NFC mobile credit card: The next frontier of mobile payment?

Telematics and Informatics xxx (2013) xxx–xxx

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Telematics and Informatics journal homepage: www.elsevier.com/locate/tele

NFC mobile credit card: The next frontier of mobile payment? Garry Wei-Han Tan a, Keng-Boon Ooi b,⇑, Siong-Choy Chong c, Teck-Soon Hew d a

Faculty of Business and Finance, Universiti Tunku Abdul Rahman, Malaysia Chancellery Division, Linton University College, Malaysia Finance Accreditation Agency, Malaysia d Faculty of Business and Accountancy, University of Malaya, Malaysia b c

a r t i c l e

i n f o

Article history: Received 27 February 2013 Received in revised form 19 May 2013 Accepted 11 June 2013 Available online xxxx Keywords: Mobile credit card Technology Acceptance Model Near Field Communication Malaysia

a b s t r a c t With the advancement of mobile devices and the emergence of Near Field Communication (NFC) technology, payment today is a mere wave-of-the-phone. However, the adoption of mobile credit card (MCC) is still not widespread despite its potential as documented. Premised on this, the study extends the Technology Acceptance Model (TAM) with four additional constructs. The moderating effect of gender was also examined. Data collected from 156 respondents were analyzed using Structural Equation Modeling (SEM) and multi group analysis. Cohen’s f-square statistic for effect size is 0.815. The results revealed that only finance-related risks and the moderating effect of gender are the non-significant factors in this study. The research provides useful theoretical and managerial implications for mobile phone manufacturers, merchants, bank decision makers, software developers, governments and private practitioners when devising their marketing campaigns and business strategies. The study also extends the applicability of TAM in the area of MCC from the perspective of an emerging market. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction In the past, shopping has often been associated with either cash or credit card payment. As mobile phone technology becomes more sophisticated, new forms of payment have since emerged within the mobile payment theme. Generally, mobile payment (MP) refers to the ‘‘payments for goods, services, and bills with a mobile device such as mobile phone, smart-phone, or personal digital assistant by taking advantage of wireless and other communication technologies’’ (Dahlberg et al., 2008, p. 165). Regardless of the definition, MP is viewed as an alternative to the old-fashioned credit card. As mobile commerce continues to gain popularity, MP will eventually play an important role to facilitate transactions between consumers and merchants (Ondrus and Pigneur, 2007). The innovation within MP has grown rapidly over the last decade with the introduction of various payment methods such as Wireless Application Protocol, Unstructured Supplementary Service Data, short messaging services, and General Packet Radio Service. While each individual MP method provides flexibility and convenience, they are still not ideal when viewed from the traditional payment context (Chen et al., 2010). This is because the traditional MP solutions are not easy to use (Ondrus and Pigneur, 2007). Leavitt (2010), for example describes the tedious process in keying in credit card numbers on the limited physical keyboards. Lee (2004) opines that for an innovation to be regarded as truly mobile, the transactions should not only take place in the virtual world but with any mobile device in a physical world. Taking into consideration of the current limitations within the traditional MP solutions, this paper ⇑ Corresponding author. Address: Persiaran UTL, Bandar Universiti Teknologi Legenda, Batu 12, 71700 Mantin, Negeri Sembilan, Malaysia. Tel.: +60 6 7587888; fax: +60 6 7587599. E-mail addresses: [email protected] (G.Wei-Han Tan), [email protected], [email protected] (K.-B. Ooi), [email protected] (S.-C. Chong), [email protected] (T.-S. Hew). 0736-5853/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tele.2013.06.002

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focuses specifically on mobile credit card (MCC) as another form of MP. MCC in this context is referred to as a contactless credit card payment using a mobile phone with the aid of the Near Field Communication (NFC) technology. In this case, physical connection between consumer payment and the terminal reader is not required since transactions can be conducted with a simple touch or wave. Although the adoption of MCC brings convenience and benefits to consumers, the adoption rate is still far from massive utilization (Bank Negara Malaysia, 2010; The Star, 2012). Both the articles reported that MCC is not widely adopted among consumers and merchants in Malaysia although the technology has been in the market since 2010. It is interesting to note that with such a low adoption rate, very little study has been conducted in relation to MP solutions from the NFC’s perspective. Most of the MP findings to date were constrained to the use of mobile telecommunications network and short range wireless technologies as a choice to transfer data. In addition, most of the studies were also confined to established markets (Ondrus and Pigneur, 2007; Teo et al., 2005) with limited perspectives from emerging markets such as Malaysia. Attentions given to MP, likewise were mainly focusing on specific themes. The viewpoints of consumers’ attitudes have been sparsely explored although they have major implications on the adoption rate. Since consumers have unpredictable behaviors yet they play important roles in MP’s success, there is an urgency to explore MCC’s acceptance from the perspective of consumers’ attitude. This study adopts the Technology Acceptance Model (TAM) since it has the ability to predict different Information Technology (IT) utilization (Tan et al., 2012). As the model only takes into consideration two constructs, the overall prediction is not considered to be complete. Taking the cue, this study incorporates two additional psychological variables, namely personal innovativeness in information technology (PIIT) and social influence (SI). The variables were included since consumers’ paying habits are grounded from the person’s characteristics as well as environmental influences. In addition, two other constructs on finance-related risks – perceived risk (PR) and perceived financial cost (PFC) were added to the model as well. This is in view of the fact that PR is among the major obstacles mentioned in most of the technology adoption studies, while the decision to adopt a particular technology is often linked to the perception of financial cost in acquiring and utilizing it. The arguments found support from Mallat’s (2007) study which concludes that PR and PFC are the two major barriers in adopting financial-related MP services. Further, since most of the research papers related to IT adoption focused solely on the technology itself and do not consider other social factors, the moderating effect of gender is also added to the model. Taken together, it is believed that the integrated model can help to explain MCC’s acceptance from the theoretical perspective, in which practical contributions can be derived at based on the study’s results. The next section reviews the literature relevant to the variables of interest. As a result, a research framework and a series of testable hypotheses are developed. The methodology is then described, followed by an analysis and interpretation of the data collected. The implications are discussed and recommendations are provided before the paper is concluded with possible future research directions. 2. Theoretical background and research model 2.1. Overview of NFC-aided mobile credit card NFC has been regarded as the future of MP services (Ondrus and Pigneur, 2007). Initially, the payment method was carried out for VISA and MasterCard Paypass program (Pasquet et al., 2008). Ruijun and Yao (2010) remarked that NFC can transfer data either in active or passive modes via a short range high frequency wireless communication technology. The operational distance under passive mode is 10 cm, while the inactive mode is 20 cm (Chen et al., 2010). Hence, the NFC technology enables transactions to be conducted merely by holding a mobile phone within the range of the NFC reader. The technology has since been adopted in USA, Canada, Hong Kong, Korea, Japan and Taiwan (Chen and Chang, 2011; Pope et al., 2011). In Malaysia, Malayan Banking Berhad (Maybank) is the only participating bank at the moment that provides such a payment convenience in collaboration with Maxis (the country’s largest telecommunication services provider), VISA, and Nokia.

