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Determinants of Intention to Borrow Consumer Credit in Vietnam: Application and Extension of Technology Acceptance Model

  • HOANG, Van Hai (School of Business Administration, University of Economics and Business, Vietnam National University) ;
  • NGUYEN, Phuong Mai (Department of Social Sciences, Economics and Management, International School, Vietnam National University) ;
  • LUU, Thi Minh Ngoc (Human Resource Management Department, School of Business Administration, University of Economics and Business, Vietnam National University) ;
  • VU, Thi Minh Hien (Marketing Department, School of Business Administration, University of Economics and Business, Vietnam National University)
  • Received : 2020.12.20
  • Accepted : 2021.03.15
  • Published : 2021.04.30
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Abstract

The purpose of this study is to examine the determinants of intention to borrow consumer credit of Vietnamese people by applying the Technology Acceptance Model (TAM) and extending it with several variables, including anxiety, perceived trust, and perceived financial costs extracted and adapted from the existing literature. A questionnaire survey was administered in the big cities of Vietnam to a total of 602 consumers. Structural equation modeling (SEM) techniques have been employed to investigate the relationship among intention determinants to borrow. Findings show that perceived usefulness mediates the impact of subjective norms on the intention to borrow consumer credit. At the same time, subjective norms also directly influence the intention to borrow. Notably, anxiety, perceived trust, perceived financial cost, perceived ease of use have no significant influence on intention to borrow. Meanwhile, education level is confirmed to have a moderate influence on intention to borrow consumer credit of Vietnamese people. However, there is not enough statistical evidence about the influence of gender and marital status on the intention to borrow consumer credit in Vietnam. Based on the findings of the Vietnamese consumer credit market, we proposed some suggestions to promote the growth of the market in the future.

Keywords

1. Introduction

Over the years, the world consumer finance market has experienced inevitable ups and downs, but maintained relatively positive growth. According to EuroMonitor, the personal consumer credit market has been significantly growing with an annual gross lending volume increase of 20%. The worldwide consumer finance market’s size is expected to grow to nearly $14 trillion by 2022. It is observed that the growing demand of individuals and households for unsecured debt solutions has inevitably driving the enormous expansion of the consumer finance market (Vandone, 2009a).

More particularly, consumer credit has been booming in 1990s worldwide. In OECD countries, household debt as a household income ratio grew from 78% to 96% during the period from 1995 to 1999 (Christensen & Mathiasen, 2002). In post-communist transition countries, despite its novelty, the growth rate of consumer credit was incredibly impressive (Cottarelli, Dell’Ariccia, & Vladkova-Hollar, 2005). Between 1997 and 2001, the annual growth rate of consumer lending reached 26% in Poland, Hungary, and the Czech Republic. At the same time, other countries in the region, which had lower starting point, but produced even more attractive growth rate (Rona-Tas, 2003). Even in less prosperous countries like China and Vietnam, banks in the post-communist world see consumer lending as the next new financial frontier (Vandone, 2009b). Not only mortgages but also purchase credit, credit cards, and auto loans have been widely advertised that attract many people apply for them to finance their current purchase with future payment (Vandone, 2009a).

In Vietnam, the consumer finance market has recently become more active and increasingly motivated by the high demand. If in the 2011–2014 period, the annual growth rate of this market only fluctuated at 30%, it accelerated to 59% in 2015. According to the National Financial Supervisory Commission report, in 2017 alone, consumer finance grew at 65%, surpassed the 50.2% growth in 2016, and far exceeded the annual growth rate of 19% for general credit. With over 92 million people, most of whom are young, the Vietnamese consumer finance market is considered one of the world’s most potential markets. The Vietnamese market’s projected value may reach $15 billion annually and mainly comes from the target market, with approximately 30 million people aged from 20 to 60 years old (StoxPlus, 2016). It is observed that people are changing their consumption habits from shopping based on savings to borrowing to finance their demand.

Unfortunately, few studies analyze the intention to borrow consumer credit in the Vietnamese context. The behavioral intention of Vietnamese borrowers in the consumer credit market has not been explained adequately. In such regard, this study investigates the Vietnamese people’s intention to borrow consumer credit and highlights the key findings for financial institutions.

This paper is structured as follows. Section 1 is the introduction to the topic. Section 2 provides an overview of related studies and hypotheses. Section 3 presents the research model, measurement instruments, sampling method. Our empirical research results and implications are discussed in section 4. Finally, section 5 mentions the contributions of this study, the limitations, and suggestions for further research.

2. Literature Review and Hypothesis Development

2.1. Consumer Credit

Consumer credit has existed for 4000 years, but just has become widespread for these recent 50 years with the advent of credit cards and the growth in homeownership and mortgage loan (Thomas, 2010). While the consumer credit bubble has expanded internationally over the last two decades, it is rooted in the 1920s in the United States, where technological developments have produced a modern mode of consumption (Olney, 1991).

