Malaysia-Indonesia International Conference on Economics, Management and Accounting (MIICEMA) 2015
Proceedings
THE 16TH MALAYSIA-INDONESIA INTERNATIONAL CONFERENCE ON ECONOMICS, MANAGEMENT, AND ACCOUNTING and DEAN ANNUAL MEETING OF WESTERN DIVISION OF ECONOMICS INSTITUTIONS COOPERATION
ISBN 978-602-73765-0-2
VOLUME 1
Conference Venue: NEO Palma Hotel, Palangka Raya, 16 – 18 December 2015
Conference Organizer: Faculty of Economics, Universitas Palangka Raya, Central Kalimantan, Indonesia
© 2015 Universitas Palangka Raya, Indonesia
0 | Universitas Palangka Raya, Kalimantan Tengah Indonesia
Malaysia-Indonesia International Conference on Economics, Management and Accounting (MIICEMA) 2015
Sambutan Dekan Fakultas Ekonomi Universitas Palangka Raya
Assalamu’alaikum Warahmatullahi Wabarakaatuh… Puji Syukur kita panjatkan ke hadirat Tuhan YME, berkat Rahmat dan Karunia-Nya, Fakultas Ekonomi Universitas Palangka Raya (FE UPR) dipercaya untuk menyelenggarakan Seminar dan Rapat Tahunan (Semirata) Dekan Badan Kerja Sama Wilayah Barat Bidang Ilmu Ekonomi dan The 16th Malaysia Indonesia International Conference On Economics, Management, And Accounting di Bumi Pancasila, Bumi Tambun Bungai, Provinsi Kalimantan Tengah. Seminar dan Rapat Tahunan (Semirata) Dekan Badan Kerja Sama Wilayah Barat Bidang Ilmu Ekonomi dan The 16th Malaysia Indonesia International Conference On Economics, Management, And Accounting yang diikuti oleh Dekan-dekan Fakultas Ekonomi dari berbagai Universitas di Indonesia Wilayah Bagian Barat, akademisi, praktisi dan berbagai undangan khusus dari berbagai institusi pemerintah maupun swasta. Pelaksanaan Kegiatan ini dilaksanakan pada tanggal 16-18 Desember 2015. Selain penguatan keorganisasian, penyelenggaraan Seminar dan Rapat Tahunan (Semirata) Dekan Badan Kerja Sama Wilayah Barat Bidang Ilmu Ekonomi dan The 16th Malaysia Indonesia International Conference On Economics, Management, And Accounting juga akan membahas isu-isu terkini dalam kaitan pembangunan ekonomi Indonesia dengan menghadirkan tokoh- tokoh nasional dan internasional. Kegiatan ini direncanakan juga akan dihadiri oleh Menteri Ristek dan Dikti Prof.H.Mohamad Nasir,Ph.D. Kami berharap, kegiatan Seminar dan Rapat Tahunan (Semirata) Dekan Badan Kerja Sama Wilayah Barat Bidang Ilmu Ekonomi dan The 16th Malaysia Indonesia International Conference On Economics, Management, And Accounting dapat berjalan dengan lancar dan dapat diikuti dengan baik oleh seluruh peserta. Terima kasih kami ucapkan kepada pihak-pihak yang telah membantu terselenggaranya kegiatan ini.
Palangka Raya, Desember 2015 Dekan FE UPR,
Dr. Gundik Gohong, MS
1 | Universitas Palangka Raya, Kalimantan Tengah Indonesia
Malaysia-Indonesia International Conference on Economics, Management and Accounting (MIICEMA) 2015
Sambutan Rektor Universitas Palangka Raya
Assalamu’alaikum Warahmatullahi Wabarakaatuh Puji dan syukur kami panjatkan kepada Tuhan YME atas Rahmat dan Karunia-Nya kita bisa datang dan Seminar dan Rapat Tahunan (Semirata) Dekan Badan Kerja Sama Wilayah Barat Bidang Ilmu Ekonomi dan The 16th Malaysia Indonesia International Conference On Economics, Management, And Accounting di kota Palangka Raya. Segenap civitas akademika Universitas Palangka Raya (UPR) mengucapkan selamat datang kepada seluruh peserta. Seminar dan Rapat Tahunan (Semirata) Dekan Badan Kerja Sama Wilayah Barat Bidang Ilmu Ekonomi dan The 16th Malaysia Indonesia International Conference On Economics, Management, And Accounting sangat penting bagi masing-masing anggota asosiasi karena di forum ini, kita berdiskusi dan bertukar pikiran mengenai peningkatan mutu pendidikan di masing-masing universitas. Membangun kerjasama yang lebih intensif baik dari dalam hal penyusunan kurikulum, penguatan Prodi/Jurusan, meningkatkan nilai akreditasi Universitas, kerjasama penulisan seperti Jurnal dan working paper serta kerjasama di bidang- bidang yang lain. Selain pengayaan secara akademis, penyelenggaraan kegiatann ini juga akan berdiskusi mengenai isu-isu ekonomi nasional terutama berkenaan dengan penguatan industri di Indonesia dengan menghadirkan tokoh-tokoh nasional dan internasional. Terima Kasih kami ucapkan kepada seluruh anggota asosiasi telah mempercayakan Universitas Palangka Raya sebagai tuan rumah penyelenggaraan Seminar dan Rapat Tahunan (Semirata) Dekan Badan Kerja Sama Wilayah Barat Bidang Ilmu Ekonomi dan The 16th Malaysia Indonesia International Conference On Economics, Management, And Accounting dan kami berharap kegiatan ini dapar berjalan lancar hingga selesainya kegiatan. Semoga para delegasi dan undangan merasa senang dan nyaman selama mengikuti Kegiatan ini. Terima kasih kami ucapkan kepada seluruh pihak-pihak yang telah membantu dan mensukseskan kegiatan ini. Palangka Raya, Desember 2015 Rektor UPR,
Prof. Dr. Ferdinand, MS
2 | Universitas Palangka Raya, Kalimantan Tengah Indonesia
Malaysia-Indonesia International Conference on Economics, Management and Accounting (MIICEMA) 2015
TABLE OF CONTENTS
Title/Authors Page
A Review of Performance Management using the Balanced Scorecard in Public Sector Amanah Pasaribu, M. Gilang Dwi Andika, Reza Rachmanda, Dermawan Wibisono ...... 8
A Study On Linkages Among Balanced Scorecard Perspectives: The Case Of Indonesian Local Bank Rillyan N R, Ganda Satria, Andi M Raihan R, Dermawan Wibisono ...... 9
Index Satisfaction Of Public Service Quality BPS In The Province Of Central Kalimantan Lyra Asaria, Helen Lusiana, Elmalia Tara, Gundik Gohong, Renhart Jemi, Prima, Eldy Indra Purnawan ...... 10
Readiness Of Small Medium Enterprises In West Sumatera Faces ASEAN Economic Community Erni Masdupi, Rahmiati, and Firman ...... 11
The Analysis of Determinants Tourist Demand Through Sustainable Ecotourism Development in Central Kalimantan Province Irawan and Marhot H. Siregar ...... 12
Effect of the Work Plan, Working Targets and Work behavior through Work Ethic employee at University of Palangkaraya Lilyansi Sarah Duling, Usup Riassy Christa, Roby Sambung ...... 13
Proposed Performance Management System For The Construction Unit In An Indonesian Electric Company Evanny Anatassia Asfinal A, Frans Risky Julifer Purba B, Denny Nugrahadi C, Dermawan Wibisono D...... 14
The Effect of Usefulness, Ease of Use, risk and Trust Toward Behavior Intention in Using Internet Banking Robino Indan, Haris Satria ...... 15
