the factors affecting biopay acceptance of people in bangkok

THE FACTORS AFFECTING BIOPAY ACCEPTANCE OF PEOPLE IN BANGKOK

BY

MISS SUKPAPORN RAWEEPAIBOON

A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF BACHELOR OF ARTS PROGRAM IN JOURNALISM AND MASS COMMUNICATION (ADVERTISING) FACULTY OF JOURNALISM AND MASS COMMUNICATION THAMMASAT UNIVERSITY ACADEMIC YEAR 2017 COPYRIGHT OF THAMMASAT UNIVERSITY


BY

MISS SUKPAPORN RAWEEPAIBOON

A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF BACHELOR OF ARTS PROGRAM IN JOURNALISM AND MASS COMMUNICATION (ADVERTISING) FACULTY OF JOURNALISM AND MASS COMMUNICATION THAMMASAT UNIVERSITY ACADEMIC YEAR 2017 COPYRIGHT OF THAMMASAT UNIVERSITY

Thesis Title

The Factors Affecting BioPay Acceptance of People in Bangkok

Author

Miss Sukpaporn Raweepaiboon

Major Field

Advertising

Thesis Committees

………………………………………… Committee Chair (Lecturer Nichakhun Tuwaphalangkun)

………………………………………… Committee (Mr. Pakorn Sakunee)

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Thesis Title


Author


Degree

Bachelor of Arts Program in Journalism and Mass Communication

Major Field/Faculty/University

Advertising Faculty of Journalism and Mass Communication Thammasat University

Thesis Advisor

Lecturer Nichakhun Tuwaphalangkun, M.D.

Thesis Co-Advisor

Pakorn Sakunee, M.D.

Academic Years

2017

ABSTRACT Throughout the world, the trend of BioPay or biometrics payment which is a technology that use biometrics authentication to identity the user and authorize the deduction of funds from bank account have been increasing which it is believe that BioPay will help payment more safety. Also, in Thailand which since project ‘Thailand 4.0’ was launched by government with the government’s efforts to transform the country into a cashless society, the interest of biometrics have been increased which some of BioPay application can be already seen in Thailand; therefore, this study examined the relationship between perceived ease of use, perceived usefulness, perceived security and privacy; and BioPay adoption intension. The sample size for this study was 205 persons who live in Bangkok between ages of 18-60 years old using stratified techniques. The questionnaire was used as the research instrument which Pearson’s correlation coefficient were used to analyze whether accepting or rejecting the null hypothesis.

As for the result of the study, overall, the respondents agreed that BioPay technology is easy to use, useful, and secure. Additionally, the results of the analysis showed that respondents agreed to use Fingerprint for their payment the most, while Vein Scanning had the lowest acceptance rate. Furthermore, the results of this study indicate that there is a significant positive correlation (p< .001) between perceived ease of use, perceived

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usefulness, perceived security and privacy; and BioPay adoption intension which positive correlations were found which perceived security and privacy was recorded in the most significant factor to influence BioPay adoption intension.

Keywords: BioPay, Biometrics Payment, Biometrics, Technology Acceptance

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ACKNOWLEDGEMENTS This dissertation could not have been completed without the expert guidance of my supervisor, Lecturer Nichakhun Tuwaphalangkun who patiently help me through the proposal, dissertation with faithfully read and commented on countless revisions; also other suggestion and recommendation over than this dissertation which I couldn’t explain how deeply grateful am I. I also wish to thank Mr. Pakorn Sakunee and Amnet Thailand team with warmly supporting and providing a lot of knowledge to me during the internship. I owe every respondents who kindly took the time to complete my questionnaire. Also, everyone that help me to spread questionnaire, thank you very much for your all kindness! Finally, special thanks to my family who are always close by with words of support and encouragement.


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TABLE OF CONTENTS Page Abstract

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Acknowledgements

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List of tables

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List of figures

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Chapter 1 Introduction

1

1.1 Background 1.2 Statement of the Problems 1.3 Objective 1.4 Scope of Study 1.5 Research Hypothesis 1.6 Definitions of Terms

1 6 6 7 7 7

Chapter 2 Literature Review

9

2.1 Technology Acceptance Model 2.2 Biometrics Technology 2.2.1 Categories of Biometrics 2.2.2 The Useful Criteria for Evaluating Biometrics Technology 2.2.3 Types of Biometrics 2.2.4 The Utilities of Biometrics Technology 2.2.5 The Biometrics Authentication Process 2.2.6 Biometrics Advantages and Disadvantages 2.2.7 Criticism of Biometrics toward Privacy 2.3 BioPay Implementation 2.4 Prior Research 2.5 Conceptual Framework

9 11 12 14 17 37 38 39 40 42 49 52

Chapter 3 Research Methodology

53

3.1 Determining Sample Size 3.2 Sampling Technique 3.3 Research Instrument 3.4 Formulating Research Instrument 3.5 Data Collection 3.6 Data Analysis and the Statistic Used in Data Analysis

53 55 55 56 57 58

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Chapter 4 Analysis and Interpretation 4.1 Demographic 4.2 BioPay’s Perceived Ease of Use 4.3 BioPay’s Perceived Usefulness 4.4 BioPay’s Security and Privacy 4.5 BioPay’s Adoption Intension 4.6 Hypothesis Testing

64 65 67 68 70 71 74

Chapter 5 Discussion, Conclusion and Recommendations

77

5.1 Conclusion 5.2 Discussion 5.3 Limitation 5.3 Recommendations for Action 5.3 Recommendations for Further Research

77 82 88 89 91

References

93

Appendices Appendix A Appendix B Appendix C

103 108 114

Biography

116

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LIST OF TABLES Tables

Page

2.1 Physiological and Behavioral Characteristics of Biometrics 2.2 Useful Criteria of Biometrics Technology 2.3 Comparison of Various Biometrics Technologies against the Seven Pillars 2.4 Emerging Biometrics Technology 2.5 Biometrics Advantages and Disadvantages 3.1 Bangkok Population between Ages of 18 to 60 Years Old 3.2 Sampling Technique 3.3 The Result of Testing Reliability of Questionnaire in Each Section 3.4 Values of Thumb on the Correlation Coefficient Sizes 3.5 Five-point Likert Scales’ Rate, Interpretation and Range 3.6 Research Hypothesis, Statistical Hypothesis, and Statistic Used 3.7 Interpretation of Pearson's Product Moment Correlation Coefficient 4.1 Frequency and Percentage of Respondents by Gender 4.2 Frequency and Percentage of Respondents by Age 4.3 Frequency and Percentage of Respondents by Educational Level 4.4 Frequency and Percentage of Respondents by Personal Monthly Income 4.5 Frequency, Percentage, Mean and Standard Deviation (SD) of Respondents’ Perceived Ease of Use 4.6 Frequency, Percentage, Mean and Standard Deviation (SD) of Respondents’ Perceived Usefulness 4.7 Frequency, Percentage, Mean and Standard Deviation (SD) of Respondents’ Perceived Security and Privacy 4.8 Frequency, Percentage, Mean and Standard Deviation (SD) of Respondents’ BioPay Adoption Intension 4.9 Pearson's correlations between Perceived Ease of Use and BioPay Adoption Intension 4.10 Pearson's correlations between Perceived Usefulness and BioPay Adoption Intension 4.11 Pearson's correlations between Perceived Security and Privacy and BioPay Adoption Intension 5.1 Result of Respondents Intension to Adopt BioPay by Categories Ranking 5.2 Result of Hypothesis Testing 5.3 Result of Hypothesis

13 15 16 18 39 53 55 57 57 58 59 63 65 65 66 66 67 68 70 71 74 75 76 80 81 85

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LIST OF FIGURES Figures

Page

1.1 Samsung Pay 1.2 JabJai for School 1.3 K Plus Application 2.1 Technology Acceptance Model (Davis, 1989) 2.2 Technology Acceptance Model (Salisbury et al., 2001) 2.3 Annual Biometrics Technology Revenue by Region, World Markets 2015-2024 2.4 Biometrics Global Market by Technology in 2015 2.5 Face Recognition 2.6 Face Recognition on Samsung Pay 2.7 Face Thermogram 2.8 Fingerprint Patterns: Arch, Loop and Whorl with Core and Delta Guiding 2.9 Fingerprint Recognition 2.10 Hand geometry 2.11 Hand geometry Scanning 2.12 Complexity and Uniqueness of Human Iris, Fine Textures on Iris Forms Unique Biometric Patterns Encoded by Iris Recognition Systems Pay 2.13 Irises verification on Samsung S8 and S8+ 2.14 Verification Methods Available in Samsung S8 and S8+ 2.15 Structure of Retina 2.16 Retinal Scanning on Vivo X5Pro 2.17 Vein Pattern Recognition 2.18 Vein Pattern Recognition in 7-Eleven, Lotte World Tower in Seoul 2.19 Silhouette Picture for Gait Recognition 2.20 The Speed and Time of Typing Turned to Data 2.21 Mouse Action Data 2.22 Signature Recognition by Explained Factors 2.23 Voice Recognition by Voiceprint 2.24 Voice Recognition to Unlock Phone on Baidu-Lenovo A586 2.25 Individual’s Ear with Annotated 55 Landmarks on Ears 2.26 Core stages and modules in the authentication process of a general biometrics system 2.27 Pay By Touch 2.28 BioPay in 7-eleven, Lotte World Tower, Seoul 2.29 Face Recognition Available on Kiosk Machine at KPRO, Hangzhou, China 2.30 Face Scanning for Payment on Kiosk Machine at KPRO, Hangzhou, China 2.31 Fujitsu PalmSecure 2.32 PayTango 2.33 Fingopay 2.34 Conceptual research model of the study 5.1. BioPay Adoption Intension by Categories Ranking 5.2. Conceptual research model guiding the study

2 3 5 10 10 12 17 19 20 21 22 23 24 25 25

26 27 28 28 29 29 30 31 31 32 33 34 36 38 43 44 45 46 47 47 48 52 79 86

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CHAPTER 1 INTRODUCTION

1.1 Background In today’s modern world, with the development of technology and people’s living quality, people are turning more attention on the diversification of the payment method which is expected to adopt new technology that would be more convenient and safe. While the technology has been developed, cyber theft has also been increasing. The traditional method like password or PIN code has been easy to hack. This has increased the need to identify method in considerable growth in the implementation and application of BioPay. BioPay is a biometrics payment applying technology that use biometrics authentication to identity user and authorize funds deduction from bank account such as fingerprint, hand geometry and etc. (Yim, 2017) The word biometrics is derived from the Greek words bio (life) and metrikos (measure). Biometrics measurements fall into two categories. They can be psychological, such as fingerprints, or behavioral, such as voice (Zorkadis and Donos, 2004). Biometrics is the science of measuring these characteristics for the purpose of determining or verifying identity (Reid, 2004). Biometrics offers a stronger security of identity as it cannot be lost or forgotten, difficult to mimic or share, and require the individual to show identity at the time of identification. However, biometrics systems are fallible, prone to errors and vulnerable making it easy to be attacked. Since biometrics information is an essential part of individuals, it is likely to threat an individuals’ privacy. The use of biometrics systems and applications raises a number of ethical questions, particularly issues of human dignity and identity (individuality) and basic rights such as privacy, autonomy, bodily integrity, confidentiality, equity and in the case of criminal investigation, due process (Irish Council for Bioethics, 2009).

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Throughout the world, there are many application of BioPay. The trend is growing tremendously, while in Thailand, there is limited use of BioPay whiich Samsuang Pay is the only application using BioPay. Samsung Pay allows customers to use their fingerprint was launched in Thailand in October 2016. The payment system is now widely available in the country working with both MasterCard and Visa cards from Krung Thai Card (KTC), City Bank (CITI), Siam Commercial Bank (SCB), Kasikorn Bank (KBANK), Krungsri Bank (BAY), Bangkok Bank (BBL), Overseas Bank (UOB) and mPay application (FindBiometrics, 2017; Samsung, 2017).

FIGURE 1.1. Samsung Pay (Mayhew, S. (2017). Retrieved from http://www.androidauthority.com/samsung-pay-everything-you-need-to-know-678123/) Although the rate of using BioPay in Thailand is still considerably low, which only Samsung Pay is available in mass market, BioPay tend to be more useful by it is widely use and apply by business unit and government. Since ‘Thailand 4.0’ was launched by government with the government’s efforts to transform the country into a cashless society, the interest of biometrics also has increases. In 2016, Thai start-up has developed biometrics technology that will enable schools to monitor and store student data called ‘JabJai for School’ by using fingerprint to buy things in school which this system benefits in increasing security of transaction. Furthermore, parents can also monitor children’s money usage from mobile application (Jabjai for school, 2017).

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FIGURE 1.2. JabJai for School (Jabjai for School. (2017). Retrieved from https://www.jabjai.school/en/about.html) Budsakorn Teerapunyachai, director of Risk Management and Information System Examination Department, Bank of Thailand stated that the Bank of Thailand has been developing the technology over the period which Mobile banking growth rate is 70 percent, which Bank of Thailand is paying attention on it. Not only mobile banking, cloud computing and biometrics technology also has gained lot of attention (Leesanguansuk, 2017). Additionally, the first quarterly report from Bank of Thailand in 2015 presented that the Bank of Thailand was studying in form of the model developed by Singapore using biometrics in electronic transaction. This is to make Thailand to shift toward digital economy with electronic payments (Bank of Thailand, 2017). Moreover, Veerathai Santiprabhob, the Governor of Bank of Thailand stated that since FinTech had merged with Bank of Thailand, it has significantly risen the use of biometrics in payment activities and it is believed that the usage will increase (ThaiPublica, 2017). Also in the business unit, Glen Robson, executive vice-president of Verifone Inc., mentioned that the use of biometric and authentication technologies, particularly fingerprint readers, will be included in the next wave of electronic data capture terminals (Siam Scope Magazine, 2017). In addition, G-ABLE co., ltd. cooperates with HYPR Corp. to launch biometrics authentication platform which Suthep Oonmettachit, the president chief sales officer revealed that in the past half year, the company will focus on using biometrics technology to strengthen 3 to 5 finance business unit by the end of 2017 (G-ABLE, 2017).

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Likewise, biometric identification systems are set to gain more momentum in Thailand this year, largely driven by the national e-payment system and the proliferation of financial technology (FinTech) services. Dutch Ng, co-founder and chief executive of i-Sprint Innovations, a Singapore-based identical management service provider stated that “apart from Singapore, Thailand has the highest potential for biometrics technology identification usage in Southeast Asia thanks to burgeoning of digital banking services”. Similarly, e-payment and FinTech services require identity verification with biometrics technology particularly fingerprint and face recognition systems, can play an important role in the verification of e-payment services. In the same tone, in 2016 the Bank of Thailand issued a notification to financial institutions to reset their customers’ password verification protocols to avoid possible fraud which Dutch Ng stated that this could be a wakeup call for banks and financial institutes to adopt biometrics technology for system verification (Leesa-nguansuk, 2017). Research examining the adoption and consumer attitude toward personal privacy of BioPay is very limited due to a small level of its adoption throughout the period. However, BioPay lately has been a vital topic in these several years on its adoption trend which tends to be increased. Although the research of biometrics technology payment is limited, there are a lot of study on biometrics technology. The study of Joshua and Koshy (2009) found that perceived usefulness, perception of safety and security influenced the attitude of users toward biometrics technology. Furthermore, Kim, Brewer, and Bernhard (2008) described that convenience, physical security and data security are among the factors for individual implementation despite personal concerns of privacy. Additionally, Hsieh, Nguyen, and Lin (2008) reported that ease of use and convenience are the main factors when biometrics technology was used for payment mechanism to prevent identity theft. Moreover, Irish Council for Bioethics (2009) explained that the use of biometrics technology has increased significantly in recent times. Biometrics technology is still a relatively new concept for many people to fully grasp. Several international studies have indicated that the use of biometrics technology often evokes fears of privacy and civil liberties infringements among the general public. Consequently, as stated above, it can be seen that the application of biometrics technology tend to be more used for payment. Therefore, researcher is interested in the

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question of identifying the factors that will be influencing Bangkok citizen’s intention to adopt BioPay. After the data were collected in September, some organizations have launched BioPay in October. Krungthai Card (KTC) has launched TapKTC mobile application using biometrics identification besides providing a pin number which is easy and convenient to use. Tosapong Rangkawara, KTC’s vice president stated that KTC is Southeast Asia’s first and only financial institution and the third in the world, to endorse this technology in collaboration with Samsung. TapKTC’s using iris recognition with trail under the Bank of Thailand’s regulatory which Tosapong said that TapKTC will become a crucial method for proactive marketing activities that has the best efficiency in connecting customers (The Nation, 2017). Moreover, Kasikorn Bank is about to launch biometrics technology on ‘K Plus’ mobile payment application which fingerprint recognition can be used on both iOS and android (Alex, 2017).

FIGURE 1.3. K Plus Application (Alex. (2017). Retrieved from https://www.appdisqus.com/2017/09/22/k-plus-will-support-fingerprint-androidupcoming-release.html) Additionally, the Finance Ministry of Thailand is launching a pilot program in testing a payment system that uses face recognition which will take place at the Finance ministry’s cafeteria called ‘MOF Digital Canteen’. Thailand government has been promoting e-payment systems, urging people to go cashless by using debit cards or QR code applications which face recognition is selected because the face pay system is costeffective. Somchai Sujjapongse, permanent secretary of Ministry of Finance stated that

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in October, 2017 all shops in Ari Street will be cashless and will be supported by the Finance Ministry of Thailand. QR Code payment system, EDC machine and FacePay will be provided (The Nation, 2017; Yooaresai, 2017). In Thailand, it is believed that biometrics technology can play an important role in the verification of e-payment services (Leesa-nguansuk, 2017). Furthermore, with the new economic model ‘Thailand 4.0’, government supports e-payments which have led to this research. In order to understand how BioPay will be adopted, technology adoption theory will be mainly used in this research. Therefore, the purpose of this study is exploring factors affecting Thai citizens to adopt BioPay.

1.2 Statement of the Problems 1.) What are the factors influencing BioPay adoption? 2.) Is there any relationship between perceived ease of use and BioPay adoption intention? 3.) Is there any relationship between perceived usefulness and BioPay adoption intention? 4.) Is there any relationship between perceived security and privacy and BioPay adoption intention?

