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Decision Factors for the Adoption of an Online Payment System by Customers
and legal policies for online privacy protection, they may be less willing to provide their personal information for online payments.
of various e-commerce activities (Gefen et al., 2004; Hsu et al., 2004; Luarn & Lin, 2005). When customers perceive the online payment system as more useful and/or easier
• Exposure of personal information: Cus-tomers can hesitate to use online payment systems because of privacy concerns. Inva-sion of privacy in the area of e-commerce includes the unauthorized collection, disclo-sure, or other use of personal information such as selling it to another e-vendor (Wang, Lee, & Wang, 1998), and safeguarding pri-vacy would typically cause an added cost to the consumers (Luo, 2002). This too is similar to Pavlou’s (2003) environmental un-certainty involving perceived risk associated with exposure of personal information.
• Concern of system security:In the network security area, Hwang et al. (2003) indicate that the existing secure electronic transac-tion protocol needs further revisions to sustain credit cardholders’ trust in banks’ online card payment networks, particularly in the environment of growing bank mergers and acquisitions. The more customers are concerned about the security of an online payment system, the greater the risk and less trustworthiness they would perceive in using pay-online transactions, and the less their intention would be to adopt the system.
to use, they should be more willing to adopt it.
• (I¿FLHQF\Daft and Lengel’s (1986) media richness theory argues that the selection of communication media depends on the task characteristics and the cost of media usage. The theory suggests that for a rather ORZHTXLYRFDOLW\WDVNRI³SD\LQJELOOVDW DVSHFL¿FDPRXQWE\DVSHFL¿FGHDGOLQH´ some leaner media (such as the paperless online media) is better at lowering costs and minimizing excessive message decoding. In addition, Chou et al. (2004) argue that the adoption speed of business innovations (e.g., e-payment system alternatives) is positively DIIHFWHG E\ WHFKQRORJLFDO HI¿FLHQF\ DQG established customer base, but not neces-sarily affected by technological complexity. By using online payment systems, a buyer FDQVXEPLWDSD\PHQW³SDSHUOHVVO\´RUHYHQ ³VSHHFKOHVVO\´DQGDVHOOHUFDQUHFHLYHSD\-PHQWVDIWHURI¿FHKRXUVDQGVKLSWKHJRRGV soon after, instead of answering phone calls (for credit card payments), waiting a few days to receive a mailed payment or taking even longer to clear a check (Sorkin, 2001).
• Convenience:Customers favor the mobility and the associated convenience of accessing
(B) Perceived Advantage their bills at any time and any place (Yu et al., 2002). The adoption of an electronic pay-
• Perceived use (PU) and perceived ease of use (PEOU): The adoption of online payments can be explained in part by the TAM (Davis, 1989). According to TAM, the intention to use a new technology is determined by the PU and PEOU for the VSHFL¿FWHFKQRORJ\7KLVPRGHOKDVEHHQ widely used and extended by researchers to study technology acceptance behavior and to identify the adoption decision determinants
ment method allows online payers to check and pay their bills when and where they want to without having to wait for their paper ELOOVWREHVHQWWRDSUHVSHFL¿HGPDLOLQJ DGGUHVVDWD¿[HGWLPHLQWHUYDO7KHUHIRUH when customers can conveniently access the Internet, they should have a greater intention to adopt an online payment system.
