A unified perspective on the factors influencing usage intention toward mobile financial services

Journal of Business Research 65 (2012) 1590–1599

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Journal of Business Research

A unified perspective on the factors influencing usage intention toward mobile financial services Yong-Ki Lee a,1, Jong-Hyun Park b,2, Namho Chung c,⁎, Alisha Blakeney d a

Department of Business Administration, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul 143-747, Republic of Korea Yangjaenam Branch, Wooribank, 60 Yangjae-dong, Seocho-gu, Seoul 137-130, Republic of Korea College of Hotel & Tourism Management, Kyung Hee University, 1 Hoegi-dong, Dongdaemun-gu, Seoul 130-701, Republic of Korea d School of Business, Auburn University at Montgomery, 96 Grove Park LoopWetumpka, AL 26093, United States b c

a r t i c l e

i n f o

Article history: Received 1 June 2010 Received in revised form 1 October 2010 Accepted 1 February 2011 Available online 12 March 2011 Keywords: Mobile financial services Technology acceptance model Task-technology fit Personal innovativeness Absorptive capacity Connectivity Structural equation modeling

a b s t r a c t This research proposes that the factors influencing the intention to use mobile financial services (MFS) include general technology perceptions, technology-specific perceptions, user characteristics, and task-user characteristics. Most previous research examines customer satisfaction with MFS. However, this research does not explain why MFS is expanding relatively more slowly than Internet financial services in general. Therefore, this study investigates this issue by determining the key drivers of MFS usage intention. Specifically, the research model includes five exogenous variables: task-fit, monetary value, connectivity, personal innovativeness, and absorptive capacity. Perceived usefulness and perceived ease of use both serve as mediators between the first four of these five factors and usage intention. Connectivity influences perceived ease-of-use directly. In addition, perceived monetary value has a significant effect on perceived usefulness, inferring MFS is not only useful for a firm, but also is useful from a time and monetary value standpoint. Personal innovativeness significantly influences perceived ease-of-use, so innovative users can take advantage of MFS more frequently. Absorptive capacity also directly affects usage intention. Finally, perceived task technology, versus a task characteristic view, significantly influences perceived usefulness. © 2011 Elsevier Inc. All rights reserved.

1. Introduction Advancements in Internet technology are transforming the modern world. The combination of information and communication technology is sparking enormous changes across society. In addition, since 1995, e-commerce, e-business, and m-business (mobile business) have increased dramatically. M-business, in particular, has the ability to change lives and ways of conducting business as consumers and mobile devices have become inseparable (Kalakota and Robinson, 2001). This outcome is especially true in Korea, where the urbanized population and government-supported broadband yields the highest penetration of high-speed Internet in the world (Strategy Analytics 2009). Korean residents are rapidly adopting the Internet for their business and financial needs. According to the Korea Bank (2007), the number of Internet financial users at the end of December 2007 was nearly 45 million, an increase of about 25% in just one year. The main

⁎ Corresponding author. Tel.: + 82 2 961 2353; fax: + 82 2 964 2537. E-mail addresses: [email protected] (Y.-K. Lee), [email protected] (J.-H. Park), [email protected] (N. Chung), [email protected] (A. Blakeney). 1 Tel.: + 82 2 3408 3158, fax: + 82 2 3408 4310. 2 Tel.: + 82 2 592 2050; fax: + 82 2 579 1840. 0148-2963/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.jbusres.2011.02.044

reasons for this include Korea's “apartment subscription mandatory system” and the ease of use of Internet banking. In addition to the Internet, Korean residents are heavy users of cellular phones, with more than nine out of ten people (44.7 million users out of a population of 48.7 million) using mobile communication (National Statistical Office, 2009). This combination of Internet availability and mobile communication would appear to make Korea a prime market for mobile financial services (MFS). MFS is useful for checking bank balances, performing account transactions, making payments, and performing stock trading via a device such as a mobile phone (Tiwari et al., 2007). However, while Koreans adoption of Internet financial services is rapid in general, the growth rate of MFS is surprisingly low. In fact, those who do Internet banking through mobile communications equipment (hereafter, “mobile banking”) account for fewer than 4% of Internet banking users. Many researchers examine adoption and diffusion of MFS, focusing primarily on user perspectives and the service's characteristics (Jung et al., 2010; Kim et al., 2009; Laforet and Li, 2005; Laukkanen, 2007; Laukkanen and Lauronen, 2005; Luarn and Lin, 2005; Mallat et al., 2004; Oh and Yoon, 2010; Riivari, 2005; Scornavacca and Barnes, 2004; Suoranta and Mattila, 2004). However, most of this research examines customer satisfaction with MFS. Why is the expansion of MFS relatively slower than that of Internet financial services in general (Kim et al., 2004)? Even though mobile devices provide safe,

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convenient, and ubiquitous capabilities, the adoption of MFS has been slow in Korea. This study investigates this issue by determining the key drivers of MFS usage intention. Internet financial service users are going mobile. At the core of this transformation, companies entice customers to use MFS by demonstrating the potential value offered, on the assumption that customers will actively use the services when they perceive them as more convenient and useful than alternatives. While the number of customers using MFS is low, those that do use the service do so with regularity. According to Marketing Insight (2007), the percentage of Koreans who do their mobile banking once or twice a week is 19.3%; three or four times a week, 22.8%; and every day, 22.6%. The relationship with task characteristics, rather than acceptance or rejection of personal characteristics or mobile technology, influences the rate of use. Therefore, this research examines the factors affecting the use of MFS: first, from the perspectives of personal, task-related, and mobile technological characteristics; second, by identifying the impact of influencing factors on usage intention. The findings of this study yield practical guidelines for choosing a MFS promotion strategy by aiding in understanding the influences on customer usage intention. The study can aid in deciding how to attract new MFS users and retain existing customers. Additionally, the results provide a unified theoretical view of information technology adoption, customer behaviors, and task fit.

