Determinants of consumers' intention to use credit card: a perspective of multifaceted perceived risk

Hoang Nam Trinh; Hong Ha Tran; Duc Hoang Quan Vuong Article Determinants of consumers' intention to use credit card: a perspective of multifaceted perceived risk Asian Journal of Economics and Banking (AJEB) Provided in Cooperation with: Ho Chi Minh University of Banking (HUB), Ho Chi Minh City Suggested Citation: Hoang Nam Trinh; Hong Ha Tran; Duc Hoang Quan Vuong (2020) : Determinants of consumers' intention to use credit card: a perspective of multifaceted perceived risk, Asian Journal of Economics and Banking (AJEB), ISSN 2633-7991, Emerald, Leeds, Vol. 4, Iss. 3, pp. 105-120, https://doi.org/10.1108/AJEB-06-2020-0018 This Version is available at: https://hdl.handle.net/10419/334036 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Determinants of consumers’ intention to use credit card: a perspective of multifaceted perceived risk Hoang Nam Trinh Faculty of Management Information Systems, Banking University of Ho Chi Minh City, Ho Chi Minh City, Vietnam Hong Ha Tran Faculty of Banking, Banking University of Ho Chi Minh City, Ho Chi Minh City, Vietnam, and Duc Hoang Quan Vuong Institute for Development Studies, Ho Chi Minh City, Vietnam Abstract Purpose –The purpose of this study is to develop a theoretical model for consumer behavioral intention by integrating the technology acceptance model (TAM) and the theory of perceived risk, which is tested on the intended use of credit cards in Vietnam. Design/methodology/approach –The data were collected from 485 bank customers through a nationwide online survey. An exploratory and confirmatory factor analyzes were performed to validate the factor structure of the measurement items while structural equation modeling was used to validate the proposed model and testing the hypotheses. Findings –The results of structural equation modeling reveal that perceived risk, perceived usefulness, social influence and perceived ease of use were significant determinants of consumer intention to use a credit card. Of them, only perceived risk discouraged the intended use of a credit card, which was synthesized from psychological, financial, performance, privacy, time, social and security risk. Research limitations/implications –This study measured the first-order risk dimensions based on the payment function of the credit card only; these measurements missed potential losses relevant to credit function of credit cards. Practical implications –This study can be beneficial to banks enacting policies to attract more consumers and to help decide how to allocate resources to retain and expand their customer base. Originality/value –The study adds value to the literature on consumer behavior by confirming the impact of second-order perceived risk on the intended use of credit cards, which most previous studies have not demonstrated. The research also provides an empirical evidence to the academic research platform on e-banking services in Vietnam, especially related to the credit card industry. Keywords Vietnam, Perceived risk, Behavioral intention, Credit card Paper type Research paper © Hoang Nam Trinh, Hong Ha Tran and Duc Hoang Quan Vuong. Published in Asian Journal of Economics and Banking. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode JEL classification –D12, D14, E42, G21 Consumers’ intention to use credit card 105 Received 23 June 2020 Revised 30 June 2020 Accepted 8 July 2020 Asian Journal of Economics and Banking Vol. 4 No. 3, 2020 pp. 105-120 Emerald Publishing Limited 2615-9821 DOI 10.1108/AJEB-06-2020-0018 The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2615-9821.htm Downloaded from http://www.emerald.com/ajeb/article-pdf/4/3/105/108530/ajeb-06-2020-0018.pdf by ZBW German National Library of Economics user on 16 December 2025 1. Introduction Credit cards, a combination of payment card and personal consumption credit, are widely used in around the world. Starting with a relationship between vendors and consumers, as well as a need to buy first and pay later, Franklin National Bank in New York, the USA, issued first-ever credit cards to market in 1951. Year after year, the rapid development of consumer demand for credit cards exceeded the bank’s responsibility and management capacity. Consequently, many international credit card organizations have been established and operated independently around the world with six famous brands including American Express, Diners Club, Japan