Analysis of Integrated Performance Measurement System in PT. X Yuki Gradiannisa, Ni Made Yunita S. , Martua F. Purba, Dermawan Wibisono ...... 16
The Influence Of User Satisfaction On E-Learning Management System Usage Rahmiati ...... 17
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Malaysia-Indonesia International Conference on Economics, Management and Accounting (MIICEMA) 2015
Determinant Factors Of Indonesian Manufacturing Listed Companies Dividend Policy Saiful And Gita Lidya ...... 24
The Role Of Intellectual Capital In The Implementation Of Corporate Governance Hiras Pasaribu, Wisnu Yatimantoro, Swanto Sirait ...... 30
Factors Affecting Budgetray Slack On The Financial Firms In Bengkulu City Isma Coryanata...... 39
Identifying The Influence Of Job Characteristics And Job Satisfaction On Organizational Citizenship Behavior Rini Sarianti; Rahmiati; Vidyarini Dwita ...... 49
Corporate Governance Mechanism And Firm Performance Husaini Riska Puspita Sari ...... 54
Corporate Ownership, Corporate Characteristics And Tax Aggresiveness In The Indonesian Manufacturing Company Listyo Cahyo Purnomo & Payamta...... 55
Determinant Factors Of Indonesian Manufacturing Listed Companies Dividend Policy Saiful, Gita Lidya ...... 56
E-Commerce On The Actors Of Business Micro Small Medium In Order To Improve Competitiveness Of SME Whyosi Septrizola, Se, Mm., Yunita Engriani, Se, Mm., Mike Triani, Se, Mm ...... 57
Effect Of Leadership, Organizational Culture And Motivation Of Work On The Performance Of Employees In The Department Of Energy And Mineral Resources West Sumatra Rino, Nur Aulia Rafika ...... 67
Finding Key Performance Indicators At Armament Industry In Indonesia Brian Lee, Rizki Lingga, Teja Pramesya Aransid...... 84
Readiness Of Small Medium Enterprises In West Sumatera Faces Asean Economic Community Erni Masdupi, Rahmiati, and Firman ...... 93
Going Global Strategy Of Leather Product Start-Up Company In Indonesia (Case : Moosh Leather Indonesia)
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Malaysia-Indonesia International Conference on Economics, Management and Accounting (MIICEMA) 2015
Ita Purwita Sundari, ST ...... 106
Corporate Governance Mechanism And Firm Performance Usaini, Saiful, Riska Puspita Sari ...... 107
Islamic Corporate Social Responsibility Disclosure And Firm Performance Of Shari’ah Compliant Companies Noorain Omar, Roshima Said, Nurul Fatihah Ilias ...... 114
Kuznet Curve Analysis For The Environment And Economy Of Indonesia Shanty Oktavilia, Firmansyah, Fx Sugiyanto ...... 115
Participatory Budgeting, Government Internal Control Systems And Implementation Of Government Accounting Standards To Achievement Of Good Government Governance Principles Nila Aprila, Se., M.Si., Ak., Ca, Fenny Marietza,Se.M.Ak, Baihaqi,Se.M.Si.Ak.Ca and Tasyah Ririzkita Reansyah H ...... 116
Relationship Between Entrepreneurial Orientation, Achievement Orientation And Business Performance Among Smes In Malaysia: A Pls Analysis Syed Shah Alam, Md Daud Ismail, Mst. Nilufar Ahsan, Mohammad Masukujjaman ...... 136 Factor Affecting Young Adults’ Intention To Involve Social Entrepreneurship In Malaysia Md Daud Ismail, Syed Shah Alam ...... 156
Fair Value Accounting And The Cost Of Equity Capital Of Asian Banks Ashwaq Dignah, Radziah Abdul Latiff, Zulkefly Abdul Karim, Aisyah Abdul Rahman ...... 168
Finance And Accounting Shared Services And Outsourcing Firms In Malaysia: Growth And Challenges Aini Aman ...... 185
Student Mobility And Knowledge Transfer: A Case In A Renowned Public University Mohd Dzul Azzwan, Ameera Ellyana Azhar ...... 199
Unit Trust Performance: Stock Selection Versus Market Timing Abilities Ruzita Abdul-Rahim, Rafidah Othman ...... 211
Influence Of Service Quality On Customers’ Perceived Value: An Empirical Study Azman Ismail, Ilyani Ranlan Rose, Nurul Afiqah Foboy ...... 227
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Malaysia-Indonesia International Conference on Economics, Management and Accounting (MIICEMA) 2015
Kecekapan Dan Risiko Kecairan Instisusi Perbankan Di Malaysia Muniroh Khalib, Aisyah Abdul-Rahman, Hawati Janor ...... 239
Brain Gain Vs. Brain Drain: Rebalancing Human Capital Mobility From The Macro And Micro Perspectives Nor Liza Abdullah, Rosmah Mat Isa, Rasidah Arshad, Noradiva Hamzah, Noor Azuan Hashim, Hazrul Izuan Shahiri, Liew Chei Siang ...... 261
Mindfulness, Enterprise Systems Use And Work-Life Balance Yusasniza Mohd Yunus, Mohd Zaher Mohd Zain, Aini Aman ...... 275
Export Strategy For Smes: Active Or Reactive Move? Abu H Ayob ...... 299
How Does Profit-Loss Sharing Affect The Soundness Of Bank During Crises? Mariani Abdul-Majid, Alireza Tamadonejad ...... 306
Persepsi Masyarakat Terhadap Pemilikan Mikroinsurans (Perception Towards Microinsurance) Hendon Redzuan, Rubayah Yakob, Nurul Ain Abdul Hameed, Hawati Janor ...... 320
Impact Of Macroeconomic Policy Variables And Economic Freedom On Malaysia GLC’s Stock Returns: An Analysis Of Augmented Fama-French Three Factor Model Zulkefly Abdul Karim, Mohd Azlan Shah Zaidi, Wan Noor Elani Binti Wan Azizi ...... 338
Analisis Mengenai Hipotesis Keluk Alam Sekitar Kuznets di Negara OIC Mohd Adib Ismaila, Hainnur Aqma Rahimb ...... 351
Testing For Efficient Market Hypothesis Using Trading Rules For Developing Market Mehrdad, Hawati Janor, Hafizi Abdul Majid...... 366
Oil Fraction Of Pome For Commercial Use: Perceived By Industry and End User Retno Agustina Ekaputri, Budiyanto ...... 384
Implications Of Social Entrepreneurship Training Business Model, Based On Potential And Local Wisdom For Strengthening The Economy In West Sumatera Armida. S Dan Rahmat Richard ...... 385
How Strategies Of A Business Start-Upmade Leather Can Compete
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Malaysia-Indonesia International Conference on Economics, Management and Accounting (MIICEMA) 2015
In A Creative Industries By Using Swot Method & Balanced Scorecard Analysis Ita Purwita Sundari, Darmawan Wibisono ...... 388
Participatory Budgeting, Government Internal Control Systems And Implementation Of Government Accounting Standards To Achievement Of Good Government Governance Principles Nila Aprila, Se., M.Si., Ak., Ca, Fenny Marietza,Se.M.Ak, Madani Hatta,Se.M.Si.Ak.Ca, Tasyah Ririzkita Reansyah Harahap ...... 398
