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• W2316402222 abstract "This paper builds the Tobit model to analyze the factors influencing credit demand via carrying out a survey in high technology SME of Heilongjiang technological region. The results show that the credit demand influenced by the main business, the business scale, the RDcredit demand;Tobit model I. COLLECTION AND COLLATION OF STATISTICAL DATA The survey project is supported by Heilongjiang Provincial Development and Reform Commission and Heilongjiang Provincial Academy of Social Sciences, the organization of more than ten people to use a sample survey, in March 2013 to May 2013 to carry out the questionnaire survey. This investigation uses the method of sampling and screening. Ultimately, effective questionnaire is 417, of which three National University Technology Parks, 347 parts, 70 parts of a provincial universities science and Technology Park, 103 software service industries, 314 other industries. II. THE DESCRIPTIVE ANALYSIS ON THE FACTORS AFFECTING THE CREDIT DEMAND OF HIGH-TECH SME A. Main business and credit demand Tab.I shows the two credit demand of different industries, which can be seen in the production of hightech industries higher demand for credit, accounting for 80%, while the service the credit demand of high-tech industries accounted for only 35% TABLE I. THE INCIDENCE OF CREDIT DEMAND OF ENTERPRISES IN DIFFERENT INDUSTRIES IN HEILONGJIANG Productive sectors Service industry The number of enterprises with credit demand 251 36 The incidence of credit demand 80% 35% B. Business performance and credit demand Through tab., we find that the operating losses of the enterprise, the year of the profit enterprise and for three consecutive years of profitable business maintain a relatively stable state of credit demand, which is mainly high tech companies are required to maintain the core competitiveness of the enterprise itself, so the capital investment is relatively large. TABLE II. THE INCIDENCE OF CREDIT DEMAND OF DIFFERENT OPERATING PERFORMANCE OF ENTERPRISES IN HEILONGJIANG Lossmaking enterprises in those years Profit enterprise in those years Three consecutive years of profit-making enterprises The number of enterprises with credit demand 25 123 139 The incidence of credit demand 73% 74% 64% C. Level of debt and credit demand TABLE III. THE INCIDENCE OF CREDIT DEMAND OF DIFFERENT DEBT LEVELS OF ENTERPRISES IN HEILONGJIANG No debt Less than 30% 30%70% 70%or more The number of enterprises with credit demand 60 81 109 37 The incidence of credit demand 80% 72% 64% 64% Through tab.III we find that high tech SME regardless of whether the debt or not have a higher demand for credit, International Conference on Education, Management, Computer and Society (EMCS 2016) © 2016. The authors Published by Atlantis Press 1100 but some companies believe that with a higher debt is difficult to refinance, there are some companies that should improve the risk investment system as soon as possible to ensure that the R & D results can be converted into funding as soon as possible. D. Business scale and credit demand Through tab. IV we see high tech SME are basically the case, with the scale of the expansion of the company's fixed asset investment, operating costs are gradually increasing. We need to ensure a certain operating profit to make business develop. However, 20 to 50 million-scale enterprises demand is relatively small, but it is not a reasonable explanation. In addition the service of high-tech small and medium enterprises is generally smaller with the basic distribution of 20 million or less. TABLE IV. CREDIT DEMAND INCIDENCE OF DIFFERENT ENTERPRISES SCALE OF ENTERPRISES IN HEILONGJIANG Below5 million 5million20 million 20 millio n -50 millio n More than 50 millio n The number of enterprises with credit demand 72 103 51 61 The incidence of credit demand 64% 77% 50% 90% E. R & D investment and credit demand Through tab. V we can see that there are credit demands of enterprises in R & D investment accounted for 66%, the other for the expansion of business scale. And service of high-tech industry in the R & D investment less demand, mainly because of its focus on R & D by the human capital. TABLE V. DIFFERENT BUSINESS R & D INVESTMENT RATE OF CREDIT DEMAND IN HEILONGJIANG Productive sectors Service sectors Number of R & D enterprise credit demand 189 14 R & D investment accounted for 75% 40% F. Credit system and credit demand No matter how the system changes in credit, business credit demand changes little, probably because of the country supporting high technology industries and making efforts to large and small and medium enterprises under investigation in the university industrial parks. The local government also has a certain degree of support, so it has little effect on changes in the credit system. TABLE VI. THE INCIDENCE OF CREDIT DEMAND OF DIFFERENT CREDIT SYSTEM OF ENTERPRISES IN HEILONGJIANG Original credit system loosening of credit system The number of enterprises with credit demand 287 303 The incidence of credit demand 69% 73% III. EMPIRICAL ANALYSIS OF CREDIT DEMAND OF HIGH TECHNOLOGY SME A. Tobit model description Tobit model, also known as censored normal regression model, is a model of the loss of the model from the following 0 points in which is a linear regression on latent variables, which can add errors are normally distributed and the same variance [1-5]. Thus, there are: y        (1) Among them, the error term is: 2 [0, ] N   (2) For different observation values, it has constant value variance 2  . This implies latent variable 2 [ , ] y N      . Observable y is in line with 0 L  . This result can be defined by the censored data, so there is:" @default.
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• W2316402222 date "2016-01-01" @default.
• W2316402222 modified "2023-09-24" @default.
• W2316402222 title "Investigation and Empirical Research on the Credit Demand of High Technology SME" @default.
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• W2316402222 doi "https://doi.org/10.2991/emcs-16.2016.270" @default.
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