MENJELASKAN PENERIMAAN PENGGUNA MOBILE BANKING SEBUAH PERSPEKTIF DARI PERLUASAN MODEL PENERIMAAN TEKNOLOGI MENGGUNAKAN VARIABEL PERSEPSI NILAI MOBILITAS DAN PERSEPI KENIKMATAN

SKRIPSI

Submitted as Partial Fulfillment of Requirements for the Degree of Sarjana Ekonomi (SE) at the Sebelas Maret University Surakarta

By

Delariza Rika Fasita F 0307036

FACULTY OF ECONOMICS SEBELAS MARET UNIVERSITY

SURAKARTA 2011

perpustakaan.uns.ac.id digilib.uns.ac.id

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MOTTO

Sungguh bersama kesulitan itu ada kumudahan, karenanya jika kamu telah

selesai (dari suatu urusan) kerjakanlah sungguh-sungguh (urusan yang lain).

Dan kepada Tuhanmulah kamu berharap

(Q.S. Alam Nasyrah: 6-8).

You live you learn, you love you learn, you cry you learn, you lose you learn (Alanis Morisette).

In the middle of difficulty lies opportunity (Albert Einstein).

Only those who dare to fail greatly, can ever achieve greatly (Robert F. Kennedy).

Hidup itu tak selamanya indah, tapi biarkan yang indah itu tetap

hidup dalam kenangan

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DEDICATION

This

skripsi

and whatsoever success

that I could achieve is dedicated to

My -greatest - beloved

Papa

and

Mama

If only there is a good enough word to

say my sincerely thanks for you two.

I Love you.

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2. Drs. Jaka Winarna M.Si., Ak., as the Head of Accounting Department, Sebelas Maret University, Surakarta.

3. Mr. Santoso Tri Hananto, M.Si,Ak., as my skripsi advisor. Thanks for your advices and support so this skripsi can be done.

4. Mr. Agus Budiatmanto SE., M.Si, Ak., as my academic advisor, thanks for all your support and advices.

5. My Papa and Mama, thank you for being my greatest parents in the worlds. Thank you for all support and endless love, even we were separated, I know you always there for me. This English skripsi is dedicated only for you two and also my sister, Rensi.

6. My “dudulz” Dedie Saifullah, thank you for all love, care, understanding, and all you’ve gave for me for whole time. I always could count on you.

ACKNOWLEDGMENT

Researcher will be grateful to Allah SWT for all the mercy and bless so that she was able to finish this research well. This Skripsi is proposed to complete all the requirements of achieves the degree of Sarjana Ekonomi of Accounting Department, Sebelas Maret University, Surakarta.

Researcher realizes that she could not have finished this skripsi without the supports and involvement of many parties both directly and indirectly. I owe a very great debt to:

1. Prof.Dr. Bambang Sutopo, M.Com., Ak., as the Dean of Economics Department, Sebelas Maret University, Surakarta.

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7. Dhinar Adi Nugroho, my best brother, thank you for the story, your support and care for me.  

8. My best friends “LOTIZ”, Dewi Listiani thank you for being my first friend at UNS until now, your trust for always share to me. Noor Anis Meikawati, thanks for our story together, I will never forget it. Murdiani Agustiati, thank you for coloring our day with your odd behavior. When we feel down, remember our heart fight to through all this. Novi Eka Rahmawati, thank you for your serenity that always make me so calm.

9. My best friends in Bekasi, Febri Alfalina Saputri, Reynaldi Oey, Allert Benedicto Ieua Noya, Dian Anggraini Kumalasari thanks for our beautiful relationship.

10. All of my best friends, Ebray, aunt Weny, Fata’s mom for being my great English editor. Joe, Hadi, Gandi “Tria” for the never ending support.

11. Thanks to pakde Dr. Nur Julianto and Bude Mochdiyati whom I lived with at Solo. My big family, Eyang Imam Soeyitno family and Embah Mochammad Family, thanks for your love, care, and support.

12. My “Agent 007” friends: Ayus, Peka, Irla, Fatania, Dewo, Mba Sri, Oppie, Nani, Rudi, Rija’, Awang, etc. HMJ Akuntansi friends, Mas Okky, Mba Desta, Mas Dancrut, Mba Ulli, Mas Fijri, Mba tryas, Mba Hanni, Mba Finik, Reza, Abhe, Anes, etc. and all of economic faculty friends for all support in the last four years.

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13. And for all parties that Researcher could not mention one by one, but you have already mentioned in my heart.

Researcher realizes that this research is far from being perfect. This research has a lot of constraint, thus any suggestions and critics are expected for the sake of improving this research.

As I close this acknowledgment, I expect that this small print writing will be useful to all parties.

Surakarta, April, 2011

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TABLE OF CONTENTS

Page

TITLE ... i

ABSTRACT ... ii

ABSTRAK ... iii

PAGE OF ADVISOR’S APPROVAL ... iv

PAGE OF APPROVAL ... v

PAGE OF MOTTO ... vi

PAGE OF DEDICATION ... vii

ACKNOWLEDGEMENT ... viii

TABLE OF CONTENT ... xi

LIST OF TABLES ... ... xiv

LIST OF FIGURE... ... xv

LIST OF APPENDIXES... ... xvi

CHAPTER I. INTRODUCTION A. Background... ... 1

B. Problem Statements ... . 7

C. Research Objectives ... . 7

D. Research Advantages ... 7

II. THEORETICAL FRAMEWORK A. Agency Theory ... ... 9

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1. Technology Concepts ... 9

2. Conceptual of Mobile Banking ... 11

3. Technology Accpeted Model (TAM) ... 14

B. Conceptualization and Hypotheses Development ... 23

C. Conceptual Framework ... 26

III. RESEARCH METHODS A. Research Design ... 27

B. Population and Sample ... 27

C. Data Source and Data Collecting Technique ... 28

D. Measurement Items ... 28

E. Data Analyze Technique and Hypotheses Test ... 31

1. Data Test Technique ... 31

2. Model Assumption Test ... 33

IV. DATA ANALYSIS A. Data Collection Analysis ... 39

1. Total Data Collection ... 40

2. Respondents Demography ... 40

B. Data Test Analysis ... 43

1. Normality Test ... 43

2. Outlier Evaluation ... 44

3. Validity Test ... 45

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C. Model Assumption Test ... 48

1. Godness of Fit Analysis ... 48

2. Model Modification ... 49

D. Hypotheses Analysis ... 51

V. CONCLUSION A. Conclusions ... 57

B. Research Constraints ... 58

C. Research Suggestion ... 59

REFERENCES ... 60 APPENDIXES

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LIST OF TABLES

PAGE

Table III.1 Research Variables 29

Table III.2 Godness of Fit Indices 37

Table IV.1 Data Research Collection 40

Table IV.2 Respondents Age 41

Table IV.3 Respondents Educational Background 42

Table IV. 4 Normality Test 44

Table IV.5 Outliers Data 45

Table IV.6 Validity Test 46

Table IV.7 Reliability Test 47

Table IV.8 Goodness of Fit Model Before Modified 48 Table IV.9 Goodness of Fit Model After Modified 50

Table IV.10 Goodness of Fit Model Summary 51

Table IV.11 Significant Level 51

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LIST OF FIGURE

PAGE

Figure II.1 Technology Accepted Model by Davis et al. (1989) 17

Figure II.2 Conceptual Framework 26

Figure III.1 TAM with Perceived Mobility Value (PMV) and Perceived

Enjoyment (PE) 38

Figure IV.1 Respondents Gender 41

Figure IV.2 Bank Where The Respondents Save Their Money in 42

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LIST OF APPENDIXES

Appendix 1 Questionnaire Form

Appendix 2 Respondents Recapitulation

Appendix 3 Research Path Diagram before Modified

Appendix 4 Research Output Path Diagram before Modified Appendix 5 Normality Test

Appendix 6 Outlier Test Appendix 7 Validity Test Appendix 8 Reliability Test

Appendix 9 Goodness of Fit Model before Modified Appendix 10 Modification Indices before Modified Appendix 11 Research Path Diagram after Modified Appendix 12 Goodness of Fit Model after Modified Appendix 13 Modification Indices after Modified Appendix 14 HypothesesTest

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A PERSPECTIVE OF THE EXTENDED TECHNOLOGY ACCEPTED MODEL (TAM) USING PERCEIVED MOBILITY VALUE AND

PERCEIVED ENJOYMENT VARIABLES

DELARIZA RIKA FASITA NIM F0307036

The objective of this research is to examine and verify that the TAM can be employed to explain and predict the acceptance of mobile banking. This study identifies two factors that account for individual differences, i.e. Perceived Mobile Value (PMV) and Perceived Enjoyment (PE) which is adapted from Huang et al. (2006). Population in this research is bank customers who use mobile banking services in Indonesia. A sample of 131 respondents was selected using a purposive sampling method whereby the respondents have to be mobile banking users to be included in the survey. The constructs’ in the model were measured using existing items adapted from some prior TAM research.

The result shows that the data fit the extended TAM well. Furthermore, the result show that perceived enjoyment and perceived mobility can affect individual intention to use mobile banking. Overall, the result support that perceived mobility value and perceived enjoyment may appropriate to use in predicting user acceptance of mobile banking.

