank or any other entity used before the new technology. Switch costs were quanti?ed using items from Eastin (2002) and Wang et al. (2003). Three items were reproduced from these studies to re?ect the costs of switching from of?ine to online banking. Two items were classi?ed as time-related costs, while the other one measured the bene?ts lost cost (Burnham et al. 2003), which mirrors the cost of replacing an old medium with a new one. Questions intending to assess of?ine loyalty came from Ping (1993) and Jones and Farquhar (2003). Although the loyalty used here is the loyalty for of?ine banking, these studies still replicated the construct. Three items were used to measure this kind of loyalty. Perceived risk items were derived from items classi?ed by Zeithaml et al. (1996). This construct is considered important as a moderating variable because it is as existential as some ‘‘disturbances’’ that may impinge on attitude and behavior. Two items were reproduced from the above-mentioned study. Computer self-ef?cacy questions were taken from measurements evaluated by Yi and Hwang (2003) and Chan and Lu

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Modified TAM model

Perceived Usefulness Computer SelfPerceived Ease of Use
Efficacy

Offline bankrelated constructs

Offline Trust Offline Loyalty Switch Cost

Attitude toward switch

Behavior intention to switch

Perceived Risk

Fig. 2. The proposed conceptual model.

(2004). The three items used aimed to assess the con?dence of the user in using an online banking platform. 3.3. Sampling The data used in this study were gathered by means of a convenience sampling survey conducted among different banks in Tainan, Taiwan in 2008. Some of the banks included Taishin Bank, Bank of Taiwan, Mega International Commercial Bank, First Commercial Bank, Shanghai Commercial and Savings Bank and Bank Sinopac. Four hundred questionnaires were distributed and 250 were returned, which represents a response rate of 62.5%. Questionnaires were completed in different places around the Tainan area, during different times of day and on different days during the data collection period. The resulting sample was thus well distributed in terms of demographic information. The questionnaire (shown in the Appendix) consisted of questions related to background information and possible factors affecting the behavioral intention to switch to online banking. Seven-point Likert scales ranging from ‘‘strongly agree’’ to ‘‘strongly disagree’’ were used as a basis for answering the questions. This scale was used in previous TAM-extension studies (Tan and Teo 2000, Wang et al. 2003). Convenience sampling is a non-probability method. This means that subjects are chosen in a nonrandom manner. To avoid the non-response bias, this study did a test of homogeneity on the demographic variables (Chang and Chen 2008). All items among the constructs were tested against demographic controls (gender, age and education) using ANOVA, which was adopted from Cho (2006). The mean scores of the items were all indifferent (p > 0.05) among the demographics. As a result, the survey response could be mixed as a single dataset for further analysis.

Table 2 Demographic characteristics of the respondents. Demographic pro?le Gender Male Female Age Under 20 21–30 31–40 41–50 Over 50 Educational attainment Under junior high school/junior high school University/college Masters/Ph.D. Respondents’ industry Student Manufacturing Service Finance Others Frequency of using online banking (per month) Less than once 2–3 times 4–5 times More than ?ve times Frequency 88 162 1 77 118 42 12 19 200 31 21 34 158 25 12 38 77 70 53 Percent (%) 35.2 64.8 .4 30.8 47.2 16.8 4.8 7.6 80.0 12.4 8.4 13.6 63.2 10 4.8 15.2 30.8 28 21.2

Most of the participants were female. The bulk of the respondents use the online banking platform more than ?ve times per month. 4.2. Factor analysis and reliability test Table 3 shows the factor analysis of PU, PEOU, of?ine trust, switching costs, of?ine loyalty, perceived risk, computer selfef?cacy, attitude towards switching and behavioral intention to switch. The factor analysis was carried out using principal axis factoring with varimax rotation as an extraction method. The goal of this method was to verify the construct validity of the measurement used. According to Hair et al. (1998), factor loadings should be greater than 0.5 to be deemed signi?cant. Following this logic, most of the factor loadings were greater than 0.5, with some above 0.9; hence, all factors used in this study had a signi?cant level of convergent validity. It is also clear from Table 3 that all of the

4. Data analysis and results 4.1. Sample characteristics The descriptive statistics of the respondents’ demographic characteristics were analyzed and are presented in Table 2. The majority of the respondents fell into the 30–40 year-old age group, which coincides with the target user group for online banking services. They were distributed among a variety of professional industries such as banking, manufacturing, social work, IT and medicine.

