An Empirical Analysis of the Factors Influencing the Switching Intention from Cash Payment to Mobile Payment in Vietnam45280

An Empirical Analysis of the Factors Influencing the Switching Intention from Cash Payment to Mobile Payment in Vietnam Thao An Tran 1 *, Yen Vinh Thi Tran 2 1 Vietnam-Korea friendship

An Empirical Analysis of the Factors Influencing the Switching Intention from Cash Payment to Mobile Payment in Vietnam

Thao An Tran (1) *, Yen Vinh Thi Tran (2)

(1) Vietnam-Korea friendship IT College, Da Nang, Vietnam

(2) Danang Architecture University, Da Nang, Vietnam

* Correspondence: thaoan66@gmail.com

Abstract: Mobile payment with huge advantages in the independence of time and location offers

customers faster, safer and easier payment experience than traditional methods However, before accepting innovation technology, users must decide to continue paying by the current method or switching to one of its substitutes To understand the main determinants that affect customers' switching intention from cash payment to mobile payment in Vietnam, this study presents a conceptual model combined the technology acceptance model (TAM) and Push-Pull-Mooring (PPM) framework The results show that alternative attractiveness is the most impact pull factor, following

by mobility Regarding push factors, low satisfaction and inconvenience of cash payment have great influences on both perceived ease of use and perceived usefulness Finally, regarding mooring effects, personal innovativeness has the significant impact on perceived usefulness, perceived ease of use as well as switching intention, whereas, perceived risk has a negative influence and the effect of mobile payment knowledge is not confirmed Moreover, the relationships between the variables are

influenced only by habit, while the impact of switching cost is not found

Keywords: Intention to Switch, Mobile Payment, Push-Pull-Mooring framework, TAM model

1 Introduction

The explosive growth of smart devices and the Internet, as well as the rapid development in Fintech services, help consumers enjoy convenience and comfort financial services In particular, mobile payment dominates with enormous benefits, such as convenience, time, place, and speed Facing many options, users must decide to switch to new technology with a lot of advantages as well as risks or staying with the incumbent payment method According to the Statistic report (https://www.statista.com), 42.66 million Vietnamese will use the smartphone in 2022 That is a motivation for the growth of the mobile payment system Nevertheless, cash is still the most popular payment method with 90% users The previous literature on mobile payment only focuses on identifying the main factors that affect the acceptance of mobile payment In addition, switching from incumbent method payments to mobile payment has not been explored yet This study aims to fill theoretical and factual gaps by researching the key factors influence switching intention from cash payment to mobile payment in Vietnam

2 Literature Review

2.1 Mobile Payment and Current Status of Payment in Vietnam

Mobile payment relates to any payment that uses a mobile device to initiate, activate confirm a payment transaction for goods, services, bills (Au and Kauffman, 2008) Besides, the usage fields of mobile payment are not limited from online transactions as m-commerce, e-commerce to offline at the cash desk, stores or restaurants (Turowski et al., 2013) In Vietnam, Quick Recognition (QR) is the most popular mobile payment method provided by most of the banks and many financial companies On the other hand, Samsung Pay has just been provided in Vietnam since September 2017 Samsung Pay allows making transaction through Near Field Communication (NFC) and Magnetic Secure Transmission (MST) technology Samsung Pay rapidly developed in Vietnam market where Apple Pay and Android Pay cannot because it is more suitable for Vietnam current payment infrastructure where 98% of POS facilities use MST technology Cash is still king with 90% users (State Bank of Vietnam) Although most of Vietnamese have a bank account, cash withdrawal from ATMs machine reaches 86.81% of total domestic’s card transaction value (State Bank

of Vietnam) Cash on Delivery also is preferred than other payment methods like bank transfer, the credit card for online shopping (Vietnam e-commerce report, 2017)