PERSPEKTIF DARI PERLUASAN MODEL PENERIMAAN TEKNOLOGI MENGGUNAKAN VARIABEL PERSEPSI NILAI MOBILITAS DAN PERSEPI

KENIKMATAN

DELARIZA RIKA FASITA NIM F0307036

Tujuan penelitian ini adalah untuk menguji dan memverifikasi apakah TAM dapat digunakan untuk menjelaskan dan memprediksi penerimaan pengguna mobile banking. Penelitian ini menggunakan dua faktor yang menjelaskan perbedaan-perbedaan individual, yaitu persepsi nilai mobilitas dan persepsi kenikmatan, yang diadaptasi dari Huang et al. (2006). Populasi pada penelitian ini adalah nasabah bank pengguna jasa mobile banking. Sampel dari 131 responden didapat dengan menggunakan metode purposive sampling di mana kriteria responden adalah pengguna mobile banking. Konstruk yang digunakan dalam model diukur dengan menggunakan item pengukuran yang diadaptasi dari penelitian TAM yang pernah ada.

Hasil menunjukkan bahwa data cocok dengan perluasan model TAM ini dan persepsi nilai mobilitas dan persepsi kenikmatan dapat mempengaruhi niat seseorang untuk menggunakan mobile banking. Secara keseluruhan, persepsi nilai mobilitas dan persepsi kenikmatan dimungkinkan untuk digunakan dalam memprediksi penerimaan pengguna mobile banking.  

perpustakaan.uns.ac.id CHAPTER I digilib.uns.ac.id

INTRODUCTORY

A. BACKGROUND

Nowadays technology, provide dynamic collaborative environments that widely recognized today, becomes an important factor in the future development (Baten, 2010). Information technology is weakening geographical constraints and changing the way people communicating to each others (Mazhar, 2006). The usage of new information technology will also change the individual behaviour (Hamzah, 2009).

The internet, one of the information technologies, has created an incredible market space. Same with it, another technology stream has emerged to play an increasingly important role in business and society: mobile communications (Feng et al. in Barati and Mohammadi, 2009). Mobile phones have become an integral part of the 21st century landscape with an expected penetration of 4.5 billion by 2011. As the number of mobile phone users is growing, purchasing products and services using mobile phones and other mobile devices are also increasing (Manochehri and Alhinai in Barati and Mohammadi, 2009).

The major change has come in the delivery of the content, application, and services to the mobile communication devices (Sadi et al., 2010). Since the mid-1990s, there has been a fundamental change in banking delivery channels toward using self-service channels such as online banking services (Pikkarainen

perpustakaan.uns.ac.id et al., 2004). Banks began to look at electronic banking (e-banking) as a means digilib.uns.ac.id

to replace some of their traditional branch functions for two reasons. Firstly, branches were very expensive to set up and maintain due to the large overheads associated with them. Secondly, e-banking products or services like Automatic Teller Machine (ATM) and electronic fund transfer were a source of differentiation for banks that utilised them. Banks can find significant savings by serving customers in the mobile channel ($0.08) rather than through the contact centre ($3.75), IVR banking ($1.25), ATM ($0.85) or even online banking ($0.17) (Eads, 2009: 1). Being a tight competitive industry, the ability of banks to differentiate themselves based on price is limited (Singh et al. in Goi, 2006).

Mobile banking, the lowest cost banking service, is defined as a way for the customer to perform banking actions on his or her cell phone or other mobile device (Miller, 2011). Mobile banking is a financial service access from using Short Message Services (SMS) technology platform for simple transaction as a customer’s asks (Hristu in Amin et al. 2006) to using Wireless Application Protocol (WAP) technology for more complex financial information. With mobile banking services, customers should not go to ATM. In the past, people were doing their transactions using ATM. This machine gives an enough solution to customers for paying without stand in a long line, but it still needs the attendant from the customers to do their transaction.

Although information technology condition in Indonesia leave behind from other countries (Harmadi and Hermana, 2005), but compared with other e-banking services, the mobile e-banking growth in Indonesia is the quickest. This

perpustakaan.uns.ac.id rush growth is because mobile banking services, use for different kinds of digilib.uns.ac.id

banking services ranging from bill payment to making investment, can answer the needs of modern citizens who have a high mobility. Customers are not the only beneficiary of this new service, commercial banks may greatly increase the market coverage and better track customer as well (Shao, 2007).

Now on, almost all banks in Indonesia apply this kind of services. The government hope with this popular channel from banking services will decrease the used of cash money. A survey research from the International Financial Institute reveals that 35% from all over the world online housing work chores will shift to mobile banking services. It predicted that the value of mobile banking services will increase two times per years and will increase four times per years after 2011. According to a study conducted by the telecommunications analyst firm the number of mobile phone banking users will exceed 150 million globally by 2011.

Based on Indonesia Bank, internet banking user reached about 2,5 million by 2009. It larger than in 2008 where the internet banking user only reached about 1,5 million (Ismartunun, 2010). The amount of BCA mobile banking transaction has increased 57%, from Rp. 27,9 billion at the first quarter by 2009 to Rp. 43,9 billion at the first quarter by 2010 (Ismartunun, 2010). 

TELKOMSEL, one of Indonesia’s cellular network provider, has 2,5 million mobile banking users, with the highest traffic from BCA and Mandiri Bank, and has predicted 4 million customers will use mobile banking services by the end of

perpustakaan.uns.ac.id 2007 (Noor in Niagara, 2008: 3). It can conclude that mobile banking users in digilib.uns.ac.id

Indonesia are quite enough perspective.

The success of mobile banking usage depends on how users would achieve the systems (Wijayanti and Akhirson, 2009). Thus, the metaphorical tide is likely to raise all boats by increasing overall customer comfort with mobile banking and mobile commerce in general, which will decrease costs and increasing profits through the new customers and more profitable transactions (Eads, 2009).

Choosing mobile banking as the object of this study analysis is due to two particular reasons. First, the need of media for people who has a high mobility is increasing overtime. Second, mobile banking helps to reduce the transaction cost and give more value-added for the customers.

Human beings, being creatures of habit, will probably view anything that is new with caution and suspicion. The same applies to multimedia banking. However, with the threat of globalization and possible squeezes in margins, banks are attempting to 'push' clients towards multimedia banking (Vijayan et al., 2005).

Many research were explained by Harmadi and Hermana (2005) in Indonesia, Lee et al. (2007) in South Korea, Kripanont (2007) in Thailand, Wessels and Drennan (2009) in Australia, Sadi et al. (2010) in Sultanate Oman about determinant adoption of internet banking is no longer generally consistent. It means that those researches not yet found the exact factors affecting the customers to use mobile banking services. Technology Accepted Models (TAM)

perpustakaan.uns.ac.id approach, developed by Davis et al. (1989) based on Theory of Reasoned Action digilib.uns.ac.id

(TRA), used by those researches, which can explain customer acceptance of information technologies.

TAM consists of six primaries constructs, namely external variables (e.g. prior experience, voluntariness, compatibility, complexity, etc.), perceived usefulness, perceived ease of use, attitude, behavioural intention, and actual usage. It shows that user behaviour is determined by perceptions of usefulness and the ease of use of the technology (Adams et al., 1992; Davis et al., 1989; Mathieson, 1991; in Huang et al., 2006). Davis (1989) observed that external variables enhance the ability of TAM to predict acceptance for future technology. In other words, the constructs of TAM need to be extended by using additional factors (Huang et al., 2006).

Many research extended their TAM with external variables in order to explain further and become the antecedent from perceived usefulness or perceived ease of use (Jogiyanto, 2008: 124). Choosing additional factors depends on the target technology, main users, and context (Moon and Kim in Huang et al., 2006). Wang et al. in Huang et al. (2006) noted that variables relating to individual differences play a vital role in the implementation of technology. The more accepting of a new information system the users are, the more willing they are to make changes in their practices and use their time and effort to actually start using the new information system (Succi and Walter in Pikkarainen et al., 2004). Usage of a system can be an indicator of information system success and computer acceptance in some cases, whether the system is

perpustakaan.uns.ac.id regarded as good or bad depends on how the user feels about the system digilib.uns.ac.id

(Pikkarainen et al., 2004).

Mobile banking services are still in infancy. It has a great deal of room for improvement. Thus, there is a need to study and understand user’s acceptance of mobile banking services in order to identify the significant motivational factors affecting their intention to use mobile banking.

From a marketing perspective the greatest advantage of mobile communication and mobile commerce is that it offers suppliers a channel of direct communication with consumers via a mobile device at any time and at any place (Lubbe and Louw, 2009). How to anticipate customer needs and develop mobile content services is not easy in a rapidly developing mobile market (Pihlstrom, 2008: 2). Mobile devices create an opportunity to deliver new services to existing customers and to attract new ones (Lubbe and Louw, 2009) and when consumers enjoy positive experience in using mobile banking, they will increase the amount of transaction (Suki and Suki, 2007). From that explanation, this study will identify two constructs, which are adopted from Huang et al. (2006), namely “perceived mobility value”, and “perceived enjoyment” in order to identify the factors that influencing user acceptance of mobile banking with TAM.

perpustakaan.uns.ac.id B. PROBLEM STATEMENTS digilib.uns.ac.id

Previous research, conducted by Huang et al. (2007), explains that user acceptance of mobile learning can be explained by TAM with two external variables, i.e. perceived mobility value and perceived enjoyment. Based on the problem background, the researcher formulates the problems of this research, using the same model with Huang et al. (2007) but with different object, in question forms “Are perceived mobility value and perceived enjoyment variables affecting user acceptance of mobile banking with Technology Acceptance Model (TAM)?”

