FACTORS AFFECTING CONSUMER BEHAVIOR OF PAYMENT CARD USERS IN VIETNAM45481

FACTORS AFFECTING CONSUMER BEHAVIOR OF PAYMENT CARD USERS IN VIETNAM Nguyen Quang Huy, Nguyen Thi Thu May Foreign Trade University ABSTRACT This study aims to examine the role of factor

FACTORS AFFECTING CONSUMER BEHAVIOR OF PAYMENT CARD

USERS IN VIETNAM

Nguyen Quang Huy, Nguyen Thi Thu May

Foreign Trade University

ABSTRACT

This study aims to examine the role of factors affecting the payment cards use of Vietnamese consumers based on the Unified Theory of Acceptance and Use of Technology model These factors are verified through a sample of

101 consumers living in Hanoi The results show that the five factors strongly impacting on the payment cards use of Vietnamese consumers (from high

to low) are Facilitating conditions; Perceived creditability; Social influence; Performance expectance; Effort expectance, which all have positive impact

on the payment cards use of Vietnamese consumers Finally, the study draws conclusion and some recommendations for commercial banks and the future researches

Key words: Payment card, Consumer behavior, Vietnamese consumers, Banking.

1 INTRODUCTION

The payment card is known as a morden and versatile technology that brings immense benefits to consumers, banks and the society It helps customers to pay faster and more easily, banks to mobilize idle capital among the population, and governments to manage the amount of money coming in and out of market more closely and effectively Hence, payment card and non-cash payment methods are becoming widely used, to be a new trend in the next era

In recent years, the card circulation market in Vietnam has made positive progress when the number of circulating cards has increased annually According to the 2019 Report of Vietnam Bank Card Association, by the end of 2018, the number of circulating cards reached over 86 milion, an increase of 12% compared to 2017 In particular, international cards grew higher than domestic cards, at 17% compared to 11% Nevertheless, the growth rate of card usage continued to decline significantly, only 8% in 2018, compared to 12% 2017 and 22% in 2016 Obviously, consumer behavior on payment card has varied from year to year Banks, therefore, need to

Performance

expectancy

Social infuence

Use behavior

Facilitating

conditions

Behavioral intention

Effort expectancy

Voluntariness

of use

change their business strategies in order to meet the consumer needs It is essential for banks to be aware of what factors influence consumer behavior of payment cards users

This study aims to clarify the role of payment cards for consumers and the society, with a group of factors affecting consumer behavior of payment card users in Vietnam The research objectives are to identify factors that influence payment card usage on consumer behavior; identify opportunities and challenges of banks in the non-cash payment market in Vietnam, and then propose solutions to promote the use of payment cards in Vietnam The following sections of this article include: (i) Literature review;

(ii) Research methods;

(iii) Empirical Results and discussion;

(iv) Conclusion and recommendations

2 LITERATURE REVIEW

2.1 Theory of consumer behavior

Consumer behavior is “behavior that consumers exhibit in finding, buying, using and evaluating products and services that they expect will satisfy their individual needs” (Peter D Bennet, 1988) It is also “a process that describes how consumers make decisions about choosing and eliminating a type of product or service” (Charles W Lamb, Joseph F Hair and Carl McDaniel, 2000)

Companies study consumer behavior to identify the customers’ needs, interests and habits Specifically, companies find what consumers want to buy, why they buy the product/service and brand, how, where and when to buy it, and how much to build a marketing strategy in order to motivate consumers to choose their products/ services (Philip Kotler, 2001) Nowadays, the study of consumer behavior has gone even further Companies study whether consumers are aware of the benefits of the purchased products/services (Performance Expectancy), how consumers perceive and evaluate after using products/services Consumers’ perception will affect subsequent purchases and the information to other consumers (Social influence)

2.2 Affecting factors

In local and international studies, the factors affecting consumer behavior have been studied officially and clearly Some typical studied factors affecting consumer behavior on the choice to use the service in general and payment cards in particular, such as:

As for research on electronic payment methods, the “Factors affecting consumer perception of electronic payment methods” research (Wendy Ming, Yen Teoh and ctg, 2013) shows that the three factors of benefit, efficiency and effort expectancy are significantly related to consumer perceptions of e-payments This study measured the impact levels of deteminants affecting consumer perceptions of e-payment services

Social infuence

Performance

expectancy

Facilitating

conditions

Use acceptance Effort expectancy

Preceived

creditability

Age Gender

H1 (+) H2 (+)

H3 (+)

H4 (+)

H5 (+)

As for internet banking research, there are two researches: “Factors affecting the use

of internet banking in Mauritius” (Dineshwar Ramdhony and Ashvin Ramjee, 2010) and “Conducting research and application of the Unified theory of Acceptance and Use Technology (UTAUT) in using internet banking” (Anoop Gorecha, 2005) Dineshwar and Ashvin’s research uses the theory of Technology Acceptance Models (TAM), Theory Plan of Behaviour (TPB) and Destroyed Theory of Plan (DTP) Anoop Gorech’s research aims to carry out the UTAUT model research into internet banking usage behavior research Both studies have been very successful in clarifying the factors that influence internet banking behavior

2.3 Consumer behavior model

Numerous researches have looked at factors that influence consumer acceptability and use Researchers use a variety of theories to explain Kwong et al (2002) showed that Reasonable Action Theory (TRA), Theory of Planed Behaviour (TPB) and Technology Acceptance Model (TAM) are used for most researches on technology adoption All the above discrete theories, along with a number of other models, are consolidated by Venkatesh & David (2003) in the unified theory of technology adoption and use - Unified Theory of Acceptance and Use of Technology (UTAUT, 2003) The model is shown as follows:

Picture 1 Unified Theory of Acceptance and Use of Technology Source: Research of Venkatesh & David (2003)

The UTAUT model identifies four factors that directly affect user’s acceptance and user’s behavior, including: Effort expectancy, Performance expectancy, Social influence and Facilitating conditions There are also peripheral factors (gender, age, willingness and experience) that adjust to the intended use In particular, Effort expectancy is defined as the extent to which an individual believes by using the

system will help to achieve high work efficiency Performance expectancy describes the ease of use of the system Social influence is the extent to which an individual perceives others that they should use the new system And Facilitating condition is defined as the extent to which an individual believes that an organization with the same technical infrastructure exists to support the use of the system

2.4 Empirical model

UTAUT has been experimentally tested, demonstrating superiority to other current models in the research method of Venkatesh (2003), Park et al (2007) and Venkatesh & Zhang (2010) However, this model is still less applicable in the study of the acceptance of using payment cards instead of cash of consumers in Vietnam In addition, through an overview of domestic and foreign research models, the authors choose to apply the UTAUT model and add a perception factor about reliability

At the same time, reducing the two factors of voluntariness of use and experience

to reflect the research areas to increase the value of explaining practical behavior related to the above problem The research model is proposed and tested as follows:

Picture 2 Empirical model selected Source: Compiled by the authors

Based on the proposed research model as above, the authors give the research hypothesis:

- H1: Consumers’ performance expectancy achieved by using a payment card which

have a positive impact on the acceptance of payment card usage.