Generally, credit is defined as the granting of goods, services, or money in return for a promise of future payment (Kamleitner & Kirchler, 2007). Regarding consumer credit, this concept focuses on credit obtained [by private households] to finance any purchase other than property (Guardia, 2002). Thus, consumer credit refers to the debts (credits) of consumers involved in buying consumers’ goods to consume themselves and their families, except mortgage debt (Westerfield, 1938).

Basically, consumer credit comprises two major forms: installment and non-installment credit (Guardia, 2002). Installment debt and credit card debt exemplify these two forms. In the case of an installment loan, referred to as closed-end credit, the amount borrowed must be repaid in a specific amount of equivalent payments. This type of credit is characterized by the contractual obligation to use the loan to purchase of a specific product or service (Guardia, 2002), so that it is frequently offered to the consumers at the point of sale. With non-installation, also known as open-ended credit, the credit is extended in advance of any purchase, so that customers do not have to re-apply each time the credit is requested. The sum owing can be repaid in full or by a series of equivalent or unequal payments, typically on a monthly basis. This type of credit includes bank credit cards, travel and entertainment accounts, service credit, and other charge accounts (Tufano, 2009). When using consumer credit, people can purchase products financed by their future income. Consequently, they do not need to wait until saving enough money (Jia, Xue, Fu, & Xu, 2018).

The term “consumer credit” in this research refers to all types and degrees of consumer debt, except for mortgage loans.

2.2. Intention to Borrow Consumer Credit

Behavioral purpose is a presupposition that explicitly results in a particular behavior. Behavioral intention is a joint function of the attitude toward performing a particular behavior in a given situation and the norms perceived to govern that behavior multiplied by the motivation to comply with those norms (Fishbein, 1967). The normative component includes both the individual’s personal beliefs about what he should do in a given situation and his perception of others’ expectations about his behavior in that situation, i.e., social normative beliefs (Ajzen & Fishbein, 1972; Fishbein, 1967).

In this study, behavioral intention (INT) refers to the individual’s willingness to borrow consumer credit.

2.3. Determinants of Intention to Borrow Consumer Credit

2.3.1. Perceived Trust

Trust allows the expression of an assumption or beliefs of a person’s future behavior based on previous experiences and another person’s characteristics (Mayer, Davis, & Schoorman, 1995). Studies about the role of trust have been conducted in a variety of fields including e-commerce, e-payment, mobile banking. Thus, trust may be founded on a fair evaluation of the individual’s capacity and honesty and feelings of concern and benevolence. Trust is therefore a multi-dimensional definition.

In the consumer credit market, perceived trust is the degree to which consumers have attitudinal confidence for reliability, credibility, safety, and integrity of financial service providers. Mansour, Kooli, and Utama (2014) confirmed that trust positively affects buying behavior. Besides, trust is the foundation for customer loyalty and encourages repeated purchases in the future (Choi & Mai, 2018).

In this study, we also assume that perceived trust would influence the intention to borrow consumer credit. Consequently, we formulate the following hypothesis:

H1: Perceived trust (PT) positively influences intention to borrow consumer credit (INT).

2.3.2. Perceived Financial Cost

Perceived financial cost (PFC) is defined as the degree to which individuals are concerned with financial costs, such as service fees when using a product or service (Nguyen & Cassidy, 2018). The relationship between PFC and purchasing intention has been addressed in previous studies about mobile banking and credit card. For example, mobile banking adoption was proved to be discouraged by economic considerations (Yang, 2009). Similarly, Yu (2012) found that the PFC had a major effect on the intentions of future customers of mobile banking. Other studies have also supported this relationship (Butt, Rehman, Saif, & Safwan, 2010; Seetharaman, Patwa, Niranjan, & Kavuri, 2016; Tan, Ooi, Chong, & Hew, 2014).

Our study aimed to examine the impact of PFC on the intention to borrow consumer credit. Thus, this study hypothesized as follows:

H2: Perceived financial cost (PFC) negatively influences intention to borrow consumer credit (INT).

2.3.3. Anxiety

Anxiety (AN) determines the degree to which a person becomes anxious when it comes to action. Anxiety has been mentioned in previous studies about e-commerce, technological products, online banking service. Doyle, Stamouli, and Huggard (2005) indicated that computer anxiety had a significant effect on self-efficacy. Similarly, Yeow, Yuen, Tong, and Lim (2008) found that customers’ anxiety would decrease if individuals had more experience with the online banking service.

This paper hypothesizes that anxiety would negatively affect an individual’s intention to borrow consumer credit. Thus, we develop the following hypothesis:

H3: Anxiety (AN) negatively influences intention to borrow consumer credit (INT).