C. RESEARCH OBJECTIVES

The objective of this research is to examine and verify that the TAM can be employed to explain and predict the user acceptance of mobile banking using two factors that account for individual differences, i.e. Perceived Mobile Value (PMV) and Perceived Enjoyment (PE).

D. RESEARCH ADVANTAGES

1. Advantages for banking provider

The researcher expects with this research, banking provider would know what factors affecting their customers using or adopting mobile banking to do their transaction so that can use for their future strategic plan, substance policy improving their productivity, and enhance their market section in this globalization era full with technology adopted.

perpustakaan.uns.ac.id 2. Advantages for bank customer digilib.uns.ac.id

This research hopefully can give advantages to the customers, so they can maximize using mobile banking services. Afterwards, for the customers who not yet known and not yet use it before will know and use it in their daily life. 3. Advantages for next research

Hopefully, this research can contribute a reference for literature development and knowledge for next research about mobile banking technology.

           

perpustakaan.uns.ac.id CHAPTER II digilib.uns.ac.id

THEORETICAL FRAMEWORK

A. Agency Theory

1. Technology Concepts

Nowadays, technology has been being an unearthed part of human life. There are so many definitions of technology. In Random House Dictionary quotes from Kumala (2008: 12) technology is defined tightly relating to life, citizens, and environment. It means that technology will not be a free valuable. A technology usually started from individual or group imagination using nature phenomenon and practical needs. From those imaginations, individual or group developed it to be an invention. According to Galbraith in Niagara (2008) technology is defined as a systematic application and obtained from formulation science knowledge concept or knowledge collection that have certain function in practical human daily live and technology as the activity that involving organizational activity and system value.

Technology is defined by Goetch in Kumala (2008: 12) as “people tools, resources, to solve problems or to extend their capabilities”. Pacey in Kumala (2008: 12) defines technology as “the application or scientific and other knowledge to the practical task by ordered systems that involve people and organization, living things and machines”. From those definitions, there are obtaining some essence: (1) technology related to eternal idea or human thought, technology existence together with human culture existence, (2)

perpustakaan.uns.ac.id technology is the human creation, so it does not come naturally, and it was digilib.uns.ac.id

artificial; (3) technology is set of means, so it can be bordered or it universals, depends on the analysis side sight; (4) technology is purposing to facilitate human endeavour, so technology must be able increasing human ability performance (Kumala, 2008: 12).

Fichman in Stylanou and Jackson (2007) introduced a related argument by distinguishing between two types of technologies in terms of the main knowledge that each type determines the user. Type 1 technologies (e.g. personal computers, word processing packages, graphics software) are generally independent use technologies that are intended to facilitate self-contained tasks performed by individual users. These technologies impose a relatively small main knowledge and typically require only a few hours of training before users achieve basic proficiency. In contrast, Type 2 technologies (e.g. software development process technologies) involve significant knowledge barriers to adoption, including a lengthier training process and a situation where the user ability, not just the willingness to use, is a determining factor. As such, experience, attitudes, training, and supervisory desires become valid predictor variables (Lee et al. in Stylanou and Jackson, 2007).

Facts in technology adoption based on the dynamic process, based on empirical literature in naturally affecting static network (Ryan and Tucker in Niagara, 2008: 12). The benefits of technology adoption is a beginning to indicate economic development and in the next steps, technology can use as

perpustakaan.uns.ac.id the economic agent of the corporation in the same industry. Decision of digilib.uns.ac.id

adopting technology can also relate to how a corporation developing information technology innovation. Thus, manager in a corporation must be prepared for what strategy will be used to adopt information technology that took by the end user as technology acceptance (Zhu and Weyant, 2000). Innovation in technology information done by vendor can be speed, quality and flexibility increasing for the end user operating (Steinmueller. 2001; Callantone, et al. 2006; in Niagara, 2008: 13).

Orlikowski and Iacono in Stylanou and Jackson (2007) point to the fact that not enough attention is paid to the technology itself as well as to the tendency to threat technologies as an independent and stable constant despite the empirical evidence that highlights the impact of system design on perceptions and use. Adopting the perspective that technology use is a function of how the technology merges with the social environment, they point to the silence of cultural, normative, and regulatory influences on the usage decision (Stylanou and Jackson, 2007).

2. Conceptual of Mobile Banking

Mobile phone is no longer known as it traditional functions, i.e. voice conversation and Short Message Services (SMS). Nowadays, the mobile phones even facilitate for a real time teleconference through 3G (Third Generations). Nonetheless, from the banking perspective, mobile phones introduce a new channel of distribution for the banking industry, and the

perpustakaan.uns.ac.id demand are keeping on increasing hence entrenched its feasibility as a new digilib.uns.ac.id

media of banking transaction (Amin et al., 2006).

In Barati and Mohammadi (2009), mobile banking is defined as the “type of execution of financial services which the customer uses mobile communication techniques in conjunction with mobile devices” (Pousttchi and Schurig, 2004). It is defined as “a channel whereby the customer interacts with a bank via a mobile device, such as a mobile phone or personal digital assistant” (Barness and Corbit, Scornavacca and Barnes, in Barati and Mohammadi, 2009). According to Amin et al. (2006), mobile banking defines as the newest channel in electronic banking to provide a convenience way of performing banking transaction, which is known as "pocket-banking". The terms m-banking, m-payments, m-transfers, m-payments, and m-finance refer collectively to a set of applications that enable people to use their mobile telephones to manipulate their bank store value in an account linked to their handsets, transfer funds, or even access credit or insurance products (Donner and Tellez, 2008).

In Amin et al. (2006), Kohli (2004) claimed that the mobile banking service gives customers the convenience of account access information and real-time transaction capabilities. Hamzah (2005) in Amin et al. (2006) said that "mobile banking" brings the convenience and enhanced value. Riivari (2005) in Amin et al. (2006) claimed that the opportunity for mobile services is three times as many mobile phone users as those who use online PCs, and they are now ready for anywhere, anytime applications that match their lifestyles.

perpustakaan.uns.ac.id According to Donner (2006) mobile banking services enable consumers, for digilib.uns.ac.id

example, to request their account balance and the latest transactions in their accounts, to transfer funds between accounts, to make, buy and sell orders, for the stock exchange and to receive portfolio and price information.

There are a variety of mobile media channels, including, SMS (Short Message Service), mobile web, mobile client application, phone banking, etc. Each mobile media channel has its strengths and weaknesses, and it is important to identify the delivery mode that is most appropriate for each banking service. According to Rahardjo in Widyastuti (2008: 32), there are some conditions for mobile banking services: (1) easy use application, (2) the services can be reached from everywhere and every time, (3) cheap, (4) secure, and (5) reliable. Mobile banking services generally classified into three type characteristics (Kumala, 2008: 15), mention as follow.

1) Informational

This type is the simplest of mobile banking. It consists of products and services information from bank provider. The risk is quite low, because this system does not connect to banks’ main server and network, but connects to web hosting server.

2) Communicative

This type is enabling communication between customers and banks systems. It can be account balance information, transaction report, customer data changed, and also member services form. The risk is higher than the first above, because there is an interaction between the customers and some banking network server, which is susceptible with programs that can harm the system such as viruses.

3) Transactional

This last type is the most complete than the others, and generally it also consist two types above. In this type, customers enable to do transaction directly. Because it has direct flow through bank main server and network, so it has the highest risk than two others. Thus, a good maintenance and direct control is necessary. Customers can directly access their bank account, paying bill, transferring fund, etc.

perpustakaan.uns.ac.id According to Alsindi et al. (2004) in Kumala (2008: 16), mobile digilib.uns.ac.id

banking services have some strengths and weaknesses. The strengths are mentioned as follow.

1) WAP provides more alternatives to connect with bank customers and to increase the number of customers.

2) Bank customer can reach their banking services anytime and anywhere. 3) It can consider as one of the markets competitive advantage.

4) The used of this technology will decrease the number of customers to visit bank or ATM and also opening new branch.

The weaknesses are mentioned as follow.

1) The number of mobile banking users is very minim.

2) Mobile banking, perhaps, considered by some customers is a complex used of technology.

3) Developing mobile banking services needs a lot of cost because it needs more effort and infrastructure assure the security to do.

4) Limitation of cell phone screen width considered as one of the weaknesses because the information than shown is limited.

Mobile banking is still in development phase which needs more concerned due to enhance the mobile banking system content to fulfill the customer needs. When it probably completing the customer needs, the acceptance of consumer will increase and bank can rise up their profitability. With driving customer loyalty, engaging new segment, and empowering it own capability, it also probably gives some opportunities to bank provider.

3. Technology Acceptance Model (TAM)

One of the most utilized models in studying information system acceptance is the Technology Acceptance Model (TAM) (Davis et al., 1989; Mathieson, 1991; Davis and Venkatesh, 1996; Gefen and Straub, 2000; Al- Gahtani, 2001) in which system use (actual behaviour) is determined by Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) relating to the attitude toward the use that relates to the intention and finally to behaviour

perpustakaan.uns.ac.id (Pikkarainen et al., 2004). TAM has become so popular that it has been cited digilib.uns.ac.id

in most of the research that deals with user acceptance of technology (Lee et al., 2003).