Table 1 Merchant list in Malaysia. S/ N

Category

Name

1 2 3 4

Hypermarket Retailer Specialty store Convenience store Petrol station Food and beverage Cinema Others

Carrefour Parkson, AEON Watson, Hush Puppies, Toy City, The Body Shop 7 Eleven

5 6 7 8

Caltex Nandos, O’Briens, A&W, Burger King, Dunkin Donuts, Steven’s Tea Garden Café, Kaya Kopitiam, Golden Oven, Station 1 Café, Daily Fresh, Mercu UEM Café, Bank Negara Café, Baskin Robbins Cathay Cineplex Tolls nationwide, Rapid KL LRT stations, Rapid KL buses, KTM Komuter, Monorail, selected parking lots (32 in Kuala Lumpur)

Source: Maybank2u.com (Maybank Malaysia, 2012).

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Table 1 summarizes the applicability of MCC in Malaysia. The table also implies that although the technology has been in existence for some time, it is still relatively untapped (The Star, 2012), therefore opening up possibilities to research into this area. 2.2. Factors influencing mobile credit card adoption A considerable number of frameworks have been developed over the years to understand consumers’ intention to adopt certain IT. Among them include the Theory of Reasoned Action (TRA) (Fishbein and Ajzen, 1975), Technology Acceptance Model (TAM) (Davis, 1989), Theory of Planned Behavior (TPB) (Ajzen, 1991), and the Diffusion of Innovation (DOI) (Rogers, 1995). TRA aims to explain that the actual behavior of an individual is determined by his or her behavioral intention, and that the intention is influenced by subjective norm and his or her attitude towards behavior (Masrom and Hussein, 2008). TPB, considered an improvement to TRA, introduces a third construct known as perceived behavioral control to account for both the cognitive and situational resources needed to perform a task (Ajzen and Madden, 1986). Like TPB, TAM was also adapted from TRA to model user’s acceptance and behavior of new information systems (Fishbein and Ajzen, 1975). However, it is thought to be more parsimonious than TRA or TPB (Mathieson, 1991). The original TAM consists of five constructs which are perceived ease of use (PEOU), perceived usefulness (PU), attitude towards using (ATU), behavioral intention to use (BI), and the actual system use (AU). As ATU is a weak predictor, the construct was subsequently eliminated in the revised TAM (Davis, 1989). TAM, however, is not adequate to portray the actual influences of technology use. Venkatesh and Davis (2000) commented that the model only describes up to 40% of its variance. Nysveen et al. (2005) opine that the main usage of TAM stresses on work-related factors than everyday’s life. More importantly, the model also neglected user’s individual characteristics (McCoy et al., 2005) and external factors (Kuo and Yen, 2009). Rogers (1995) introduces the DOI, which provides a perspective of how innovation among consumers moves from early adoption to mass adoption. As the speed of adoption to innovation differs for each of the consumer groups, Rogers segmented the market into five categories based on the relative passage of time. In addition, consumers’ readiness to adopt a new innovation is also influenced by personality traits (Serenko, 2008). Agarwal and Prasad (1998) observe that consumers with higher personal innovativeness have a higher likelihood to develop positive attitudes towards IT adoption compare to lesser innovative consumers given the same level of belief. They are usually risk takers and have the tendency to break the general rules. Hence, innovators tend to purchase new products quicker than others (Midgley and Dowling, 1978). Despite DOI’s contributions, LaRose et al. (2007) raise a concern on the exclusion of potential adopter’s individual capabilities and psychological factors from the model. Since each of the frameworks has its advantages and disadvantages, the TAM is adopted in this study together with four additional variables, i.e. SI, PIIT, PR and PFC. In addition, gender is also included as a moderating variable in this study. In view of the limitation of using attitude as a dependent variable, it is replaced with intention which is in line with the TAM. Fig. 1 shows the research framework which guided the development of conjectured relationships between the variables.

Perceived Usefulness (PU) H7

Gender H1

Perceived Ease of Use (PEOU) TECHNOLOGY ACCEPTANCE MODEL

Social Influence (SI)

Personal Innovativeness in Information Technology (PIIT) PSYCHOLOGICAL SCIENCE CONSTRUCTS

H2

MODERATING VARIABLE H8

H3 H4

INTENTION TO ADOPT MOBILE CREDIT CARD

H5 H6

Perceived Risk (PR)

Perceived Financial Cost (PFC) FINANCE-RELATED RISKS Fig. 1. Research framework.

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3. Hypotheses development 3.1. Perceived usefulness PU is the degree to which a prospective adopter believes that by using a particular system would improve his or her job performance (Davis, 1989). In order for MCC to be accepted, the innovation must possess more advantages when compared to cash or credit card payment. Zmijewska (2005) describes the benefits of MCC in terms of quicker checkout since signature is not required. Similarly, as the transaction is conducted via a wave-of-the-phone, the tedious process of entering credit card numbers is eliminated (Leavitt, 2010). The speed of transaction using MCC has been confirmed by Finextra (2004) where it is 6 s faster than PayPass cards. If consumers believe that MCC adoption can increase their productivity, this will encourage usage. As such, it is posited that PU will have a positive effect on the intention to adopt MCC. The following hypothesis is thus constructed: H1. PU has a significant relationship with MCC adoption in Malaysia.

