Information School

INF6000 Dissertation COVER SHEET (TURNITIN) Registration Number 160237548 Family Name SI First Name WEN

Use of unfair means. It is the student's responsibility to ensure no aspect of their work is plagiarised or the result of other unfair means. The University’s and Information School’s advice on unfair means can be found in your Student Handbook, available via http://www.sheffield.ac.uk/is/current Assessment Word Count ____14491______________. If your dissertation has a word count that is outside the range 10,000 – 15,000 words or if you do not state the word count then a deduction of 3 marks will be applied Late submission. A dissertation submitted after 10am on the stated submission date will result in a deduction of 5% of the mark awarded for each working day after the submission date/time up to a maximum of 5 working days, where ‘working day’ includes Monday to Friday (excluding public holidays) and runs from 10am to 10am. A dissertation submitted after the maximum period will receive zero marks. Ethics documentation should be included in the Appendix if your dissertation has been judged to be Low Risk or High Risk. þ (Please tick the box if you have included the documentation) A deduction of 3 marks will be applied for a dissertation if the required ethics documentation is not included in the appendix; and the same deduction will be applied if your research data has not been available for inspection when required. The deduction procedures are detailed in the INF6000 Module Outline and Dissertation Handboo

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The impact of the Third-Party Payment on Consumer

Behaviours: a case study of Alipay in China

A study submitted in partial fulfilment of the requirements for the degree of Master of Information Management

at

THE UNIVERSITY OF SHEFFIELD

by

WEN SI

September 2017

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Abstract Background: Today, the rapid development of the global economy has led to people suffering increasing amounts of pressure. This has led to convenience and efficiency

becoming key words and main trends. One such aspect of these trends has been the

emergence of third-party payment systems, which have been particularly popular in

China. In a short time, such systems have developed rapidly and gradually come to

replace cash as the main payment method used in China.

Aims: The main aim in this study is to research the success of the Chinese third-party payment platform Alipay, how it has impacted Chinese consumer behaviours, and the

consequences of such systems becoming so popular.

Methods: This will be carried out through a quantitative research method, with a questionnaire adopted to collect data which will then be analysed through SPSS with

Microsoft Excel. This analysis takes multiple forms and will include descriptive

analyses, reliability analyses, Muti-linear Regression tests, and one-way ANOVA

methods to examine and analyse the results.

Results: Through the review of current studies, it was found few researchers gave an accurate definition of third-party payment, and most only focused on the influencing

factors such as personal attitudes, social norms, perceived ease of use, perceived

usefulness, perceived trust, perceived risk, and individual traits. Moreover, it was found

as the popularity of Alipay led to the emergence of third-party payment platforms,

China is moving towards a cashless society, with such systems replacing cash,

becoming the main payment method used in the country today.

Conclusions: Third-party payment platforms were chosen as the theme in this dissertation, and used to fill the gaps found in the current literature. The results show,

in China, using third-party payment platforms is a major trend, among both the young

and old. From this it can be suggested third-party payment in China has developed from

a new fashionable payment system amongst younger people to a main payment method

used by all people of all ages.

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Acknowledgement Fist and foremost, I would like to express my gratitude to my supervisor Dr. Christopher

Foster gave me lots of support and guidance on the topic and the final title. This support

continued throughout the process and was indispensable for me in finishing this

dissertation.

Furthermore, thanks go to Dr George Turner, one of my academic teachers who

deserves special mention for his kind and useful advice and academic support. This was

particularly helpful in my dissertation structure and improving my writing skills.

Thirdly, thanks go to all the friends who have supported me in the process, with special

mention for Joshua Beaumont, who gave me lots of academic support, and taught me

how to write in a more native and academic manner.

Finally, I want to thank my parents, to whom I am grateful for their constant

encouragement and support in my study at the University of Sheffield.

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Table of content ABSTRACT .............................................................................................................................. 3

ACKNOWLEDGEMENT ....................................................................................................... 4

CHAPTER 1. INTRODUCTION ........................................................................................... 9 1.1 DISSERTATION RESEARCH BACKGROUND ..................................................................... 9 1.2 DISSERTATION RESEARCH GAP .................................................................................... 10 1.3 DISSERTATION RESEARCH AIM, OBJECTIVES AND QUESTIONS ................................. 11

1.3.1 Dissertation research aim ...................................................................................... 11 1.3.2 Dissertation research objectives ............................................................................ 12 1.3.3 Dissertation research questions ............................................................................. 12

1.4 THE OUTLINE OF THIS DISSERTATION ......................................................................... 12 CHAPTER 2. LITERATURE REVIEW ............................................................................. 13

2.1 INTRODUCTION ............................................................................................................. 13 2.2 THE THIRD-PARTY PAYMENT PLATFORMS ..................................................... 13

2.2.1 The definition of third-party payment platform .................................................... 13 2.2.2 The characteristics of third-party payment system ............................................... 16 2.2.3 The third-party payment platform’s status in China ............................................ 17

2.3 CONSUMER BEHAVIOURS ............................................................................................. 22 2.3.1 Definition of consumer behaviours ....................................................................... 22 2.3.2 The relationship between consumer behaviours and lifestyle .............................. 22 2.3.3 An integrative framework ...................................................................................... 23

2.4 THE ANALYSIS OF FACTORS INFLUENCING CONSUMER BEHAVIOURS ...................... 24 2.4.1 Social Factors ......................................................................................................... 25 2.4.2 Situational factors .................................................................................................. 25 2.4.3 Consumer's perceptions ......................................................................................... 26

2.5 SUMMARY ...................................................................................................................... 28 CHAPTER 3 METHODOLOGY ......................................................................................... 29

3.1 INTRODUCTION ............................................................................................................. 29 3.2 RESEARCH METHODOLOGY: POSITIVISM .................................................................. 29 3.3 RESEARCH METHODS ................................................................................................... 30

3.3.1 Deductive: questionnaire survey ........................................................................... 30 3.3.2 Inductive ................................................................................................................. 31

3.4 RESEARCH STRATEGY .................................................................................................. 31 3.4.1 Questionnaire Design ............................................................................................ 31 3.4.2 Samples & Ethics ................................................................................................... 33

3.5 DATA ANALYSIS ............................................................................................................ 34 4.2 DESCRIPTIVE STATISTICS ANALYSIS ........................................................................... 38

4.2.1 The method of measures of the different variables .............................................. 38 4.2.2 Demographic information ..................................................................................... 43 4.2.3 Usage statue of Alipay ............................................................................................ 46 4.2.4 The general reasons for using Alipay ................................................................... 48 4.2.5 Selection of the payment method ........................................................................... 51

4.3 RELIABILITY ANALYSIS ................................................................................................ 51 4.4 MULTI-LINEAR REGRESSION ........................................................................................ 54

4.4.1 Introduction ............................................................................................................ 54 4.4.2 Model 1: UI & PU, PEU, PR, PT, PA ................................................................... 55 4.4.3 Model 2: COI & PU,PEU,PR,PT, PA ................................................................... 58

4.5 ONE-WAY ANOVA ....................................................................................................... 60 4.5.1 Assessing the strength of relationship ................................................................... 60

CHAPTER 5 DISCUSSION ................................................................................................ 63

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5.1 OVERVIEW ...................................................................................................................... 63 5.2 RESEARCH QUESTIONS RESPONSES ............................................................................... 63

CHAPTER 6: CONCLUSION ............................................................................................. 69 6.1 OVERVIEW ..................................................................................................................... 69 6.2 RESEARCH RESULTS ..................................................................................................... 69 6.3 DISSERTATION RESEARCH LIMITATIONS ................................................................... 70

REFERENCES ....................................................................................................................... 72

APPENDIX ............................................................................................................................. 77 APPENDIX1: QUESTIONNAIRE .............................................................................................. 77 APPENDIX2: APPLICATION ................................................................................................... 87 APPENDIX3: APPROVAL LETTER .......................................................................................... 92 APPENDIX4: INFO CONSENT ................................................................................................ 93 APPENDIX5: ACCESS TO DISSERTATION .............................................................................. 95 APPENDIX6: CONFIRMATION OF ADDRESS AFTER COMPLETION FORM ............................... 96

