The factors driving adoption of proximate mobile payment methods, in the Chinese economy

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The factors driving adoption of proximate

mobile payment methods, in the Chinese

economy.

Master Thesis

MSc in Business Administration – Digital Business

Amsterdam Business School

University of Amsterdam

Author: Yulun Ma

Student Number: 11709057

Supervisor Name: Shameek Sinha

Final Version

June 21, 2018

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Acknowledgements

I appreciate the invaluable guidance and inspirations from my supervisor Dr. Shameek Sinha. He always motivates me to pursue a higher goal, which seems unable to accomplish,

during my work of thesis. His insights bring this thesis to a higher level. Especially during the data collection period, I could not find a way to reach a larger number of suitable

participants without his encouragements and suggestions.

I also thank my family members and friends. They greatly supported me during my work of

thesis. And they also give me huge confidence when I encountered problems.

Yulun Ma

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Statement of originality

This document is written by Student Yulun Ma who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating

it.

The Faculty of Economics and Business is responsible solely for the supervision of

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Abstract

Chinese people hardly experienced the credit card system, and mainly rely on cash for their small payment. Recently, Chinese directly jumped into the mobile payment era. There are

numerous drivers affecting Chinese users to adopt mobile payment as their main payment method, for example, the policy of the government, the technology trend, and the

determinant factors of individuals.

Based on the TAM or UTAUT model, lots of previous research examined the determinant

factors that drive users to accept mobile payment (Bachfischer, Lawrence, & Steele, 2004;

Dahlberg, Mallat, & Öörni, 2003; Shin, 2009; Zhou, Lu, & Wang, 2010). But few of them investigate the factors for both sides’ users. However, this research will focus on the

factors of two sides’ users, the buyer side and seller side users, in the Chinese market. The

comprehensive conceptual model of this research is based on the TAM and UTAUT model with other incorporated influential factors (e.g. network externality, trust, and intimacy).

Specifically, the factors of seller side users are grouped based on the two-factor theory of

Herzberg, in order to better facilitate the understanding of the intrinsic reasons that influence seller side users’ adoption intention.

Two separate online questionnaires have been used to collect data. Finally, there are 920 completed questionnaires which contribute to the buyer side analysis. There are 839

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perceived usefulness, perceived ease of use, personal innovativeness, and social influence

are positively related to the buyer’s attitude towards mobile payment. Furthermore, the buyer’s attitude towards mobile payment is positively related to the buyer’s intention to use.

As for the seller side analysis, results show that network externality, intimacy, financial cost, and trust are positively related to the seller’s intention to use. In addition, there is a

positive relationship between seller’s intention to use and the actual adoption of seller side users.

This research enriches the existing literature in the mobile payment field. Specifically, it

contributes to the analysis of factors for two side users in the Chinese market, and confirmed previous researches (Chen, 2008; Schierz, Schilke, Wirtz, 2010; Kim,

Mirusmonov, & Lee, 2010). This research provides insights for mobile payment users

about how to facilitate the adoption of mobile payment for both sides’ users. It also highlights the important factors for both sides’ users.

Keywords:

Mobile payment, China, two side users, TAM model, UTAUT model, Herzberg’s

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List of Tables

Table 1: Overview of Chinese top two mobile payment providers………2

Table 2: Descriptive statistics and correlations (buyer side’s model)………..37

Table 3: Descriptive statistics and correlations (seller side’s model)………..38

Table 4: Regression analysis for hypothesis 1 to hypothesis 4………40

Table 5: Regression analysis for hypothesis 5……….40

Table 6: Regression analysis for hypothesis 6………..41

Table 7: Regression analysis for hypothesis 7 to hypothesis 10………..41

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List of Figure

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1. Introduction

Several years ago, Chinese people still preferred cash when they purchased products and

services. But now, many Chinese prefer to use mobile payment to conduct financial

transactions, including both buyers and sellers in the market. Chinese hardly experienced the bank-based credit system, thus, a lot of Chinese people lack the credit card culture and have

the cash-centric culture comparing with American or European countries (Lu, Yang, Chau, &

Cao, 2011). Chinese directly jumped into mobile payments.

There are many important drivers from different levels that affect Chinese users to adopt

mobile payment as their payment methods, for example, the government policy and the raised

technology trend in China. This research discusses the determinant factors driving the seller side users to accept mobile payment on the firm level, and it also analyzes the crucial factors

driving the buyer side users to adopt mobile payment on the individual level, from mixed perspectives that involved psychological, cognitive, and behavioral factors.

Under certain technical and national conditions, Chinese market showed a very interesting phenomenon that the rapidly developed third-party mobile payment platforms

obtain the market trust and provide their services to the users conveniently with a high level of guarantee and security in a minimized customers’ capital cost. Hereby, it is necessary and

valuable for researchers to investigate the corresponding factors, which affect the adoption of

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December 2016, the number of Chinese mobile Internet users reached 695 million, with the

growth rate surpassing 10% for three consecutive years. From the report of iResearch Global (2016), Chinese mobile payment transaction volume reached US$1.45 trillion in 2015. In

comparison, the equivalent figure for the United States is US$8.71 billion in 2015. However, the Chinese market is almost monopolized by two major players. AliPay and WeChat Pay

account for roughly 54% and 40% of market shares of the Chinese mobile payment market, respectively (Meeker, 2017).

Table1. Overview of Chinese top two mobile payment providers

Corporation Technology Business Model Market Share

AliPay QR Code Independent Service Provider (Third-party Centric Operator)

53.73%

WeChat Pay QR Code Independent Service Provider (Third-party Centric Operator)

39.35%

Then, the buyer side users and seller side users will both benefit from this booming

technology. For the buyer side users, they can easily enjoy the convenience of cashless

lifestyle, for example, no worries about losing their wallet or needing of small changes, and they can easily manage their financial status with the expense bills that can be queried in real

time. For the seller side users, they can simply improve the diversity of payment methods,

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They can also analyze their customer’s purchase behavior with the help of big data, which

will be available if they access the mobile payment platforms, and conduct marketing campaigns in a precise way. Next, the seller side users will manage their business and

financial status easier by simply accessing to the financial reports.

The huge amount of researches examined the factors that affect the mobile payment

adoption from the buyer side perspective (Schierz, Schilke, & Wirtz, 2010; Chen, 2008; Olieria, Thomas, Baptista, & Campos, 2016). And only limited research is conducted from

the perspective of the seller side users (Hayashi & Bradford, 2014; Mallat & Tuunainen,

2008).