2.3.4. Perceived Usefulness and Perceived Ease of Use

Perceived usefulness (PU) and perceived ease of use (PEOU) are two main factors in the Technology Acceptance Model (TAM). Since its first introduction in 1989, TAM has been extensively used in many studies to predict the consumers’ intention behavior when choosing technology-related products such as information systems, payment systems, mobile banking, credit card.

According to Davis, Bagozzi, and Warshaw (1989) perceived usefulness (PU) is the degree to which a person believes that using a particular system will enhance their performance. Meanwhile, perceived ease of use (PEOU) is considered the extent to which a person believes that using a product or service would be free of effort (Davis et al., 1989).

PU is a strong determinant of usage intentions (Venkatesh, Morris, Davis, & Davis, 2003). In the context of e-banking adoption, PU refers to several attributes such as transaction speed, user-friendliness, user experience, accuracy, convenience, and the like. Thus, PU can be measured as a multi-dimensional quality factor (Liao & Cheung, 2002). Furthermore, PU is an essential factor for technology acceptance and sometimes has a substantial impact on usage behavior (Jahangir & Begum, 2008). Regarding consumer credit, PU means that the financial service providers can be available and helpful for customers on a 24/7 basis (Bugembe, 2010). In other words, the financial service is believed to be useful when it satisfies the needs of people and is closely related to productivity, and effectiveness, such as the improvement of financial management skill, time management, and satisfaction from the convenience in purchasing goods with the support of consumer credit service.

PEOU refers to the easy access to credit institutions’ processes and procedures when choosing consumer credit services. PEOU has been considered in many studies about consumer behavior and determines user adoption of a new product or service such as internet-based banking, mobile banking, credit card, intention to borrow credit (Bugembe, 2010; Mai, Ngoc, & Dzung, 2019; Nguyen & Lien, 2019; Nguyen & Cassidy, 2018; Venkatesh et al., 2003).

It is argued that PU and PEOU will predict an individual’s intention to borrow consumer credit. Moreover, PEOU is believed to affect PU (Venkatesh & Davis, 2000). Therefore, three hypotheses were developed as follows:

H4: Perceived ease of use (PEOU) positively influences perceived usefulness (PU).

H5: Perceive the ease of use (PEOU) positively influences intention to borrow consumer credit (INT).

H6: Perceived usefulness (PU) positively influences intention to borrow consumer credit (INT).

2.3.5. Subjective Norms

Subjective norm (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 (Ajzen, 1991). Subjective norm is a direct determinant of behavioral intention in the Theory of Reasoned Action (TRA) and the subsequent Theory of Planned Behavior (TPB).

The direct effect of SN on intention is initiated because people may choose to take an action if they believe one or more critical referents think they should. Even in some cases, SN motivates an individual to intend to act even if the individual himself does not have a positive attitude about this action (Venkatesh, Thong, & Xu, 2012). In this paper, SN represents the degree to which an individual perceives that meaningful and relevant others believe they should borrow consumer credit.

The significant positive effect of SN on behavior intention has been addressed in previous research (Venkatesh et al., 2003; Yeow et al., 2008; Yu, 2012). However, some studies showed no direct effect of SN on an individual’s intention (Davis, 1989; Mathieson, 1991), whereas Taylor and Todd (1995) explored a significant effect.

In addition, Venkatesh and Davis (2000) introduced the second generation of the model called TAM2 to explain how SN affect PU and intentions. It is assumed in TAM2 that, in a computer usage context, the direct effect of SN on intention over and above PU and PEOU, will occur in mandatory but not voluntary (Venkatesh & Davis, 2000). Moreover, SN also has positive impact on consumers’ PU (Venkatesh & Davis, 2000; Zhang, Zhu, & Liu, 2012), and the consumers’ PEOU of using a technology system (Abramson, Dawson, & Stevens, 2015). More specifically, Nguyen and Cassidy (2018) in a study about credit card adoption in Vietnam, confirmed that SN influences behavioral intention both directly and indirectly through PU and PEOU.

From previous studies, we adopted TAM to test the effect of SN on the intention to borrow consumer credit through PU and PEOU. Therefore, three hypotheses were developed as follows:

H7: Subjective norms (SN) perceived usefulness (PU).

H8: Subjective norms (SN) perceived ease of use (PEOU).

H9: Subjective norms (SN) positively influences intention to borrow consumer credit (INT).

2.4. Demographic Factors and Intention to Borrow Consumer Credit

The demographic variables have been put into consideration to explain consumer credit attitude and behaviors (Lown & Ju, 1992). As regards the impact of gender on the intention to borrow consumer credit, studies showed substantial differences between the degree of participation of men and women in consumer credit borrowing (Schor, 1999). Gender has been put into much research of intention to borrow consumer credit either as a controlling variable or a mediator. In this study, we assumed that gender controls the relationship between antecedents and intention to borrow. Based on the above arguments, we formulated the following hypothesis.