120 Table 3 Summary of measurement scales. Construct Perceived usefulness Variables PU1 PU2 PU3 PU4 PU5 PEOU1 PEOU2 PEOU3 PEOU4 PEOU5 OT1 OT2 OT3 SC1 SC2 SC3 LT1 LT2 LT3 ATT ATT ATT ATT 1 2 3 4

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Factor loading .724 .816 .871 .793 .675 .897 .882 .819 .826 .834 .837 .933 .914 .866 .807 .916 .859 .847 .834 .949 .897 .933 .924 .885 .858 .997 .897 .899 .922 .947 .946 .916 .916

Eigenvalue 3.033

Cumulative explanation (%) 60.656

Corrected item-total correlation .768 .668 .754 .656 .621 .829 .813 .724 .720 .729 .667 .836 .799 .686 .602 .783 .667 .649 .623 .905 .820 .878 .862 .725 .683 .741 .702 .739 .828 .876 .877 .831 .831

Cronbach’s a .829

Perceived ease of use

3.632

72.635

.904

Of?ine trust

2.407

80.249

.877

Switching costs

2.240

74.669

.829

Of?ine loyalty

2.161

71.699

.780

Attitude towards switching

3.428

85.698

.944

Behavior toward switching

BTS1 BTS2 BTS3 BTS4 BTS5 CS1 CS2 CS3 PR1 PR2

3.137

82.799

.756

Computer self-ef?cacy

2.641

88.044

.931

Perceived risk

1.831

91.570

.908

variables tended to have a high coef?cient item-total correlation (greater than 0.5), which suggested a high degree of internal consistency for each dimension. Moreover, the Eigenvalues were greater than 1 and Cronbach’s a values were greater than the 0.8 cutoff (Peterson 1994). These results further con?rmed the reliability of the measurement items. No items were deleted from the analysis. This factor model was used to analyze the switching attitude and behavior intention towards online banking. 4.3. Test of hypotheses Seven hypotheses were formulated in this study, and multiple regression analysis was used to test them. The independent variables (perceived usefulness, perceived ease of use, of?ine trust, offline loyalty and switching costs) were regressed against attitude towards switching. control variables such as age, gender and education were also added to the model to give precedence to user characteristics (Venkatesh and Morris 2000, Lai and Li 2005). Table 3 presents the results of the regression. The overall model was statistically signi?cant (R2 = 0.490; pvalue <0.05) and had high explanatory power for this type of research. The results were within the generally accepted range of 0.4. The Durbin–Watson test (DW = 2.140), which looks at serial correlation among residuals, was also within the generally accepted range of 1.5–2.5. As can be seen in Table 4, the control variables were found to be insigni?cant in explaining the attitude towards switching. These constructs have different effects on diverse sets of studies. Most studies regard them as moderating effects (such as Lin and Qiu

Table 4 Results of multiple linear regression. Independent variables Attitude towards switching b Gender Age Education Perceived usefulness Perceived ease of use Of?ine trust Of?ine loyalty Switching costs R2 Adjusted R2 F-value p-value Durbin–Watson .011 ?.032 .11-1 .149 .405 .260 ?.162 ?.247 .490 .473 28.967 0.0000 2.140 t-value .231 ?.655 1.705 2.874 6.812 3.392 ?1.987 ?4.674 p-value .817 .513 .040 .004 .000 .001 .048 .000

(2004) and Venkatesh and Morris (2000), while others treated them as direct effects (Igbaria and Iivari 1995, Chau and Lai 2003). Among the background variables (age, gender and education), only education demonstrated a positive, signi?cant effect. Age and gender were insigni?cant and thus do not in?uence attitude towards switching, consistent with the ?ndings of Jones and Hubona (2006). While age and gender do not signi?cantly affect the attitudes of users towards information systems, the qualities of the users (such as education) clearly shape their attitudes and behavior. Table 4 shows H1–H4 and all are supported. H1a and H1b followed the logic of TAM, and H1a (perceived usefulness) was statis-