2. Background The propensity to accept new information systems can serve as the viewpoint from which to examine users' adoption of MFS. For over two decades, information systems studies apply a technology acceptance model (TAM; Davis, 1989) as well as absorptive capacity (Cohen and Levinthal, 1990). These studies suggest that describing the purposes of MFS use requires an approach that integrates various theories. First, with regard to TAM, Davis (1989) presents a concise and universally applicable approach based on the theory of reasoned action (TRA) and the theory of planned behavior (TPB) by Fishbein and Ajzen (1975), Ajzen and Fishbein (1980). Davis (1989) claims this framework is employable to evaluate the usefulness of information technology. TAM defines attitude, subjective norm, and self-efficacy as determinants of users' behavioral intentions. Meanwhile, Davis (1989) states that the sequential relationship of belief–attitude– intention–behavior in TAM can predict the acceptance of information technology by users, and asserts that perceived usefulness and ease of use lead to acceptance (Lederer et al., 2000). Logically, perceived usefulness and ease of use may play a key role in the acceptance of MFS as a type of information system. Studies on information technology regard the attitude and belief of individuals as key factors in determining use (Davis, 1989; Doll and Torkzadeh, 1991). They suggest that attitudes toward the system, in conjunction with environmental factors, elicit intention to use and ultimately increase usage. However, in some cases, individuals will use information technology because of the appropriateness for their job, rather than convenience or usefulness. This perspective is consistent with the theory of task-technology fit (Goodhue and Thompson, 1995), which claims that if users find the functions of an available technology to be a good fit for their work, they will use that technology. Goodhue and Thompson's theory illustrates two important points. First, the use of information technology must be appropriate for the given job. Second, individual difference characteristics are important because the individual determines exactly what is appropriate. Although MFS is an optional system, adopters tend to be frequent users. Thus, consider the relationship of MFS with job or individual characteristics.

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Many areas, including management strategies, organizational learning, and information technology acceptance, use the concept of absorptive capacity as Cohen and Levinthal (1990) propose. Although varying by scholar, absorptive capacity usually consists of three components (Suh et al., 2005): (1) prior knowledge, which provides the basis of assessing the value of new knowledge; (2) knowledge internalization ability, which combines new knowledge with the knowledge base one already has; and (3) knowledge utilization ability, in which individuals can apply new knowledge to their jobs. Applying absorptive capacity MFS means that people may use the service if they have some prior knowledge about it, basic knowledge about using information technology, and the actual ability to use it. Since using MFS can generate additional costs, such as using a mobile phone, researchers need to consider its use from the viewpoint of monetary value. Essentially, the question addresses, “Are mobile financial services worth the cost?” Researchers also need to assess value from the viewpoint of an Internet connection, because MFS can be used anytime and anywhere. Finally, the analysis should include the viewpoint of personal innovativeness for using new technology. 3. Research design The discussion of prior research suggests that in describing the intention of using MFS, one should take a unified view of the general and specific characteristics of information technology, the characteristics of individuals using MFS, and the characteristics of the task to which MFS is applied. Fig. 1 presents the research model. 3.1. General technology perception General technology perception refers here to users' recognition of the technologies in mobile financial services (Hong and Tam, 2006) and is conceptualized as two constructs: perceived usefulness and ease of use (Davis, 1989; Lederer et al., 2000; Venkatesh, 2000; Venkatesh and Davis, 2000; Venkatesh et al., 2003). MFS perceived usefulness means that a bank customer recognizes the service as being able to improve performance, increase productivity, and enhance effectiveness. Ease of use captures how readily a customer can use it. This study hypothesizes that the keys to adopting the Internet and MFS are similar. TAM specifically explains computer usage behavior, using TRA as a theoretical basis for specifying the causal linkages among five key constructs: perceived usefulness, perceived ease of use, attitude toward use, behavioral intention to use, and actual system usage. Incorporating findings from over a decade of information systems research, TAM may be especially well suited for modeling computer acceptance, including Internet services (Venkatesh et al., 2003). In this research, the belief variables perceived usefulness (from a TAM model) and perceived ease of use influence the attitude of use and the usage intention. Simply stated, usefulness and ease of use increase usage intention. Therefore, the first three hypotheses are: H1. Perceived usefulness has a positive impact on usage intention toward mobile financial services. H2. Perceived ease of use has a positive impact on perceived usefulness toward mobile financial services. H3. Perceived ease of use has a positive impact on usage intention toward mobile financial services. 3.2. Task-technology fit Many studies suggest that the perception of whether a particular technology fits well with users' present tasks can be a basis for influencing their use of that technology. Goodhue (1998) defines fit as

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Task Characteristics Task-Fit

Technology-Specific Perception

H4

General Technology Perception

Monetary Value

H5

Perceived Usefulness

Connectivity

H6

H1 H2

Individual Characteristics

Perceived Ease of Use

H7

Usage Intention H3

Personal Innovativeness

H8

Absorptive Capacity

Fig. 1. Proposed model.