- H2: Consumers’ effort expactancy have a positive impact on the acceptance of the acceptance of payment cards

- H3: Social influence has a positive impact on consumers’ acceptance of payment cards usage

- H4: Consumers’ perceived creditability has a positive impact on the acceptance of payment cards usage

- H5: Consumers’ facilitating conditions have a positive impact on the acceptance

of payment cards usage

- H6: Consumers’ gender has the effect of regulating their acceptance of payment cards usage

- H7: Consumers’ age has the effect of regulating their acceptance of payment cards usage

3 RESEARCH METHODS

3.1 Sample collection

Research topics include market analysis, searching, information surveys, synthesizing information, applying theory to practice, etc Because of limited research time and scope, the authors focus on implementation in Hanoi, from April 29, 2019 to May 4,

2019 Subjects surveyed are consumers who have been using payment cards and are living in Hanoi Convenient, non-probability sampling methods, questionnaires sent via social networks and popular online communication channels in Vietnam

3.2 Research methods

- Quantitative method: The author collects information for studying by conducting a

direct consumer survey in Hanoi

- Descriptive statistics analysis:

Data processing project uses SPSS 20 software with the following tools:

+ Testing Cronbach’s Alpha reliability coefficients to verify whether data collection is reliable or not by Alpha coefficient;

+ Exploring factor analysis EFA finds valuable factors in measurement;

+ Means analysis of factors;

+ Using multivariate regression models to determine the influence of each factor on consumers’ payment cards usage

4 EMPIRICAL RESULTS AND DISCUSSION

Statistical sample characteristics: The female sample consists of 58 women

accounting for 57.4%; 43 men account for 42.6% The majority of people aged 18-22 years old, 36 people account for 35.6%; at the age of 23-30, there are

33 people, accounting for 32.7%; in the age group of 31-40, there are 18 people,

accounting for 17.8%, the remaining over 40 years old are 14 people, accounting for 13.9% Education level, most of them have University degree with 77 people (76.2%), there are 13 people at postgraduate level accounting for 12.9%, Intermediate 6 people make up 5.9%, the rest College with 5 people (accounting for 5%) In terms

of card usage time, most consumers use it for more than 3 years with 59 people (accounting for 58.4%) The results are listed in the table below:

Table 1 Statistical sample characteristics

Criteria Quantity Percentage (%) % Aggregate

Gender

Age

Education level

Card usage time

Source: Compiled by the authors based on research results

Table 2 Summary table of measurement results for scales

deleted

Source: Compiled by the authors based on research results

All scales have Cronbach’s Alpha coefficient greater than 0.7 which has good measurement significance Hence, after the reliability assessment, these factors will

be included in the next EFA discovery factor analysis

Compatibility test of Exploratory Factor Analysis model (KMO) and correlation test of observed variables (Barllet’s Test)

Table 3 KMO аnd Bartlett’s Test

Source: Compiled by the authors based on research results

The measure KMО (Kaiser - Meyer - Olkin) has the value equal 0.853 which satisfies 0.5 ≤ KMO ≤ 1 Thus, the factor analysis of the model consistent with real data The Barllett test has a value of sig equal to 0.0000 < 0.05 In conclusion, observed variables are correlated with many factors in each group

Table 4 Rotated Component Matrix a

Source Compiled by the authors based on research results

The result’s EFA analysis of the independent variables of the above rotated component matrix shows that factor loading of observed variables are satisfied all conditions of analysis which is factor loading ≥ 0.4 and number of factors of factor analysis is five factors with thirty-first observed variables

Test of proper level of model

Table 5 Model summary

Model R squareR Adjusted R square Std Error of the Estimate Durbin-Watson

Source Compiled by the authors based on research results

Table 6 Anova

Source Compiled by the authors based on research results

In the multiple regression model, because of many independent variables, we must use the adjusted determination coefficient R2 (Adjusted R Square) to assess the suitability of the model The summary results of multiple regression models are presented in Table5 showing that the determination coefficient R2 = 0.798 (≠ 0) and adjusted R2 = 0.787 It means 78.7% of the variables of the dependent Acceptance

of use is explained by 5 independent factors This results shows that this linear regression model fits the sample data set at 78.7% The F test (Table 4.6 ANOVA analysis of variance) shows the significance level Sig = 0.000 < 0.05 deduced we can conclude that the model is meaningful to infer the whole