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List of Figures

Figure 2.1 The third-party payment system operational mode ----------------12

Figure 2.2 The framework of M-payment -------------------------------------------14

Figure 2.3. GMV of China's Third-party online payment--------------------------17

Figure 2.4 Chia's Third-Party Mobile payment--------------------------------------18

Figure 2.5 Market Share of China's Third-party Internet Payment Companies ---19

Figure2.6. Share of China's Top Third-party Mobile Payment------------------19

Figure 2.7 Top 500 Apps in China by Monthly Active Users-------------------20

Figure 3.1 The structure of this dissertation methodology---------------------28

Figure 4.1 The framework of analysing research results-----------------------36

Figure 4.2 The gender of respondents---------------------------------------42

Figure 4.3 The Age & Education level--------------------------------------------44

Figure 4.4 The occupation & income of respondents ------------------45

Figure 4.5 How long for the respondents has been used Alipay-------47

Figure 4.6 Respondents of the payment selection &their preference--------------51

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List of Tables

Table 4.1 Measures of the different variables--------------------------37

Table 4.2 The frequency for respondents using Alipay--------------46

Table 4.3 The spending via Alipay in each month---------------------48 Table 4.4 The gengerL REASONS FOR USNING Alipay---------------------48

Table 4.5 The results of reliabity analysis----------------------------------------52

Table 4.6 The result of R2 in model 1----------------------------------------------55

Table 4.7 The result of F-test in model 1-----------------------------------------56

Table 4.8 The result of t-test in model 1--------------------------------------------57

Table 4.9 The result of R2 and F test in model 2 ---------------------------------59

Table 4.10 The result of t-test in model 2-----------------------------------------59

Table 4.11 The table of ANOVA------------------------------------------------61

Table 4.12 The table of Multiple-comparisons----------------------------------62

Table 5.1 Education and Frequency------------------------------------------------64

Table 5.2 Income and Frequency------------------------------------------------65

Table 5.3 Gender & Frequency-----------------------------------------------------65

Table 5.4 Which aspects of consumers' lives were impacted by third-party payment....66

Table 5.5 The expenditure increase after using Alipay------------------------------67

Table 5.6 Perceived Risk from respondents' answers------------------------------68

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The impact of the Third-Party Payment on Consumer

Behaviours: a case study of Alipay in China

Chapter 1. Introduction 1.1 Dissertation research background

Over the past decade, as the result of the development of web and e-commerce’s

websites, the traditional shopping model has been gradually replaced by the E-

commerce shopping models (Dahlberg, Guo & Ondrus, 2015; Zhao & Xin, 2012). An

electronic payment (E-payment) system may be considered as a means to connect with

consumers with new e-commerce systems. At present, the e-commerce system is being

further developed and is becoming more popular, secure and reliable. As a result, e-

payment systems are rapidly increasing in China, especially third-party payment

method. This new payment system may involve mobile payment as a form of third-

party payment methods and it has become a new fashion lifestyle. Today, comparing

with the cash payment and online bank payment method and others various payment

forms, third-party payment is now considered much more convenience for consumers

and this accounts for its popularity (Ba, Whinstone & Zhang, 2003).

Recently in China, there has been a growing an interest in using mobile payment

systems(M-payment). According to a Chinese online payment report from STATISTIC

(2009) in 2009 China’s online payment transactions volume (OPTV) was ¥505 billion

(about £58 billion), but the third- party mobile OPTV was only ¥59 billion (about £6.4

billion). In 2004, Alipay, M-phone payment application was launched. At that time,

Chinese OPTV was beginning to increase significantly. In 2013, Chinese OPTV ¥5,400

billion (£620 billion), in addition third-party mobile OPTV expanded considerably

reaching ¥1,219.74 billion in sales (£140.2 billion). Until 2015, third-party mobile

OPTV amounted to ¥9,527.6 billion (£1095.1billion).

As one of many China's payment platforms, Alipay has achieved a 50% share of the

Chinese consumer segments, becoming the largest third-party platform in China. Liu

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(2015) claims that Alipay is the most widely used application in China. In 2014, Alipay

had already generated ¥600 million (£5,340 million) through their registered users. It

expanded its operations to include cooperation with more than sixty-five merchants and

business banks, for instance, VISA, MasterCard, which in order enhance their

reliability and attract more users (Alipay, 2017). It has been claimed that Alipay has

become one of the most popular methods of payment for many Chinese consumers (Xie

&Lin, 2014). Its rapid and unprecedented success may be considered an interesting case

study which could serve as useful model for other similar enterprises in other countries.

It is a new concept in payment and linked up banking services.

At present, Alipay is not just limited to online transactions, it also can be used off online.

For example, it can be used to pay for university fees, restaurant bills, or utility bills: in

fact, any type of payment. This is an important third-party E-payment tool for millions

of Chinese consumers, to date, Alipay has 450 million users (Alipay, 2017). In a sense,

it can create its own third-party ecosystem. The latter refers to a linked-in system

whereby many retail outlets are integrated in order to facilitate payment. Many consider

this development as an important stage in moving towards a cashless society and China

is on the way, moreover, this transformation is strongly advocated by the Jack Ma who

is the founder of Alibaba Group and Alipay (CNBC, 2017; Xinhua, 2017). This third-

party payment system has generated the success of Alipay which has motivated my

interest in this topic. It is considered an insightful case study worthy of dissertation

research as it points towards future E-payment applications.

1.2 Dissertation research gap Currently, many studies have focused on various types of factors which can impact on

consumer spending behaviours and how e-payment has affect these trends. However,

they have mainly concentrated on technological innovation and changing consumer

preferences (Ma, 2015; Dennis, Merrilees, Jayawardhena & Wright, 2008; Yoon &

Occe˜na, 2015). There are several differences between E-payment and third-party

payment. The formal continues to only one bank, whereas, the latter is linked to more

than one bank: in fact, there is no limit to their number. Another major difference is that

the 3rd part payment is an escrow system which is quite unlike the usual E-payment. To

date, there has been little attention given to the effects of third party payment systems

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on consumer behaviours and how it can influence lifestyles. This system has developed

rapidly in China in a very short period of time and it has already become the primary

payment method in China (Lv & Xia, 2010). Third-party payment system has now

developed into a mature E-payment ecosystem but with distinct Chinese characteristics

(Guo & Bouwman, 2016). In essence, an effective E-payment ecosystem tends to rely

on three vital elements: a valid business model; an analysis of consumer behaviours and

variables related to the physical environment. The latter refers to the geographical

locations, such as large urban concentrations, where it be accessed and initially used

(Ondrus, Lytinen & Pigneur, 2009).

China is not like other western countries in that is does not have a consumer culture

which depends heavily on credit cards. In fact, many consumers in China consider these

systems as insecure and that they can encourage over spending and higher consumer

debt. Over the past thirty years, Chinese E-commerce has developed relatively slowly

compared with western countries. However, since the emergence of the third-party

payment system in 2003, there has been a rapidly increase in its use. The present

Chinese E-payment system has the highest in the world overtaking economies, such as

the US and Japan, and it continues to expand (Huang, Dai & Liang, 2016; Gao, Chen,

Zheng and Zhou, 2012). However, a review of the current literature reveals a gap in the

research field in that few researchers have studied the significant impact of E-payment

on urban lifestyle. One possible reason for this could be the very rapid rise in its use

and the popularity of third-party payment systems. This trend has no parallels in other

western countries. It thus appears to be a particularly Chinese phenomenon which is

closely linked to the rapid changes in economic growth and corresponding consumer

behaviours.

1.3 Dissertation research aim, objectives and questions 1.3.1 Dissertation research aim

The main aim in this study is to investigate the success of Alipay as a third-party

payment platform and how it has impacted on Chinese consumer behaviours. It is also

considered important to determine the reasons why consumers have enthusiastically

embraced this system instead of other methods of payment. Relevant to the aim, it is

also necessary is to examine in what way their daily lives have what changed after

adopting third-party payment via Alipay. The results of this study may be conducive to

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the development of third-party payment systems or as a useful information provided to

others researchers who are interested in Alipay in China.

