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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 © 2011 ACADEMY PUBLISHER 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 practice- years 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 Entrepreneur Association, 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 activities in that year; ② average time spend on social activities 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
JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER 2011 1821 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. After deleting the data those are incomplete or obviously invalid data, we carry out mean value analysis 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 TABLE 4-1 SAMPLES INFORMATION 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: Gender Education Age Type: Male No:2328 89.9% Type: Female No:261 10.1% Type No. % Type No. % Primary 49 2.1 Under 25 11 0.5 Junior 434 18.6 26~35 301 12.9 Senior 983 42.2 36-45 1024 44.0 College 764 32.8 46-55 798 34.3 Master 98 4.2 Above 55 194 8.3 Primary 7 2.7 Under 25 0 0 Junior 31 11.9 26~35 44 16.9 Senior 117 44.8 36-45 122 46.7 College 90 34.5 46-55 76 29.1 Master 16 6.1 Above 55 19 7.3 Table 4-2 ANOVA Result Gender Work experience Industry 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 37 1.6 Manufacturing category ( Agriculture, Mining, Manufacturing, Geology, Construction, Electricity & Gas) 1306 56.1 2-5 years 142 6.1 Service category(Food service, Finance, Insurance, Real State, Social Services, Education, Scientific Research, Health) 806 34.6 6-10 years 339 14.6 Others 216 9.3 More than 10 years 1810 77.7 Female Less than 1 year 7 2.7 Manufacturing category ( Agriculture, Mining, Manufacturing, Geology, Construction, Electricity & Gas) 102 39.1 2-5 years 14 5.4 Service category(Food service, Finance, Insurance, Real State, Social Services, Education, Scientific Research, Health) 136 52.1 6-10 years 34 13.0 Others 23 8.8 More than 10 years 206 78.9 © 2011 ACADEMY PUBLISHER exploratory factor analysis by using formal questionnaire date. The data’s KMO = 0.660, and Bartlett's test of the value of spherical is 6678.330, whose Sig =.000
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