Analisis Faktor-Faktor yang Mempengaruhi Kebijakan Penyaluran Kredit Perbankan Di Indonesia Robby Dharma, Lusiana, Rio Andika Putra...... 399
Strategy Map Formulation For Designing Strategic Plan In Indonesian Transportation Organization Fina Hafnika A, Okki Hamdani B, Roytama Januar Simbolon C, Dermawan Wibisono D ...... 401
The Impact of Manufacturing Industry Efficiency on the Indonesian Economic and Welfare Firmansyah, Wahyu Widodo ...... 407
The Intention Analysis of the Utilization and Use of Credit Analysis Software : Unified Theory Of Acceptance And Use Of Technology (UTAUT) Approach MadaniHatta SE.M.Si.,Ak, Fenny Marietza,SE.,M.Ak, Nila Aprila,SE., M.Si.Ak.CA, Bayu Gumilang Saputra ...... 408
True Nature of Supply Network Communication Structure: A Network Analysis Comparative Study Lokhman Hakim Osman ...... 409
Valuation Of Young Technology Company With Negative Earnings: Study Case of Tesla Motors, Inc Pasthika Yoga Parmana, Subiakto Sukarno ...... 425
Analysis of Organizational Performance Measurement Zakat in Jember Nur Hisamuddin, Reza Alvionita ...... 427
7 | Universitas Palangka Raya, Kalimantan Tengah Indonesia
Malaysia-Indonesia International Conference on Economics, Management and Accounting (MIICEMA) 2015
The Intention Analysis of the Utilization and Use of Credit Analysis Software : Unified Theory Of Acceptance And Use Of Technology (UTAUT) Approach
By MadaniHatta SE.M.Si.,Ak, Fenny Marietza,SE.,M.Ak, Nila Aprila,SE.,M.Si.Ak.CA, BayuGumilangSaputra University of Bengkulu
ABSTRACT
This study aims to test the intention analysis of the utilization and use of credit analysis software : Unified Theory Of Acceptance And Use Of Technology (UTAUT) Approach. There are 80 samples used in this study. They are employees from analyst credit department. The independent variable of this study is performance expectation, effort expectation, and social factors, while the dependent variable is the interest of utilizing and using SI. This study uses multiple regression analysis with SPSS version 16.0. As the result, from the first hypothesis test, it shows that performance expectation provides a positive influence to the intention of credit analysis software utilization. Whilstthe second hypothesis test shows that effort expectation gives a good influence towards the intention of credit analysis software utilization as well. As the third hypothesis test shows that the social factors influence the utilization of credit analysis software in a positive way. In the fourth hypothesis test, it shows that the facilitating condition positively influences the intention of utilizing credit analysis software. Equally in the fifth hypothesis test whichthe intention of utilization gives a good impact to the intention of utilizing credit analysis software.
Key words : Students. Supervisor
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1. INTRODUCTION 1.1 Background Banking is an industry that provide financial service wich is have the main activity as intermediary institutions that raise fund from the society in the form of deposits and distribute that with the society in the form of loans. As a service company, the quick quality of service and the quality is very needed to achieves customer satisfications that will increase the profit for the banks. Therefore, each bank has realized how important technology informationt to support information system at the company. Credit is a main of income for each bank through interest income and other income that related to the provision of credit, and also as the largest operating business risk that mainly related to arrears repayment of the debtor. Therefore, in granting this credit, bank have to following the systems and procedures that has been determined by the bank. The important procedure while distributing credit is the phase of credit analysis. The decisions for giving the credit to the prospective borrowers must be based on proper analysis because of the bank must consider the risk that will be faced if it does not perform analysis credit correctly. Therefore, technological support like software is needed to helps the task of the credit analyst who agreed to grant the credit, so the decisions that has taken may be more appropriate. The software that used by the analyst typically includes the fiture that relating with analysis data from prospective borrowers, especially financial data, features to process credit documents and features relating to the collectibility credit. This software usually connected with the internet because there are few data such us Curriculum Vitae, collateral, Business financial statements that should be sent to the central office and for delivery and demand associated data with Bank Indonesia. One of the theories explaining the acceptance model and use of technology is a model that developed by Venkatesh et al.,(2003) is an Unified Theory of Acceptance and Use of Technology (UTAUT) model. UTAUT is a newest acceptance model which combined multiple prior acceptance model that has a weakness, such us Technologi Acceptance Models (TAM) developed by Davis (1989). TAM has a few weakness in reserach of Jogiyanto (2007) as TAM just providing simply informations or general result about interest and behavior of system users on the receiving technology information. TAM only explained beliefs of why user intends to use the system behavior is believed that the system will be used handy and easy to be use, but TAM has not provided yet the information and explained why system users has that beliefs. Prior research have shown that the technological features changed users behavior to the thecnology (Harrison dan Datta, 2007). However, there is still little research on technology in the form of software using an Unified Theory of Acceptance and Use of Technology (UTAUT) model. Venkatesh et al. (2003) explainded that the expectations of an individual performance in use the techmology information if that technology system can help to improve their performances. While the expectations of business performance is an ease level of use of IT system. The social factor is the influence of the surrounding environment is convincing individual to use IT system.