1.3 Objectives This study aims to identify the factors influencing individuals’ decision to purchase by adopting BioPay. The objectives of the study are stated as follows: 1.) To identify the factors influencing BioPay adoption. 2.) To examine the relationship between perceived ease of use and BioPay adoption intention. 3.) To examine the relationship between perceived usefulness and BioPay adoption intention. 4.) To examine the relationship between perceived security and privacy and BioPay adoption intention.

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1.4 Scope of Study The scopes of this study are to identify the factors influencing BioPay adoption in Bangkok. The respondents are from Bangkok population between the age of 18 to 60 years old, accounted for 3,517,850 persons (The Bureau of Registration Administration, 2016). The study was carried out in September, 2017.

1.5 Research Hypothesis The following hypotheses were stated as follows: H1: There is statistically significant relationship between perceived ease of use and BioPay Adoption Intension. H2: There is statistically significant relationship between perceived usefulness and BioPay adoption intention. H3: There is statistically significant relationship between perceived security and privacy and BioPay adoption intention.

1.6 Definitions of Terms Key terms used in this study are defined as follows: Biometrics Technology is an automated process of recognizing or verifying the identity of a living person based on a physiological or behavioral aspect. BioPay or Biometrics payment is a payment method that allow consumers to make transactions by identifying themselves with a part of their body called biometrics technology which linked to a preassigned credit card or bank account. BioPay Adoption Intention is the degree to which an individual’s intention to adopt or reject BioPay.

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Fingerprint Recognition is the assessing of characteristic patterns of forks and ridges on the fingertips by using optical, capacitive or thermal techniques to distinguish one person from another. Hand Geometry is the measuring of the physical dimensions of the hand (for example, the span of the length of the fingers) when it is spread out on a flat surface. Iris Scanning is an image comparison of the user’s iris with previously stored image. The iris is the coloured circle surrounding the pupil of a person's eye. Perceived Ease of Use is the degree to which an individual believes that using BioPay would be free from physical and mental effort. Perceived Security and Privacy is the degree to which an individual believes that using technology would be safe and increasing individuals’ privacy. Perceived Usefulness is the degree to which a person believes that using BioPay would make the user receive benefits such as facilitating users’ shopping easier or prevent identity fraud. Retinal Scanning is the scanning of the distinctive vein patterns on the retina. The retina is a thin layer of tissue that lines the back of the eye on the inside. Signature Analysis is the assessing of a handwritten signature that is captured using a special pen and/or pad: static analysis simply assesses the resulting pattern, whereas dynamic systems also measure the pressure and speed of the signature. Vein Scanning is the assessing of the characteristic vein patterns in the back of the hand by using infrared light. Voice Verification is comparing user’s voice with previously stored ‘voiceprint’. Can be performed on a text-dependent basis (that is, when speaking a known word or phrase) or text-independently.

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CHAPTER 2 LITERATURE REVIEW

The objective of this chapter is to review the literature and gain better understanding of factors affecting the adoption of BioPay. The first section is on technology adoption theory which technology acceptance model (TAM) is the guiding principle of this study. The next section, an overview of BioPay are reviewed. Then, the following section is the literature review providing an overview of biometrics technology which consist of categories of biometrics, the seven pillars of biometrics technology, types of biometrics, the utilities of biometrics technology, the biometrics authentication process, biometrics advantages and disadvantages, and criticism of biometrics technology on privacy. The final section of the literature review focuses on prior adoption research.

2.1. Technology Acceptance Model Davis (1989) created the technology acceptance model (TAM), measuring the intentions to use a specific system. Originated from the theory of reasoned action (TRA) from Fishbein and Ajzen (1975), the TAM model’s behavioral intention is influenced by two beliefs: perceived usefulness and perceived ease of use. Davis stated that “Perceived usefulness is defined as the prospective user’s subjective probability that using a specific application system will increase his or her job performance within an organizational context”. It also reiterated that “Perceived ease of use refers to the degree to which the prospective user expects the target system to be free of effort”. These concepts will also be used in this study. Both of perceived usefulness and perceived ease of use are significant indicators of people’s intentions to accept technology. As the model shows, external variables have an impact on the perceived usefulness and on the perceived ease of use of a technology. According to Davis, perceived usefulness is a major factor of people’s intention to use new technologies (in his study the new

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technology is ‘using computers’) and perceived ease of use is a second significant determinant of these intentions. The following figure explains the technology acceptance model. Perceived Usefulness (U) Attitude toward Using (A)

External Variables

Actual System Use

Behavioral Intension to Use(BI)

Perceived Ease of use (E)

FIGURE 2.1. Technology Acceptance Model (Davis, 1989) Salisbury, Pearson, Pearson, and Miller (2001) developed the TAM which security and privacy construct were selected as additional factors because of their dominant influence in their research; ‘Perceived security and World Wide Web purchase intention’. In this TAM model’s, behavioral intention is influenced by three beliefs: perceived usefulness, perceived ease of use, and perceived security and privacy. The researchers found that increased levels of perceived web security will lead to greater intend to purchase products on the World Wide Web. The TAM model of Salisbury et al. is stated as follow: Perceived Usefulness

Perceived Ease of use

Behavioral Intension to Use

Security and Privacy

FIGURE 2.2. Technology Acceptance Model (Salisbury et al., 2001)

Actual System Use

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For this study, the theory framework based on TAM with security and privacy factor was added by Salisbury et al., perceived usefulness, perceive ease of use, and security and privacy were studied factors to identify behavioral intension to use BioPay. Due to the BioPay implementation has not been widespread yet in Thailand, this research only focus on behavioral intention to use which actual system used is not included.

2.2 Biometrics Technology The term biometrics is derived from the Greek words bio and metrikos which mean life and measure in English. Biometrics measurements are divided into two categories. It can be psychological, such as fingerprints, or behavioral, such as voice (Zorkadis and Donos, 2004). Biometrics is the science of measuring these characteristics for the purpose of determining or verifying identity (Reid, 2004). Biometrics technology increase the level of security for an individual’s account since biometric systems is highly reliable in validating enrolled account holder as the actual one that is requesting authorization. This is the main difference between the use of biometrics and passwords because passwords can be compromised, shared and forgotten. (Bolle et al, 2004). The biometrics systems have emerged in the latter half of the twentieth century, coinciding with the emergence of computer systems. The nascent field experienced an explosion of activity in the 1990s and began to surface in everyday applications in the early 2000s (Mayhew, 2017). Biometrics technology has now become the foundation of a wide range of collections of highly secured identification and verification mechanisms available for identity management. The contemporary meaning of biometrics technology emphasized the automated process (Lease, 2005). According to a report from Tractica, biometrics technology market revenue will increase to 67 billion dollars worldwide over the next 10 years which Asia Pacific will be the largest market for biometrics technology (Tractica, 2015).

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FIGURE 2.3. Annual Biometrics Technology Revenue by Region, World Markets 20152024 (Tractica. (2015). Retrieved from https://www.tractica.com/newsroom/pressreleases/biometrics-market-revenue-to-total-67-billion-worldwide-over-the-next-10years/)

2.2.1 Categories of Biometrics Biometrics is classified into two distinct areas: physiological and behavioral (Acharya, 2006; Bromba, 2007). Zorkadis and Donos (2004) stated that “biometrics technologies rely on who you are (physiological) or what you do (behavioral), as opposed to conventional methods, which rely on what you know (knowledge of passwords or other secrets such as cryptographic keys) and/or what you possess (such as a token or an ID card).” In table 1, each category and related description is presented. Many adults will be familiar with one or two of these biometrics techniques (Weber, 2006).

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Table 2.1 Physiological and Behavioral Characteristics of Biometrics Method Physiological Face recognition

Description Extracts key measurements from a digital image of the user’s face and compares them with a stored ‘face print’

Facial thermogram

Characterizes individuals by using varying temperatures emanating from different regions of the face

Fingerprint

Assesses characteristic patterns of forks and ridges on the

recognition

fingertips by using optical, capacitive, or thermal techniques to distinguish one person from another

Hand geometry

Measures the physical dimensions of the hand (for example, the span of the length of the fingers) when it is spread out on a flat surface

Iris scanning

Compares an image of the user’s iris with a previously stored image

Retinal scanning

Scans the distinctive patterns on the retina

Vein checking

Assesses the characteristic vein patterns in the back of the hand by using infrared light

Behavioral

Description

Gait recognition

Characterizes individuals by the way in which they walk

Keystroke analysis

Monitors typing activity to determine characteristic rhythms; can be performed on the basis of known text (for example, in conjunction with a username and password) or keyboard inputs in general

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Table 2.1 Physiological and Behavioral Characteristics of Biometrics (continues) Method Behavioral Mouse dynamics

Description Monitors mouse-related activity and attempts to characterize users on the basis of measures such as speed and accuracy

Signature analysis

Assesses a handwritten signature that is captured using a special pen and/or pad: static analysis simply assesses the resulting pattern, whereas dynamic systems also measure the pressure and speed of the signature

Voice verification

Compares a user’s voice with a previously stored ‘voiceprint’. Can be performed on a text-dependent basis (that is, when speaking a known word or phrase) or textindependently

Note. From ‘Privacy invasions: New technology that can identify anyone anywhere challenges how we balance individuals’ privacy against public goals. European Molecular Biology Organization’ Weber, K., 2006.

2.2.2 The Useful Criteria for Evaluating Biometrics Technology The seven pillars provided useful criteria for evaluating biometrics technology to achieve better results. They provide decision inputs to biometrics vendors for the manufacture of hardware and software applications. It is essential to note that the degree to which each biometrics technology fulfills a given criterion varies among one another (European Commission, 2005).

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Table 2.2 Useful Criteria of Biometrics Technology Criteria

Description

1. Universality

All individuals should have the characteristic.

2. Distinctiveness

The characteristic should be sufficiently different to distinguish between any two individuals.

3. Permanence

The characteristic should remain largely unchanged throughout the individual’s life.

4. Collectability

It should be relatively easy for the characteristic to be presented and measured quantitatively.

5. Performance

Refers to the level of accuracy and speed of recognition of the system given the operational and environmental factors involved.

6. Acceptability

Refers to an individual’s willingness to accept the use of that characteristic for the purpose of biometric recognition.

7. Resistance to

Refers to the degree of difficulty required to defeat or bypass

Circumvention

the system.

Note. From ‘Introduction to Biometrics’ by Jain et al. (2006)

In table 2.3, different types of biometrics modality and how each compared against the seven pillars is presented.

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Table 2.3 Comparison of Various Biometrics Technologies against the Seven Pillars Types of Biometrics

Universality

Distinctiveness

Permanence

Collectability

Performance

Acceptability

Circumvention

Face

High

Low

Medium

High

Low

High

Low

Fingerprint

Medium

High

High

Medium

High

Medium

High

Hand Geometry

Medium

Medium

Medium

High

Medium

Medium

Medium

Keystrokes

Low

Low

Low

Medium

Low

Medium

Medium

Hand Vein

Medium

Medium

Medium

Medium

Medium

Medium

High

Iris High

High

High

High

Medium

High

Low

High

Retinal Scan

High

High

Medium

Low

High

Low

High

Signature

Low

Low

Low

High

Low

High

Low

Voice Print

Medium

Low

Low

Medium

Low

High

Low

Facial Thermograms High

High

Low

High

Medium

High

High

Odor

High

High

High

Low

Low

Medium

Low

DNA

High

High

High

Low

High

Low

Low

Gait

Medium

Low

Low

High

Low

High

Medium

Ear

Medium

Medium

High

Medium

Medium

High

Medium

Note. From ‘Introduction to Biometrics’ by Jain et al. (2006)

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2.2.3 Types of Biometrics Several biometrics technologies are available commercially such as signature, fingerprint, hand geometry, retina, iris, face, and voice. Other technologies are in development including odor sensing, nailbed identification, and skin pattern recognition (U.S. Treasury, 2005). In 2015, the use of biometrics in global market consisted of irisi, automated fingerprint, identification system (AFIS), face, fingerprint, voice, vein and signature. These technologies accounted for over ten percent (Acuity Market Intelligence, 2017).

FIGURE 2.4. Biometrics Global Market by Technology in 2015 (Acuity Market Intelligence. (2017). Retrieved from http://ww1.prweb.com/prfiles/2007/04/03/516626/Slide3.JPG) Due to the lack of updated information of current implementation of biometrics technology since 2005, researcher updated the current use of biometrics technology by acquiring mix data from Acuity Market Intelligence (2017) in table 2.4.

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Table 2.4 Emerging Biometrics Technology Types of Biometrics

Status

1. Face Recognition

Commercially available*

2. Facial Thermogram

Commercially available

3. Fingerprint Recognition


4. Hand Geometry


5. Iris Scanning


6.Retinal Scanning


7.Vein Pattern Recognition

Commercially available

8. Gait Recognition

Still in a research

9. Keystroke Analysis

Still in a research*

10. Mouse Dynamic


11. Signature Analysis


12. Voice Recognition


13. Odor Recognition

Still in a research

14. DNA Recognition

Still in a research

15. Ear Geometry

Still in a research

Note. Edited from ‘The Use of Technology to Combat Identity Theft’ by U. S. Treasury (2005). The data with mark * is added by researcher. Since some of biometrics technology are in research process (face thermogram, gait recognition, keystroke dynamics, mouse dynamics, odor recognition, DNA recognition, and ear geometry), this research only studied on 8 types of biometrics that are already used in market. These include fingerprint recognition, hand geometry, face recognition, iris scanning, retinal scanning, vein scanning, signature analysis and voice verification.

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Types of biometrics are stated as follows: 1.) Face Recognition Face recognition is an automated or semi-automated process, which records and compares the spatial geometric distinguishing features of the face which are based on two dimensional (2D) and three dimensional (3D) images and even included infrared facial scans. (European Commission, 2005; Woodward, Webb, Newton, Bradley, and Rubenson, 2001). This include the location and shape of facial attributes – including the eyes, eyebrows, nose, lips, chin – and their spatial relationships, analysis of the entire facial images and even the analysis of skin texture (Jain et al., 2006) During enrolment, a sensor (e.g. a camera) captures an image or series of images of the user’s face, which is then converted to a digital format. An algorithm then extracts the relevant features and measurements which then creates a template, which is much smaller than the original image (Organization for Economic Cooperation and Development, 2004).

FIGURE 2.5. Face Recognition (Kofman, A. (2016). Retrieved from https://theintercept.com/2016/10/13/how-a-facial-recognition-mismatch-can-ruin-yourlife/) Facial recognition systems are used for physical and logical access control in numerous settings; for example, application in banks, casinos, offices and as an access to computer systems.

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Facial recognition also used in mobile phone for identification such as Samsung Galaxy S8 and S8+. It functions is facial recognition to unlock the phone.

FIGURE 2.6. Face Recognition on Samsung Pay (Neurogadget.. (2017). Retrieved from http://neurogadget.net/2017/04/25/samsung-galaxy-s8-facial-recognition-feature-justanother-gimmick/54953) Facial recognition tend to grow more and more where a research done by Acuity Market Intelligence reported that face recognition in the global market was 12.9 percent in 2015 (Acuity Market Intelligence, 2017). Despite the fact that face recognition is less accurate than fingerprints, nevertheless, it tends to be less invasive, passive, and unobtrusive. It can be extremely effective in scanning large crowds for known criminals and terrorists (Baird, 2002; U.S. Treasury, 2005). The advantages of face recognition include factors such as no physical contact required, easy to use (just look at the camera), images can be captured from a distance, can capture many faces in public, and able to integrate with other systems (Chirillo and Blaul, 2003; Lease, 2005; Nakashima, 2007; Woodward, Horn, Gatune, and Thomas, 2003). On the other hand, there are some drawbacks of face recognition such as a high level of error rate, individual’s appearance may change over time and affect operations, significantly greater threats to privacy than other biometrics. Other flaws included poor lighting, eyeglasses, facial hair, and facial expressions may affect performance (Lease, 2005; Nakashima, 2007).

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2.) Face Thermogram Facial thermogram is unique to individuals and that it could be potentially used to devise methods and system for biometrics identification. Thermograms, generally are ‘visual displays of the amount of infrared radiation (IR) energy emitted, transmitted, and reflected by an object, which are then converted into a temperature. It is displayed as an image of temperature distribution.’ (Prokoski and Riedel, 1999). Facial thermography measures the amount of thermal radiation (heat) emitted from an individual’s face (Prokoski and Riedel, 1999). It has been suggested that the pattern of heat radiated by the human face is suitable for recognition purposes (Irish Council for Bioethics, 2009). Thermography works very much like facial recognition, except that an infrared camera is used to capture the images in place of a regular digital camera which based on two dimensional (2D) and three dimensional (3D) images (Prokoski and Riedel, 1999) Facial thermogram have some advantages, for example it works even in total darkness, nearly invariant to illumination changes and facial expressions. It also can be used when a user is under heavy artificial makeup (Kong, 2005).

FIGURE 2.7. Face Thermogram: Conventional photograph (in A) and thermographic images of the areas of contact between the face and the mask (in B) and between the nasal dorsum and the mask (in C) (Pneumol, J. P. (2017). Retrieved from http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1806-37132017000200087)

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3.) Fingerprint recognition Fingerprint technique has a long history which can be traced back past 100 years. (Jamieson, R., Stephens, G. and Kumar, S., 2005) Fingerprint scan is used to measure the ridge patterns of the fingertips (Nakashima, 2007). A fingerprint image can be captured involuntarily or unconsciously (European Commission, 2005). Sometimes, people leave fingerprint trails on surfaces that they touch by leaving oil that coats the ridge of the print. The residue that is left behind is called a latent fingerprint. Such fingerprints can be enhanced using special powders and brushes and can be processed to be used for credentialing (U. S. Treasury, 2005). The three major fingerprint features used for pattern recognition are arches, loops and whorls that are normally found on a fingerprint (Deb, 2004).

FIGURE 2.8. Fingerprint Patterns: Arch, Loop and Whorl with Core and Delta Guiding (Joshi, P. (2012). Retrieved fromhttps://prateekvjoshi.com/2012/07/22/fingerprint-recognition/) Fingerprint identification technology has benefited from technological advances. This has led to rapid, completely automated commercial fingerprint systems for verification (Acharya, 2006). The improvement in fingerprint technology led to the integrated, automated system that law enforcement agencies use today such as the Federal Bureau of Investigation (FBI) and Criminal Justice Information Services (CJIS) which fingerprints has been implemented extensively in crime investigation, identity verification, and fraud protection. It is considered to be a matured biometrics technology which is called the Integrated Automated Fingerprint Identification System (IAFIS) which contains the fingerprint and criminal history of over 47 million subjects in the criminal master file.