credit bureau, Visa, MasterCard and Chinese union pay. Banks join these institutions and are licensed to issue and acquire credit cards. To expand the credit card market segment, banks are constantly issuing cards to new customers and encouraging existing customers using them in daily spending. Based on practical requirements, many researchers are interested in consumer intended and actual use of credit cards. Studies of consumer behavior on credit cards have mainly focused on the decisive role of individual demographic characteristics, credit card attributes and personal perception about credit cards. Some authors proved that differences in demographics such as age, gender, occupation and financial status lead to differences in his intention to use credit cards (Dewri et al., 2016;Foscht et al.,2010;Porto and Xiao, 2019). Others have confirmed that consumers decide to use credit cards because of their advantages compared to other payment methods such as cash, e-money or debit card (Chahal et al., 2014;Ooi and Tan, 2016;Qureshi et al., 2018). Assuming consumers are always rational in their behavior (Fishbein and Ajzen, 1975), some authors believed that a person decides using credit cards because of their ability to finance his daily expenses effectively (Porto and Xiao, 2019;Tan et al.,2014;Trinh and Vuong, 2017). Moreover, some empirical studies have highlighted that social groups such as family, friends and colleagues have a significant influence on consumer intended use of credit cards (Ali et al., 2017;Amin, 2013;Tan et al., 2014;Varaprasad et al.,2013). Reasonable consumers are not only interested in the benefits of using a credit card but also they care about their potential losses (Fishbein and Ajzen, 1975;Mitchell, 1999). Many authors agreed that perceived risk is a major barrier to the intended use of e-services (Roy et al., 2017;Yang et al.,2015). Similarly, perceived risk has been considered as a deciding factor for the intention to use credit cards (Nguyen and Cassidy, 2018;Tan et al.,2014; Tseng, 2016;Varaprasad et al., 2013). However, their outcomes were inconsistent; perceived risk had significantly negative impact (Nguyen and Cassidy, 2018), significantly positive influence (Varaprasad et al.,2013) or insignificant effect on consumer intended use of credit cards (Tan et al.,2014;Tseng, 2016). As the credit card market becomes more competitive, a better understanding of consumer behavior becomes imperative for banks. However, unlike previous research studies, this study focuses on the impact of perceived risk on the intended use of credit cards. To achieve this goal, the study begins with a brief review of consumer behavior. As a result, a theoretical model and testable hypotheses are developed, followed by the methodology and data collected. The findings are described and discussed before making some conclusions, as well as future research directions. 2. Literature review and proposed theoretical model 2.1 Literature review Several research frameworks have been developed over the years to explain consumer intended and actual behavior. Prominent among them, theory of perceived risk (TPR) (Bauer, 1960) focuses on how consumers are concerned about the potential losses that influence on their intention in a specific purchase situation. However, consumers are not AJEB 4,3 106 Downloaded from http://www.emerald.com/ajeb/article-pdf/4/3/105/108530/ajeb-06-2020-0018.pdf by ZBW German National Library of Economics user on 16 December 2025 only risk averse but also rational; they intent to do something when they find this behavior useful, easy to do or they are encouraged by influencers, which are inherited from theory of reasoned actions (Fishbein and Ajzen, 1975), technology acceptance model (TAM) (Davis et al.,1989), theory of planning behavior (TPB) (Ajzen, 1991) or unified theory of acceptance and use of technology (UTAUT) (Venkatesh et al.,2003). These theories are applied independently or together in many studies on consumer intended use of e-services (Alalwan et al.,2017;Liu et al., 2019;Pelaez et al.,2019;Tam and Oliveira, 2017). Credit card is a technology product, used on electronic devices with two basic functions, namely, payment and credit (Foscht et al., 2010). Credit cardholder can buy first, pay later based on the bank’s commitment (Amin, 2013). Accordingly, the issuing bank will pay the biller on behalf of the cardholder, who is responsible for returning full and timely (Foscht et al., 2010). In modern commerce, credit