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tically signi?cant (b = 0.149; p < 0.05). As previous studies have consistently demonstrated that perceived usefulness has a signi?cant and positive in?uence on attitude towards use, such is the case in this study with respect to attitude towards switching. H1b (perceived ease of use) (b = 0.405; p < 0.005) has also been presented in previous research and has been proven to affect attitude towards use (see Tan and Teo 2000, Venkatesh and Morris 2000). In summary, if users perceive online banking as useful and easy to use, then they are more inclined to use the online banking platform. The evidence for H2 (Of?ine Trust) is consistent with a study by Lee et al. (2007) showing that of?ine trust is the key to using online banking. Of?ine trust refers to the fact that customers who patronize their traditional of?ine banking channel are more likely to utilize its online banking service. Hirschman (1980) also suggested that greater positive perception might lead to a greater acceptability of products, hence increasing the chance that the new products will be adopted. In the context of this study, an individual would be more willing to switch to an online banking service if he or she had enough experience with its supporting channel (i.e., the physical bank). Of?ine Trust was positive and signi?cant (b = 0.260; p < 0.05). On a similar note, Of?ine Loyalty ful?lled the expectation of H3 and was negative and signi?cant (b = ?0.162; p < 0.05). If the customer’s loyalty to their physical bank is high, they will be unwilling to switch from physical to virtual banking. Therefore, Of?ine Loyalty is relevant in explaining attitude towards switching to online banking. The results for Switching Costs supported the outcome of H4 (b = ?0.260; p < 0.05). According to Chen and Hitt (2002), switching costs can adversely affect the attitude of the consumer towards switching because they take into account the different factors that could be avoided due to the resulting behavior. Perera and Kim (2002) also argued that the larger the switching costs are to the consumer, the more resistant they will be towards switching. 4.3.1. Test of moderating effects In Table 5, Model 1 examines the relationship between attitude towards switching and behavioral intention to switch. The results indicated that attitude towards switching is positive and signi?cant (b = 0.762; p < 0.001). These results thus support H5. To examine the moderating effects of computer self-ef?cacy and perceived risk, a regression analysis was employed among the four variables, as shown in Table 5. The interaction terms of perceived risk and computer self-ef?cacy were added to Model 3. The interaction of attitude towards switching and computer selfef?cacy (b = 0.341; p < 0.001) had a signi?cant in?uence on the behavioral intention to switch; hence, H6 was supported. To further analyze the said relationship, the means of attitude

6.5

------- high self

BI to switch

6.0 5.5 5.0 4.5 4.0 1 L 2 H

Attitude towards switching
Fig. 3. The moderating effect of computer self-ef?cacy.

towards switching and computer self-ef?cacy were clustered into two high and low groups and a graph of the interaction between the independent variable and the moderator variable was constructed. As shown in Fig. 3, the two regression lines are not parallel. The slopes of the regression lines between behavior and attitude are different for different categories of users’ computer self-ef?cacy. This difference indicates that computer self-ef?cacy may in?uence the relationship between attitude and behavior towards switching. If computer self-ef?cacy is high and satisfactory, the relationship between attitude and behavior is strengthened. Conversely, if computer self-ef?cacy is insuf?cient to facilitate the switch, then the relationship may be weakened. In sum, computer self-ef?cacy will moderate the in?uence of attitude towards switching on the behavior towards switching. As can be seen in the regression results, Perceived Risk was not signi?cant. Previous studies have included background variables (age, gender, income and education) in the analysis of attitude and behavior and have shown that these can have moderating effects (see Venkatesh et al. 2002, 2003). However, these results can vary, have low explanatory power and are not generalizable due to factors that may also inhibit or stimulate behavior, such as the present technological environment, in?uence of organizational factors (Sun and Zhang 2005) and subjective norms (Wu and Chen 2005). This reasoning possibly explains why perceived risk is not empirically supported in this research. 5. Conclusions 5.1. Discussion This study has discussed consumers’ behavioral intentions to switch to online banking using the technology acceptance model (TAM), which has been extended to include new variables obtained from the online banking acceptance literature. The conceptualized model suggested that attitude towards and behavioral intention to switch to online banking can be developed using variables derived from TAM (PU and PEOU) and the variables referring to the physical market, including of?ine trust, of?ine loyalty and switching costs. The empirical evidence revealed that PU and PEOU were significant in explaining consumers’ attitude towards switching to online banking services. This result refers to the fact that consumers use online banking for the advantages it provides in contrast to using a physical bank. In addition, one variable related to of?ine banking, the trust in physical banks (of?ine trust), will enhance customers’ attitude towards switching service from physical to online banking. On the contrary, the more the customer’s loyalty

Table 5 he moderating effects of computer self-ef?cacy and perceived risk. Independent variables Attitude towards switch Computer self-ef?cacy Perceived risk Attitude towards Switching ? computer self-ef?cacy Attitude towards Switching ? perceived risk R2 Adjusted R2 F-value Durbin–Watson Behavioral Model 1 .762??? Intention to switch Model 2 .595??? .224??? ?.693 Model 3 .350?? .201? ?.124 .341???