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The record provides automated fingerprint search capabilities, electronic image, storage, and electronic exchange of fingerprints and responses. It operates 24 hours a day, 365 days a year (European Commission, 2005) Fingerprint identification has universal application which rated in high uniqueness, permanence and performance (Jain et al., 2006). A misidentification rate of fingerprint is 1/1,000 with a medium level of security (Nyasulu & Fomene, 2001). Iris, however, has a false reject probability rate of 1/11,400 (Khaw, 2002). The advantages of fingerprint include the ability to use multiple fingers to scan for a template. The fingerprint pattern is permanent and does not change with age. Apart from that, it is easy to use and the sensors are inexpensive. Meanwhile, the disadvantages of fingerprint recognition include issues with public perceptions regarding its use such as hygienic factor when touching the sensor will exposed to spreading of germs. Also the scanned image of the fingerprint could be reproduced and misusing it for criminal activities (NSTC, 2006).

FIGURE 2.9. Fingerprint Recognition (CSE. (n.d.). Retrieved from https://www.csenet.co.uk/keeping-an-eye-on-biometrics/) Currently, the fingerprint recognition are widely used in phone using identification application such as iPhone which was introduced by Apple’s fingerprint sensor that was firstly introduced in iPhone 5s back in 2013. In 2015, the use of fingerprint recognition in global market accounted for 15 percent (Acuity Market Intelligence, 2017).

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4.) Hand Geometry Hand geometry recognition systems measure the physical dimensions of a hand (or finger) from a 3D image. Hand geometry analyzes the geometrical structure of the human hand or finger by measuring the length, width, thickness, and surface area (Jain et al., 2004). The user places their hands on the sensor, which includes guidance poles to ensure the correct positioning of the user’s hand and fingers. The sensor capture the image using camera by taking images of the top and the side of the hand (see figure 4) (NSTC, 2006). The sensor recorded the image as black and white image which the sensor does not record any surface details, such as finger or palm prints, scars or skin color (Zunkel, 1999).

FIGURE 2.10. Hand geometry (Shahnewaz, M. (2015). Retrieved from http://www.m2sys.com/blog/guest-blog-posts/about-hand-geometry-identification/) The significant benefits of hand geometry are it is easy to use and ability for large scale application with high usability and collectability. Hand geometry is generally considered to be stable, particularly once an individual reaches adulthood. Moreover, newer hand geometry sensors can ‘learn’ of minor changes in the size of the hand that might be associated with growth or ageing. The system is able to update the template accordingly (Zunkel, 1999). In contrast, hygiene issues have been raised due to multiple users that can be contaminated due to various users that emit physical contact. Also, the performance can be adversely affected by environmental factors such as sunlight and extreme cold (Jain et al., 2004). In 2015, the use of hand geometry only accounted for one percent when compares to the whole market (Acuity Market Intelligence, 2017).

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FIGURE 2.11. Hand geometry Scanning (Nguyen, L. (2014). Retrieved from http://www.biometricupdate.com/201412/coal-mining-company-fails-to-accommodateemployees-objection-to-using-biometric-hand-scanner-eeoc)

5.) Iris Recognition Iris recognition is a process using the pattern of the iris of an individual as a unique identifier (Ernst, 2002). The iris of an individual is absolutely unique. In the entire human population, there are no identical irises of any individual to another in their mathematical detail (Argus, 2007). The iris is the colored portion of a person’s eye and a muscle within the eye that regulates the size of the pupil, controlling the amount of light that entered the eye (NSTC, 2006).

FIGURE 2.12. Complexity and Uniqueness of Human Iris, Fine Textures on Iris Forms Unique Biometric Patterns Encoded by Iris Recognition Systems Pay (Sinharoy, I. (2014). Retrieved from https://indranilsinharoy.com/2014/12/05/dissertation_series/)

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Iris technology has the lowest error rate and the highest level of overall accuracy according to the seven pillars of universality, distinctiveness, collectability, performance, and acceptability is outstanding (European Commission, 2005; Lease, 2005). The strengths of iris recognition are that it is very stable and generally remains the same throughout an individual’s lifetime. Also, it is relatively difficult to fake or copy due to the internal biometric pattern. Furthermore, iris pattern characteristics are very unique and no two irises can be identical. On the other hand, there are some drawbacks of iris technology such as the cost of iris technology is higher than other biometrics technology. Lighting and other environmental conditions can affect image acquisition. It also can be difficult to scan the iris from a distance due to its small physical anatomy. (Chirillo and Blaul, 2003; European Commission, 2005; Lease, 2005; Nakashima, 2007; U. S. Treasury, 2005). Iris recognition adoption have been increased gradually from seven percent in 2007 to fourteen percent in 2015 (Acuity Market Intelligence, 2017). In addition to the previous statement about Samsung S8 and S8+, not only face recognition is used for unlocking the phone, iris recognition is also included along with other verification methods including fingerprint, pattern, password and PIN.

FIGURE 2.13. Irises verification on Samsung S8 and S8+ (Samsung. (2017). Retrieved from http://www.samsung.com/th/samsungpay/)

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FIGURE 2.14. Verification Methods Available in Samsung S8 and S8+ (Samsung. (2017). Retrieved from http://www.samsung.com/th/samsungpay/)

6.) Retina Recognition Retina recognition is based on the comparison of the complex pattern of the blood vessels located at the back of the eye (NSTC, 2006). Similarly to iris pattern, retina vasculature is also unique for each individual and for both eyes of the same individual. The position of the retina at the back of the eye keeps it well protected and consequently, the vascular network exhibits very high stability over time. The user looks into an eyepiece and focuses on a designated point in the viewing field, which helps align the eye correctly and fixes the area of the retina that will be imaged. Due to the small size of the area to be imaged, the user needs to be quite close to the sensor during capturing of the image, approximately between 2 and 5 cm away (Hill, 1999; Jain et al., 2004; Woodward et al., 2001). When it is closed to infrared light, which is invisible to the user, the vascular network of the retina will be illuminated and this is reflected back to the sensor as a wavelength. The algorithm then creates a unique ‘signature’ (template) based on the retina blood vessel pattern of that individual (Irish Council for Bioethics, 2009).

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FIGURE 2.15. Structure of Retina (Lubopitko-bg. (n.d.). Retrieved from http://encyclopedia.lubopitko-bg.com/The_Retina.html) One of the significant characteristic of retina is its universality, while the collectability of this it can be problematic, for example multiple areas of the retina can potentially be presented to the sensor; therefore, each user needs to be trained in using the equipment to align their eyes correctly (Jain et al., 2004). Additionally, scanning of the retina is often considered to be invasive and health concerns have been raised related to potential thermal damage to the eye (European Commission, 2005).

FIGURE 2.16. Retinal Scanning on Vivo X5Pro (Shams. (2016). Retrieved from http://webcusp.com/list-of-all-eye-scanner-iris-retina-recognition-smartphones/)

7.) Vein Pattern Recognition Vein pattern recognition uses a high resolution camera and infrared light to capture the pattern and structure of blood vessels visible on the back of an individual’s hand or finger. The vascular pattern of the human body is unique to a specific individual. It does not change as people aged, even though the vein size may change as individuals

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grow, the shape of the pattern remains distinct (NSTC, 2006). The result appears as the shadow of the veins as black lines and the rest of the hand structure is seen as white. The extracted vein template is then compared with the previously stored patterns and will be matched together confirming its recognition (Wang and Leedham, 2005). Vein pattern can be taken from different body parts but current recognition technologies concentrates on the hand vascular pattern that differs throughout the human body. For example, the pattern of the left hand is different from the right of the same person (Sanya-Isijola and Ademuyiwa, 2010). The most significant benefit of vein pattern recognition is its ease of use. However, images cannot be collected at a distance (NSTC, 2006).

FIGURE 2.17. Vein Pattern Recognition (Fujitsu. (n.d.). Retrieved from http://www.fujitsu.com/jp/group/frontech/en/solutions/businesstechnology/security/palmsecure/)

FIGURE 2.18. Vein Pattern Recognition in 7-Eleven, Lotte World Tower in Seoul. (Yeong, P. M. (2017). Retrieved from http://eng.dt.co.kr/contents.html?article_no=20170517105941000390

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8.) Gait Recognition Gait Recognition is a specific way of how an individual walks (Jain et al., 2004). In terms of biometric recognition of gait, a video camera is used to capture the specific repeating pattern of individual shape and/or the dynamics of the body movement. This pattern will be predominantly assessed through silhouette matching. An algorithm is used to determine the mathematical relationship between the movements of each point of the body and create a signature pattern (template) necessary for recognition (Sarkar and Liu, 2008).

FIGURE 2.19. Silhouette Picture for Gait Recognition (Hakim, S. (n.d.). Retrieved from http://biometrics.derawi.com/?page_id=38) Gait is not a universal biometric trait, since not all individuals are able to walk and is prone to variation over time; for example, due to changes in body weight, pregnancy, injuries (especially to the legs or feet) and even drunkenness (Jain et al., 2004). However, gait can be collected from a distance and from a number of angles, even using a low resolution video camera (Sarkar, Phillips, Liu, Vega, Grother, and Bowyer, 2005).

9.) Keystroke Dynamics Keystroke Dynamics is the individual characteristic way of typing on a keyboard such as speed and pressure, which is sufficient for use in biometric recognition systems (Jain et al., 2004; Woodward et al., 2001). Keystroke dynamics are distinctive enough to verify an individual’s identity. However, not all individuals can exhibit keystroke dynamics, for example due to

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insufficient literacy levels or competency in using computers. This trait can be collected quite easily and unobtrusively, which may assist in the acceptance of this method of recognition (Heyer, 2008).

FIGURE 2.20. The Speed and Time of Typing Turned to Data (Goodin, D. (2015). Retrieved from https://arstechnica.com/information-technology/2015/07/how-the-wayyou-type-can-shatter-anonymity-even-on-tor/)

10.)

Mouse Dynamics

Mouse Dynamics is defined as the set of actions received from the mouse movement data for a user while interacting with a specific graphical user interface that can be used for several security applications which similar to keystroke dynamics, mouse dynamics does not required special hardware or device for data collection. To identify the user we have to collect data from the mouse action and movement pattern such as Mouse-Move, Drag and Drop, Point and Click action (S. Benson Edwin and A. Thomson, 2009).

FIGURE 2.21. Mouse Action Data (Apple, 2017). Retrieved from https://support.apple.com/en-us/HT204895; Raj, S. B. E. and Santhosh, A. T. (2009). Retrieved from http://paper.ijcsns.org/07_book/200904/20090450.pdf)

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11.)

Signature Dynamics

Signature recognition is an automated method of examining an individual’s signature. These systems assess specific features of the signature writing process, including the speed, direction and pressure of writing, the time the stylus contacted with the surface, the total time taken to write the signature and where the stylus raised and lowered on the surface (Woodward et al., 2001).

FIGURE 2.22. Signature Recognition by Explained Factors (ANDXOR Corporation. (2013). Retrieved from http://www.andxor.com/supported-signatures.html) Although signatures are not considered to be very distinctive, they have been accepted as a means of verification for various government, legal, financial and commercial transactions (Jain et al., 2004). Signature dynamics is not a stable biometrics characteristic as a person can changes his or her signature over time. Also it can be affected by an individual’s physical or emotional state (NSTC, 2006). The acceptability of this biometrics system is facilitated by its ease of collection and the familiarity of using ordinary written signatures for verification, although it is considered not to be very accurate (Organisation for Economic Cooperation and Development, 1980).

12.)

Voice Recognition

Voice recognition or speaker recognition uses an individual’s voice characteristic for recognition purposes which certain features of an individual’s voice are based on the shape and size of their vocal tracts, mouth, nasal cavities, lips and etc. There are basically two types of voice recognition system, text dependent and text independent systems (Jain et al., 2004; NSTC, 2006). In a text dependent system, the user speaks a particular, predetermined, or passphrase such as a sequence of numbers. In a text independent system the user’s voice

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is recognized whatever they say. It is believed that text independent offer greater security against abusive activities than text dependent systems, but are more difficult to design (Jain et al., 2004; Organisation for Economic Cooperation and Development, 1980). Voice biometrics are usually used in verification-based applications and have been implemented in the financial services sector, especially e-commerce and e-banking (The Economist, 2006). Voice recognition is also developing as a means of purchasing for goods and services via telephone. In general, sound waves from an individual’s voice recording are calculated as feature vectors, which are then modelled as a voiceprint (template) for that individual (NSTC, 2006). During the recognition process, the sequences of feature vectors from the sample and enrolled voiceprints are compared using pattern analysis. The advantage of voice recognition is that the sensor needed to acquire the voice print is commonly available which is quick and easy to collect. Each person’s voice has high level of universality. However, the disadvantages of voice recognition are that there can be a high false rate. This is because voice is prone to change over time due to ageing, emotional state and medical conditions such as person who has a cold (Bolle et al., 2004; Jain et al.,2004; NSTC, 2006).

FIGURE 2.23. Voice Recognition by Voiceprint (Pyatt, J. E. (2011). Retrieved from http://www.personal.psu.edu/ejp10/blogs/tlt/2011/09/understanding-speechrecogniti.html)

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FIGURE 2.24. Voice Recognition to Unlock Phone on Baidu-Lenovo A586 (Lee, K. A., Li, H., and Ma, B. (2013). Retrieved from http://archive.signalprocessingsociety.org/technical-committees/list/sl-tc/spl-nl/201302/SpeakerVerificationMakesItsDebutinSmartphone/)

13.)

Odor Recognition

Odor recognition is the recognition of a characteristic component of odor emitted by a given individual. The odor emitted from pores all over an individual’s body. This system operates by circulating air around the body part being analyzed and over an array of chemical sensors. All individuals emit different smells and type of odor, components of which are considered to be distinctive. However, it can be affected by certain foods and medications including deodorants and perfumes. Individual’s smell are extracted and classified into a template (Heyer, 2008; International Biometric Group, 2008; Jain et al., 2004; Organisation for Economic Cooperation and Development, 1980).

14.)

DNA Recognition

In biometrics technology, DNA is used as an identification method. DNA is present in all individuals and the structure of an individual’s which DNA does not change over time and unique for everyone except identical twins. To obtain a DNA profile as part of biometric identification, a sample of skin cells is swabbed from the

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inside of the cheek and individual unique DNA profile is created by a specialized laboratory procedure. This profile is then stored as a reference for later use. The DNA swab is then destroyed for protecting the privacy of individual. The analysis generates a full DNA profile of the sixteen loci (specific locations on the gene). It can be used and accepted for identification purposes across the world. These loci do not contain genetic information related to medical history or illness. One of the outstanding advantages of DNA is that it is a highly accurate form of identification, while this method has some drawbacks, for example DNA requires precise collection, exspensive equipment, highly trained staff and the procedure is not done in real time operation. Moreover, privacy and security concerns surrounding the collection and storage of DNA is a serious issue (International Biometrics + Identity Association, n.d.; Jain et al., 2004).

15.)

Ear Geometry

Ear Geometry system based on analyses of the shape of the outer ear, the ear lobes and bone structure which have two dimensional (2D) and three dimensional (3D) images. A sensor collects a side profile image of the individual’s head, from which the system automatically locates the ear and isolates it from the surrounding hair, regions of the face and the user’s clothes. The algorithm has to account for differences in skin tone (caused by lighting variation), as well as differences in ear size, ear shape, hair occlusion and the presence of earrings (Organisation for Economic Cooperation and Development, 1980). Ears have a good universality and the rich structure of the ear is unique enough to permit it to be used as a biometrics technology. However, there are some experts that have the opinion and consider that ears changes over time (Jain et al., 2004).

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FIGURE 2.25. Individual’s Ear with Annotated 55 Landmarks on Ears (Intelligent Behaviour Understanding Group , (n.d.). Retrieved from https://ibug.doc.ic.ac.uk/resources/ibug-ears/) In summary, this research only focusing on 8 kinds of biometrics which consisted of fingerprint recognition, hand geometry, face recognition, iris scanning, retinal scanning, vein scanning, signature analysis and voice verification. This is according to Acuity Market Intelligence (2017) and U.S. Treasury (2005) which suggest that only 8 types of biometrics has been currently adopted in mass market, while the other 7 types are still under extensive research process.

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2.2.4 The Utilities of Biometrics Technology Biometrics technology used area are stated as follow: Verification Mode: The verification mode is the process of validating an individual’s identity by comparing the captured biometric data with the person’s biometrics template stored in the system’s database (Jain et al., 2004). The verification process is the basis for authentication systems (U. S. Treasury, 2005). In verification mode approach, the system answers the question “Are you the person you claim to be?” or “Is this X?” After the individual claims to be X, the individual’s claimed identity is either confirmed or denied based on biometrics templates in the database which this is referred to as a 1:1 (one-to-one) match (Geising, 2003; Lease, 2005; Newton and Woodward, 2001) For example, individual’s using PIN at automatic teller machine (ATM) to do banking transaction such as deposit, withdrawal, fraud detection, prevention and protection (Harris, 1999; Jain et al., 2004). Watch-list Mode: The watch-list mode is the method of comparing a presented biometrics against a smaller collection of reference biometrics. In the watch-list task, the biometrics system determines if the individual’s biometrics signature matches a biometrics signature of someone in the database of the watch-list as one-to-few match systems. It is normally used in the surveillance of known criminals or suspects while the watch-list form of biometrics application is not commonly discussed in the literature, unlike the verification and identification functions. (Blackburn, 2004; Lewis, 2007; U. S. Treasury, 2005). This mode is referred to as screening watch-list and used in airport security, in public events and surveillance applications (Hong, Jain, Pankanti, Prabhakar, Ross, and Wayman, 2004).