1.3.2 Dissertation research objectives

In this study, there are three specific research objectives:

1) To provide clear definitions of third-party payment and stating the third-party

payment status in China.

2) To define consumer behaviours and identify which important factors can affect

consumer behaviours.

3) To identify the advantages of using this third-party payment system as provided

by Alipay and how it has transformed Chinese consumer lifestyles.

1.3.3 Dissertation research questions

1) What are the determiner factors which affect consumer decision making when

adopting a third-party payment platform?

2) Do different types of consumers show different degrees of frequency in regard to

their use of Alipay?

3) In what way has the third-party payment system affected the Chinese consumer

behaviours?

4) What are the challenges that China faces in becoming a cashless society?

1.4 The outline of this dissertation This study consists of six chapters: the introduction, literature review, methodology,

results, discussion and conclusion chapters. Specifically, the second section of the

literature review focuses on reviewing studies on third-party payment, M-payment and

consumer behaviours. There is also a critical evaluation and summary of the most

relevant sources related to this topic. The methodology chapter describes the most

appropriate methods for collecting and analysing data. The fourth chapter analyses the

results: an appropriate tool (SPSS) is selected in order to analyse the data and describe

the research results. The discussion chapter discusses the interpretation of the findings

and addresses the research questions. It also compares and contrasts the study’s findings

with that of the current literature and offers possible explanations for the results. Finally,

the conclusion chapter offers a summary of the main findings and their contribution to

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this recent field of research involving third party payment applications. There is a

reflective review of the dissertation as well as recommendations for further research.

Chapter 2. Literature Review

2.1 Introduction This section reviews the previous and current studies on the third-party payment and

consumer behaviours as these elements are the two main themes of this study. The

review consists of six main sections. First, it is important to provide a clear and precise

definitions of the ‘third-party payment’ and ‘escrow services’ as these terms will be

used throughout this study. Similarly, it is essential to defines the concept of consumer

behaviours as well as to summaries the characteristics of a fully functional third-party

payment platform. Second, sources related to the e-commerce, third party payment and

consumer environment of China are examined as well as the present role of Alipay in

this sector. Third, the review focuses on the literature describing theoretical models

which are considered pertinent to this study, namely: The Theory of Reasoned Action

(TRA); the Technology Acceptance Model (TAM) and the unified Theory of

Acceptance and Use of Technology (UTAUT). These models can be used to analyse

factors which can affect the consumer behaviours, for instance: personal traits,

psychological factors, social and situational factors.

2.2 The third-party payment platforms 2.2.1 The definition of third-party payment platform

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By definition third-party payment is an escrow service. This means that on the behalf

of the transacting parties, trading money is held by a third party until both the seller and

buyer demonstrate to the third party that they are satisfied with this transaction. The

third-party payment platform will then release the trading money to the seller; otherwise,

the trading money will be returned to the buyer (Shen, 2012; Zhao & Sun, 2012). The

whole process of third-party payment method operation is shown in Figure 2.1.

Figure 2.1 The third-party payment system operational mode source (This author, 2017)

M-payment method is defined as using a mobile device using WI-FI, internet or

wireless and other communication technologies to pay for goods, services and all kinds

of bills in internet (Yang et al., 2011; Dahlberg, Mallat, Ondrus & Zmijewska, 2008;

Chen, 2008).

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Figure 2.2 The framework of M-payment (Source: Dahlberg, Guo and Ondrus, 2015)

It is clear that compared to the E-payment system, M-payment without the position

limit, is more flexible. However, other researchers, for example Stapleton (2013)

Dahlberg, Guo and Ondrus (2015) and Guo and Bouwman (2016) similarly point out a

weakness that the M-payment system required everyone operator must be cooperate

with a pay service providers, for instance, bank institution, VISA and Master. There is,

however, a drawback in that each account is just allowed to link with one pay service

provider. If the buyer and seller belong to different pay service providers, they will be

charged exchange service fees which invariably increase user costs. Compared to M-

payment, third-party payment system has another attractive consumer advantage. In the

M-payment system, when consumers transfer any amount of money, either large or

small, they still need to pay an additional transfer service fee to their bank. However,

by contrast, in daily transactions, the third-party payment platform allows a free transfer

to users: it is free of charge. (Shi, Zhang, Arthanari and Liu, et al., 2014). Several

researchers have advocated the third-party payment system. They argue that the third-

party payment system is one of the most critical drivers for internet exchange by

enhancing the process of monetary transactions. Nevertheless, in this new e-commerce

economy, the emergence of third-party payment system allows the consumer to share

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and transfer more information through technology than ever before. This new economy

also created opportunities for greater electronic crime (Rachel & Caterina, 2012). There

are criticisms of this system: for instance, Dai, Grundy and WNLo (2001) point out the

potential high security risks for mobile device payment mode. In certain circumstances,

consumer's bank information can be easy stolen by hackers and once consumers' money

is stolen there is often no legal redress. It is clear that in regard to mobile payment there

needs to be more legal control. Kim, Song, Braynow and Rao (2005) cited in Ma’s

dissertation (2016) hold the view that third-party payment is an independently third

party licensed institutional, which means the third-party platform is permitted by the

state. Moreover, in this third-party platform, each third-party payment user is allowed

to link with various banks accounts in order to transfer the money and pay the bill. This

new function duffers considerably from traditional one to one E-payment patterns

between consumer and bank. In conclusion, third-party payment system is an enhance

M-payment system and making it more effective tool for satisfying to the needs of

consumers (Choi & Sun, 2016). Currently, the main well-known third-party payment

platform operators are PayPal, Alipay, Apple pay, WeChat pay, Tenpay and Samsung

Pay, etc. PayPal was the first one third-party payment platform, however, Alipay is

currently the most popular third-party payment platform (Dahlberg, Cerpa, Bouwman

& Guo, 2015; Huang, Dai & Liang, 2014).

2.2.2 The characteristics of third-party payment system

According to the current literature, it is generally agreed that this new third-party

payment system combines all of the advantages of E-payments and M-payments.

Furthermore, as a third-party platform, this new element plays a vital role in this new

system as it makes users put more trust into the third-party payment system (Liu, 2015;

Shen,2012; Zhao & Sun, 2012; Choi and Sun, 2016). To date, an increasing number of

researchers are interested in third-party payment system, especially since the

emergence of Alipay, and its success in China's e-commerce sector in the past ten years

(Choi, Sun, 2016; Dahlberg, Bouwman, Cerpa & Guo,2015). According the current

literature, the main characteristics of third-party payment system may be summarized

as follows:

(1) Third-party payment system offer escrow service that can enhance user

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reliability and satisfaction (Bae, B., Ahn, & Infobank, 2012). The online escrow service

is designed to protect the debtor and the debt collector. During the process of transfer

money, payment is transferred to the escrow account rather than the debt collector's.

The debt collector can only receive the payment transferred by the third-party payment

platform when the seller confirms the received goods. The third-party payment platform

plays an intermediary role in this payment process. For consumers and merchants, third-

party payment platform not only means a fairer system but it can also reduce the cost

of banking services (Pinson, I., 2013; Shrader & Duflos, 2014).

(2) The third-party payment platform is much more flexible for its users. First,

third-party payment system can be utilized for different models, such as business to

consumer (B2C) or in these recent years the new emergence of consumer to consumer

(C2C) e-commerce model, allowing users more freedom to choose an appropriate

option for them (Lu, Yang, Chau, & Cao, 2011). Second, third-party payment is a non-

bank financial institution; however, the bank is not the only one financial organization.

The third-party payment platform also provides a digital deposits service and therefore

it is possible to use the third-party payment platform for remittance with very few

restricts (Jin, Song & Zhang, 2007). It is also important to point out that third-party

payment institution can ensure the security of user money (Zhao & Sun, 2012).

(3) The third-party payment platform can save costs as well as enhance information

security services. Furthermore, the third-party payment institution provides a unified

application interface by cooperating and liaising with many mainstream banks (Zhao &

Sun, 2012). These features are an improvement on other payment systems as they can

overcome the drawbacks of the other systems. This means users can enjoy the same

price in the one third-party payment platform when they conduct transactions with

different banks. They can then avoid paying multiple bank service fees when they

transfer or collect their money.