cards are becoming increasingly important and popular all over the world (Porto and Xiao, 2019). Studies on credit cards are conducted and published in prestigious scientific journals, in which perceived risk from TPR, perceived usefulness from TAM/UTAUT, perceived ease of use from TAM/TPB/UTAUT and social influence from TPB/UTAUT are frequently used to predict consumer intended use of credit cards. These concepts are briefly described as followed: Perceived usefulness was proposed as the degree to which a person believes that using a particular system would enhance his/her performance (Davis et al.,1989;Venkatesh et al., 2003). Credit cards are appreciated for non-cash payments and personal consumer credit (Chahal et al., 2014). Consumers prefer credit cards due to uncertainty when carrying cash (Khare et al.,2012) or special discounts from famous brands (Dali et al., 2015). They use credit cards as a source of revolving credit with long grace period (Chahal et al.,2014;Khare et al.,2012). They can even withdraw cash by credit cards as required (Chahal et al.,2014). As a result, consumer appreciate the performance of credit card usage, so they are more likely to use it in their daily expenses (Amin, 2013;Nguyen and Cassidy, 2018;Ooi and Tan, 2016;Trinh and Vuong, 2017;Varaprasad et al.,2013). Ajzen (1991) and Davis et al. (1989) considered perceived ease of use as the degree to which a person believes that using a particular system would be easy. Ajzen (1991) assumed that this perception is determined by a total set of accessible control beliefs. Qureshi et al. (2018) stated consumers are easy to register a credit card with a quick and simple procedure. Chahal et al. (2014) and Dali et al. (2015) posited credit card’s non-stop usability in numerous electronic devices. Moreover, the credit card payment process is so simple that cardholders do not need much effort to learn and use it regularly (Khare et al., 2012). Consequently, many studies have confirmed that consumers appreciate credit cards and tend to use them for daily (Ali et al.,2017;Amin, 2013;Nguyen and Cassidy, 2018;Porto and Xiao, 2019;Trinh and Vuong, 2017;Tseng, 2016). Social influence referred to a degree to which a consumer perceives that important people believe that he/she should or should not perform a particular behavior (Ajzen, 1991; Venkatesh et al.,2003). Consumers are irresistible to observe and evaluate credit card features, they feel uncomfortable when their friends, colleagues always use and talk about them (Qureshi et al.,2018). Amin, 2013 argued that consumers tend to acquire and imitate the financial attitudes behaviors of family members. Moreover, media, which is designed specifically to reach a large audience or viewers has contributed to raising consumer awareness about credit cards (Ali et al.,2017). Empirical evidence suggested that social groups’perspective may enhance one’s intended use of credit cards (Ali et al.,2017;Amin, 2013;Nguyen and Cassidy, 2018;Trinh and Vuong, 2017;Varaprasad et al.,2013). However, Leong et al. (2013) suggested that social influence only effects indirectly on the intended use of credit cards through perceived usefulness and perceived ease of use. Consumers’ intention to use credit card 107 Downloaded from http://www.emerald.com/ajeb/article-pdf/4/3/105/108530/ajeb-06-2020-0018.pdf by ZBW German National Library of Economics user on 16 December 2025 Perceived risk, in consumer behavior perspective, refers primarily to consumer subjective expectations for incident losses (Bauer, 1960;Featherman and Pavlou, 2003). Consumers are granted a credit line to pay their bills, and they must spend a lot of time, money and effort to use it safely and effectively (Chahal et al., 2014;Yang et al.,2015). However, their payments are not always successful because of operational breakdowns or system malfunctions (Varaprasad et al.,2013). Meanwhile, the losses of personal privacy and system security are serious and consumers may be accounted until the authorities clarify the responsibilities of stakeholders (Tan et al., 2014;Tseng, 2016). As a result, consumers are less like to use credit cards when they are deeply concerned about their uncertainty (Nguyen and Cassidy, 2018). However, some studies found that user’scredit card adoption is not from how they perceives the losses caused by its use (Tan et al.,2014; Tseng, 2016). Varaprasad et al. (2013) argued that the bank’s efforts make consumers choose credit cards even if they are afraid