2.2 Technology Acceptance Model (TAM)

Davis (1989) developed TAM to determine the factors that influence consumers' intention to use new technology and explain users' behaviors Two key factors were proposed in TAM model, namely perceived ease of use and perceived usefulness Lee (2011) pointed out that technology acceptance and switching are different While technology acceptance just the act of using technology to perform a certain task, switching concerns to the tendency or intention to change from one method to the others Therefore, Lee (2011) modified the original TAM model to explain intention to switch from offline bank to online bank Similarly, in this study, “intention to use” was modified as “intention to switch” This study adapted this modified TAM to study switching intention from cash payment to mobile payment

2.3 The Push-Pull-Mooring (PPM) Framework

PPM framework was developed base on the “Law of Migration” of Ravenstein (1885) which illustrates the impact of push and pull effects on human migration from one place to another (Lee, 1966) Bansal et al., (2005) suggested that the PPM framework also applies can also be applied to research individuals’ switching behavior in the marketing field shifting from one provider to another PPM model has been modified and applied in wide different fields of studies from service industry (Keaveney, 1995) to credit cards (Burnham et al., 2003) and mobile telecommunication services (Kim et al., 2004)

3 Model Development and Hypotheses

3.1 Push Factors

Push factors illustrate negative factors of the existing service or product or provider that motivate customers to switch to one of its substitutes (Lee, 1966; Moon, 1995) In this study, push factors show a disadvantage of cash payment that is studied through low satisfaction at cash payment and inconvenience of cash payment

3.1.1 Low Satisfaction at Cash Payment

In marketing literature, satisfaction is an important factor that has a positive impact

on the repurchase intention and loyal of customers (Oliver, 1999; Chang et al., 2014) Low levels of satisfaction are the reason for switching intention of customers from an incumbent product (Burnham et al., 2003; Kim et al., 2004) Hence, in this research, low satisfaction of customer when using cash payment is the push factor that motivates users to switch to mobile payment Therefore, we hypothesize

H1a: Low satisfaction at cash payment positively influences on perceived usefulness H1b: Low satisfaction at cash payment positively influences on perceived ease of use 3.1.2 Inconvenience of Cash Payment

With cash payment, users must carry an amount of cash in the wallet and suffer uncomfortable feelings, such as worrying about losing their wallet or limiting in spending what they have on hand or just a “thick” or “heavy” wallet In term of the payment speed, cash is lower than credit cards or mobile payment With mobile payment, customers can make the financial transaction very simple for the goods or services at anytime from anywhere, save time and reduce personal risk (Zhou, 2011) Moreover, Keaveney (1995) and Lai et al (2012) pointed out that inconvenience is a negative factor that pushes customers away from the existing service provider, leading to the following hypothesis

H2a: Inconvenience of cash payment positively influences on perceived usefulness

H2b: Inconvenience of cash payment positively influences on perceived ease of use

3.2 Pull Factors

Pull factors refer to positive attributes or advantages of alternative service providers over the incumbent (Moon, 1995, Bansal et al., 2005) In this research, pull factors show the advantage of mobile payment that is measured through alternative attractiveness and mobility

3.2.1 Alternative Attractiveness

Alternative attractiveness is defined as customers expecting the outcome achievable

or characteristics of the alternative provider better than those of the incumbent provider (Park et al., 2010) In other ways, customers feel satisfied with competing alternatives service provider in the marketplace (Chang et al., 2014) The advantages of mobile payment such as speed, mobility, availability, conveniences and reducing personal risk are more dominant than other payment methods (Nickerson, 2013; Teo et al., 2015) Furthermore, customers are attracted by promotion programs from mobile payment providers Thus, we have:

H3a: Alternative attractiveness positively influences on perceived usefulness

H3b: Alternative attractiveness positively influences on perceived ease of use

3.2.2 Mobility

Kleinrock (1996) claimed that mobility is the key advantage of mobile payment Mobility means that users can access to the ubiquitous services regardless of time and place (Au and Kauffman, 2008) In respect of mobile payment, mobility is explained as the ability

of customer’s accesses and make financial transactions anywhere and anytime without intermediaries through their mobile devices (Dahlberg et al., 2003) Compared with traditional payment methods, mobile payment allows users to make payments for goods, services, and bills flexibly and more freedom and value (Amberg et al., 2004) Moreover, the significant positive relationships among mobility, the perceived usefulness and perceived ease of use were confirmed in previous studies (Dahlberg et al., (2003) and Tran et al., (2018)) Hence, we develop the following hypothesis