However, it is necessary to conduct a research on both sides of users, because of the

interdependence between the buyer side users and seller side users. The buyer side users will

not accept mobile payment as their payment methods when the payment scenarios are limited, due to the restricted adoption from seller side users. Same for the seller side users, if their

customers hardly accept mobile payment, then, they will not consider to adopt it.

Also, the factors of existing researches are mainly based on the factors from the TAM model or UTAUT model, for example, the perceived ease of use (PEOU) and perceived

usefulness (PU), few of them connect the psychological part to analyze the factors that affect the adoption of mobile payment. The interested factors from this research are not only

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but also incorporated other important factors (e.g. network externality). Specifically, factors

of seller side are grouped based on the two-factor theory of Herzberg, by the suggestion from previous research in the technology related field (Liu, Guo, & Lee, 2011). Again, few

researches focus on specific areas, for example, Chinese market.

Then, this study will choose Chinese market for a deeper analysis, because of the unique

Chinese scenario – The leap-frog in technology. And this study will conduct research on the factors that determine the adoption of mobile payment from both seller’s and buyer’s

perspective to fulfill the literature gap regarding research on both perspectives.

For now, there is a great number of Chinese that have adopted mobile payment as their major transaction solution. Mobile payment is a broad topic, which has been subdivided into

several categories, for example, remote payment and proximate payment. Although many

researchers made efforts toward the analysis of mobile payment adoption, based on TAM model or UTAUT model, few of them specifically focus on the unique Chinese scenario – the

jump in technology. Also, few articles narrowed down to the analysis from the individual

level of the adoption of proximate mobile payment methods, for example, WeChat pay. And it is even harder to find researches on both perspectives to analyze the influential factors of

mobile payment adoption. Therefore, it is necessary to formulate a research to fill this gap. Hereby, this thesis will mainly focus on the determinant factors driving adoption of

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sellers, in the Chinese economy. Based on the specific standpoint, this research proposes two

interesting research questions, as follows:

1. What are the determinant factors that affect the individual’s adoption of proximate payment methods (for both buyers and sellers’ side) over other payment methods (e.g. cash and credit card)?

2. How does the adoption differ across the two distinct seller side and buyer side?

In such way, this thesis endeavors to make contributions, which could perfect the existing theoretical constructs in mobile payment field and help the practitioners to gain insights to

better understand the needs of users and improve the quality of services.

Recently, there were few articles, which analyzed the mobile payment adoptions,

published in the first tier journals (Leong, Hew, Tan, & Ooi, 2013; Tan, Ooi, Chong, & Hew, 2014). Therefore, from the academic side, the thesis will make contributions to this field by

enriching the existing literature, which will promote the knowledge growth about mobile

payment adoption in China. Several authors highlighted the importance of mobile payment to both buyer side users and seller side users (Lai & Chuah, 2010; Mallat, 2007), but more

stressed on the importance from the technology perspective that the wide adoption of mobile

payment is based on a secure payment system (Chang, Chen, & Zhou, 2009). Furthermore, research shows that culture has a stronger influence on the mobile internet services than other

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technology applications (Lee, Chou, Kim, & Hong, 2004). Therefore, it will be more

meaningful to focus on a concrete culture to discuss and analyze the determinant factors which drive the adoption of proximate mobile payment. Therefore, with the help of two sides

analysis, specifically under the discussion of the unique Chinese scenario, the thesis enriches the current literature by effectively examining and comparing the influential factors of mobile

payment adoption. In addition, it also encourages future researchers to investigate the influential factors in other cultures and to explore the phenomenon of jump in technology in

other economies.

For the business side, this thesis is based on the unique view of users from both sides, which can help the existing firms from mobile payment industry to better understand the

needs of their customers and provide the new players with evidence based knowledge.

Moreover, by analyzing and comparing the buyer side’s and seller side’s users, the findings of this thesis will assist the practitioners to strategically develop the current market by promoting

the services to their users from the two distinctive sides.

The remainder of this research is structured as follows. The second chapter discusses the theoretical backgrounds on the mobile payment in general and in Chinese market environment,

the main findings, and Herzberg’s two-factors theory, after that, the conceptual model of this research and the ten hypotheses were established. The third chapter mainly discusses the

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chapter discusses the main findings of the analysis. The last chapter includes the discussion of

this research, the academic and practical contributions, and finally the limitation of this research and suggestions for future research.

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2. Theoretical Background

This chapter provides the literature review about previous researches in the related field,

and therefore determines the research areas that need to be further discussed. Firstly, section

2.1 is about the general discussion of mobile payment. Secondly, section 2.2 discusses the mobile payment in the Chinese market, and especially introduces the prerequisites

foundations and opportunities for the development of the Chinese third-party mobile payment

providers. Thirdly, in section 2.3, it discusses the previous generic adoption models and the adoption model of this research. Fourth, section 2.4 discusses each factor that related to buyer

side users’ adoption issues, and followed with the proposed hypotheses of this research. Fifth,

section 2.5 discusses the factors and generated hypotheses that are related to seller side users and the two-factor theory of Herzberg. At the end of this chapter, the conceptual model of this

research is given. 2.1 Mobile Payment

Paying by phone is not a new idea. During the early period of the 2000’s, purchasing ringtones and games by using mobile phone and finalizing the payment through billing

systems of mobile telecom operators was already available. Thereafter, mobile payment has

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In this article, the definition of mobile payment is specified for the following argument.

“M-payment is a transaction of a monetary value for services or products with a mobile device (such as mobile phones, smartphone, tablet, or any wireless enabled device or card) for

the initiation, authorization and confirmation of payment processes, using wireless and/or other communication technologies” (Guo & Bouwman, 2016, p.148).

Proximate payment is categorized from the technology perspective, and is also the main focus of this research. For proximate mobile payment, it needs to exchange the payment

information between payers’ mobile devices and payees’ POS terminal, which was realized

by the short-range technologies, for example, near field communication (Becker, 2007).

According to iResearch (2011), the reason for adopting proximate mobile payment is the

instant and fast speed of the transaction. However, due to the absence of individuals’ trust that

resulted from the vulnerability of cybersecurity towards both mobile payment systems and the mobile devices. End-users may generate a negative feeling for adopting the mobile payment

(Slade, Williams, Dwivedi, & Piercy, 2015).