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Identification: The identification process is an essential function of biometrics technology in which individuals must be recognized, verified, given access and permission privileges by the system searching the templates of all users in the database for a match (Jain et al., 2004). In the identification mode, the system conducts a 1: N (one-to-many) comparison to establish an individual’s identity and fails if the subject is not enrolled in the database. The identification mode is different from the verification mode of 1:1 (one-to-one) match system and the watch-list of one-to-few match systems. Identification is a critical component in recognition process where the system will establish either positive or negative identity (Blackburn, 2004; Giesing, 2003; Jain et al., 2004; Lease, 2005; U. S. Treasury, 2005).

2.2.5 The Biometrics Authentication Process During the enrollment phase, biometrics data of the user such as the fingerprints are acquired, captured and processed by the sensor, quality component and database (Hong et al., 2004; Jain et al., 2004; Lewis, 2007; Tilton, 2006). The template which is stored in a database will be used to determine a biometrics match and establish identity (Lewis, 2007). When the user returns to the system, an analogous process to enrollment will occur. The user’s relevant biometrics data will be extracted and then compared with the previously stored template in the database. If the score is within allowable or preset threshold criteria, the decision will be made either to match or not to match (Deschaine, 2005; Hong et al., 2004; Jain et al.; Lewis, 2007; Tilton, 2006) Enrollment Sample acquisition

Present to the biometric s en s or

Capture the raw biometrics data

Process the biometrics f ea tu r e s

Capture the raw materials

Process the biometrics features

Verification

Quality control

Database of reference t emp la t es

Match

Compare

Decision

YES/NO No Match

Figure 2.26. Core stages and modules in the authentication process of a general biometrics system.

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2.2.6 Biometrics Advantages and Disadvantages The biometrics advantages and disadvantages are stated in following table. Table 2.5 Biometrics Advantages and Disadvantages Advantages

Disadvantages

- Preventing identity theft and fraud in

- Biometrics technology is inherently

the use of travel documents, stolen credit

individuating and it interfaces easily with

cards and fake checks scams.

database technology, making it easier to

- Surveillance of known criminals,

commit privacy violations.

suspects or suspected terrorists

- Some biometrics technologies are

- Increased security which biometrics

discriminatory.

technology serves as the gatekeeper of

- The cost of failure is high and may be

confidential personal data.

consequential.

- Offer significant cost savings or

- Performance is a big issue when the

increasing return in investment (ROI) in

database is large.

areas such as loss prevention, time and

- The problems of accuracy and speed

attendance.

that can affect the system.

- Controlling access to sensitive facilities

- Biometrics system may be intrusive or

such as an airports for passengers’ safety. personally invasive. - Eliminate problems caused by lost IDs

- Neither verification systems nor

or forgotten passwords.

identification systems generate perfect

- Authenticate the user through

matches.

behavioral and physiological traits, which are better than other methods of authentication which may be shared or observed such as passwords. - Integrate a wide range of biometrics solutions and technologies, customer applications and databases into a robust

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Table 2.5 Biometrics Advantages and Disadvantages (continues) Advantages

Disadvantages

and scalable control solution for facility and network access such as a transaction. - Prevent fraudulent use of stolen cards, especially in conjunction with point-ofsale payments. - Employ hard-to-forge technologies and materials to reduce and control welfare fraud. Note. from Abernathy et al., 2007; Barry 2002; Chirillo and Blaul, 2003; Electronic Frontier Foundation, 2006; Jain et al., 2004; King et al., 2008; Matyas & Riha, n.d; Nakashima, 2007; Questbiometrics, 2005; Rosenzweig et al., 2004; Woodward, 2001

2.2.7 Criticism of Biometrics toward Privacy The issues of biometrics on privacy are frequently discussed. Although many people believe that biometrics help individuals to protect their privacy better, others are of the opinion that biometrics invade individuals’ privacy. Privacy is considered one of the most important human rights. According to United Nation, several of articles about privacy and security are stated in Universal Declaration of Human Rights. The declarations are as follow: - Article 3: Everyone has the right to life, liberty and security of person. - Article 12: No one shall be subjected to arbitrary interference with his privacy, family, home or correspondence, nor to attacks upon his honour and reputation. Everyone has the right to the protection of the law against such interference or attacks. (United Nations General Assembly in Paris, 1948). Moreover, the latest constitution of the Kingdom of Thailand stated in Chapter 3 Rights and Liberties of the Thai People, Section 32 that “A person shall enjoy the rights

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of privacy, dignity, reputation and family. An act violating or affecting the right of a person under paragraph one, or an exploitation of personal information in any manner whatsoever shall not be permitted, except by virtue of a provision of law enacted only to the extent of necessity of public interest” (Constitution Drafting Committee, 2017 On one hand, biometrics is believed as a method to help people gain more privacy protection by offering greater privacy protection. Biometrics modalities offer a stronger identity assurance as they cannot be lost or forgotten, they are difficult to copy, forge or share. Furthermore, the systems require the individual to be present at the time of identification (Irish Council for Bioethics, 2009). Since the worldwide focus on terrorism, soaring identity fraud and identity theft, the need of using biometrics as a control measures are increased (King et al., 2008). On the other hand, there are rising concerns that biometrics technology has the ability to invade confidentially and violate individual rights (Vollmer, 2006). Privacy apprehension was one of the significant problems confronting not only the biometrics industry but also any organization that gathered personal information (American National Standards Institute, 2007). Cavoukian (1999) also stated that if an individual’s data are tagged with biometrics ID, people will lose control of their identity. The debate over the adoption of biometrics is about physical privacy that focused on user freedoms and continue to raise greater anxiety of the state watching (Acharya, 2005; The American National Standards Institute, 2005; Rand, 2001; Woodward et al., 2001). Privacy advocates object to the use of biometrics and other verification tools for collecting individual’s information for fear of having a ‘surveillance society’ in which governments and private corporations were collecting increasing amounts of personal data, sometimes without justification” (Acharya, 2005). For example, in 2013, Snowden — a former contractor for the CIA revealed that the US National Security Agency (NSA) was collecting telephone records of tens of millions of Americans. NSA had also tapped directly into the servers of nine internet firms, including Facebook, Google, Microsoft and Yahoo, to track online communication in a surveillance programme known as Prism which US government claimed that they have a right to spy on people to protect national security (Citizenfour, 2014). Additionally, Vollmer (2006) mentioned that as governments continued to adopt and rapidly implement the technology, the privacy of the individual has been threatened.

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To gain a depth view of biometrics used in BioPay, this section will review categories of biometrics (section 2.3.1), the seven pillars of biometrics technology (section 2.3.2), types of biometrics (section 2.3.3), the utilities of biometrics technology (section 2.3.4), the biometrics authentication process (section 2.3.5), biometrics advantages and disadvantages (section 2.3.6), and criticism of biometrics toward privacy (section 2.3.7). The framework will be used to set the questions in questionnaire.

2.3 BioPay Implementation BioPay is a payment method that allows consumers to make transactions by identifying themselves with part of their body called biometrics technology which linked to a preassigned credit card or bank account. Biometrics identifies people by measuring some aspect of individual anatomy or physiology such as face, finger, hand, iris and speaker recognition. These technologies are commercially available today and are already in use (Podio and Dunn, 2002). Sample Case Study In 2002, Pay By Touch was founded by John P. Rogers. Pay By Touch developed a biometrics authentication system that allowed customers to pay and get loyalty rewards with their fingerprint. It allowed secure access to checking, credit card, loyalty, healthcare, and other personal information. It functions through the unique characteristics of an individual's biometrics features, thereby creating a highly secure anti-identity theft platform. Although Pay By Touch had more than 3.6 million consumers for its service and company raised over $300M dollars, in 2008 it was shut down and no longer in operation (Shubha, 2015).

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FIGURE 2.27. Pay By Touch (Lovemark. (n.d.). Retrieved from http://www.lovemarks.com/lovemark/pay-by-touch/) In 2017, 7-Eleven has launched its first smart convenience store equipped with a BioPay system at Lotte World Tower in Seoul. This is the first such store in the world opened by 7-Eleven for testing new technology, available only to Lotte employees named ‘HandPay’. It recognizes a person's unique palm vein pattern. This technology is easier to use and more comfortable than traditional scanning of fingerprints or iris. The customer places their hand on a transparent support and the system recognizes it and charges the customer's account. To benefit from this payment system, customers will have to have a Lotte Card – at least initially. Lotte is a massive Korean distribution conglomerate with a wide range of business interests. 7-Elevens in Korea are a joint venture between Lotte and the U.S.-based 7-Eleven organization and collectively known as Korea Seven. The company hopes to offer the HandPay system to customers not using the Lotte card by the end of August this year. The store also boasts electronic price tags, refrigerators that open and shut automatically, a special Smart Safe cigarette vending machine and a smart CCTV surveillance system. When customers have made all their selections, they head over to unmanned automated checkout points. They place their items on a conveyor belt where the barcodes are scanned. And they will not have to worry about when placing the items on the belt in a certain way – each item is scanned on all sides to locate the barcode. Eventually, even that impressive feature will be done away with, with the introduction of an artificial intelligence system that can identify items without the need for any barcodes (Watson, 2017).

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FIGURE 2.28. BioPay in 7-eleven, Lotte World Tower, Seoul (Al Jazeera English. (2017). Retrieved from http://newsvideo.su/video/6878036; Watson, T. (2017). Retrieved from https://newstarget.com/2017-05-24-7-11-korea-launches-smart-storeallowing-customers-to-pay-with-parts-of-their-body-linked-to-a-credit-card.html) In the same year, South Korea’s Financial Services Commission (FSC) is planning to use BioPay by launching a pilot projects that will allow consumers to make payments at the point of sale using palm vein authentication technology, without the need of a physical payment card. The Bank of Korea is also pushing ahead by implementing its Coinless Society Project plans, which will be made up of a variety of pilot projects with the aim of establishing a ‘well-constructed’ digital payments infrastructure in the country (Boden, 2017). Additionally in September, 2017, KPRO, a new fast-casual-esque concept under KFC brand in the eastern city of Hangzhou had launched the new payment method that allow customers to pay by their face called ‘Smile to Pay’. It is the first physical store in the world to use face recognition technology for payment. The payment process only took two steps; scanning their face at the kiosk machine and then entering the mobile phone number which linked to Alipay – a virtual bank account created by Ant Financial, the affiliate financial service of Chinese e-commerce giant Alibaba. Currently, Alipay have active users over 500 million which face recognition has been available since 2015 with one third of users using this face scan system. The accuracy of this face-detection on Alipay reaches 99.8 percent. This system using technology to combined a 3D camera and liveness detection algorithm which Ant Financial claimed that Smile to Pay can effectively block spoofing attempts using other people’s photos or video recordings and ensure account safety.

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FIGURE 2.29. Face Recognition Available on Kiosk Machine at KPRO, Hangzhou, China (Reuters. (2017). Retrieved from https://www.reuters.com/article/us-alibabapayments-facialrecognition/just-smile-in-kfc-china-store-diners-have-new-way-to-payidUSKCN1BC4EL) Additionally, Chen Jidong the director of Ant Financial, Alibaba stated that with the newly developed algorithm methods considering multi-biometric features, users can identify customers when they make faces or stand crowded in front of the camera. We can even tell the difference between two identical twins. Other factors like aging calculation, face and body movements, and location information are used to realize multiple layers of security confirmation. However, as facial recognition technology is widely applied, the prospect of using it in payment systems still arouses users' concerns about its security. Wrong recognition has potentially devastating consequences when money is concerned (Nan, 2017; Reuters, 2017, Wang 2017).

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FIGURE 2.30. Face Scanning for Payment on Kiosk Machine at KPRO, Hangzhou, China (Foodbuzz. (2017). Retrieved from https://www.foodbuzz.fr/payer-sourire-cestpari-de-kfc-smile-to-pay-%F0%9F%98%81/) In addition, Fujitsu Frontech North America has partnered with Keyo, granting the biometrics retail and hospitality payment solutions provider access to and distribution rights in Fujitsu’s patented PalmSecure biometrics technology. Fujitsu PalmSecure uses a near-infrared light to capture a user’s palm vein pattern, creating a unique biometrics template that is matched against the palm vein patterns of preregistered users. This technology significantly improves the value proposition for retailers by reducing transaction fees and the risk of fraud, while simplifying and improving the human experience by eliminating physical cards, keys and tickets. It also cuts down on the amount of time spent in checkout queues. Both of Fujitsu’s PalmSecure sensors and PalmSecure for SSO (single sign-on) only recognize the palm vein pattern if blood is actively flowing within the individual’s veins, eliminating any chance of forgery. The use of vascular pattern recognition technology provides highly reliable authentication with low false acceptance and rejection rates, It also ensures fast and easy enrollment (Fujitsu, 2017).

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FIGURE 2.31. Fujitsu PalmSecure (Security-Systems Technology. (n.d.). Retrieved from http://www.sstgroup.co.uk/solutions/palm-vein-scanners/) Furthermore, PayTango, a system that links a person’s fingerprint with a form of payment like a credit card or debit card which allow customers to simply places their index and middle finger on the biometrics fingerprint scanner. Eventually the software will recognize the user automatically. Fingerprints could also be linked to a store’s loyalty or discount program. PayTango was developed by four students from Carnegie Mellon University, the initial test run of PayTango is occurring at three locations on the Carnegie Mellon campus. In order to setup a new account with PayTango, students and other customers can register at the terminal when checking out. After scanning their fingerprints, customer slides the card they want to link to their account and enter their cell phone number to complete the registration process which the entire process only takes about six seconds (Flacy, 2017).

FIGURE 2.32. PayTango (Culter, K. (2017). Retrieved from https://techcrunch.com/2013/03/25/paytango/)

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Additionally, in England, Costcutter supermarket has launched the new payment method by using finger vein biometrics technology. Costcutter is the first supermarket in the world that use finger vein scanning called Fingopay. Simon Binns, commercial director of Sthaler stated that vein scanning is the most secure version of identification because it cannot be copied or stolen due to uniqueness of fingerprint’s vein pattern. “The technology uses an infrared light to create a detailed map of the vein pattern in your finger. It requires the person to be alive, meaning in the unlikely event, the criminal hacks off someone’s finger” and “this makes payments so much easier for customers. They don’t need to carry cash or cards. They don’t need to remember a pin number. You just bring yourself. This is the safest form of biometrics. There are no known incidences where this security has been breached” Simon Binns stated. Moreover, Nick Telford-Reed, director of technology innovation at Worldpay UK said that finger vein scanning has a number of advantages compared to fingerprint. “This deployment of Fingopay in Costcutter branches demonstrates how consumers increasingly want to see their payment methods secure and simple.” Biometrics security has become an important part of payment authentication technology in recent years. The research of Worldpay UK found that 69% of consumers are happy in using their fingerprints as a method of authentication. The growth of mobile and contactless payments make fingerprint the most preferred biometric application (Madhvi , 2017).

FIGURE 2.33. Fingopay (BBC. (2017). FINGOPAY. Retrieved from http://www.bbc.com/news/technology-41346717)

For this study, this section is essential to understand in the context of the BioPay and its current use.

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2.4 Prior Research Prior researches on biometrics adoption are stated as follows: Kritica Law (2007, pp.65-67) studied on the ‘Impact of Perceived Security on Consumer Trust in Online Banking. MCIS. Auckland University of Technology’ by applying quantitative research using survey research method. The research found that perceived of security is a significant factor which perception of privacy had the highest and most significant impact on trust while other components have no significant effect. Similarly, the result showed that the mechanisms that were indicated by the website had a significant impact on the participants’ perceptions. Visible security mechanisms for privacy, availability, authentication and authorization had the maximum impact on the consumers’ perceptions. There is a strong correlation between visible security mechanisms and perception of security indicated that the users of internet banking websites are aware and influenced by the presence of these indicators which lead to the importance in indicating the types of mechanisms used and educating the users on these mechanisms. The researcher also suggested that online banking interfaces should inform their consumers on the methods used to ensure the user’s information and transactions privacy. This effort would be able to improve the perception of privacy and help increase the level of trust in e-banking applications. Lanouar Charfeddine and Narsi Wadie (2013, pp.16, 24) studied on ‘The Behavior Intention of Tunisian Banks’ Customers in using Internet Banking’. The research applied a quantitative approach using survey research method. The research found that security and privacy is a significant factor which this factor is added to TAM. The result of this study clearly shows that users find the internet banking system useful and easy to use. Likewise, attitude, perceived behavior control and perceived usefulness are the critical factor for users to adopt internet banking. The researchers recommended that internet banking professional should build a positive perception among their customers on the application of internet banking services by offering free information. Additionally, in order to promote internet banking new marketing campaign, it have to target more elderly people, informing on the facilities of this service, and to make this technology easy to use. The researchers also suggested that banks should promote the advantages of online banking compared to traditional banking. Commercial banks

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should ensure that security and privacy of the internet banking systems are properly developed and practical to users. Furthermore, banks should be aware of their systems security and ensure personal information is protected. Gideon U. Nwatu (2011, Abstract, PP.208-216) studied about ‘Biometrics Technology: Understanding Dynamics Influencing Adoption for Control of Identification Deception within Nigeria’ which used the mixed method research. Descriptive and inferential study used interview and survey questionnaires for data collection. The research found that there were correlation between biometrics technology adoption and three other variables; perceived ease of use, usefulness, security concerns and awareness. A significant majority (67 percent) of the research participants and the interpretation of findings from the analyses conducted showed that ease of use is a dynamic factor that will influence adults’ perceptions toward the adoption and usability of biometrics technology. 75 percent of adults who were interviewed confirmed that ease of use of biometrics technology would influence adoption. Additionally, a significant majority (70 percent) of the participants agreed that perceived usefulness will influence adults’ perceptions toward the adoption and usability of biometrics technology which shows that it is 8 times more likely to accept the adoption of biometrics technology to prevent identity fraud. 65 percent of adults who were interviewed confirmed that perceived usefulness of biometrics technology would influence their perception in adoption. Moreover, 42 percent of adult participants agreed that security concerns will influence their behavior toward the adoption and usability of biometrics technology. While 85 percent of interviewed participants indicated that security is a factor that will seriously influence their behavior toward adoption and usability. Personal security and the protection of banking transactions and assets were areas of concern for, which biometrics technology can be used to mitigate victimization. Furthermore, 66 percent of participants answering the questionnaire and 90 percent of the interviewed participants agreed that awareness of biometrics technology will influence their behavior toward adoption and usability. The interviewed participants confirmed that it is difficult to determine the usefulness and security advantages of biometrics technology without awareness of the system.

