Commercial bank credits for high - Tech agriculture in lam dong province

ss; Sixth,high-tech agriculture chain value also enablecommercial banks to create niche high-tech agriculture loan products, such as lending through value chains, lending through focal institutions, lending to projects or co-operations, etc. Seventh, the loans for high-tech agriculture are usually lower than its demand; Eighth, high-tech agriculture lending requires the credit officer’s effort to grasp new technologies, production processes, etc. to be confident in the evaluation and make credit proposals; Ninth, purchasing contracts prior to the production is considered as important key to loan evaluation and proposal in casesbusiness don’t have secured assets in high-tech agriculture lending. 2.3.3. The role of commercial bank credits for high-tech agriculture 2.3.4. Developing commercial bank credits to high-tech agriculture 2.3.4.1. Perspectives on developing commercial bank credits for high-tech agriculture 9 Developingcommercial bank credits for high-tech agriculture needs simultaneously developboth of its width and depth. Width developing is the growth in terms of credit volume, credit structure, and high-tech agriculture credit customer segments. Depth developing is the quality of credit facilities, quality of credit services, improve customer satisfaction and meet the reasonable needs of high-tech agriculture customers based on loanrisk control. 2.3.4.2. Developing indicators of commercial bank credits in high-tech agriculture Growth indicators on loan sales of high-tech agriculture Growth indicators on loan outstanding balance of high-tech agriculture Growth indicatorsabout high-tech agriculture customer quantity Growth indicatorson evaluatehigh-tech agriculture credit quality 2.3.4.3. Factors affecting the development of commercial bank credits for high-tech agriculture Objective factors group of the commercial bank • Credit policy of commercial bank • The bank information technology system • Internal control system that related to credit activities • The bank marketing activities • Implementingcommercial bank credit activities and the quality of workforce Subjective factorsgroup • Legal environment • Natural conditions and socio-economic environment 2.4. Theoretical frameworks of behavior 2.4.1. The theory of planned behavior (TPB) The theory of planned behavior was developed byAjzen and Fishbein (1980). The planned behavioral theory is meant to interpret most human behaviors. According to TPB, the true behavior of an individual in the conduct of a particular action is come frombehavioral intention of said individuals.The intention of which is influenced by three main factors: attitude, perceived behavioral control and subjective norm (Figure 2.1). Figure 2.1 The theory of planned behavior (TPB) 2.4.2. Theory of Technology Acceptance model (TAM) Technology Acceptance Model is also developed from the Theory of Reasoned Action (TRA) by Fisbein&Ajzen (1975), according to TAM, a person acceptance behavior of using a certain system is formed from their attitude toward using that system and their perceived usefulness (PU) of said system. In which, the attitude and perception about system usefulness is influenced by the perceived ease-of-use (PEOU). 10 + Figure 2.2The technology acceptance model (TAM) 2.4.3. The Expectation – Confirmation Theory (ECT) The Expectation – Confirmation Theory was proposed by Oliver (1980), see Figure 2.3 below. According to the ECT model, customer’s post-purchase behaviors are formed on four steps. (1) Expectations, (2) performance or quality of the product or service, (3) confirmation and finally, perceived of the confirmation, then customer will form an attitude about said product or service, that is the satisfaction. Figure 2.3 Theexpectation – confirmation theory model (ECT) 2.4.4. Theory of Perceived Risk Featherman and Pavou (2003) defined perceived risk is the human perception of possible losses when they pursue a specific goal. 2.4.5. Integrated Models 2.4.5.1. Lee's Integrated Model (2009) 11 Figure 2.4 Lee's Integrated Model (2009) Lee (2009) had integrated the TPB and TAM models while adding additional theoretical theories, risk awareness – the perception of benefits to formulate an integrated model that explaining the internet banking using intention of Taiwanese people's (See Figure 2.4). The integrated model has been successfully tested through quantitative research and the prefixes of both TAM and TPB models, such as perceived usefulness (PU), perceived ease of use (PEOU), subjective norm (SN) and perceived behavioral control (PBC).They’re all contributed to the explain customer's attitude and intention in choosing to use internet banking. 