3.2. Perceived ease of use PEOU is the extent to which using a new system is expected to be free of efforts for prospective adopters (Davis, 1989). PEOU is one of the major concerns for most consumers, especially in the old MP adoption (Saxena et al., 2005) because there are several steps involved in the payment process which can be very complicated. Further, the device is also restrained by slow text input facilities, limited resolutions, pocket-sized screens, and short battery lifetime (Curran and Huang, 2008). As MCC transaction is conducted with a simple wave, the barriers inherent in the old MP would have been eliminated. Hypothetically, the greater the perception that MCC does not require much mental efforts to use, the more likely consumers will exert a positive attitude towards MCC. A considerable number of studies have identified PEOU as having a significant effect on PU (Nysveen et al., 2005). Amin (2007) in his study on the traditional MP adoption in Malaysia also provides similar evidence. This suggests that if MCC is easy to use or easy to learn, individuals will also perceive the method as useful and therefore are more likely to adopt it. The following propositions thus ensue: H2. PEOU has a significant relationship towards MCC adoption in Malaysia.

H7. PEOU has a significant relationship with PU. 3.3. Social influence SI can be divided into three components, namely subjective norm (SN), image, and voluntariness (Karahanna et al., 1999). According to Fishbein and Ajzen (1975, p. 302), SN is defined as a ‘‘person’s perception that most people who are important to him think he should or should not perform the behavior in question’’. The construct emphasizes on the views and roles of friends, peer groups, relatives, and superiors (Lopez-Nicolas et al., 2008; Teo et al., 2012a,b). Since SN plays an important role in the study of new technology adoption (Venkatesh and Davis, 2000), the construct is likely to influence MCC adoption. Image, on the other hand, refers to the degree of which an innovation can raise a user’s position in the society (Moore and Benbasat, 1991). Teo and Pok (2003) explain that consumers adopt new mobile services to enhance their image and social status. Mobile phone ownership is also seen as a symbol of social progress in developing nations (Lopez-Nicolas et al., 2008). It is thereby hypothesized that the greater the perception of SI (SN and image), the greater the intention to adopt MCC voluntarily. H3. SI has significant relationship towards MCC adoption in Malaysia.

3.4. Personal innovativeness in information technology PIIT is a trait reflecting users’ willingness to adopt a new system (Agarwal and Prasad, 1998). Lu et al. (2008) elucidate that highly innovative individuals are active seekers of new ideas, thus they are able to cope with uncertainties leading to the development of positive intention on IT adoption. Yi et al. (2006) further remarked at how innovators have the ability to imagine, understand, and appreciate the benefits associated with an innovation. This explains why innovators are usually quicker in purchasing new products when compared to other consumers (Midgley and Dowling, 1978). In view that PIIT influences behavioral intention (Crespo and Rodriguez, 2008), an individual with higher PIIT are more likely to be a risk taker with positive belief on MCC. Therefore, they are more likely to develop positive intention to adopt MCC. This argument leads to the following proposition: Please cite this article in press as: Tan, G.-W.H., et al. NFC mobile credit card: The next frontier of mobile payment? Telemat. Informat. (2013), http://dx.doi.org/10.1016/j.tele.2013.06.002

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H4. PIIT has a significant relationship towards MCC adoption in Malaysia.

3.5. Perceived risk PR is the expectation of losses related to purchase and it is one of the major barriers discouraging consumers from making a purchase (Zhou, 2011; Wong et al., 2012). Chang (2010) confirmed that PR is the most important factor in evaluating whether to adopt mobile phones for commercial transactions such as shopping in Australia. Accordingly, technology failure could lead to financial or psychological loss. In the context of MCC adoption, 63% of American customers are concerned with financial risks (The Star, 2011). Since the study by Lu et al. (2011) also revealed that PR significantly influence students’ behavioral intention to adopt MP in China, it is therefore posited that PR will also influence consumer decision to adopt MCC as an alternative payment method. The following hypothesis is thus suggested: H5. PR has a significant relationship towards MCC adoption in Malaysia.

3.6. Perceived financial cost The other barrier in the intention to adopt MCC is the cost of acquiring mobile phone, the transactional fees to use the services, and the cost to maintain and upgrade it (Luarn and Lin, 2005; Wang et al., 2006). In addition, there are also other hidden transaction charges which may further exaggerate the costs (Wu and Wang, 2005). Taken together, these barriers measure users’ perception of financial cost (PFC). Lu et al. (2011) discover the negative relationship between behavioral intention and PFC to adopt MCC. Likewise, Chong et al. (2012) found that cost has significant influence on m-commerce adoption in a China–Malaysia cross-country study. In Malaysia, mobile phones with NFC-enabled technology come with either a 12- or 24-month contract (Maybank Malaysia, 2012). This suggests that the cost may be more expensive for average consumers when compared to mobile phones without contract plans. Since the adoption rate of a new innovation is affected by the PFC, this leads to the following proposition: H6. PFC has significant relationship towards MCC adoption in Malaysia.

3.7. Gender Moderating variables such as gender has been generally missing from most of the TAM-related research although there is evidence to suggest its possible moderating effect on MCC adoption. Building upon the model of Eagly and Wood (1991), it can be argued that differences in decision-making among gender are linked to gender roles in terms of how men and women should behave. Gefen and Straub (1997) describe that men are more competitive and assertive, while females are encouraged to be more cooperative and nurturing. In a study on the relationship between PIIT and the intention to adopt wireless mobile data system, Lu et al. (2006) demonstrate that gender has a significant moderating effect where male is found to be a significant direct determinant in the adoption compared to female. This could be due to the fact that men have higher level of openness to ideas (Costa et al., 2001) and that they are bolder to try new technological products (Morris et al., 2005). Similarly, gender also has been found to moderate PU, PEOU, and intention. Venkatesh and Morris (2000) provide evidence that women are strongly influenced by PEOU while men’s decision is influenced by PU. This is because women emphasize more on the importance of services and therefore they appreciate the importance of PEOU. Moreover, female also have lower computer self-efficacy (Venkatesh and Morris, 2000) and this thus leads to higher PEOU. On the other hand, Sun and Zhang (2000) found men to be more pragmatic and task-oriented and thus they tend to seek for utility function such as PU. Venkatesh and Morris (2000) also found that women are strongly influenced by SN where they are more concerned with other’s opinions and feelings during interaction. As far as PR is concerned, Aguirre-Urreta and Marakas (2010) conclude that males perceive lesser risk compared to females in a similar situation. Because of the perception of risk, females are also more vigilant to PFC due to the hidden charges (Wu and Wang, 2005) which they may not be aware of. These arguments lead to the following hypothesis: H8. Gender moderates the relationships among the variables in the research framework.