Due to the great potential and opportunities of mobile payment, different kinds of solution providers were involved in the market. Among those different models, the

independent service provider or third-party provider acts independently as a “neutral” intermediary in the middle of financial agency and operators, and it can provide service of

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mobile payment between customers (C2C) and between customers and merchants (C2B)

(Chaix & Torre, 2011; Smart Card Alliance, 2008)

However, due to the complexity and the time constraint, this paper will mainly focus on

the perspective of end-users (both buyer and seller side) to investigate the potential reasons of C2B proximate mobile payment adoption under the independent service provider model in

China.

2.2 Mobile payment in China

Mobile payment has its own advantages to grow fast in China. The mobile network and

financial infrastructures are relatively well-developed by the Chinese mobile network

operators and banks, which are unable to develop the operator-centric and bank-centric model for building their own mobile payment platform. Therefore, it gives the third-party operators

valuable opportunity for rapid growth.

Rather than the fully matured landline infrastructures equipped by developed countries,

the relatively strong mobile telecommunication infrastructure was built by the Chinese state-owned mobile network operators (Lu et al., 2011), which are only able to realize a

limited mobile payment sensorial, for example, pay the phone bills. The mobile network operators did not build their own mobile payment platforms but provided strong mobile

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The Chinese banks were unable to build their strong and powerful mobile payment

platforms but also helped the third-party operators to build a strong financial foundation and infrastructure. Firms like AliPay and WeChat payment positioned themselves as the third

party payment solution providers after they build the trust foundation among the Chinese internet users by firstly providing the third party transaction guarantee services for many

years. Those firms connected multiple parties and greatly reduced the financial burdens for the mobile payment users and finally resulted in a booming trends of proximate mobile

payment in China, which is largely due to the refined QR-code technology. The mobile

payment firms largely expanded their customers by realizing real-time transactions between their personal bank account and the account of apps.

In short, the Chinese banks made an endorsement for the mobile payment firms, and the

mobile payment users are still putting their money in their bank accounts by linking their accounts of mobile payment to their bank accounts (Wong & King, 2017). Therefore, mobile

payment becomes the intermediary of banks and customers, and is actually only used for

small payments.

Then, Chinese third-party centric mobile payment providers, mainly referred to as

AliPay and WeChat pay, provided an efficient solution to customers and filled the niche of the micro-payment market in Chinese (Lu et al., 2011).

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Therefore, the Chinese mobile payment market is so special, in terms of the perquisites

foundations and opportunities, which is hardly able to utilize the existed western experience in the field of mobile payment. Chinese people also experienced a special path to trust the

mobile payment providers before they addicted to their services. In fact, the Chinese users build their trust of mobile payment on the top of Chinese government, banks, and mobile

network operators, instead of the service providers themselves. In order to uncover the correspondent factors which are driving them to adopt the mobile payment as their payment

solutions. This research makes a quantitative analysis, which is based on the two separate

questionnaires for buyer side users and seller side users, and conclude from the results of tested hypotheses.

2.3 Adoption Models

Technological adoption model (TAM) was developed by Davis (1989), which had been utilized to predict the individual’s perception towards the acceptance of new technology.

Davis built TAM based on extending the two constructs of TRA theory from Fishbein and Ajzen (1975), which are perceived usefulness (PU) and perceived ease of use (PEOU). Davis

(1989) states that the actual usage of the new technology can be determined by individual’s behavior intention (BI), which can be determined by individual’s attitude towards the use of

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(Park & Chen, 2007; Joo & Sang, 2013; Jung, Chan, & Park, 2011; Lee, Kim, Ryu, & Lee,

2011) effectively validated the significance of TAM model by exploring the adoption intention of mobile technologies and services through their established theatrical model based

on the TAM model (Kim & Sundar, 2014).

The unified theory of acceptance and use of technology (UTAUT) model was built by

Venkatesh, Morris, Hall, Davis, and Walton (2003), which proposed four main constructs of the determinants of behavior intention. The four constructs are effort expectancy,

performance expectancy, social influence, and the facilitating conditions. UTAUT model can

relatively more comprehensively predict the success of acceptance and understand the driving factors, because the UTAUT model was based on the previous studies like TRA, TAM, and

the innovation diffusion theory (IDT) (Lu et al., 2005).

The TAM and UTAUT model are generic models and equally focus on the general acceptance issues, which are unable to explain or predict the specific technology or system in

detail. Also, the generic models may neglect some other influential issues of the determinants

of acceptance, for example, the personal innovativeness (Lu et al., 2005).

In the mobile payment related studies, in order to investigate the determinant factors of

users, some previous researches mainly applied TAM model (Bachfischer, Lawrence, & Steele, 2004; Schierz et al., 2010; Dahlberg, Mallat, & Öörni, 2003), and other researches

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obvious that both TAM and UTAUT models are matured and able to be employed in the

research of determinant factors that drive users to adopt mobile payment as their payment methods. However, previous researches only separately applied those useful models and

seldom further extend with other influential factors, for example, intimacy. In addition, previous researches have also applied the TAM and UTAUT model in China and other Asian

countries’ mobile payment studies (Tan et al., 2014; Lu et al., 2011; Thakur & Srivastava, 2014), which gives this research with great confidence to further explore the unique Chinese

mobile payment market with those two models. Therefore, those applicable models will also

be integrated in this research. But in order to prevent the potential drawbacks resulted from the usage of a single model, this research will comprehensively apply the TAM and UTAUT

model with incorporated other influential factors, for example, personal innovativeness,

network externality, and intimacy.

Therefore, the comprehensive conceptual model of this research is based on TAM model,

UTAUT model, and also includes some other crucial factors. Specifically, for the buyer side

users, perceived usefulness and perceived ease of use (both from TAM model), social influence (from UTAUT model), and personal innovativeness are incorporated, because of

that the paying habits of consumers are developed from people’s character and external environment. For seller side users, the network externality, intimacy, trust, and financial cost

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mentioned the importance of those two factors (Tan et al., 2014; Mallat, 2007; Guo &

Bouwman, 2016; Mallat & Tuunainen, 2008).

The following several sections introduce each interested variable specifically, which are

also developed with the hypothesis for testing. At the end of this chapter, the conceptual model of this research is also presented.

2.4 Buyer Side Users

2.4.1 Perceived Usefulness and Perceived Ease of Use

The technological adoption model (TAM) is frequently used to predict the user acceptance of information systems, and mainly focuses on two theoretical constructs,

perceived usefulness (PU) and perceived ease of use (PEOU), which are theorized to be the

fundamental determinants of system use (Davis, 1989).