the degree to which users believe a certain technology meets their needs and organizational values. Goodhue and Thompson (1995) introduce task-technology fit to evaluate the consumer's performance in using information technology. Goodhue's theory defines task-technology fit as the degree to which information technology meets a user's task needs. A number of empirical studies illustrate that task-technology fit has a significant impact on perception and usage behavior. Cooper and Zmud (1990) find that a higher compatibility (fit) between task and technology promotes information technology acceptance by increasing the consumer's positive perceptions of using the technology. Chen et al. (2002) find that fit is the strongest determinant of customer attitude toward using virtual stores. Venkatesh and Davis (2000) provide additional support, suggesting that job relevance for a technology may be a basis for the perceptions of use of the technology (i.e., perceived usefulness). In other words, if the functions of technology support users' tasks, or are appropriate to those tasks, the technology will be used (Goodhue, 1998; Goodhue and Thompson, 1995). The implication is perceived fit between the task at hand and MFS technology will enhance the usefulness of that technology. This leads to the hypothesis: H4. Task-fit has a positive impact on perceived usefulness toward mobile financial services. 3.3. Technology-specific perception Technology-specific perception refers here to customers' recognition of MFS-related technique characteristics (Hong and Tam, 2006). This study operationalizes technology-specific perceptions on two dimensions: monetary value and connectivity. Perceived monetary value is how customers feel about the effectiveness and benefit of a product purchase. One of the causal variables that induce customer action is customer perceived value (Anderson and Narus, 1988; Corfman et al., 1991; Ravald and Grönroos 1996; Zeithaml, 1988). Researchers conceptualize value as the monetary or non-monetary benefit of purchasing a product or service (Ravald and Grönroos, 1996; Kotler, 1994; Naumann, 1995). In other words, customer value is a tradeoff in which the benefits are contrasted with sacrifices to arrive at a value judgment.

In the current study, perceived monetary value is a concept that emphasizes financial perspective. Do customers believe any fees (such as the service charge for Internet or mobile financial services) are offset by the benefits they experience through MFS (Hong and Tam, 2006)? Customers recognize these fees as a dichotomy of either “expensive” or “cheap” (Jacoby and Olson, 1977). According to Monroe and Krishnan (1985), these subjective prices are a sacrifice or asset (quality) of a service in terms of cost. However, customers' perceived monetary value is positive when the perceived quality is greater than the perceived sacrifice (Dodds et al., 1991). That is, customers' perceived monetary value influences judgment of the usefulness of a product or channel. If the perceived monetary value is high, then the customer recognizes the product as very useful. Thus: H5. Monetary value has a positive impact on perceived usefulness toward mobile financial services. Customers would be able to continue a mutual action without time or place limits through mobile technology (Kannan et al., 2001). Characteristics of MFS are more prominent than existing Internet financial services, and include ubiquity, user identity, and localization based on connectivity (Kannan et al., 2001; Kalakota and Robinson, 2001; Hong and Tam, 2006). Therefore: H6. Connectivity has a positive impact on perceived ease of use toward mobile financial services. 3.4. Individual characteristics The study investigates two additional factors: personal innovativeness and absorptive capacity. Personal innovativeness is a key individual difference characteristic influencing the adoption of an innovation, and relates to the users' willingness to embrace a new information technology (Rogers, 2003). Innovative consumers feel less perceived danger and are much more open to new technology (Joseph and Vyas, 1984). They are information explorers actively seeking new ideas and accepting the associated dangers and uncertainties (Rogers, 2003). Furthermore, such consumers easily embrace opportunities to buy unfamiliar products and are willing to try new, cutting-edge innovations. In sum, consumers with higher

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innovativeness accept new technology more readily (Rogers, 2003; Venkatesh and Davis, 2000; Ko et al., 2010). Empirical studies report personal innovativeness affects the acceptance of information technology, Internet shopping, and web broadcasting (Donthu and Garcia, 1999; Venkatesh and Davis, 2000; Lin 2006). In particular, Pagani (2004) shows that perceived innovativeness of individuals influences their acceptance attitude toward mobile services. Agarwal and Prasad (1998) empirical test whether personal innovativeness moderates perceived usefulness, perceived ease of use, and compatibility, reporting only compatibility is moderated. However, Agarwal and Prasad (1998) stop short of concluding usefulness and convenience have no moderating effects, suggesting additional testing with various samples or other information technology contexts is warranted (p. 213). The current study operationalizes personal innovativeness as how easily people accept innovation; thus, affecting perceived ease of use rather than perceived usefulness. Based on these previous studies and the application here, the next hypothesis is:

Table 1 Descriptive statistics of user groups. Demographics Gender Age

Education: level completed

Monthly income (unit: USD)

