The Research on the Influencing Factors of Financing Strategy of Woman Entrepreneurs in China

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1 JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER The Research on the Influencing Factors of Financing Strategy of Woman s in China Xiong Xiong College of Management and Economics, Tianjin University,Tianjin, China xxpeter@tju.edu.cn Rong Fu, Wei Zhang, Yongjie Zhang College of Management and Economics, Tianjin University,Tianjin, China diana1228cn@yahoo.com.cn, weiz@tju.edu.cn, yjz@tju.edu.cn Lin Xiong The Robert Gordon University,Scotland, Aberdeen,UK l.xiong@rgu.ac.uk Abstract Based on the data from the nationwide surveys of SMEs in "China's Private Economic Research in 2002, this paper examines gender differences among Chinese entrepreneurs seeking financing pattern, including external and internal financing, and studies on the factors those affect women entrepreneurs financing strategies through theoretical analysis and model validation from the human capital and social capital perspective. We find that human capital and social capital have positive influence on seeking external financing. There is also some evidence that the impact in Administrative system may promote external financing in China. Index Terms Women entrepreneurship, Human capital, Social capital, financing strategy I. INTRODUCTION With the development of economy, increasing women begin to start their own business, no matter in developed or developing countries. Although female entrepreneurship has drawn wide attention only in recent 20 years, the female entrepreneurship developed rapidly, and women enterprise has become an important driving force for the global economic growth. According to the GEM report (2005), female entrepreneurship is booming worldwide, and more than a third of entrepreneurs are woman. According to GEM 2007, the Chinese women's entrepreneurial activity index was as high as 11.16%, higher than the global average. However, contrast to the entrepreneurial enthusiasm, woman enterprises rely more on self-accumulation of capital and develop relatively slow. For the research on women entrepreneurs financing strategy, many scholars believe that there are some Corresponding author: Yongjie Zhang. differences between male entrepreneurs and women entrepreneurs in financing patterns. Women entrepreneurs face more difficult in obtaining financing, and seem to have some specific financing [1]. Women face significant difficulties in external financing, particularly bank loans, venture financing. In the past 40 years, the United States about 40% of the enterprises are owned or managed by women, but less than 5% of the venture capital invest in the women-led enterprises [2]. In order to analyze this subject, scholars give various explanations from different points of view. On the supply side (behaviors of bankers and public funders), some scholars believe that women entrepreneurs encountered with some credit discrimination when seeking to external financing. Women were required higher interest rates and more additional conditions when applying for loan [3]. Using the methods of experimental and qualitative analyze, Sara Carter et al [4] found that the loans lenders assess different conditions when dealing with the application of loan from male and female appliers. When the variables such as business industry, credit market structure are controlled, women business owners still have to pay more for the loan, and there is no evidence to prove that women enterprises are greater risk than men s. On the demand side (behaviors of women entrepreneurs), Orser [5] believe that women entrepreneurs are not that eager for business growth, and usually consider more on risks. In the process of applying for bank loans, women business owners are more negative, although their applications will not be easier to reject by banks. Some scholars also attempted to explain this phenomenon from human capital and social capital perspective. Nancy [2] find that only education level of women business owners has a significant impact on the choice of external equity financing strategy, while social capital is not directly affected. doi: /jcp

2 1820 JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER 2011 II. LITERATURE REVIEW Women entrepreneurs financing pattern has been noticed by many scholars recently. Some of them tried to study the issue from human capital perspective. By studying the Finnish company, Cooper et al. [6] found entrepreneurs education level have a significant impact on the enterprise's survival and development. Loscocco et al. [7] believe one of the key factors leading to the success of small businesses is the relevant industry experience. They found that women has disadvantage in this area, because the female entrepreneurs than male entrepreneurs usually have less relevant experience in specific industry. Bosma et al. [8] studied more than 1,000 Dutch companies, and confirmed the industry experience prior to starting one s own business has played an important role in profitability and growth of small business. Bates [9] found that college-educated entrepreneurs has a higher rate of success of starting one s own company than less educated entrepreneurs, and they apply for a loan from commercial banks easier. Fabowale et al [10] found that banks increased rejection rate of lending loan when women business owners had few management experience. Boden and Nucci [11] believe that women has less opportunity to accumulate human capital because of their lower payment and less management experience. Some of scholars also tried to carry out the research from social capital perspective. Uzzi s [12] research shows that the strong and weak links of the network between companies and banks are favorable for applying loan, and obtaining lower interest rates. Higgins and Gulati [13] found that the more extensive networks business owners have, the more excellent investment banks willing to underwrite the company s IPO. Shane and Stuart [14] found that venture capitalists seem to be more willing to invest in emerging companies those they are familiar with, especially the entrepreneurs who had previously sponsored or had close