Perceived usefulness is “the degree to which a person believes that using a particular

system would enhance his or her job performance” (Davis, 1989, p.320). If a system is high in

the perceived usefulness, the user will, even more, believe there is a positive relationship in its usefulness. The perceived ease of use is "the degree to which a person believes that using a

particular system would be free from effort" (Davis, 1989, p.320).

Research supported that the perceived ease of use can be the determinant of usage

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supports (Hu, Chau, Sheng, & Tam, 1999; Lucas & Spitler, 1999). PEOU is a major concern

of the mobile payment system, because of the complicated steps that may be involved in the transactions (Saxena, Das, & Gupta, 2005). In addition, many other issues still may influence

the usage of devices and finally affect the user experience of whole mobile payment service, for example, the limited resolutions, size of the screen, and the short battery lifetime (Curran

& Huang, 2008).

According to Davis (1989), the perceived ease of use of an application will lead to a

positive attitude of adopting mobile payment by users. Nevertheless, compared to the PEOU,

it is believed that the perceived usefulness of an application is stronger correlated to the positive attitude of adopting systems, which should not be omitted (Davis, 1989). Those

different findings could result from their own different situations or technologies. In the

environment of mobile payment of this research, the PEOU is defined according to the extent of the difficulty of learning and using the mobile payment system. And the PU has defined

according to that whether the mobile payment is a useful, quicker, and effective payment

solution. Therefore, it is hypothesized.

H1: There is a positive relationship between the perceived usefulness of buyer side users

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H2: There is a positive relationship between the perceived ease of use of buyer side users

and the attitude of mobile payment adoption. 2.4.2 Personal Innovativeness

Among the frequently adopted technology acceptance model – TAM model (Davis, 1989), the revised TAM model: TAM 2 from Venkatesh and Davis (2000), the TAM 3 from

Venkatesh and Bala (2008) and the UTAUT (Venkatesh et al., 2003), the personal

innovativeness is absent when analyzing the adoption of new technologies.

Lu et al. (2005) state that a person who has relatively higher personal innovativeness is

more likely to generate a positive attitude towards the target technology. According to

Agarwal and Prasad (1998), personal innovativeness in information technology (PIIT) is the individual’s willingness to try new information technologies, and it is described as a represent

of the risk-taking propensity only existed in limited numbers of individuals. It is also considered as a critical factor to explain the attitude towards the individual’s adoption

behavior. There is a significant positive relationship between individual’s PIIT level and their attitudes toward new technologies (Agarwal & Prasad, 1998). Agarwal and Prasad (1998)

also believed that the personal innovativeness should be integrated into the TAM model from Davis (1989), and proposed that the higher level of innovativeness will lead to a positive

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the positive adoption intention of a technology. Recent studies from Asian countries, India

and Malaysia, found that the personal innovativeness will affect users’ adoption intention toward the mobile payment (Thakur & Srivastava, 2014; Tan et al., 2014).

In the light of previous researches, specifically, the researches had been done in Asia, which will give confidence for this research to integrate personal innovativeness in the

conceptual model for fulfillment. Then, in the environment of mobile payment, it is hypothesized.

H3: There is a positive relationship between the attitude of mobile payment adoption and

the personal innovativeness of buyer side users.

2.4.3 Social Influence

There are two forms of social influence. The first type is the information influence,

which occurs when the information is adopted by people as the evidence of reality. The second type is the normative influence, which occurs when people correspond to others’

expectation (Bearden, Calcich, Netemeyer, & Teel, 1986).

Social influence has been widely considered as the important factor in explaining the adoption of new technologies. (Cooper & Zmud, 1990; Karahanna, Straub, & Chervany, 1999)

The social influence could be referred to as the perceived pressures from individual’s social network, which will influence the final behavior decision of individual (Lu et al., 2005).

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Triggered by the uncertainty of whether to adopt the innovative technologies, individuals tend

to involve the social network for consulting and reducing their anxieties. (Karahanna et al., 1999). The UTAUT model incorporated the social influence as a crucial factor to determine

individual’s adoption intention towards the technology (Venkatesh et al., 2003).

In the mobile-technologies based adoption researches, many researchers applied the

social influence for building the model and presenting with supportive conclusions (Hong & Tam, 2006). Also, according to Lu et al. (2005), social influence will shape an individual’s

attitude towards the confidence of using the system. Researchers indicated that the wireless

mobile internet services are designed to provide convenience and efficiency for users (Siau, Sheng, & Nah, 2004). However, the social influence will shape the estimation of individuals’

confidence or ability towards using a system or technology (Lu et al., 2005).

Therefore, in the environment of mobile payments, people’s perception toward the mobile payment may be influenced by other’s opinion or attitude, especially when people still

not familiar with this technology and need to discover it by themselves. Accordingly, it is

hypothesized.

H4: Social influence will be positively related to the attitude of mobile payment adoption

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2.5 Attitude, Intention to use and Actual adoption

The definition of attitude is “an individual’s positive or negative feelings about performing the target behavior” (Fishbein & Ajzen, 1975, p.216). And the attitude has long

been identified as the cause of intention (Suki & Suki, 2011). Also, Kyun, Kim, and McMillan (2009) state that the user’s attitude towards the mobile devices will affect their intention to use

it.

The intention is an indication of people's readiness to conduct certain behavior and

"assumed to be the immediate antecedent of behavior" (Ajzen, 2002, p.665).

According to TAM model (Davis, 1989), the actual usage of a technology system is determined by user’s behavior intention. Then, the actual adoption and behavior intention is

jointly determined by the attitude of technology system adoption. Therefore, it can infer that

the relationships among attitude, intention and actual adoption can be applied in the environment of mobile payment in the same way. In contrast, Mallat and Tuunainen (2005;

2008) state that the majority of seller side users did not perceive the mobile payments as a fully mature technology to become one of their payment solutions, although the mobile payment holds lots of potential in some business. This conflictive argument, which is from the mobile payment field, contradicts with the most credible and famous generic adoption model. Then,

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the difference urges this research to further explore the relationship between adoption intention and actual adoption of mobile payment. Thus, it is hypothesized.

H5: There is a positive relationship between the attitude of mobile payment adoption and

the buyer’s intention to use.

H6: There is a positive relationship between the seller’s intention to use mobile payment

and the mobile payment adoption of the seller. 2.6 Seller Side Users

2.6.1 Network Externality

Network externality has been used to explain the value creation in the network related markets, furthermore, in the mobile payment context, because of the indirect network

externalities of the payment technology, it has been recognized as an influential factor determines the adoption of mobile payment (Economides, 1996; Liebowitz, 2002; Van Hove,

1999).