2.4.5.2. The integrated model of Bhattacherjee (2001) Figure 2.5 Post-acceptance model The post-acceptance model of Bhattacherjee (2001) has been verified by quantitative methods in research context both in terms of customer acceptance and retention to online banking services in 12 the United States. The results of the study showed that Bhattacherjeehad successfully built the validation scale and integrated it appropriately along with TAM model to explain customer behavior. 2.4.5.3. The integrated model of Liao et al (2007) Figure 2.6 Integrated model by Liao et al. (2007) Author Liao and his associates had integrated the TPB and TAM theories to build a model to explain the intention of continuing to use the online learning system (e-learning) of the students in Taiwan (see Figure 2.6). The results of the study showed that the model was successfully built and confirmed with all the theories being supported, and the model had explained 70% for the intention of continuing to use the e-learning system of Taiwanese students. 2.5. Relevant studies assessment and inheritance First, through the examination of the relevant experimental studies, thesis found that the study byWaqarAkram, Zakir Hussain, MH Sial and Ijaz Hussain (2008) has a wide range of suitable content in the purpose of study high-tech agriculture customer’s access to commercial bank credit capitals; and integrated with group discussions between experts in banking sectors, business owners, co-ops and farmer households. This thesis created customer surveys on the demand of credit capitals for high-tech agriculture in Lam Dong Province, and used analysis tools and methods to point out issues in the process of accessing credit capital of high-tech agriculture customers. Second, from behavioral theories and from analyzing three integrated patterns of behavior, with the spirit to inherit anddevelop further the direction studies of mentioned Bhatteacherjee (2001), Liao and Associates (2007) and Lee (2009), the author builds the theoretical research model as shown in Figure 2.7 below. Figure 2.7 Theoretical research model In the research model, the final dependent variable of the model is the Behavioral intention (INT), the intermediate dependent variables are: Attitude (ATT) and Perceived of Usefulness (PU). Five independent variables of the model are: Perceived ease of use (EU); Perceived of Risk (RIS); Subjective norm (NOR); Cognitive Behavioral Control (PBC) and Confirmation (CONF). The result of above model can be present in following regression equations: 13 INT = α1 + β1ATT + β2NOR + β3CONF + β4PBC + Ɛ1 ATT = α2 + β5PU + β6EOU + β7CONF – β8RIS + Ɛ2 Two main research subjects are: • The intention to grant high-tech agriculture credit of credit officer who has never done high-tech agriculture credit contract before. • The intention to continue to grant high-tech agriculture credit of credit officer who did the high-tech agriculture credit contract before. Conclusion of Chapter two Chapter 2 introduces the theory of the commercial bank credit for high-tech agriculture and explains the theoretical concepts involved. Based on analyzing and synthesizing previous research models and behavioral theories, a research direction on customer credit demand is formed, and a new theoretical research model is proposed. The research methodology and testing of specific model will continue to be mentioned in Chapter 3. CHAPTER THREE: RESEARCH METHODS With the objective stated, the thesis has done two separate studies as follows: 3.1. The first research design 14 3.1.1. Research process Figure 3.1 First research process 3.1.2. Research Method 3.1.2.1. Qualitative research Qualitative research using group discussions to build the survey question. 3.1.2.2. Quantitative research Quantitative research was done using a convenient (non-probability) sampling method. Data collected will be analyzed using tools such as Excel and SPSS software. 3.1.3. Research sample The research subject is the farm owners and business owners who are producing flower or vegetables on the survey area. According to Nguyen DinhTho (2011), the minimum sample size for quantitative research is n = 150. The thesis collects 161 surveys to perform descriptive analytic. 3.1.4. Building questionnaires The closing questions use measurable scale that are inherited from the previous study of Akram and Hussain (2008) and other experts in the qualitative research. 3.1.5. Data analysis methods Use methods of analysis, synthesis, comparison and evaluation. 3.2. The second research design 15 Figure 3.2 Second research process 3.2.2. Research model and assumptions In the context of study high-tech agriculture credit of the thesis, the theoretical research model proposed in Chapter 2 can apply to both of two research subjects: -The intention to grant high-tech agriculture credit of credit officer who had never done high-tech agriculture credit contract before. -The intention to continue to grant high-tech agriculture credit of credit officer who did the high- tech agriculture credit contract before. 