2.2.3 The third-party payment platform’s status in China

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Choi and Sun (2016) emphasize the fact that in China, the third-party payment occupies

the largest market share. In the first quarter of 2017, the Gross Merchandise Volume

(GMV) of the Chinese third-party online payment volume reached a massive 6.4 trillion

Yuan (£7,272 billion). It increased by 56.1% compared to the whole year of 2016

(iResearch, 2017). Between the first quarter of 2016 to 2017, the GMV of China’s third-

party status was shown in Figure 2.3.

Figure 2.3. GMV of China's Third-party online payment (Source: iResearch, 2017)

From the above figure, from 2016 to 2017, the GMV of China’s third-party payment

showed an increasing trend. In the M-payment sector, according to iResearch (2017)

the newest report statistics show that the GMV of China's third-party M-payment rose

steadily as shown in Figure 2.4.

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Figure 2.4 Chia's Third-Party Mobile payment (Sources: iResearch, 2017)

According to the figure above, the GMV of third-party payment on the mobile market

clearly increased, generating 22.7 trillion yuan (£2.6 billion) in the first quarter of 2017,

increasing by 113.4% compared in 2016.

The current study found that despite the sluggish global e-economy since 2015, China

has now become the world’s largest B2C E-Commerce Markets (Loesche, 2017). The

B2C E-commerce sales of China showed a significant rise in sales by 27.2% from

$766.5 billion in 2015 to $975 billion in 2016 (Ecommerce Europe, 2017). What is

surprising is that third-party payment in China can have such significant impact on

consumers spending behaviours. Based on the online market share of China's Third-

party Internet Payment companies in 2017 (Figure 5). The top ranking is Alipay with

30.7% of the market share in China, and Tenpay (22.2%) in second position.

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Figure 2.5 Market Share of China's Third-party Internet Payment Companies

(Sources: iResearch, 2017)

Alipay is not only limited to online transactions. From 2013, Alipay moved from online

transactions to a mobile platform which has become increasingly popular and widely

used in daily transactions. For example, for collecting cinema tickets, it is possible to

quickly scan QR code and then finish the transactions via the Alipay mobile application.

Significantly, this ticket deal on third-party payment platform transaction is free, except

for the use of mobile data (Chinadaily, 2017). This new function means that third-party

payment also supports physical payment when used in department stores and

supermarkets. This innovation means that shoppers do not need to rely on cash or even

their wallets.

Figure2.6. Share of China's Top Third-party Mobile Payment (Sources: iResearch, 2017)

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From iResearch (2017) the mobile payment market share of China's top third-party

payment report (Figure 2.6) reveals that Alipay is not only one of the online market

leaders but also occupies the leading position in the mobile payment market, attaining

54% more than half of mobile payment market.

Figure 2.7 Top 500 Apps in China by Monthly Active Users

(Sources: iResearch, 2017)

According to the above figure 2.7, it is interesting to note that Alipay is the only one

payment application which has entered the top 20 ranking: it is ranked in the top six;

and thus, confirming its popularity. Alipay is not just limited to operations in China, it

also supports Chinese consumers abroad. Boden (2016) reports that since 2016,

Chinese visitors can use Alipay application in the UK to pay bills. From 2012, most

schools and university in the UK have allowed Chinese students to pay their education

fees by Alipay (Universities News, 2012). In 2017, the China Daily reported that the

Beijing subway system would allow passengers to use third-party payment for tickets

and passes. In China's agricultural market, authorities also offer scan QR codes for

payment. The transaction is completed in Alipay or WeChat, the two largest online

third-party payment platforms in China (China Daily, 2017). Moreover, Finnish Air

also announced this June that they would allow passengers use Alipay and WI-FI on all

Chinese routes. (Zhu, 2017). The above examples emphasise the deep penetration of

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Alipay into many sectors of the Chinese consumer markets and sectors. It reflects its

influence on consumer behaviours and the rapid movement towards a cashless society.

2.3 Consumer Behaviours 2.3.1 Definition of consumer behaviours

Given the aims and objectives of this study, it is necessary to determine how potential

customers have responded to the emergence of the third-party payment systems such as

Alipay. In general, a successful payment system largely depends on consumer

acceptance levels, and hence it is necessary to investigate which factors can influence

of consumer acceptance of new technology.

A useful definition is offered by Schiffman and Wisenblit (2015):

"Consumers behaviours refers to the study of consumers' actions during searching for,

purchasing, using, evaluating and disposing of products and services that they expect

will satisfy their needs. It further explains how individuals make decisions to spend

their available resources on goods that marketers offer for sale" (p.30).

Other researchers have explained the concept of the consumer behaviours. It involves

researching of individuals or groups; studying their consumer mentality, habits and

customs, and how they select products. It is predicted that in different situations

consumers may be affected in different ways in regard to purchasing or during the

process of decision-making (Kuester, 2012). Similarly, Kahle and Close (2011) report

that further insights into consumer behaviours may be gained by referring social

anthropology, psychology and social psychology theories. They are also explaining

how people’s buying inclinations and especially the final processes of purchasing

products can be affected by emotional and time factors; personal preferences and

perceived degrees of convenience.

However, in reviewing the current literature, the main focus tends to be on how

consumer adapt to new on the technology and how it changes their decision-making

process. However, few studies have concentrated on the impact of the third-party

payment on consumer behaviours.

2.3.2 The relationship between consumer behaviours and lifestyle

23

Consumer behaviours is the determining factor of their pattern of lifestyle, whereas,

pattern of lifestyle reflects consumer behaviours, for instance, a person’s choices,

person’s attitude, the current trend of social (Solomon, Bamossy, Askegaard & Hogg,

2014). Furthermore, Zablocki and Kanter (1976) stated that patterns of lifestyle do not

last forever: they may change according to individuals’ preferences and their responses

to innovations in technology developing. It is important to evaluate how third-party

payment has impacted on consumer behaviours and lifesyles.

2.3.3 An integrative framework

An integrative framework attempts to synthesise relevant theories in order to explain

consumer behaviour. The Theory of Reasoned Action (TRA), for instance, is a basis

model of analysis of consumer behaviour. Developed by, Fishbein and Ajzen (1975),

the theory proposes that consumer behaviours are determined by consumer attitudes

and subjective norms. Consumer attitudes refer to consumers’ individual feelings and

the motivation to adopt to new technologies. Subjective norms are considered other

vital factors. Fishbein and Ajzen explained that subjective norm refers to social factors,

which means other people’s behaviours or attitude will affect consumer made decision.

For instance, family or friends and their way of thinking may affect consumer their

purchase intentions. However, studies into consumer acceptance of the third-party

payment systems, have found that TRA is too limited in regard to the effect of social

variables.

Investigating the impact on the consumer acceptance of digital payment information

system is clearly an importance area of study in this digital age. For example, Davis et

al. (1989) reflecting on the future of TRA address two new elements: perceived

usefulness (PU) and perceived ease of use (PEU), made this new theory that is called

the Technology Acceptance Model (TAM) more comprehensive and TAM its research

field focused on the acceptance of mobile payment method in information system

(Dennis, Merrilees, Jayawardhena & Wright, 2009). TAM model identifies consumer

use or purchase intention is influenced by four factors: attitude toward the

behaviours(ATB), subject norm (SN), perceived usefulness (PU) and perceived ease of

use(PEU) (Ma, 2016). Davis et al. (1989) demonstrate that consumers' user experience

is a determined factor for consumers' acceptance of the new technologies. PU is

considered one of the key factors for consumer decision-making. Therefore, knowing

24

the level of PU for consumers is an important step in understanding purchasing

behaviour and their levels of satisfaction. Lee, Kozar and Larsen (2003) agree Davis'

view and point out that the TAM can be considered as the most significant and the

popular research theory used to explain consumer adoption behaviour in the

information system (Davis, Bagozzi & Warshaw,1989).

