4.1. Introduction
This chapter presents the study’s findings through a data analysis process. The collected data (primary data) was analyzed using SPSS software (Statistical Package for the Social Sciences) version 16.0 and diagrams. The results derived from the analysis were used to answer the research questions and to test the four hypotheses suggested in the previous chapter. Finally, a summary of hypotheses testing will be included in this part to remind readers about the researcher’s achievements.
4.2. Data collection results
In order to ensure the timeliness of the collected data, the secondary data collection has been started from the beginning of the study till this point of time. This data helps the researcher form the literature review about credit risk management (chapter two) and describe business lending of BIDV (chapter three). The two chapters focus on four factors including credit information, technology, credit staff and loan policy which influence NPL ratio.
In addition, the researcher tried to gather primary data through the survey questionnaires and interview method to achieve the study objectives.
Thanks to being a member of BIDV, the researcher takes advantages in collecting primary data. Therefore, number of superiors who response in the survey is high, occupies a half of the sample (twenty branch managers/vice managers accepted to
Chapter 4: Data analysis and findings Page 57 be interviewed via telephone, thirty credit department leaders and fifty officers fill in the research questionnaire and give back to the researcher. The data collected from these managers were extremely important in answering the research questions because of the sensitivity and reliability of the information provided.
Furthermore, survey questionnaires were distributed to a list of 150 target credit staffs. After one week, the researcher received feedback from 100 respondents. Since 100
respondents are considered an acceptable number to derive the findings for this study (adapted from Trong & Ngoc, 2008), the implementation of the delivery and collection questionnaires is unnecessary.
4.3. Data analysis Collected data (primary data) was analyzed using SPSS software version 16.0. The detail of the study findings which derived from the above analysis will be presented and discussed.
4.3.1. Descriptive statistic
Descriptive statistic is a useful tool in describing background data such as: age, income and education status of respondents in the research (Orcher 2005). Therefore, this study employed this method to present the respondents’ information in term of working years, respondent’s current position and respondent’s background/faculty. This information is fundamental for judging the reliability of collected data from those participants.
4.3.1.1 Respondents position
It is necessary for this study to approach different position levels to improve the quality and reliability of collected information because different levels tend to have different perspectives or knowledge about the same object. Since a lot of effort has been made, 50% of participants are from top management levels and the rest are at staffing levels.
Current position Frequenc
y Percen Valid Cumulative
Valid Manager/Vi
ce 20 20.
0 20.0 20.0
Credit
department 30 30.
0 30.0 50.0
Officer 50 50.
0 50.0 100.0
Total 100 100.
0 100.
Figure 4.1: Respondents’ position0
Source: derived from SPSS analysis (Appendix B) 4.3.1.2 Respondents length of time working in
BIDV
Obviously, the understanding and knowledge of people about an industry or certain areas depend on the length of time they have worked in that industry and areas. Therefore, respondents’
working years is one criterion to access the reliability of their answers. Figure 5.2 shows a small percentage of only 1%
participants who have less than 1 year working experience in banking industry whereas the percentage of 1-3, 3-5 and 5-10 working-years credit officers are 18%, 14% and 25% respectively.
Respondents with more than 10 years working experience accounts for the largest proportion (42%).
Member of BIDV Frequen
cy Perce Valid Cumulative
Valid Less than 1
year 1 1. 1.0 1.0
1-3 years 18 18.
0 18.0 19.0
3-5years 14 14.
0 14.0 33.0
5-10years 25 25.
0 25.0 58.0
More than
10 42 42.
0 42.0 100.0
Total 100 100.
0 100.
Figure 4.2: Respondents’ working years in BIDV0 Source: derived from SPSS analysis (Appendix B) 4.3.2. Measures of combination reliability
Four hypotheses with their assumptions about the relationship between those variables: credit information, technology, credit officers and loan policy have been developed at the beginning of this study. However, each variable is affected by a variety of following individual aspects:
Variable name Aspects
1. Impact of credit information
Credit
information
2. Source of credit information 3. Credit information selecting and systemizing
4. Credit information sharing 5. Credit information updating
Technology
1. Technology operation
2. Credit staff competence and technology matching
3. Frequency of facility maintenance
4. Technology investment & BIDV's growth progress matching
5. Modern technology
Credit staff
1. Characteristic of credit staffs 2. Comprehensive training course 3. Proper supervision system
4. Important role of board of directors 5. Reward policy
Loan policy
1. Impact of loan policy 2. Well-defined loan policy
3. Well-communicated loan policy 4. Frequency of amendment
Table 4.1: Four variables with their different aspects Since the research utilizes perception survey, the survey
questionnaire was designed to ask respondents to provide their response in a predetermined level of agreement:
Rate Level of Agreement
1 Strongly disagree
2 Disagr
3 Normalee
4 Agree
5 Strongly agree
Table 4.2: Level of agreement in survey questionnaires.