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Hylke Huys (2013-2014, pp.42-44) studied on ‘Consumer Acceptance of Identification Technology’ by applying quantitative research by survey research method among 187 fingerprint and 151 iris scanning and facial recognition. The research found that behavioral intention to accept identification technologies by consumers depends significantly on perceived usefulness of the technology, perceived ease of use, relative advantage of the technology compared to other payment methods or marketing tools, the privacy concerns of consumers regarding the technologies, anxiety of consumers towards these technologies, innovativeness of consumers in general, facilitating conditions surrounding these technologies and experience consumers have with the technology. The research also discover that privacy concerns is a greater effect of the technology anxiety which people are less anxious about using the technology, but more about what happens to their personal data. Therefore, retailers should reduce the privacy concerns of consumers by providing enough information on what happens with the data that is collected by the pay by touch mechanism and observed by the iris and facial scanner., Hiring experts or extra employees assisting people and answering questions about the technology to facilitate people’s experience. Additionally, although this research did have big respondents sample that have many years of experience in this area, the intention of consumers to accept the technology would be enhanced if they have more experience. The results also demonstrate that consumers are significantly more comfortable with the fingerprint recognition system. Therefore, researcher suggested that if the retailers would like to enhance customers’ experience by an identification technology, they should start with fingerprint scanner. Michael C. Breward (2009, Abstract) studied about ‘Factors Influencing Consumer Attitudes towards Biometric Identity Authentication Technology within the Canadian Banking Industry’ by applying mixed method research. The study was divided into three parts; qualitative, qualitative and quantitative. In the first part, qualitative is used to identify what avenues of exploration Canadian banks considered to be the most salient with respect to consumer perceptions of biometrics authentication technology. In this part, semi-structured interviews of experts are included. In the following part, qualitative was presented by asking consumers across Canada what they perceived as potential benefits and concerns with biometrics authentication technology being used to access their bank accounts. Finally, quantitative study was carried out and

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the proposed research model was tested using this model identified on both contextual antecedents and innate traits. Both of these may influence consumer attitudes towards using biometrics to access their bank accounts via automated teller machine (ATM). In addition, the aspects of control and voluntariness were manipulated, through the presentation of various scenarios, to examine their effects upon both attitudes as well as the direct antecedents of privacy and security concerns and usefulness. The proposed model was assessed using structural equation modeling. The research found that the contextual factors of privacy and security concerns and usefulness have a bigger impact upon attitudes as compared to innate personality traits. On the other hand, voluntariness appears to have no effect. Moreover, the control has a significant impact upon attitude as well as privacy and security concerns and usefulness (Madhvi, M., 2017).

2.5 Conceptual Framework The research model that guided the study is shown in figure 2.31 as below: Perceived Ease of Use

Perceived Usefulness

Perceived Security and Privacy FIGURE 2.34. Conceptual research model of the study

BioPay Adoption Intension

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CHAPTER 3 RESEARCH METHODOLOGY

This study ‘The Factors Affecting BioPay Acceptance of People in Bangkok’ used a survey questionnaire to test the proposed hypotheses. A close-ended survey questions used in questionnaire were formulated on a check-list question by employing five-point Likert scale. Since this study focused on Thai citizen, the questionnaire were both in English and Thai. A total of 205 survey responses were collected from people in the Bangkok metropolitan area. The respondents were adults between 18 and 60 years old. This research is conducted according to the following of methodology:

3.1 Determining Sample Size The sample size of this research is based on Taro Yamane (Yamane, 1973) formula with 95 percent confidence level with 7 percent error. (According to Bangkok population, there are 3,517,850 persons between the age of 18 to 60 years old from the data of the Bureau of Registration Administration official report, 2016) (see appendix C) Table 3.1 Bangkok Population between Ages of 18 to 60 Years Old

Age 18 Yrs. 19 Yrs. 20 Yrs. 21 Yrs. 22 Yrs. 23 Yrs. 24 Yrs. 25 Yrs. 26 Yrs.

Population divided by age December 2016 Persons Age Persons 69,444 92,722 39 Yrs. 79,047 92,882 40 Yrs. 80,047 89,764 41 Yrs. 85,135 89,547 42 Yrs. 82,105 87,302 43 Yrs. 77,108 90,862 44 Yrs. 76,773 93,612 45 Yrs. 75,434 91,964 46 Yrs. 75,256 90,589 47 Yrs.

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Table 3.1 Bangkok Population between Ages of 18 to 60 Years Old (continues) Population divided by age December 2016 Age Persons Age Persons 72,058 93,213 27 Yrs. 48 Yrs. 73,228 90,304 28 Yrs. 49 Yrs. 69,772 87,741 29 Yrs. 50 Yrs. 73,653 90,362 30 Yrs. 51 Yrs. 78,471 90,901 31 Yrs. 52 Yrs. 80,192 87,367 32 Yrs. 53 Yrs. 83,951 85,411 33 Yrs. 54 Yrs. 86,750 82,587 34 Yrs. 55 Yrs. 89,627 83,423 35 Yrs. 56 Yrs. 93,202 79,557 36 Yrs. 57 Yrs. 93,521 73,471 37 Yrs. 58 Yrs. 91,016 68,479 38 Yrs. 60 Yrs. Total 3,517,850 The calculation formula of Taro Yamane is presented as follows: 𝑛=

𝑁 1 + 𝑁(𝑒)2

Where: 𝑛 = sample size required 𝑁 = number of people in the population 𝑒 = allowable error (percent)

Substitute numbers in formula: 𝑛=

3,517,850 1 + 3,517,850 (0.07)2

𝑛 = 205 (Rounded)

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After calculating the sample size by substituting the numbers into the Taro Yamane formula, the number of sample calculated is 204.069794 persons. A sample size of 205 was accounted for data collection.

3.2 Sampling Technique After achieving the sample size presented in section 3.1, stratified sampling techniques and snowball sampling techniques were applied select sample. Table 3.2 Sample Size Age Range

Population

Persons

18-25

625,093

37

26-33

606,581

35

34-41

729,484

43

42-49

727,393

42

Above 50

829,299

48 Total 205

3.3 Research Instrument For the study, questionnaire is used as the research instrument. The questionnaire consists of 5 parts. The questionnaire is close-ended question which consists of check-list question and five point Likert scale. 5 parts of questionnaire are stated as follows: PART 1: The demographic information of the respondents using check-list questions. PART 2: The question on respondents’ perceived ease of use using five-point Likert scale. PART 3: The question on respondents’ perceived usefulness using five-point Likert scale.

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PART 4: The question on respondents’ perceived security and privacy which is divided question into two sub-categories: perceived security and perceived privacy using five-point Likert scale. PART 5: The question on respondents’ BioPay adoption intension using fivepoint Likert scale.

3.4 Formulating Research Instrument The questionnaire is formulated through the following steps: 1. Understanding the study conceptual framework 2. Brain storming for questions that will be used in questionnaire 3. Classified problems 4. Selecting relevant question and sequencing the questions in order 5. Test the reliable of the questionnaire

Test the reliable of the questionnaire Validity In order to test the validity of questionnaire, researcher brought questionnaire to professor to check the validity of content, question structure, wording, content coverage and studied variables for increasing questionnaire validity. Reliability The questionnaire in the Thai version was pretested with 30 native Thai who were invited to do a questionnaire and were asked to give suggestions on the questionnaire in terms of question content, wording, sequence, form, layout, question difficulty and instructions. The results of the pretesting revealed minor instances of ambiguous wording and the need for additional information of some items that were subsequently revised to improve the clarity and understandability.

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The questionnaire reliability separated in four parts using computer software for statistical analysis in interpreting the result. The Cronbach alpha coefficients in each section are stated as follows: Table 3.3. The Result of Testing Reliability of Questionnaire in Each Section Section

Variables

Questions

Cronbach’s Alpha

2

Perceived Ease of Use

3 questions

α = 0.724

3


5 questions

α = 0.816

4

Perceived Security and Privacy

5 questions

α = 0.924

5

BioPay Adoption Intension

13 questions

α = 0.937

The value of α is more than 0.7 in every section, therefore, the questionnaire is acceptable. Table 3.4 Values of Thumb on the Correlation Coefficient Sizes (Tavakol and Dennick 2011) Cronbach’s Alpha

Internal Consistency

α ≥0.9

Excellent

0.9 > α ≥ 0.8

Good

0.8 > α ≥ 0.7

Acceptable

0.7 > α ≥ 0.6

Questionable

0.6 > α ≥ 0.5

Poor

0.5 > α

Unacceptable

3.5 Data Collection The questionnaires were through internet randomly distributed to Bangkok dwellers. Data collection was conducted in September, 2016.

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3.6 Data Analysis and the Statistic Used in Data Analysis The data will be analyzed by computer software using statistical analysis as follows: 1. The demographic background information of respondents using check-list questions were analyzed and presented using descriptive statistics in the form of frequency and percentage. 2. Information of perceived ease of use, perceived usefulness, security and privacy using five-point Likert scales questions were analyzed by using Pearson's product moment correlation coefficient to identify the magnitude of relationship between variables. 3. The BioPay adoption intention using five-point Likert scales questions were analyzed by using Pearson's product moment correlation coefficient to identify the magnitude of relationship between variables. The five-point Likert scales are stated as follows: Table 3.5 Five-point Likert Scales’ Rate, Interpretation and Range Range

Interpretation

4.21 - 5.00

Strongly Agree

3.41 - 4.20

Agree

2.61 - 3.40

Moderately Agree

1.81 - 2.60

Disagree

1.00 -1.80

Strongly Disagree

Scale rating of class interval of Best (1970) are used to interpret 𝐶𝑙𝑎𝑠𝑠 𝑖𝑛𝑡𝑒𝑟𝑣𝑎𝑙 =

𝑀𝑎𝑥𝑖𝑚𝑢𝑚 − 𝑀𝑖𝑛𝑖𝑚𝑢𝑚 𝐶𝑙𝑎𝑠𝑠 𝑁𝑢𝑚𝑏𝑒𝑟

=

5−1 5

= 0.8

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1. = 1.00 - 1.80 means Strongly Disagree (Not true at all) 2. = 1.81 - 2.60 means Disagree (True to a minimal degree) 3. = 2.61 - 3.40 means Moderately Agree (True to a moderate degree) 4. = 3.41 - 4.20 means Agree (True to a high degree) 5. = 4.21 - 5.00 means Strongly Agree (Absolutely True) Table 3.6 Research Hypothesis, Statistical Hypothesis, and Statistic Used Hypothesis

Statistic Used

Research Hypothesis 1: There is a relationship between perceived ease of use and BioPay adoption intention.

Pearson's product moment correlation coefficient

Statistical Hypothesis 1: H0: There is no relationship between perceived ease of use and BioPay adoption intention. H1: There is a relationship between perceived ease of use and BioPay adoption intention.

Research Hypothesis 2: There is a relationship between perceived usefulness and BioPay adoption intention. Pearson's product moment Statistical Hypothesis 2: H0: There is no relationship between perceived usefulness and BioPay adoption intention. H1: There is a relationship between perceived usefulness and BioPay adoption intention.

correlation coefficient

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Table 3.6 Research Hypothesis, Statistical Hypothesis, and Statistic Used (continues) Hypothesis

Statistic Used

Research Hypothesis 3: There is a relationship between perceived security and privacy, and BioPay adoption intention.

Statistical Hypothesis3:

Pearson's product moment

H0: There is no relationship between perceived

correlation coefficient

security and privacy and BioPay adoption intention. H1: There is a relationship between perceived security and privacy and BioPay adoption intention.

Statistics used in data analysis 1. Basic statistics 1.1) Percentage 𝑃=

𝑓 × 100 𝑁

Where: P = Percentage 𝑓 = Frequency to be converted to percentage 𝑁 = Numbers of frequencies

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1.2) Mean 𝑥̅ = (∑ 𝑥) 𝑁 Where: 𝑥̅ = Mean ∑ 𝑥 = Summation of the scores 𝑁 = Numbers of data 1.3) Standard Deviation ∑ (𝑋 − 𝑋̅)2 √ 𝑆𝐷 = 𝑁−1

Where: SD = Standard Deviation Σ = the sum of 𝑋 = Each value of data set 𝑥̅ = Mean 𝑁 = Numbers of data

2. Statistic for analyzing the reliability of the questionnaire Analyzed for the reliability of questionnaire, which set the rating scale by using Cronbach’s alpha-coefficient:

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𝛼=

∑ 𝑉𝑖 𝑛 (1 − ) 𝑛−1 𝑉𝑡𝑒𝑠𝑡

Where: 𝛼 = Reliability 𝑛 = Number of questions in questionnaire Σ𝑉𝑖 = the sum of variability of each of question score 𝑉𝑡𝑒𝑠𝑡 = Variability of each of overall questions’ score on the entire test

3. Statistic for hypothesis testing 3.1) Pearson's product moment correlation coefficient 𝑟=

𝑁Σxy − (Σx)(Σy) √[𝑁Σ𝑥 2 − (Σ𝑥)2 ][𝑁Σ𝑦 2 − (Σ𝑦)2 ]

Where: 𝑁 = Number of pairs of scores Σxy = the sum of the products of paired scores Σx = the sum of x scores Σy = the sum of y scores Σ𝑥 2 = the sum of squared x scores Σ𝑦 2 = the sum of squared y score *Note: Predetermined alpha level of significance is .05.

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According to Miller (1998), the interpretation of the Pearson's product moment correlation coefficient (r) are stated as follows: Table 3.7 Interpretation of Pearson's Product Moment Correlation Coefficient Size of Correlation

Interpretation

0.70 or higher

Very strong relationship

0.50 – 0.69

Substantial relationship

0.30 – 0.49

Moderate relationship

0.10 – 0.29

Low relationship

0.01 – 0.09

Negligible relationship

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CHAPTER 4 ANALYSIS AND INTERPRETATION

The data analysis for this study of ‘The Factors Affecting BioPay Acceptance of People in Bangkok’ is statistically analyzed by computer software. Therefore, in order to comply with purpose of the research and answering the questions of the study, data was gathered from 205 respondents. In order to analyze and present the result, data analysis is by divided into 6 parts as follow: PART 1: Descriptive statistic on the respondents’ demographic information in terms of frequency and percentage. PART 2: Descriptive statistic on the respondents’ perceived ease of use in terms of frequency and percentage. PART 3: Descriptive statistic on the respondents’ perceived usefulness in terms of frequency and percentage. PART 4: Descriptive statistic on the respondents’ perceived security and privacy in terms of frequency, percentage, mean and standard deviation. PART 5: Descriptive statistic on the respondents’ BioPay adoption intention in terms of frequency and percentage, mean and standard deviation. PART 6: Hypothesis Testing 1.) Inferential statistic on the relationship between perceived ease of use and BioPay adoption intention using Pearson’s product moment correlation coefficient. 2.) Inferential statistic on the relationship between perceived usefulness and BioPay adoption intention using Pearson’s product moment correlation coefficient. 3.) Inferential statistic on the relationship between perceived security and privacy and, BioPay adoption intenton using Pearson’s product moment correlation coefficient.

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4.1 Demographic The respondents’ basic personal background information which was collected through the first part of the questionnaires is shown in table 5.1 to table 5.4. Table 4.1 Frequency and Percentage of Respondents by Gender Gender

Frequency (Persons)

Percentage

Male

75

36.6

Female

130

63.4

205

100.0

Total

Table 4.1 shows that 36.6 percent of respondents are male (75 persons) and 63.4 percent of respondents are female (130 persons).

Table 4.2 Frequency and Percentage of Respondents by Age Age

Frequency (Persons)

Percentage

18-25

37

18.0

26-33

35

17.1

34-41

43

21.0

42-49

42

20.5

50-60

48

23.4

205

100.0

Total

Table 4.2 shows the age distribution of the respondents which were collected by using stratified sampling techniques calculated from Bangkok population. Respondents between 50 and 60 are the majority, which accounted for 23.4 percent or 48 persons of the total respondents. Respondents between 34 and 41 are the second largest group represented by 21.0 percent or 43 persons. The third largest group is the age between 42 and 49 which accounted for 20.5 percent or 42 persons. The forth group is the age between 18 and 25 with 18.0 percent or 37 persons. Lastly, respondents between 26 and 33, accounted for 17.1 percent or 35 persons.

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Table 4.3 Frequency and Percentage of Respondents by Educational Level Educational Level

Frequency (Persons)

Percentage

Lower than Bachelor Degree

17

8.3

Bachelor Degree

121

59.0

Higher than Bachelor Degree

67

32.7

Total

205

100.0

Table 4.3 shows that for educational level, bachelor degree has the greatest number of respondents’, accounting 59.0 percent or 121 persons, followed by higher than bachelor degree and lower than bachelor degree with 32.7 percent or 67 persons and 8.3 percent or 17 persons, respectively.

Table 4.4 Frequency and Percentage of Respondents by Personal Monthly Income Personal Monthly Income

Frequency (Persons)

Percentage

Less than 5,000 THB

16

7.8

5,001-10,000 THB

22

10.7

10,001-15,000 THB

10

4.9

15,001-20,000 THB

17

8.3

20,001-25,000 THB

13

6.3

More than 25,001 THB

127

62.0

Table 4.4 shows that overwhelming majority of respondents’ personal monthly income is recorded in more than 25,001 THB, accounting 62.0 percent or 127 persons. Follow by 5,001-10,000 THB which accounted 10.7 percent or 22 persons. The third is 15,001-20,000 THB with 8.3 percent or 17 persons. The forth is less than 5,000 THB with 7.8 percent or 16 persons. This is followed by 20,001-25,000 THB, accounting 6.3 percent or 13 persons. Lastly, the minority is 10,001-15,000 THB with only 4.9 percent or 10 persons.