It has been already approved that the network externality can positively related to the critical mass (Himmelberg, 1995). For the seller side users, Kauffman, McAndrews, and

Wang (2000) state that the mobile payment represents a highly networked service, which the

amount of participation has a great impact on the benefits of service. In addition, Mallat (2007) states that the adoption intention of buyer side users is significantly affected by the number of

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actual users from seller side, because it determines the potential opportunities allow buyer

side users to actually use the technology. Each new adopter from buyer side will increase the number of seller side users, then, the adoption intention of both sides’ users is actually

interdependent and based on their perceived amount of actual users for another side.

Additionally, according to Mallat and Tuunainen (2008), the low adoption rate of buyer

side user will lead seller side user to suspect the maturity of mobile payment market, furthermore, pessimistic with their financial return after the adoption of mobile payment.

Then, seller side users will concern the possibility of realization of reaching a larger number

of consumers by adopting the mobile payment. Thus, the insufficient network externalities that resulted from the lack of critical mass will affect the adoption of mobile payment of seller

side users. Au and Kauffman (2008) proposed that the users will only able to work with limited

versions of mobile payment platforms to maximize the utility of the instruments and finally cause the termination of credit cards, in addition, the network externality will benefit buyer side users with convenience and connectivity, and it will bring efficiency to the seller side users, finally, comparing with the offer from credit card companies, it will encourage consumers to eventually adopt the mobile payment instruments as their main payment solution. Considering the current situation of Chinese mobile payment market, which is largely coped with the arguments from Au and Kauffman (2008). Therefore, it gives strong confidence for this

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research to incorporate the network externality as an influential factor in the conceptual model.

Then, it is hypothesized.

H7: The network externality is positively related to the intention to use the mobile

payment for seller side users. 2.6.2 Intimacy

According to Treacy and Wiersema (1993), customer intimacy is considered as one of

three value disciplines for companies to deliver values to their customers, and it requires companies to understand their customers and even further satisfy their special needs. Many

companies want to build up a rapport with their customers. The intimacy is a significant

constitute of customer influence toward the service provider (Froehle & Roth, 2004).

From the research of Froehle and Roth (2004), the intimacy between customer and

merchant could be realized by face-to-face contact or the technology-mediated environments. Guo and Bouwman (2016) state that the users from seller side adopted or intend to adopt the

mobile payment are more focused on the customer intimacy and see the mobile payment as an innovative payment method. When the merchant adopted the mobile payment, it suggests that

they always put the needs of their customers at a first place.

According to Jones, Mothersbaugh, and Beatty (2000), the customers will increase their

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feel the trustworthiness of their services result from the competence of the services. Therefore,

it is categorized as the satisfier of the two-factor theory (Liu et al., 2011). In the mobile payment environment of this research, the intimacy also viewed as a satisfier. Then, it is hypothesized.

H8: Seller side users that focus on customer intimacy is positively related to the intention of mobile payment adoption.

2.6.3 Financial Cost

Financial costs include several parts. The transactional fees of using the service, the

additional costs of maintaining and upgrading the services, and other uncovered fees during

the transactions together integrated as the financial cost (Luarn & Lin, 2005; Wu &Wang, 2005). The perceived fees have a prominent effect on the perceived value, in the context of

the general mobile internet (Kim, Chan, & Gupta, 2007).

The mobile payment service providers, who update for software and hardware of POS, and the training of employees may acquire high commissions and fees when the seller side

users are adopting the mobile payment as a transaction solution (Mallat & Tuunainen, 2008).

Therefore, the financial cost can be one of the major barriers for seller side users to adopt the mobile payment (Alexander, Howells, & Hine, 1992). According to Mallat and Tuunainen

(2008), the cost has a significant impact on the mobile payment adoption. The sellers, who pass the transaction fees to their customers are unlikely to succeed. Therefore, the sellers are

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seeking a low-cost transaction instrument. It is also reported that the merchants are requiring

cost-effective and competitive pricing before their adoption of the mobile payment, and claiming with low fees in the early phase of the usage of mobile payment solution. Kleijnen,

Wetzels, and Ruyter (2004) state that the perceived financial cost is a significant determinant of the usage intention of wireless financial services.

Specifically, in the context of mobile payment, there is a negative relationship between premium priced mobile payment system and adoption willingness (Mallat & Tuunainen,

2008). In China, the financial cost of mobile payment for seller side users is not high,

compared to fees of other payment solution providers. And it is in line with the argument from Mallat (2007) that business model of mobile payment providers should not rely on the

charges of using the platform by consumers. Also, it should be lower than the commissions of

other payment instruments (Mallat & Tuunainen, 2008). It provides great confidence for this research to incorporate the financial cost in the model. Furthermore, according to the

mentioned previous studies, the financial cost will be categorized as a hygiene factor in this

research. Thus, it is hypothesized.

H9: Financial cost is negatively related to the intention to use mobile payment for seller

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2.6.4 Trust and Security Issues

According to Rambus (2018), for the current situation of Chinese mobile payment, another additional security issue will be the static QR code, which is never changing, and

always staying as they are. Then, it can be printed on a piece of paper but extremely easy to be exploited by simply replace it with another one.

Therefore, trust is considered as a subjective belief that one party will accomplish the obligation in line with the expectation of trusting party. It is important that gaining trust will

reduce fears and uncertainties (Gefen, Karahanna, & Straub, 2003; McKnight, Choudhury, &

Kacmar, 2002). Previous researchers found that the trust as an influential determinant will affect the willingness of customers to conduct the transaction of e-commerce (Gefen et al.,

2003), a step further, in the mobile environment, trust of service provider and the system itself

will significantly determine the success of transaction (Siau, Sheng, Nah, & Davis, 2004). Specifically, the previous researcher found that in the context of mobile payment, the trust of

consumers is more related to authentication and confidentiality of personal data (Dewan, &

Chen, 2005).

Hayashi and Bradford (2014) proposed and explained about the concerns in details,

which are regarding the security issues, seller side users worried about the safety of consumers’ mobile devices. For example, whether the sensitive information of sellers will be

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concern, especially for the third-party provided mobile payment solutions. Because the

ownership of customer data is unclear when the third party is involved. Then, it leads to further uncertainty of responsibility for customer data security. Once the customer data

related to the purchase history is breached, the reputation of the seller will be harmed, even if the seller does not own the data. Therefore, according to Mallat and Tuunainen (2008), the

trust and security issues in payment solutions are important prerequisites for seller side users to adopt the mobile payment. However, the trust and security issues are not considered as the

significant barriers to mobile payment adoption. As an influential determinant, trust is

categorized as a hygiene factor, due to the fact that it must be qualified to maintain the relationship between consumer and service provider (Liu et al., 2011). Thus, it is

Hypothesized.