Mobile Banking Usage Frequency

H7. Personal innovativeness has a positive impact on perceived ease of use toward mobile financial services. Management strategy, organizational learning, and information technology all use Cohen and Levinthal's (1990) concept of absorptive capacity. Although the definition differs slightly among researchers (Pavlou and El Sawy, 2006; Xu and Ma, 2008), three categories generally comprise absorptive capacity: prior knowledge, knowledge internalization capacity, and knowledge utilization capacity (Suh et al., 2005). First, prior knowledge is simply the base of familiarity with the new knowledge. Second, knowledge internalization capacity combines new knowledge with prior knowledge and internalizes it. Finally, knowledge utilization capacity is the ability of the individual to use the new knowledge in his or her tasks. Therefore, one can apply the concept of absorptive capacity to MFS. In other words, if individuals have prior knowledge of Internet and mobile applications they can more easily understand MFS technology and likely feel more confident in using it. Therefore: H8. Absorptive capacity has a positive impact on usage intention toward mobile financial services. 4. Research methodology 4.1. Data collection The Woori Bank of Korea assisted with this study by hosting an online survey on its website. The population is customers using Internet banking services of local banks, with customers responding to the questionnaire about MFS on the website. Screening methods insure the quality of data. First, cyber money (money exchanged electronically to buy products as rewards) encourages people to participate in the online survey. Second, when a respondent fails to answer a question, the survey engine refuses to proceed to the next question until the omitted question is completed. Finally, if answers appear invalid or lacking thought—for example, multiple questions answered by clicking the same number or obvious patterns in the response clicks—the respondent is dropped from the Internet survey. A total of 240 respondents participated between September 20, 2008, and October 15, 2008. Table 1 displays the respondent characteristics. The sample consists of more females (69.6%) than males (30.4%), most (50.4%) are between the ages of 20 and 29, and the majority have a university or higher level of education (55.8%), implying that people with higher education are more likely to use mobile banking. Most frequent occupation categories are managers or officials (37.1%), followed by sales or service personnel (34.0%), and the average monthly household income is between $5000 and $10,000.

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Most frequent use of mobile financial service

Most frequent mobile banking usage place

Telecommunication company

Mobile banking method

Male Female b30 30–40 40–50 N50 High school (2 year) College Bachelor's degree Master's degree b1000 1000–2000 2000–3000 3000–4000 4000–5000 5000–10,000 N10,000 Everyday 3–4 times a week 1–2 time a week 1–2 times a month Less than 1 time a month Deposit inquiry/Transfer Foreign currency exchange GIRO bill Fund Credit card Add on service Home Office School Vehicle Anytime, anywhere SKT KTF LGT IC-chip method Virtual machine method

n = 240

%

73 167 121 81 29 9 56 50 120 14 4 43 33 29 23 75 33 18 40 73 61 48 218

30.4 69.6 50.4 33.8 12.1 3.8 23.3 20.8 50.0 5.8 1.7 17.9 13.8 12.1 9.6 31.3 13.8 7.5 16.7 30.4 25.4 20.0 90.8

2 6 6 5 3 19

0.8 2.5 2.5 2.1 1.3 7.9

40 12 27 142 133 77 30 92 148

16.7 5.0 11.3 59.2 55.4 32.1 12.5 38.3 61.7

The highest frequency of use of mobile banking is once or twice per week (30.4%), followed by once or twice per month (25.4%). The main service used is account inquiry/transaction (90.8%). The main place to use mobile banking is anywhere (59.2%), illustrating the portability of mobile banking. The most frequently used mobile company is SK Telecom (SKT) (55.4%), followed by Korea Telecom (KT) (32.1%) and LG Telecom (LGT) (12.5%). Finally, the method of performing mobile banking is changing from IC-chip to virtual machine (VM, a mobile banking software implementation of a machine that executes programs like a physical machine) because the number of VM users (61.7%) is substantially higher than those who had the IC-chip (38.3%). 4.2. Instrument development The study uses instruments from previous studies modified to suit the context of mobile banking (see Table 2). The scale items are measured on a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). To minimize cross-cultural methodology issues, back-translation (with the material translated from English into Korean and then back into English, the versions compared, and discrepancies resolved) is used to ensure consistency between the survey versions (Mullen, 1995; Singh, 1995). Perceived usefulness is measured by four items adopted from Lee et al. (2005, 2006), Venkatesh and Davis (2000), and Venkatesh (2000). The coefficient alpha for usage intention toward MFS is 0.89. Perceived ease-of-use is measured using a scale of four items revised from Venkatesh and Davis (2000) and Venkatesh (2000). The

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Table 2 Measurement model resulting from confirmatory factor analysis.a Constructs and variables

Standardized factor loadings (t-value)

Usage intention I intend to reuse mobile financial services shortly. Assuming I have access to mobile financial services, I intend to use it. Given that I have access to mobile financial services, I predict that I would use it.d Perceived usefulness Using mobile financial services improves my performance in my finances. Using mobile financial services increases my productivity in my finances.d Using mobile financial services enhances my effectiveness in my finances.d I find mobile financial services to be useful in my finances. Perceived ease of use The interaction with mobile financial services is clear and understandable. Interaction with mobile financial services does not require a lot of mental effort. I find mobile financial services easy to use. I find it easy to get mobile financial services to do what I want it to do. Task fit Use of the mobile financial services is relevant for my work. Use of the mobile financial services is helpful for my work. Use of the mobile financial services is desirable for my work.d Monetary value I expect that mobile financial services would be reasonably priced. Mobile financial services would offer a good value for the money. I believe that at the current price and fee, mobile financial services would provide a good value.d Connectivity I expect that I would be able to use mobile financial services at anytime, anywhere. I would find mobile financial services to be easily accessible and portable. I expect that mobile financial services would be available to use whenever I need it. Personal innovativeness If I heard about a new information technology, I would look for ways to experiment with it. Among my peers, I am usually the first to try out new information technologies. In general, I am hesitant to try out new information technologies.d I like to experiment with new information technologies.d Absorptive capacity I have the technical competence to absorb mobile financial services. I have the necessary knowledge to understand mobile financial services. I have a clear understanding of the goals, tasks, and responsibilities of mobile financial services.d I have information on state-of-the art of mobile financial services.d a b c d