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4.2 BioPay’s Perceived Ease of Use The respondents’ perceived ease of use collected through the first part of the questionnaires is shown in table 5.5. Table 4.5 Frequency, Percentage, Mean and Standard Deviation (SD) of Respondents’ Perceived Ease of Use Level of Agreement or Disagreement Stron Perceived Ease of

Stron -

Use

gly

Agree

Agree

Mode

Disag-

gly

-rate

ree

Disag-

Agree

Total

Mean

SD

0.828

ree

1. Using BioPay will be easier than other payment

1

9

75

83

37

205

3.71

(0.5)

(4.4)

(36.6)

(40.5)

(18.5)

(100.0)

(Agree)

0

26

85

68

26

205

3.46

(0.0)

(12.7)

(41.5)

(33.2)

(12.7)

(100.0)

(Agree)

1

8

55

95

46

205

3.86

(0.5)

(3.9)

(26.8)

(46.3)

(22.4)

(100.0)

(Agree)

methods. 2. Using BioPay will not require advanced

0.871

knowledge. 3. Using BioPay is easy because it does not need anything else than my own body; for example, scanning fingerprint, hand geometry, face

0.823

recognition, or retinal scanning to deduction of funds from bank account instead of carry cash or use credit card. 3.68 Total

(Agree)

0.841

68

Table 4.5 shows that overall respondent agree that BioPay is easy to use having a mean of 3.68. The question of ‘Using BioPay is easy because I do not need anything else than my own body; for example, scanning fingerprint, hand geometry, face recognition, or retinal scanning to deduction of funds from bank account instead of carry cash or use credit card.’ has the highest degree with a mean of 3.86 (agree). Followed by the question of ‘Using BioPay will be easier than other payment methods.’ with a mean of 3.71 (agree). The lowest mean the question of ‘Using BioPay will not require advanced knowledge.’ having a value of 3.46 (agree).

4.3 BioPay’s Perceived Usefulness The respondents’ perceived usefulness which was collected through the first part of the questionnaires is shown in table 5.6. Table 4.6 Frequency, Percentage, Mean and Standard Deviation (SD) of Respondents’ Perceived Usefulness Level of Agreement or Disagreement Stron Perceived

Stron -

Usefulness

gly

Agree

Agree

Mode

Disag-

gly

-rate

ree

Disag-

Agree

Total

Mean

SD

0.827

ree

1. Being to pay with BioPay will

0

8

61

86

50

205

3.87

make purchasing

(0.0)

(3.9)

(29.8)

(42.0)

(24.4)

(100.0)

(Agree)

will help to prevent

4

20

60

66

55

205

3.72

identity fraud such

(2.0)

(9.8)

(29.3)

(32.2)

(26.8)

(100.0)

(Agree)

product and service easier. 2. Using BioPay

as forcing a signature.

1.027

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Table 4.6 Frequency, Percentage, Mean and Standard Deviation (SD) of Respondents’ Perceived Usefulness (continues) Level of Agreement or Disagreement Stron Perceived

Stron -

Usefulness

gly

Agree

Agree

Mode

Disag-

gly

-rate

ree

Disag-

Agree

Total

Mean

SD

0.890

ree

3. BioPay will deliver faster service than other

0

16

56

84

49

205

3.81

(0.0)

(7.8)

(27.3)

(41.0)

(23.9)

(100.0)

(Agree)

2

7

52

85

59

205

3.94

(1.0)

(3.4)

(25.4)

(41.5)

(28.8)

(100.0)

(Agree)

1

16

45

88

55

205

3.88

(0.5)

(7.8)

(22.0)

(42.9)

(26.8)

(100.0)

(Agree)

payment methods. 4. Using BioPay is convenient.

0.875

5. Other payment methods might be lost; for example, forget password, but with BioPay, biometrics identification (e.g.

0.913

scanning fingerprint, hand geometry, face recognition, or retinal scanning) will never lost. 3.84 Total

0.900

(Agree)

Table 4.6 shows that that overall respondent agree that BioPay is useful having a mean value of 3.84. The question of ‘Using BioPay is convenient.’ has the highest mean value of 3.94 (agree). This follows by the question of ‘Other payment methods might be lost; for example, forget password, but with BioPay, biometrics identification (e.g. scanning fingerprint, hand geometry, face recognition, or retinal scanning) will never lost.’ with a mean of 3.88 (agree). Question with the lowest mean is ‘Using BioPay will help to prevent identity fraud such as forcing a signature.’ with a value of 3.72 (agree).

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4.4 BioPay’s Perceived Security and Privacy The respondents’ perceived security and privacy which was collected through the first part of the questionnaires are shown in table 4.7. Table 4.7 Frequency, Percentage, Mean and Standard Deviation (SD) of Respondents’ Perceived Security and Privacy Level of Agreement or Disagreement Stron Perceived Security

Stron -

and Privacy

gly

Agree

Agree

Mode

Disag-

gly

-rate

ree

Disag-

Agree

Total

Mean

SD

0.842

ree

Security 1. The system using in that

3

9

92

72

29

205

3.56

(1.5)

(4.4)

(44.9)

(35.1)

(14.1)

(100.0)

(Agree)

3

11

92

69

30

205

3.55

(1.5)

(5.4)

(44.9)

(33.7)

(14.6)

(100.0)

(Agree)

3

13

71

83

35

205

3.65

(1.5)

(6.3)

(34.6)

(40.5)

(17.1)

(100.0)

(Agree)

4

20

60

82

39

205

3.64

(2.0)

(9.8)

(29.3)

(40.0)

(19.0)

(100.0)

(Agree)

3

20

57

89

36

205

3.66

(1.5)

(9.8)

(27.8)

(43.4)

(17.6)

(100.0)

(Agree)

BioPay is trustable. 2.Transactions through the BioPay

0.860

are secure. Privacy 3. BioPay makes payment more safety in term of

0.887

privacy. 4. BioPay makes biometrics identity (e.g. fingerprint)

0.963

hard to steal. 5. BioPay makes other persons reach personal

0.929

information harder. 3.61 Total

(Agree)

0.896

71

Table 4.7 shows that respondent overall agree that BioPay is secure and can protect privacy with a mean of 3.61. The question of ‘BioPay makes other persons reach personal information harder.’ has the highest mean of 3.66 (agree). Followed by the question of ‘BioPay makes payment more safety in term of privacy.’ with a mean of 3.65 (agree). The lowest mean value is the question of ‘BioPay makes biometrics identity (e.g. fingerprint) hard to steal.’ with 3.64 (agree).

4.5 BioPay’s Adoption Intension The respondents’ BioPay adoption intention which was collected through the first part of the questionnaires is shown in table 5.8. Table 4.8 Frequency, Percentage, Mean and Standard Deviation (SD) of Respondents’ BioPay Adoption Intention Level of Agreement or Disagreement Stron BioPay Adoption

Stron -

Intention

gly

Agree

Agree 1. Intention to download

Mode

Disag-

gly

-rate

ree

Disag-

Agree

Total

Mean

SD

0.926

ree

7

19

88

68

23

205

3.40

(3.4)

(9.3)

(42.9)

(33.2)

(11.2)

(100.0)

(Mode -rate

application that use

Agree)

BioPay. 2. Intention to choose the shop

8

33

78

67

19

205

3.27

(3.9)

(16.1)

(38.0)

(32.7)

(9.3)

(100.0)

(Mode -rate

that BioPay are

Agree)

available. 3. Intention to connect credit card or debit card with application that use BioPay.

0.972

6

25

88

63

23

205

3.35

(2.9)

(12.2)

(42.9)

(30.7)

(11.2)

(100.0)

(Mode -rate Agree)

0.936

72

Table 4.8 Frequency, Percentage, Mean and Standard Deviation (SD) of Respondents’ BioPay Adoption Intention (continues) Level of Agreement or Disagreement BioPay

Stron -

Adoption

Stron -

Intention

gly

Agree

Agree

Mode

Disag-

gly

-rate

ree

Disag-

Agree

Total

6

payment

(2.9)

27 (13.2)

85 (41.5)

SD

ree

4. Intention to make a

Mean

66 (32.2)

21 (10.2)

205 (100.0)

3.34

0.934

(Mode -rate

through

Agree)

BioPay. 5. Intention to consider BioPay as first

7

38

81

56

23

205

3.24

(3.4)

(18.5)

(39.5)

(27.3)

(11.2)

(100.0)

(Mode

choice when I

0.995

-rate

purchase

Agree)

things. 6. Intention to use fingerprint recognition

8

18

69

74

36

205

3.55

(3.9)

(8.8)

(33.7)

(36.1)

(17.6)

(100.0)

(Agree)

7

25

68

79

26

205

3.45

(3.4)

(12.2)

(33.2)

(38.5)

(12.7)

(100.0)

(Agree)

1.007

with BioPay. 7. Intention to use hand geometry with

0.977

BioPay. 3.37

8. Intention to use face recognition

8

27

79

63

28

205

(Mode

(3.9)

(13.2)

(38.5)

(30.7)

(13.7)

(100.0)

-rate

with BioPay.

1.004

Agree)

9. Intention to use iris scanning with BioPay.

9

23

69

62

42

205

3.51

(4.4)

(11.2)

(33.7)

(30.2)

(20.5)

(100.0)

(Agree)

1.074

73

Table 4.8 Frequency, Percentage, Mean and Standard Deviation (SD) of Respondents’ BioPay Adoption Intention (continues) Level of Agreement or Disagreement BioPay

Stron -

Adoption

Stron -

Intention

gly

Agree

Agree

Mode

Disag-

gly

-rate

ree

Disag-

Agree

Total

Mean

SD

1.103

ree

10. Intention

12

40

72

51

30

205

3.23

to use retinal

(5.9)

(19.5)

(35.1)

(24.9)

(14.6)

(100.0)

(Mode -rate

scanning with

Agree)

BioPay. 11. Intention to use vein

15

41

82

45

22

205

3.09

(7.3)

(20.0)

(40.0)

(22.0)

(10.7)

(100.0)

(Mode

1.067

-rate

scanning with

Agree)

BioPay. 12. Intention to use signature

9

41

80

57

18

205

3.17

analysis

(4.4)

(20.0)

(39.0)

(27.8)

(8.8)

(100.0)

(Mode

(pressure,

0.991

-rate

direction and

Agree)

speed) with BioPay. 3.11

13. Intention to use voice

12

43

84

42

24

205

(Mode

verification

(5.9)

(21.0)

(41.0)

(20.5)

(11.7)

(100.0)

-rate

with BioPay.

1.054

Agree) 3.31 (Mode Total

1.003

-rate Agree)

Table 4.8 shows that respondents overall have are moderately agree on intention to adopt BioPay with a mean of 3.31. According to the table, the question of ‘Intention to download application that uses BioPay’, ‘Intention to choose the shop that BioPay are available.’, ‘Intention to connect credit card or debit card with application that uses

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BioPay.’, ‘Intention to make a payment through BioPay.’, and ‘Intention to consider BioPay as first choice when I purchase things.’ has a moderate mean value of 3.40, 3.27, 3.35, 3.34 and 3.24, respectively. In terms of intention to adopt BioPay by categories, fingerprint recognition has the highest mean at 3.55 (agree), followed by iris scanning and hand geometry with a mean of 3.51 and 3.45, respectively. Vein scanning has the lowest mean value of 3.07 (moderate agree).

4.6 Hypothesis Testing Research Hypothesis 1: There is a relationship between perceived ease of use and BioPay adoption intention. Statistical Hypothesis 1: H0: There is no relationship between perceived ease of use and BioPay adoption intention. H1: There is a relationship between perceived ease of use and BioPay adoption intention. Table 4.9 Pearson's correlations between Perceived Ease of Use and BioPay Adoption Intention Variable BioPay Adoption Intention

Correlation r = .628*** p-value = .000 (n = 205 persons)

p-value is significant at the .001 level (2-tailed). Table 4.9 displays the correlation of perceived ease of use and BioPay adoption intention at 0.628. This shows that there is a substantial relationship between the variable. The p-value is 0.000 which shows a significant effect. Therefore, the researcher failed to reject the null hypothesis (H0) and accept the alternative hypothesis (H1) that there is a relationship between perceived ease of use and BioPay adoption intention. There is a positive correlation which implies that to increase perceived ease of use, BioPay adoption intention will also increase.

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Research Hypothesis 2: There is a relationship between perceived usefulness and BioPay adoption intention. Statistical Hypothesis 2: H0: There is no relationship between perceived usefulness and BioPay adoption intention. H1: There is a relationship between perceived usefulness and BioPay adoption intention. Table 4.10 Pearson's correlations between Perceived Usefulness and BioPay Adoption Intension Variable BioPay Adoption Intension


p-value is significant at the .001 level (2-tailed). Table 4.10 displays the correlation of perceived usefulness and BioPay adoption intention at 0.696 which shows that there is substantial relationship between the variable. The p-value is 0.000 which shows that there is a significant effect Therefore, the researcher failed to reject the null hypothesis (H0) and accept the alternative hypothesis (H1) that there is a relationship between perceived usefulness and BioPay adoption intention. There is a positive correlation which implies that to increase perceived usefulness, BioPay adoption intention will also increase.

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Research Hypothesis 3: There is a relationship between perceived security and privacy and BioPay adoption intention. Statistical Hypothesis3: H0: There is no relationship between perceived security and privacy and BioPay adoption intention. H1: There is a relationship between perceived security and privacy and BioPay adoption intention. Table 4.11 Pearson's correlations between Perceived Security and Privacy and BioPay Adoption Intension Variable BioPay Adoption Intension


p-value is significant at the .001 level (2-tailed). Table 4.11 displays the correlation of perceived security and privacy and BioPay adoption intention at 0.722 which shows that the relationship between the variable is very strong. The p-value is 0.000 which shows that there is a significant effect. Therefore, the researcher failed to reject the null hypothesis (H0) and accept the alternative hypothesis (H1) that there is a relationship between perceived security and privacy and BioPay adoption intention. There is a positive correlation which implies that to increase perceived usefulness, BioPay adoption intention will also increase.

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CHAPTER 5 CONCLUSION AND RECOMMENDATIONS This study on ‘The Factors Affecting BioPay Acceptance of People in Bangkok’ was to determine the factors that would influence the adoption of BioPay. The identified factors of influence used in this study were: perceived ease of use, perceive usefulness and perceive security and privacy. In this chapter, the researcher provides conclusion, discussion and recommendations for further studies.

5.1 Conclusion 5.1.1 Demographic The studied population in this research was 205 persons. The majority of respondents was females with 63.4 percent (130 persons), while there was only 36.6 percent of males (75 persons). The age range of respondents was divided into five groups using stratified sampling techniques the highest proportion recorded for age was above 50 which has the greatest population in Bangkok, accounted 23.4 percent (48 persons); followed by respondent of the age of 34-41 which accounted 21.0 percent (43 persons). The least respondents were at the age of 26-33 which accounted 17.1 percent (35 persons). In term of educational level, the majority was recorded for bachelor degree, accounted 59.0 percent (121 persons), while the number of higher than bachelor degree and lower than bachelor degree was 32.7 percent (67 persons) and 8.3 percent (17 persons), respectively. Moreover, majority of the respondents have more than 25,001 THB, accounted for 62.0 percent (127 persons), and followed by 5,001-10,000 THB with 10.7 percent (22 persons). The lowest number of respondents’ personal monthly income was recorded in 10,001-15,000 THB which accounted 4.9 percent (10 persons).

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5.1.2 BioPay’s Perceived Ease of Use Overall, the respondents agreed that BioPay is easy to use (3.68). The respondents agreed highest that using BioPay is easy because it does not need anything else than their own body; for example, scanning fingerprint, hand geometry, face recognition or retinal scanning to deduct funds from bank account instead of carry cash or use credit card (3.86). This followed by using BioPay will be easier than other payment methods and does not require advanced knowledge having mean value of 3.71 (agree) and 3.46 (agree), respectively. 5.1.3 BioPay’s Perceived Usefulness Overall, the respondents agreed that BioPay is useful (3.84). The result shows that all questions had agreeable level with the highest mean was recorded in respondents’ perceive that using BioPay is convenient with a mean of 3.94 (agree). The second highest mean was marked in respondents’ perceiving that other payment methods might be lost; for example, forget password, but with BioPay, biometrics identification (e.g. scanning fingerprint, hand geometry, face recognition, or retinal scanning) will never lost at mean of 3.88 (agree). This is followed by being able to pay with BioPay will make purchasing product and service easier at the mean of 3.87 (agree). 5.1.4 BioPay’s Perceived Security and Privacy Overall, the respondents agreed that BioPay is secure and can protect privacy (3.61) which all of the questions were recorded in having direction of agreeable level. The mean of respondents’ perceived that BioPay makes other persons reach personal information harder was the highest at 3.66 (agree), followed by the respondents’ perceiving that BioPay makes payment more safety in term of privacy and makes biometrics identity (e.g. fingerprint) hard to steal with mean of 3.65 (agree) and 3.64 (agree), respectively.

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5.1.5 BioPay’s Adoption Intension Overall, the respondents moderately agree to adopt BioPay with a mean of 3.31. Respondents’ moderately agreed that they have intention to download application that uses BioPay, connect credit card or debit card with application that uses BioPay, and choose shop that BioPay are available, accounted a mean of 3.40 (moderate agree), 3.35 (moderate agree) and 3.27 (moderate agree), respectively. In terms of biometrics technology by categories to use in BioPay, respondent’s intention to adopt BioPay by ranking was shown in figure 5.1 and table 5.2 which shows the highest recorded in fingerprint recognition having a mean of 3.55 (Agree), followed by iris scanning and hand geometry, with a mean of 3.51 (agree) and 3.45 (agree), respectively. Vein scanning has the lowest mean at 3.07 (moderate agree).

BioPay Adoption Intension 3.60

3.55 3.51

3.50

3.45 3.37

3.40 3.30

3.23 3.17

3.20

3.11 3.10

3.09

3.00 Fingerprint Iris Recognition Scanning

Hand Face Retinal Geometry Recognition Scanning

Signature Voice Vein Analysis Verification Scanning

Mean

FIGURE 5.1. BioPay Adoption Intention by Categories Ranking

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Table 5.1 Result of Respondents Intention to Adopt BioPay by Categories Ranking Biometrics Categories to Use in BioPay

Mean

Interpretation

1.) Fingerprint Recognition

3.55

Agree

2.) Iris Scanning

3.51

Agree

3.) Hand Geometry

3.45

Agree

4.) Face Recognition

3.37

Moderate Agree

5.) Retinal Scanning

3.23

Moderate Agree

6.) Signature Analysis

3.17

Moderate Agree

7.) Voice Verification

3.11

Moderate Agree

8.) Vein Scanning

3.09

Moderate Agree

5.1.6 Conclusion from Hypothesis Testing This study focused on three research questions. Each question examined the correlation between perceived ease of use, perceive usefulness, perceive security and privacy, and BioPay adoption intention. Each research question is reiterated and summary of the related results are presented in this section. Overall, there were substantial relationship with positive correlation in all of hypothesis which factor of perceived security and privacy and BioPay adoption intension had the highest correlation at 0.722 which p-value was .000. Therefore, perceived security and privacy was the most significant factor to influence BioPay adoption intention.

