Factors influencing

e d 1, M Theory of planned behavior (TPB) Online banking Perceived risk Perceived benefit ank ugh atio ss f ban banking to form a positive factor named perceived benefit. In addition, drawing from perceived risk the- sized with perceived benefit as well as integrated with the technology acceptance model (TAM) and theory of planned behavior (TPB) model to propose a theoretical model to explain customers’ intention et tech l role n plat line sh purchases (Donna et al., 1999), primarily due to risk concerns (Jarvenpaa et al., 1999; Pavlou, 2001) and, thus perceived risk is posited as a prominent barrier to consumer acceptance of online banking. Compared to online purchases, the adoption of online banking adoption is typically more complex, as it initiates a long- term relationship between the consumer and online banking services. There is a lot at stake for consumers as they contemplate entering into a business relationship with distant, faceless online limited empirical work which captures the success factors or posi- tive factors of online banking to help form a strategic agenda. In this study, besides negative factors, we explore and integrate the advantages of online banking to develop a predictor named per- ceived benefit to explain and predict customer intention to adopt online banking. In order to provide a solid theoretical basis for examining the adoption of online banking services, this paper draws on two schools of thought regarding the nomological structure of the the- ory of reasoned action (TRA): (1) the technology acceptance model (TAM) (Davis et al., 1989), and (2) the theory of planned behavior Electronic Commerce Research and Applications 8 (2009) 130–141 Contents lists availab es .e E-mail address: lmc@npic.edu.tw speed and lower handling fees (Kalakota and Whinston, 1997), there are still a large group of customers who refuse to adopt such services due to uncertainty and security concerns (Kuisma et al., 2007; Littler and Melanthiou, 2006). Therefore, understanding the reasons for this resistance would be useful for bank managers in formulating strategies aimed at increasing online banking use. Consumers have shown reluctance to complete simple online acteristics of the perceived risks. We divided perceived risk into five categories: performance, financial, time, social and security/ privacy risks, as theorized by Jacoby and Kaplan (1972), in order to clarify which risk facets are more important in this field. Although several research projects have focused on the factors that impact on the adoption of information technology or Internet for the past decade (Heijden, 2003; Taylor and Todd, 1995), there is line banking provides many advantages, such as faster transaction Internet stock trading and so on. However, despite the fact that on- provide a deeper understanding of the perceived risks of adopting online banking, we carried out a more in-depth study of the char- 1. Introduction With the rapid growth of Intern has played an important and centra which provides an online transactio e-commerce applications such as on 1567-4223/$ - see front matter � 2008 Elsevier B.V. A doi:10.1016/j.elerap.2008.11.006 to use online banking. The results indicated that the intention to use online banking is adversely affected mainly by the security/privacy risk, as well as financial risk and is positively affected mainly by perceived benefit, attitude and perceived usefulness. The implications of integrating perceived benefit and per- ceived risk into the proposed online banking adoption model are discussed. � 2008 Elsevier B.V. All rights reserved. nology, online banking in the e-payment area form to support many opping, online auction, banking services. Although consumer perceptions of the risks of adopting online banking have been studied by many researchers (Liao et al., 1999; Tan and Teo, 2000; Yousafzai et al., 2003), the perceived risk variable has only been modeled as a single con- struct, which fails to reflect the real characteristics of perceived risk and explain why consumers resist such banking services. To Keywords: Technology acceptance model (TAM) ory, five specific risk facets – financial, security/privacy, performance, social and time risk – are synthe- Factors influencing the adoption of intern and TPB with perceived risk and perceive Ming-Chi Lee Department of Information Engineering, National Pingtung Institute of Commerce, No. 5 a r t i c l e i n f o Article history: Received 11 May 2008 Received in revised form 25 November 2008 Accepted 25 November 2008 Available online 7 December 2008 a b s t r a c t Online banking (Internet b over the last decade. Altho on the adoption of inform neously captures the succe customers to adopt online Electronic Commerce R journal homepage: www ll rights reserved. t banking: An integration of TAM benefit insheng E. Rd., Pingtung, Taiwan, ROC ing) has emerged as one of the most profitable e-commerce applications several prior research