4. Research methodology 4.1. Sampling and data collection As the paper focuses on MCC adoption, the respondents should own at least a credit card and mobile phone. Sensibly, these individuals are more likely to adopt MCC compared to those who do not own and use mobile phones and credit Please cite this article in press as: Tan, G.-W.H., et al. NFC mobile credit card: The next frontier of mobile payment? Telemat. Informat. (2013), http://dx.doi.org/10.1016/j.tele.2013.06.002

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cards. Using a self-administered questionnaire, the study was conducted on customers of a major bank in the state of Perak, Malaysia. The bank was selected because it has diverse customer groups comprising of various ethnics, ages, and backgrounds. This allows wider representation in terms of customer segmentations compared to single race countries, thus providing a better picture with regards to multi-faceted customer segmentation. Using the systematic sampling method, every second customer who enters the bank was selected. The participants were asked to describe their level of agreement to each statement by circling their responses in the questionnaire. After a period of four weeks, 220 customers participated in the survey. Of these, 33 samples were rejected because of incomplete responses, yielding a total response of 187. Based on the Mahalanobis distance d2, 31 outliers were discarded, resulting in the actual sample size of 156 to be used in the SEM analysis. 5. Data analyses 5.1. Profile of respondents The respondents consist of 42.3% males and the remaining are females (Table 2). The majority of respondents are between the age of 21 and 30 years old and that most of them are single. Most of the respondents come from the education industry, followed by financial institutions, banking, manufacturing, IT-related, and retail. No respondents from the telecommunications and tourism industries were surveyed. The majority of them have used credit cards for less than 3 years and that they reported to have used credit cards of between 1 and 3 times a month. 5.2. Survey instruments In order to test the 8 hypotheses constructed, the six independent variables were adapted from the literature (Table 3). Five survey questions were developed to measure the dependent variable. All the items have a score of 1–5, ranging from 1 = strongly disagree to 5 = strongly agree. Prior to dissemination, the questionnaire was piloted where slight modifications were made to improve the clarity of some statements.

Table 2 Demographic profile of respondents. Item

Frequency

Percent

Gender

Male Female

66 90

42.3 57.7

Age

Below 20 21–25 26–30 31–35 36–40 Above 40

10 92 28 15 3 8

6.4 59.0 17.9 9.6 1.9 5.1

Marital status

Single Married

126 30

80.8 19.2

Highest education level

No college degree Diploma/advanced diploma Bachelor degree/professional qualification Postgraduate qualification

16 21 84 35

10.3 13.5 53.8 22.4

Respondent’s industry

Banking Financial institutions IT-related Manufacturing Retail Telecommunications Tourism Education Others

6 8 5 4 4 0 0 77 52

3.8 5.1 3.2 2.6 2.6 0.0 0.0 49.4 33.3

Period of credit card use

Less than 3 years 3–6 years Over 6 years 1–3 times 4–10 times 11–20 times Others

116 21 19 84 33 6 33

74.4 13.5 12.2 53.8 21.2 3.8 21.2

Frequency of credit card use (per month)

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G.Wei-Han Tan et al. / Telematics and Informatics xxx (2013) xxx–xxx Table 3 Questionnaire source and number of items. Constructs

Number of items

Sources

Perceived usefulness (PU) Perceived ease of use (PEOU) Social influence (SI) Personal innovativeness in information technology (PIIT) Perceived financial cost (PFC) Perceived risk (PR) Intention to use (IU)

5 4 6 5 3 3 5

Davis et al. (1989) and Tan et al. (2010) Davis et al. (1989) and Tan et al. (2010) Tan et al. (2012) Goldsmith and Hofacker (1991) Cruz and Laukkanen (2010) Tan et al. (2010) Tan et al. (2010)

5.3. Statistical analysis In line with the two-stage Structural Equation Modeling (SEM) procedures advocated by Anderson and Gerbing (1988) and adopted by many researchers (Leong et al., 2011a, 2012, 2013b; Zhou, 2011), confirmatory factor analyses (CFA) and SEM with maximum likelihood estimation (MLE) are carried out consecutively. SEM combines multiple regressions with CFA to simultaneously estimate a set of interrelated causal relationships and that it is the second generation of multivariate technique. 5.4. Validity and reliability measures It is imperative that content validity must be tested prior to any statistical analysis (Bharati and Chaudhury, 2004). Content validity refers to the representativeness and comprehensiveness level of an item. To ensure content validity, items in this study have been adapted from prior studies whereas all definitions were adopted based on an in-depth literature review. Besides, criterion validity – how good the predictive factors can predict the outcomes of a dependent variable – was also tested. The directions and magnitudes of all correlation coefficients between all the factors and IU are significant and consistent with theoretical expectations (Table 4). Since information from the independent and dependent variables were collected from the same respondents, the issue of common bias may arise. Common Method Bias (CMB) is defined as the overlapping between two variables due to high correlations between the underlying constructs. For this reason, Harmon’s single factor analysis was performed and the common variance is just 26.056% (<50%). We then continue with the common latent factor (13.69%) and marker variable method (12.96%) in Analysis of Moment Structure (AMOS). Based on the results, it is confirmed that CMB is not an issue in this study. After the questionnaire was examined through pre and pilot tests, an Exploratory Factor Analysis (EFA) with varimax rotation is performed. The Kaiser–Meyer–Olkin’s (KMO) sampling adequacy and Barlett’s sphericity test (Table 5) verified

Table 4 Discriminant and criterion validity.

Note: PU = perceived usefulness; PEOU = perceived ease of use; PFC = perceived financial cost; PR = perceived risk; SI = social influence; PIIT = personal innovativeness in information technology; IU = intention to use. ⁄⁄ Correlation is significant at 0.01 level (2-tailed); major diagonal shows the square root of the AVE. a Fornell and Larcker (1981); shaded cells indicate criterion validity.

Table 5 KMO and Barlett’s tests. Kaiser–Meyer–Olkin measure of sampling adequacy

0.794

Barlett’s test of sphericity Approx. Chi-square df Sig.