3.2.2.1 The first research model The subject of first research model is behavioral intention of granting credit to high-tech agriculture customer of credit officer. 16 Figure 3.3 First research model 3.2.2.1 The second research model The subject of second research model is behavioral intention of continuing to grant credit to high- tech agriculture customer of credit officer. Figure 3.4. The second research model 3.2.3. Research design The research was conducted in two main steps: Preliminary research and formal research. 3.2.3.1. Preliminary research Was conducted by two methods: qualitative and quantitative. The qualitative research was intended to explore, moderate and complete the observed variables used to measure the concepts of research. The qualitativeresearch was done through group discussion and deep interview. Quantitative researchwas conducted for two purposes: first was to check the reliability of the measure scale before conducting formal research, second was to estimate the response rate of the survey subjects to predict the number of surveys may be obtained if doing surveys at the commercial banks in the survey area. 3.2.3.2. Formal research The formal research was conducted by quantitative methods with the survey subject was the credit officer who never did and who didgranting high-tech agriculture credit. The collected data was cleaned up and implemented analysis steps include: Reliability analysis with Cronbach’s Alpha measure, exploratory factor analysis-EFA, and confirmatory factor analysis-CFA. The consumptions of two research models will be tested by structural equation modeling (SEM). Finally, using Bootstrapfeature to check the accuracy of the estimates in model. 3.2.4. Research sample 17 The research used the cluster sampling method, with the overall sampling subjects was all credit officers who never did and who did high-tech agriculture credit contract at commercial banks in survey area. 358 questionnaires were collected for formal research sample and divided into 2 groups, group 1 including 175 questionnaires were those credit officers who have not previously granted high-tech agriculture credit and 183 questionnaireswere the credit officers who have grantedhigh-tech agriculture credit.The sample size qualified minimum sample size (n = 150) Nguyen DinhTho (2011). 3.2.5. Measure scale The measure scale of theoretical concepts was inherited from the previous studies by Lee (2009) and Bhattacherjee (2001), through qualitative research with experts to complete and correct the content, in accordance with research context was credit activities at commercial banks. The research used Likert measuring scale with 5 levels respectively (1: Completely disagree – 5: Completely agreed). 3.2.6. Data analysis methods The data was processed through two common steps on structural equation modeling (SEM) with both measurement model and a structural model that were evaluated by software AMOS 22.0. The measurement model was analyzed by the Cronbach’ alpha test and the exploratory factor analysis (EFA) for preliminary assessment of the measure scale. The confirmatory factor analysis (CFA) was used to verify composite reliability, convergent validity and discriminant validity and general compatible rate of the scale. Structural model was used SEM technique with maximum likelihood (ML) for estimating the overall relevance to the research model and the stated research consumptions. 3.2.6.1. Descriptive analysis 3.2.6.2. Measure scale reliability analysis 3.2.6.3.Exploratory factor analysis (EFA) 3.2.6.4. Confirmatory factor analysis (CFA) 3.2.6.5. Testing the research model Chapter Three Conclusion Chapter 3 has designed and described the full range of research methods to fulfill four objectives of the thesis, the research results is shown in Chapter 4. 18 CHAPTER FOUR: RESEARCH RESULTS 4.1.Overview of the socio-economic status and agricultural production in Lam Dong Province 4.1.1. Natural condition of Lam Dong Province 4.1.2 Socio-economic status in Lam Dong Province 4.1.3. The status of high-tech agriculture production in Lam Dong Province 4.3.1.1. The status of agricultural production in Lam Dong Province 4.3.1.2. The status of high-tech agriculture production in Lam Dong Province 4.2. The status of the commercial bank credit development for high-tech agriculture in Lam Dong Province 4.2.1. Overview of the activities of credit institutions in Lam Dong Province 4.2.2. Demand for credit in high-tech agriculture production in Lam Dong Province The total capital to executethe high-tech agriculture program between 2001-2015 and 2016-2020 was 10,083,782 million dong. In which, the state budget capital was 3,174,000 million dong and the