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Table 5.2 Result of Hypothesis Testing Hypothesis H1: There is statistically significant relationship between Perceived Ease of Use and BioPay Adoption Intention. H2: There is statistically significant relationship between Perceived Usefulness and BioPay Adoption Intention. H3: There is statistically significant relationship between Perceived Security and Privacy and BioPay Adoption Intention.

Hypothesis Testing Result Accept hypothesis (r = .628*** p-value < .001) Accept hypothesis (r = .696*** p-value < .001) Accept hypothesis (r = .722*** p-value < .001)

5.1.6.1 Hypothesis 1 H1: There is statistically significant relationship between Perceived Ease of Use and BioPay Adoption Intention. The findings of the study revealed that there was a positive correlation with substantial relationship between perceived ease of use and BioPay adoption intention.

5.1.6.2 Hypothesis 2 H2: There is statistically significant relationship between Perceived Usefulness and BioPay Adoption Intention. The findings of the study revealed that there was a positive correlation with substantial relationship between perceived usefulness and BioPay adoption intention.

5.1.5.3 Hypothesis 3 H3: There is statistically significant relationship between Perceived Security and Privacy and BioPay Adoption Intention. The findings of the study revealed that there was a positive correlation with very strong relationship between perceived security and privacy and BioPay adoption intention.

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5.2 Discussion 5.2.1 Demographic The population for this research was 205 persons calculated by using Taro Yamane (Yamane, 1973) formula with 95 percent confidence level with 7 percent error. The respondents of this research were female with 63.4 percent (130 persons) and male 36.6 percent (75 persons). This is consistent with the data from the Bureau of Bangkok Registration Administration which revealed that there are more female residents in Bangkok. The respondents were divided into five groups using stratified sampling techniques with proportional allocation. Respondent between the age of 18-25 accounted 18.0 percent (37 persons), between the age of 26-33 accounted 17.1 percent (35 persons), between the age of 34-41 accounted 21.0 percent (43 persons), between the age of 42-49 accounted 20.5 percent (42 persons), and respondent whom age are above 50 accounted 23.4 percent (48 persons). According to the data of the Bureau of Registration Administration official report (2016), the highest population of Bangkok are person whom age are above 50 which this research collected respondents from five groups mentioned above in order to gain the widespread sample. In terms of education level, the overwhelming majority was recorded having bachelor degree at 59.0 percent (121 persons) which correspond to the data that was collected in people over 18 years old and snowball sampling techniques was used which therefore, the bachelor degree had the greatest number. For the household income, the majority was in people who have monthly income more than 25,001 THB which accounted 62.0 percent (127 persons). Due to the researcher’s acquaintances are person whom have monthly income more than 25,001 THB by applying snowball sampling techniques that was used through Facebook posting and Line. Facebook and Line are the most popular platform in Thailand with 41 and 33 million users, respectively (Positioning Magazine, 2016). Therefore, majority of the respondents of this research were people in this group. 5.2.2 BioPay’s Perceived Ease of Use The result on BioPay’s perceive ease of use found that, overall the respondents agreed that BioPay is easy to use with a mean value of 3.68 (agree). The highest mean was recorded for the question ‘Using BioPay is easy because it does not need anything

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else than my own body; for example, scanning fingerprint, hand geometry, face recognition, or retinal scanning to deduction of funds from bank account instead of carry cash or use credit card.’ with a mean of 3.86 (qgree). The study of Huys (2014) on ‘Consumer Acceptance of Identification Technology’ where feedback received from 187 respondents that use fingerprint and 151 respondents that use iris scanning and facial recognition. The study of Hays found that respondents perceived that iris scanning and face recognition is easy to use with a mean value of 3.59 (agree) and fingerprint recognition is easy to use with a mean of 4.08 (agree). The result of Hays’s study is consistent to this study where respondents agreed that BioPay is easy to use. 5.2.3 BioPay’s Perceived Usefulness For the result on BioPay’s perceived usefulness, it was found that overall the respondents agreed that BioPay is useful with a mean of 3.84 (agree). This implies that majority of respondents perceived that using BioPay is convenient with a mean of 3.94 (agree). This finding also in congruent with the finding of Visa Thailand that Thai people are gradually carrying less cash around and adopting digital forms of payment as they are starting to see it as more convenient (Techsauce, 2017). Consequently, the essential reason for people to adopt BioPay is its convenient feature. Additionally, the study of Huys (2014) who studied about ‘Consumer Acceptance of Identification Technology’ found that the respondents perceived that iris scanning and face recognition; and fingerprint recognition is easy to use with a mean of 2.78 (moderate qgree) and 3.65 (agree), respectively. It is consistent with the study of Hays showing the similar agreeable trend that related to this study on the respondents perceiving that BioPay is useful. 5.2.4 BioPay’s Perceived Security and Privacy In terms of the result of BioPay’s perceived security and privacy, it was found that overall the respondents agreed that BioPay is secure and can protect privacy with a mean of 3.61. The highest mean was in respondents’ perceiving that BioPay makes other persons reach personal information harder with a mean of 3.66 (agree). The result of this finding had linkage with the statement of Suripong Tantiyanon, Visa Country Manager, Thailand that “82 percent of Thai people expect their payment experience to

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be safe, fast and reliable.” It is also consistent with survey of Visa Thailand which found that millennials were the group that are most likely to use online payments with 83 percent, and 60 percent of them said that carrying too much cash is not safe (Techsauce, 2017). Hence, the substantial reason for people to adopt BioPay is that BioPay makes other people harder to reach personal information. 5.2.5 BioPay’s Adoption Intension The result on BioPay’s adoption intention found that overall respondents had the intention to adopt BioPay with a mean of 3.31 (moderate agree). Fingerprint recognition had the highest mean of 3.55 (agree), followed by iris scanning and hand geometry, 3.51 (agree) and 3.45 (agree), respectively. While vein scanning has the lowest mean at 3.07 (moderate agree). The result of this research related to the survey of Visa Thailand that 75 percent of Thai consumer feels comfortable in using Biometrics technology such as fingerprint recognition and face recognition which has the highest adoption intention among Southeast Asian country (Techsauce, 2017). Moreover, people tend to be more familiar with fingerprint recognition (Huys, 2014)) such as scanning finger to unlock the phone or to enter building. This might result in fingerprint recognition having the highest mean. Results of this study related to the study of Huys (2014) who studied about ‘Consumer Acceptance of Identification Technology’. The study found that fingerprint recognition system was highly accepted by consumers when compared to others type of biometrics technology. However, this research found that vein scanning has the lowest mean at 3.07 (moderate agree). In the study of Worldpay UK found that finger vein scanning has a number of advantages over fingerprint. One it is that each people has unique fingerprint’s vein pattern that cannot be copied or stolen (Madhvi, 2017). Likewise, in Korea vein scanning also selected as a payment method in 7-eleven, Lotte World Tower, Seoul.

5.2.6 Discussion on Research Framework This study examined the factors influencing the adoption of BioPay. The identified factors of influence used in this study were: perceived ease of use, perceive usefulness, and perceive security and privacy which based on the theoretical concept of Technology Acceptance Model (TAM). Security and privacy construct were selected as

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additional factors (Salisbury et al., 2001). The sample size for this study was 205 persons who live in Bangkok between the ages of 18-60 years old. The data were collected during September, 2017. Pearson correlation coefficients were calculated to determine the correlation between variables. The results showed a statistically significant correlation between perceived ease of use, perceive usefulness, and perceive security and privacy, and BioPay adoption intention.

In a review of the original three hypotheses in this research model, H1, H2 and H3 was supported.

Table 5.3 Result of Hypothesis Hypothesis

Result

H1: There is statistically significant relationship between

Supported

Perceived Ease of Use and BioPay Adoption Intention.

(r = .628*** p-value < .001)

H2: There is statistically significant relationship between Perceived Usefulness and BioPay Adoption Intention.

Supported (r = .696*** p-value < .001)

H3: There is statistically significant relationship between Perceived Security and Privacy and BioPay Adoption Intention.

Supported (r = .722*** p-value < .001)

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Therefore, the theory of TAM of Salisbury et al. (2001) used in this study had supported that perceived ease of use, perceived usefulness, and perceived security and privacy were significant in influencing BioPay adoption ,which perceived security and privacy result as the most significant factor (r = .722). Perceived Ease of Use

r= .628***


r= .696*** r= .722***

Perceived Security and Privacy

BioPay Adoption Intention

*** p-value < .001 FIGURE 5.2. Conceptual research model guiding the study

5.2.7 Discussion on Hypothesis Testing 5.2.7.1 Hypothesis 1 H1: There is statistically significant relationship between Perceived Ease of Use and BioPay Adoption Intention. Charfeddine and Wadie (2013) who studied on ‘The Behavior Intention of Tunisian Banks’ Customers in using Internet Banking’ found that perceived ease of use is the critical factor for users to adopt internet banking. Furthermore, the study of Nwatu (2011) on ‘Biometrics Technology: Understanding Dynamics Influencing Adoption for Control of Identification Deception within Nigeria’ using mixed method research where it was found that ease of use had significant influence on biometrics technology adoption. The study of Huys (2014) who studied about ‘Consumer Acceptance of Identification Technology’ also found that the perceived ease of use was an influence factor in fingerprint, iris scanning and facial recognition adoption. With these the finding of this research shows that there was a positive correlation with substantial relationship between perceived ease of use and BioPay adoption intention (r = 0.628***; p-value = .000). Consequently perceived ease of use significantly influences BioPay adoption intention using the system which users intend to use the system more

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frequently as the system becomes easy to use. Therefore, this result confirmed the TAM as the theoretical model that guided this study.

5.1.7.2 Hypothesis 2 H2: There is statistically significant relationship between Perceived Usefulness and BioPay Adoption Intention. The study of Charfeddine and Wadie (2013) on ‘The Behavior Intention of Tunisian Banks’ Customers in using Internet Banking’ found that perceived usefulness is the substantial factor for users to adopt internet banking. Also in the study of Nwatu (2011) who studied on ‘Biometrics Technology: Understanding Dynamics Influencing Adoption for Control of Identification Deception within Nigeria’ found that perceived usefulness was the significant factor in influencing biometrics technology adoption. Therefore, this research found that perceived usefulness as the significant factor affecting BioPay adoption intention with a substantial correlation of perceived usefulness and BioPay adoption intention (r = 0.696***; p-value = .000). Perceived usefulness by the individual influences the BioPay adoption intention in using the system which significantly affect users intention to use the system more frequently when it is proven as a useful utility. This result confirmed the TAM (Salisbury et al., 2001), as the theoretical model that guided this study.

5.1.7.3 Hypothesis 3 H3: There is statistically significant relationship between Perceived Security and Privacy and BioPay Adoption Intention. According to Law (2007), the study on the‘Impact of Perceived Security on Consumer Trust in Online Banking’ found that perceived of security is a significant factor with the perception that privacy had the highest and most significant impact on Consumer Trust in Online Banking, while other factors did not have great impact. Moreover, Charfeddine and Wadie (2013) who studied about ‘The Behavior Intention of Tunisian Banks’ Customers on using Internet Banking’ also found that perceived security and privacy are the most critical factor for users to adopt internet banking. Likewise, the research of Breward (2009) on ‘Factors Influencing Consumer Attitudes towards Biometric Identity Authentication Technology within the Canadian Banking

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Industry’ found that the factors of privacy and security concerns have a significant impact on attitudes while factor of voluntariness appears to have no effect. Additionally, the study on ‘Biometrics Technology: Understanding Dynamics Influencing Adoption for Control of Identification Deception within Nigeria’ by Nwatu (2011) using mixed method research which found that there were correlation between biometrics technology adoption and perceived security concerns. Also, in qualitative research, the researcher found that ease of use, usefulness, security concerns and awareness have significant influence on biometrics technology adoption. Likewise, in the study of Huys (2014) that studied about ‘Consumer Acceptance of Identification Technology’ found that fingerprint recognition system was highly accepted by consumers when compared to others type of biometrics. With the results of this study, researcher also found that there was a very strong relationship between BioPay’s perceived security and privacy, and BioPay adoption intention which perceived security and privacy was found to be a significant factor with the correlation of 0.722***; p-value = .000. It can be concluded that perceived security and privacy by the individual influences the BioPay adoption intention using the system which can significantly influence users intention to use the system more frequently when it is proven that it is secure and protect their privacy. Therefore, this result confirmed the TAM (Salisbury et al., 2001), as the theoretical model that guided this study.

5.3 Limitation This research was conducted in Bangkok, Thailand in September 2017. 205 respondents were had participated on studying the implication of BioPay in mass market as a payment method. It was very limited which Samsung Pay and TapKTC are the only payment application that is available in mass market in Thailand. Therefore, the respondents had to imagine how BioPay would be like. Also, some of biometrics technology was not widely available in Thailand for respondents to use such as vein pattern recognition which might be the barrier for respondents to conceive how BioPay would work. However, there is a start-up company named ‘JabJai for school’ that developed biometrics technology that enabled schools to monitor and store student data. Parents are able to monitor children’s money usage from mobile application by using fingerprint to buy things in school. Although, there were several new implementation of

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BioPay in Thailand, the idea of BioPay is still new for consumers. Additionally, this research only examines the BioPay adoption intention due to the condition of BioPay implication in Bangkok as stated above.

5.4 Recommendations for Action 1.) The result of hypothesis found that relationship between perceived security and privacy and BioPay adoption intension has the highest positive correlation. In order to make BioPay more adopted, business or government sector that intend to use BioPay should communicate or educate consumer in terms of security and privacy of BioPay. This research has found that this factor is the most significant factor that effect BioPay adoption intention. Additionally, the highest mean was recorded in the respondents’ perceived on BioPay that can provide other persons to reach personal information harder. Therefore, business owners and government sector should focus on this point more. 2.) The result of hypothesis found that there was a positive correlation with substantial relationship between perceived usefulness and BioPay adoption intention. Therefore, the increasing of the usefulness of BioPay is promoted by the business or government sector that intend to use BioPay. This will eventually increase the adoption intention of BioPay. Moreover, the highest mean was recorded in the respondents’ perceiving that using BioPay is convenient. Business owners and government sector should communicate on the usefulness of BioPay in terms of its convenience in order to increase BioPay adoption intention. 3.) The result of hypothesis found that there was a positive correlation with substantial relationship between perceived ease of use and BioPay adoption intention. Hence, business owners and government sector that intend to use BioPay should also promote BioPay in terms of its easy to use in order to increase BioPay adoption intention. Furthermore, the highest mean was recorded in the respondents’ perceiving that using BioPay is easy because it does not need anything else other than user’s own body; for example, scanning fingerprint, hand geometry, face recognition, or retinal scanning to

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deduct funds from bank account instead of carry cash or use credit card. Therefore, the business and government sector should communicate the ease of use of BioPay with regard to increase BioPay adoption intention. 4.) The result of the study found that the top three BioPay adoption intention in terms of biometrics categories was fingerprint recognition, iris scanning and hand geometry. Vein scanning had the lowest mean. Hence, business owners or government sectors that intend to use BioPay should choose the biometrics technology that had high adoption rate such as fingerprint recognition, iris scanning and hand geometry. In addition, the respondents had highest intention to download application that uses BioPay. Likewise, in terms of BioPay adoption intention by biometrics category fingerprint recognition had the highest mean of agree level, while vein scanning had the lowest mean. Related stakeholders that intend to use BioPay should start with the fingerprint scanner that consumers most favor and avoid biometrics that consumers disfavor. 5.) Although this research found that vein scanning were the least biometrics technology that respondents intend to adopt, vein scanning was in use in several well-known franchise including 7-eleven, Lotte World Tower, Seoul and Costcutter supermarket in Korea and England. It is claimed that vein scanning is the most secure version of biometrics identification where it cannot be copied or stolen. Consequently, business owners and government sectors that intend to use BioPay should educate the vein scanning to customer more and create a friendly name to avoid fear when customer uses it. For example, Korea use the name ‘HandPay’ for hand vein scanning in 7eleven and England use the name ‘Fingopay’ for finger vein scanning in Costcutter supermarket.

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5.5 Recommendations for Further Research The following are the recommendations for the study of ‘The Factors Affecting BioPay Acceptance of People in Bangkok’:

1.) The research should add in-depth interview or focus group discussion method, in order to gain more insight data of each biometrics technology on BioPay adoption; for example, why consumers tend to adopt fingerprint more than other biometrics methods or why consumer tend to adopt vein scanning less than other biometrics methods. Thus, business owners or government sectors that intend to use BioPay will understand consumer more. 2.) Due to the respondents not familiarize with some of biometrics technology such as iris scanning or vein scanning, the research should provide more information about biometrics technology to respondents more; for example, exhibits each of the biometrics machines to respondents for trail. 3.) The research should include the opinion of entrepreneur or government personnel that prone to use BioPay that can help 4.) The research should expand target population to collect Thailand’s nationwide respondents to compare data of people in urban area where difference and similarity of BioPay adoption between these areas. Likewise, in case that there is a gap between people in urban area and rural area, the research should find solution to resolve the gap. 5.) Comparative research studies should be used to compare the BioPay adoption intention in Thailand with other countries, or compare with countries that BioPay or biometrics technology that widely available, which there might be a difference between varying countries with the available of biometrics technology and BioPay. 6.) In many countries, the societies have been transforming into aging society which people who getting old tends to experience many physical difficultly. This could be in not remember password or inability to distinguish the value on the cash. Therefore BioPay would be a better solution for them which

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they only use their own body for making payment. Consequently, the research should include the study with aging people. 7.) The people knowledge, attitude and performance always change with time. Therefore, research should be conduct in difference time to select the right biometrics system, communication approach and appropriate message with that period.