2508.046 351 0.000

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Table 6 Exploratory factor analysis: rotated component matrixa. Components 1

2

3

PU2 PU3 PU4 PU5

4

5

6

7

0.867 0.783 0.686 0.673

PEOU1 PEOU2 PEOU3 PEOU4

0.792 0.746 0.753 0.681

PFC1 PFC2 PFC3

0.942 0.923 0.836

PR1 PR2 PR3

0.870 0.881 0.897

SI1 SI2 SI3 SI5 SI6

0.810 0.877 0.716 0.717 0.583

PIIT1 PIIT2 PIIT3 IU1 IU2 IU3 IU4 IU5 Initial eigenvalue % Variance explained Cumulative% variance explained

0.825 0.829 0.767 0.774 0.788 0.852 0.779 0.723 7.258 26.882 26.882

2.977 11.026 37.907

2.633 9.753 47.660

2.121 7.855 55.515

1.740 6.443 61.958

1.531 5.672 67.630

1.326 4.911 72.541

Note: aRotation converged in 6 iterations; PU = perceived usefulness; PEOU = perceived ease of use; PFC = perceived financial cost; PR = perceived risk; SI = social influence; PIIT = personal innovativeness in information technology; IU = intention to use.

the suitability of conducting an EFA. The extraction of factors is based on Kaiser’s criterion of eigenvalue of at least 1. Besides, all factor loadings must be greater than 0.50 and load only on one factor. Through the EFA, 7 factors explaining 72.541% of the total variance have been extracted (Table 6). The reliability of the scale was tested using Cronbach’s alpha coefficient, composite reliability (CR) and average variance extracted (AVE). Hew and Leong (2011), and Nunnally and Bernstein (1994) recommend that an alpha coefficient of 0.70 or more is considered to be adequate. Besides, the CR which uses actual loadings of the construct as the weight instead of setting an equal weight was also computed. Kline (2005) asserts that the CR must be greater than 0.70 while the AVE should be at least 0.50. Convergent validity refers to the capability of a construct to yield the same results even though different approaches are engaged. To assess convergent validity, the criteria set by Fornell and Larcker (1981) as described in the following are employed: (a) all factor loadings should be statistically significant and exceed 0.50; (b) all CR should exceed 0.70; and (c) all AVE should exceed the variance due to measurement error (i.e., AVE > 0.50). Table 7 indicates that all of these criteria have been statistically verified and therefore, it can be concluded that this study has achieved a good convergent validity. Discriminant validity, on the other hand, refers to the degree of difference between the construct and its indicators relative to other constructs and their indicators. Table 4 confirms the existence of discriminant validity since the square root of AVE has exceeded its correlation coefficients (Leong et al., 2011a). Furthermore, it also shows that the Fornell–Larcker ratio for all the constructs is less than 1.00, therefore verifying the existence of discriminant validity (Fornell and Larcker, 1981). Hair et al. (2010) mention about some assumptions that need to be met in order to perform multivariate analysis. In terms of outliers, the Mahalanobis d2 distances are examined and data which has a p2 = 0.000 probability are discarded. Normality of the dataset is ascertained by using standardized residual P–P plots and histograms with normal plot. Besides, the maximum magnitudes of skewness (1.249) and kurtosis (2.278) are less than the thresholds of 3 and 10, respectively which Please cite this article in press as: Tan, G.-W.H., et al. NFC mobile credit card: The next frontier of mobile payment? Telemat. Informat. (2013), http://dx.doi.org/10.1016/j.tele.2013.06.002

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G.Wei-Han Tan et al. / Telematics and Informatics xxx (2013) xxx–xxx Table 7 Convergent validity and construct reliability. Constructs and items Perceived usefulness (PU) Using MCC increases my productivity/performance Using MCC would enhance my effectiveness in my daily work Using MCC makes the handling of payment easier Overall I would find MCC to be advantageous Perceived ease of use (PEOU) Learning to use MCC will be easy for me Using MCC does not require a lot of mental efforts It would be easy for me to become skillful at using MCC Since a MCC uses my mobile phone, hence a mobile phone credit card is easy to use Perceived financial cost (PFC) The annual fees of MCC services is expensive for me The transaction fees is expensive The cost of mobile phone is high for me Perceived risk (PR) The risk of an unauthorized third party overseeing the transaction is low using MCC The risk of abuse of usage information (e.g., payment amount) is low when using MCC The risk of abuse of billing information (e.g., credit card number, bank account data) is low when using MCC Social influence (SI) Friend’s suggestions and recommendations will affect my decision to use MCC Family/relatives have influence on my decision to use MCC I will use MCC if my colleagues use it I will use MCC if the service is widely used by people in my community MCC will enable me to improve my social status Personal innovativeness in information technology (PIIT) I am very curious of how things work I like to experiment with new ways of doing things I like to take a chance Intention to use (IU) I am likely to use MCC in the near future Given the opportunity, I will use MCC I am willing to use MCC in the near future I will think about using a mobile phone credit card I intend to use mobile payment services when the opportunity arises

Indicator

Factor loadings

Critical ratio (p-value)

PU2 PU3

0.828 0.754

9.078 (***) 8559 (***)

PU4 PU5

0.686 0.690

7859 (***) n.a.

PEOU1 PEOU2 PEOU3 PEOU4

0.743 0.774 0.732 0.819

9308 (***) 8354 (***) 9205 (***) n.a.

PFC1 PFC2 PFC3

0.937 0.911 0.711

11.647 (***) 11.213 (***) n.a.

PR1

0.813

12.994 (***)

PR2

0.886

14.612 (***)

PR3

0.898

n.a.

SI1

6.479 (***)

SI2

7.590 (***)

SI3 SI5

7.103 (***) 7.471 (***)

SI6

n.a.

PIIT1 PIIT2 PIIT3

7343 7397 n.a.