.178 .580 .579 343.071??? 2.042 .606 .602 126.303??? 2.096 .610 .602 76.245??? 2.134

Note: ?p < 0.05;??p < 0.01;???p < 0.001

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is to physical banking (of?ine loyalty), the lower their enthusiasm towards switching to online banking will be. These ?ndings show that the dimensions related to physical banking would offer some explanatory power for the user’s acceptance of online banking. Further, the empirical ?ndings also indicated that consumers take into account the switching costs they may face when switching from of?ine to online banking. The greater the switching costs, the less likely they are to engage in switching behaviors. The interaction effects of computer self-ef?cacy and perceived risk were also tested using behavior towards switching as the dependent variable. The results indicated that the interaction between attitude towards switching and computer self-ef?cacy was signi?cant, which further strengthens the hypothesis of computer self-ef?cacy’s moderating effect on the behavioral intention to switch. This result could mean that individuals self-evaluate how comfortable they are in using information systems, such as an online banking platform, and thus, high and low ratings of computer self-ef?cacy can moderate the effect of attitude towards switching on the resulting behavioral intention to switch. 5.2. Theoretical contributions While TAM has been extensively veri?ed in the factors that affect users’ intention to engage in virtual markets, prior research has mostly focused on the factors that might be effective in predicting attitude towards using and behavioral intention to use (Davis 1989, Adams et al. 1992, Gefen and Straub 1997, Tan and Teo 2000, Cheng et al. 2006). This paper’s research provided a fresh perspective on the TAM model by stressing the aspect of switching to analyze the attitudes and behaviors of consumers in technology acceptance. The act of switching has been neglected in the TAM literature, as many studies have focused on acceptance behavior and not on switching behavior. Before a consumer accepts a technology, he or she performs a switch from one technology to another. At ?rst, the empirical results support the arguments in this study: users’ acceptance and adoption of online banking re?ects not only the acceptance behavior of the new technology, but also the switching behavior between the conventional and new technology. Although previous studies discussed the degree of users’ acceptance of Internet technology, such as online banking, online stores and e-mail, most of these studies performed analyses by mainly focusing on the usage attitude and intention (Bhattacherjee 2001, Chau and Hu 2001, Verhagen et al. 2004, Lai and Li 2005, Chang and Tung 2008). The main contribution of this study was examining the potential substitution effect between old and new technologies. This study concentrated on the switching attitude and intention. Therefore, the results of this study are able to re?ect the potential impact of an old technology when users accept or use a new technology. According to the literature, previous studies based on TAM did not review this point of view in depth. Thus, the viewpoints made in this study create another layer of thinking in this topic. Second, this paper contributes by offering ideas about factors that in?uence switching to online banking by including variables related to the traditional, physical bank. Prior research, such as that of Cockrill et al. (2009) and Lee (2009), extended TAM by incorporating other variables like web security and reluctance to change channels. Other authors incorporated features of the new technology itself as variables, such as its PU or PEOU. Additionally, others focused on the characteristics of the users (personal creativity, professional background and online experience) or on social perspectives, such as trust and subjective norms (Bhattacherjee 2001, Chau and Hu 2001, Featherman and Fuller 2003, Gefen et al. 2003). These researchers extended the TAM model, but failed to consider the substitution effect between the physical and virtual markets and its potential effect on the physical market.

Third, background variables such as gender and age were found to have a relatively weak relationship with attitude towards switching, which is in contrast with previous studies of online banking such as Chau and Lai (2003), Sun and Xiao (2006) and Luarn and Lin (2005). However, Gefen and Straub (1997) proved in their research that the use of online banking varies across cultures. Table 6 summarizes the research contributions of this study. 5.3. Managerial implications The implications that this study provides for managers are the following: if bank managers want to expand their business from of?ine to online banking, it may not be enough to consider only the bene?ts of online banking (i.e., the usefulness and ease of use). Managers must keep in mind that users’ adoption of online banking is not just an acceptance of the new technology, but a behavioral switch from physical banking to online banking. Thus, the factors relating to physical banks will play an important part in the switching process. A ?rst step for a corporation is to work on enhancing its customers’ trust in its of?ine banking. In this way, it will help customers reduce their uncertainty in virtual banks, thereby improving their attitude towards switching from of?ine to online banking. Further, bank managers should also understand that of?ine loyalty will affect their customers’ attitude towards switching. Managers can consider lowering their of?ine loyalty by offering incentives to customers who switch to online banking, such as discounts or bonuses for using online services. Finally, because switching costs provide a mental accounting for consumers, banks should not charge their customers for their online banking services. Users have to incur other non-monetary costs, such as learning and investing time. If it is not possible to provide free online banking, then bank managers should ensure that the costs of manual transactions do not go beyond the costs of of?ine banking. Only in this way can users’ intentions to switch be effectively promoted and the behavior of using online banking be further enhanced. 5.4. Limitations and further research The study has some limitations. In a recent study by Bagozzi (2007), he criticized TAM as a ‘‘theory’’ that lacks feasibility, has