TAM is based on the Theory of Reasoned Action (TRA), which is concerned with the determinants of consciously intended behaviours (Fishbein and Ajzen in Pikkarainen et al., 2004). Behavioural intention will determine individual behavioural. Expression from behavioural intention should be relating with high accurate prediction of related volitional action (Jogiyanto, 2007: 26). From the information systems' perspective one relevant element of TRA is its assertion that any other factor that influences behaviour, for example, systems design variables user, characteristics, task characteristics, political influences and organizational structure do so only indirectly by influencing an attitude toward behaviour, subjective norm or their relative weights (Davis et al. in Pikkarainen et al., 2004).

Since 1967 TRA has been developed, tested and used extensively and its extension, the Theory of Planned Behaviour (TPB) utilized widely since 1988 by Ajzen. Ajzen included a construct which was not use yet in TRA. This construct namely perceived behavioural control which is used to control individual behaviour that is limited by their weaknesses and their boundaries from lack of sources used to realize their behaviour (Jogiyanto, 2007: 61).

Although the TAM and the TRA share many issues they have some considerable differences. The first difference is that according to TRA beliefs are bound to context, and hence they cannot be generalized. Contrary to that,

perpustakaan.uns.ac.id TAM states that PEOU and PU are issues that affect acceptance of all digilib.uns.ac.id

information systems. The other significant difference is that in TRA all beliefs are summed together, but in the TAM both beliefs are seen as distinct constructs. Modelling each belief separately allows researchers to better trace influences of all the affecting factors on information system acceptance (Davis et al. in Pikkarainen et al., 2004).

TAM has been tested in many studies (e.g. Davis, 1989; Davis et al., 1989; Mathieson, 1991; Adams et al., 1992; Davis, 1993; Segars and Grover, 1993; Taylor and Todd, 1995), and it has been found that TAM’s ability to explain the attitude toward using an information system is better than other model’s (TRA and TPB) (Mathieson in Taylor and Todd, 1995). In other words, the use of an information system acts as an indicator for information system’s acceptance. There are five main constructs used in TAM:

1) perceived usefulness, 2) perceived ease of use,

3) attitude towards behaviour or attitude towards using technology, 4) behavioural intention or behavioural intention to use,

perpustakaan.uns.ac.id Figure II.1 digilib.uns.ac.id

Technology Accepted Model by Davis et al. (1989)

3.1 Perceived Usefulness

Several studies on TAM perceived usefulness as an important antecedent of computer utilization (Davis et al. and Igbaria et al. in Selamat et al., 2009). Davis (1989) defined PU as the degree to which an individual believes that using the system will enhance his job performance (Alrafi, _____). From that definition, it is known that perceived usefulness as a belief about decision making process (Jogiyanto, 2007: 114). Many research found strong relationships between perceived usefulness and technology usage. In the study of mobile banking acceptance Luarn and Lin (2005) in Selamat et al. (2009) found that perceived usefulness has a positive impact on the willingness to use mobile banking. Therefore, it is highly predictable that people use information technology because they find it useful. Its construct is made by six items, i.e. work more quickly, job performance, increase productivity, effectiveness, make job easier, and useful.

External  Variables 

Attitude  Toward  Using  Perceived 

Ease of Use  Perceived  usefulness 

Behavioural  Intention to 

use 

Actual  system use 

perpustakaan.uns.ac.id 3.2 Perceived Ease of Use (PEOU) digilib.uns.ac.id

Quote from Selamat et al. (2009), PEOU is a major factor that affects acceptance of an information system (Davis et al., 1989). PEOU is defined as the degree to which an individual believes that using computer or computerized system will be free from physical and mental efforts (Davis in Alrafi, ______). From the definition, it is known that PEOU also a belief about decision making process (Jogiyanto, 2007: 115).

According to Teo (2001) if a system is easy to use, it requires less effort on the part of users, thereby increasing the likelihood of adoption and usage. Conversely, if systems that are complex or difficult to use are less likely to be adopted, since it requires significant effort and interest on the user. Franco and Roldan (2005) in Selamat et al. (2009) found the relationship between PEOU, and PU was significant and positively related. This means a difficult system is less useful. The construct of PEOU is formed by many items (Jogiyanto, 2007: 115), i.e. easy of learn, controllable, clear and understandable, flexible, easy to become skilful, and easy to use.

3.3 Attitude Towards Using

Attitude toward using the system is defined as the degree of evaluative affect that an individual associates with using the target system in his or her job (Davis et al. in Jogiyanto, 2007: 116). It refers to the person’s general feeling, favorable or otherwise, for the use of the

perpustakaan.uns.ac.id new technology. TAM conceptualizes individual perceptions of digilib.uns.ac.id

usefulness based on instrumentality as being strongly related to attitude toward technology use. It is also defined by Mathieson (1991) as the user’s evaluation of the desirability of his or her using the system (Jogiyanto, 2007: 116). Prior research showed that attitude has positive influence to the behavioural intention, and some showed negative results. Thus, some researches do not include this construct (Jogiyanto, 2007: 116).

3.4 Behavioural Intention

The behavioural intent constructs as a proxy to predict the actual usage had been successful thus far (Ramayah and Ignatius, 2003). Warshaw and Davis (1985) define behavioural intention as “the degree to which a person has formulated conscious plans to perform or not perform some specified future behaviour” (Ramayah and Ignatius, 2003). This is in line with the Theory of Reasoned Action (Fishbein & Ajzen, 1975) and its successor Theory of Planned behaviour (Ajzen, 1985), which contend that behavioural intention is a strong predictor of actual behaviour. In the application of information systems, the TAM has been successfull used by many researchers to predict behavioural intent towards the use of information technology (Ramayah and Jantan, 2003; Ramayah, Sarkawi and Lam, 2003; Legris, Ingham, and Collerette, 2002; in Ramayah and Ignatius, 2003).

perpustakaan.uns.ac.id digilib.uns.ac.id

3.5 Behaviour (Actual Usage)

The behavior construct represents a user’s subjective estimate of the amount of time or frequency that he/she actually spends using the technology (Stylianou and Jackson, 2007). Igbaria et al. (1995) defined perceived usage as the amount of time interacting with a technology and the frequency of use (Gardner and Amoroso, 2004). They found strong relationships with behavioural intent to use the technology. Igbaria et al.

in Gardner and Amoroso (2004) found that individuals are likely to use a system if they believe it is easy to use and will increase their performance productivity.

Actual usage, as originally conceptualized in the Davis (1989) study, was measured by the frequency of use and the length of time of use (Szajna, 1996). Objective measures of actual use are difficult to obtain for Internet-based technologies and therefore, many of the TAM studies either left out usage as a dependent variable, focusing solely on behavioural intention or else moved to perceived usage. The construct captures both work and entertainment related use. The mobile banking conceptualization examines use as a function of the time spent transaction on the mobile banking. Szajna (1996) recommended the examination of self-reported usage. Sun (2003) in Gardner and Amoroso (2004), reports that most TAM studies used a perceptual self-report usage. Felasufazein (2010) and Kusumo (2010) has proven that actual

perpustakaan.uns.ac.id usage did not fit to the model to their research for mobile banking digilib.uns.ac.id

acceptance. 3.6 External Variables

Although TAM is a model applicable to a variety of technologies (Adams et al., 1992; Chin and Todd, 1995; Doll et al., 1998), it has been criticized for not providing adequate information on individuals’ opinions of novel systems (Mathieson, 1991; Moon and Kim, 2001; Perea y Monsuwe et al., 2004; in Huang et al., 2006). Davis (1989) observed that external variables enhance the ability of TAM to predict acceptance of future technology. In other words, the constructs of TAM need to be extended by incorporating additional factors. Choosing additional factors depends on the target technology, main users and context (Moon and Kim in Huang et al., 2006). Wang et al. (2003) in Huang et al. (2006) noted that variables relating to individual differences play a vital role in the implementation of technology. Additionally, empirical research based on TAM has discovered strong relationships between individual differences and information technology acceptance (Agarwal and Prasad in Venkatesh, 2000).

To understand user perception of mobile banking, this study use two individual difference variables, namely “perceived mobility value” and “perceived enjoyment”, into the proposed TAM model. These two constructs are described as follow. Perceived Mobility Value (PMV) denotes user awareness of the mobility value of mobile banking. Mobility

perpustakaan.uns.ac.id has three different elements, including convenience, expediency and digilib.uns.ac.id

immediacy (Seppala and Alamaki in Huang et al. 2006). Mobility permits users to gain access to service or information anywhere at anytime via mobile devices. Previous studies found that mobile users valued efficiency and availability as the main advantages of mobile banking, and these advantages are a result of the “mobility” of a mobile device (Chen et al., 2003; Hill and Roldan, 2005; Ting, 2005; in Huang et al., 2006). From paper build by exploring customer perceived value in the mobile service field, the majority of respondents show positive critical incidents when users perceived mobile services to be especially valuable them, description of reasons why and under which condition they had used the service, and description of consequences of service use in their own language (Pihlstrom, 2008: 65). Therefore, mobile banking is valuable because of its mobility. Consequently, the perceived mobility value is a critical factor of individual differences affecting users’ behaviors (Huang et al., 2006).