H4a: Mobility positively influences on perceived usefulness

H4b: Mobility positively influences on perceived ease of use

3.3 Mooring Effects

The impacts of culture, social elements, spatial or personal factors on users’ decision staying with a current service provider or switching to others is referred as mooring effect (Bansal et al., 2005) In this study, mooring effects were measured through mobile payment knowledge, personal innovativeness, and perceived risk

3.3.1 Mobile Payment Knowledge

Users often confront substantial risk when changing service provider, especially in the IT field because of unidentified outcomes (Sharma et al., 2000) Users who have a broad knowledge of the products or services or type of providers in the marketplace will have better skills to evaluate alternatives, thereby reducing risks and easily moving to from providers to another (Bell et al., 2005) Customers will recognize that mobile payment is an optimal alternative for cash payment because of its convenience, safety if they have a high degree of knowledge of the mobile payment Thus, we have:

H5a: Mobile payment knowledge positively influences on perceived usefulness H5b: Mobile payment knowledge positively influences on perceived ease of use

3.3.2 Personal Innovativeness

Rogers (1983) claims that personal innovativeness is the degree of an individual to actively explore new information systems and technologies The great positive effect of personal innovativeness on the acceptance of new technologies recognized in a lot of previous studies (Agarwal et al., 1998; Tran et al., 2018) In switching research marketing, individual with high personal innovativeness exhibits the willing to take uncertainty result and risk of alternatives if the substitutes are better than incumbent product or service (Han

et al, 2010; Lopez, 2006) Therefore, we hypothesize

H6a: Personal innovativeness positively influences on perceived usefulness

H6b: Personal innovativeness positively influences on perceived ease of use

3.3.3 Perceived Risk

Perceived risk relates to the expectation of losses or sacrifices in purchasing or using

a risk technology (Sweeney, 1999; Zhou, 2011; Wong et al., 2012) Like other payment services, private financial information such as the identity, confidential data which required

to transmit and store in the mobile payment process, can be stolen by hackers to access and conduct unauthorized monetary transactions Therefore, we hypothesize

H7a: Perceived risk positively influences on perceived usefulness

H7b: Perceived risk positively influences on perceived ease of use

3.4 Perceived Ease of Use, Perceived Usefulness, Intention to Switch

The user's behavioral intentions in the TAM model are driven by perceived ease of use and perceived usefulness While perceived ease of use is "the degree to which a person believes that using a particular system would be free of effort”, perceived usefulness refers

to “the degree to which a person believes that using a particular system would enhance his

or her job performance” Davis (1989) According to the modified TAM model (Lee, 2011),

we develop the following hypothesis

H8: Perceived ease of use positively influences on the perceived usefulness

H9: Perceived ease of use positively influences on switching intention to mobile payment in Vietnam

H10: Perceived usefulness positively influences on switching intention to mobile payment in Vietnam

3.5 Moderator Effects

3.5.1 Switching Cost

The switching cost is the main negative factors because people only switch when the benefits should outweigh the costs (Lee et al, 2001; Anderson, 1994) The costs of switching from cash payment to mobile payment are both the economic costs, including the actual mobile equipment costs, transaction costs, and access fees (Wu, 2005) and non-monetary costs as time and effort, emotional and psychological cost, unfamiliarity, uncertainty and learning costs (Han et al., 2011; Jones, 2002) In this research, switching cost has a moderating effect on customers' decision to switch from cash payment to mobile payment

H11: Switching cost will moderate the relationships between the variables

3.5.2 Habit

Habit is the main barrier for switching because people like to stay with the current product or service which become a familiar part of their daily routine (Jones, 2002) In this study, payment habit means cash is mostly used to pay for purchasing services or product and COD is chosen to pay for online transactions Clearly, the intention to switch from cash payment to mobile payment can be moderated by habit Thus, we hypothesize