H10: For the seller side users, there is a positive relationship between the trust of mobile payment and the intention of mobile payment adoption.

2.6.5 Herzberg’s two-factor theory

Herzberg, Mausner, and Snyderman (1959) state that there are two factors can affect the motivation of workers, hygiene factors, and motivation factors (or satisfiers). Hygiene factors

refer to the organization’s contextual features that support workers (e.g. salary, firm’s policy and work conditions). Without hygiene factors, workers may feel grievances and even against

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gained from satisfactory job performance e.g. the feeling of accomplishment and the

appreciation from colleagues (Herzberg, 1968). Hygiene factors and motivation factors are

correlated to job dissatisfaction and satisfaction, respectively. But, the two factors represent

two distinct spectra rather than the opposite two ends of a spectrum (House & Wigdor, 1967). The two-factor theory is not only applicable in the work environment but also applied in

many other research contexts, for example, it is used to investigate the behavior motivation caused by internet services (Liang & Lai, 2002; Ong, Chang, & Lee, 2013). Previous

researches indicated, in the information system context, the enablers, motivators or satisfiers,

refer to the factors that motivate users to accept the products or services, as for the inhibitors, de-motivators or hygiene factors, refer to the factors that prohibit the actual usage (Cenfetelli

& Schwarz, 2011; Park & Ryoo, 2013). Furthermore, the enablers and inhibitors are not

completely opposite to each other. Instead, the absence of inhibitors will not necessarily facilitate the adoption. The growth of electronic transaction will largely decrease the usage of old payment solutions and facilitate the adoption of mobile payment. For the adoption analysis of seller side users of mobile payment, with the help of two-factor theory, the behind psychological loss/ gains will be easier to clarify. And we can infer that the enablers will facilitate the seller side users to apply the service, for example, due to the perceived increased intimacy between seller side users and their own customers, it will lead to their increased positive adoption intention of mobile payment. However, for the inhibitors, like the trust issues,

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if seller side users do not trust the service providers, then, it will decrease their adoption intention.

Accordingly, in the research of Liu et al. (2011), the two-factor theory has been used as

an effective instrument to systematically examine and study the customer satisfaction related factors, in the context of mobile telecommunication. In the researches of related field, hygiene

factors are seen as a “must” for a business, whereas the motivation factors are “extra feel-good features”. The trust is a hygiene factor. While the intimacy is motivation factor (Liu

et al., 2011).

This research will use two-factor theory to explain the mobile payment adoption of seller side users, also, in line with the previous studies, the issues like trust and financial cost will be

categorized into hygiene factors. The issues like network externality and intimacy will be

categorized into motivation factors.

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Conceptual model

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3. Research Methodology

This chapter introduces the method used to collect and analyze data. The section 3.1

describes the data collection process. Section 3.2 describes the measurement of factors. The

section 3.3 shortly describes the procedure of data analysis. 3.1 Population and sample

The aim of this research is to examine the determinant factors that affect the adoption of

mobile payment in China, from both buyer side and seller side users’ perspective. To test the proposed hypotheses, a cross-sectional survey design was conducted because it is easier to be

applied by a master’s level student and is frequently used in quantitative research.

Considering the realistic fact that the English level of Chinese is not commonly high, which can affect the rate of participation and may also harm the reliability of data collection.

Therefore, the data were collected through two separate Chinese questionnaires, which were both directly translated from the original English version after the proof translation from

another native Chinese speaker, for both buyer side and seller side version respectively. Those questionnaires were designed and distributed with the help of an online application, the

“Qualtrics”, which allows the respondents to enter in via a received link, and the corresponding responses were recorded automatically. The survey applied snowball method,

which will largely save the financial cost of the survey with a high number of fit participants.

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layer participants) were asked to distribute those surveys to different selected people (second

layer participants) through their own social networks, for example, their friends and colleagues. Because there are two separate surveys, so, the close relatives were contacted

firstly, in order to make sure the buyer side and seller side version will be sent to the appropriate people. Besides, everyone was requested to pre-select the participants before each

sending, in case of the surveys would be successively sent to others by the second layer participants. Therefore, no matter how many times that the surveys were sent, it can be sure

that the distinct surveys were filled out by the appropriate corresponding participants, because

of the careful selections and detailed explanations. In the final dataset, 1198 people filled in the buyer side questionnaire, but 920 completed all questions, including 370 males (40.2%)

and 550 females (59.8%). 40.4% of buyer side users ranging from 25 to 34 years old, 24%

and 20% of buyer side users belong from 35 to 44 ages and 45 to 54 ages respectively. More than half (53.2%) of buyer side respondents got a bachelor degree, and 70.7% of buyer side

respondents have less than four family members. 70.1% of respondents reported that they

don’t have a religious belief and 19.1% of them believe in Buddhism. Respondents with annual income ranging from 20,000 to 50,000 RMB and 50,000 to 80,000 accounts for the

proportion of 27.3% and 29.3% separately. Furthermore, there is a similar proportion of participants who earn less than 20,000 RMB (17%), compared with people who earn 80.000

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For the seller side dataset, 839 of 1111 respondents completed the questionnaire, which

comprises 419 males (49.9%) and 420 females (50.1%). About the demographic data, among the seller side users, there is 51.8% of the participants belong from 25 to 34 years old; 26.8%

of seller side participants aged between 35 to 44 years old. Compared with buyer side participants, 68.3% of seller side participants have a bachelor’s degree, which number is

obviously larger than them. About the organizational characteristics, 33.5% and 24.1% of seller side users worked in a 'small or midsize business' and 'large organization' respectively.

The turnover of the organization presents an extreme result, which is 42.1% of sellers

reported more than 10 million RMB and 39.6% of sellers reported less than 0.5 million RMB. 57.2% of respondents worked in a local organization, and 40.3% of sellers worked in a

national organization. Moreover, 90.7% of respondents worked in the finance industry.

3.2 Measurements

Two different questionnaires were distributed to buyer side and seller side users

respectively. The demographic information, including age, gender, and education level, were

answered by all respondents. For buyer side, annual income, number of family members, and religious belief also be asked. For seller side, questions about organization characteristics

were answered, including the type of organization, turnover, sector type, and presence of the organization. All items were answered by using a five-point Likert scale ranging from 1

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Davis (1989) were used to measure the perceived usefulness with a high reliability of 0.90.