Others similar digital technology adoption theories have also emerged. Kim,

Mirusmonov and Lee (2010) argue that although TAM is better than TRA, nevertheless,

TAM fails to account for certain relevant factors. The Unified Theory of Acceptance

and Use of Technology (UTAUT) purports to enhance the TAM by adding personal

traits (PT), situation factors and product characteristics these factors (Venkatesh et al.,

2003). Ylänne-McEwen (2000) concluded that by omitting other factors, it is still a

need to address the Theories of Planned Behaviours (TPB) and perceived behavioural

control (PBC) into above consumer adopt technology models. These can be considered

as a foundation framework to investigative consumer adopt technologies intention

study. In the future, accordingly, researchers can use this framework to address

conditions used to analysis consumer behaviours in different situations.

Moreover. Another theory used to explain consumer adoption behaviours is called

diffusion of innovation(DOI), DOI had been some researchers used in their conceptual

or literature study. Dahlberg, Guo and Ondrus (2015) hold the view that the consumer

adoption behaviours have become the most heated theme in the field of E-payment

literature research in the recent year. Reviewing the current literature, most of the key

mobile payment adoption theories or models have been covered in this dissertation

framework.

2.4 The analysis of factors influencing consumer behaviours According to the above research framework, lots of reasons will affect on consumer

behaviours, from 2.3.2 the framework, there are some mainly factors, which have strong

correlation with third-party payment method and enough to affected consumer’s

decision were analyzed and summarized as follow: Social Factors, Situational factors

and Individual Traits these three factors.

25

2.4.1 Social Factors

Social factors can be divided into attitude and subjective norms (SN), and it is the vital

factor in TRA, which created by Fishbein and Ajzen (1975). Today, SN also has been

widely used in information system research. Attitude refers to consumers' intention,

prefer to psychology aspect. Mooij (2004) stated that there were three elements of

attitude: cognitive, affective and behavioral. Attitude reflect consumer the degree of

preference, and then the different of degree will drive consumer have different

behaviour. Usually, consumer's attitude will be affected on their mood, motivation and

feeling. Solomon, Bamossy, Askegaard and Hogg (2014) also confirm this point in their

book and stated that the attitude toward the behaviours research will be useful in the

widely field, for example, company product strategy and some small companies or

industries modify future developing. Sometimes another people's behaviour will affect

consumer's attitude, for example, today the third-party payment is very popular and has

been became a main trend, almost everyone use this new mobile payment system.

Under this background, consumer's attitude may be will affect by the main trend and

other people's adoption, then attracting consumer adopt third-party payment this new

payment system.

Subjective norms are another significant element of social factors. Aishbein and Ajzen

(1980) claim that subjective norms mean consumer's habit, other people's behaviours

or others people's attitude, for example, heeding family or friends' advice or

recommendations. These may ultimately affect consumer's purchasing decisions and

patterns of behaviour. Fishbein and Ajzen note that social factors can drive consumer

behaviours. And TAM, UTAM also adopt it as vital factor in their theories.

2.4.2 Situational factors

Situational factor refers to "…a situational characteristic of the interaction between an

individual and the situation" (Moon & Kim, 2000, p12). The concept is based on Davis

et al.’s (1989) representation in their study who highlight this factor in their ATM model.

Moon and Kim (ibid) report that this situational characteristic also can refer to degrees

of motivation which can be divided into extrinsic and intrinsic motivation. Extrinsic

motivation refers to interaction between individuals and their present situation, while

extrinsic motivation is linked to situational factors. For instance, culture is conceived

26

as one of the situational factors influencing consumer behaviours (Schiffman &

Wisenblit, 2015). By contrast, intrinsic motivation refers to consumer personal

emotions, which may be considered as an attitudinal variable by researchers which can

be effectively applied in their analyses.

2.4.3 Consumer's perceptions

Consumer perception is another key factor affects consumer acceptation of third-party

payment system, it was seen that, despite some researchers argued here that consumer's

perceptions maybe refer to individual's experience, so they would put it at attitude factor

classify by its definition, however, in this dissertation, it as an independent factor

argued, because judging the third-party payment system. In general consumer

behaviours, which can be divided into consumer perceived usefulness, perceived risk,

and perceived trust these three aspects, perceived risk and perceived trust are the top

two influential factors in TAM. Additionally, in the third-party payment system

perceived trust have negatively influenced perceived risk and vice versa (Dahlberg,

Guo & Ondrus, 2015).

Perceived usefulness (PU) refers to consumer's perceptions that using third-party

payment platform will enhance their daily lives. Compared to other types of payment

method, Alipay offers a convenient and secure platform to whereby consumers

complete transactions anywhere and at any time. One of the advantages for third-party

payments is that if consumer do not feel satisfied transactions, they can still obtain

refunds within days, which is another advantage of using Alipay.

Perceived Risk (PR) refers to consumer's perceptions that using third-party payment

platform is safe. It is a determining factor for the consumer whether accept third-party

payment system, as it involves to security for the consumer’s private information and

money. PR can be divided into market risk, finance risk, credit risk and technology and

operation risk. Finance risk generally refers to situations when a user may encounter

money loss; privacy disclosure or other related issues. In other words, consumer may

be concerned about finance, credit, as well as technology and operation risks. Consumer

may feel these risks are too high when considering to adopt a new system (Hong, Yu

and Shen, 2011). Third-party payment platform whether can effective forecast as well

as to avoid risk, which is one of vital aspect for consumer consider whether it adopts.

27

Lu, Yang, Chau and Cao (2011) in their study conducted that perceived risk in the third-

party payment negatively influences consumer acceptance it.

Perceived trust (PT) refers to consumer 's perceptions that using third-party payment

platform not only because it convenient, safety but also it can make them satisfaction,

in short, consumers know there were potential risks in the third-party payment system,

despite all that, they still believe and then accept this new system as another main

payment method.

Jin, Song and Zhang (2007) in their research study presented that the security for private

information is the biggest factor influencing consumers’ decision to choosing which

payment method when they pay the bill. Qing, Xinhua, Chuan and Baoxu (2014) also

state that the important reason for the third-party payment method can develop so

quickly in the short time in China is that every third-party payment platform has its own

business license; in other words, it regulated by the state. In the premise of security

assurances, consumers easier effect on the present fashion trend, brand loyal, and risk

aversion before their made decision, particularly for the young consumer (Zhao & Sun,

2012).

2.4.4 Individual traits (IT)

Individual traits mainly refer to characteristic of individual's information: age, gender,

income and education background. IT as another influenced factor have addressed at

TAM (Dennis et al., 1992). Likewise, further literature about consumer adoption of the

technology in the information system can also be found that Chen et al. (2002) and

Venkatesh et al. (2003) also add IT at UTAM in their studies, and Venkatesh et al. also

emphasis the fact that adding IT this new aspect in the model. This useful for identifying

the factors which can influenced consumer adoption of the third-party payment system

from a comprehensive of aspect (Yang et al., 2012).

28

2.5 Summary In summary, this chapter examined definition of various third-party payment system

and introduces the concept of third-party payment systems in China. It discussed related

theories of user acceptance of technology and critical factors. In the theory of TAM,

perceived usefulness(PU) and perceived ease of use(PEU) were identified as important

variables, which influence users’ behaviours and their lifestyle.

The main weakness of this content of literature review is that this dissertation theme

third-party payment, Alipay is not quite the same with others’ types of mobile payment

methods, such as mobile banking application and micro-payment still not be

significantly presented and without deeply discussed their effect on consumer

behaviours. Moreover, there has been little discussion on their effect on consumer

behaviour patterns.

29

Chapter 3 Methodology

3.1 Introduction The aim of all social research methodology is to service the theme and purpose of the

research (Clough & Nutbrown,2012). The structure of this methodology is defined by

the research 'onion' theory, as defined by Saunders, Lewis and Thornhill (2015). This

is a widely used research methodology shown below in Figure 3.1.