H12: Habit will moderate the relationships between the variables

A conceptual framework for the paper is developed including 17 hypotheses are formulated to address the research problems and objectives as shown in Figure 1

Figure 1 Research Model

4 Research Method

The questionnaire was redrawn and modified from literature to match the switching

to mobile payment context A seven-point Likert scales from 1 to 7 corresponding to strongly disagree to strongly agree was applied for all constructs Four items on low satisfaction at cash payment were adapted from Oliver and Swan (1989) Three items on the inconvenience

of cash payment were adapted from Verhoef (2001) Four items for attractive alternatives from Kim (2006), Bansal et al., (2005) were adapted and personal innovativeness with three items from Goldsmith et al., (1991), Agarwal et al., (1998) Four items on mobility were adapted from Huang et al., (2007) The scale of mobile payment knowledge (4 items) was revised from Sharma et al., (2000) The scale of perceived risk (3 items) was revised from Brown et al (2003) and Tan and Teo (2000) Five items for habit from Limayem (2007), Venkatesh (2012) were adapted and switching cost with five items from Zhang et al., (2008), Jones (2002) We adapted the perceived usefulness (4 items), perceived ease of use (4 items) and intention to switch (4 items) from Davis (1989) and Venkatesh et al., (2000) The survey was conducted on randomly Vietnamese from April to July 2019 Among the 420 questionnaires distributed, 406 questionnaires were chosen to analysis after removing 14 negative questionnaires

5 Data Analysis and Results

5.1 Demographic Analysis

Table 1 illustrates the characteristics of respondents Most of the responses are generated by a male, which occupied 63.05% of our sample size whereas female just accounts for around 36.95% Regarding age, respondents under 35 years old dominated

(62.07%) In terms of income, the respondents are mainly distributed under 10 million per month (76.60%) In addition, the majority of respondents are a student (31.287%) and office worker (30.79%)

Table 1 Sample Demographics Demographic Category Count % Demographic Category Count %

Age

Under 25 92 22.66

Career

Office

Income (Million VND)

Under 5 166 40.89

Over 15 48 11.82 5.2 Measurement Model Assessment

5.2.1 Reliability and Validity

Cronbach's alpha was applied to check the internal consistency and reliability of the items Cronbach’s Alpha test results (Table 2) show the value in all cases (0.796 ~ 0.913) over 0.7, which implies that the data are reliable The Exploratory Factor Analysis (EFA) was performed which divided factors into 12 components All items were well loaded with factor loading more than 0.5

Table 2.The Result of Cronbach’s Alpha Check

Construct name Variabl

e

Cronbach’s

α Construct name

Variabl

e

Cronbach’s

α

Low Satisfaction at

Inconvenience of

Attractive

Mobile Payment

Personal

Perceived Ease of

Intention to

5.2.2 Measurement Model Assessment

Confirmatory Factor Analysis (CFA) was assessed to check reliability and construct validity As the CFA results, the seven model-fit measures were satisfactory which is good evidence for the validity of the model (χ2 = 871.752; df = 584; χ2/df = 1.493; CFI = 0.967; NFI

= 0.908; IFI = 0.968; RMSEA = 0.035) According to Table 3, the average variance extracted (AVE) for all cases (0.501~ 0.785) were higher than 0.5 and all CR value also exceeded 0.7 (0.801~0.916) These values showed strong evidence of convergent validity (Fornell et al., 1981) To test the discriminant validity of the constructs, we compared the square root AVE

of each construct with the correlation coefficients The correlation matrix in Table 3 illustrates that the highest value of correlation coefficient (0.644) is smaller than the lowest values of square root AVE (0.708), indicating the evidence of the discriminant validity (Fornell et al., 1981)