H10: Gender (GEN) positively influences intention to borrow consumer credit (INT).

Besides, the educational background may also affect how people choose consumer credit services to a certain extent. According to the study by Zhu and Meeks (1994), the interactive impact of higher education and a more desirable specific approach to credit led to a tremendous amount of credit outstanding in 1986. It is confirmed that well-educated and high financial conscious consumers might have a higher demand for credit and more access to credit information (Chavali, Raj, & Ahmed, 2021; Chien & Devaney, 2001; Danes & Hira, 1990). They discovered that respondents who claimed that credit cards should be used for installment purposes were more likely to use more credit cards and accumulate finance charges more frequently.

Thus, the following hypothesis was formed.

H11: Education background (EDU) positively influences intention to borrow consumer credit (INT).

Furthermore, some studies have insisted on the positive relationship between marital status and consumer credit behavior, such as the studies of Chien and Devaney (2001), Kennickell, Starr-McCluer, and Surette (2000). In this study, we also argued that marital status would positively affect borrowing consumer credit. Thus, hypothesis 12 was developed as follows:

H12: Marital status (M_Stat) positively intention to borrow consumer credit (INT). influences

3. Research Methodology

3.1. Research Model

The research model in this study was adapted and extended from the Technology Acceptance Model (TAM) to examine the factors that influence intention to borrow consumer credit in Vietnam. As TAM has been widely used in explaining individuals’ attitudes and behaviors in many contexts, we believe that the model might be applied and extended in the Vietnamese context.

The dependent variable in this research model was the intention to borrow consumer credit (INT). Meanwhile, independent variables in this research model include perceived trust (PT), perceived financial cost (PFC), anxiety (AN), subjective norms (SN), perceived usefulness (PU), and perceived ease of use (PEOU).

Some controlling variables in the model were gender, education level, and marital status. Figure 1 gives details of the research model.

Figure 1: Research Model

3.2. Measurement Instruments

This research’s constructs were adapted from existing literature related to consumer credit services, mobile banking, and credit cards. A 5-point Likert scale was applied to measure all variable items ranging from 1 = strongly disagree to 5 = strongly agree.

In this study, the intention to borrow consumer credit was measured on three items adapted from the study of Venkatesh and Davis (2000).

Besides, six independent variables were included in the research model. TAM variables were perceived usefulness (four items) and perceived ease of use (four items). These two measurements were adapted from the previous studies of Davis (1989) and Venkatesh et al. (2003). Subjective norms (four items) were adopted from the studies of Venkatesh and Davis (2000), Abramson et al. (2015), and Lim et al. (2016). Moreover, perceived trust (four items) was adapted from the study of Salloum and Al-Emran (2018). Perceived financial cost (three items) was modified from the studies of Butt et al. (2010), Yu (2012), Nguyen and Cassidy (2018). In this study, anxiety is also included as a determinant of intention to borrow consumer credit. This variable has three items adapted from the studies of Compeau and Higgins (1995), Phau and Woo (2008), Nguyen and Cassidy (2018).

Based on all constructs mentioned above, the questionnaire was designed and presented in an unbiased and straightforward wording manner, whereby respondents could easily understand the questions and provide the answer based on their perception.

3.3. Sampling Method

The population of this study includes Vietnamese citizens who are over 18 years old. We choose a sample of citizens living in big cities of Vietnam, including Hanoi, Hai Phong, Thanh Hoa, Da Nang, Ho Chi Minh City, due to these areas’ high density of financial service providers and local economic growth.

We conducted a questionnaire survey by using two methods. For the online method, we sent a Google Form-based questionnaire to potential respondents through the authors’ friend circle. At first, 100 initial people were randomly picked up from the authors’ friends on social networks, and the online questionnaire was sent to them. Then, these initial respondents were encouraged to spread our survey through their circles of friends. The questionnaire was delivered in public places such as supermarkets, parks, and electronic stores for the offline method in selected cities.

After six months, we received 610 responses from both online and offline channels, and 602 responses were valid; eight responses were removed due to the lack of some information. SPSS and AMOS version 22 was employ for data analysis. The sample characteristics are presented in Table 1.

Table 1: Sample Characteristics (n = 602)

4. Results and Discussions

4.1. Preliminary Analysis

Firstly, we performed the Cronbach’s alpha test for seven constructs to verify the reliability of the measurement instruments. Results showed that all scales have moderate to good reliability, with all Cronbach’s alpha values over 0.6, and the inter-total item correlation score is higher than 0.3 except for PEOU4. So, the items of six instruments remained unchanged. Only one item (PEOU4) was removed from the perceived ease of use (PEOU) scale.