Figure 3.1 The structure of this dissertation methodology (source: This author, 2017)

3.2 Research Methodology: Positivism Positivism was selected in this dissertation. During the early twentieth-century,

positivism was identified as a pervasive scientific method by Auguste Comte, a famous

French philosopher. In methodology research, philosophers, such as Crotty (1998)

clarified and refined the definition of positivism, which refers to the notion that a fact

must be measured and tested by the nature of science. This involves two essential

common parts: current theory and existing accurate knowledge. Similarly, Gill and

Johnson (2010) point out that the purpose of positivism is to establish the objectivity of

knowledge and to combine this with empirical measurement. In the current context of

social science, Kerlinger and Lee (2000) emphasize that positivism is empirical

30

research methodology, as well as claiming that it is a requirement for positivist

researchers. They stress the need to maintain objective and neutral attitudes while

conducting positivism-based research.

3.3 Research methods Research methods can serve as approaches or analytical tools, which function to support

a researcher's philosophical choice. for instance, when collecting data for an analysis

segment (Gouldner, 1970). Based on the view of empirical research, Mutchnick and

Berg (2002) state that there are two research approaches in positivism: deductive and

inductive. Both of these approaches belong to positivism, although neither of them are

viewed as better than the other, just different perspectives in which to approach the

scope of research.

3.3.1 Deductive: questionnaire survey The deductive and inductive approaches are two vital aspects of positivism. A deductive

approach assumes that if an assumption before the investigation is true, then the

investigation’s result must be true. Moreover, the generalization of the deductive

approach is from general to the specific. For example, the aim of this dissertation

research is to collect data on third-party payment platforms, Alipay and Chinese

consumer behaviours, with this data then being used to evaluate the research questions,

as well as the relationship of this data to current situation and theories.

Aquestionnaire survey was selected as the only research method in this dissertation and

it is also the most popular survey method used to collect data from the deductive

approach. This scope of this research is targeted to China, combined with the

characteristic of questionnaires being to research quantity. Moreover, researchers send

the questionnaire to participants and collect the results via email, the cost of which is

very low, along with the time spent.

31

3.3.2 Inductive The inductive approach is another research method of positivism focussing on

qualitative research, for instance, by researching the behaviour and attitudes of people

through focus group interviews(Saunders et al., ibid). The characteristics an interview

more accuracy in results. In addition, the researcher can change their questions based

on the participant’s answers, in order to get the answers they want.

3.4 Research Strategy The aim of this research is to determine which third-party payment factors impact on

consumer behaviours, as well as the impact of third-party payment systems on Chinese

consumer behaviours. The limitation of this research strategy is that it only uses the

deductive research method, with questionnaires having been chosen to collect data.

Furthermore, as only quantitative research strategies have been used, this research is

not as comprehensive as it would be if this strategy was combined with qualitative

methods on consumer behaviour as well.

3.4.1 Questionnaire Design In this quantitative research, the questionnaire was selected as the tool to collect data

with. Therefore, the effectiveness of the research results rely on the content of the

questionnaire. This means that every step of the questionnaire design must be linked

with the aims and objectives of this research. For example, through the form of the

questionnaire and the type of question used.

According to the different types of deliveries and collections used for this data

collection method, , the questionnaire can be divided into internet questionnaires,

postal(mail) questionnaires, and delivery and collection questionnaires. The Internet

questionnaire can be further divided into the web questionnaire and the mobile

questionnaire, both of which are also known as self-completed questionnaires.

Compared to others, this research adopted the internet questionnaire because it can

reduce the time spent sending the questionnaire to participants, and it is also easier to

transfer results onto a computer for analysis.

32

A closed-ended (forced-choice) questionnaires was selected, which refers to the fact

that all possible answers are already provided under the question, so the respondent

only needs to choose the most appropriate answer (Dawson, 2009; De Vaus, 2014). In

addition, using suitable language is another vital consideration. Buchanan et al. (2013)

suggests that the use of language in a questionnaire depends largely on the research

target. Using participants’ mother tongue can make the participants feel more

comfortable and allow them to more effectively to fill in the questionnaire. Therefore,

Mandarin was selected because this research is in a Chinese context, and the

participants are all from China. Therefore, in order to allow the participants to more

easily read and understand the questionnaire, and to fill in the results more reliably, the

questionnaire was presented in Mandarin.

There were four different question types used: list questions, category questions, rating

questions and multiple choices questions. The first part of the questionnaire, which

informed respondents of ethical considerations, used list questions. This included

'Yes/No' and/or 'agree/disagree' question types, and required the respondents to have

already understood the meaning of questions and to give a certain answer.

The second part of the questionnaire used category questions, which mainly focussed

on collecting respondents’ demographic characteristics, such as gender, age, occupation,

and education level. These question types were designed to cover all possible answers,

whereas, there was only one category fitted to each respondent's answer. Fink (2003)

explains how this particular design allows researchers to analyse results more easily.

The third part of the questionnaire was aimed at researching the respondents'

perceptions of third-party payments and Alipay. According to literature reviewed in the

section above, it was clearly noted that perceived trust, perceived risk, perceived

convenience, perceived usefulness, and subject norms were the five main factors

influencing on consumer behaviours. Moreover, in order influence the reliability of the

answers, this section's questions adopted a likert scales style with five possible

responses. Bruner (2013) claims that likert-style rating is the most frequently used in

questionnaires. This section’s questions were presented with five categories, with

participants being asked how strongly they agree or disagree, or how strongly they are

satisfied or are not satisfied. Answers were divided from one to five, with the possible

33

answers being labelled as strongly disagree, disagree, neutral, agree and strongly

agree(Dillman et al., 2014). This measurement method can make the results more

accurate.

The last section of the questionnaire used multiple choice questions, and were

predominantly used to ask respondents questions related to their behaviours. This

question type’s answers also provided options for all possible answers, but this there

were more possible responses to choose from in this category than in the other question

types with given answers detailed above.

3.4.2 Samples & Ethics Generally, the validity of the survey results are positively correlated with the sample

number. The larger a research’s sample size is, the lower the possibility of errors in the

results (Lenth,2001). Luckily, this survey received a total of 447 questionnaires, with

417 valid questionnaires and 30 invalid ones. Moreover, the age range is the sample

was from 18 to over 50 years old, with a limitation on gender, occupation and the

education level, which means this research's results are very meaningful, and the results

can be used to fill the research gap.

As for ethical considerations, the ethical risks of this research were tested by the

university of Sheffield, and were considered to be of low risk. In addition, at the

beginning of the questionnaire, the participants must be informed of why they were

invited to do this questionnaire, as well as what the aim, the content, and the purpose

of this questionnaire was. Moreover, it is important to note that the participant were

told that they could delete their information at any point in the research, and the

researcher ensure their information would only be used for the purposes of this research,

and that their information will be kept safe. In addition in regards to security of privacy,

if a participant didn’t want to continue, or didn’t know what Alipay was, the

questionnaire would allow the participant to end the process without any of the

information being used for the research.. These questionnaires would then be

considered as invalid and would not be analysed. . In other words, these considerations

ensured that all data collected and analysed during the research process had received

the permission of every participant

34

3.5 Data Analysis Quantitative data belongs to primary data, and is used for quantitative analysis and

calculations, without any meaning yet being taken from the results (Saunders et al.,

ibid). It therefore seems that quantitative analysis determines the quality of the

quantitative research results. Therefore, it is important that the researcher knows and

clearly lays out every step in the quantitative analysi process. Furthermore,

classification of the quantitative data is done only according to a hierarchy of

measurement, so the most appropriate measurement methods can be selected to do the

analysis (Berman Brown and Saunders, 2008; Willett, 2017).

A quantitative survey must use quantitative analysis and quantitative analysis tools.

SPSS (Statistical Product and Service Solution) and Excel were selected to do the

descriptive analysis, reliability analysis, multi-linear regression, and one-way ANOVA

analysis.

Descriptive analysis was adopted to present the current state of Alipay in China. In

addition, the different variables were used to classify different questions, with likert-

style questions being classified into the following six categories: PU, PEU,PR, PT, COI

and UI, which were mainly to do with the impact on consumer behaviours factors.

Secondly, reliability analysis was used to examine the reliability and validity of the 417

respondents' answers, as an internet questionnaire could not ensure the credibility of

the answers.