One example item is “Mobile payment is a useful payment method”.

Three items from Venkatesh, Thong, and Xu (2012) were used to measure perceived

ease of use, but the reliability is 0.44. After deleting the last item, the reliability increased to 0.86. One example item is “Mobile payment technology is easy to learn”. Also, three items

from the same resource were used to measure social influence with high reliability of 0.82, the example item of social influence is “People who influence my behavior think that I should

use mobile payment” (Venkatesh et al., 2012). Moreover, three items were used to measure

intention to use with a very high reliability of 0.94. The example item is “I plan to use mobile payment frequently”. Three items from Agarwal and Prasad (1998) were used to measure

personal innovativeness, and the reliability is 0.74. One example item is “In general, I am not

hesitant to try out new information technologies”.

Four items from Oh et al. (2003), van der Heijden (2003), and Yang and Yoo (2004)

were used to measure attitude towards mobile payment with the reliability 0.92. The example

item is “Using mobile payment is wise”.

For seller side questionnaire, three items from Yu and Tao (2007), and Katz and Shapiro

(1992) were used to measure network externality, and the reliability is 0.86. An example item is “If more and more merchants accept mobile payment, then the quality of mobile payment

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Intimacy was measured by using three items from Kim, Part, and Jeong (2004). The

reliability is 0.91 and one example is “My mobile payment service provider cares for its customer”. Also, three items from the same source were used to measure trust with reliability

0.90. The example is “My mobile payment always provides safe financial services”. Three items from Constantinides (2002) were used to measure financial cost with reliability 0.90.

One example item is “I think the access cost is expensive of using mobile payment”. The measured items of 'seller’s intention to use' are same as the items that used in the buyer-side

questionnaire. The item of adoption is not found from existing literature. Therefore, the

specific one question was written by myself, which is “I prefer to apply the mobile payment services instead of the alternative kinds of payment methods, for example, the credit card and

cash”. The reliability cannot be tested because there was only one item.

3.3 Analysis

The analyses were executed in SPSS version 22. Firstly, the missing data were checked.

Secondly, the reliability of the scales was tested by using Cronbach’s alpha. Then, PCA was

used to test the construct validity of scales, and an eigenvalue greater than 1 was used as criteria for determining factors. Moreover, the correlations between all variables were being

examined. Lastly, four regression analyses were conducted to test the hypotheses, with a significant level of !=0.05.

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4. Results

This chapter mainly focuses on findings. Section 4.1 reports all the correlations. Section

4.2 introduces all the results of tested hypotheses.

4.1 Correlations

Table 2 shows the mean, standard deviation and correlations of all variables in the buyer

model. For the correlation between demographic information and measured variables,

perceived usefulness is negatively correlated with age (r= -.12, p<.01), but positively correlated with education level (r=.13, p<.01). Perceived ease of use is negatively correlated

with age (r= -.18, p<.01) and family member (r= -.07, p<.05), but positively correlated with

education level (r= .11, p<.01). Personal innovativeness is negatively correlated with gender (r= -.10, p<.01) and age (r= -.11, p<.01), but positively correlated with annual income (r=.10,

p<.01). Moreover, there is a negative correlation between social influence and gender (r= -.09, p<.01), but a positive correlation between social influence and annual income (r=.08, p<.05).

Attitude towards mobile payment is negatively correlated with age (r= -.12, p<.01), but positively correlated with education level (r=.13, p<.01). Buyer’s intention to use is

negatively correlated with age (r= -.18, p<.01), but positively correlated with education level (r=.14, p<.01) and annual income (r=.07, p<.05). Furthermore, correlations among the

personal innovativeness(PIIT), the perceived ease of use (PEOU), the perceived usefulness

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! 37!

Table 2: Descriptive Statistics and Correlations (Buyer’s side model)

Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11 1 Gender 0.60 0.49 (-) 2 Age 3.59 1.06 -.03 (-) 3 Education Level 4.53 1.02 .04 -.24** _(-) 4 Aunual Income 2.81 1.33 -.18** .17** .22** (-) 5 Family Member 2.73 1.34 -.10** -.08* -.10** -.00 (-) 6 Perceived Usefulness 4.13 0.72 -.01 -.12** .13** .01 -.04 (.90)

7 Perceived Ease Of Use 4.04 0.70 .01 -.18** .11** .01 -.07* .66** (.86)

8 Personal Innovativeness 3.57 0.68 -.10** _-.11** _.01 _.10** _-.06 _.37** _.49** _(.74)

9 Social Influence 3.59 0.78 -.09** -.05 .00 .08* -.02 .36** .41** .54** (.90)

10 Attitude 3.92 0.69 -.03 -.12** _.13** _.06 _-.06 _.53** _.61** _.49** _.52** _(.92)

11 Intention To Use 3.99 0.72 -.01 -.18** _.14** _.07* _-.03 _.54** _.61** _.46** _.44** _.80** _(.94)

Note. N=920. Gender was coded as 0=Male, 1=Female. The Perceived Usefulness, the Perceived Ease Of Use, the Personal Innovativeness, the Social Influence, the Attitude, and the Intention To Use were measured on a scale from 1 to 5. Between the parentheses, alpha coefficients have been presented on the diagonal between parentheses. And the significant convergent correlations have presented in bold-face.

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! 38! Table 3: Descriptive Statistics and Correlations (Seller’s side model)

Variable M SD 1 2 3 4 5 6 7 8 9 10 1 Gender 0.50 0.50 (-) 2 Age 3.41 0.85 -.11** _(-) 3 Education Level 4.66 0.67 .05 -.16** (-) 4 Annual Turnover 3.09 1.84 -.03 -.07 .05 (-) 5 Network Externality 3.90 0.86 .02 -.02 .06 .02 (.86) 6 Intimacy 3.75 0.84 -.02 -.04 .03 -.02 .70** (.91) 7 Financial Cost 2.99 0.94 .07 -.05 -.04 -.04 .12** .18** (.90) 8 Trust 3.43 0.81 -.02 .00 -.01 -.03 .46** .55** .31** (.90)

9 Intention To Use Seller 3.84 0.75 .01 -.10** .10** -.01 .57** .61** .13** .65** (.94) 10 Adoption 3.70 0.87 .02 -.10** .12** -.01 .45** .52** .14** .56** .77** (-)

Note. N=920. Gender was coded as 0=Male, 1=Female. The Network Externality, the Intimacy, the Financial Cost, the Trust, the Intention

To Use Seller, and the Adoption were measured on a scale from 1 to 5. Between the parentheses, alpha coefficients have been presented on the diagonal between parentheses. And the significant convergent correlations have presented in bold-face.

