臺灣博碩士論文加值系統

A lot of aspects have to be put into consideration before bank decides to invest into companies in a form of loan. For any single bad loan, bank will suffer not only financial losses, but also significant damage in its reputation. That is why before any loan decision is made; applications must undergo a long analysis procedure to screen bad loans from the beginning. In this decision making process, bank officers will take into account a lot of different kind of attributes related to the borrower’s background and experience, the business and conditions surrounding it. As from the lender’s side, human capital factor might influence the decision, simply because human judgment plays important portion in loan decision.
This study used both quantitative and qualitative research methods. For the quantitative examination, primary data which are collected from loan officers working in Bank Central Asia, Indonesia. In total there are 291 respondents who participated in the experimental study (conjoint analysis). Respondents were presented with 10 hypothetical companies applying for SME loan. These companies carried a certain mixes of five borrower’s attributes - relationship with the bank, value of collateral, firm size, business experience, and share of investment in the company. The respondents had to give a rating of how likely they will approve the application as if in the real setting. While collecting the quantitative data, interviews and observations were conducted in order to obtain more in-depth information.
The study found that borrower’s attributes have positive relationships toward the likelihoods of SME loan approval, with the following ranks of importance: 1) business experience, 2) value of collateral, 3) share of investment, 4) relationship with the bank, and 5) firm size. Lender’s human capitals do not affect the process. Further investigations discovered that the influence of lender’s human capitals has diminished because of the new computerized risk-rating system adopted in the bank. This system has enabled a more rigid and uniform brackets in SME approval, leaving less leeway for human judgment to affect the process. There is also an additional finding which highlights that risk and business department have different values only on collateral attribute. If this difference can be solved and both departments have similar point of view toward the matter, the bank will work more efficiently.
The results can be used to reevaluate the credit analysis practice, whether it is in line with the bank’s policy. They can also be used as a base to create the supporting analytical tools necessary to help the job of loan officers. As the role of human capital was found not vital in SME decision process, human resource relocation can be considered. Those with higher human capital can be transferred to credit departments with higher level which require more complicated analyses, leaving the novice or those with lower level of human capital work in the SME department with the assistance of the computerized risk-rating system. Hopefully this study will enrich literature in academic world, give insights to the banking industry, and finally help to creating efficiency in the common practice of loan granting.

A lot of aspects have to be put into consideration before bank decides to invest into companies in a form of loan. For any single bad loan, bank will suffer not only financial losses, but also significant damage in its reputation. That is why before any loan decision is made; applications must undergo a long analysis procedure to screen bad loans from the beginning. In this decision making process, bank officers will take into account a lot of different kind of attributes related to the borrower’s background and experience, the business and conditions surrounding it. As from the lender’s side, human capital factor might influence the decision, simply because human judgment plays important portion in loan decision.
This study used both quantitative and qualitative research methods. For the quantitative examination, primary data which are collected from loan officers working in Bank Central Asia, Indonesia. In total there are 291 respondents who participated in the experimental study (conjoint analysis). Respondents were presented with 10 hypothetical companies applying for SME loan. These companies carried a certain mixes of five borrower’s attributes - relationship with the bank, value of collateral, firm size, business experience, and share of investment in the company. The respondents had to give a rating of how likely they will approve the application as if in the real setting. While collecting the quantitative data, interviews and observations were conducted in order to obtain more in-depth information.
The study found that borrower’s attributes have positive relationships toward the likelihoods of SME loan approval, with the following ranks of importance: 1) business experience, 2) value of collateral, 3) share of investment, 4) relationship with the bank, and 5) firm size. Lender’s human capitals do not affect the process. Further investigations discovered that the influence of lender’s human capitals has diminished because of the new computerized risk-rating system adopted in the bank. This system has enabled a more rigid and uniform brackets in SME approval, leaving less leeway for human judgment to affect the process. There is also an additional finding which highlights that risk and business department have different values only on collateral attribute. If this difference can be solved and both departments have similar point of view toward the matter, the bank will work more efficiently.
The results can be used to reevaluate the credit analysis practice, whether it is in line with the bank’s policy. They can also be used as a base to create the supporting analytical tools necessary to help the job of loan officers. As the role of human capital was found not vital in SME decision process, human resource relocation can be considered. Those with higher human capital can be transferred to credit departments with higher level which require more complicated analyses, leaving the novice or those with lower level of human capital work in the SME department with the assistance of the computerized risk-rating system. Hopefully this study will enrich literature in academic world, give insights to the banking industry, and finally help to creating efficiency in the common practice of loan granting.

TABLE OF CONTENTS
ACKNOWLEDGEMENT I
ABSTRACT II
TABLE OF CONTENTS IV
LIST OF TABLES VII
LIST OF FIGURES VIII
CHAPTER ONE INTRODUCTION 1
1.1 Research Background. 1
1.2 Banking System in Indonesia. 2
1.2.1 Rural Banks (Bank Perkreditan Rakyat). 3
1.2.2 Commercial Banks (Bank Umum). 4
1.3 Bank Central Asia (BCA). 6
1.3.1 The History. 6
1.3.2 The Reputation. 7
1.3.3 The Financial Performance. 7
1.4 SME in Indonesia. 9
1.5 Research Objectives and Procedures. 10
1.5 Research Structure. 11
CHAPTER TWO LITERATURE REVIEW 12
2.1 Loan Approval. 12
2.2 Borrower’s Attributes. 15
2.2.1 Relationship with the Bank. 15
2.2.2 Value of Collateral. 16
2.2.3 Firm Size. 17
2.2.4 Related Business Experience. 18
2.2.5 Share of Investment. 19
2.3 Lender’s Human Capital. 20
2.3.1 Education. 22
2.3.2 Banking Experience. 23
2.3.3 Lending Experience. 23
2.3.4 Recent Exposure to SME Loans. 24
CHAPTER THREE RESEARCH DESIGN AND METHODOLOGY 25
3.1 Research Design. 25
3.2 Variables. 26
3.2.1 Possibility of Loan Approval. 26
3.2.2 Borrower’s Attributes. 27
3.2.3 Lender’s Human Capital. 27
3.2.4 Other Control Variables. 28
3.3 Stimulus. 28
3.4 Questionnaire Design. 30
3.5 Procedure. 31
3.6 Participants. 32
3.7 Data Analysis Procedure. 32
3.7.1 Descriptive Statistic Analysis. 32
3.7.2 Conjoint Analysis. 33
3.7.3 T-test and One-way ANOVA. 34
3.7.4 Hierarchical Linear Modeling (HLM). 34
3.8 Summary of Hypotheses. 35
CHAPTER FOUR RESEARCH RESULTS 36
4.1 Descriptive Statistic Analysis. 36
4.2 Conjoint Analysis Result. 37
4.2.1 The Relationships between Borrower’s Attributes and the Likelihood of Loan Approval. 37
4.2.2 The Level of Importance of Borrower’s Attributes. 40
4.3 Pair Sample T-test and One-way ANOVA. 44
4.3.1 Awareness towards the Difference between Levels. 44
4.3.2 Differences among Categories within Demographic Groups. 44
4.3.3 The Influence of Human Capital Factors on Loan Approval. 47
4.4 Hierarchical Linear Modeling Results. 51
CHAPTER FIVE CONCLUSION AND SUGGESTIONS 54
5.1 Discussions and Implications. 54
5.2 Research Limitations. 55
5.3 Suggestions for Future Research. 56
REFERENCES 58
APPENDICES 64
Appendix 1 Questionnaire in English 64
Appendix 2 Questionnaire in Indonesian 69

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