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Malaysia-Indonesia International Conference on Economics, Management and Accounting (MIICEMA) 2015
Research of Thompson (1991) states that there are six factors that influence the use of information system, such us social, affect, complexity, suitability of duty, and long-term consequences and conditions that facilitate the user. Handayani (2005) has conducted a research on the factors that affect the interests of utilization and use of information systems in manufacturing firms in BEI. These results indicate that the performance expectations, expectations of business and social factors positively affects the interest of information system to the use of information system. The conditions that facilitate affects positively with the use of information sysetem. While, interest in the use of infomation sytem does not affects with the use information system. In this study, researchers will test the intentions of credit analysis software utilization on banking company in Bengkulu City. Information system that reffered on this research has been supported by the use of information technology in the form of software credit analysis which is mandatory in assisting the employees credit analyst that responsible for analyzing financial data and non- financial prospective borrowers. 1.2 Problems Based on the background above, it can be formulation of the problems on these research, these are : 1) Wether there is influence of performance expectations to intentions of credit analysis software utilization by employees of the credit analyst at the banking company in the Bengkulu City? 2) Wether there is influence of effort expectation to intentions of credit analysis software utilization by employees of the credit analyst at the banking company in Bengkulu City? 3) Wether there is influence of social factor to intentions of credit analysis software utilization by employees of the credit analyst at the banking company in Bengkulu City? 4) Wether there is influence of conditions that facilitate for the use of credit analysis software by employees of the credit analyst at the banking company in Bengkulu City? 5) Wether there is influence of intentions of credit analysis software utilization to the use of credit analysis software by employees of the credit analyst at the banking company in Bengkulu City?
2. LITERATURE REVIEW 2.1 The Unified Theory of Acceptance and Use of Technology (UTAUT) The Unified Theory of Acceptance and Use of Technology (UTAUT) is a newest technology acceptance model developed by Vankatesh, et al. (2003). They use theories that already exists to develop a new integrated development model. The motivation of Venkatesh et al. (2003) makes a model which built by the weakness of theories that already exists. UTAUT combined the features that has been success from eighth acceptance theory become the first theory. The eighth leading unified theory in UTAUT (Jogiyanto, 2007) are: 1) Theory of Reasoned Action (TRA), 2) Technologi Acceptance Model (TAM), 3) Motivational Model (MM) 4) Theory of Planned Behaviour (TPB)
410 | Universitas Palangka Raya, Kalimantan Tengah Indonesia
Malaysia-Indonesia International Conference on Economics, Management and Accounting (MIICEMA) 2015
5) Combinations of TAM and TPB model 6) Model of PC Utilization (MPCU) 7) Innovation diffusion theory (IDT) 8) Social Cognitive Theory (SCT). From these eight model, Vankatesh found five main construct that has an important role as direct determinant, namely: 1) Peformance expectations is where the levels to which individuals believe that the use of the system will help to achieve gains in performance. 2) Effort expectation is associated with a level of ease of use of a system. 3) Social influence is defined as the extent to which an individual prepares interests that are trusted by others that affect the use of the new system. 4) The conditions that facilitate defined as the extent to which a person believes that the organizational and technical infrastructure available to support the system. 5) The influence of the use of an oak spresi intention of desire or intention by a person, where the desire is influenced by social factors.
2.1.2 DSS (Desicion Support System) Generally DSS (Decision Support System) is an is a set of systems that can solve the problem efficiently and effectively, which aims to help decision makers choose various alternatives decision is the result of processing the information obtained / available by using models of decision makers. Another definition of DSS (Decision Support System) is a system-based computer that used to assist decision makers in order to solve the complex problems that is impossible to do with a manual calculation in a way through the simulation interactive where data and analytical models as the main component ( Sparague et al., 1993).
2.1.3 The Use of Information Technology in Banking industry of Indonsesia Banking Industry in Indonesia began to recognize and apply the technology around 70s. At that time only the computer's role as a calculating machine. The next decade, the application of information technology in the banking industry is still limited to the automation of business processes that were previously done manually. Applications are limited to the bank's internal support system and oriented on technical issues. The new information technology becomes a trend since the early 1990s era of banking deregulation, in line with the rapid developments in information technology advances and intense competition in the national banking industry and the world. Application of information technology not only on the things that are to solving business problems, but also able to give answers to the needs of customers who increasingly diverse and complex. At this period of back-office and databese has been through by online, which is able to connect the entire network of branch offices. Funding products based on information technology with a market retail target become a kind of national banking industry trends in the decade of the 90s. To anticipate the development of the use of information technology quickly by national banks, on March 31, 1995 Bank Indonesia issued a regulation on the use of information technology by the national banks through the Decree of Directors of Bank Indonesia Number: 27/164 / KEP / DIR on the Use of
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Information System Technology by Banks, At this time, all banks in Indonesia are already applying information technology. While most of the branches, sub- branches and cash offices also computerized by applying on-line actual time system between branch offices. Customer related to bank users of IT can feel the advantages such as: (1) ease of use of banking services, (2) the flexibility service time, (3) the speed and accuracy of service, (4) security services, (5) a diversity of services and, (6) the cost and effort lower.
2.2 Development Hypotesis 2.2.1 Performance Expectations towards Intention Utilization of Credit Analysis Software Performance Expectations is defined as the degree to which an individual believes that by using the system will help in improving performance. This concept illustrates the benefits of the system for the wearer related to perceived usefulnees, extrinsic motivation, job fit, the relative advantage (Venkatesh et al., 2003). Venkatesh et al. (2003) stated that the performance expectations construct a strong predictor of intentions the use of SI in setting voluntary or mandatory. Hand (2005) also found that the positive influence on the performance expectations intention of utilization of information systems. Based on the theoretical description and some previous research on the influence of the interest in the use of performance expectations IS, then the first hypothesis stated: H1: Performance expectations has a positive influence on the intention of the utilization of credit analysis software.
2.2.2 Effort expectation of the intention Utilization of Credit Analysis Software Effort expectation is the level of ease of use of the system will be able to reduce the effort and time of individuals in their work. This means that individuals who use the IS in the job will be easier than the manual way. Three constructs that make up this concept is the perceived ease of use, usability and complexity (Venkatesh et al., 2003). Venkatesh et al., (2003), expectations of the business has a significant relationship with the intention of exploiting SI only during the post-training period but then become insignificant in the period of implementation. Handayani (2005) also found that the positive influence on the effort expectation intention of utilization of information systems. Based on that it can be formulated second hypothesis that will be tested were as follows: H2: Effort Expectations has a positive influence on the intention of the utilization of credit analysis software
2.2.3 Social Factors to Intention of Utilization of Credit Analysis Software The social factor is defined as the level which an individual assumes that everyone else convinced him that should use the new system. Within an organization's environment, social factors will determine the success of the use of information system. Social factors as direct determinants of interest represented by the use of information system is representatived by related constructs that subjective norms, social factors and image (Venkatesh et al., 2003).
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According to the results of research Venkatesh et al., (2003) and Handayani (2005) who found that social factors have a positive influence on the use of information system and other supporting empirical evidence the third hypothesis that will be tested are as follows: H3: Social factors have a positive influence on the intention of the utilization of credit analysis software.