capital mobilized from credit institutions, businesses and households was 6,909,782 million dong. If the estimated demand for credit capital is about 70%, then total credit demand for high- tech agriculture production in the phase 2011-2020 was about 4,836,847 million dong. 4.2.3. Status of credit for agricultural in countryside area at commercial banks in Lam Dong Province About agricultural in countryside area, total sales of agricultural in countryside area lending in 2018 was more than 104,949 billion dong, up 237.5% to the previous year 2017, increase abundantly of 60,764 billion dong and went-up more than 80 times to the number in 2012. In terms of outstanding loan balance to agricultural in countryside area, the total outstanding loan balance in 2018 was more than 58,775 billion dong, up to 33.4% over the year 2017 with an increase of 14,726 billion dong and went-up more than 25 times to of year 2012. Credit balance growth of commercial banks in the agricultural in countryside areagrew average 71% per year. 4.2.4. The status of commercial bank credits for high-tech agriculture in Lam Dong Province - In loan sales: The total loan sales of high-tech agriculturein the 2012-2018 period was 1,021 billiondong, averaged 255.2 billion/year (2015-2018). Loan sales by commercial bank: with 26 commercial bank branches in Lam Dong province by 31/12/2018, there were 6 commercial bank branches had credit for high-tech agriculture with the total loan sales in 2015-2018 was 1,021 billion dong. In which, the loan sales of AgribankLam Dong was 474,504 billion dong, accounted for 46%; AgribankLam Dong 2 was 286,865 billion VND, accounted for 28%; Vietinbank Lam Dong was 29,493 billion dong, accounted for 3%, Vietcombank Lam Dong was 122,971 billion dong, accounted for 12%; Military Bank was 41,411 billion dong, accounted for 4% and Lien Viet Post Bank was 66,081 billion, accounted for 6%. The proportion of loan sales for high-tech agriculture in total loan sales for agricultural in countryside area: Figure 4.1 below shows that in the 2015 – 2018 period, loan sales proportion of high-tech agriculture to total loan sales of agricultural in countryside area was very low (in 2015 was 1.489%; in 2016 was 0.374%, in 2017 was 0.791% and in 2018 was 0.342%). 19 Source: Summary of State Bank - Lam Dong Branch Figure 4.1 Commercial bank loan sales proportion of agricultural in countryside area and high-tech agriculture in Lam Dong Province - Regarding the proportion of outstanding loan balance of high-tech agriculture to outstanding loans balance of agricultural in countryside area: in 2015-2018 period, outstanding loan balanceofhigh-tech agriculture accounted for very low percentage (0.255% in 2015, 0.264% in 2016, 1.267% in 2017 and 0.481% in 2018). -Regarding customers of high-tech agriculture loans: the total number of customers received high- tech agriculture loans in the 2015-2018 period was 798 customers.In which, there were 21 customerswerebusinesses and co-ops and 777 customers were households. The number of high- tech agriculture customers in this period compared to agricultural in countryside area loan customerswas very low (Businesses < 7% and households < 2%).In 2017, total customersreceived loansfor high-tech agriculture pursuant toDecision No. 813 (loans with preferential rate) was 443 customers (8 businesses and 435 households). In 2018, the number was 18 customers (9 businesses and 9 households). -Regardinghigh-tech agriculture customers with outstanding loan balance: the number of high- tech agriculture customers with outstanding loan balance accounted for 0.2% of total agricultural in countryside area customers. The number of business customers with outstanding loan balance was only 1.1% of total businesses customers with outstanding loan balance in agricultural in countryside area. -Regarding non-performing loans (NPLs) in agricultural in countryside area and high-tech agriculture: NPLs of agricultural in countryside area in 2015-2017 period accounted for about 25% of total NPLs, and there was none NPLs of high-tech agriculture. In 2018, NPLs of agricultural in countryside area accounted for 92.6% of total NPLs, and NPLs of high-tech agriculture accounted for 1.47% of total NPLs in agricultural in countryside area. 4.2.5. Discussing the results of current high-tech agriculture bank credits in Lam Dong Province First, growth in loan sales and in outstanding loan credit balance of agricultural in countryside area in 2012-2018 period was very high. Meanwhile, the growth in high-tech agriculture loan sales was not much. The average increases was 36% per year; in the whole period, total high-tech agriculture loans was 1,021,325 million, accounted for 0.51% of agricultural in countryside area loan sales. 