projects have focused on the factors that impact n technology or Internet, there is limited empirical work which simulta- actors (positive factors) and resistance factors (negative factors) that help king. This paper explores and integrates the various advantages of online le at ScienceDirect earch and Applications lsevier .com/locate /ecra (TPB) (Azjen, 1991). Since TAM and TPB have been used in many studies to predict and understand user perceptions of system use and the probability of adopting an online system (Gefen et al., 2003; Hsu and et al., 2006; Wu and Chen, 2005), they are the most appropriate tools for understanding online banking adoption. This study proposes to integrate the five facets of perceived risk listed above with the TAM and TPB in order to provide a more compre- hensive model of online banking evaluation and adoption. This study enlarges the scope of the adoption decision to explic- life; therefore, measures of physical risk were not included in this study. We define perceived risk in online banking as the subjec- tively determined expectation of loss by an online bank user in contemplating a particular online transaction. The dimensions of perceived risk were defined in Table 1. 2.1. Perceived risks of online banking The present research investigated five types of risk – security/ g as ting ll as en in s the M.-C. Lee / Electronic Commerce Research and Applications 8 (2009) 130–141 131 itly include both negative (perceived risk) and positive factors (perceived benefits) simultaneously. The research may give practi- tioners an increased understanding of customers’ risk perceptions which can then be used to devise risk-reducing strategies and trust-building mechanisms to encourage online trading adoption, especially in the emerging area of e-payments. The purposes of this study are as follows: 1. To investigate whether perceived risk and benefit significantly impact customers’ behavioral intention to use online banking adoption. 2. To clarify which factors are more influential in affecting the decision to use online banking. 3. To evaluate whether the integration of TAM with TPB provide a solid theoretical basis for examining the adoption of online banking. This paper proceeds as follows: Section 2 introduces perceived risk, perceived benefit and the theoretical foundations. Section 3 outlines our research model and hypotheses. Section 4 details the methodology and research design, and Section 5 presents the data analysis and hypotheses testing results. Section 6 discusses our re- search findings. Section 7 provides implications, and finally Section 8 concludes with this paper’s limitations, and potential topics for future research. 2. Perceived risk, perceived benefit and theoretical background Since the 1960s, perceived risk theory has been used to explain consumers’ behavior. Considerable research has examined the im- pact of risk on traditional consumer decision making (Lin, 2008). Peter and Ryan (1976) defined perceived risk as a kind of subjective expected loss, and Featherman and Pavlou (2003) also defined per- ceived risk as the possible loss when pursuing a desired result. Cunningham (1967) noted that perceived risk consisted of the size of the potential loss (i.e. that which is at stake) if the results of the act were not favorable and the individual’s subjective feelings of certainty that the results will not be favorable. Most of scholars claimed that consumers’ perceived risk is a kind of a multi-dimen- sional construct. Six components or types of perceived risk have been identified: financial, performance, social, physical, privacy, and time-loss (Jacoby and Kaplan, 1972; Kaplan et al., 1974; Roselius, 1971). However, the dimensions of perceived risk may vary according to the product (or service) class Featherman and Pavlou, 2003. Online banking does not incur any threat to human Table 1 Dimensions of perceived risk. Dimension Definition Performance risk The possibility of the product malfunctioning and not performin Social risk Potential loss of status in one’s social group as a result of adop Financial risk The probability that a purchase results in loss of money as we Privacy risk Potential loss of control over personal information, such as wh case is where a consumer is ‘‘spoofed” meaning a criminal use Time risk Consumers may lose time when making a bad purchasing decision b or service only to have to replace it if it does not perform to expec Physical risk The probability that a purchased product results in a threat to hum privacy, financial, social, time/convenience, and performance loss, and the details of these five risks related to online banking are de- scribed as follows: 1. Security/privacy risk: This is defined as a potential loss due to fraud or a hacker compromising the security of an online bank user. Phishing is a new crime skill by which phishers attempt to fraudulently acquire sensitive information, such as usernames, passwords and credit card details, by masquerading as a trust- worthy entity in an electronic communication (Reavley, 2005). A phising attack takes places when a user receives a fraudulent email (often referred to as a spoof email) representing a trusted source that leads them to an equally fraudulent website that is used to collect personal information (Entrust, 2008). Both fraud and hacker intrusion not only lead to users’ monetary loss, but also violate users’ privacy, a major concern of many Internet users. Many consumers believe that they are vulnerable to iden- tity theft while using online banking services (Littler and Melanthiou, 2006). 