Individuals engage in activities because these activities lead to enjoyment and pleasure (Teo and Lim, 1997). According to Davis et al. (1992), Perceived Enjoyment (PE) is defined as “the extent to which the activity of using the technology is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated” Jogiyanto, 2007: 131). In this study, perceived enjoyment denotes the extent to which an individual finds the interaction of mobile banking

perpustakaan.uns.ac.id intrinsically enjoyable or interesting. Perceived enjoyment is seen as an digilib.uns.ac.id

example of intrinsic motivation, and it has been found to influence user acceptance significantly. Furthermore, research on the role of enjoyment suggested the importance of enjoyment on users’ attitudes and behaviors (Igbaria et al., 1995; Teo and Lim, 1997; Wexler, 2001; Yi and Hwang, 2003; in Huang et al. 2006).

B. Conceptualization And Hypotheses Development

1. Perceived Mobility Value (PMV)

PMV tested by Huang et al. (2006), it relates to users’ personal awareness of mobility value. Mobility enables users to receive and transmit information anytime and anywhere (Huang et al., 2006). The mobility associated with time-related needs will encourage users to adopt mobile technology since enhanced accessibility is expected to affect dynamic interaction and high levels of engagement (Anckar and D’Incau, 2002 in Huang et al., 2006). Earlier research supports the importance of conditional value, in that people in general lack motivation to use new mobile services unless these services create value in situations where mobility really matters and thereby affect people’s lives positively (Jarvenpaa et al. in Pihlstrom, 2008: 183)

Hence, users who perceive the value of mobility also understand the uniqueness of mobile banking and have a strong perception of its usefulness. In other words, perceived mobility value has a positive effect on the

perpustakaan.uns.ac.id perceived usefulness of mobile banking. Therefore, this work treats perceived digilib.uns.ac.id

mobility value as a direct antecedence of perceived usefulness.

H1: Perceived mobility value has a positive effect on perceived usefulness of mobile banking.

2. Perceived Enjoyment

The concept of perceived enjoyment (PE) adapted from Davis et al. (1992) means that users feel enjoyable from the instrumental value of using mobile banking. Prior studies on technology acceptance behaviour examined the effects of perceived enjoyment on perceived ease of use (Igbaria et al.,

1996; Venkatesh, 2000; Venkatesh et al., 2002; Yiand Hwang, 2003; in Huang et al., 2006). New technologies that are considered enjoyable are less likely to be difficult to use. By extending these results to the context of the mobile banking, we can therefore postulate that perceived enjoyment will have a positive effect on perceived ease of use.

H2: Perceived enjoyment has a positive effect on perceived ease of use of mobile banking.

There is a causal relationship between perceived enjoyment and attitudes. When users feel that mobile banking is enjoyable, the stimulus of happiness in turn enhances their perception of mobile banking. Venkatesh (2000) found that perceived enjoyment indirectly influences users on adoption. Another research showed that attitudinal outcomes, such as happiness, pleasure, and satisfaction, result from the enjoyable experience (Childers et al., 2001; Moon and Kim, 2001; Van der Heijden, 2003; Yu et

perpustakaan.uns.ac.id al., 2005; in Huang et al., 2006). These findings indicate that enjoyment digilib.uns.ac.id

highly correlates with the users’ positive attitudes.

H3: Perceived enjoyment has a positive effect on attitude toward using mobile banking.

3. Perceived Ease of Use, Perceived Usefulness, Attitude, and Behavioural Intention

Perceived ease of use has been found to influence the usefulness, attitude intention, and actual use (Chau in Gardner and Amoroso, 2004). Chau study revealed that perceived ease of use significantly affected perceived usefulness, but did not significantly affect intention to use. In the context of the mobile banking, we can postulate positive relationships between perceived ease of use and two constructs, perceived usefulness of mobile banking and attitude toward using mobile banking.

H4: Perceived ease of use of the mobile banking has a positive effect on perceived usefulness of mobile banking.

H5: Perceived ease of use of the mobile banking has a positive effect on attitude toward using mobile banking.

Perceived usefulness is the degree to which an individual believes that using a particular system would enhance his or her performance. Usefulness has been confirmed to be the most important factor affecting user acceptance with few exceptions (Sun in Gardner and Amoroso, 2004). Hence, perceived usefulness of mobile banking is likely to be positively related to attitude toward using mobile banking.

H6: Perceived usefulness of mobile banking has a positive effect on attitude toward using the mobile banking.

perpustakaan.uns.ac.id In TAM, behavioural intention is influenced by both perceived digilib.uns.ac.id

usefulness and attitude. This relationship has been examined and supported by many prior studies (Adams et al., 1992; Davis et al., 1989; Hu et al., 1999; Venkatesh and Davis, 1996, 2000; in Huang et al., 2006). Therefore, this study presents the following hypotheses.

H7: Perceived usefulness of mobile banking has a positive effect on behavioural intention toward using the mobile banking.

H8: Attitude has a positive effect on behavioural intention toward using the mobile banking.

C. Conceptual Framework

According to prior research, the objective of this research is to examine and verify that the TAM can be employed to explain and predict the acceptance of mobile banking using two factors that account for individual differences, i.e. Perceived Mobile Value (PMV) and Perceived Enjoyment (PE). It will adopt prior research by Huang et al. (2006), which use extended variables of TAM.

Figure II.2 Conceptual Framework

PMV  PU 

PEO

PE

ATT BI 

H1 

H2 H3 

H4 

H5 

H6  H7

H8

Key: 

PMV = Perceived mobility value        PU = Perceived usefulness        PEOU = Perceived ease of use  ATT   = Atitude            BI  = Behavioral intention   PE = Perceived enjoyment 

perpustakaan.uns.ac.id CHAPTER III digilib.uns.ac.id

RESEARCH METHODOLOGY

A. Research Design

This research tries to explain an effect of perceived mobile value on perceived usefulness and perceived enjoyment on attitude towards using mobile banking and perceived ease of use with TAM model. It uses quantitative research method with hypothesis test. Sekaran (2000: 108) defines that hypothesis is a logically conjectured relationship between two or more variables expressed in the form of a testable statement.

B. Population and Sample

Population in this research is bank customers who use mobile banking service in Indonesia. The sample is bank customers who use mobile banking service who stay in Jakarta. Sample size has an important role for SEM interpretation result. Sample size becomes based on sampling error estimation. With estimation model using Maximum Likelihood (ML), it requires at least 100 samples. When the sample raises more than 100, the ML sensitivity will increase to detect differential among data. When sample size become large (400-500 samples), ML will be a very sensitive and will always result in significant differential so goodness of fit measurement will be poor. Ghozali (2008: 64) recommends sample size for ML estimation method is 100-200 samples.

perpustakaan.uns.ac.id C. Data Source and Data Collecting Technique digilib.uns.ac.id

This research will use primary data, which is directly obtained from the respondents, with purposive convenience sampling technique. Purposive convenience sampling is collecting information from members of the population who are conveniently available to provide it. Each respondent will be asked to give their evaluation about the statements or questions by choosing answers served with a Likert scale ranging from 1 for totally disagreeing to 4 for totally agree.

D. Measurement Items

Measurement items used in this research particularly for the core constructs (six key determinants) of the proposed research model have been adapted from the measurement items originally used in many theories. All original measurement items used in measurements of the core constructs of the theories or models including perceived mobility value, perceived enjoyment, perceived usefulness, perceived ease of use, attitude toward using, behavioral intention had statistical explanation and prediction under investigation by Gardner and Amoroso (2004), Huang et al. (2006), and Jogiyanto (2007). The measurement item can be seen at table III.1.

29

 

 

Table III.1 Research Variable

Variable Description Constructs

Type Items Source Questionnaire

PE Perceived enjoyment

Independent 3 Moon and Kim (2001);

Yi and Hwang (2003); Yu et al. (2005); Huang et al (2006)

PE1 Saya akan senang menggunakan mobile banking.

PE2 Mobile banking akan menjadi hal yang menarik.

PE3 Mobile banking akan membuat saya merasa baik.

PMV Perceived mobility

value

Independent 4 Huang et al (2006) PMV1 Saya tahu bahwa perangkat mobilitas (handphone, laptop,

dsb) adalah media untuk mobile banking.

PMV2 Saya merasa mudah mengakses mobile banking di mana saja

dan kapan saja.

PMV3 Mobile Banking memungkinkan saya melakukan transaksi

pada saat itu juga (real time data/transaction).

PMV4 Mobilitas adalah keuntungan utama dari mobile banking.

PU Perceived usefulness

Independent/ Dependent

6 Davis (1989, 1993);

Venkates and Davis (1996); Yang (2005); Huang et al. (2006); Jogiyanto (2007)

PU1 Penggunaan mobile banking dapat mempercepat penyelesaian transaksi.

PU2 Penggunaan mobile banking dapat meningkatkan kinerja saya.

PU3 Penggunaan mobile banking dapat memudahkan pekerjaan saya.

PU4 Penggunaan mobile banking dapat menghemat waktu saya.

PU5 Penggunaan mobile banking dapat meningkatkan efektivitas saya dalam bertransaksi.

PU6 Secara keseluruhan, mobile banking akan sangat bermanfaat.

30

 

 

Source: Adopted from Gardner and Amoroso (2004), Huang et al. (2006), and Jogiyanto (2007)

Variable Description Constructs

Type Items Source Questionnaire

PEOU Perceived ease of use

Independent/ dependent

4 Davis (1989, 1993);

Venkates and Davis (1996); Yang (2005);

PEOU1 Menggunakan mobile banking merupakan hal yang

mudah bagi saya.

PEOU2 Penggunaaan mobile banking jelas dan mudah dipahami.

Huang et al. (2006); PEOU3 Penggunaan mobile banking fleksibel.