IU1 IU2 IU3 IU4 IU5

n.a. 13.860 (***) 12.774 (***) 9.807 (***) 11.203 (***)

0.761

Cronbach’s alpha (a)

Composite reliability (CRa)

Average variance extracted (AVEb)

0.829

0.826

0.550

0.832

0.851

0.589

0.894

0.893

0.738

0.904

0.900

0.751

0.834

0.832

0.500

0.782

0.788

0.556

0.891

0.870

0.573

***

Significant at p < 0.001; n.a. = not applicable; MCC = mobile credit card. Composite reliability = (square of the summation of the factor loading)/{(square of the summation of factor loadings) + (summation of error variances)}. Average variances extracted = (summation of the square of the factor loadings)/{(summation of the square of factor loadings) + (summation of error variances)}. Criteria: Cronbach’s a > 0.70 (Nunnally and Bernstein, 1994), composite reliability, CR > 0.70, Average variance extracted (AVE) > 0.50 (Hair et al., 2010). a

b

imply a normal distribution (Kline, 2005). The assessment of normality in AMOS reveals a Mardia’s critical ratio of 6.559 which is slightly above the threshold of 1.960 at p = 0.05 level for the multivariate kurtosis (Mardia, 1974). Hence, to further investigate the multivariate normality, Bollen-Stine Bootstrap was performed. The p-value of 0.713 > 0.05 indicates that the distribution is normal. The linearity of the dataset is then examined via the matrix scatter plots. To address multicollinearity problem, Variance Inflation Factor (VIF) and tolerance are examined (Leong et al., 2011b; Teo et al., 2012a,b). All VIF values are less than 10 while the respective tolerance is greater than 0.10 (Table 8). Finally, the sample size of 156 is within the acceptable range for SEM analysis (Hair et al., 2010). Hence, there were no violations to the multivariate analysis in this research. Several goodness-of-fit indices based on the seven constructs with 31 items were obtained from AMOS for the CFA model (Table 9). The chi-square statistics (v2 = 8.795, df = 9, p = 0.456) indicates that the model has a very good fit. Since v2 is sensitive to sample size (Hair et al., 2010), we calculated the normed chi-square statistics (v2/df = 0.977) which is less than 3 (Bagozzi and Yi, 1988). Other indices (GFI = 0.982; AGFI = 0.958; CFI = 1.000; IFI = 1.000; TLI = 1.000, RMR = 0.018 and

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G.Wei-Han Tan et al. / Telematics and Informatics xxx (2013) xxx–xxx

Table 8 Multicollinearity test – coefficientsa. Model

1

(Constant) PU PEOU PFC PR SI PIIT

Unstandardized Coefficients

Standardized coefficients

B

Std. error

beta

0.593 0.173 0.193 0.006 0.053 0.180 0.249

0.319 0.066 0.068 0.040 0.041 0.050 0.059

0.197 0.215 0.009 0.085 0.240 0.273

t

Sig.

1.859 2.627 2.847 0.149 1.302 3.593 4.227

0.065 0.010 0.005 0.882 0.195 0.000 0.000

Collinearity statistics Tolerance

VIF

0.656 0.646 0.980 0.863 0.828 0.882

1.524 1.549 1.020 1.159 1.207 1.134

Note: aDependent variable: IU; PU = perceived usefulness; PEOU = perceived ease of use; PFC = perceived financial cost; PR = perceived risk; SI = social influence; PIIT = personal innovativeness in information technology; IU = intention to use.

Table 9 Goodness-of-fit indices: the recommended and actual values. Model fit indices

Recommended value

CFA model

Structural model

v2/df

63.00a >0.05a P0.90a P0.90a P0.90a P0.90b P0.90b 60.05c 60.08d

0.977 0.456 0.982 0.958 1.000 1.000 1.000 0.018 0.000

0.622 0.760 0.991 0.969 1.000 1.000 1.000 0.013 0.000

p-Value GFI AGFI CFI IFI TLI RMR RMSEA

Note: v2/df = the ratio between Chi-square and degrees of freedom; GFI = goodness of fit index; AGFI = adjusted goodness of fit index; CFI = comparative fit index; IFI = incremental fit index; TLI = tucker-lewis index; RMR = root mean square residual; RMSEA = root mean square error of approximation. a Source: Bagozzi and Yi (1988) and Hair et al. (2010). b Source: Arbuckle (2008), Byrne (2001) and Hair et al. (2010). c Source: Gefen et al. (2000). d Source: Browne and Cudeck (1993) and Jöreskog and Sörbom (1994).

Table 10 Multi-group analysis: gender-invariant test of measurement and structural model for male and female. Original model without imposing equality constraints

Measurement model Structural model

Difference between original and constrained models

Model with equality constraints imposed

v2 Male (df)

v2 Female (df)

v2

df

P

11.388 (9) 7.722 (8)

6.858 (9) 6.787 (8)

18.246 14.509

18 16

24.800 23.545

v2

P 24 29

df

D v2

D df

6.554 9.036

6 13

v20.05 Critical value

Gender variance

12.592 22.362

NS NS

Note: NS = not significant.

RMSEA = 0.000) were well above the recommended values. In summary, there is a very good fit of the CFA model with the dataset. A multi-group analysis (MGA) with SEM has been engaged to examine the gender-invariance of the measurement model. Based on the chi-square difference between the model with all factor loadings constrained as equal and the original model without constraints, the gender-invariance of the measurement model can be further verified. Gender-invariance is confirmed if the chi-square difference (Dv2) is greater than the critical value of the chi-square at p = 0.05 with the corresponding difference in degrees-of-freedom (Ddf) (Feng et al., 2006). Table 10 shows that the measurement model does not show any significant difference between both the genders.

6. Hypotheses results The analysis of the structural model shows that the chi-square statistics is 4.980 (df = 8, p = 0.760) and that the normed chi-square statistics (v2/df) is 0.622. Table 9 shows that generally, the structural model fits very well with the dataset based Please cite this article in press as: Tan, G.-W.H., et al. NFC mobile credit card: The next frontier of mobile payment? Telemat. Informat. (2013), http://dx.doi.org/10.1016/j.tele.2013.06.002

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G.Wei-Han Tan et al. / Telematics and Informatics xxx (2013) xxx–xxx Table 11 SEM results. Hypothesis

Path

Estimate (b)

Std. error

Critical ratio

p-Value

Supported

H1 H2 H3 H4 H5 H6 H7

PU ? IU PEOU ? IU SI ? IU PIIT ? IU PR ? IU PFC ? IU PEOU ? PU

0.173 0.193 0.180 0.249 0.053 0.006 7.314

0.064 0.066 0.049 0.058 0.040 0.039 51.199

2.695 2.928 3.693 4.317 1.334 0.153 0.143

0.007⁄ 0.003⁄ 0.000⁄⁄ 0.000⁄⁄ 0.182 0.878 0.886

Yes Yes Yes Yes No No No

Note: ⁄p < 0.01; ⁄⁄p < 0.001; square multiple correlation, R2 = 0.449; f2 = 0.815. PU = perceived usefulness; PEOU = perceived ease of use; PFC = perceived financial cost; PR = perceived risk; SI = social influence; PIIT = personal innovativeness in information technology; IU = intention to use.