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use are significantly positive. The strongest correlation exists between buyer’s attitude

towards mobile payment and buyer’s intention to use (r=.80, p<.01).

Table 3 shows the mean, standard deviation, and correlations of all variables in the seller

side model. For the correlation between demographic characteristics in individual and organizational level and measured variables, seller side user’s age is negatively correlated

with seller’s intention to use (r= -.10, p<.01) and adoption (r= -.10, p<.01). In contrast, the seller’s education level is positively correlated with seller’s intention to use (r=.10, p<.01) and

adoption (r=.12, p<.01). Other correlations are insignificant. For the measured variables in the

seller’s model, all correlations among network externality, intimacy, financial cost, trust, seller’s intention to use and adoption are significantly positive.

4.2 Hypotheses testing

To test the hypothesis 1 to hypothesis 4, a linear regression analysis was conducted, along with regard perceived usefulness, perceived ease of use, personal innovativeness, and

social influence as independent variables; regarding buyer’s attitude towards mobile payment

as a dependent variable. From table 4, all four independent variables positively predicted the buyer’s attitude towards mobile payment. Standardized coefficient between perceived

usefulness and attitude is positive and significant (β=.17, p<.01), so hypothesis 1 is supported. Perceived ease of use has the strongest effect on the attitude towards mobile payment (β= .34,

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innovativeness and attitude (β=.13, p<.01), as well as social influence and buyer’s attitude

towards mobile payment (β=.25, p< .01). Therefore, hypothesis 3 and 4 are both supported.

Table 4: Regression analysis for hypothesis 1 to hypothesis 4

Unstandardized Coefficients

Standardized Coefficients

Model B Std. Error Beta t Sig.

1 (Constant) .63 .12 5.44 .00

Perceived Usefulness .16 .03 .17 5.35 .00

Perceived Ease of Use .34 .03 .34 10.02 .00

Personal Innovativeness .13 .03 .13 4.21 .00

Social Influence .22 .03 .25 8.54 .00

a. Dependent Variable: Attitude

Another simple regression analysis was used to test hypothesis 5, which has shown a

positive relationship between buyer’s attitude towards mobile payment and buyer’s intention to use. The result (Table 5) shows that the coefficient is positive and significant (β=.80,

p<.01). Therefore, hypothesis 5 is supported.

Table 5: Regression analysis for hypothesis 5

1 (Constant) .74 .08 8.92 .00

Attitude .83 .02 .80 39.65 .00

a. Dependent variable: Intention To Use

To test hypothesis 6, which states there is a positive relationship between seller’s

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(Table 6) shows the coefficient is positive and significant (β=.77, p<.01). Therefore,

hypothesis 6 is supported.

Table 6: Regression analysis for hypothesis 6

1 (Constant) .26 .10 2.53 .01

Intention To Use Seller .90 .03 .77 34.36 .00

a. Dependent variable: Adoption

A linear regression analysis was executed to test hypothesis 7 to hypothesis 10, including

regarding the network externality, intimacy, financial cost, and trust as independent variables and regards the seller’s intention to use as a dependent variable. Results (Table 7) show that

the network externality (β=.21, p<.01), intimacy (β=.22, p<.01) and trust (β=.46, p<.01) all positively predicted seller’s intention to use. Therefore, hypothesis 7, hypothesis 8 and

hypothesis 10 are all supported. Moreover, the coefficient of financial cost is negative and

significant (β= -.08, p<.01). Therefore, hypothesis 9 is supported, which states there is a negative relationship between financial cost and seller’s intention to use.

Table 7: Regression analysis for hypothesis 7 to hypothesis 10

1 (Constant) 1.13 .10 11.27 .00

Network Externality .18 .03 .21 6.23 .00

Intimacy .20 .03 .22 6.32 .00

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Trust .42 .03 .46 15.52 .00 a. Dependent variable: Intention To Use Seller

The following table introduces all the results of tested hypotheses.

Table 8: The result of tested hypothesis

Hypothesis Result H1: There is a positive relationship between the perceived usefulness and buyer’s

attitude towards the adoption of mobile payment. Supported

H2: There is a positive relationship between the perceived ease of use and

buyer’s attitude towards the adoption of mobile payment. Supported H3: There is a positive relationship between the personal innovativeness and

buyer’s attitude towards the adoption of mobile payment. Supported H4: There is a positive relationship between social influence and buyer’s attitude

towards the adoption of mobile payment. Supported

H5: There is a positive relationship between the attitude of mobile payment

adoption and the buyer’s intention to use. Supported

H6: There is a positive relationship between the seller’s intention to use mobile

payment and the mobile payment adoption of seller. Supported H7: There is a positive relationship between network externality and seller’s

intention to use. Supported

H8: There is a positive relationship between intimacy and seller’s intention to

use. Supported

H9: There is a negative relationship between financial cost and seller’s intention

to use. Supported

H10: There is a positive relationship between the trust of seller side users and

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5. Discussion

This chapter is designed to deepen the discussion of the results after a short summary of the findings and provide the answers to the raised research questions in the first chapter. This

chapter also provides the academic and practical implications. The limitations of the research and the suggestions for future research will be given, in order to better resolve the questions

raised. Finally, an overall conclusion, which is based on the analysis of the collected data and the literature review, will be provided at the end.

5.1 Conclusions

Hypothesis 1 assumed that the buyer side users perceived more usefulness if they have a positive attitude towards the adoption of mobile payment. Considering the positive and

significant standardized coefficient (β=.17, p<.01), hypothesis 1 is supported. Therefore, this

research confirmed the previous study that the high perceived usefulness of a system would help the user believe in the existence of positive use-performance relationship (Davis, 1989).

Hypothesis 2 assumed that the buyer side users would perceive more ease of use if they have

a positive attitude towards the adoption of mobile payment. Hypothesis 2 was also supported (β=.34, p<.01), thus, this research confirmed the previous study conducted by Davis in 1989,

the user will more easily accept an application, which is perceived to be easier to use than another (Davis, 1989). Davis states that the perceived usefulness should be more influential

The factors driving adoption of proximate mobile payment methods, in the Chinese economy