of un-expectations caused by this type of payment instrument. Despite some differences, most of these studies have shared a one-dimensional approach to perceived risk on credit cards. This approach refers perceived risk as a common perception, defined by several observed variables, and therefore, does not reflect consumer valuation of different types of potential losses relevant to credit card use. 2.2 Proposed research model Based on the above review about consumer behavior and prior studies on the intention to use credit cards, the study proposes a theoretical model of the intended behavior by integrating some prominent adoption theories. The model suggests perceived risk, usefulness, ease of use and social influence as exploratory factors to predict consumer intended use of credit cards. These constructs and their hypotheses are described below: Perceived usefulness is one of the most important factors in TAM and has been studied comprehensively as a main determinant of consumer adoption of modern electronic services, including e-shopping (Chhonker et al.,2017), e-payment (Liu et al.,2019) and e-banking (Zhang et al., 2018). In the context of credit cards, perceived usefulness can be considered as an indicator of the degree to which a person believes that using a credit card would enhance his/her payment for daily expenses. Empirical evidences showed that perceived usefulness plays an important role in consumer intended use of credit cards (Leong et al., 2013;Nguyen and Cassidy, 2018;Tan et al.,2014;Trinh and Vuong, 2017;Tseng, 2016;Varaprasad et al., 2013). Therefore, this study hypothesizes that: H1. Perceived usefulness affects positively the intention to use credit cards. Consumers are rational, who are not only interested in benefits but also in losses whenever they make decision, especially for those behaviors, which they cannot see or touch, just feel only how they work. These concerns are mentioned as the risk perceptions, which were first proposed in TPR (Bauer, 1960). Nowadays, this concept becomes more seriously in the context of e-services, where data are transferred between connected e-devices. Such e-transactions are invisible to consumers, who may be faced to unexpected outcomes and this may prevent them to perform behaviors. Some literature reviews about perceived risk are conducted in technology adoption, including e-shopping (Pelaez et al.,2019), e-payment (Patil et al.,2018) and e-banking (Mutahar et al.,2018). Among many approaches of using perceived risk in studies on consumer intended use of technology, (Featherman and Pavlou, 2003;Hanafizadeh and Khedmatgozar, 2012) summarized perceived risk is situation specific and is considered as a second-order factor, which is commonly formed by performance, financial, social, time, psychological, security, privacy factors (Table 1). This approach has AJEB 4,3 108 Downloaded from http://www.emerald.com/ajeb/article-pdf/4/3/105/108530/ajeb-06-2020-0018.pdf by ZBW German National Library of Economics user on 16 December 2025 been used in many empirical studies (Martins et al., 2014;Mutahar et al., 2018;Tandon et al., 2016;Yang et al.,2015). As such, this study hypothesizes that: H2. Perceived risk is a second-order construct of seven first-order risks, including financial, performance, psychological, social, time, security and privacy risk. H2a-g. Financial, performance, psychological, social, time, security and privacy risk perception have positively related to perceived risk. Perceived risk, the main construct of TPR, is often considered as a main barrier of consumer intention to use e-services. Its negative effect on behavioral intentions has been confirmed in e-services (Cao and Niu, 2019;Martins et al.,2014;Mutahar et al.,2018;Roy et al., 2017; Tandon et al., 2016). These researchers agreed that the more consumers’aversion to potential losses are lowered, the more they are likely to adopt e-services. They also found that only individuals who perceive using an e-service as a low-risk undertaking would have a tendency to perceive it as useful. Therefore, this study hypothesizes that: H3. Perceived risk affects negatively perceived usefulness on credit cards. H4. Perceived risk affects negatively the intention to use credit cards. Perceived ease of use is another important factor in TAM and has been mentioned as a main antecedent of consumer intended use of modern electronic services, including e-shopping (Chhonker et al.,2017), e-payment (Liu et al., 2019) and e-banking (Zhang et al., 2018). Based on original TAM (Davis et al., 1989), this study describes credit card’s perceived ease of use as the perception of complexity to learn and use for potential customers when adopting to a credit card. Empirical evidences showed that perceived ease of use have a direct effect on the intended use of credit cards or indirect influence by mediating the perceived usefulness (Leong et al., 2013;Nguyen and Cassidy, 2018;Tan et al., 2014;Trinh and Vuong, 2017; Tseng, 2016;Varaprasad et al.,2013). Therefore, this study hypothesizes that: Table 1. Multi-dimensional perceived risk Dimension of perceived risk Definition FIR Potential financial losses due to purchasing a subscription to a poorly performing eservice or potential internet-based fraud PER Potential performance problems, malfunctioning, transaction processing errors, reliability and/or security problems, and therefore, not performing as expected SOR Potential losses to their perceived status in their social group as a result of using an eservice PSR Potential losses to their self-esteem, peace of mind or self-perception (ego) due to worrying, feeling frustrated, foolish or stressful as a result of using an e-service TIR Potential losses to convenience, time and effort caused by wasting time researching, purchasing, setting up, switching to and learning how to use the e-service SER Potential losses involving transmitting sensitive data through e-services that breach technological data protection PRR Potential losses to the privacy and confidentiality of their personally identifying information and that e-service usage exposes them to potential identity theft Sources: Featherman and Pavlou (2003);Hanafizadeh and Khedmatgozar (2012) Consumers’ intention to use credit card 109 Downloaded from http://www.emerald.com/ajeb/article-pdf/4/3/105/108530/ajeb-06-2020-0018.pdf by ZBW German National Library of Economics user on 16 December 2025 H5. Perceived ease of use affects positively perceived usefulness on credit cards. H6. Perceived ease of use affects positively the intention to use a credit card. Social influence was proposed in TPB (Ajzen, 1991), UTAUT (Venkatesh et al., 2003) and became an indispensable construct in studies on technology adoption (Chhonker et al.,2017;Liu et al.,2019; Zhang et al., 2018). As a member of a community, consumers are very influenced by the surrounding friends, colleagues, especially who are important to them. These influences may be great motivation in the early stages of adoption, when consumers have a limited knowledge or experience of a new technology (Venkatesh et al.,2003). This study supposes social influence as a degree to which consumer perceives that important others such as family, peers and colleagues believe he/she should adopt and use a credit card in daily consumption. Empirical studies confirmed that consumers are directly influenced by social groups in their behavioral intention (Cao and Niu, 2019;Malaquias and Hwang, 2019;Martins et al.,2014;Sripalawat et al.,2011). Meanwhile, Liébana et al. (2017) and Pelaez et al. (2019) found that consumer’s perception on new technology’s performance may change depending on whether his important influencers appreciate it, then, in turn, their opinions encourage him adopting this technology. Therefore, this study hypothesizes that: H7. Social influence affects positively perceived usefulness on credit card. H8. Social influence affects positively intended use of credit card. Based upon above discussions, a theoretical model is developed to predict consumer intended use of credit cards with four explanatory factors, including perceived usefulness, perceived risk, perceived ease of use and social influence, where perceived risk is a secondorder construct related to seven first-order risk dimensions, including financial, performance, social, psychological, time, security and privacy risk (Figure 1). 