Then, we ran the confirmatory factor analysis (CFA) to assess the measurement instruments’ validity and convergence. Twenty-four items of the seven constructs were put into CFA. We used the composite reliability (CR) index and average variance extracted (AVE) index to evaluate the measurements’ reliability.

Table 2 showed that for most instruments, CR and AVE were higher than the required criteria for all the measures (CR > 0.6; AVE > 0.5) (Bagozzi & Yi, 1988). Three constructs have AVE values below 0.5, but these scales are still acceptable because their CR values are higher than 0.6. Moreover, CFA results of seven constructs also indicated that all items had substantial and significant loadings on their corresponding factor, which provided evidence of convergent validity. CFA results also showed a reasonable fit to the data (see Table 2).

Table 2: Reliability, Validity, Convergence of the Measurement Instruments

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4.2. SEM Analysis

Structural equation modeling (SEM) analysis was run to examine the effect of determinants on the intention to borrow consumer credit of Vietnamese people. Table 3 showed the results.

Table 3: SEM Analysis Result

Notes: *p < 0.05; **p < 0.01; ***p < 0.001.

As illustrated in Table 3, four out of 12 hypotheses were supported, including H6, H7, H9, and H11. The remaining hypotheses were rejected. Among six independent variables, only perceived usefulness (β = 0.553, p < 0.01) and subjective norms (β = 3.009, p < 0.001) positively affected intention to borrow consumer credit. In the meantime, among the three controlling variables, only education level (β = 0.045, p < 0.05) positively impacted intention to borrow consumer credit. There was not enough evidence in this study to prove the effect of gender and marital status on the intention to borrow consumer credit.

4.3. Discussion and Implications

This study shed light on the effect of independent variables on the intention to borrow consumer credit in the Vietnamese consumer finance market. Findings from this study can be illustrated as follow.

Firstly, subjective norms had both direct and indirect impacts on the intention to borrow consumer credit. It is illustrated in this study that subjective norms had a positive impact on intention to borrow consumer credit (β = 3.009, p < 0.001). Moreover, subjective norms positively affect perceived usefulness (β = 1.438, p < 0.001), and then perceived usefulness also had a positive impact on intention to borrow consumer credit (β = 0.053, p < 0.01). Thus, perceived usefulness mediated the relationship between subjective norms and intention to borrow. This finding is congruent with another study by Nguyen and Cassidy (2018) that confirmed the significant positive influence of subjective norms on consumer credit card acceptance in Vietnam. It also similar to the results of Lee (2016) about the user behavior of mobile enterprise applications, Wu, Chou, Weng, and Huang (2011) about website user behavior, Xiao, Tang, Serido, and Shim (2011) about the risky credit behavior.

Secondly, it is notable that education level positively affected intention to borrow. This finding implies that the more knowledgeable the consumers are, the higher intention they have to borrow consumer credit to support their spending. Similar findings of the relationship between education and credit use have been confirmed in the study of Mandell (1973), which affirmed that people with higher education were more likely to know about credit. This finding is reasonable as the consumer financial market is still at an embryonic stage in Vietnam. Many people still do not have full knowledge about consumer financial services, particularly those at a low education level.

Thirdly, there is not enough evidence to support the positive impact of perceived trust (β = 0.011, p > 0.05) and perceived ease of use (β = –0.016, p > 0.05) on intention to borrow consumer credit. It is understandable that trust did not affect the intention to borrow the consumer credit of Vietnamese people. The consumer finance market is not familiar to the majority of Vietnamese people. A lot of information about the market structure and scheme, the consumer loan process is not thoroughly comprehended by the users. Thus, there is still doubt about the credibility of financial service providers. In addition, perceived ease of use (PEOU) was not confirmed to have a positive impact on perceived usefulness (PU) and intention to borrow (INT). Consequently, both hypotheses H4 and H5 were rejected.

This result in the Vietnamese consumer credit market is contrary to other studies, including an e-payment adoption comparative study between Vietnam and Taiwan (Lin & Nguyen, 2011), a study of credit card adoption in Vietnam (Nguyen & Cassidy, 2018). This finding is attributed to the reality in Vietnam that 60% of customers of financial companies are laborers in the free market who have relatively low education levels and limited access to sources of information about the consumer finance market. Furthermore, the result of S&P Global Finlit Survey also revealed that Vietnam is among the group of countries that have the lowest financial literacy level among adults (24%) and much lower than Thailand (27%), Indonesia (32%), Malaysia (36%), Myanmar (52%) and Singapore (59%) (Lusardi & Oggero, 2015). Thus, Vietnamese people might hardly perceive ease of use when looking for consumer credit services.

