Impact of Individual Factors on the Business Performance of Women Entrepreneurs in Sri Lanka

Sri Lankan women have only recently taken an active role in the business sector of the country. However, they are seemed competing on an equal footing with the men, in the business field. This empirical investigation attempted to understand the impact of personal and industrial factors on the business performance of women entrepreneurs in Sri Lanka. This empirical study examined the applicability of five theoretical perspectives derived from studies in USA and Israel. These five theoretical perspectives include Motivation and Goals, Social Learning Theory, Network Affiliation Human Capital and industrial factors. Each of these variables was operationalized using 4 or 5 dimensions and sub dimensions. This study sheds light on the explanatory power of these five perspectives in the Sri Lankan context. Six hypotheses and six major research questions were developed and tested. A survey was conducted to gather primary data by selecting 100 small business women from Galle, Colombo and Ratnapura districts and that data was analyzed using multiple linear regression analysis and Pearson’s correlation analysis. The reliability was tested using Chronbatch’s Alfa value method and all the variables carried equal or above 0.65. The best fit model of the multiple linier regression includes only three variables such as Motivation & Goals, Human Capital and Industrial factors and excludes the Social Learning Theory and Network Affiliation because their ability to predict the Business Performance is negligible. The correlation analysis too showed strong positive correlations between Business Performance of Sri Lankan women entrepreneurs and Motivation & Goals and Human Capital. Social Learning Theory and Network Affiliation showed positive correlations with Business Performance. But the relationships were not much vivid. There is a very strong positive correlation between Business Performance of small business women and Industrial factors with a 0.721 of coefficient of correlation at a 99% significant level. Sabaragamuwa University Journal Volume 11 Number 1; December 2012, pp 139-165


Research Background
In a market economy, being a female entrepreneur means accepting the risks involved in selling either goods or services to make a profit. Entrepreneurs run their own business; pay taxes and either work alone or employ others, and thus achieve a degree of social standing. Even though women accept the risks and have to expend an enormous amount of time and energy upon the organization, an increasing number of women are seeking to become entrepreneurs (Hisrich & Brush, 1987). USA, Canada and Britain are among the countries in which female entrepreneurship thrives.
Sri Lankan women have only recently taken an active role in the business sector of the country. But still the number of female entrepreneurs is relatively low. However, the situation has now changed and there is evidence to prove that Sri Lankan women significantly contribute in every economic, political and social aspect of the country (Hemalatha, 2005). In fact they seemed to be competing on an equal footing with men, in such fields.
Especially in the business sector of Sri Lanka, it is conspicuous that women have shown significant progress over the men during the last few decades. A large number of women have started their own businesses and most of them have been able to meet with success. Some of them now compete at international level.
There are variations among these female entrepreneurs in terms of the determinants of their decision to become entrepreneurs, the level of success (or failure), and the determinants of their business performance and of many other aspects. This research study was carried out as an empirical study on the individual factors, which influence the business performance of women entrepreneurs of Sri Lanka. entrepreneurs, and sometimes have posed threats to their male counterparts by outperforming them. Researchers of Western countries like USA, Canada and Great Britain and of some Asian countries too have thoroughly studied the success factors of women entrepreneurs and/or the factors that influence the business performance of female entrepreneurs. In fact, there are some studies (Hisrich & Brush, 1988;Hisrich et al., 1997) which attempted to examine the relationship between the individual factors and the business performance of women entrepreneurs. In Sri Lanka it seems that researchers in the field have paid less attention towards this topic, despite there being a number of successful businesses owned and operated by Sri Lankan women.

I.
To determine whether the Individual Factors of the female entrepreneurs in Sri Lanka have an impact on their Business Performance.

II.
To determine whether there is a relationship between Motivations and Goals of female entrepreneurs and their business performance.
III. To find whether there is a relationship between the Social Learning Theory of female entrepreneurs and their business performance.
IV. To find whether there is a relationship between the Network Affiliation of female entrepreneurs and their business performance.

V.
To find whether there is a relationship between the Human Capital of female entrepreneurs and their business performance.
VI. To determine whether there is a relationship between the Environmental Influences and the business performance of female entrepreneurs.
H 5 : There is a positive correlation between Human Capital and Business Performance of women entrepreneurs in Sri Lanka.
H 6 : There is a positive correlation between Environmental Factors and Business Performance of Women entrepreneurs in Sri Lanka.