3. Methodology The empirical data for this study are obtained through an online survey, which were based on our review of prior studies relevant to the proposed theoretical model. Some expressions were customized to fit the context of credit cards. The research was anchored on a five-point Likerttype scale measurement varying from “1 (strongly disagree)”to “5 (strongly agree).”Apre-test was also performed with five banking experts with a background on credit cards to ensure that the questionnaire has no semantic problems. Some modifications of content and structure were amended based on the provided feedback. The instruments were then further pilot-tested with 15 consumers, who have experienced in using credit cards for paying bills. Insignificant changes were made to the wordings resulted from the tests. A final questionnaire focuses on 11 first-order constructs corresponding to the proposed model with 46 questions asked (Table 2). The survey was conducted by using 724 respondents selected through convenient sampling of Vietnamese bank customers, who are potential customers encouraged by the bank to register and use credit cards. Only 485 responses were valid and usable, yielding a valid response rate of 67% among volunteered participants. With 46 observed variables, the required sample size is from 138 to 230 (Cattell, 1978). The data from 485 respondents are, therefore, compatible. Based on collected data, both exploratory factor analysis and confirmatory factor analysis (CFA) are conducted to select and arrange the significant variables to particular factors (Byrne, 2010;Hair et al.,2014). Finally, structural equation modeling is used for building the model of determinants of the intention to use credit cards (Anderson and Gerbing, 1991;Byrne, 2010). AJEB 4,3 110 Downloaded from http://www.emerald.com/ajeb/article-pdf/4/3/105/108530/ajeb-06-2020-0018.pdf by ZBW German National Library of Economics user on 16 December 2025 4. Findings 4.1 Profile of respondents and intention to use credit cards The data presented in Table 3 provides the demographic details on a gender, marital status, occupation, age and highest level of academic qualification of the respondents. These controlled variables are considered in this study based on prior studies relevant to consumers’intended use of credit cards. Prior studies supposed that the differences in these Table 2. Questionnaire source and number of items Constructs No. of items Sources Perceived usefulness (PU) 7 Trinh and Vuong (2017) Perceived risk (PR) FIR 4 Hanafizadeh and Khedmatgozar (2012) PER 4 Yang et al. (2015) SOR 4 Yang et al. (2015) PSR 3 Yang et al. (2015) TIR 3 Yang et al. (2015) SER 4 Hanafizadeh and Khedmatgozar (2012) PRR 4 Hanafizadeh and Khedmatgozar (2012) Perceived ease of use (EOU) 5 Trinh and Vuong (2017) Social influence (SI) 4 Trinh and Vuong (2017) Intention to use credit card (IU) 4 Trinh and Vuong (2017) Figure 1. Proposed theoretical model Consumers’ intention to use credit card 111 Downloaded from http://www.emerald.com/ajeb/article-pdf/4/3/105/108530/ajeb-06-2020-0018.pdf by ZBW German National Library of Economics user on 16 December 2025 demographic characteristics may lead to the differences in the intention to use credit cards (Dewri et al.,2016;Porto and Xiao, 2019;Qureshi et al.,2018). Of our samples, majority of the respondents are male (51.3%), married (61.4%) compared to female (48.7%) and single (38.6%). Survey participants are mostly young adulthood with 73% of them below the age of 45. The results also show that 20.5% of respondents have college education; 44.7% of them are graduated and 34.8% remaining are post-graduated. Regarding the respondents’occupation, their largest proportion belongs to public services (30.5%), followed by trading services (26.4%), financial services (25.4%) and industries (15.1%). However, the one-way ANOVA tests in comparing means of intention to use credit card insist that there is no significant difference between independent groups divided by these demographic variables, which is inconsistent to prior studies (Dewri et al.,2016;Porto and Xiao, 2019;Qureshi et al.,2018). 4.2 Factor analysis Applying exploratory factor analysis on data collected from survey questionnaires, 10 factors are extracted from 39 observed variables, except PU4, FIR1, SOR1, which are eliminated from the analysis because its loading factors are less than 0.5 (Hair et al.,2014). These extracted factors are suitable to the proposal model (Table 4). The Kaiser-Meyer-Olkin measure coefficient is 0.847 with a statistical significance of 0.000, indicates that the exploratory factor analysis (EFA) of the Table 3. Descriptive statistics and mean comparative analysis Variable Freq. 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Corresponding author Hoang Nam Trinh can be contacted at: [email protected] For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected] AJEB 4,3 120 Downloaded from http://www.emerald.com/ajeb/article-pdf/4/3/105/108530/ajeb-06-2020-0018.pdf by ZBW German National Library of Economics user on 16 December 2025