2.2.4 Conditions that Facilitate to Use of Credit Analysis Software Behavior can not occur if the objective conditions in the environment prevented (Triandis, 1980). Conditions that facilitate the use of information system according Triandis(1980) defined as objective factors that can be ease to do an action. The objective factors include provisions that support users in exploiting the IS, such as training and help the users when facing the difficulties. Venkatesh et al. (2003) and Handayani (2005) stated that the conditions that facilitate users have an influence on the use of information systems by employees. The fourth hypothesis to be tested are as follows: H4: The conditions that facilitate the users have positive influence on the use of credit analysis software.
2.2.5 The Intention of the Utilizations of Credit Analysis and the Use of Analysis Credit Software Triandis (1980) found that individual behavior as an indivudual expressions of desire or intention which want to affects by (1) social factors, (2) affect, (3) perceive consequences. Davis et al., (1989) states that the usefulness perceived by users of information system, while Thompson et al.,(1991) states that individual beliefs about the usefulness of information system would increase their intention and finally that they will use information system in their works. The research of Venkatesh et al. (2003) states that there is a direct relations between intention of the utilizations of the usefelness of system information with the use of information system. Based on that research results, the researcher proposed the fifth hyphotesis that will be tested as are follows: H5: The intention utilizations of the use analysis credit software have positive influence with the use of analysis credit software.
2.3 Theoritical Framework
Performance Expectations The intentions The use of Effort of utilizations of Accounting Expectation the IS users Information System Social Factors
Conditions that facilitate the users Figure 2.1 Research Model
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3. METHODOLOGY 3.1 Types of Research This research is quantitavies research with survey approachment. Survey research is whose collected the information from respondents using a questionnaire. Survey research is limited by the research data which collected by the sample the population to represent the entire population (Singarimbun dan Effendi, 2004). This research aims to tested causal relations and hypothesis test as form of the independent variables and dependent variables.
3.2 Population and sample Population is group of people, event or everything that have specific characteristic (Indriantoro dan Supomo, 2001). Population on this research are the employees who works on the credit department of banking companies in Bengkulu City. Sample is a half of the population elements Sampel adalah sebagian dari elemen- elemen populasi (Indriantoro dan Supomo, 2002). Sample from this research are the employees who used anaysis credit software. This research will test the acceptance and the use of information technology as form analysis credit software that is used by the employees who works on the analys credit department.
3.3 Data Collection Method The data in this research is a primer data. Questionnaires were distributed to the respondents in order to be more effective distribution, so will done by delivering itself or use the collector and will be collected by the collector. To guarantee the respondent who fill that questionnaires as hopes on this research such us the credit employees, so for each banking company that will straight attended to meet one of credit employees and subsequently entrusted to other employees. When unable to meet the credit employees, researchers will meet the relevant sections in research permition, for example general divisions or secretary of the company and ask the dirrections to fill this questionnaire with the intended respondent.
3.4 Data Anaysis Techniques This research data was analyzed by means of statistical test by using SPSS 16.0 software with testing as outlined below:
3.4.1 Statistic Descriptive To further clarify the object under this research will be presented an overview of the demographics of survey respondents include gender, age, education and work experience. Researchers used the absolute frequency distribution table whic is showing the average rate, median, range and standard deviation.
3.4.2 Data Validity Test 3.4.2.1 Validity test Validity test used to masure whether or not a legitimate or valid questionnaires. A valid questionnaire will said if the quetions on the questionnaire were able to reveal something that will be measured by the questionnaire. Validity test can be conducted by bivariate correlation between each respective indicator scores with a
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total score of constructs. An indicator is said to be a valid statement if the correlation of each indicator showed significant results (Ghozali, 2005). 3.4.2.2 Reliability Test Reliability test is intended to measure a questionnaire as an variable indicator. A reliable questionnaire will said reliable or reliable if individual answers on the statement is consistent or stable over time. SPSS provides the facility to measure the reliability on the statistic test of Croanbach Alpha (α), as a reliable variable if the value of Croanbach Alpha > 0.60 (Nunnally dalam Ghozali, 2006).
3.4.3 Classic assumption Test Hypothesis tes in this research using the multiple regression analysis, it is necessary for classical assumption test are follows : 3.4.3.1 Normality Test Normality test intended to test whether the regression model used in this study or residual confounding variables that are normally distributed.. A good regression model is to have data distribution that is normal or nearly normal (Ghozali, 2005). Normality test used is a non-parametric statistical tests One- Sample Kolmogorov-Smirnov Test. Significant value from residual normally distributed if the value Asymp. Sig (2-tailed) in the test One-Sample Kolmogorov- Smirnov Test which is greater than 0,05. It can concled that in regression there are residual variable or confounding variable that normally distributed (Ghozali, 2005).
3.4.3.2 Multicolinearity Test Multicolinearity Test intended to test wether the regressions model found a correlations between independent variables. A good regression model should not have correlations between independent variables. Multicoliniearity can be review by the VIP (Variance Inflation Factor) and tolerans values. If the tolerans value > 0,10 or equals with VIF value < 10, means that there is no correlations between independent variables or have no multicolinearity between independent variables (Ghozali, 2005). 3.4.3.3 Heteroskidastisity Test This test is to determined wether the regressions model occurred inequality residual variance from one observations to another observations. If the residual variance from one observations to another observations remained it is called homokidastisity or there is no heterokidastisity. Heteroskidastisity with Gletser test which have significance level of 5% (Ghozali, 2005), if there is probability value significance level above 5% from confidence. If all independent variable has probability value more than 0,05 it means the regressions on this research there is no heteroskidatisity.
3.4.4 Analysis Regressions Statistic method that will be used to test Hypothesis proposed on this research is multiple regressions with SPSS programs version 16.0. This method used to test the strength relations with performance expectation, effort expectation and social factors with the intentions of credit analysis software utilization and conditions that facilitated the users to the use of analysis credit software. The regressions model on this research are as follows :
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Y1 = α + β1X1 + β2X2 + β3X3 + ε ………………………………….. (1) (for Hypothesis 1, 2 and 3) Y2 = α + β4Y1 + β5X4 + ε …………………………………………... (2) (for Hypothesis 4 and 5) Descriptions : Y1 : The Intentions of Utilizing information System Y2 : The Use of Information System X1 : Performance Expectations X2 : Effort expectation X3 : Social Factors X4 : The Conditions that Facilitated the users α : Constanta β : Coefisient Regression ε : Error
3.4.5 Coefisient of Determination Test (R2) This Coefisient of Determination (R2) is used to ilustrated the ability of the model in explaining the variations that occurs in the dependent variables (Ghozali, 2005). Coeficient of determinations (R2) is expressed by a precentage. This corelations coefficient values (R2) ranges between 0 < R2 < 1.
3.4.6 F Test Basically F test shows wether the regressions has demonstrate viable models to be used on hypotesist test (Ghozali, 2005). A decisions making by seeing the significance value, if > 0,05, the regressions model is not feasible (fit) to use. Meanwhile if < 0,05,than the regressions model worthy (fit) to use.