Significance of the Study
Women are a significant force in the entrepreneurial world of Sri Lanka and in the world as well. Over the past three decades, the increase of the number of women entrepreneurs has been at a significant level. This growth can be identified in terms of the number of the businesses, revenue generation and employment. A number of factors have influenced this growth.
This study will focus on identifying the relationship between individual factors and the business performance of Sri Lankan women entrepreneurs.
There is a big gap in this research area especially in the Sri Lankan context and in the world as well. Therefore this research study will be very important to the researchers in this subject field as a preliminary study which would give a foundation for their future researches.
The findings of this research would foster the theoretical background of women entrepreneurship and be helpful to all the scholars who are interested in the field of women entrepreneurship and the factors that influence the business performance of women entrepreneurs.
Further, this research would give important information for the government and non-government decision makers in the sector of entrepreneurship development and women affairs especially in designing and implementing the Entrepreneurship Training and Development Programmes for women.

Personal and Industrial factors influencing the performance in women-owned businesses
This chapter is dedicated to explain the Personal and Industrial factors factors those influence the business performance of women entrepreneurs. The most comprehensive summary of Personal and Industrial factors influencing performance was noted by Cooper and Gascon (1992), which examined such factors as experience, education, occupation of parents, gender, race, age, and entrepreneur's goals. This summary, drawn from previous literature examining the performance (Stevenson and Jarillo, 1990;Vesper, 1980;Gartner, 1985;Cooper, 1989), concluded that successful ventures are more often started by men who are achievement motivated, manage risk, and engage in systematic planning. Although Cooper and Gascon (1992) cogently summarized the state of research on the topic to date, their discussion of independent variables is not organized according to particular theories or approaches. Studies of performance of women entrepreneurs are few (Brush, 1992), with the majority of research not being comparative among groups of women and men. However, individual level variables explaining performance in United States studies were frequently the same as those noted in male-owned businesses: previous occupational experience, business skills, level of education, and personal factors such as motivations and having a mentor.
The largest comparative study in the United States was conducted by Kalleberg and Leicht (1991) who examined the determinants of survival and success among small businesses headed by men and women, using longitudinal data. They found that women's businesses were no more likely to fail and were just as successful as men's, which was contrary to conventional wisdom regarding women's inferiority in entrepreneurship. This research also found that the determinants of survival and success operated in much the same way for men and women "suggesting that the processes underlying small business performance are similar irrespective of an entrepreneur's gender" (Kalleberg and Leicht, 1991). These findings were supported in a comparative study conducted by Johnson and Storey (1994) in the United Kingdom, which found no significant differences in survival rates of male-and female-owned businesses, although women-owned firms were typically smaller and more likely to employ women. Most recently, Chaganti and Parasuraman (1994) examined strategic approaches of male and female entrepreneurs as these related to performance, finding similarities across gender, with the exception that women tended to emphasize quality more than men.
Although these recent studies add to our understanding of performance similarities and differences, "performance" in entrepreneurial businesses was operationalized differently, making it difficult to compare across studies. Most frequently used operationalizations of performance include survival, growth in employees, and profitability (Srinivasan et al., 1994). For instance, Kalleberg and Leicht (1991) and Johnson and Storey (1994) have examined survival, whereas Chaganti and Parasuraman (1994) examined financial performance and documented organizational factors as they related to performance.

Motivations and Goals
Psychological motivations such as achievement, independence, and locus of control have been widely investigated with regard to their influence on business start-up (Brockhaus and Horwitz, 1986). Fewer studies examined their relationship to business performance. U.S. research has found that individual motivations and owner/founder goals are related to performance in women-owned businesses, where opportunity motivation was related to survival and independence was associated with "no growth" (Hisrich and Brush, 1987).
Other studies indicate few significant performance differences related to male versus female achievement motivation or individual self-esteem. Women reported lower levels of self-confidence than did males (Miskin and Rose, 1990).