2. Financial risk: It is defined as the potential for monetary loss due to transaction error or bank account misuse. According to Kuisma et al. (2007), many customers are afraid of losing money while performing transactions or transferring money over the Internet. At present online banking transactions lack the assurance provided in traditional setting through formal proceedings and receipts. Thus, consumers usually have diffi- culties in asking for compensation when transaction errors occur (Kuisma et al., 2007). 3. Social risk: This refers to the possibility that using online bank- ing may result in disapproval of one’s friends/family/work group. It is possible that one’s social standing may be enhanced or diminished depending on how online banking is viewed. It may well be that people have unfavorable or favorable percep- tions of online banking that in turn affect their views of its adopters; or, alternatively, not adopting online banking may also have negative or positive connotations. 4. Time/convenience risk: It may refer to the loss of the time and inconvenience incurred due to the delays of receiving the pay- ment or the difficulty of navigation (finding appropriate services and hyperlinks). Two leading causes of dissatisfying online experiences that may be thought of as a time/convenience risk include a disorganized or confusing Web site and pages that are too slow to download (Forsythe and Shi, 2003). It may also be related to the length of time involved in waiting the website or learning how to operate online banking website. it was designed and advertised and therefore failing to deliver the desired benefits a product or service, looking foolish or untrendy the subsequent maintenance cost of the product formation about you is used without your knowledge or permission. The extreme ir identity to perform fraudulent transactions y wasting time researching and making the purchase, learning how to use a product tations an life weighted by their motivation to comply with that referent. This is expressed as: SN ¼Pnbi �mci.Perceived behavioral control arch 5. Performance risk: This refers to losses incurred by deficiencies or malfunctions of online banking websites. Customers are often apprehensive that a breakdown of system servers or disconnec- tion from the Internet will occur while conducting online trans- actions because these situations may result in unexpected losses (Kuisma et al., 2007). 2.2. Perceived benefit Online banking has recently come to be considered as one of the most effective banking transaction methods (Huang et al., 2005) because it possesses many advantages which offline banking chan- nels can not offer. Thus, online banking managers aim to utilize these advantages to increase the online banking adoption rate. Based to a certain extent on reasons offered by Lee (2008), there are two main types of perceived benefits, which can be categorized as direct and indirect advantages. Direct advantages refer to imme- diate and tangible benefits that customers would enjoy by using online banking. For example, customers can benefit from a wider range of financial benefits, faster transaction speed, and increased information transparency. First, this wider range of financial bene- fits includes the lower transaction handling fees, higher deposit rates, opportunities to win prizes and extra credit card bonus points. Second, the faster transaction speed obviously means that time can be saved since online banking does not need paper docu- ments, the processing of which can give rise to errors and delays, and which also requires more personnel. Online banking auto- mates this process by mediating transactions through websites and electronic data interchange, and can also reduce the need for customers to communicate with bank staff regarding transaction details because they can be obtained at a website. Third, during the transaction, online banking allows customers to monitor con- tractual performance at any time, or to confirm delivery automat- ically. In other words, more relevant information is immediately available and transparent to customers. Indirect advantages are those benefits that are less tangible and difficult to measure. For example, online banking allows customer to perform banking transactions anywhere in the world and enjoy 24-hour service, as well as offering customers more investment opportunities and services, such as stock quotations and news up- dates. The factors outlined above are the perceived benefits that will be considered in the preliminary model of online banking adoption. 