Jogiyanto (2007) PEOU4 Penggunaan mobile banking tidak membutuhkan terlalu

banyak usaha berpikir.

ATT Attitude toward using

Independent/ dependent

4 Bagozzi et al. (1992);

Hu et al. (1999); Huang et al (2006); Jogiyanto, (2007)

ATT1 Menurut Saya, mobile banking sangat dibutuhkan.

ATT2 Saya mendapat hasil positif dari mobile banking..

ATT3 Saya ingin menggunakan mobile banking.

BI Behavioral intention

Dependent 5 Gardner and Amoroso

(2004):

BI1 Saya memilih menggunakan mobile banking dalam penyelesaian transaksi saya.

Huang et al. (2006); BI2 Saya berencana untuk menggunakan mobile banking

untuk penyelesaian transaksi di masa yang akan dating

Jogiyanto (2007)

BI3 Di masa depan, saya berniat untuk menggunakan mobile banking secara rutin.

BI4 Jika saya diminta untuk mengungkapkan pendapat saya, saya bermaksud untuk mengatakan sesuatu yang menguntungkan.

perpustakaan.uns.ac.id E. Data Analyze Technique and Hypotheses Test digilib.uns.ac.id

1. Data Test Technique a. Validity test

Validity is the extent to which the data collected truly reflect the phenomenon being studied. For the sake of the clarity, Sekaran (2000) can group validity test under three broad headings: content validity, criterion-related validity, and construct validity. This research use construct validity test because this approach is more objectives, simple and it use in many research.

Construct validity testifies to how well the results obtained from the use of the measure fit the theories around which the test is designed (Sekaran, 2000: 208). Any biases could also be detected if the respondents had tended to respond similarly to all items or stuck to only certain points on the scale (Sekaran, 2000: 208). To test whether latent constructs are unidimensional or indicators measurement constructs are valid. First, we must see whether indicators are statistically significant or not. Second, we must see convergent validity value or loading factor value for each indicator. Some established research use 0,70 for good validity value. While convergent validity 0,50-0,60 still acceptable for earlier research (Ghozali, 2008: 132).

b. Reliability Test

The reliability of a measure indicates the extent to which the measure is without bias (error free) and hence offers consistent

perpustakaan.uns.ac.id measurement across time and across the various items in the instrument digilib.uns.ac.id

(Sekaran, 2000: 204). According to Ticehurst and Veal (2000) in Kripanont (2007: 128), reliability is the extent to which research findings would be the same if the research were to be repeated at a later date, or with a different sample of subjects. A construct or variable is said reliable, if the Cronbach’s alphavalue is >0,70 (Ghozali, 2008: 137). According to Sekaran (2006) in Bhilawa (2010: 33), reliability less than 0.6 is considered to be poor, those in the 0.7 is acceptable, and those over 0.8 is good. The closer the reliability coefficient gets to 1.0 is the better.

c. Normality Data Assumption

SEM requires normal distribution of data. If data distributes abnormal, maybe it will influence data analysis resulting to high bias data. In this research, normality test is counted by using computerized program, AMOS 18. The postulate used in this research to examine data normality is the critical ratio (cr) value. The data distribution is normal if cr skewness value or kurtosis cr value is between -2,58 and +2,58 (Wijaya, 2009: 134). Curran et al. in Bhilawa (2010: 34) divides normality data level into three parts, they are:

• normal, if z statistic value (critical ratio or c.r.) skewness < 2 and c.r. kurtosis value is < 7,

• moderately non-normal, if c.r skewness is between 2 to 3 and c.r kurtosis value is between 7 to 21,

perpustakaan.uns.ac.id • extremely non-normal, if c.r. skewness is >3 and c.r. kurtosis is> digilib.uns.ac.id

21.

d. Outlier Evaluation

Outlier is the observation that appears with extremely values, which have a unique different characteristic from other observation, and it appears on extreme value, whether it on one variable or combination variables (Hair et al. in Bhilawa, 2010: 33). Outlier can be handled with erasing one or some data which far from the certain spot center.

Test to multivariate outliers is done using Mahalanobis Distance criteria at the level p<0,001. Mahalanobis Distance evaluated using χ2 at free degree as big as variables sum, which is used in research (Ferdinand in Bhilawa, 2010: 33). This outlier evaluation is done with computer’s software, AMOS 18.

2. Model Assumption Test

This research uses Structural Equation Modeling (SEM) multivariate analyzing to examine hypotheses using AMOS 18 software. SEM is a statistical model that provides approximate calculation of the strength of the hypothesis on the relationship between variables in a theoretical model, either directly or through intervening variables (Maruyama in Wijaya, 2009: 1). SEM refers to the relationship between endogenous variables and exogenous variables, which is the variable can not be observed or calculated directly (unobserved variables) or latent variables (Pedhazur in Wijaya, 2009: 2).

perpustakaan.uns.ac.id AMOS 18 used to examine whether the estimated model has goodness of fit digilib.uns.ac.id

and has causality relation as hypothesized. The test consists of: a. Goodness of Fit Measurement

Structural model categorized as “good fit” if it fulfills these conditions below.

1)Chi-Square (χ2) Measurement Statistic (CMIN)

This analysis is purposing to develop and examine a model which appropriate with the data. Chi's square is so sensitive to very small sample as well as to very large sample. Thus, this examination needs to complete with another examine the instrument (Ghozali, 2008: 130). CMIN shows the likelihood ratio chi-square statistic for each fitted model (tested against the saturate model). If the p value for each model is greater than 0.05, this means that the data do not depart significantly from the model.

Furthermore, if at each step up the hierarchy from the unconstrained model to the measurement residuals model, the increase in chi-square is never much larger than the increase in degrees of freedom (a non-significant chi-square, p value greater than 0.05), the model up the hierarchy is preferable otherwise, the model up the hierarchy is worse (a significant chi-square, p value less than 0.05) (Arbuckle in Kripanont, 2007: 147).

perpustakaan.uns.ac.id 2)Minimum Probability Value Level digilib.uns.ac.id

P value is the probability of getting as large a discrepancy as occurred with the present sample under appropriate distributional assumptions and assuming a correctly specified model. So P is a “p value” for testing the hypothesis that the model fits perfectly in the population. Therefore, this is a method to select the model by testing the hypothesis to eliminate any models that are inconsistent with the available data (Kripanont, 2007: 192). The minimum probability value level that needs is 0,1 or 0,2, but for probability level about 0,05 is still able. (Hair et al. in Bhilawa, 2010: 36).

3)Normed Chi-Square (CMIN/DF)

This index is chi square value divided with degree of freedom. According to Wheaton et al. (1977), ratio value ≤ 5 is a reasonable measurement. Other researchers such as Byrne (1988) suggest to this value ratio < 2 is a fit measurement (Ghozali, 2008: 67). CMIN/DF (χ2 / df) is the minimum discrepancy divided by its degrees of freedom; the ratio should be close to 1 for correct models. Although Arbuckle (2005) claimed that it is not clear how far from 1 we should let the ratio get before concluding that a model is unsatisfactory. In contrast, Byrne (2006) suggested that ratio should not exceed 3 before it cannot be accepted. Since the chi-square statistic (χ2) is sensitive to sample size it is necessary to look at others that also support goodness of fit (Kripanont, 2007: 193).

perpustakaan.uns.ac.id 4)Measures Based on the Population Discrepancy digilib.uns.ac.id

The Root Mean Square of Approximation (RMSEA) indicates expected goodness of fit if the model estimated in population. Recommended RMSEA acceptant value is ≤ 0,08 (Wijaya, 2009: 7). According to Ghozali (2008: 67), RMSEA value between 0,05 to 0,08 is acceptable.

5)Goodness of Fit Index (GFI)

GFI is a goodness- of- fit index for ML (Maximum likelihood) and ULS (Unweighted Least Squares) estimation (Kripanont, 2007: 193). GFI is used to calculate the weighted proportion of the variance in the sample covariance matrix described by the covariance matrix in estimated population (Wijaya, 2009: 8). Recommended acceptant level by GFI is ≥ 0,90 (Ghozali, 2008: 67).

6)Adjusted Goodness of Fit Index (AGFI)

AGFI is GFI development, adjusted with degree of freedom that is available to test whether the model accepted. Recommended value is > 0,90 (Ghozali, 2008: 67). Wijaya (2009: 8) also recommends AGFI value for at least equals or greater than 0,90.

7)Tucker Lewis Index (TLI)

TLI is an incremental fit index alternative that compares a tested model against a baseline model (Wijaya, 2009: 8). TLI is a index fit measure that less influenced by sample size. Recommended acceptance value by TLI is ≥ 0,90 (Ghozali, 2008: 68).

perpustakaan.uns.ac.id 8)Comparative Fit Index (CFI) digilib.uns.ac.id

CFI is also known as Bentler Comparative Index. CFI is incremental fit index which also compares the tested model with null model (Wijaya, 2009: 8). This index is quite good for measuring the goodness of fit because it is not influenced by sample size. Recommended value by CFI is ≥ 0,90 (Wijaya, 2009: 9).

9)Normed Fit Index (NFI)

NFI is a comparison measurement between proposed model and null model. NFI value is various starting from 0 (no fit at all) to 1 (perfect fit). In parallel with TLI, NFI does not have an absolute standard value, but generally it recommends for equals or more than 0,90 (Ghozali, 2008: 68).