contacted before. Priscilla chu[15] and other scholars studied the Chinese entrepreneurs in Hong Kong and Canada, and found that social capital of entrepreneurs could provide an access to critical resources for enterprises development, such as market, technology, capital, knowledge. In addition, Tjosvold [16] also believe social capital could be used in getting support from government. Pearce and Robinson [17] also pointed out that in China, enterprises leaders usually set up long term relationship among political parties, administrative leaders and other business executives, and the relationship with government officials is often essential for industrial and commercial enterprises on business success. David [18] found that at present stage, both private entrepreneurs and managers of state-owned enterprises in China all maintain close and good social relations with the government and the Party, which is one of important ways to obtain economic resources. On the other hand, Renzulli, Aldrich, and Moody [19] found that compared with men s, most women s networks are lack of diversity, which will hinder women entrepreneurs to identify entrepreneurial opportunities as well as search for scare resources. Just as Nancy [20] mentioned, the lack of diversity of female s networks results from the lack of contact with those who control key resources and critical introducers in their past experience. III. RESEARCH QUESTIONS AND METHOD A.Research questions Several specific financing patterns of women-owned businesses and relative explanations have been identified in the previous section. In this review, we try to verify several hypothesis and interpret the reasons of women entrepreneurs financing strategies from human capital and social capital perspective. To identify human capital, we use 2 variables. 1 the result of formal education education level; 2 knowledge gained from work experience and practiceyears of work experience. To quantify social capital, we design 3 categories of variables, including the type of social network, the scale of social network, and the intensity of social network. The network we surveyed are following 6 types of network, the member of Deputy to People s Congress, CPPCC National Committee member, the Federation of Industry member, member of the Individual and Private, Chinese Communist Party, and Democratic Party. We also investigated the highest duties and the levels of network in the first 4 networks. The scale of network is measured by the number of membership. The greater the number of membership is, the larger network is. The intensity of network is measured by the following indicators: 1 the cost spends on social in that year; 2 average time spend on social weekly. The greater the value of these two indicators shows, the greater the intensity of the network is. Meanwhile, we have three hypotheses to verify in Chinese market. H1: There are gender differences when entrepreneurs choose financing strategy. H2: More human capital female entrepreneurs have, external financing are more likely to be used. H3: More social capital female entrepreneurs have, external financing are more likely to be used. The conceptual model could be established in following way: Figure.1 conceptual model of analysis

3 JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER B.The data and research method This study is based on the data from the 2002 China Private Economy Research survey, a national wide survey conducted by the CPC Central Committee United Front Work Department, China Federation of Industry and State Administration for Industry. To ensure data s integrity, reliability, we use Microsoft Excel to eliminate invalid original survey for the preliminary screening. Then we use SPSS17.0 statistics software to process the data, and the main analysis methods include reliability and validity analysis, descriptive statistics analysis, ANOVA, exploratory factor analysis, Logistic regression. IV. EMPIRICAL ANALYSIS Before starting our analysis, we summarize basic information of our samples as follows, shown in table 4-1: TABLE 4-1 SAMPLES INFORMATION Gender Education Age Type: Male No: % Type: Female No: % Type No. % Type No. % Primary Under Junior ~ Senior College Master Above Primary Under Junior ~ Senior College Master Above After deleting the data those are incomplete or obviously invalid data, we carry out mean value analysis Table 4-2 ANOVA Result Gender Work experience Industry According to above analysis, we obviously notice that men and women business owners have significant difference when choosing whether using external financing (Sig = 0.001), and averagely men prefers to external financing than women (Mean Male > Mean Female). However, the proportion of start-up capital in individual investors does not appear significant difference in genders. To identify the number of factors, and the correlation between the observed variables, we carried correlation between the observed variables, we carried out and ANOVA based on gender differences. The results are shown in Table 4-2: Type No. Percent Type No. Percent Male Less than 1 year Manufacturing category ( Agriculture, Mining, Manufacturing, Geology, Construction, Electricity & Gas) 2-5 years Service category(food service, Finance, Insurance, Real State, Social Services, Education, Scientific Research, Health) 6-10 years Others More than years Female Less than 1 year Manufacturing category ( Agriculture, Mining, Manufacturing, Geology, Construction, Electricity & Gas) 2-5 years Service category(food service, Finance, Insurance, Real State, Social Services, Education, Scientific Research, Health) 6-10 years Others More than 10 years exploratory factor analysis by using formal questionnaire date. The data s KMO = 0.660, and Bartlett's test of the value of spherical is , whose Sig =.000 <0.05. Therefore, exploratory factor analysis is acceptable to be used on these data. Then, we used principal component analysis to extract common factors. The result is as follows:

4 1822 JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER 2011 TABLE 4-3 FACTORS MATRIX AFTER ROTATION F10 Post in Private F11 Component F3 No. of social networks.638 F8 Industry and Commerce.511 Level F9 Industry and Commerce Post.444 F6 CPPCC Post.897 F7 CPPCC Level.873 F4 NPC Post.887 F5 NPC Level.882 F2 Work experience F1 Education.761 F12 Cost for social.681 F13 Time spends on social.530 To further study the factors those impact women entrepreneurs financing strategy, we tried to establish a Logistic Regression Model. The results are as follows, shown in table 4-4, 4-5, and 4-6: Table 4-4 Hosmer and Lemeshow Test Step Chi-square df Sig According to above tables, we can conclude our equation: P=1/(1+e-z) And z= *Work experience+0.057*cost for social *Industry and Commerce * *NPC Post. We may notice that the coefficients of work experience, cost for social, whether to join the Industry and Commerce, and the NPC Post, which are as measures of human capital and social capital, are positively correlated with external financing. Thus, this result confirmed hypothesis H2 and H3, that is the more human capital and social capital women entrepreneurs have, the more they tend to use external financing. We can explain these results from the previous literature and the social status. (a) human capital: The work experience, as an indicator of human capital, may promote using external financing. From the demand side, it may enhance the relevant skills and accumulate of the managerial experience. From the investors points of view, whether having relative work experience is considered an important measure, when applying bank s loan and attracting venture investment, which has also been confirmed in previous research. However, another measure of human capital, education background, was not evolved in our final model equation. Therefore, the higher education of women entrepreneurs does not means they would tend to use external financing. But this result is in accordance with Hu Huaimin's finding, that is the number of women entrepreneurs and their education level distributed in inverted U ", that is to say, both less educated and higher educated women are not that interested in starting their own business. (b) the intensity of network: Cost for social, as an indicator of the intensity of network, may reflect the maintenance of social networks by women entrepreneurs to some extent. Scholars have found that there are triangular interactions among emotions, resources and interactive reciprocal relationship, which is the more the interaction between individuals; the more likely they are to participate in group to share feelings, the more likely to exchange resources. Therefore, spending more on social and participating in more interactive, women entrepreneurs may obtain external financing resources easier. (c) networks between enterprises and industries: Although Chinese people values family relationship deeply, family network still cannot meet the needs of enterprises development. Therefore, joining in the Industry and Commerce, of Private s may expand women entrepreneurs social circle and benefit their career. On one hand, among these networks between enterprises and industries, network members are engaged in similar, and exchange information of different market, related technical advice and financing with women entrepreneurs. On the other hand, female entrepreneurs have more opportunity to the key figures mastering scarce financial resources by these networks. (d) Administrative impact:the coefficient of NPC Post, a measure of administrative impact, is much greater than the other three, which explains this factor has larger impact of using external financing. In China, the people's congress is China's highest authority. Deputies generally have high social reputation in social life, and also have some administrative influence in the administrative system. Therefore, when women entrepreneurs take some deputies duties, such as director or deputy director of the Standing Committee, it will increase their personal reputation to a large extent. Because of financial market imperfections and lack of policy stability, informal constraints in the economy during the transition period has played an important role in society, and network and people's trust has become extremely important. Thus, taking some duties in NPC is beneficial to attract external financing. Besides, based on previous studies, women, lacking of access to critical resources to grasp chances, are often disadvantaged in the network status. In China, about 75% of the deputies to the NPC are officials. Therefore, by serving as a certain NPC Post, women entrepreneurs will have an access to the

5 JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER some scarce financial resources and optimize their network infrastructure. According to above analysis, we establish our model as follows. Figure.2 Women entrepreneur s financing strategy V. CONCLUSION The main finding of this paper including: (a) From the result of ANOVA, we may notice that during the development of enterprise, men and women Step 1 a Cost for social Table 4-5 Variable in equation entrepreneurs have a significant difference in using internal or external financing. (b) Female entrepreneurs who have more human capital are more likely to use external financing. Work experience of women entrepreneurs has positive correlation to external financing, while education background is not significant in China. (c) Social capital on entrepreneurial financing strategies was significant. From the logistic regression equation, we find that Cost for social, Industry and Commerce, of Private s and NPC Post are the key factors of promoting using external capital for Chinese women entrepreneurs. ACKNOWLEDGMENT This research is supported by NSFC (Project ) and Royal Society of Edinburgh and National Natural Science Foundation of China for financial support (Project ). B S.E. Wald df Sig. Exp(B) Constant Step 2 b Cost for social Constant Step 3 c Cost for social NPC Post E Constant Step 4 d Work experience Cost for social NPC Post E Constant Step 5 e Work experience Cost for social Industry and Commerce NPC Post E Constant a. Variable(s) entered on step 1: Cost for social. b. Variable(s) entered on step 2:.c. Variable(s) entered on step 3: NPC Post. d. Variable(s) entered on step 4: Work experience. e. Variable(s) entered on step 5: Industry and Commerce.