3.5 Hypothesis Test Basically, Hypothesis test shows the extend of the influence of independent variable and dependent variables (Ghozali, 2005). A decisions making by look at the significance (=0,05) for each independent variebles. If significance less than 5%, H0 is rejected, and vice versa Ha can not be accepted.
4. RESULTS AND DISCUSSION 4.1 Research Data 4.1.1 Descriptions of Respondents The research data are collected by distributing 80 questionnaire to the employees of credit analyst on the banking company Bengkulu City by delivering directly way to the respondent. The required time to collecting the data is about 15 days, on February 26th until March 13th 2014. These are the details of delivery and receipt process of distributing the questionnaire can be seen in Table 4.1 belows: ======Insert Table 4.1======Based on table 4.2, the amount of questionnaires that have been sent are 80, that was returned 59 questionnaire. There are 5 returned questionnaires which is can not be used as the data on this research because its contents were incomplete and incorrect. So, the amount of questionnaires on this research are 54, wich has respon rate 73,75% and usable response rate 67,50%.
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The profiles of 54 respondent that has participated on this research are shown in Table 4.2 as follows : ======Insert Table 4.2======Table 4.2 informs that most of male (62,97%) are aged 25-20 (53,70%), the majority has the educational background of S1 (85.18%) and has work experience for 1-3 years (62.96%) in the banking company.
4.1.2 Descriptive Statistics The descriptive statistics for the variables used to give an overview of the respondent's opinion to the research variables. Descriptive statistical results are presented in Table 4.3 as follows: ======Insert Table 4.3======Based on table 4.3 above, the average values response of the performance expectations for actual range are above the average value of theoretical range, this indicating that respondents believe that the credit analysis software that they used can improve their performance. Effort expectations variables has an average actual range value amount to 14.74 is above the average value of theoretical range, indicating that respondents believe that the software they are easy to used and can reduce their time and effort to works. Social factors variables have the actual range of 14.35 are above the average value of theoretical range, indicating that respondents' assessment whic are others important people to believes them for use credit analysis software is high. Conditions facilitate variables having an average value with actual range amounted to 14.41 are above the average value of theoretical range, indicating that respondents' assessment of the existence of organizational and technical infrastructure that supports the use of credit analysis software is high. Intention of utilization of information systems variables has a actual range of 11.19 above the average value of theoretical range, indicating that respondents have a desire to continue to use credit analysis software to help their duties. Use of credit analysis software variables actually has an average value for the actual range above the average value of theoretical range, indicating that respondents use credit analysis software with the frequency and duration of time.
4.2 Quality Test Result 4.2.1 Validity Test Result To measure the validity on this research used Coeficient correlation pearson by calculating each corelations scores from each quetions with the total score, Ghozali (2001). The results from SPSS 16.0 program shows coeficient correlation pearson values of each variables can be seen on Table 4.4 belows: ======Insert Table 4.4======From Table 4.4 above can be seen that the correlation between each scores of the questions on the total score of the variables showed significant results (at the level of 0.01). So it can be concluded that each questions on the variable construct research is valid.
4.2.2 Reability Test Result A questionnaire is reliable if people answers the questions consistently or stable over time (Ghozali, 2005). According to Nunally (1969) on Ghozali (2005),
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a construct or variables is reliable, if the value of cronbach alpha > 0,60. The test results using SPSS 16.0 showed Cronbach alpha values of variables studied are presented in Table 4.5 below: ======Insert Table 4.5======Based on Table 4.5 it can be known that the Cronbach alpha value of each instrument that used in this research was > 0.60 indicating that the data collected using questions instruments are reliable.
4.3 Classical Assumption Test Result 4.3.1 Normality Test Result Normality tests used One Sample Kolmogorof-Smirnov Test, the data are normally distributed if the value Asymp Sig (2-tailed) generated is greater than the value of alpha is equals to 0.05 (5%). The results of normality test by One Sample Kolmogorof-Smirnov Test is presented in Table 4.6 below: ======Insert Table 4.6======Based on the analysis result above it can be concluded that for each variable used in this research, has a pattern of normal data distribution. This can be shown by the Sig. Kolmogorov Smirnov Test insignificant, is greater than 0.05 (5%).
4.4.2 Multicolinearity Test Result Multicolinearity will not occur in a good equations model. To detect the presence or absence of multicolinearity in a regression model in this research is done by looking at the value of tolerance and variance inflation factor (VIF). Cut off value commonly used to indicate the presence of multicolinearity is the value of tolerance <0.10 and VIF> 10 (Ghozali, 2005). Multicollinearity test results can be seen in Table 4.7 below: ======Insert Table 4.7======Based on the analysis above it can be seen that the model used in this research there is no multicollinearity problem. This is indicated by the value of tolerance between the two independent variables is greater than 0.01 and VIF less than 10.
4.3.3 Heteroskidastisity Test Result A good regression model is a homoskidastisity regression model. To test whether there is heteroskidastisity can be seen from the Gletser test result. Heterokidastisiy test results in this research can be seen in Table 4.7 below: ======Insert Table 4.8======From heterokidastisity test result in Table 4.7 above, indicates that significance probability value > 0.05, which means it can be concluded that the regression model which used no symptoms heteroskedastisitas.
4.4 Hyphotesis Test Result 4.4.1 Hyphotesis Test 1, Hyphotesis Test 2, and Hyphotesis Test 3 Result Testing the hypothesis 1, hypothesis 2 and hypothesis 3 using multiple regression analysis which is have equation model 1 is: Y1 = α + β1X1 + β2X2 + β3X3 + ε. Hypothesis test results by SPSS 16.0 are presented in Table 4.8 below: ======Insert Table 4.9======ANOVA test results or F test indicates the number 23.125 with p-value of 0.000, which means decent regression model one used to test the influences of
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performance expectations, effort expectations and social factors against the intention of the use of accounting information systems variables. Adjusted R² value of 0,556 = 55.6% indicated that a change of performance expectations variable , effort expectations and social factors can explain the changes to the intention of the use of software analysis of credit of 55.6%, while the remaining 54.4% is explained by others factors are not put into the equation. From Table 4.8 shows that the regression coefficients for the variables performance expectations of 0,305 is positive, which means the performance expectations of a positive influence on the intention of utilization software credit analysis. The test results showed significant p value of 0.000 which is less than 0.05, so it can be concluded that the hypothesis 1 which states that the positive effect on the performance expectations intention of utilization software credit analysis is accepted. Regression coefficients value for the effort expectations variables for 0.170 is positive, which means effort expectations positively affects on intention utilization of software credit analysis. The test results showed significant p value of 0.034 which is less than 0.05, so it can be concluded that the second hypothesis which states that the positive influence on the effort expectations intention utilization of software credit analysis is accepted. Regression coefficient value for the variable social factors of 0.133 is positive, which means that social factors positively affects the intention of exploiting software credit analysis. The test results showed significant p value of 0.041 which is less than 0.05, so it can be concluded that the hypothesis 3 which states that the positive influence of social factors on the intention utilization of software credit analysis is accepted.