Table III.2 Goodness of Fit Indices

Fit Indices Cut Off

Value Source

Chi-Square Approaches 0 Wijaya, 2009

Probability level ≥ 0.05 Wijaya, 2009

CMIN/DF ≤ 2 Ghozali, 2008

RMSEA < 0.05 Ghozali, 2008

GFI 0-1 Ghozali, 2008

AGFI Approaches 1 Ghozali, 2008

TLI ≥ 0.90 Ghozali, 2008

Wijaya, 2009

CFI ≥ 0.95 Bentler and Bonnet, 1995

NFI Approaches 1 Ghozali, 2008

Wijaya, 2009 Source: Wijaya (2009), Ghozali (2008), Huang et al. (2006)

perpustakaan.uns.ac.id Figure III.1 digilib.uns.ac.id

TAM with Perceived Mobility Value (PMV) and Perceived Enjoyment (PE)

3CHAPTER III

               

perpustakaan.uns.ac.id CHAPTER IV digilib.uns.ac.id

DATA ANALYSIS

This chapter will describe the data analysis and research results about mobile banking acceptance with external variables using perceived mobility value variable and perceived enjoyment variable with Technology Accepted Model (TAM). It will be divided into three parts: (1) describing about data research collection and respondents demographic descriptions, (2) data test analysis, (3) model assumption analysis, and (4) hypotheses test.

A. Data Collection Analysis

1. Total Data Collection

Data collected from 80 questionnaires were directly distributed to respondents and 110 questionnaires were distributed by email. Based on the sample criteria discussed above, this study has obtained 67 respondents by direct distribution and 65 respondents by email distribution, so 132 samples total are obtained. From table IV.1 we can see that level of returned questionnaires is 69.47% from 190 distributed questionnaires which one of them can not be processed. So, there are 131 questionnaires that can use for this research test.

perpustakaan.uns.ac.id Table IV.1 digilib.uns.ac.id

Data Research Collection

Source: Primary data processing (2011)

2. Respondents Demography a. Respondents Characteristics

From table IV.2 we can see that majority of respondents’ age range between 21-25 years old with 62 respondents (47.33%), and the second majority is between 26-30 years old with 22 respondents (16.79%). It shows that there are much more productive respondents than unproductive respondents. The minority respondents’ age is between 51-55 years old and >51-55 years old (2.29%). Researcher has the youngest respondent with 19 years old and the oldest with 83 years old.

b. Respondent Gender

Based on data collection, respondent gender characteristic describes as follows. There are 62 men respondents (47%), 62 women respondents (47%), and seven respondents did not answer it (6%).

DESCRIPTION TOTAL PERCENTAGE

Questionnaire distributed 190 100%

Questionnaires returned 132 69.47%

Questionnaire which can not be processed 1 0.76% Questionnaire which can be processed 131 99.24%

perpustakaan.uns.ac.id Table IV.2 digilib.uns.ac.id

Respondent Age

Source: Primary data processing (2011) Figure IV.1

Respondent Gender

c. Respondent Educational Background

Based on data collection, respondent educational background characteristic describes as follows. There are 81 S1 graduates as majority educational background (61.83%). Second, 30 respondents are D3

graduates (22.9%). Then 10 respondents are SLTA or equals graduates Age Range Total Percentage

≤20 9 6.87%

21-25 62 47.33%

26-30 22 16.79%

31-35 7 5.34%

36-40 12 9.16%

41-45 4 3.05%

46-50 5 3.82%

51-55 3 2.29%

>55 3 2.29%

NO ANSWERS 4 3.05%

TOTAL 131 100%

perpustakaan.uns.ac.id Table IV.3 digilib.uns.ac.id

Respondents Educational Background

d. Bank Where the Respondents Save Their Money in

From 131 respondents, there are nine different banks where the respondents save their money in. The first majority is BNI with 70 respondents (53%), and the second is BCA with 25 respondents (19%). The other banks are Mandiri, BRI, Danamon, CIMB Niaga, Muamalat and Bukopin, as we can see on the figure IV.2 below.

Figure IV.2

Bank Where the Respondents Save Their Money in

Education Total Percentage

SLTA/equals 10 7.63%

D3 30 22.90%

S1 81 61.83%

Masters Degree (S2) 4 3.05%

NO ANSWER 6 4.58%

Total 131 100%

Source: Primary data processing (2011)

perpustakaan.uns.ac.id B. Data Test Analysis digilib.uns.ac.id

1. Normality Test

Normality data can be evaluated with skewness critical ratio value criteria at -2.58<c.r<2.58 at 0.01 significant level. Data conclude has a normal distribution if skewness critical ratio value is between the absolute value of ±2.58. The result of normality data is seen as table IV.4. From the skewness critical ratio value is seen that all indicators have normal distribution except for PU5 and PMV1. Analysis to non-normal distributed data can bias the interpretation because the result of Chi-square value analysis the lean increases so probability level decreases.

Data that used in this study represents as the real condition which is obtained from primary data based on the very various answers of the respondents so it was difficult to get perfect normal data distribution. According to Hair et al. in Bhilawa (2010: 52) large sample size leans decreasing the analysis distortion from non-normality data that will be analyzed. Further more, Maximum Likelihood Estimates (MLE) used in this study is not too robust to non-normal data, so we can still do the next analysis.

perpustakaan.uns.ac.id Table IV.4 digilib.uns.ac.id

Normality Test

Variable Min Max Skew c.r. kurtosis c.r.

BI1 2.000 4.000 -.049 -.227 .207 .479

BI2 2.000 4.000 .226 1.048 1.224 2.838

BI3 2.000 4.000 .014 .063 -.130 -.302

BI4 2.000 4.000 .121 .560 1.428 3.311

PE1 2.000 4.000 .215 .999 -.412 -.956

PE2 2.000 4.000 .448 2.076 .257 .597

PE3 2.000 4.000 .038 .175 .213 .495

ATT1 2.000 4.000 -.072 -.334 -.393 -.912

ATT2 2.000 4.000 .156 .722 .620 1.438

ATT3 2.000 4.000 .111 .513 .244 .567

PEOU1 2.000 4.000 .023 .105 .304 .706

PEOU2 2.000 4.000 -.023 -.105 .304 .706

PEOU3 2.000 4.000 .054 .249 1.604 3.718

PEOU4 2.000 4.000 -.164 -.761 .393 .911

PU6 2.000 4.000 -.107 -.496 -.695 -1.610

PU5 2.000 4.000 .573 2.659 -1.134 -2.630

PU4 2.000 4.000 .215 .997 -1.513 -3.507

PU3 2.000 4.000 .029 .134 .057 .133

PU2 2.000 4.000 .002 .009 .071 .165

PU1 2.000 4.000 .256 1.189 -.830 -1.924

PMV4 2.000 4.000 .505 2.343 -1.232 -2.856

PMV3 2.000 4.000 -.031 -.144 -.888 -2.058

PMV2 2.000 4.000 -.081 -.374 -.525 -1.218

PMV1 3.000 4.000 .564 2.614 -1.682 -3.900

Multivariate 72.966 11.729

2. Outlier evaluation

Outlier is an observation condition from data, which has unique characteristic that looks so different from other observations and exists in extreme value, whether it for a single variable or combination variables. Detection to multivariate outliers tested with Mahalanobis distance. Criteria Source: Primary data processing (2011)

perpustakaan.uns.ac.id that use are based on Chi-squares at degree of freedom 24 is total variables at digilib.uns.ac.id

a significant level p<0.001. The value of Mahalanobis distance is 51,17, result from Chiinv formulas (0,001, 24), where 24 is total indicator variables at significancy level p<0,001. It means all cases that have Mahalanobis distance value higher from 51,17 is outliers multivariate.

Table IV.5 Outliers Data

From table IV.5, there are two cases (observation number 93 and 44) categorized as the outlier, but those cases no need to take out. That because in research analysis if there is no specific reason to take out the outlier case so that case should be taking on the research (Ferdinand, 2006 in Bhilawa, 2010: 55)

3. Validity Test

Valid instrument is measurement tools used to get valid data and use to measure what things would be measured (Sugiyono in Bhilawa 2010: 47). Because of the construct that will be tested is adapted from the prior research which has success to identify factors that build the construct so this research will use Confirmatory Factor Analysis (Ghozali, 2008: 121).

Value of a factor loading on a standardized estimates model is defined as construct the validity test. General standard for factor analysis is lambda

Observation number

Mahalanobis d-squared

p1 p2

93 53.515 0.000 0.063

44 52.594 0.001 0.003

perpustakaan.uns.ac.id value or factor loading value is more than 0.4 (Ferdinand in Bhilawa, 2010: digilib.uns.ac.id

47). The result of a factor loading calculation can be seen at table IV.6 below. As we can see that all constructs are valid so the study can be continued to the next test analysis.