Fourthly, it is not proven that perceived financial cost (β = 0.021, p > 0.05) and anxiety (β = –0.495, p > 0.05) had a negative impact on intention to borrow consumer credit. So, hypothesis H2 and H3 were rejected. This finding implies that anxiety and perceived financial cost might not prevent people from borrowing consumer credit. To a certain extent, this finding reflects the current situation of the Vietnamese consumer credit context. According to the statistics of Economist Intelligence Unit, in 2015, the consumption ratio over GDP of many countries in the world is high, such as United Kingdom 65%, German 54%, Japan 59%. This ratio of Vietnam is even higher than the average rate of developing countries, reaching 67%. This is an impressive number reflecting the high consumption demand of Vietnamese people currently and in the future. As a result, high demand for consumption will lead to high demand for consumer credit services regardless of the cost.

Based on the survey of 602 consumers, the research results presented in this article hope to provide more information for credit institutions to adjust promotional activities of their products and services to exploit the Vietnamese consumer credit market. First of all, credit institutions should focus on product characteristics to increase the perceived usefulness and convenience for customers. They might reduce the interest rates, make the loan repayment period more flexible, offer promotional programs, and provide transparent information about the product and services. Moreover, credit institutions should also focus on diversifying consumer credit products to satisfy the needs of consumers. Currently, the product package for consumers is minimal.

In addition, credit institutions should coordinate with schools to implement consumer finance education activities for consumers. These activities can be carried out in the form of workshops, extra-curricular activities to provide consumers with complete and accurate information about the credit market. With better knowledge, consumers will access formal consumer credit loans instead of “black credit, ” which might quickly lead them to a bad debt situation. Informed and knowledgeable consumers will be better for both consumer financial services providers as well as service users. Consequently, the consumer finance market in Vietnam will become more transparent and increasingly prosperous.

5. Conclusion and Limitations

From the theoretical perspective, this research contributed to the existing literature in several ways. First, this study provided insights on the factors that seem to determine the intention to borrow consumer credit by using an integrative approach. Research results hint that subjective norms play a critical role in an embryonic consumer finance market. Second, this article extended TAM with anxiety, perceived trust, and perceived financial cost. However, these factors seem to have no effect on the intention to borrow consumer credit in Vietnam from the database of this study. Consequently, it is argued that there should be more investigation about the collective impact of these factors on the behavioral intention of consumer credit borrowers.

In terms of practical perspective, the results of this research provided managers of financial institutions some information about the behavior of borrowers, focusing on factors that affecting their intention. Vietnam’s consumer finance market is expected to have much space for growth thanks to the enormous population of more than 92 million and a GDP growth rate of about 6% per year. Thus, formal financial institutions might consider their marketing and PR activities to bring transparent information and educate customers to use their products and services. Developing consumer credit in the formal sector will support pushing back black credit – a type of credit that causes significant damage and risks to Vietnamese people.

The sampling method is a weakness of this study due to the difficulty in approaching financial services customers. Moreover, the study has not addressed the impact of other demographic factors such as income, household size, past experience with consumer credit on the intention of borrowers. The factors that affect the consumers’ behavior may also be investigated in stages of the credit decision. Therefore, future studies might add variables in the research model, expand the sample size to increase the representativeness of the research, and use a new approach to study the topic.

Acknowledgements

This research is funded by Vietnam National University, Hanoi (VNU) under project number QG.18.24.