Table 6 Summary of research contributions. Aspect Focus Prior research contributions to the TAM literature Attitude and behavioral intention to use virtual market (online technology) This study’s contribution to the TAM literature Attitude and behavioral intention to switch form physical to virtual market Considering the potential effect of physical market a. TAM-related variables

Variables included

a. TAM-related variables, perceived web security (Cheng et al. 2006, Adoption of Internet Banking in Hong Kong) b. Awareness, PEOU, Security, Cost, Reluctance to Change, Security (Alam et al. 2009. Customer’s Adoption of Internet Banking: Malaysia Case) Signi?cant relationship (Venkatesh and Morris 2000, Gefen and Straub 1997)

b. Physical market related variables (Of?ine Trust, Of?ine Loyalty, Switching Costs) and Moderating variables (CSE and Perceived Risk) Weak relationship of demographic variables to attitude toward switching

Relationship of control variables (gender, age, education)

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questionable heuristic value and limited explanatory and predictive power. However, this study’s framework changed from a highly parsimonious model to one that is a bit more complicated and is much more effective at predicting behavior intention towards switching. The empirical evidence also showed that the R2 in this research is suf?cient to explain the variance of the attitude and behavior intention towards switching. Second, the survey was conducted only among Taiwanese consumers using the convenience method of sampling. This method is used to make the research procedure faster by obtaining a large number of completed questionnaires in a rapid and ef?cient fashion. This has an effect on the generalization of the ?ndings. Sample bias might be present due to a ?aw in the sample selection process; hence, it cannot speak for the entire population. This could affect the validity of the study. However, as shown in the non-response test, no signi?cant differences were
Table A.1 Question items used in the study. Construct Measure

found among the demographic controls. This improves the external validity of the questionnaire. Finally, another limitation is that other factors may affect the users’ behavioral intention to switch. These other factors may include the web interface, such as the ‘‘look’’ and ‘‘feel’’ of the website. Hausman and Siekpe (2009) concluded that web design elements could affect the intentions of conducting the transaction and the intentions to repeat it. Furthermore, this may affect their PU and PEOU. These limitations are avenues of further research. Moreover, another area that could be investigated is the marketing strategy literature, such as that of consumer behavior, to further analyze the behavioral intention to switch. While the authors ?rmly believe that this work sheds new light on understanding the precedence of technology acceptance, more research considering the potential effect of the factors related to the physical market must be performed, both theoretical and empirical.

Source Davis et al. (1989), Venkatesh and Davis (2000), and Moon and Kim (2001)

Perceived usefulness PU 1 Using online banking would improve my performance in conducting transactions PU PU PU PU 2 3 4 5 Using online banking would increase my productivity Using online banking would enhance my effectiveness I would ?nd online banking useful Internet banking gives me greater control over ?nancial banking activities

Perceived ease of use PEOU 1 It is easy for me to learn how to utilize online banking PEOU PEOU PEOU PEOU 2 3 4 5 I ?nd it easy to get online banking do what I want to do It is easy to remember how to use online banking My interaction with the online banking site is clear and understandable I ?nd online banking useful for my banking activities

Davis et al. (1989), Venkatesh and Davis (2000), and Suh and Han (2002)

Of?ine trust OT 1 My bank has the ability to meet its promises OT 2 My bank would not do anything against my interests OT 3 My bank always treats me with goodwill Of?ine loyalty LO 1 I say positive things about the bank to other people LO 2 I intend to continue to do business with the present bank LO 3 I intend to do more business with the present bank Switch cost SC 1 This bank provides services that cannot be easily replaced by other banks SC 2 It takes me a great deal of time and effort to get used to a new platform SC 3 In general, it would be a hassle switching to a new platform Attitude towards switch ATT 1 In my opinion, it is desirable to switch from of?ine to online banking ATT 2 ATT 3 ATT 4 I think it is good for me to switch from of?ine to online banking In my view, switching from of?ine to online banking is a wise idea I feel that switching from of?ine to online banking is pleasant