In order to make it easy for the researcher to test those hypotheses and create the study findings, those aspects that belong to each variable were combined into one. Each variable
was computed by average of its aspects. Thus, Cronbach’s alphas were used to decide whether the combination is reliable.
Based on the result derived
from SPSS version 16.0 (Table 4.3), all the values of Cronbach’s alphas are greater than 0.70 so the combination is really reliable. This cut-off value 0.7 was implemented because if the alpha values were any lower, the R2 or “coefficient of determination” would be less than 0.5: 0.70 x 0.70 = 0.49 (Vogt 2007).
In addition, if any item (aspect) of the four factors is deleted, Cronbach’s Alpha will be less than the current Cronbach’s Alpha (see Appendix C). Therefore, none of predetermined aspect should be deleted.
Variables Number of
Items Values of
Cronbach s alpha Credit
information
5 0.70
Technology 5 0.813
Credit staff 5 0.817
Loan policy 4 0.859
Table 4.3: The overall score of 1 Cronbach’s alpha Source: Computed
by SPSS (Appendix C) 4.3.3 Statistical hypothesis testing
T-test technique is used to test the suggested hypotheses in the previous chapter. The technique help researcher determine mean of each variable is really greater than 3 or the respondents agree that the four factors including credit information, technology, credit staff and loan policy positively affect level of credit risk or non-performing loan ratio in BIDV.
In this study, the researcher believes that four variables positively affect credit risk management (or mean of each variable is greater than 3). The researcher formulates the null hypothesis that the
means are equal to 3. In the other words, the researcher assumes that four variables including credit information, technique, credit staff and loan policy don’t influence non-performing-loan ratio in BIDV.
The null hypothesis is that the means are equal 3:
Ho: à = 3
The alternative hypothesis is that the means are not equal 3:
H1: à # 3
One-Sample Statistics N Mean Std.
Deviation Std. Error Credit information 100 4.08 0.46558 Mean 0.04655
Technology 100 3.97 0.48515 0.04851
Credit staff 100 4.04 0.54203 0.05420
Loan policy 100 4.03 0.58309 0.0583
One-Sample Test
Test Value = 3 t df Sig.
(2-tailed )
Mean Differen ce
95%
Confidence Interval of Lowerthe Upper
Credit 23.3253 99 0.0 1.08 0.99361 1.17838
Technology 20.1171 99 0.0 0.97 0.87973 1.07226 Credit staff 19.2607 99 0.0 1.04 0.93644 1.15155 Loan policy 17.6643 99 0.0 1.03 0.91430 1.14569
Table 4.4a: The t-test results Source: Computed by SPSS (Appendix)
Variables Mean Std.
Deviatio n
Low er Limi
Upp er Limi
Region of rejecti Credit
informati 4.08
6 0.46558
7 2.9076
2 3.0923
8 <2.907
62 >3.092 Technology 3.97 0.48515 2.9037 3.0962 <2.903 >3.09638 Credit staff 4.04 0.54203 2.8924 3.1075 <2.892 >3.107 Loan policy 4.03 0.58309 2.8843 3.1157 <2.884 >3.115
Table 4.4b: The t-test results Source: Computed according to
Zikmund (1997)
The t-test results in the tables 4.4a&b help the researcher reject the null hypothesis at the confident level of 95% because of two reasons. Firstly, t-value of each variable is greater than the t- value associated with the confidence level of 95% (1.984) (Trong & Ngoc, 2008 and Zikmund, 1997). Secondly, mean of each
variable is contained in the region of rejection because the mean is greater than upper limit (Zikmund, 1997).
The results indicate that mean of each variable is greater than 3 or the respondents agree that the four variables positively affect level of credit risk or non-performing loan in BIDV.
4.4Comparison and discussion of findings
After collected data has been analysed and the model for the effective credit risk management strategies has been set up, this section will discuss those findings and compare with the extant literature.
4.4.1 Credit information
The findings from data analysis indicated that the credit information variable has a positive effect on the reduction of non- performing-loan ratio in BIDV. Therefore, by improving the quality of credit information variable, BIDV can effectively manage their level of credit risk.
Furthermore, the combination of five aspects that influence the quality of credit information (CI): the impact of CI, source of CI, CI selecting and systemizing, CI sharing and CI updating is reliable based on high Cronbach alpha (0.703). Thus, BIDV should focus on improving these aspects in order to upgrade their quality of information.