Social Learning Theory
The second theoretical approach emphasizes the role of entrepreneurial socialization, which is anchored in social learning theory (Bandura, 1977) as an explanation of entrepreneurial behavior and career development. Social learning can occur through the observation of behavior in others, often referred to as role models. The individual's socialization process, which occurs in the family setting, transmits social norms, language, educational aspirations, and shapes career preferences through observational learning and modeling (Bandura, 1977).
The utility of social learning theory to explain entrepreneurial career preferences was explored in several studies. The effects of observational learning through perceived parental entrepreneurial role model performance was explored by Scherer et al., (1989). These authors found that the presence of a parent in an entrepreneurial role was associated with increased education and training aspirations, task self-efficacy, and expectancy for an entrepreneurial career. The study also showed that individuals with a parent performing an entrepreneurial role were often high performers and significantly different from individuals without role models, who were low performers. Similarly, a Canadian study found that 33% of the women entrepreneurs in this sample reported their fathers were entrepreneurs (Belcourt et al., 1991).

Network Affiliation
The third perspective states entrepreneurship is embedded in a complex network of social relationships. Within these networks, entrepreneurship is facilitated or constrained by linkages between aspiring entrepreneurs, resources, and opportunities (Aldrich and Zimmer, 1986). According to this view, the presence or absence of networks, such as access or membership in associations, play a role in influencing performance. The facts that women entrepreneurs are embedded in different personal and social networks than men and that division and barriers limit the reach and diversity of their networks have far-reaching consequences for business performance (Aldrich, 1989).
and business skills will influence business performance. Cooper (1989) proposed experience and education were "antecedents" to the decisions to start a company and ultimately affected performance. Several studies showed that years of formal education of the entrepreneur before establishing a new firm were related to eventual performance of the firm (Box et al., 1993;Brush and Hisrich, 1991). Box et al., (1993) also found a relationship between higher levels of education and increased performance among manufacturing firms in Oklahoma. Testing the effects of education on business ownership in a longitudinal study, Dolinsky et al., (1993) reported that the levels of staying and reentering a business increased with higher levels of education. They argued that less educated women might face financial or human capital constraints that limited their business pursuits.

Industrial Factors
Environmental influences presume that factors including the differential structure of opportunity, location, sectoral activities, and sociopolitical variables (i.e., availability of government assistance) are critical determinants of performance. Economic measures of venture profitability; revenues and number of employees are related to environmental economic conditions, such as the market structure, regional opportunities, investment climate, availability of labor, and other features (Gibb, 1988). Similarly, resource availability, including venture capital, technical labor force, loans, support services, and a favorable entrepreneurial subculture are also a major influence on performance (Bruno and Tybjee, 1982). Dess and Robinson (1984) concluded that performance is a complex and multidimensional phenomenon that is difficult to operationalize without using a combination of objective and subjective measures.

Business Performance
The present study is based on the following operational definition of Business Performance.
Operational definition: The Business Performance can be defined as how the company is doing financially, ability to achieve improvements in specified areas like competition, market growth and achievements and measure of how effectively each of these areas has been addressed.
The concept of Business Performance was conceptualized using five dimensions proposed by Brush (1982, 1985). These dimensions include the increase of revenue, increase of profit, ability to face competition, business expansion and business achievements. The dimensions of Business Performance were then measured with the use of five question items specially developed by the researcher for this purpose.

Conceptual Framework
Studies of personal factors influencing performance are prevalent, although the majority of these were carried out in the United States, Canada, and the United Kingdom. This study will focus on individual level factors and industrial factors as they significantly influence performance. The following discussion is organized according to five theoretical perspectives, each of which has a corresponding body of empirical research. These five perspectives are: Motivations and goals, Social learning, Network affiliation, Human capital and Industrial factors (See Figure 1).

Motivation & Goals
Operational definition: Motivation is a process that account for and individual's intensity, direction and persistence of effort towards attaining goals.
The researcher conceptualized the concept of motivation and goals with the use of four dimensions based on a theoretical framework, which, was originally developed by Brush (1982, 1985) and was replicated (subject to the changes of conceptual and sampling procedures) by . These four dimensions are achievement motives, independence motives, economic necessity motives and personal goals. The dimensions of motivation and goals were then measured with the use of four question items specially developed by the researcher for this purpose. Each question was given five responses, to be selected by the respondents considering their real motives.

Social learning theory
Operational definition Social learning theory is the individuals' socialization process, which occurs in the family setting, transmits social norms, language, educational aspirations, and shapes career preferences through observational learning and modeling.
Conceptualization of the concept of Social Learning Theory is based on four dimensions of Social Learning Theory measurements proposed by Brush (1982, 1985). They are parent/s entrepreneur, husband entrepreneur; some one was a role model and the economic status at childhood. The dimensions of Social Learning Theory were then measured with the use of five question items specially developed by the researcher for this purpose.