• )LQDQFLDOEHQH¿WVExisting studies inves-tigate various economic factors that might
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Decision Factors for the Adoption of an Online Payment System by Customers
LQÀXHQFHWKHRXWFRPHRIFUHGLWVDOHVDQG such factors include customer search cost, membership cost, and interchange fees (e.g., Wright, 2003). Adopting an online pay-PHQWV\VWHPZLOOVLJQL¿FDQWO\UHGXFHWKH paperwork, cut the postage cost of sending ELOOVDQGLQFUHDVHWKHRSHUDWLQJHI¿FLHQF\ of vendors such as credit-providing banks. As a result, some credit card issuing banks provide a bonus to customers who switch WRD³SDSHUOHVV´RQOLQHELOOLQJV\VWHP&KHQ and Tseng (2003) studied the performance of marketing alliances between Taiwan’s credit card issuing banks and the tourism industry, DQGWKHLU¿QGLQJVVXJJHVWWKDWFUHGLWFDUG clients consider the attached promotional ERQXV RI WUDYHO GLVFRXQWV DV LQÀXHQWLDO therefore having positive effects on alli-ance performance. Nevertheless, Lucas and %RZHQ¿QGWKDWLQWKHFDVLQRLQGXVWU\ SURPRWLRQDO SHULRGV IDLO WR VLJQL¿FDQWO\ LQÀXHQFHVDOHVDQGWKHPDJQLWXGHRISUL]H PRQH\JHQHUDWHVDSRVLWLYHEXWLQVLJQL¿FDQW economic impact. As for the e-business, Wilson and Abel (2002) examine the issues that must be considered for developing a successful Internet marketing plan. They emphasize the importance of online and of-ÀLQHSURPRWLRQDODFWLYLWLHV6RLWDSSHDUVWKDW WKHLQÀXHQFHRIXVLQJSURPRWLRQDOERQXVHV for marketing new products and/or services FRXOGEHDQLQGXVWU\DQGPDUNHWVSHFL¿F issue. Whether a promotional bonus can materially enhance customers’ willingness to adopt online payment methods is therefore tested.
7KHK\SRWKHVHVUHJDUGLQJ³SHUFHLYHGFKDU-acteristics of the online payment system” are summarized as follows:
H1: 3HUFHLYHGULVNDQGEHQH¿WRIXVLQJDQRQOLQH SD\PHQWV\VWHPVKRXOGKDYHDVLJQL¿FDQWLPSDFW
on a customer’s intention to adopt online payment methods.
H1-1: The intention to adopt online payments should be negatively associated with perceived risk factors.
H1-2: The intention to adopt online payments should be positively associated with perceived EHQH¿WIDFWRUV
Vendor’s System Characteristics
(A) Vendors’ Service Features
&XVWRPHUV FDQ EHQH¿W IURP DGRSWLQJ RQOLQH billing and payment systems by minimizing pay-PHQWHIIRUWV³FOLFNWRSD\´VDYLQJSRVWDJHFRVW REWDLQLQJSD\PHQWFRQ¿UPDWLRQFLUFXPYHQWLQJ mail delay, avoiding past-due penalties, and scheduling recurring payments. With regard to automated deductions, however, automated online payments require customers’ careful timing and SHUVRQDO¿QDQFLDOSODQQLQJWRZRUNFRQVLVWHQWO\ as some of these forgetful and unorganized cus-tomers could run into unexpected overdrafts. To solve this problem, some online payment systems SURYLGHFXVWRPHUVPRUHÀH[LELOLW\DQGFRQWURO over how much they want to pay and when they want the payment to be made, and even allow online payers to cancel the pending scheduled payments if they feel need to. Debruyne et al. DQG9DQ6O\NH/RXDQG`D\¿QG that the market tends to be more responsive to a new product that can be assessed within an existing product category and less responsive to radical innovations or new products that employ a niche strategy. Their evidence suggests that the public should have relatively less resistance for adopting an online billing and payment system, which is merely an extra new feature added to some well-established existing services (credit
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Decision Factors for the Adoption of an Online Payment System by Customers
sales, automated bank deposits) rather than a ³UDGLFDOLQQRYDWLRQ´
(B) Vendors’ Web site Features
Existing empirical research suggests that both the DYDLODELOLW\DQGWKHTXDOLW\RIGHVLJQVLJQL¿FDQWO\ affect customers’ interest in and performance of e-business Web sites (Lee et al., 2005; Rangana-than & Ganapathy, 2002). Designing a good Web site is essential for an online payment system, and Liang and Lai (2002) argue that a good design must provide adequate functional support to meet e-commerce customers’ needs at each stage of their decision processes.
H2:Vendor’s system characteristics should have DVLJQL¿FDQWLPSDFWRQDFXVWRPHU¶VLQWHQWLRQWR adopt online payment methods.
H2-1: The intention to adopt online payments should be positively associated with a customer’s overall perception of the features offered on the vendor’s Web site.