The findings confirm the importance of credit information in reducing non- performing-loan ratio. Beside the aspect of credit information reviewed in chapter 2 including source of CI (Hempel and Simonson 1999), CI sharing (Chau 2009) and CI updating (Thomas 2006), the findings also underline the importance of CI selecting and systemizing in enhancing BIDV’s capacity to manage credit risk management.
4.4.2 hnology
The findings from data analysis indicated that the technology variable has a positive effect on the reduction of non-performing- loan ratio in BIDV. Therefore, by improving the technology variable, BIDV can effectively manage their level of credit risk.
Furthermore, the combination of five aspects that influence the effectiveness of technology: effective technology operation, credit staff competence and technology
matching, frequency of facility maintenance, technology investment & BIDV growth progress matching and modern technology is reliable based on high Cronbach alpha (0.817).
Thus, BIDV should focus on improving these aspects in order to upgrade their technology capacity.
The findings confirm the importance of technology in reducing non- performing-loan ratio. Beside the four aspects of technology which Chau (2009) mentioned, the findings suggest one more aspect: credit staff competence and technology matching.
4.4.3 Credit staff
The findings from data analysis indicated that the credit staffs variable has a positive effect on the reduction of non-performing- loan ratio in BIDV. Therefore, by improving the credit staffs variable, BIDV can effectively manage their level of credit risk.
Furthermore, the combination of five aspects that influence the quality of credit staff: characteristic of credit staff, comprehensive training course, proper supervision system, important role of board of directors and reward policy is reliable based on high Cronbach alpha (0.819). Thus, BIDV should focus on improving these aspects in order to upgrade their credit staff quality.
The findings of this study assert the importance of two aspects which mentioned in chapter 2, including ethics (characteristic aspect and proper supervision system aspect) and skills (training course aspect) of credit staff in credit risk management. The findings confirm the importance of credit staffs in credit risk management. Beside the four aspects of credit officer which Chau (2009) mentioned, the findings suggest two more aspects:
characteristic of credit staff and important role of board of directors.
4.4.4 Loan policy
The findings from data analysis indicated that the loan policy variable has a positive effect on the reduction of non-performing- loan ratio in BIDV. Therefore, by improving the loan policy variable, BIDV can effectively manage their level of credit risk.
Furthermore, the combination of four aspects: impact of loan policy, well-defined policy, well-communicated policy and frequency of policy amendment is reliable based on high Cronbach’s alpha (0.851). Thus, BIDV should focus on improving these aspects in order to upgrade the effectiveness of their loan policy.
The findings are quite similar to literature. Sinkey (1998) and Chau (2009) suggested ways to improve the effectiveness of loan policy: loan policy must be well-defined, well-communicated and frequently amended.
4.5Result on Hypotheses testing
This study aims to confirm the influence of the four factors on reducing non- performing-loan in BIDV. In order to do this, the researcher used the t-test technique to test the hypotheses by testing the influence of four variables: credit information, technology, credit staff and loan policy on reducing non- performing- loan in BIDV. The result from t-test analysis indicates that all four hypotheses were rejected and confirms that the four factors positively affect the reduction of non- performing-loan ratio. The following table is a summary of the hypotheses testing results:
No Null Hypotheses Testing Evidence
1 Credit information does not influence non-performing-loan ratio in BDV.
Rejected Section 2.7.1 &
4.4.1 Table 4.3, 4.4a
& 4.4b 2 Technology does not
influence non- performing- loan ratio in BDV.
Rejected Section 2.7.2 &
4.4.2 Table 4.3, 4.4a
& 4.4b 3 Credit staffs do not
influence non- performing- loan ratio in BDV.
Rejected Section 2.7.3 &
4.4.3 Table 4.3, 4.4a
& 4.4b
4 Loan policy does not influence non- performing- loan ratio in BDV.
Rejected Section 2.7.4 &
4.4.4 Table 4.3, 4.4a
& 4.4b 4.6Summa
ry:
Table 4.5: Summary of hypotheses testing result
The purpose of this chapter is to create the findings for this study which derived from a data analysis process. First, a descriptive statistics was used to summarize the respondents’
background: working years and positions. The result from this statistic
reveals the quality of collected data based on respondents’
experience and knowledge. Second, the researcher implemented a reliability measurement with the purpose of testing the possibility of combining individual aspects to one variable. High Cronbach’s alpha values (0.7 and above) proved that the combination purpose is satisfied. Third, the researcher used t-test technique in order to test the influence of four variables: credit information, technology, banks staff and loan policy and the reduction of non-performing-loan ratio. The result from this analysis reveals that the four factors positively affect the reduction of non-performing-loan ratio.
Chapter 5: Recommendations and conclusions Page 66 CHAPTER
FIVE