Perceived Usefulness (PU)

β = 0.173; p = 0.007*

β = -7.314; p = 0.886 Perceived Ease of Use (PEOU)

β = 0.193; p = 0.003*

TECHNOLOGY ACCEPTANCE MODEL

Social Influence (SI)

Personal Innovativeness in Information Technology (PIIT) PSYCHOLOGICAL SCIENCE CONSTRUCTS

β = 0.180; p = 0.000** β = 0.249; p = 0.000**

R2 = 44.9%, f2 = 0.815 INTENTION TO ADOPT MOBILE CREDIT CARD

β = 0.053; p = 0.182

Perceived Risk (PR) β = 0.006; p = 0.878 Perceived Financial Cost (PFC)

Note: * p < 0.01; ** p < 0.001

FINANCE-RELATED RISKS Fig. 2. SEM results.

on the goodness-of-fit indices (GFI = 0.991; AGFI = 0.969; CFI = 1.000; IFI = 1.000; TLI = 1.000, RMR = 0.013 and RMSEA = 0.000). As shown in Table 11, PU (b = 0.173, p = 0.007); PEOU (b = 0.193, p = 0.003); SI (b = 0.180, p = 0.000) and PIIT (b = 0.249, p = 0.000) have positive significant causal relationships with IU. On the other hand, PR (b = 0.053, p = 0.182) and PFC (b = 0.006, p = 0.878) do not have significant relationships with IU, and PEOU (b = 7.314, p = 0.886) was found to have insignificant relationships with PU. PIIT has the strongest impact on IU, followed by PEOU, SI and PU. Fig. 2 further illustrates these causal relationships where dotted lines indicate insignificant paths. The squared multiple correlations show that the structural model has been able to explain 44.9% of the variances in IU. Based on Cohen’s f-square, the effect size of the model is 0.815. Likewise, a MGA is also performed to examine the gender-invariance of the structural model. Table 10 indicates there is no significant difference between both the genders. Hence, it can be concluded that the structural model is robust across both the genders. In order to examine the moderating effects of gender on the causal relationships, a MGA is also conducted. Recommended by Jöreskog and Sörbom (1993), this technique consists of four steps: (1) dividing the dataset into two subgroups based on the gender of the respondents; (2) performing path analysis for each of the subgroups by imposing equality constraints, i.e. regression weight of all paths are fixed to be equal between the subgroups; (3) performing path analysis for each of the sub-groups without equality constraints imposed on each of the respective paths in the model, i.e. estimation of path coefficient is allowed to be vary between subgroups; and (4) determining the moderating effects based on the chi-square difference test.

Please cite this article in press as: Tan, G.-W.H., et al. NFC mobile credit card: The next frontier of mobile payment? Telemat. Informat. (2013), http://dx.doi.org/10.1016/j.tele.2013.06.002

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G.Wei-Han Tan et al. / Telematics and Informatics xxx (2013) xxx–xxx

Table 12 Multi group analysis: moderating effects of gender. Hypothesis

H1 H2 H3 H4 H5 H6 H7

Path

Estimate of path coefficient

PU ? IU PEOU ? IU SI ? IU PIIT ? IU PR ? IU PFC ? IU PEOU ? PU

Male

Female

0.160⁄ 0.200⁄ 0.142⁄ 0.285⁄⁄⁄ 0.062 0.008 2.702

0.197⁄ 0.184 0.229⁄⁄ 0.193⁄ 0.043 0.022 3.102

Dv2ð1Þ

p-Value (two-tailed)

Significant

0.115 0.021 0.855 0.643 0.060 0.147 2.358

0.7345 0.8848 0.3551 0.4226 0.8065 0.7014 0.1246

No No No No No No No

Note: ⁄p < 0.05; ⁄⁄p < 0.005; ⁄⁄⁄p < 0.001; critical value for Dv2ð1Þ = 3.841 at p = 0.05. PU = perceived usefulness; PEOU = perceived ease of use; PFC = perceived financial cost; PR = perceived risk; SI = social influence; PIIT = personal innovativeness in information technology; IU = intention to use.

Table 13 Critical ratios for differences between parameters. Parameter for male PU ? IU Parameter for female

PU ? IU PEOU ? IU PFC ? IU PR ? IU SI ? IU PIIT ? IU PEOU ? PU

PEOU ? IU

PFC ? IU

PR ? IU

SI ? IU

PIIT ? IU

PEOU ? PU

0.572 0.442 0.302 0.481 1.084 0.952 0.104

Note: PU = perceived usefulness; PEOU = perceived ease of use; PFC = perceived financial cost; PR = perceived risk; SI = social influence; PIIT = personal innovativeness in information technology; IU = intention to use.

Accordingly, a positive moderating effect is confirmed if ‘‘the path is higher for the group which scored higher in some moderators and a drop in chi-square between the restricted and unrestricted model with one fewer degree of freedom is statistically significant’’ (Lee, 2009, p. 15). Thus, a Dv2ð1Þ value greater than 3.841 is statistically significant at the 5% level (p = 0.05). Table 12 shows that there are no significant moderating effects of gender. No paths are significantly affected by gender and thus the structural model is robust against gender differences. Alternatively, the pairwise parameter comparisons (Table 13) also revealed that there is no significant difference in the path coefficients between the gender groups since the absolute values of all critical ratios are less than 1.960 at p = 0.05. Thus, there are no significant moderating effects of gender on IU. 7. Discussion 7.1. Technology Acceptance Model The findings suggest that PU is a significant construct in predicting the intention to adopt MCC. The results corroborate previous research conducted on traditional MP solutions in Malaysia (Amin, 2007) as well as on mobile services acceptance (Wang et al., 2006). Since convenience is an important factor in MP adoption (Hashemi and Soroush, 2006), if consumers find the innovation useful, they are more likely to adopt the services offered. This is particularly in view of the advantages brought about by MCC over cash or credit card payment in terms of quicker and more convenient transactions. The findings will have implications in light of promoting usage based on the advantages of MCC. Likewise, PEOU is a significant factor in predicting MCC acceptance. The finding is consistent with prior study on MCC by Amin (2008) and Leong et al. (2013a) in Malaysia. In view that MCC works with the aid of the NFC technology, there is no need to input data compared to the traditional payment process. As such, there is a general perception that MCC is less complicated to use where it only requires a simple wave when conducting any transaction. While the absence of any relationship between PEOU and PU contradicts with many TAM studies such as Nysveen et al. (2005) and Wang et al. (2006) which suggest that the PEOU will influence the formation of PU, the finding is consistent with the results of Davis et al. (1989) during the initial adoption where it was revealed that the relationship was only significant after a certain period of usage. The finding was not surprising since MCC is still at the infancy stage in Malaysia. Please cite this article in press as: Tan, G.-W.H., et al. NFC mobile credit card: The next frontier of mobile payment? Telemat. Informat. (2013), http://dx.doi.org/10.1016/j.tele.2013.06.002