4.4.2 Hyphotesis Test 4 and Hyphotesis Test 5 Result The fourth and fifth hypothesis testing using multiple regression analysis by using the model equation 2: Y2 = α + β4Y1 + β5X4 + ε. The hypothesis test results by SPSS 16.0 is presented in Table 4.9 below: ======Insert Table 4.10======ANOVA test results or F test indicates the number 27.606 with a p-value of 0.000, which means 2 decent regression model 2 was used to test the effect of conditions that facilitate the utilization of the intention variable and the intention of use of the software analyzes the actual credit variable. Adjusted R² value of 0.501 = 50.1% indicated that a change of conditions that facilitate the intentions of credit analysis software utilization variables can explain the changes to the use of the software analyzes the actual credit of 50.1%, while the remaining 49.9% is explained by others factors that are not put into the equation. From Table 4.11 above shows that the value of the regression coefficients for facilitate the conditions variables of 0.158 is positive, but the significance test showed p value of 0.030 which is less than 0.05, so it can be concluded that the hypothesis 4 which states that the conditions that facilitate have positive influence on the intention of the use of the software analyzes actual credits received. The value of the regression coefficient for the variable utilization intention of 0.419 is positive and shows significant value p value of 0.001 which is smaller than 0.05, so it can be concluded that the hypothesis 5, which states that the intention of utilization have positive influence on the intention of the use of the software
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analyzes actual credits received. Summary of the test results of the fifth hypothesis in this research can be seen in Table 4.10 below: ======Insert Table 4.11======
4.5 Discussion 4.5.1 Influence on Performance Expectations Intentions of Credit Analysis Software Utilization The first hypothesis states that performance expectations have a positive influence on the intention to use the software credit analysis empirically proven to be supported by empirical data of respondents. This means that the employees of the credit analysts believe that the credit analysis software they use is beneficial and can improve performance, so that they have the intention to continue using the credit analysis software. This result is in line with research Venkatesh, et al. (2003) and Handayani (2005) which states that the performance expectations have a positive influence on the intention of the use of information systems. The results also support the UTAUT model which states that performance expectations construct a strong predictor of the intention of credit analysis software utilization (Venkatesh et al., 2003).
4.5.2 Influence of Effort Expectations of the intention of Credit Analysis Software Utilization The second hypothesis which states that effort expectations have a positive influence on the intention of credit analysis software utilization is empirically proven to be supported by empirical data of respondents. This means that the employees of the credit analysts believe that credit analysis software that the used are easy to use and can reduce their time and effort to works so that they have the intention to continue using the analysis credit software. The result is in line with research Venkatesh, et al. (2003) and Handayani (2005) which stated that the effort expectations have a positive influence on the intention of the use of information systems. The results support a UTAUT model which states that effort expectations is a strong determinant in determining the intention of credit analysis software utilization
4.5.3 Influence of Social Factors on the Intention of Credit Analysis Software Utilization The third hypothesis states that social factors have a positive influence on the intention of credit analysis software utilization empirically proven to be supported. This means that the employees of the credit analysis believe that the intention of credit analysis software utilization is influenced by the people who are in their working environment which affect these employees to use the analysis credt software. The results support the research Venkatesh, et al. (2003) and Handayani (2005) which states that the social factors have positive influence on the use of credit analysis software. The results also support the UTAUT model which states that social factor is a strong factor in determining the intention of credit analysis software utilization
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4.5.4 The Influence of Conditions that Facilitate on the Use Actual Credit Analysis Software The fourth hypothesis which states that the conditions that facilitate have a positive influence on the use of credit analysis software which empirically proven to be supported. This means that the employees of the credit analysts believe that the organizational infrastructure and technical support in the use of credit analysis software at the their company affect the use of software analysis of the actual credit. The results support the research Venkatesh, et al. (2003) and Handayani (2005) which states that the conditions that facilitate have positive influence on the use of credit analysis software. The results also support the UTAUT model which states that the conditions facilitate is one of the strength factors that determined the influence to use credit anlysis software.
4.5.5 The Influence of the Intention of Actual Credit Analysis Software Utilization The fifth hypothesis which states that the intention of use has a positive impact on the use of actual credit analysis software means empirically proven to be supported. This means that the employees of the credit analysts who have the intention or desire to use the software accounting directly affects the use of the sactual credit analysis software. The results support a UTAUT model which states that the intention of credit analysis software utilizations affect the use of actual credit analysis software.
5. CONCLUSIONS AND SUGGESTIONS 5.1 Conclusions This research background on the research development on the intention of the use of credit analysis software using an acceptance model, Unifed Theory of Accepatance and Use of Technology (UTAUT) models on the banking company in the form of software or computer accounting. Based on the data analysis results and hypothesis test, then can concluded as follows: 1) Performance expectations variables have a positive influence on the intention of the use of credit analysis software utilization, which means respondents are confident that by using the system will help in improving the performance and would increase their intention to use the credit analysis software. 2) Effort expectations variables have apositive influence, which means that the respondent would have the intention to use the software credit analysis if they feel that the system is easy and requires no need times and efforts to works. 3) Social factors have positive influence on the intention of the use of credit analysis software. This means that the social environment surrounding the accounting employees as leaders and co-workers to support or influence them in utilizing the system and system utilization will improve their status. 4) Variables of conditions that facilitate users shown to affect the use of actual credit analysis software. The existence of organizational infrastructure and technical support provided by the company related to the use of credit analysis software which is accompanied by the tendency of the increasing use of analysis credit software.
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5) Variable intention of credit analysis software utilizations shown to affect the use of actual credit analysis software, it means an intention utilization system is a desire of employees from the attitude of their acceptance of the system, but the use of software credit analysis are actually more related to their needs which adjusted their task.
5.2 Research Limitations Researchers realized that in this research there are some limitations that are likely to interfere with the research results, are as bellow: 1) Limitations of time that do not allow researchers to test the users intrinsic factor credit analysis software as variables that may moderate the relationship between independent and dependent variables. 2) This research s not yet to explore the complexity of the used software. 3) Question instruments for actual use only consists of two questions, which should have 3 questions minimum.
5.3 Suggestions for Next Research Next researchers can consider to use UTAUT models that have been associated with moderating variables, such as gender, age, whether the nature of the use of the software is mandatory or voluntary and others.
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Handayani, R. 2005. ―Analisis Fakto-Faktor yang Mempengaruhi Minat Pemanfaatan Sistem Informasi dan Penggunaan Sistem Informasi‖. Unpublished Thesis
Harrison, M. J. Dan P. Datta. 2007. ―An Empirical Assesment of Usr Percetions of Feature Versus Application Level Usage‖. Communication Association Informastion System, Vol. 20, pp. 300- 321.
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Jogiysnto, H. M. 2007, “Sistem Informasi Keperilakuan”. Andy Offset. Jogjakarta.