Network affiliation
Operational definition: Network Affiliation is the complex network of social relationships of an individual.
The concept of Network Affiliation was conceptualized using four dimensions such as the number of networks, use of advisors, membership in associations and mentors, as done by Brush (1982, 1985). The dimensions of Network affiliation were then measured with the use of five question items specially developed by the researcher for this purpose.

Human capital
Operational definition: The human capital can be defined as the supportive skills or abilities and capabilities of an individual developed through education and experience.
The concept of Human Capital was conceptualized using six dimensions proposed by Brush (1982, 1985). These dimensions include the education level, education area, previous occupation, previous entrepreneurial experience, experience in the industry and business skills. The dimensions of Network affiliation were then measured with the use of five question items specially developed by the researcher for this purpose.

Industrial Factors
Operational definition: Industrial Factors presume that factors including the differential structure of opportunity, location, sectoral activities, and sociopolitical variables (i.e., availability of government assistance) are critical determinants of performance.
The concept of Environmental Influences was conceptualized using five dimensions proposed by Brush (1982 and1985). These dimensions include sources of finance, market situation, socio political environment, location, and resource availability.

Methodology
The Population

Sampling Method
The sample includes 100 women entrepreneurs from Colombo, Galle and Rathnapura districts. The sampling procedure was non-probability sampling. The convenience method of sampling was used to select the women entrepreneurs (small scale business women).

Data Collection
The researcher applied two systematic methods of data collecting that are used in methods to collect data in this research. They are the self developed questionnaire and the direct interviews. Primary data, which were collected with the use of the questionnaire, the main research instrument, were used to come up with the answers to the research questions developed at the beginning of the research. The questionnaire was originally developed by the researcher and distributed among the selected sample of women entrepreneurs to collect data related to the depending variable and independent variables. All the questionnaires were filled through personal discussions.

Reliability Test
The researcher used the Chronbach's Alpha value method to test the reliability of the questionnaire used to collect primary data. Table 3 discribes the results of tha realiabilty test.

Correlation Analysis
According to the results obtained by the Correlation analysis it is evident that there are positive relationships between the dependent variable (BUSIPER) and all the independent variables (MGOAL, SOLETH, NETAF, HUMCAP and ENVINF).  There is a strong positive relationship between Business Performance (BUSIPER) and Motivation & Goal (MGOAL). In this case the coefficient of correlation is 0.631 at a 99% significant level. (r= 0.631, p= 0.000) There is also a positive relationship between Business Performance (BUSIPER) and the Social Learning Theory (SOLETH), but it is not much strong. Here the coefficient of correlation is 0.443; which is less than 0.5. Therefore; the relationship is weak. The significant level is 99%. ( p= 0.000) The relationship between Business Performance (BUSIPER) and the Network Affiliation (NETAF) is also positive; but the relationship is not strong. The coefficient of correlation is 0.359 at a 99% significant level. (r= 0.359, p= 0.000) According to the results of the correlation analysis, there is a strong positive relationship between Business Performance (BUSIPER) and Human Capital (HUMCAP). Here the coefficient of correlation is 0.626 at a significant level of 99%. (r= 0.626, p= 0.000) The strongest positive relationship among all the independent variables and the dependent variable exists between Business Performance (BUSIPER) and Environmental Influences (ENVINF). Here the coefficient of correlation is 0.721 at a 99% significant level. (r= 0.721, p= 0.000)

Multiple Linear Regression
The multiple linear regression analysis was carried out to investigate the relationship between the dependent variable (Business Performance) with each independent variable such as Motivation and Goals (MGOAL), Social Learning Theory (SOLETH), Network Affiliation (NETAF), Human Capital (HUMCAP) and Environmental Influences (ENVINF). Since there are many independent variables in the model, the selection procedure; stepwise was applied to identify the most important variables in the model. Table 5.6 shows the model summary after applying the stepwise multiple regression procedure. The final model includes such variables as ENVINF, MGOAL and HUMCAP. All the variables are significant at 99% level of significance and none of the other variables are significant at 99% level.  According to the stepwise procedure, the most important variable at the first step is ENVINF. After including this variable to the model, the R 2 value is 52%, implying that variable ENVINF explains 52% of the total variability of the dependent variable.
After including the variable ENVINF, the second most important variable is MGOAL. This model explains more than 59% of the total variability of Business Performance. These two variables are highly significant at 99% level.