20 Second, growth in loan sales and outstanding loan balance in agricultural in countryside area and high-tech agriculture spiked in 2 periods (2014-2015) and (2016-2017). Third, the short-term debt in agricultural in countryside area and high-tech agriculture customers still accounted for the majority. Four, the number of agricultural in countryside area loanscustomers increased very quickly during the whole period.Meanwhile, the number of high-tech agriculture loan customers wassmall (only 21 businesses, co-opswas borrowed for high-tech agriculture). 4.3. Results of customer survey on commercial bank capital access demand 4.3.1. Overview of the survey sample 4.3.2. Survey results • Survey result on agricultural production characteristics • Survey results on high-tech agriculture production • Survey results of customers’ concerns when participating in high-tech agriculture Table 4.1 Survey of customers’ concerns when participating in high-tech agriculture Concerns Quantity Rate Lacking of capital 134 83,2% Lacking of businesses to buy veggies and flowers after harvesting 102 63,4% Unfocused product brand 84 52,2% Lack of knowledge and experience 72 44,7% Not understanding the current quality standards 61 37,9% The mixing of the low-quality agricultural goods 47 29,2% Lacking of management skills 38 23,6% Lack of support from the government 33 20,5% Lack of labor 24 14,9% Do not know to choose which high-tech agriculture product 19 11,8% Unstable market 8 5,0% Lacking of farm land 3 1,9% Source: from survey results • Customer survey results on access to capital credit -Survey results aboutcommercial bank loan purposes for high-tech agriculture production Table 4.2 Survey on loan purposes of high-tech agriculture loan customers Loan purposes Quantity Rate Investing on facilities (net houses, greenhouse, etc.) 62 82,7% Investing on production system (machinery, irrigation systems, lighting system) 57 76,0% Production (seedlings, fertilizers, pesticide, labor salary, etc.) 54 72,0% Paying debts 25 33,3% Buying or renting agricultural land 16 21,3% Other purpose 8 10,7% Source: from survey results • Other funding sources of survey subjects Table 4.3 Survey on subjects about other capital sources for high-tech agriculture production Other capital sources Quantity Rate 21 Buy on credit from suppliers 37 49,3% Borrow from relatives 34 45,3% Advance payment from buyers 31 41,3% Not using any others capital source 13 17,3% Participate tontine 7 9,3% Other credit sources 5 6,7% Source: from survey results • Survey results on the type of bank loans collateral Table 4.4 Survey on subject’s loan collateral Collateral type Quantity Rate House, real-estate 58 77,3% Agricultural land 21 28,0% Other people's collateral 11 14,7% Machinery, factory or private property 7 9,3% Non-collateral loan 2 2,7% Source: from survey results • Survey on customers don’t borrow from commercial banks for high-tech agriculture production Table 4.6 Reasons customers didn’t borrow from commercial banks for high-tech agriculture production yet Reasons customers don’t borrow from bank Quantity Rate No need to borrow 35 40,7% Already borrowed from other source 51 59,3% There’s need but don’t know where to borrow 5 5,8% Reason for non-approved loan No collateral 2 2,3% Insufficient production capacity 2 2,3% Don’t know how to complete the loan application 20 23,3% Poor loan propose 21 24,4% Credit restriction policy of commercial bank 3 3,5% Other reasons for not want to borrow Bribing cost for credit officer 15 17,4% Got other capital sources 46 53,5% Don’t want to pay interest 14 16,3% Complicated procedure 17 19,8% Too far from the bank branches 6 7,0% Costly procedure process 21 24,4% Time-consuming to make propose and wait for approving 40 46,5% Source: from survey results • Survey result of difficulties when borrowing from commercial bank Table 4.7 Survey of difficulties in commercial bank loan process for high-tech agriculture production 22 Difficulties Quantity Rate Low evaluating collateral asset 51 68,0% Loan term is too short 46 61,3% Not many other forms of collateral 29 38,7% Approved loan amount is lower than need 26 34,7% Application take long time to approved 15 20,0% Must have collateral asset 15 20,0% Complicated procedure 8 10,7% Source: from survey results • Survey results about the quality of credit services for high-tech agriculture in Lam Dong Provinces - Customer survey results about the importance of the credit service quality evaluation criteria for high-tech agriculture In order to understand the hindrances in agricultural credit financing process of commercial bank, the thesis used Importance-performance analysis. Table 4.8 Evaluation criteria used in importance-performance analysis Symbol Evaluation Criteria Level of importance Credit service quality X1 Loan interest rate 4,37 3,11 X2 Loan procedure 4,37 3,31 X3 Loan approval time 4,60 3,17