Third, multi-linear regression and One-way ANOVA were both used to answer the

different research questions. Multi-linear regression can have more independent

variables at the same time and is usually used to test the effect of various factors. Multi-

linear regression was therefore used to examine the correlation between the main

influencing factors, which were mentioned in the literature review. As for one-way

ANOVA, it was only used to determine which independent factor (age, gender,

education level) influenced the use (including frequency of use) of Alipay. Each data

analysis method will be examined in detail in the following chapter on results.

35

3.6 Chapter Summary In conclusion, this chapter examined the philosophy and method used for this

research, including providing the definition of positivism, the deductive approach, and

an explanation of the quantitative questionnaire survey. Indeed, every step of the

research designing and rationale were presented in this chapter in detail.

Furthermore, the research sample and the tools for analysing quantitative data were

also discussed and illustrated.

36

Chapter 4 Results and Discussion 4.1 Introduction In this quantitative research, all the results of my statistical analysis will be illustrated

via table, pie chart and bar chart. Indeed, the process of results analysis involved the

descriptive analysis for Different variables, Individual traits, usage status of Alipay,

reasons for using Alipay and the selection of Alipay. The first section is the purpose of

the reliability analysis, multi-linear regression and one-way ANOVA. For instance,

reliability analysis is to test the results of descriptive analysis of different variable to

see whether reliability as well as the true information collected is linked with

consumer's attitude for Alipay or the Alipay frequency etc.

the purpose of the second part is the discussion and conclusion chapters. For instance,

the results of reliability analysis can serve as evidence to support discussion. And the

results of multi-linear Regression and one-way ANOVA are used to answer the research

questions. Moreover, the framework of this chapter is showed in Figure 4.1.

37

Figure 4.1 The framework of analysing research results (Source: This author, 2017)

This

research

results

DescriptiveAnalysis

Different variables(Q23-42)

Demographic info. (Q9-Q13)

Usage statue of Alipay

(Q18-20)

Use Alipay frequency

(Q18)

How long has been used

Alipay (Q19)

Spending how much via

Alipay in each month

(Q20)

Reasons for using Alipay (Q17,21 and 22)

Selection of Alipay (Q14 & 43)

ReliabilityAnalysis

PerceivedUsefulness( PU) Q23-24

Perceived ease of use (PEU) Q25-27

Perceived risk(PK) Q30-31

Perceived Trust(PT) Q32-34

Consequences of Innovation (COI)

Q35-40

Usage intention(UI)Q41-43

Multi-linearRegresion Y=Usage

intention(UI)

Y=Consequences of Innovation (COI)

One-way ANOVA

38

4.2 Descriptive statistics analysis Descriptive analysis is usually used to examine and summarise individual variables

with the use of: numbers, graphs and charts (Brace, Kemp & Snelgar, 2012). The central

tendency and the dispersion were the two aspects on which the descriptive statistics

analysis needed to focus. While examining the central tendency, frequency (mode),

median and mean were the three main values that were needed to measure the central

tendency. The mode refers to the value that cumulates most frequently and the median

stands for middle value of the entire sample. Researchers prefer to use mode and median

for comparisons, usually if the mode is bigger than the median, the result of the data is

good. This is usually contrasting when the results on the other hand display the opposite

effect. Statisticians identified the mean as the average; as it can indicate what the trend

is. However it doesn’t have any affect on the distribution (Saunders et al., ibid).

Alternatively, the analysis of dispersion in this dissertation only requires the

understanding of the standard deviation, which is compared accordingly with the mean.

This is such that if the standard deviation is closer to the mean, it can be inferred that

most of the data does not deviate from the mean so much. Nevertheless, if the standard

deviation is a number quite far from the mean, the data can arguably be expressed as

useless (Black, 2009). Furthermore, due to the analysis done by SPSS, some of the

names in the name expression might have had some differences.

4.2.1 The method of measures of the different variables

Table 4.1 Measures of the different variables (N=417)

Categories Mean Median Std. Deviation

Perceived usefulness (PU)

More convenient

（Q23） 4.63 5.00 0.762

39

Satisfy all demands

（Q24） 4.23 5.00 0.939

Perceived easier of use (PEU)

Only bring mobile

phone go out （Q25） 4.35 5.00 0.931

Easy to register (Q26) 4.41 5.00 0.850

Password or fingerprint

(Q27) 4.65 5.00 0.699

Perceived Risk (PR)

Info Disclosed (Q30) 2.88 3.00 1.218

Stolen (Q31) 2.78 3.00 1.325

Perceived Trust (PT)

Safety system (Q32) 3.86 4.00 1.006

No false information

(Q33) 3,47 3.00 1.141

Risk (Q34) 3.83 4.00 1.016

Consequents of innovation (COI) Expenditure increase

(Q28) 4.01 4.00 1.155

Use Alipay more

when eating outside

(Q35)

4.12 4.00 1.078

Use Alipay more

when booking hotel

(Q36)

4.17 5.00 1.103

Use Alipay more for

utilities bills (Q37) 3.71 4.00 1.319

40

Use Alipay more to

top up mobile (Q38) 4.15 5.00 1.713

Use Alipay more both

online and offline

(Q39)

3.99 4.00 1.118

Usage Intention (UI)

Satisfaction (Q40) 4.27 4.00 0.828

Continue to use

(Q41) 4.46 5.00 0.802

Replace cash (Q42) 4.07 4.00 1.090

First, the method of measurement for the different variables refers to how the questions

were conducted in the questionnaire, as they were partitioned into several groups or

categories. This was because the answer held different purposes, thus leading to

different analysis methods. In this research, this method was used to adopt the Likert-

style questions, which has been mentioned in methodology. Furthermore, the research

aimed to compare the mean (frequency), median and std. deviation to test the normality

of the distribution by using the correspondence analysis method. This method was

coined as the measures of the central tendency (Anderson et al., 2014).

The mean in this situation describes how many people took part in the questions, which

was done by comparing the value of mean and the value of median. If the mean was

smaller than the median, it means that the data will represent a long tail to the left,

showing that the quantitative data is negatively skewed. This indicates that there are

more people who agree to the views than the ones who disagree. However, If the mean

is greater than the median, their will be a long tail to the right, representing that the

quantitative data is positively skewed. This condition stands when there are fewer

people who agree to the views than disagree. Another indicator for the data was kurtosis,

which decides the length of the tail; moreover, the kurtosis shape of the distribution

stands for the degree of the deviation. This is portrayed when the Std. Deviation is

greater than one, (a negative kurtosis value), as the shape of the kurtosis distribution

will be flatter, indicating that respondents' have answered more broadly. Conversely, if

the value of Std. Deviation is less than one (meaning that the kurtosis value is positive),

41

the distribution of the kurtosis shape will be more peaked, usually representing that

most of respondents' held the same opinions (Dancy & Reidy, 2011).

From the above table 4.1, all of the variables were classified into six categories: PU

PEU, PR, PT, COI, and UI. First, PU was combined Q23 and Q24. By definition, these

two questions had a median 5, with Q23's mean being 4.63, which is comparably closer

to 5 than that of Q24. This means that most of the respondents' answered the same for

Q23. Moreover, these two questions had a Std. Deviation that was less than 1, which

means that they both had a more peaked distribution for their kurtosis shapes. Therefore,

both their kurtosis values were positive, thus portraying again that most of the

respondents in these questions answered the same option.

Secondly, the PEU factor was separated and presented with Q25, Q26 and Q27. Their

mean and median's were all very close, portraying the respondents' identity of views.

Additionally, the Std. Deviation of these questions all had a value less than 1. This

means the quantitative data for these questions were positively skewed.

Third, PR involved to Q30 and Q31. For these questions, whilst comparing the value

of the mean and median, it turned out that there was not much difference between the

two. Thus it can be viewed that in these questions, most of participants adopted the

same option. However, these two question's had a Std. Deviation of more than 1 means

that the data represents a normal distribution (bell-shaped curve), therefore it's kurtosis

value is negative.