Testing the Hypotheses
The researcher for achieving the overall and specific objectives of this research developed six hypotheses. Those six hypotheses were then tested with the use of the results obtained by the correlation analysis run with regards to each independent variable and the dependent variable. The Pearson's coefficient of correlation is = 0.631** (P=0.000); which is close to plus one. It implies that there is a strong positive correlation between Motivation and Goals and Business Performance of Women Entrepreneurs of Sri Lanka.

Hypothesis 01
The results of stepwise regression analysis too came up with the same finding that there is a positive relationship between the Motivation and Goals (MGOAL) and the Business Performance of women entrepreneurs. Out of the three regression models developed by the stepwise regression analysis, two models include MGOAL as a good predictor of Business Performance. First, of which the R 2 is 0.596 (59%) the β value for MGOAL is 0.332. Second, the best fit regression model includes (MGOAL) as a good predictor of Business Performance. There R 2 is 0.621 (62%) and the β value for (MGOAL) is 0.284. (p=0.000)

BUSIPER= -0.397 + 0.431 ENVINF + 0.284 MGOAL + 0.208 HUMCAP + E i
The Business Performance will be increased by 0.284 when Motivation and Goals increases by one unit. Therefore we have evidence to reject the null hypothesis of no positive correlation between Business Performance and Motivation and Goals at 99% significant level.

Hypothesis 03
H 0 : There is no positive correlation between Social Learning Theory and Business Performance of Women Entrepreneurs of Sri Lanka. H 1 : There is a positive correlation between Social Learning Theory and Business Performance of Women Entrepreneurs of Sri Lanka.
We have evidence to reject the null hypothesis of no positive correlation between Business Performance and Social Learning Theory at 99% significant level. The Pearson's coefficient of correlation is 0.443**; which is not very much close to plus one. But the significant level is (P-value) 0.000 which is < 0.01 < 0.05. Therefore, we can conclude that though there is a positive correlation between Business Performance and Social Learning Theory, it is not much strong.
The stepwise multiple linier regressions analysis excludes Social Learning Theory (SOLETH) from the best regression model. Therefore we can conclude that the influence of Social Learning Theory in predicting the Business Performance is low.

Hypothesis 04
H 0: There is no positive correlation between Network Affiliation and Business performance of Women Entrepreneurs of Sri Lanka. H 1 : There is a positive correlation between Network Affiliation and Business Performance of Women Entrepreneurs of Sri Lanka.
We have evidence to reject the null hypothesis of no positive correlation between Business Performance and Network Affiliation at 99% significant level. The Pearson's coefficient of correlation is 0. 359**, and is not very much close to plus one. But the significant level is (P-value) 0.000 which is < 0.01 < 0.05. Therefore, we can conclude that though there is a positive correlation between Business Performance and Network Affiliation, it is not much strong. But the stepwise multiple linier regressions analysis excludes Network Affiliation (NETAF) from the best regression model as the influence of NETAF in predicting Business Performance is ignorable.

Hypothesis 05
H 0: There is no positive correlation between Human Capital and Business Performance of Women Entrepreneurs of Sri Lanka. H 1 : There is a positive correlation between Human Capital and Business Performance of Women Entrepreneurs of Sri Lanka.
We have evidence to reject the null hypothesis of no positive correlation between Business Performance and Human Capital at 99% significant level. The Pearson's coefficient of correlation is 0.626**; which is close to plus one. The significant level is (P-value) 0.000 which is < 0.01 < 0.05. Therefore, we can conclude that there is a strong positive correlation between Business Performance and Human Capital.
Here also the stepwise multiple linier regressions analysis includes Human Capital (HUMCAP) to the best regression model as it is a good predictor of Business Performance of women entrepreneurs of Sri Lanka. The Business Performance will be increased by 0.21 when Human Capital increases by one unit.

Hypothesis 06
H 0: There is no positive correlation between Environmental Factors and Business performance of Women Entrepreneurs. H 1 : There is a positive correlation between Environmental Factors and Business Performance of Women Entrepreneurs We have evidence to reject the null hypothesis of no positive correlation between Business Performance and Environmental Factors at 99% significant level. The Pearson's coefficient of correlation is 0.721**which is very much close to plus one. The significant level is (P-value) 0.000 which is < 0.01 < 0.05. Therefore, we can conclude that there is a strong positive correlation between Business Performance and Environmental factors.
The stepwise multiple linier regressions analysis strongly proves this relationship and includes Environmental Factors (ENVINF) to the best regression model as the most important variable which mostly explains the total variability of the dependent variable. The Business Performance will be increased by 0.49 when Human Capital increases by one unit.