G.Wei-Han Tan et al. / Telematics and Informatics xxx (2013) xxx–xxx

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7.2. Psychological science constructs SI is another influential factor predicting MCC acceptance. This finding found support from TAM-related studies such as one by Lu et al. (2008). As envisaged by Lu et al. (2003), young consumers are usually vulnerable to environmental influences. Since the majority of respondents are young, their decisions whether to adopt certain technologies are likely to be influenced by the roles and opinions of their family members, friends, colleagues, superiors, and classmates (Lopez-Nicolas et al., 2008). This is evident from the results that MCC can help to enhance users’ social status among their friends (Moore and Benbasat, 1991; Teo and Pok, 2003). The findings on PIIT and MCC adoption is congruent with prior IT studies (Crespo and Rodriguez, 2008; Liu et al., 2010) where PIIT is a significant construct in influencing the intention to adopt a technology. Since the majority of respondents are highly educated and young, they are likely to be more confident and more willing to try a new technology which helps them to overcome the uncertainties associated with it. This explains why they have the tendency to develop a positive intention on adopt MCC. 7.3. Finance-related risks PR however is a non-significant construct in this study. The finding contradicts Lu et al.’s (2011) study on traditional MP solutions in China. One possible explanation is due to the respondents’ age where the majority of them are young users and therefore they may not understand the financial risks involved when adopting MCC. Further, their willingness to try a new technology such as MCC may have helped them to overcome the risks associated with it. Similarly, PFC was found to be a non-significant factor. The finding contradicts with the study by Lu et al. (2011) on MP solutions in China. According to Nicole et al. (2010), price is not an important indicator in the decision to adopt a certain technology for market segments of early adopters. The evidence is clear since MCC is a new trend which has yet to be fully explored by Malaysians. 7.4. Gender Finally, the moderating effect of gender is non-significant which implies that the intention of both the genders followed the same patterns equally. As most of the studies also revealed inconclusive results (Calisir et al., 2009; Lu et al., 2006; Venkatesh and Morris, 2000), the moderating effect of gender requires further investigation. 8. Implications From the theoretical perspective, the research has contributed to existing literature by considering NFC technology which has largely been neglected by current scholars. By providing insights on the factors contributing to MCC acceptance in Malaysia, the research has contributed from the viewpoint of developing markets. In addition, the study has successfully extended the TAM by including SI, PIIT, PR and PFC. The integrated model provides a clearer explanation on adoption intention than TAM alone. More importantly, the same model can be replicated or extended to different economies to determine whether the findings are similar or otherwise. Lastly, we have also contributed to the existing knowledge by incorporating the moderating effect of gender. In view of the high development cost in designing the infrastructure for MCC, it is important that the innovation be adopted. From the managerial standpoint, since PU influences MCC adoption, advertising campaigns by banking institutions should concentrate on the advantages provided by its adoption. These benefits may include time saving, convenience, reduction in cash handling, safety, and flexibility in terms of payment. The importance of PEOU implies that hand phone manufacturers should stress on the friendly features of MCC compared to traditional MP solutions when attempting to educate consumers. Banks should simplify the registration process for MCC use and improve the support provided to consumers through the availability of competent personnel. At best, such support should be made available both online and offline. Since SI is another significant factor in this research, banks could consider employing opinion leaders, word-of-mouth effects or celebrity endorsements. In view of the importance of image, advertising campaigns should factor into account a positioning strategy which could reflect on the upper class status of adopting MCC. In light with the findings on PIIT, the device providers should segregate the market and to tailor to the specific needs of this niche market with the appropriate services required. This is imperative because the services required by innovative consumers may differ from those of non-innovative consumers. Finally, since the moderating effect of gender is not significant in this study, marketing practitioners may not need to differentiate between the two groups when designing their marketing campaigns. 9. Limitation and future research Although rigorous statistical procedures are employed in this study, the paper is not without limitations. The first shortcoming relates to the overall research model. In order to achieve parsimony, the model was constrained only to the psychological science constructs, finance-related risks, and the TAM constructs. Other factors such as perceived trust, government support and so on are may be included in future research. The second limitation concerns the sample size. In view that data collection was geographically constrained; the extents to which the findings are generalizable to other environments remain Please cite this article in press as: Tan, G.-W.H., et al. NFC mobile credit card: The next frontier of mobile payment? Telemat. Informat. (2013), http://dx.doi.org/10.1016/j.tele.2013.06.002

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G.Wei-Han Tan et al. / Telematics and Informatics xxx (2013) xxx–xxx

ambiguous. A larger sample size, probably cross-national, is required in future studies. Thirdly, since the respondents are actually mobile phone and credit card users, by focusing exclusively on this group may pose potential bias since behavioral differences may be significant between users and non-users (Sarel and Marmorstein, 2003). This suggests that future research should consider non-users as well so that comparison can be made. Fourth, the study only takes into consideration the consumers’ perspective. The adoption rate of MCC is also dependable on the availability of MCC terminals and therefore future research should gather the merchants’ viewpoints. Fifth, the study was limited to the moderating effect of gender. Future studies could include age, experience, education, income (Loke et al., 2011; Amin, 2012) and so on in furthering their exploration on MCC. Lastly, this is a cross-sectional research. As Sim et al. 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