CCRb

AVEc

0.88

0.79

0.77

0.63

0.87

0.56

0.83

0.56

0.89

0.81

0.86

0.67

0.88

0.79

0.87

0.77

0.89 (17.07) 0.88 (16.70) – 0.70 (11.72) – – 0.87 (15.13) 0.75 0.82 0.81 0.76

(13.24) (15.02) (14.75) (13.47)

0.85 (15.69) 0.83 (15.33) – 0.90 (16.96) 0.90 (17.03) – 0.85 (15.56) 0.78 (13.90) 0.82 (14.77) 0.89 (16.84) 0.88 (16.33) – – 0.84 (15.43) 0.91 (17.23) – –

χ2 = 185.14, d.f = 124 (χ2/d.f = 1.49), p = 0.0003, GFI = 0.92, AGFI = 0.88, RMSEA = 0.045, NFI = 0.98, CFI = 0.99. Composite construct reliability. Average variance extracted. The item was deleted after confirmatory factor analysis.

coefficient alpha for usage intention toward MFS is 0.88. Connectivity is measured using a three-item scale adopted from Dey (2000) and Hong and Tam (2006). The coefficient alpha for usage intention toward mobile financial services is 0.90. Monetary value is measured by three items capturing cost and time savings based on Monroe and Krishnan (1985), Dodds et al. (1991), and Hong and Tam (2006). The coefficient alpha for usage intention toward MFS is 0.92. Personal innovativeness is measured using four items based on Goldsmith and Flynn (1992). The coefficient alpha for usage intention toward MFS is 0.90. Absorptive capacity is measured using four items adopted from Cohen and Levinthal (1990), Suh et al. (2005), and Xu and Ma (2008). The coefficient alpha for usage intention toward MFS is 0.90. Task fit is measured using three items based on Goodhue and Thompson (1995), Goodhue (1998), and Zigurs and Buckland (1998), with a coefficient alpha for usage intention toward MFS of 0.94. Finally, three usage intention items are adopted from Venkatesh and Davis (2000) and Venkatesh (2000), with a coefficient alpha for usage intention toward MFS of 0.89. 5. Analysis and results 5.1. Measurement model Self-reported data on two or more variables collected from the same source has the potential to lead to common method variance. Harman's single-factor tests for such bias (Podsakoff et al., 2003). If a high level of common method variance is present, entering all of the variables together will result in one factor accounting for a majority of

the variance. In this study, an exploratory factor analysis results in eight factors with eigenvalues greater than one, with the first factor accounting for 11.63% of the variance in the items. The results do not indicate that a single factor structure accounts for most of the variances, suggesting that common method bias is not a concern in the data. Overall measurement quality is assessed using confirmatory factor analysis (Anderson and Gerbing, 1992). Although measurement quality is sometimes assessed factor by factor, in the current study each multiple-item indicator is considered simultaneously to provide for the fullest test of convergent and discriminant validity. As shown in Table 2, nine items with low factor loadings (below 0.50) are dropped from further analysis (Anderson and Gerbing, 1992; Babin and Boles, 1998). All loadings exceed 0.50, and each indicator t-value exceeds 11.7 (p b 0.001). The χ2 fit statistics show 185.14 with 124 degrees of freedom (χ2/df = 1.49) (p b 0.001). The root mean square error of approximation (RMSEA) is 0.04; the comparative fit index (CFI) is 0.99; the adjusted goodness-of-fit index (AGFI) is 0.88; and the normed fit index (NFI) is 0.98. All statistics supported the overall measurement quality given the number of indicators (Anderson and Gerbing, 1992). Furthermore, evidence of discriminant validity exists when the proportion of variance extracted in each construct exceeds the square of the Φ coefficients representing its correlation with other factors (Fornell and Larcker, 1981). One pair of scales with a high correlation is perceived ease of use and task fit (Φ = 0.66, Φ² = 0.43) (see Table 3). The variance extracted estimates are 0.56 for both

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Table 3 Construct intercorrelations, mean, and standard deviation. 1 1. 2. 3. 4. 5. 6. 7. 8.

Usage intention Perceived usefulness Perceived ease of use Task fit Monetary value Connectivity Personal innovativeness Absorptive capacity

1.00 0.61⁎⁎ 0.63⁎⁎ 0.66⁎⁎ 0.58⁎⁎ 0.57⁎⁎ 0.58⁎⁎ 0.57⁎⁎

2 1.00 0.60⁎⁎ 0.60⁎⁎ 0.61⁎⁎ 0.54⁎⁎ 0.45⁎⁎ 0.45⁎⁎

3

1.00 0.66⁎⁎ 0.54⁎⁎ 0.52⁎⁎ 0.46⁎⁎ 0.51⁎⁎

4

5

1.00 0.61⁎⁎ 0.66⁎⁎ 0.58⁎⁎ 0.61⁎⁎

1.00 0.51⁎⁎ 0.52⁎⁎ 0.46⁎⁎

6

1.00 0.48⁎⁎ 0.52⁎⁎

7

Mean

SD

1.00 0.66⁎⁎

3.66 3.71 3.61 3.57 3.41 3.83 3.35 3.63

0.93 0.81 0.78 0.85 0.83 0.79 0.94 0.84

⁎⁎ p b 0.01.