Loudon, K. C., dan J. P. Laudon. 2006. “Management Information System”. 8th Edition. New Jersey : Prentice-Hall, Inc.
Mcleod, R dan George. 2004. “Sistem Informasi Manajemen”, eight edition. Jakarta: Indeks.
Moore, G. C., dan Benbasat, I., 1991. ―Development of an Instrument to Measure the Perseption of Adopting an Informtion Technology Innovation,‖ Information System Research, Vol. 2, No. 3, pp. 192-222.
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Venkatesh, V., F. D. Davis. 2007. ―A Theoretical Extension of The Technology Acceptance Model: Four Longitudinal Field Studies‖. Managenent Science, Vol. 45, No. 2, pp. 186-204.
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APPENDIX Tabel 4.1 Rincian Pengirimandan Pengembalian Kuesioner
Keterangan Jumlah Persentase Total kuesioner yang disebar 80 100% Jumlah kuesioner yang kembali 59 73,75% Kuesioner yang tidak dapat digunakan 5 6,25% Kuesioner yang dapat digunakan 54 67,50% Sumber: data primer diolah 2014
Tabel 4.2 Profil Responden
Keterangan Frekuensi Persentase Jenis Kelamin Pria 34 62,96% Wanita 20 37,04% Umur Responden < 20 tahun - - 20 – 25 tahun 25 46,30% 25 – 30 tahun 29 53,70% > 30 tahun - - Tingkat Pendidikan SMA - - Diploma 4 7,41% S1 46 85,18% S2 4 7,41% S3 - - Jabatan Analis Kredit 54 100% Pengalaman Berkerja < 1 Tahun 6 11,11% 1 – 3 Tahun 34 62,96% 3 – 5 Tahun 14 25,93% > 5 Tahun - - Lama Menggunakan Software < 1 Tahun 9 16,67% 1 – 3 Tahun 27 50% 3 – 5 Tahun 18 33,33% > 5 Tahun - - Sumber: data primer diolah 2014
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Tabel 4.3 Statistik Deskriptif Teoritis Aktual Variabel Rata- Rata- Kisaran Kisaran SD rata rata Ekspektasi Kinerja 4 – 20 12 9-19 14,44 2.820 Ekspektasi Usaha 4 – 20 12 12-19 14,74 2.571 Faktor Sosial 4 – 20 12 10-20 14,35 2.489 Kondisi yang 4 – 20 12 9-19 14,41 2.778 Memfasilitasi Niat Pemanfaatan 3 – 15 9 9-15 11,19 1.672 Penggunaan Sesungguhnya 2 – 10 6 5-10 7,48 1.463 Sumber: data primer diolah 2014
Tabel 4.4 Hasil Uji Validitas Variabel Pearson Corelation Keterangan Ekspektasi Kinerja 0,682** - 0,858** Valid Ekspektasi Usaha 0,779** - 0,900** Valid Faktor Sosial 0,470** - 0,873** Valid Kondisi yang Memfasilitasi 0,658** - 0,860** Valid Niat Pemanfaatan 0,794** - 0,867** Valid Penggunaan sesungguhnya 0,902** - 0,918** Valid ** Correlation is significant at the 0.01 level (2-tailed). Sumber: Data primer diolah, 2014
Tabel 4.5 Hasil Uji Reliabilitas Variabel Cronbach Alpha Keterangan Ekspektasi Kinerja 0,714 Reliabel Ekspektasi Usaha 0,860 Reliabel Faktor Sosial 0,608 Reliabel Kondisi yang Memfasilitasi 0,608 Reliabel Niat Pemanfaatan 0,777 Reliabel Penggunaan sesungguhnya 0,791 Reliabel Sumber : Data primer yang diolah, 2014
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Tabel 4.6 Hasil Uji Normalitas Data
Kolmogrov- Asymp. Sig. Nama Variabel Keterangan Smirnov Z (2-Tailed) Ekspektasi Kinerja 1,303 0,067 Normal Ekspektasi Usaha 1,241 0,092 Normal Faktor Sosial 1,319 0,062 Normal Kondisi yang Memfasilitasi 1,288 0,072 Normal Niat Pemanfaatan 1,004 0,256 Normal Penggunaan Sesungguhnya 1,298 0,060 Normal Sumber : Data Primer diolah, 2014
Tabel 4.7 Hasil Uji Multikolinearitas Variabel Tolerance VIF Keterangan Persamaan 1 : Ekspektasi Kinerja 0,613 1,632 Bebas Ekspektasi Usaha 0,585 1,710 Multikolinearitas Faktor Sosial 0,942 1,062 Persamaan 2 : Kondisi yang 0,518 1,931 Bebas Memfasilitasi 0,518 1,931 Multikolinearitas Niat Pemanfaatan Sumber : Data Primer diolah, 2014
Tabel 4.8 Hasil Uji Heteroskedastisitas Nama Variabel Sig. Keterangan Ekspektasi Kinerja 0,067 Bebas Heteroskedastisitas Ekspektasi Usaha 0,363 Bebas Heteroskedastisitas Faktor Sosial 0,658 Bebas Heteroskedastisitas Kondisi yang Memfasilitasi 0,999 Bebas Heteroskedastisitas Niat Pemanfaatan 0,297 Bebas Heteroskedastisitas Sumber : data primer diolah, 2014
Tabel 4.9 Hasil Uji Hipotesis (Model 1) Variabel Koefisien Nilai Koefisien t value p value Ekspektasi Kinerja β1 0,305 4,401 0,000 Ekspektasi Usaha β2 0,170 2,179 0,034 Faktor Sosial β3 0,133 2,093 0,041 R-square= 0,581 Adjusted R-square= 0,556 F value= 23,125 p value= 0,000 < 0,05 Sumber : data primer diolah, 2014
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Tabel 4.10 Hasil Uji Hipotesis (Model 2)
Nilai t p Variabel Koefisien Koefisien value value Kondisi yang Memfasilitasi β4 0,158 2,227 0,030 Niat Pemanfaatan β5 0,419 3,555 0,001 R-square= 0,520 Adjusted R-square= 0,501 F value= 27,606 p value= 0,000 < 0,05 Sumber : data primer diolah, 2014
Tabel 4.11 Ringkasan Hasil Pengujian Hipotesis
Hipotesis Kesimpulan Ekspetasi Kinerja berpengaruh positif terhadap niat H1 Diterima pemanfaatan software analisis kredit Ekspetasi Usaha berpengaruh positif terhadap niat H2 Diterima pemanfaatan software analisis kredit Faktor Sosial berpengaruh positif terhadap niat pemanfaatan H3 Diterima software analisis kredit Kondisi yang memfasilitasi berpengaruh positif terhadap niat H4 Diterima pemanfaatan software analisis kredit Niat pemanfaatan berpengaruh positif terhadap niat H5 Diterima penggunaan software analisis kredit
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