References

  1. Abramson, J., Dawson, M., & Stevens, J. (2015). An examination of the prior use of e-learning within an extended technology acceptance model and the factors that influence the behavioral intention of users to use m-learning. SAGE Open, 5(4), 1-9. https://doi.org/10.1177%2F2158244015621114 https://doi.org/10.1177%2F2158244015621114
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-t
  3. Ajzen, I., & Fishbein, M. (1972). Attitudes and normative beliefs as factors influencing behavioral intentions. Journal of Personality and Social Psychology, 21(1), 1-9. https://psycnet.apa.org/doi/10.1037/h0031930
  4. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94. https://doi.org/10.1007/BF02723327
  5. Bugembe, J. (2010). Perceived usefulness, perceived ease of use, attitude and actual usage of a new financial management system: A case of Uganda National Examinations Board. Master thesis. Makerere University.
  6. Butt, B. Z., Rehman, K. U., Saif, M. I., & Safwan, N. (2010). Customers credit card selection criteria in perspective of an emerging market. African Journal of Business Management, 4(14), 2934-2940. https://doi.org/10.5897/AJBM.9000177
  7. Chavali, K., Raj, P. M., & Ahmed, R. (2021). Does financial behavior influence financial well-being? Journal of Asian Finance, Economics and Business, 8(2), 273-280. https://doi.org/10.13106/jafeb.2021.vol8.no2.0273
  8. Chien, Y. W., & Devaney, S. A. (2001). The effects of credit attitude and socioeconomic factors on credit card and installment debt. Journal of Consumer Affairs, 35(1), 162-179. https://doi.org/10.1111/j.1745-6606.2001.tb00107.x
  9. Choi, Y., & Mai, D. Q. (2018). The sustainable role of the e-trust in the B2C e-commerce of Vietnam. Sustainability, 10(1), 291-309. https://doi.org/10.3390/su10010291
  10. Christensen, B. S., & Mathiasen, T. M. (2002). Household financial wealth: trends, structures and valuation methods. Paper presented at the 27th General Conference of The International Association for Research in Income and Wealth-Stockholm, Svedorszag.
  11. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211. https://doi.org/10.2307/249688
  12. Cottarelli, C., Dell'Ariccia, G., & Vladkova-Hollar, I. (2005). Early birds, late risers, and sleeping beauties: Bank credit growth to the private sector in Central and Eastern Europe and in the Balkans. Journal of Banking & Finance, 29(1), 83-104. https://doi.org/10.1016/j.jbankfin.2004.06.017
  13. Danes, S. M., & Hira, T. K. (1990). Knowledge, beliefs, and practices in the use of credit cards. Home Economics Research Journal, 18(3), 223-235. https://doi.org/10.1177/1077727X9001800304
  14. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  15. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
  16. Doyle, E., Stamouli, I., & Huggard, M. (2005). Computer anxiety, self-efficacy, computer experience: An investigation throughout a computer science degree. Paper presented at the Proceedings Frontiers in Education 35th Annual Conference.
  17. Fishbein, M. (1967). Attitude and the prediction of behavior. In M. Fishben (Ed.), Readings in attitude theory and measurement. New York: Wiley.
  18. Guardia, N. D. (2002). Consumer credit in the European Union. ECRI Research Report, 1, 1-39.
  19. Jahangir, N., & Begum, N. (2008). The role of perceived usefulness, perceived ease of use, security and privacy, and customer attitude to engender customer adaptation in the context of electronic banking. African Journal of Business Management, 2(2), 032-040.
  20. Jia, L., Xue, G., Fu, Y., & Xu, L. (2018). Factors affecting consumers' acceptance of e-commerce consumer credit service. International Journal of Information Management, 40, 103-110. https://doi.org/10.1016/j.ijinfomgt.2018.02.002
  21. Kamleitner, B., & Kirchler, E. (2007). Consumer credit use: a process model and literature review. Revue Europeenne de Psychologie Appliquee/European Review of Applied Psychology, 57(4), 267-283. https://doi.org/10.1016/j.erap.2006.09.003
  22. Kennickell, A. B., Starr-McCluer, M., & Surette, B. J. (2000). Recent changes in US family finances: Results from the 1998 Survey of Consumer Finances. Federal Reserve Bulletin, 86, 1-9.
  23. Lee, S. (2016). User behavior of mobile enterprise applications. KSII Transactions on Internet & Information Systems, 10(8), 3972-3985. https://doi.org/10.3837/tiis.2016.08.030
  24. Liao, Z., & Cheung, M. T. (2002). Internet-based e-banking and consumer attitudes: an empirical study. Information & Management, 39(4), 283-295. https://doi.org/10.1016/S0378-7206(01)00097-0
  25. Lin, C., & Nguyen, C. (2011). Exploring e-payment adoption in Vietnam and Taiwan. Journal of Computer Information Systems, 51(4), 41-52.
  26. Lown, J. M., & Ju, I.-S. (1992). A model of credit use and financial satisfaction. Financial Counseling and Planning, 3(1), 105-124.
  27. Lusardi, A., & Oggero, N. (2015). Millenials and financial literacy: A global perspective. Global Financial Literacy Excellence Center, The George Washington University School of Business.
  28. Mai, N. P., Ngoc, L. T. M., & Dzung, T. H. (2019). Factors affecting students's intention of borrowing consumer credit in Hanoi. VNU Journal of Science: Economics and Business, 35(1), 97-111. https://doi.org/10.25073/2588-1108/vnueab.4199