constructs, indicating adequate discriminant validity. Allaying concern about the discriminant validity of task fit and connectivity constructs, the correlation between task fit and connectivity is 0.66 (Φ² = 0.44). The variance extracted estimates for these scales are 0.71 and 0.67, respectively. Thus, according to this assessment, the measures appear to have acceptable levels of validity. 5.2. Overall model results Table 4 presents the maximum-likelihood estimates for the various overall fit parameters. The χ² statistic suggests that the data do not fit the model (χ² = 230.71, df = 134, p b .01). However, because of the sensitivity to sample size, the χ² statistic is not always an appropriate measure of a model's goodness-of-fit. Therefore, multiple fit indices assess the overall evaluation of fit (Bagozzi and Yi, 1988; Bollen, 1989; Hair et al., 2006). The goodness-of-fit index (GFI) is .90; the Bentler and Bonett (1980) normed fit index (NFI) and nonnormed fit index (NNFI) are 0.97 and .95, respectively. RMSEA is 0.05 and CFI 0.98. These multiple indicators suggest that the model has good fit, justifying further interpretation. The squared multiple correlations (SMCs; R²) for the structural equations for perceived ease of use, perceived usefulness, and usage intention are high, as shown in Fig. 2. More than half of the variance (SMC = 0.71) in usage intention is explained by the direct effects of absorptive capacity, perceived ease of use, and perceived usefulness, and the indirect effects of perceived job fit, monetary value, connectivity, and personal innovativeness. For perceived usefulness

(SMC = 0.71), an even greater amount of the variance is explained by the direct effects of perceived job fit and monetary value. For perceived ease of use (SMC = 0.50), the variance is explained by the direct effects of connectivity and personal innovativeness. Table 4 presents the standardized parameter estimates. H1–H3 address the structural relationships among perceived usefulness, perceived ease of use, and usage intention in terms of general technology perception. Perceived usefulness has a positive effect on usage intention (β31 = 0.47, t-value = 4.75), and is statistically significant at the p b 0.001 level, supporting H1. The significant positive impact of perceived ease of use on perceived usefulness supports H2 (β12 = 0.40, t-value= 5.21, p b 0.001). This is consistent with findings by McCrae and Costa (1991). Perceived ease of use also positively impacts usage intention (β32 = 0.20, t-value = 2.41, p b 0.01), supporting H3. H4 states that perceived task fit is associated with perceived usefulness in mobile financial services. Perceived job fit of MFS has a significant positive effect on perceived usefulness (γ 11 = 0.25, t-value = 2.51, p b 0.01), providing support for H4. H5 and H6 posit that monetary value and connectivity are positively associated with perceived usefulness and perceived ease of use for MFS in terms of technology-specific perception, respectively. As expected, monetary value has a significant positive effect on perceived usefulness (γ 12 = 0.33, t-value = 3.90, p b 0.001), supporting H5. Connectivity has a significant positive effect on perceived ease of use (γ 23 = 0.52, t-value = 6.62) and is statistically significant at the p b 0.001 level, supporting H6.

Table 4 Analysis of competing structural models. Hypotheses

H1 H2 H3 H4 H5 H6 H7 H8

Path

Perceived usefulness → Usage intention Perceived ease of use → Perceived usefulness Perceived ease of use → Usage intention Task fit → Perceived usefulness Monetary value → Perceived usefulness Connectivity → Perceived ease of use Personal innovativeness → Perceived ease of use Absorptive capacity → Usage intention Task fit → Usage intention Monetary value → Usage intention Connectivity → Usage intention Personal innovativeness → Usage intention R2 Perceived ease of use 0.50 (50.4%) Perceived usefulness 0.71 (71.5%) Usage intention 0.71 (71.1%) Fit indices χ2 230.71 Df 134 P 0.00

tcrit σ = .05 = 1.96. tcrit σ = .01 = 2.58. a χ2 = 230.71, d.f = 134, p = 0.000, GFI = 0.90, AGFI = 0.86, RMSEA = 0.05, NFI = 0.97, CFI = 0.98. b χ2 = 223.40, d.f = 130, p = 0.000, GFI = 0.91, AGFI = 0.86, RMSEA = 0.05, NFI = 0.97, CFI = 0.98.

Estimates (t-value) Proposed modela

Alternative modelb

0.47 (4.75) 0.40 (5.21) 0.20 (2.41) 0.25 (2.51) 0.33 (3.90) 0.52 (6.62) 0.26 (3.62) 0.29 (4.46)

0.28 0.43 0.18 0.21 0.43 0.52 0.26 0.09 0.12 0.04 0.09 0.18

(2.54) (5.63) (1.98) (2.12) (3.57) (6.58) (3.55) (0.97) (0.81) (0.57) (0.79) (1.97)

0.67 (67.0%) 0.49 (49.7%) 0.71 (71.0%) 223.40 130 0.00

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Task-Fit

Perceived Usefulness R2=0.715

Monetary Value

Usage Intention R2=0.711

Connectivity Perceived Ease of Use R2=0.504 Personal Innovativeness

Absorptive Capacity

Fig. 2. Results of the LISREL analysis of the proposed model.