2.3. Technology acceptance model TAM is an adaptation of the theory of reasoned action (TRA) by Fishbein and Ajzen (1975) and was mainly designed for modeling user acceptance of information technology (Davis et al., 1989). This model hypothesizes that system use is directly determined by behavioral intention to use, which is in turn influenced by users’ attitudes toward using the system and the perceived usefulness of the system. Attitudes and perceived usefulness are also affected by perceived ease of use. Perceived usefulness, reflecting a person’s salient belief in the use of the technology, will be helpful in improving performance. Perceived ease of use is a person’s salient belief that using the technology will be free of effort (Taylor and Todd, 1995). The appeal of this model lies in that it is both specific and parsimonious and displays a high level prediction power of technology use. These determinants are also easy for system devel- opers to understand and can be specifically considered during sys- tem requirement analysis and other system development stages. 132 M.-C. Lee / Electronic Commerce Rese These factors are common in technology-usage settings and can be applied widely to solve the acceptance problem (Taylor and Todd, 1995). (PBC) reflects a person’s perception of the ease or difficulty of implementing the behavior in question. It concerns beliefs about the presence of control factors that may facilitate or hinder their performing the behavior. Thus, control beliefs about resources and opportunities are the underlying determinant of perceived behavioral control and can be depicted as control beliefs (cbi) weighted by perceived power of the control factor (pi) in question. This is expressed as PBC ¼P cbi � pi. In sum, grounded on the ef- fort of TRA, TPB is proposed to eliminate the limitations of the ori- ginal model in dealing with behavior over which people have incomplete volitional control (Azjen, 1991). In essence, TPB differs from TRA in that it has the additional component of perceived behavior control. 3. Research model and hypothesis 3.1. Research model We drew upon two primary research streams, information tech- nology (IT) adoption theory and perceived risk theory, to develop this study’s research model and associated hypotheses. Over the past decade, TAM and TPB have been widely applied to examine IT usage and e-service acceptance (Davis, 1993; Hsu, 2004; Hsu and et al., 2006). However, neither TAM nor TPB have been found to provide consistently superior explanations or behavioral predic- tions (Chen et al., 2007). Recently, a growing body of research has focused on integrating them to examine IT usage and e-service acceptance because the two models are complementary, and the results have showed that the integration model had better explor- atory power than the individual use of TAM and TPB (Bosnjak et al., 2006; Chen et al., 2007; Wu and Chen, 2005). Since the focus of this study is online banking service adoption, which is an instance of acceptance of innovative technology intertwined with social sys- tems and personal characteristics, the integration of TAM and TPB for our research framework should be comprehensive in order to examine the consumers’ intentions towards, and acceptance of, online banking. There are 12 constructs in our model, which 2.4. Theory of planned behavior The TPB underlying the effort of TRA has been proven success- ful in predicting and explaining human behavior across various information technologies (Ajzen, 2002, 1991). According to TPB, a person’s actual behavior in performing certain actions is directly influenced by his or her behavioral intention and, in turn, is jointly determined by his or her attitude, subjective norms and perceived behavioral controls toward performing the behavior. Behavioral intention is a measure of the strength of one’s willingness to exert effort while performing certain behaviors. Attitude (A) explains a person’s favorable or unfavorable assessment regarding the behavior in question. Furthermore, a favorable or unfavorable atti- tude directly influences the strength of the behavior and beliefs regarding the likely outcome. Accordingly, attitude (A) is equated with attitudinal belief (abi) linking the behavior to a certain out- come weighted by an evaluation of the desirability of that out- come (ei). This is expressed as: A ¼ P abi � ei. Subjective norm (SN) expresses the perceived organizational or social pressure of a person who intends to perform the behavior in question. In other words, the subjective norm is relative to normative beliefs about the expectations of other people. It can be depicted as indi- vidual’s normative belief (nbi) concerning a particular referent and Applications 8 (2009) 130–