Table IV.6 Validity Test

Variable Item Factor

Loading Validity Perceived mobility Value (PMV) PMV1 0.536 Valid

PMV3 0.651 Valid

PMV2 0.597 Valid

PMV4 0.68 Valid

Perceived Usefulness (PU) PU1 0.612 Valid

PU2 0.637 Valid

PU3 0.686 Valid

PU4 0.649 Valid

PU5 0.639 Valid

PU6 0.536 Valid

Perceived Easy of Use (PEOU) PEOU4 0.576 Valid

PEOU3 0.764 Valid

PEOU2 0.862 Valid

PEOU1 0.768 Valid

Attitude (ATT) ATT3 0.796 Valid

ATT2 0.817 Valid

ATT1 0.74 Valid

Perceived Enjoyment (PE) PE3 0.668 Valid

PE2 0.805 Valid

PE1 0.85 Valid

Behavioural Intention (BI) BI4 0.558 Valid

BI3 0.854 Valid

BI2 0.683 Valid

BI1 0.705 Valid

perpustakaan.uns.ac.id 4. Reliability Test digilib.uns.ac.id

Reliability test in this study is purposed to know how far the measurement result still consistent whether it measuring twice or more with the same symptom and measurement tools. A researcher measured the reliability using Cronbach’s Alpha value from each variable item. A construct variable is reliable if it has Cronbach’s Alpha Value >0.60 (Nunnaly in Ghozali, 2008). Reliability test did to each questionnaire item which has passed the validity test. From the calculation using SPSS version 19, the result is as seen as table IV.7.

From table IV.7 above, it concludes that Perceived Enjoyment (PE), Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Using (ATT), Behavioral Intention (BI) has good reliability because the value is above 0.8. Not same for the Perceived Mobility Value, it has Cronbach’s Alpha value between 0.60-0.79 (0.707) so the reliability is accepted.

Table IV.7 Reliability Test

Variable Item Cronbach's

Alpha Reliability

PE PE1 - PE 3 0.814 Good Reliability

PMV PMV1-PMV4 0.707 Accepted Reliability

PU PU1-PU6 0.809 Good Reliability

PEOU PEOU1-PEOU4 0.824 Good Reliability

ATT ATT1-ATT3 0.841 Good Reliability

BI BI1-BI4 0.813 Good Reliability

Key: 

PMV = Perceived mobility value        PU = Perceived usefulness        PEOU = Perceived ease of use  ATT   = Attitude            BI  = Behavioral intention   PE = Perceived enjoyment 

perpustakaan.uns.ac.id C. Model Assumption Test digilib.uns.ac.id

1. Goodness of Fit Analysis

Analyzed model is a recursive model (no relation reciprocal regression between the latent variables) with 131 samples. Chi-square value is 432,247 with degree of freedom 244 and probability 0. The chi-square result shows that zero hypotheses explaining the model equals empirical data is declined, means that the models did not fit (Ghozali, 2008: 130). Good model should not decline zero hypotheses; it means should not be significant on a statistic. However, it is important that chi-square is very sensitive to samples total. Larger the sample, it will be more significant. For that reason, chi-squares value in this study will be neglected, and it will use another measurement for goodness of fit models.

Table IV.8

Goodness of Fit Model Before Modified

Fit Indices Cut Off Value

Value Before Modified

Model Evaluation

Chi-Square Approaches 0 432,247 Marginal Probability level ≥ 0.05 0,000 Marginal

CMIN/DF ≤ 2 1,772 Good

RMSEA < 0.05 0,077 Marginal

GFI 0-1 0,789 Marginal

AGFI Approaches 1 0,741 Marginal

TLI ≥ 0.90 0,849 Marginal

CFI ≥ 0.95 0,867 Marginal

NFI Approaches 1 0,744 Marginal

perpustakaan.uns.ac.id 4. H4: Perceived ease of use of the mobile banking has positive effect on digilib.uns.ac.id perceived usefulness of mobile banking

The objective of hypothesis 4 is to identify whether Perceived Ease of Use (PEOU) affects Perceived Usefulness (PU) of mobile banking or not. With probability value 0,003, less than 0,05, this hypothesis is accepted. PEOU 35,9% affects PU and 64,1% affected by another variables. It supports the result of Chau (1996), Gardner and Amoroso (2004), and also Huang et al. (2006).

5. H5: Perceived ease of use of the mobile banking has positive effect on attitude toward using mobile banking.

The objective of hypothesis 5 is to identify whether Perceived Ease of Use (PEOU) affects attitude toward using mobile banking (ATT) of mobile banking or not. PEOU significantly affects ATT at probability level 0,03, less than 0,05, so H5 is accepted. PEOU 23,5% affects ATT and 76,5% affected by another variables. It supports Chau (1996), Malholtra and Galletta (1999), and also Huang et al.(2006) research.

6. H6: Perceived usefulness of mobile banking has a positive effect on attitude toward using mobile banking.

The objective of hypothesis 5 is to identify whether Perceived Usefulness (PU) affects attitude towards using mobile banking (ATT) or not. Perceived usefulness (PU) significantly affects ATT with probability level is less than 0,05, H6 is acceptable. PU 49,4% affects ATT and 50,6% affected

perpustakaan.uns.ac.id by another variables. It supports Malholtra and Galletta (1999) and Huang et digilib.uns.ac.id al.(2006) research.

7. H7: Perceived usefulness of mobile banking has a positive effect on Behavioural intention toward using mobile banking.

The objective of hypothesis 7 is to identify whether Perceived Usefulness (PU) affects Behavioural Intention towards using mobile banking (BI) or not. PU significantly affects BI at probability level 0,003, less than 0,05, so H7 is accepted. BI affected by PU at 46% and 54% affected by another variable. It also supports Maholtra and Galletta (1999) research and Huang et al. (2006) research.

8. H8: Attitude has a positive effect on behavioural intention toward using the mobile banking.

The objective of hypothesis 8 is to identify whether attitude toward using mobile banking (ATT) affects Behavioural Intention towards using mobile banking (BI) or not. Attitude significantly affects behavioural intention toward using the mobile banking at probability level 0,009, less than 0,05, H8 is accepted. ATT affects BI at 36,9% and 63, 1% affected by another variable. It also supports Maholtra and Galletta (1999) and Huang et al. (2006) research.

perpustakaan.uns.ac.id Figure IV.4 digilib.uns.ac.id Coefficient Path of TAM

                                      Key:

PMV = Perceived mobility value PU = Perceived usefulness PEOU = Perceived ease of use ATT = Attitude BI = Behavioral intention PE = Perceived enjoyment

PMV  PU

PEOU

PE

ATT BI 

H2 0,504  ***  H3 0,369  *** H4 0,359 

0,003  _0,235H5  

0,03  H6 0,494  ***  H7 0,46  0,003  H8 0,369  0,009  Note:

The figure shows the regression weight and probability level. 

 

Source: Primary data processing (2011)

H1 

0,582 

perpustakaan.uns.ac.id CHAPTER V digilib.uns.ac.id CONCLUSION

A. Conclusions

As mentioned in the previous chapters, the objective of this research is to test and verify that the TAM can be employed to explain and predict the acceptance of mobile banking using two factors that account for individual differences, i.e. Perceived Mobile Value (PMV) and Perceived Enjoyment (PE). This research uses 131 respondents sample which is gotten from primary data. From the analysis that we have done, we reach several conclusions.

First, TAM is one of the most accepted theories for explaining the acceptance of technologies. In this research, TAM can be employed to explain and predict the acceptance of mobile banking. Perceived Ease of Use (PEOU) affects significantly to Perceived Usefulness (PU). However, PU affects individual attitudes more than PEOU does and Perceived Enjoyment (PE) also affects individual attitude more than PEOU does. PU affects Behavioural Intention (BI) more than ATT does.

Second, this study shows the effect of Perceived Mobility Value (PMV) to an individual’s acceptance of mobile banking. The most significant feature of mobile technology is mobility which enables customer to do their transaction at anytime and anywhere. So the advantages of mobility are crucial to users.

Third, the fact that enjoyable is quite significant to attract users. When customers enjoy positive experience of mobile banking usage and find that

perpustakaan.uns.ac.id enjoyable may free from complexity, they will use it. Fourth, this study uses the digilib.uns.ac.id same model which is conducted by Huang et al. to explain and predict technology acceptance by user. The affective level of those two extended variables, i.e. perceived mobility value and perceived enjoyment, for mobile banking acceptance is quite high and it supports Huang et al (2006). research.

B. Research Constraints

This study has several limitations that need to be considered for the further research. Some constraints in this research are mentioned as follow.

1. A researcher did not do the pilot survey. Pilot survey is conducted to detect weaknesses in design and instrumentation and to provide proxy data for selection. Any biases could also be detected if the respondents had tended to respond similarly to all items or stuck to only certain points on the scale. 2. This study has a number of sample 131 respondents which are few and

limit for mobile banking user in Jakarta.

3. The research scope is only in the Jakarta region, so it less represent all over perception.

4. Inherent limitation on this survey method is researcher cannot control the respondents’ answers, whether respondents were not honest in answering the question asked or were not completed the questionnaire.

5. Actual usage frequency did not measure in this study, so it cannot be defined perceived usage as the amount of time interacting with the mobile banking and the frequency of use.

perpustakaan.uns.ac.id C. Research Suggestions digilib.uns.ac.id The following is suggestions to future research development.

1. Recommending this instrument for future research is the needs to do the pilot survey to decrease the probability of invalid indicators so it will reflect the clearer picture about the real condition.

2. Study scope for next research must be larger so the level of population generalization will be wider.

3. Interview method may be recommended to obtaine non-bias data.

4. For future research, it may include the mobile banking actual usage variable so it can define perceived usage as the amount of time interacting with the mobile banking and the frequency of use.

5. Replacing or even adding more variables to specified respondent answers. 6. This study gives the opportunity for further research to investigate the

others variables (e.g. self-efficacy, compatibility, voluntariness, etc.) which can not be observed by researcher.