Fourth, the PT category was combined by Q32, Q33 and Q34. After comparing the

mean and medians for these three questions, it was clear that Q32 and Q34 had a mean,

which was smaller than their medians. It can be inferred in this case that most of

respondents' chose the same options for most of the questions. However, Q33 had a

mean value in which it was greater than its median value, implying that most of the

respondents disagreed with view portrayed by this question. Moreover, these three

questions' had a Std. Deviation that was less than 1, in short, implying that the views of

the respondents' was not very consistent.

42

Fifth, the COI factor was presented by Q28 and Q35 to Q39. These six questions

conjured a mean in which it was smaller than their median, thus meaning that they are

negatively skewed and have a long tail to the left. This implies that most of people agree

to this view. Furthermore, all six questions had a kurtosis value of more than one, thus

implying that the kurtosis value is negative. This ultimately implies that the answers to

these six questions created a flat distribution.

Last, UI involved Q40-42. Except for Q40’s mean being greater than its median

(showing a positive skew), the Q41 and Q42 both enjoyed a negative skew, implying

that for these two questions most of the people agreed with the statement of the question.

Moreover, Q42 had a Std. Deviation of greater than one, whilst the other two questions

had positive kurtosis values. However, even though there were differences in the

inference for UI, all in all, there was a clear indication that most of people still prefer

to use Alipay.

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4.2.2 Demographic information (1) The gender of respondents

Figure 4.2 The gender of respondents (Source: This author, 2017)

Figure 4.2 showed that the males occupied 36.45% of the participants and that females

occupied 63.55%. As it can be seen, this pie chart reflects the number of the female

participants almost doubles the amount of males.

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(2) The Age and education level of the respondents

Figure 4.3 The Age & Educational level (source: This author, 2017)

The result of Q10 is presented with this bar chart, which illustrates that the participants

in this study involved young people, middle-aged people and the elderly. Moreover, it

can be seen that the 18-24 demographic occupied the largest population, with 42.4% of

the total population (meaning there was 177 people). The second largest demographic

was the 25-35 years old category, which had 164 people (39.3%). According to figures

it can be shown that, Alipay is very popular in China and that it holds users from various

age groups. However, only six individuals who were aged more than 50 years olds were

in the whole sample of 417 people, which is arguably a limitation for this research,

although it attempted to cover all different ages.

Another bar chart was used to represent the education level of the respondents. In

question 12, the educational level was divided into four segments: high school or lower,

Bachelor's degree, Master's degree and PhD's. A surprising result from the question is

that the accumulative total number of people that have bachelor's degree arrived at 259

persons, occupying 62.1% of the total population. Moreover, it should be noted that

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Master Degree segment also occupied a large amount of the total population.

Furthermore, the above figures mainly reflect that most of the participants in this

questionnaire are young and very well educated.

(3) The occupation and income of respondents

Figure 4.4 The occupation & income of respondents

(Source: This author, 2017)

Figure 4,4 combined question 11 and 12's results for the purpose of a more detailed

inference. Q11 researched occupation status of the participants, with the results being

portrayed with the pie chart in Figure 4.4. From such, it can be concluded that the

officers in government administration (represented by the pink section) and the students

(represented by the green part) respectively occupied 38.85% and 27.58% of the total

population; the following was freelancer section, which held 17.03%

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In addition, the bar chart on the right side of figure 4.4 represents the results of Q13,

which refers to the different four income segments of the respondents:

¥ 0-999 (£0-118), ¥ 1,000-2,999 (£119-353), ¥ 3,000-4,999 (£354-588), ¥ 5,000 or

above (£589+). This questioned aimed to examine if Chinese people would spend more

money on Alipay, respective to higher or lower salaries. Nevertheless, the result was

surprising, as most of the participants were high earners. However this did no correlate

with the former graph, which indicated that most of the participants were students.

4.2.3 Usage statue of Alipay The following segments will be used to describe general usage of Alipay. More

specifically, the following points will be analysed: the frequency for the respondents

use of Alipay (Q18), how long the respondents have been using Alipay (Q19) and how

much money via Alipay is spent each month (Q20).

(1) The frequency for respondents using Alipay

Table 4.2 The frequency for respondents using Alipay (N=417)

Frequency Percent (%) Always 228 54.7

Often 114 27.3

Sometimes 44 10.6

Few 24 5.8

Never 7 1.7

From the above table, the frequency of respondents using Alipay was splited into:

Always, Often, Sometimes, Few and Never. It can be clearly seen that 228 respondents

claim to always use Alipay. This specific answer occupied 54.7%of the total

respondents, more than half of the total number. There were 114 respondents who

claimed they used Alipay often, which occupied 27.3% of the total respondents (making

it the second most frequent answer). Only 7 respondents said they have never used

Alipay, therefore it can be inferred from this table that Alipay is very popular in China.

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(2) How long for the respondents has been used Alipay

Figure 4.5 How long for the respondents has been used Alipay

(Source: This author, 2017) The above figure portrays the result of Q19, which researched how long the respondents

had been using Alipay. Both a bar chart and a pie chart were used to show all 417 of

the individual’s answers. It was surprising to know that there were 278 people who had

been using Alipay for more than 2 years, which is 66.7% of the total population. With

the other three segments aggregately making up even half of the total answers, the

results clearly indicate that Alipay enjoys a high degree of customer loyalty.

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(3) Spending how much money via Alipay in each month

Table 4.3 The spending via Alipay in each month (N=417)

Frequency Percent (100%) ¥ 0-500 (£ 0-59) 109 26.1

¥ 501-1000 (£59-118) 111 26.6

¥ 1001-3000 (£118-353) 120 28.8

¥ 3001-5000 (£353-588) 42 10.1

¥ 5000 above (£588 +) 35 8.4

Question 20 was split up into 5 groups and it can be seen from the above table that there

was not an incredible gap between each segment. 28.8% of the respondents selected the

third group (¥ 1001-3000), 26.6% of the respondents selected group two (¥ 501-1000)

and 26.1% of respondents selected the top group (¥ 0-500). In another word, more than

80% of the respondents thought they spend at least ¥500 (£59) in each month via Alipay.

According to the summary of the above three aspects: the frequency of use, how long

it’s been used and the amount spent each month, it is obvious that amongst this sample

Alipay is clearly very popular. This is due to the fact that there was a high frequency of

users, most of them have used it for more than 2 years. Furthermore, it can also been

concluded that more than 80% of participants spent less than ¥3,000 (£353) every

month, implying that Alipay is more frequently used for Daily life payments, rather

than extravagant purchases.

4.2.4 The general reasons for using Alipay

Table 4.4 The general reasons for using Alipay (N=417)

Frequency Percent (100%)

Reasons of Alipay (Q17, multiple-choice)

Safe and reliable 154 63.1

Convenient 357 85.6

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Low-cost 91 21.8

Popular trend 58 13.9

Recommended 31 7.4

All of the above 58 13.9

Others 20 4.8

Influenced by others people to use Alipay (Q21)

Yes 80 19.2

No 337 80.8

Advantages of Alipay (Q22, multiple-choice)

Convenient and swift 352 84.4

Easy to use 280 67.1

Safe 143 34.3

Merchant offers 144 34.5

All of the above 109 26.1

Others 26 6.2

Alipay can provide which convenience (Q29, multiple-choice)

Reducing the number of times to go to

the bank 241 57.8

Reducing the number of times to go out

shopping 153 36.7

Carrying less cash 284 68.1

Saving time 242 58.0

All of the above 150 36.0

Others 20 4.8

The results of Q17 and the Q22 indicated that convenience was the determining factor

why consumers have chosen to use Alipay, as this option for both of the questions

received a significantly large portion of the answers. Moreover, the conclusion that

Alipay is mostly used due to its convenience is even more so justified in Q17 and Q22’s

to show that the other answers in the multiple-choice questions did not get a high

response rate in comparison.

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The results of Q21 showed that only 19.2% of the respondents agreed that others

(friends, families etc.) influenced them to start using Alipay. This factor also appeared

in the literature review section of this dissertation and is decrypted as one of the vital

effect on consumer behaviours' factors. It was summarized as one of the alternative

factors in social factors, called personal attitudes (Fishbein et al., ibid).

The aim of this section was to test via the actual findings, whether the factors that

impact