H2-2: The intention to adopt online payments should be positively associated with a customer’s overall perception of the Web site’s design.
Customer’s Characteristics
(A) Client-Side Technology
The level of anti-virus and/or anti-spyware protec-WLRQFRXOGDIIHFWDFXVWRPHU¶VFRQ¿GHQFHWRSD\ bills online as the threat of network invasion has been increasing (Hill, 2003). The effectiveness of customers’ computer operating systems and the VSHHGRIDFFHVVLQJWKH,QWHUQHWFRXOGDOVRLQÀXHQFH WKHLUFRQ¿GHQFHIRUPDNLQJRQOLQHSD\PHQWV
(B) Demographic Variables
Existing studies indicate that men will be more likely than women to purchase over the Internet because on average men perceive a relatively lower level of risk in online purchasing (Garbarino & 6WUDKLOHYLW]$OVRZKHQDGRSWLQJVSHFL¿F information technologies such as instant mes-saging, men value perceived relative advantage, result demonstrability and critical mass more than women, whereas women value PEOU and visibility more than men (Ilie et al., 2005). Us-ing the 2001 U.S. Census Bureau’s population survey data, Banerjee et al. (2005) also found more males use the Internet than females to do ¿QDQFLDOWUDQVDFWLRQVLQFOXGLQJVHFXULW\WUDGLQJ and banking. On the other hand, as people age, they tend to exhibit more negative perceptions to-ward new technologies and feel greater reluctance to adopt new technologies (Gilly & Ziethaml, 1985; Pommer, Pommer, Berkowitz, & Walton, 1980). More recently, Akhter (2003) examined the LQÀXHQFHRIJHQGHUDJHHGXFDWLRQDQGLQFRPH on the likelihood to purchase over the Internet, DQGKLV¿QGLQJVVXJJHVWWKDWPDOHVLQFRQWUDVW to females, younger people in contrast to elders, more educated in contrast to less educated, and wealthier people in contrast to less wealthy are more likely to use the Internet for purchasing symphony tickets. After reviewing prior literature, our study aims to test those relevant hypotheses in the online payment context.
(C) Internet Experience
Eastin (2002) employs the diffusion model to investigate the adoption of four e-commerce ac-tivities: (1) online shopping, (2) online banking, (3) online investing, and (4) electronic payment for an Internet service (such as online auction site or exclusive club membership). The results indicate that when users decide to adopt one of
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Decision Factors for the Adoption of an Online Payment System by Customers
these activities, they tend to also adopt another. Therefore, a customer’s e-commerce background FRXOGDOVRLQÀXHQFHKLVKHUWHQGHQF\WRXVHRQOLQH payments. Five factors (computer knowledge, online shopping experience, online trading ex-perience, online auction experience, and online vending experience) are thus selected for testing WKHLQÀXHQFHRIFXVWRPHUV¶,QWHUQHWH[SHULHQFH on the adoption of online payment systems.
H3:&XVWRPHU¶VFKDUDFWHULVWLFVKDYHVLJQL¿FDQW impact on the adoption of an online payment system.
H3-1: The reliability, effectiveness, and security of client-side technology should be positively as-sociated with the customer’s intention to adopt online payment methods.
H3-2: The customer’s income level should be positively associated with the customer’s intention to adopt online payment methods.
H3-3: Males are more likely to adopt online pay-ment methods than females.
H3-4: The customer’s age should be negatively associated with the intention to adopt online payment methods.
H3-5: The customer’s level of education should be positively associated with the customer’s intention to adopt online payment methods.
H3-6: The level of a customer’s Internet experi-ences should be positively associated with the intention to adopt online payment methods.
The addressed factors that presumably affect one’s intention to adopt online payment methods and their corresponding literature are summarized in Appendix I.