The significant positive impact of personal innovativeness on perceived ease of use (γ 24 = 0.26, t-value = 3.62, p b 0.001) is consistent with H7. Finally, absorptive capacity is associated with usage intention (γ 35 = 0.29, t-value= 4.46, p b 0.001), supporting H8. 5.3. Alternative model testing In addition to the proposed model illustrated in Fig. 1, an alternative model is tested (see Fig. 3). Such a comparison focuses on assessing model fit and comparing the fit of competing and theoretically plausible models. In this model, task fit, monetary value, connectivity, and personal innovativeness are postulated to have direct effects on MFS usage intention. That is, the proposed model is nested within the alternative model. The rationale for direct effects of four dimensions (i.e., task fit, monetary value, connectivity, and personal innovativeness) on MFS usage intention is that users may evaluate the service based not only on usefulness,

ease of use, and absorptive capacity, but also on the extent to which that system addresses other related issues, such as task fit, monetary value, connectivity, and personal innovativeness. The alternative model in this study also has a good level of fit (χ2 = 223.40, d.f = 130, p = 0.000, GFI = 0.91, RMSEA = 0.05, NFI = 0.97, CFI = 0.98). Because both the proposed model and the alternative model fit the data appropriately, a chi-square difference test is employed to determine if one of these structures performs better than the other (Anderson and Gerbing, 1992). As shown in Table 4, the chi-square statistics between the proposed model and the alternative are compared using the proposed model as a reference point. The chisquare statistics' difference between the models is not significant (Δχ2 = 7.31, d.f = 4, p = 0.120). This finding indicates that adding the direct paths from four antecedent factors (task fit, monetary value, connectivity, and personal innovativeness) to MFS usage intention does not significantly improve its fit.

Task-Fit

Monetary Value

Perceived Usefulness R2=0.715 Usage Intention R2=0.711

Connectivity Perceived Ease of Use R2=0.504 Personal Innovativeness

Absorptive Capacity

Fig. 3. Results of the LISREL analysis of the alternative model.

Y.-K. Lee et al. / Journal of Business Research 65 (2012) 1590–1599

Thus, all of the measures of the proposed model are at least equal to or better than those of the alternative model. The additional paths (see Table 4) provide little, if any, incremental explanatory power. More specifically, by incorporating the direct effect of the four antecedent factors on usage intention, a significant direct effect of personal innovativeness on intention is confirmed. The remaining three antecedent factors are insignificant. Overall, these results show that the proposed model explains MFS usage intention better than the alternative model.

6. Discussion The aim of this study is to analyze the factors that influence the intention of using mobile financial services in an integrated manner, including the technical characteristics, user characteristics, and job characteristics. The analysis reveals many interesting points compared to prior studies. First, in terms of general technical perceptions, the study finds that perceived usefulness and convenience both have significant effects on the intention to use MFS. Perceived usefulness and perceived ease of use clearly indicate intentions of using information systems. The path coefficient of convenience is significant along with that of usefulness, which implies that convenience is an important factor in the use of MFS, which is an optional system. Therefore, MFS design should garner much interest in the usability related to design. Second, in terms of technology-specific perception, connectivity, which is a key MFS feature, has a significant effect on perceived convenience. The most important characteristic of MFS is that users can perform bank transactions anytime, anywhere they have an Internet connection. Connectivity plays a greater role in perceived ease of use than personal innovativeness. The monetary value provided by MFS has a great effect on perceived usefulness, with a path coefficient of 0.33. Although MFS is both useful method for companies and offers high value to individuals as well in terms of time and money. This monetary value allows users to sufficiently sense the usefulness of MFS. Third, in terms of user characteristics, personal innovativeness has a significant effect on perceived convenience, and innovative consumers use MFS more than others. In addition, the absorptive capacity of individuals has a significant, direct effect on the intention to use MFS. Therefore, prior knowledge about MFS appears to contribute to some degree to the intention to use it. Finally, in terms of job characteristics, the perceived tasktechnology fit has a significant effect on perceived usefulness as well. This suggests that, in addition to monetary value, MFS can be more useful if the consumer's job is appropriate for that technology. The following points deserve consideration in order to expand and accelerate the use of mobile financial services. First, MFS's ease of use and usefulness should be highlighted, rather than merely the technical characteristics. As this study suggests, MFS will appeal more to customers' intentions to use if the promotion strategy focuses on the relatively emotional aspect of convenience instead of only emphasizing the practical aspect of usefulness. Second, promoting the connectivity and monetary value that MFS offers is important. This study indicates that connectivity, which is the most important feature, must be clearly impressed on users, as well as the positive effect of the monetary value on perceived usefulness. Promotion must well inform the users of the value MFS offers. Third, personal innovativeness is a factor that deserves consideration. Not surprisingly, innovative consumers are more easily enticed to accept MFS. Furthermore, the absorptive capacity of individuals has a direct effect on the intention to use MFS. Potential users should be given more opportunities to learn about the services. For example, demonstrations of MFS in physical bank locations or perhaps through promotional means can increase the use of MFS.