Dimitriadis and Kyrezis (2008)

Ping (1993) and Jones et al. (2003)

Eastin (2002) and Wang et al. (2003)

Davis et al. (1989), Venkatesh and Davis (2000), and Moon and Kim (2001)

Behavior intention to switch BTS 1 I would switch from of?ine to online banking for my banking needs BTS 2 BTS 3 BTS 4 BTS 5 Switching to online banking to handle my banking transactions is something I would do I can see myself switching from of?ine to online banking to handle my transactions I expect to switch from of?ine to online banking to handle my ?nancial transactions in the future I will strongly recommend others to use Internet banking

Davis et al. (1989), Venkatesh and Davis (2000), and Moon and Kim (2001)

Perceived risk PR 1 I feel the risk associated with online transactions is low PR 2 The bank will not misuse my personal information Computer self-ef?cacy CS 1 I am con?dent in using Internet banking if I have only the online instructions for reference CS 2 I am con?dent in using Internet banking even if there is no one around to show me how to do it CS 3 I am con?dent in using Internet banking even if I have never used such a system before

Zeithaml et al. (1996)

Yi and Hwang (2003) and Chan and Lu (2004)

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ABSTRACT The study investigated the influence of price change on consumption of fast moving consumer goods, a survey of fast food restaurants Eldoret, Ua...
...in the Information Age: The case of the Reserve Bank of ....unkown
NATIONAL UNIVERSITY OF SCIENCE AND TECHNOLOGY An investigation of the changing roles of librarians in the Information Age: The case of the Reserve Bank of...
Investigating antecedents of consumers' recommend intentions ....unkown
//www.tandfonline.com/loi/fsij20 Investigating antecedents of consumers' recommend intentions and the moderating effect of switching barriers Chung-Yu Wang ...
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investigating the efficacy of your current ...The consumer may be responsive to other types of...above this, you'd switch to the dryers' option...
...EXPLORING THE ANTECEDENTS OF CONSUMER INTENTION TO SWITCH ....unkown
Journal of Electronic Commerce Research, VOL 15, NO 4, 2014 FROM FREE TO FEE: EXPLORING THE ANTECEDENTS OF CONSUMER INTENTION TO SWITCH TO PAID ONLINE ...
Investigating the Role of Online Community Engagement and ....unkown
Investigating the Role of Online Community Engagement and Consumer Online Collective Empowerment for Consumer Price Fairness Perception A thesis submitted for the...
Switching barriers in consumer markets: an investigation of ....unkown
the end of this article Switching barriers in consumer markets: an ... Financial services Abstract Much research looks at why customers switch ...
...trial investigating the efficacy and safety of switching ....unkown
HIV Medicine (2005), 6, 426 CORRIGENDA r 2005 British HIV Association HIV Medicine 2005; 6: 353-359 A randomized controlled trial investigating the ...
...the Scope for the Transport Sector to Switch to Electric ....unkown
the Scope for the Transport Sector to Switch to Electric Vehicles and Plug-in Hybrid Vehicles Contents Page Executive Summary.......................
Investigations into the capabilities of switchgrass to phyto....unkown
//lib.dr.iastate.edu/etd Part of the Entomology Commons Recommended Citation Albright, Vurtice, "Investigations into the capabilities of switchgrass to ...
Using the Internet and Computer Corpora to Investigate ....unkown
(orientador) IV agradecimentos First and foremost I would like to thank God because were it not for the will to learn, the strength to try and the ...
...randomized trial to investigate the impact of switching ....unkown
Deeper levels of response with Tasigna compared to Glivec Wednesday, 14 December 2011 Phase III clinical trial data contribute to the growing ...
A Study Design to Investigate the Influence of FTC/ISO Tar ....unkown
A Study Design to Investigate the Influence of FTC/ISO Tar Yield and Tar Band Switching on Cigarette Smoke Dose as Determined by Filter Analysis and ...
An Investigation into the Impact of the Switch to Liquid Sugar.unkown
UBC Social Ecological Economic Development Studies (SEEDS) Student Report An Investigation into the Impact of the Switch to Liquid Sugar Kristian Plakaris, ...
...of the fate of lignin structures of poplar and switchgrass....unkown
Investigation of the fate of lignin structures of poplar and switchgrass during various pretreatments to understand its impact in biomass recalcitrance Y. Pu...
...of the frequency spectrum released by the switchover to ....unkown
Prepared Progira Radio Communication AB Date 2006-04-24 Document Technical investigation of the usage of the frequency spectrum released by the switch...
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