RESEARCH METHODOLOGY AND DATA DESCRIPTION
Questionnaire Design, Data Collection, and Descriptive Statistics
To test the series of research hypotheses, a survey-EDVHG¿HOGVWXG\ZDVGHVLJQHG3ULRUHPSLULFDO and conceptual research (see Appendix I) was carefully reviewed to provide the basis for our survey questions, which are listed in Appendix II. 7KHTXHVWLRQQDLUHLQFOXGHV³VXEMHFWLYH´LWHPV (Q1-Q22) measured on a Likert-type scale, rang-LQJIURP³VWURQJO\GLVDJUHH´WR³VWURQJO\ agree”). As respondents may hesitate to provide their income information to a non-business-related surveyor, we did not directly inquire about their VSHFL¿FLQFRPHOHYHOEXWLQVWHDGXVHGD³VXEMHF-tive” item (Q22) to indirectly investigate whether DFKDQJHLQWKHLQFRPHOHYHOPLJKWLQÀXHQFHWKHLU intention to use an online payment system. There are also 8 multiple-choice questions (Q23-Q30) UHODWHGWRWKHUHVSRQGHQWV¶³REMHFWLYH´FKDUDFWHU-istics, including their demographic background and Internet experience.
The survey was administered to students and faculty members at a state university located in the Midwestern U.S. The university enrolls ap-proximately 21,000 undergraduate and graduate students with various backgrounds, ranging from full-time students, working people, to retired senior citizens who seek further education; all meet the age requirements to apply for credit cards and/or online banking accounts. After screening the university’s Blackboard® user database (listed in alphabetical order with contact information) and selecting at random one out of each 20 users, a total of 200 surveys were randomly distributed through regular campus mail and email beginning early in the semester. A reminder was sent approximately six weeks after the survey was initially distributed. In total, 172 (86%) responded. To test for non-
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Decision Factors for the Adoption of an Online Payment System by Customers
UHVSRQVHELDVZHXVHGWKH0DQQ:KLWQH\³8´ test for comparing the data obtained from those ZKRUHVSRQGHGDIWHUWKH¿UVWLQTXLU\DJDLQVWWKH data obtained from those who responded after the second inquiry. Respondents were compared in several key survey areas, including use inten-WLRQSHUFHLYHGULVNDQGSHUFHLYHGEHQH¿WV1R VLJQL¿FDQWGLIIHUHQFHVZHUHIRXQGEHWZHHQWKHWZR sets of data. After excluding those who provided LQFRPSOHWHDQVZHUVWKH¿QDOVDPSOHFRQVLVWHGRI 148 (74%) with 98 undergraduates, 43 graduate students, and seven faculty members.
Table 1a summarizes the frequency distribu-tions of respondents’ personal characteristics, including gender, age, education level, computer knowledge background, and their experience with online business. Within our sample of 148 respon-dents, approximately 43% are females, 90% are between 20 and 39 years old, about 5% possess a Doctoral degree, 75% have an Associate’s or Bachelor’s degree, and about 95% have at least ¿YH\HDUVRIFRPSXWHUH[SHULHQFH,QDGGLWLRQ more than 60% of the respondents have been involved in some sort of online business activity,
Table 1a. Frequency distributions of respondents’ background information (gender, age, education, computer knowledge, online business experience)
Variable
Q23 (Gender)
Response (n = 148)
Female Male
43.2% 56.8%
Q24 (Age)
Q25 (Education)
Q26 (Computer Experience)
Q27 (Online Shopping)
Q28 (Online Stock trading)
Q29 (Online Auction Bidding)
Q30 (Online Vending)
20-29 77.7% High School 10.8%
\HDU
1.4%
Never
13.5%
Never
45.3%
Never
33.1%
Never
39.9%
30-39 12.2%
Associate
55.4%
2-4 years
4.1%
1-5/mo.
42.6%
1-5/mo.
40.5%
1-5/mo.
39.9%
1-5/mo.
38.5%
40-49 8.1%
Bachelor’s
20.9%
5-7 years
35.1%
6-10/mo.
29.1%
6-10/mo.
8.8%
6-10/mo.
17.6%
6-10/mo.
16.2%
50-59 1.4%
Master’s
8.1% 8-10 years 20.9% 11-15/ mo. 12.2% 11-15/ mo. 2.7% 11-15/ mo. 6.8% 11-15/ mo.
1.4%
0.7%
Doctoral
4.7% >10 years 38.5%
>15/mo.
2.7%
>15/mo.
2.7%
>15/mo.
2.7%
>15/mo.
4.1%
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