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APPENDICES

103

APPENDIX A QUESTIONNAIRE (ENGLISH VERSION) ‘The Factors Affecting BioPay Technology Acceptance of People in Bangkok’

This questionnaire is a part of dissertation submitted in partial fulfillment of the requirements for the Degree Bachelor of Arts Program in Journalism and Mass Communication, Program in Advertising, Faculty of Journalism and Mass Communication, Thammasat University. All given information is confidential and will not be used for other objectives. * BioPay is the biometrics payment which is a technology that use biometrics authentication to identity the user and authorize the deduction of funds from a bank account; for example, using fingerprint or hand geometry to purchase things. I live in Bangkok.

Yes

No (End questionnaire)

My age is between 18 - 60 years old.

Yes

No (End questionnaire)

SECTION 1: General Background Information Please select your response to the statement by place “✓” in boxes below. 1.1 Gender:

Male

Female

1.2 Age:

18-25

26-33

42-49

50-60

1.3 Educational Level:

34-41

Lower than Bachelor Degree Bachelor Degree More than Bachelor Degree

1.4 Personal Monthly Income:

Less than 5,000 THB

5,001-10,000 THB

10,001-15,000 THB

15,001-20,000 THB

20,001-25,000 THB

More than 25,001 THB

104

SECTION 2: Perceived Ease of Use Please select your response to the statement by place “✓” to which degree you agree or disagree in the column. Question

Strongly Agree

Agree

Moderate Agree

Disagree

Strongly Disagree

2.1) Using BioPay technology will be easier than other payment methods. 2.2) Using BioPay technology will not require advanced knowledge. 2.3) Using BioPay technology is easy because I do not need anything else than my own body; for example, scanning fingerprint, hand geometry, face recognition, or retinal scanning to deduction of funds from bank account instead of carry cash or use credit card.

SECTION 3: Perceived Usefulness Please select your response to the statement by place “✓” to which degree you agree or disagree in the column. Question 3.1) Being to pay with BioPay technology will make purchasing product and service easier. 3.2) Using BioPay technology will help to prevent identity fraud such as forcing a signature. 3.3) BioPay technology will deliver faster service than other payment methods. 3.4) Using BioPay technology is convenient.

Strongly Agree

Agree

Moderate Agree

Disagree

Strongly Disagree

105

Question

Strongly Agree

Agree

Moderate Agree

Disagree

Strongly Disagree

3.5) Other payment methods might be lost; for example, forget password, but with BioPay technology, biometrics identification (e.g. scanning fingerprint, hand geometry, face recognition, or retinal scanning) will never lost.

SECTION 4: Perceived Security and Privacy Please select your response to the statement by place “✓” to which degree you agree or disagree in the column. Question Security 4.1) The system using in that BioPay technology is trustable. 4.2) Transactions through the BioPay technology are secure. Privacy Protection 4.3) BioPay technology makes payment more safety in term of privacy. 4.4) BioPay technology makes biometrics identity (e.g. fingerprint) hard to steal. 4.5) BioPay technology makes other persons reach personal information harder.

Strongly Agree

Agree

Moderate Agree

Disagree

Strongly Disagree

106

SECTION 5: BioPay Adoption Intension Please select your response to the statement by place “✓” to which degree you agree or disagree in the column. Question 5.1) Intension to download application that using BioPay technology. 5.2) Intension to choose the shop that BioPay technology are available. 5.3) Intension to connect credit card or debit card with Application that using BioPay technology. 5.4) Intension to make a payment through BioPay technology. 5.5) Intension to consider BioPay technology as first choice when I purchase things. 5.6) Intension to use fingerprint recognition with BioPay technology. 5.7) Intension to use hand geometry with BioPay technology. 5.8) Intension to use face recognition with BioPay technology. 5.9) Intension to use iris scanning with BioPay technology. 5.10) Intension to use retinal scanning with BioPay technology. 5.11) Intension to use vein scanning with BioPay technology.

Strongly Agree

Agree

Moderate Agree

Disagree

Strongly Disagree

107

Question 5.12) Intension to use signature analysis (pressure, direction and speed) with BioPay technology. 5.13) Intension to use voice verification with BioPay technology.

Strongly Agree

Agree

Moderate Agree

Disagree

Strongly Disagree

108

APPENDIX B QUESTIONNAIRE (THAI VERSION)

‘ปัจจัยที่ส่งผลกระทบต่อการยอมรับเทคโนโลยี BioPay ของประชากรในเขตกรุงเทพมหานคร’ แบบสอบถามนี้เป็นส่วนหนึ่งในการจัดทาสารนิพนธ์ระดับปริญญาตรี หลักสูตรปริญญา วารสารศาสตรบัณฑิต กลุ่มวิชาโฆษณา คณะวารสารศาสตร์และสื่อสารมวลชน มหาวิทยาลัยธรรมศาสตร์ เพื่อศึกษาปัจจัยที่ส่งผลกระทบต่อการยอมรับเทคโนโลยี BioPay ของ ประชากรในเขตกรุงเทพมหานคร ขอความร่วมมือในการตอบแบบสอบถามทุกข้อตามความเป็นจริง ข้อมูลที่ได้จะนาไปใช้ประโยชน์เพื่อการศึกษาเท่านั้น และขอขอบคุณทุกท่านที่ได้ให้ความร่วมมือมา ณ โอกาสนี้ *BioPay คือ การชาระเงินด้วยเทคโนโลยีไบโอเมทริกซ์ (Biomatrics) ซึ่ง เป็นข้อมูลทาง ชีวภาพของบุคคล เพื่อเป็นการระบุตัวตนของผู้ใช้งานในการตัดวงเงินจากบัญชีธนาคาร เช่น การ สแกนลายนิ้วมือ ฝ่ามือ ใบหน้า หรือม่านตาเพื่อใช้ในการชาระเงินซื้อสินค้าและบริการ เป็นต้น ท่านอาศัยอยู่ในกรุงเทพ

ใช่

ไม่ใช่ (จบแบบสอบถาม)

ท่านมีอายุระหว่าง 18 – 60 ปี

ใช่

ไม่ใช่ (จบแบบสอบถาม)

ส่วนที่1: ลักษณะทางประชากร กรุณาใส่เครื่องหมายถูก (✓) ลงในช่องคาตอบที่ตรงกับท่านมากที่สุด 1.1 เพศ:

ชาย

1.2 อายุ:

18-25 ปี

26-33 ปี

42-49 ปี

50-60 ปี

1.3 ระดับการศึกษา:

หญิง

ต่ากว่าปริญญาตรี ปริญญาตรี สูงกว่าปริญญาตรี

34-41 ปี

109

1.4 รายได้ส่วนตัวเฉลี่ยต่อเดือน:

ต่ากว่า 5,000 บาท

5,001-10,000 บาท

10,001-15,000 บาท

15,001-20,000 บาท

20,001-25,000 บาท

สูงกว่า 25,001 บาท

ส่วนที่2: ความง่ายในการใช้งานเทคโนโลยี BioPay กรุณาใส่เครื่องหมายถูก (✓) ลงในช่องว่างที่ตรงกับระดับความเห็นด้วยหรือไม่เห็นด้วยของท่าน ข้อคาถาม

2.1) การใช้เทคโนโลยี BioPay จะ ง่ายกว่าการจ่ายเงินรูปแบบอื่น ๆ 2.2) การใช้เทคโนโลยี BioPay ไม่ จาเป็นต้องใช้ความรู้ขั้นสูง 2.3) การใช้เทคโนโลยี BioPay นั้น ง่ายเพราะใช้เพียงองค์ประกอบของ ร่างกาย เช่น การสแกนลายนิ้วมือ ฝ่ามือ ใบหน้า หรือม่านตาเพื่อตัด วงเงินจากบัญชีธนาคาร แทนการพก เงินสด หรือบัตรเครดิตสแกน

เห็นด้วย อย่างยิ่ง

เห็นด้วย

เห็นด้วย ปานกลาง

ไม่เห็น ด้วย

ไม่เห็น ด้วย อย่างยิ่ง

110

ส่วนที่3: ประโยชน์ของการใช้งานเทคโนโลยี BioPay กรุณาใส่เครื่องหมายถูก (✓) ลงในช่องว่างที่ตรงกับระดับความเห็นด้วยหรือไม่เห็นด้วยของท่าน ข้อคาถาม

3.1) การชาระเงินด้วยเทคโนโลยี BioPay ทาให้การซื้อสินค้าและ บริการต่าง ๆ ง่ายขึ้น 3.2) การใช้เทคโนโลยี BioPay จะ ช่วยป้องกันการถูกโกง โดยการ ปลอมแปลงหลักฐานส่วนบุคคล เช่น การปลอมลายเซ็น เป็นต้น 3.3) การใช้เทคโนโลยี BioPay จะทา ให้การชาระเงินรวดเร็วกว่าการ จ่ายเงินรูปแบบอื่น ๆ 3.4) การใช้เทคโนโลยี BioPay นั้น สะดวกสบาย 3.5) การจ่ายเงินด้วยวิธีอื่น ๆ มี ความเสี่ยงด้านข้อมูลสาคัญอาจสูญ หาย หรือถูกขโมย เช่น การลืมพาส เวิร์ด ซึ่งแตกต่างจากเทคโนโลยี BioPay ที่ใช้ข้อมูลไบโอเมทริกซ์ (เช่น การสแกนลายนิ้วมือ ฝ่ามือ ใบหน้า หรือม่านตา) ดังนั้นข้อมูล ส่วนบุคคลจะไม่สูญหาย






111

ส่วนที่4: ความปลอดภัยและความเป็นส่วนตัวในการใช้งานเทคโนโลยี BioPay กรุณาใส่เครื่องหมายถูก (✓) ลงในช่องว่างที่ตรงกับระดับความเห็นด้วยหรือไม่เห็นด้วยของท่าน ข้อคาถาม

ด้านความปลอดภัย 4.1) เทคโนโลยี BioPay สามารถ เชื่อถือได้ 4.2) การชาระเงินผ่านเทคโนโลยี BioPay มีความปลอดภัย ด้านการรักษาความเป็นส่วนตัว 4.3) เทคโนโลยี BioPay ทาให้การ ชาระเงินมีความปลอดภัยในด้านการ รักษาความเป็นส่วนตัวมากขึ้น 4.4) เทคโนโลยี BioPay ทาให้ ข้อมูลไบโอเมทริกซ์ เช่น ลายนิ้วมือ จะถูกขโมยได้ยาก 4.5) เทคโนโลยี BioPay ทาให้ บุคคลอื่นเข้าถึงข้อมูลส่วนตัวได้ยาก มากขึ้น





ไม่เห็น ด้วย อย่าง ยิ่ง

112

ส่วนที่5: แนวโน้มในการใช้งานเทคโนโลยี BioPay กรุณาใส่เครื่องหมายถูก (✓) ลงในช่องว่างที่ตรงกับระดับความเห็นด้วยหรือไม่เห็นด้วยของท่าน ข้อคาถาม

5.1) มีความตั้งใจที่จะดาวน์โหลด แอปพลิเคชันที่ใช้เทคโนโลยี BioPay 5.2) มีความตั้งใจที่จะเชื่อมต่อบัตร เครดิตหรือบัตรเดบิตของฉันเข้ากับ แอปพลิเคชันที่ใช้เทคโนโลยี BioPay 5.3) มีความตั้งใจที่จะเลือกร้านค้าที่ มีเทคโนโลยี BioPay สาหรับการ ชาระเงิน 5.4) มีความตั้งใจที่จะชาระเงินผ่าน เทคโนโลยี BioPay 5.5) มีความตั้งใจที่จะเลือก เทคโนโลยี BioPay เป็นตัวเลือกแรก เมื่อซื้อสินค้าและบริการต่าง ๆ 5.6) มีความตั้งใจที่จะใช้การสแกน ลายนิ้วมือ (Fingerprint Recognition) ร่วมกับเทคโนโลยี BioPay 5.7) มีความตั้งใจที่จะใช้การสแกนฝ่า มือ (Hand Geometry) ร่วมกับ เทคโนโลยี BioPay 5.8) มีความตั้งใจที่จะใช้การสแกน ใบหน้า (Face Recognition) ร่วมกับเทคโนโลยี BioPay






113

ข้อคาถาม




5.9) มีความตั้งใจที่จะใช้การสแกน ม่านตา (Iris Scanning) ร่วมกับ เทคโนโลยี BioPay 5.10) มีความตั้งใจที่จะใช้การสแกน เส้นเลือดในม่านตา (Retinal Scanning) ร่วมกับเทคโนโลยี BioPay 5.11) มีความตั้งใจที่จะใช้การสแกน หลอดเลือดดา (Vein Scanning) ร่วมกับเทคโนโลยี BioPay 5.12) มีความตั้งใจที่จะใช้การ วิเคราะห์น้าหนัก ทิศทาง และ ความเร็วในการเซ็นลายเซ็น (Signature Analysis) ร่วมกับ เทคโนโลยี BioPay 5.13) มีความตั้งใจที่จะใช้การยืนยัน ด้วยเสียง (Voice Verification) ร่วมกับเทคโนโลยี BioPay *ขอขอบพระคุณที่ให้ความร่วมมือในการตอบแบบสอบถาม*



114

APPENDIX C THE BUREAU OF REGISTERATION ADMINISTRATION OFFICIAL REPORT Population divided by age December 2016 Age < 1 Yrs. 2 Yrs. 4 Yrs. 6 Yrs. 8 Yrs. 10 Yrs. 12 Yrs. 14 Yrs. 16 Yrs. 18 Yrs. 20 Yrs. 22 Yrs. 24 Yrs. 26 Yrs. 28 Yrs. 30 Yrs. 32 Yrs. 34 Yrs. 36 Yrs. 38 Yrs. 40 Yrs. 42 Yrs. 44 Yrs. 46 Yrs. 48 Yrs. 50 Yrs.

Male 21,939 25,770 28,585 27,866 29,410 30,900 32,652 32,507 35,728 35,180 40,312 43,441 38,176 37,006 35,964 35,541 38,338 40,712 43,726 42,256 42,951 41,127 41,581 41,962 42,415 39,669

Female 20,646 24,124 27,567 26,263 28,248 29,272 31,205 31,677 34,864 34,264 39,735 38,664 38,597 38,250 37,264 38,112 41,854 46,038 49,476 48,760 49,931 48,420 49,281 50,002 50,798 48,072

Total 42,585 49,894 56,152 54,129 57,658 60,172 63,857 64,184 70,592 69,444 80,047 82,105 76,773 75,256 73,228 73,653 80,192 86,750 93,202 91,016 92,882 89,547 90,862 91,964 93,213 87,741

Age 1 Yrs. 3 Yrs. 5 Yrs. 7 Yrs. 9 Yrs. 11 Yrs. 13 Yrs. 15 Yrs. 17 Yrs. 19 Yrs. 21 Yrs. 23 Yrs. 25 Yrs. 27 Yrs. 29 Yrs. 31 Yrs. 33 Yrs. 35 Yrs. 37 Yrs. 39 Yrs. 41 Yrs. 43 Yrs. 45 Yrs. 47 Yrs. 49 Yrs. 51 Yrs.

Male 24,231 26,337 27,979 29,109 31,163 31,578 32,574 32,321 33,295 39,782 44,199 38,594 37,499 35,541 34,038 37,723 39,788 42,128 43,899 42,904 41,379 40,110 42,756 41,262 41,234 40,909

Female 23,096 24,627 26,791 27,710 29,318 30,332 31,164 31,773 33,138 39,265 40,936 38,514 37,935 36,517 35,734 40,748 44,163 47,499 49,622 49,818 48,385 47,192 50,856 49,327 49,070 49,453

Total 47,327 50,964 54,770 56,819 60,481 61,910 63,738 64,094 66,433 79,047 85,135 77,108 75,434 72,058 69,772 78,471 83,951 89,627 93,521 92,722 89,764 87,302 93,612 90,589 90,304 90,362

115

Population divided by age December 2016 Age 52 Yrs. 54 Yrs. 56 Yrs. 58 Yrs. 60 Yrs. 62 Yrs. 64 Yrs. 66 Yrs. 68 Yrs. 70 Yrs. 72 Yrs. 74 Yrs. 76 Yrs. 78 Yrs. 80 Yrs. 82 Yrs. 84 Yrs. 86 Yrs. 88 Yrs. 90 Yrs. 92 Yrs. 94 Yrs. 96 Yrs. 98 Yrs. 100 Yrs.

Male 41,305 38,431 37,386 33,020 30,496 27,532 24,248 21,969 18,201 14,239 12,370 10,559 8,898 8,681 7,320 5,570 4,419 3,046 2,076 1,394 856 607 455 275 257

Female 49,596 46,980 46,037 40,451 37,983 36,076 31,947 28,928 24,486 19,670 17,290 15,168 12,724 13,031 11,421 9,103 7,727 5,451 4,122 2,792 1,715 1,097 639 405 344

Total 90,901 85,411 83,423 73,471 68,479 63,608 56,195 50,897 42,687 33,909 29,660 25,727 21,622 21,712 18,741 14,673 12,146 8,497 6,198 4,186 2,571 1,704 1,094 680 601

Age 53 Yrs. 55 Yrs. 57 Yrs. 59 Yrs. 61 Yrs. 63 Yrs. 65 Yrs. 67 Yrs. 69 Yrs. 71 Yrs. 73 Yrs. 75 Yrs. 77 Yrs. 79 Yrs. 81 Yrs. 83 Yrs. 85 Yrs. 87 Yrs. 89 Yrs. 91 Yrs. 93 Yrs. 95 Yrs. 97 Yrs. 99 Yrs. > 100 Yrs.

Male 39,481 37,425 35,785 31,631 27,760 25,658 23,154 20,635 16,191 13,057 11,681 10,555 9,237 7,871 6,134 4,833 3,551 2,444 1,729 1,121 755 480 353 292 218

Female 47,886 45,162 43,772 39,301 35,844 33,303 30,868 27,676 21,607 18,164 16,392 15,465 14,029 11,888 10,068 7,848 6,439 4,769 3,536 2,168 1,446 862 484 363 350

Total 87,367 82,587 79,557 70,932 63,604 58,961 54,022 48,311 37,798 31,221 28,073 26,020 23,266 19,759 16,202 12,681 9,990 7,213 5,265 3,289 2,201 1,342 837 655 568

116

BIOGRAPHY

Name


Date of Birth

March, 03, 1994

Educational Background

Khemasiri memorial school Satriwithaya School

E-mail

[email protected]

the factors affecting biopay acceptance of people in bangkok

the factors affecting biopay acceptance of people in bangkok

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