Table 3 Convergent Validity and Correlation Matrix of Latent Constructs

AL 0.824 0.610 0.781

MB 0.900 0.693 0.426 0.832

LS 0.896 0.682 0.452 0.506 0.826

PE 0.873 0.633 0.524 0.502 0.541 0.796

IN 0.916 0.785 0.161 0.228 0.250 0.333 0.886

IC 0.857 0.599 0.573 0.593 0.554 0.562 0.185 0.774

KN 0.801 0.501 0.254 0.357 0.463 0.355 0.178 0.387 0.708

PU 0.861 0.609 0.601 0.567 0.587 0.644 0.310 0.600 0.403 0.780

IS 0.875 0.637 0.577 0.643 0.558 0.546 0.195 0.665 0.490 0.632 0.798

RI 0.838 0.633

-0.186

-0.217

-0.194

-0.261 0.040

-0.184

-0.190

-0.299

-0.287 0.795

5.3 Structural Model Analysis

Table 4 Results of Estimated Structural Coefficients

Hypotheses Path Std

Weights

S.E

0.650

Supported

0.504

Supported

H6b IN→PE 183 047 4.138 *** Supported

0.494

Structural equations modeling (SEM) was used to examine the hypotheses of the proposed model The model fitting indices of the constructs model (χ2 = 973.189; df = 591; χ2/df = 1.647; CFI = 0.957; NFI = 0.897; IFI = 0.957; RMSEA = 0.040) met the appropriate levels Inspection of the path coefficients was assessed to check the research hypotheses Table 4 and Figure 2 show the results of the tests of the hypotheses with fifteen of the seventeen hypotheses were adopted and two hypotheses were rejected In the push factors, low satisfaction at cash payment and inconvenience of cash payment were both found to have a significantly positive effect on perceived usefulness (β = 0.155, p = 0.005 and β = 0.128, p = 0.044, respectively) and perceived ease of use (β = 0.187, p = 0.002 and β = 0.196, p = 0.005, respectively), thus supporting H1a, H2a, H1b, H2b In the pull factors, alternative attractiveness has the most significant effects on perceived usefulness (β = 0.256, p = 0.000) and perceived ease of use (β = 0.218, p = 0.000), supporting H3a and H3b Moreover, the influence of mobility on perceived usefulness (β = 0.171, p = 0.001) and perceived ease of use (β = 0.122, p = 0.037) also confirmed, thus supporting H4a, H4b Finally, regarding mooring effects, personal innovativeness has significant impact on perceived usefulness (β = 0.099, p

= 0.016), perceived ese of use as (β = 0.183, p = 0.000), whereas, perceived risk has a negative impact (β = -0.116, p = 0.007 and β = -0.124, p = 0.008, respectively), thus supporting H6a, H7a, H6b, H7b However, the effect of mobile payment knowledge is not confirmed (β = 0.079, p = 0.105 and β = 0.046, p = 0.392, respectively), thus rejecting H5a, H5b In addition, H8, H9, and H10 showed a positive relationship between perceived ease of use, perceived usefulness and switching intention in the TAM model that were well-supported at a significance level of 0.001 Results found that perceived ease of use exerts the great impact

on perceived usefulness (β = 0.173); perceived usefulness and perceived ease are the key drivers of intention to use (β = 0.546 and β = 0.213 respectively)

Regarding explanatory power, the model explained 65.0% of the variation in perceived usefulness, 50.4% of the variation in perceived ease of use Moreover, the switching intention from cash payment to mobile payment was explained by 49.4% of the variance in the model To summarize, Figure 2 presents the estimation results

Figure 2 The Result of Hypothesis Test

5.4 The Test of The Moderating Effect

Table 5 Results Testing Moderator Effect of Switching Cost

Hypotheses Path

Standardized Estimate

Critical Ratio for Differences Between Parameters

Result

Group

A (101)

Group B (305)

*** p < 0.001, ** p < 0.01, * p < 0.05 level of significance

A pairwise parameter comparison was analyzed to test the hypothesis regarding the moderation of switching cost and habit To do so, respondents were divided into low switching cost group (Group A) with an average point less than or equal 4 with 101 persons, the remaining 305 persons categorized in Group B with high switching cost Similarly, high habit group (Group C) contained 122 respondents with an average point over than 4 and

294 respondents belonged low habit group (Group D) To verify the difference between groups, pairwise parameter comparisons were conducted by computing the critical ratios