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Finally, firms must identify the many tasks appropriate for MFS and incorporate these in advertisements. The study shows the fit of a job with MFS plays no less important a role than technical or user characteristics is very useful. Recently, the Korean MFS market entered a new stage as Woori Bank, Shinhan Bank, and others released a new service that supports mobile banking. These institutions are using a software method called VM, which is suited for WCDMA (Wideband Code Division Multiple Access) mobile phones. This service implements mobile banking by software instead of an IC-chip. Although allowing the user to download software to a mobile phone for banking without the need for an IC-chip, as with most other banks' services, the security level and service expandability of VM are lower than those with the IC-chip. However, the findings of this study suggest that customers are not particularly interested in the technical characteristics of MFS. Nonetheless, vendors seem focused more on technical aspects than the service aspects perceived by users. In this regard, banks developing mobile financial services should consider approaching customers more from the perspective of service value rather than merely a technical perspective. 7. Conclusions, limitations and future research This research theorizes that the factors influencing the intention to use mobile financial services, from a unified perspective, include general technology perceptions, technology-specific perceptions, user characteristics, and task user characteristics of the service. A research model that includes acceptance factors of information technology, innovativeness, and absorptive capacity serves as a means to test the research theories. The analysis reveals support for eight of the eleven hypotheses. The results are very interesting in light of existing research. First, both perceived usefulness and perceived ease of use significantly affect the intention to use MFS. This implies that these are two key factors of an information system's usage intention. However, the path coefficient of ease of use is larger than for usefulness, implying that MFS is a spontaneous system and the ease of use is a more important factor. Thus, a high level of interest in the usability of design in the development of the systems is present. Second, developers must acknowledge that the connectivity of MFS is influential to the perceived ease of its use. The most outstanding characteristic of these mobile services is that a bank account is available online anywhere, any time. Note that the connectivity provided by MFS plays a larger role in perceived ease of use than does personal innovativeness. The resulting perceived value has a significant effect on perceived usefulness. MFS is not only useful for a firm but also provides time and monetary value for consumers. Third, personal innovativeness significantly influences perceived ease of use; therefore, innovative users would use MFS more frequently. Absorptive capacity also directly affects usage intention. Accordingly, at least some information about MFS is helpful in persuading consumer trial. Fourth, perceived task technology from a task characteristic view also significantly influences perceived MFS usefulness. The usefulness is greater when combined with the user's task, including monetary value. This research shows that the following deserve attention. First, to encourage trial emphasize that ease of use is a technological characteristic of MFS. However, instead of emphasizing only the practical aspects of MFS, the more emotional issues also may effectively increase adoption. Second, constantly promoting connectivity and the monetary value of MFS is a necessity. Because connectivity is the most distinct characteristic of the service and should be impressed upon users. Because monetary value positively influences perceived usefulness, users must be well aware of the value MFS provides. Third, personal innovativeness deserves attention for diffusion of this technology. Directing promotion toward the most innovative

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customers and influencing them to accept MFS will enhance adoption. Because personal absorptive capacity also directly affects usage intention, users should be offered the opportunity to learn about MFS through demonstrations. To conclude, identifying and promoting appropriate tasks for MFS is critical. As shown by this research, task fit technology plays a critical role in mobile financial users' decisions. This is a stimulating discovery. 7.1. Limitations This research attempts to present an integrated model and a theoretically guided field study of the factors that influence perceived usefulness and perceived ease of use and, ultimately, usage intention. However, the study has some limitations, which provide the possibility of fruitful future research. First, because studies on mobile financial services are quite limited, especially considering the many prior studies on information technology adoption and innovation diffusion, theoretical grounds for the relationships among constructs are not solid. Second, women dominated the gender distribution of the study subjects by more than a 2 to 1 ratio. However, the demographics of Internet financial service users show that gender distribution is about even. This is a limitation of sampling resulting from the online data collection method adopted by this study. Nevertheless, investigating potentially differing influences on male and female adoption of MFS would be interesting. Third, this study did not investigate the potential moderating effects of such demographic characteristics as gender, age, income, or education of respondents. Because studies show that gender and age can have a moderating effect on the intention to use a service like mobile banking, additional examination could be insightful. Fourth, the consumer characteristics used as independent variables in this study can also be moderating variables. Some studies also regard personal innovativeness as a moderating variable; therefore, an additional examination of alternative models is necessary to best explain the intention to use MFS. Finally, while usage intention is used here as a dependent variable, actual usage should also be examined. Only about 4% of Internet financial service users utilize MFS and the diffusion is still very slow. This study indicates that for these services to spread successfully, they need to become a tool with direct application in the business field, rather than simply seen as convenient or useful. To that end, banks should focus on various publicity methods for promoting MFS and the development of more business convenient features, so consumers can easily use the service for their jobs anytime, anywhere. Acknowledgements The authors thank Kevin James and Barry Babin, Louisiana Tech University, and David S. Suh, Hanyang University, for their editing; and Mitch Griffin, Bradley University, for contributing to insightful comments and revisions on this research paper. References Agarwal R, Prasad J. A conceptual and operational definition of personal innovativeness in the domain of information technology. Inf Syst Res 1998;9(2):204–15. Ajzen I, Fishbein M. Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall; 1980. Anderson JC, Gerbing DW. Assumptions and comparative strengths of the two-step approach. Sociol Methods Res 1992;20(3):321–33. Anderson JC, Narus JA. A model of distributor firm and manufacturer firm working partnerships. J Mark 1988;54(1):42–58. Babin BJ, Boles JS. Employee behavior in a service environment: a model and test of potential differences between men and women. J Mark 1998;62(2):77–91. Bagozzi RP, Yi Y. On the evaluation of structural equation models. J Acad Mark Sci 1988;16(1):74–94. Bentler PM, Bonett DG. Significance tests and goodness of fit in the analysis of covariance structures. Psychol Bull 1980;88(3):588–606. Bollen KA. Structural equations with latent variables. Toronto: Wiley; 1989.

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