6 1824 JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER 2011 TABLE 4-6 THE MODEL AFTER REMOVING SOME OF THE VARIABLES Change in -2 Log Variable Model Log Likelihood Likelihood df Sig. of the Change Step 1 Cost for social Step 2 Cost for social Step 3 Cost for social NPC Post Step 4 Work experience Cost for social NPC Post Step 5 Work experience Cost for social Industry and Commerce NPC Post REFERENCES [1] Nancy Carter, Candida Brush, Patricia Greene, Elizabeth Gatewood and Myra Hart, Financing High-Growth Enterprise: Is Gender an Issue?, J. Women in Business: Access and Success, 2003: [2] Candida G. Brush, Nancy M. Carter, Patricia G. Greene, Myra M. Hart, The role of social capital and gender in linking financial suppliers and entrepreneurial firms: a framework for future research, J. Venture Capital, 2002, Vol. 4, Issue 4: [3] Coleman S, Access to capital and terms of credit: A comparison of men-and women owned small businesses, J. Journal of Small Business Management, 2000, 38 (3):37-5. [4] Sara Carter, Eleanor Shaw, Wing Lam and Fiona Wilson, Gender, ship and Bank Lending: The Criteria and Processes Used by Bank Loan Officers in Assessing Applications, J. ship Theory and Practice, 2007, 31(3): [5] Barbara Orser, Sandra Hogarth-Scott, Opting for Growth: Gender Dimensions of Choosing Enterprise Development, Canadian Journal of Administrative Sciences, 2002, 19(3): [6] Cooper, A. C, F. J. Gimeno-Gascon and C. Y. Woo, Initial Human and Financial Capital as Predictors of New Venture Performance, Journal of Business Venturing, 1994, 9: [7] Loscocco, K. A., J. Robinson, R. H. Hall and J. K. Allen, Gender and Small Business Success: An Inquiry into Women s Relative Disadvantage, J. Social Forces 1991, 70(1): [8] Bosma, N., M. van Praag, R. Thurik and G. de Wit, The Value of Human and Social Capital Investments for the Business Performance of Startups, J. Small Business Economics, 2004, 23(3): [9] Bates, T, Human Capital Inputs and Small Business Longevity, J. The Review of Economics and Statistics, 1990, 72(4): [10] Fabowale, L., Orser, B. and Riding, A., Gender, structural factors and credit terms between Canadian small businesses and financial institutions, J. ship Theory and Practice, 1995,19(4): [11] Boden, R. J. and Nucci, A. R., On the survival prospects of men s and women s new business ventures, J. Journal of Business Venturing, 2000, 15(4): [12] Uzzi B, Embeddedness in the Making of Financial Capital: How Social Relations and Networks Benefit Firms Seeking Financing, J. American Sociological Review, 1999, 64(4): [13] Higgins M, Gulati R, Getting off to a good start: the effects of upper echelon affiliations on underwriter prestige, J. Organization Science, 2003, 14(3): [14] Shane S, Stuart TE, Organizational endowments and the performance of university startups, J. Management Science, 2002, 48(1): [15] Priscilla Chu, Social Network Models of Overseas Chinese ship: The Experience in Hong Kong and Canada, J. Canadian Journal of Administrative Sciences, Dec.1996; 13(4): [16] Tjosvold, Dean, Weieker, David, Cooperative and competitive networking by entrepreneurs: A critical incident study, J. Journal of Small Business Management. Milwaukee: 1993, 31(1):11 一 22. [17] Zhilong Tian, Yongqiang Gao et al, Chinese Enterprises Political Strategy and Behavior Study, J. Management World, 2003, 12.