Table 7 Coefficients

Model

Unstandardized Coefficients StandardizedCoefficients

Collinearity Statistics

Source Compiled by the authors based on research results

The table above shows that the values of Tolerance are both higher than 0.1 and

VIF are both higher than 2 Conclusion: There is no multi-collinear phenomena

in the model The results showed that five independent variables in the model all influence consumers’ acceptance of payment card usage and this influence

is statistically significant Regarding the magnitude of the influence (importance)

of the independent variables on dependent variables is compared through the standard Beta coefficient Based on the results in Table 7, we see that there are five factors affecting the acceptability of use which are presented in order of decreasing importance, as follows: effort expectancy (β = 0,126); performance expectancy and social influence (β = 0,224); Perceived creditablity (β = 0,281); facilitating conditions (β = 0,314)

Results of testing hypothesis

Table 8 Results of testing hypothesis

H1 Consumers’ performance expectancy achieved by using a payment card which have a positive impact

on the acceptance of payment card usage. P < 0.05 Accept

H2 Consumers’ effort expactancy have a positive impact on the acceptance of the acceptance of payment

H3 Social influence has a positive impact on consumers’ acceptance of payment cards usage. P < 0.05 Accept

H4 Consumers’ perceived creditability has a positive impact on the acceptance of payment cards usage. P < 0.05 Accept

H5 Consumers’ facilitating conditions have a positive impact on the acceptance of payment cards usage. P < 0.05 Accept

Source Compiled by the authors based on research results

Testing the influence of gender

To assess the difference between consumer groups by gender, the authors used the Independent - Sample T - test with the same meaning level = 5%, the authors collected the result:

Table 9 Levene’s test

Levene’s test T - test for Equality of Means

F Sig T Df Sig.(2 -

tailed)

Mean Difference DifferenceStd Error

95% Confidence Interval of the Difference Lower Upper Equal

variance

assumed .988 .323 -1.369 99 .174 .007095 .10520 -.3528 06470 Equal

variance not

assumed -1.328 79.27 .188 .007095 .10846 -.3599 07184

Source: Compiled by the authors based on research results

The results in Table 10 show that, in Levene test, Sig value of using payment card is 0.174> 0.05, the difference between two sex groups of consumers is not different The author will use t-test results in the assumption of equal variance with Sig of using payment cards equal to 0.332 > 0.05 Therefore, the author can conclude that: At the 95% confidence level, there is no statistically significant difference in the payment cards usage of Vietnamese consumers between male and female groups The hypothesis H6 is rejected

Testing of the difference by age

To assess the difference between consumer groups by age, the author conducted ANOVA variance analysis with alpha level = 5% (95% confidence level), obtaining the following results:

Table 10 ANOVA

Sum of squares Df

Mean

SD

Source: Compiled by the authors based on research results

ANOVA test results from SPSS 20 show that: Table 4.15 Levene test for significant homogeneous variance (Sig = 0.112> 0.05), meaning that there is no difference in variance of groups Next, the results of testing the differences between groups also show that there is no difference between groups (Sig = 0.294> 0.05 (Table 4.16) Thus, it can be concluded: At the 95% confidence level, there is no statistically significant difference in Vietnamese consumers’ payment cards usage among the four different age groups of consumers The H7 hypothesis is rejected

5 CONCLUSION AND RECOMMENDATIONS

5.1 Conclusion

Based on theories of behavior and previous researches, the author has built a model

of factors affecting the payment cards usage of Vietnamese consumers The results show that the scales for Facilitating conditions, Preceived Creditablity, Social Influence, Performance Expectancy and Effort Expectancy all have direct and positive impacts

on payment card usage of consumers This research result is completely consistent with the research result of the model of UTAUT of Venkatesh & David (2003) In addition, there is no difference in behavior between men and women and between different ages

Although the study has attempted different demographic sampling to enhance representation, samples were only collected in Hanoi Further research may expand the scope of the survey to obtain better information At the same time, expand the study to non-cash payment methods in general