  29. Mandell, L. (1973). Consumer knowledge and understanding of consumer credit. Journal of Consumer Affairs, 7(1), 23-36. https://doi.org/10.1111/j.1745-6606.1973.tb00518.x
  30. Mansour, K. B., Kooli, K., & Utama, R. (2014). Online trust antecedents and their consequences on purchase intention: An integrative approach. Journal of Customer Behaviour, 13(1), 25-42. https://doi.org/10.1362/147539214X14024779343677
  31. Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191. https://doi.org/10.1287/isre.2.3.173
  32. Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709-734. https://doi.org/10.5465/amr.1995.9508080335
  33. Nguyen, H. H., & Lien, T. T. (2019). Factors influencing family business decision for borrowing credit from commercial banks: Evidence in Tra Vinh province, Viet Nam. Journal of Asian Finance, Economics and Business, 6(2), 119-122. https://doi.org/10.13106/jafeb.2019.vol6.no2.119
  34. Nguyen, O. D. Y., & Cassidy, J. F. (2018). Consumer intention and credit card adoption in Vietnam. Asia Pacific Journal of Marketing and Logistics, 30(4), 779-796. https://doi.org/10.1108/APJML-01-2017-0010
  35. Olney, M. L. (1991). Buy now, pay later: advertising, credit, and consumer durables in the 1920s. Chapel Hill, NC: University of North Carolina Press.
  36. Phau, I., & Woo, C. (2008). Understanding compulsive buying tendencies among young Australians: The roles of money attitude and credit card usage. Marketing Intelligence & Planning, 26(5), 441-458. https://doi.org/10.1108/02634500810894307
  37. Rona-Tas, A. (2003). The construction of credit card markets in Hungary, Poland and the Czech Republic. Economist, 2, 27-51.
  38. Schor, J. B. (1999). The overspent American: Why we want what we don't need. New York: HarperPerennial.
  39. Seetharaman, A., Patwa, N., Niranjan, I., & Kavuri, S. P. (2016). The impact of increased credit card usage on costs incurred by merchant establishments in Singapore. Journal of Asian Finance, Economics and Business, 3(4), 43-56. https://doi.org/10.13106/jafeb.2016.vol3.no4.43.
  40. StoxPlus. (2016). Vietnam consumer finance market. Retrieved December 20, 2020 from http://stoxresearch.com
  41. Tan, G. W.-H., Ooi, K.-B., Chong, S.-C., & Hew, T.-S. (2014). NFC mobile credit card: the next frontier of mobile payment? Telematics and Informatics, 31(2), 292-307. https://doi.org/10.1016/j.tele.2013.06.002
  42. Taylor, S., & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International Journal of Research in Marketing, 12(2), 137-155. https://doi.org/10.1016/0167-8116(94)00019-K
  43. Thomas, L. C. (2010). Consumer finance: Challenges for operational research. Journal of the Operational Research Society, 61(1), 41-52. https://doi.org/10.1057/jors.2009.104
  44. Tufano, P. (2009). Consumer finance. Annual Review of Financial Economics, 1(1), 227-247. https://doi.org/10.1146/annurev.financial.050808.114457
  45. Vandone, D. (2009a). The determinants of consumer credit: A review of the literature Consumer Credit in Europe. Contributions to Economics. (pp. 7-22). Heidelberg, Germany: Physica-Verlag HD.
  46. Vandone, D. (2009b). The profitability of the consumer credit industry Consumer Credit in Europe (pp. 45-68). Berlin: Springer.
  47. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
  48. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425-478.
  49. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 157-178.
  50. Westerfield, R. B. (1938). Effect of Consumer Credit on the Business Cycle. The ANNALS of the American Academy of Political and Social Science, 196(1), 99-110. https://doi.org/10.1177/000271623819600116
  51. Wu, M.-Y., Chou, H.-P., Weng, Y.-C., & Huang, Y.-H. (2011). TAM-2 based study of website user behavior-using web 2.0 websites as an example. WSEAS Transactions on Business and Economics, 4(8), 133-151.
  52. Xiao, J. J., Tang, C., Serido, J., & Shim, S. (2011). Antecedents and consequences of risky credit behavior among college students: Application and extension of the theory of planned behavior. Journal of Public Policy & Marketing, 30(2), 239-245. https://doi.org/10.1509%2Fjppm.30.2.239 https://doi.org/10.1509%2Fjppm.30.2.239
  53. Yang, A. S. (2009). Exploring adoption difficulties in mobile banking services. Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences de l'Administration, 26(2), 136-149. https://doi.org/10.1002/cjas.102
  54. Yeow, P. H., Yuen, Y. Y., Tong, D. Y. K., & Lim, N. (2008). User acceptance of online banking service in Australia. Communications of the IBIMA, 1(22), 191-197.
  55. Yu, C. S. (2012). Factors affecting individuals to adopt mobile banking: Empirical evidence from the UTAUT model. Journal of Electronic Commerce Research, 13(2), 104-121.
  56. Zhang, L., Zhu, J., & Liu, Q. (2012). A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior, 28(5), 1902-1911. https://doi.org/10.1016/j.chb.2012.05.008
  57. Zhu, L. Y., & Meeks, C. B. (1994). Effects of low income families' ability and willingness to use consumer credit on subsequent outstanding credit balances. Journal of Consumer Affairs, 28(2), 403-422. https://doi.org/10.1111/j.1745-6606.1994.tb00859.x