Discussion on the relationship between Business Performance and Motivation and Goals.
According to the evidence given by the data analysis, there is a strong positive correlation between Business Performance and Motivation & Goals of Women entrepreneurs. In this case the coefficient of correlation is 0.631 at a 99% significant level. These values were obtained by doing the 1-tailed test.
The results of stepwise regression analysis came up with the same finding that there is a positive relationship between the Motivation and Goals (MGOAL) and the Business Performance of women entrepreneurs. Out of the three regression models developed by the stepwise regression analysis, two models included (MGOAL) as a good predictor of Business Performance. First, of which the coefficient of determination (R 2 ) is 0.596 (59%) the β value The Impact of Individual factors on the Business Performance of Women Entrepreneurs in Sri Lanka.
for (MGOAL) is 0.332. Second, the best fit regression model too included (MGOAL) as a good predictor of Business Performance. There R 2 is 0.621 (62%) and the β value for (MGOAL) is 0.284 (p=0.000).
It is noticeable that the Motivation and Goals (MGOAL) of the selected sample of women entrepreneurs show a high level as its Mean is 3.13 and the Standard Deviation is 0.685. That means, the average value of Motivation and Goals of women entrepreneurs vary between 2.445 (3.13 -0.685) and 3.985 (3.13 + 0.685), which is a very much close value to 4; the maximum value of the scale.

Comparison of the findings of the research with the previous researchs on the same variable
Psychological motivations such as achievement, independence, and locus of control have been widely investigated with regard to their influence on starting up a business (Brockhaus and Horwits, 1986). There are a few studies that examined their relationship to the Business Performance.
The research conducted by Hisrich and Brush at United States has found that individual Motivation and Goals of owners are related to performance in women owned businesses, where opportunity motivation was related to survival and independence was associated with "no growth" (Hisrich and Brush, 1987). This study partly supports the findings of the study conducted by the Brush and Hisrich in 1987 in the United States.
The findings of the research conducted by Lerner, Brush and Hisrich in the Israeli context revealed that Motivation and Goals had a significant influence on Business performance of women entrepreneurs. All three groups of motives (achievement, economic necessity, and independence) were associated with various aspects of performance. That means, the economic necessity was correlated with profitability; achievement was strongly associated with income; and independence was associated with revenues . This study strongly supports the findings of the study conducted by the Lerner, Brush and Hisrich in 1997 in the Israeli context. Kaleberg and Leicht (1991) suggest that there is a few significant performance differences related to male versus female motives of achievement and independence. Women have reported lower levels of self-confidence than did male (Miskin and Rose, 1990).

Discussion on the relationship between Business Performance and Social Learning Theory
The results of the Pearson's correlation analysis give evident that there is a positive relationship between Business Performance (BUSIPER) and the Social Learning Theory (SOLETH) also; but not much vivid. Here, the coefficient of correlation is 0.443; which is less than 0.5. The significant level is 0.000. These values were obtained by doing the 1-tailed test.
The stepwise regression analysis develops the best fit model excluding the Social Learning Theory as a predictor of Business performance of women entrepreneurs. That means, the influence of SOLETH in predicting the Business Performance of women entrepreneurs is negligible According to the values given by the descriptive statistics to the questions related to Social Learning Theory (SOLETH), the selected sample of women entrepreneurs shows an average level socialization. Its Mean is 2.08 and the Standard Deviation is 0.865. That means the average value of Social Learning Theory of women entrepreneurs vary between 1.115 (2.08-0.865) and 2.94 (2.08 + 0.865), which is presumably close value to 2; the mid value of the scale.
As the corresponding answer to the value 2 is neither agree nor disagree, the individual socialization process in this sample does not take any side or extreme: They are neither highly socialized nor highly un-socialized women entrepreneurs. In other words, the sample of women entrepreneurs do not very much learn from the family or the society but they preferably learn from the parents, husbands and role models.

Comparison of the findings of the research with the previous researches on the same variable
The importance of Social Learning Theory to explain entrepreneurial career preferences was explored in several studies.
According to Bandura (1977) the individual socialization process, which occurs in the family setting, transmits social norms, language, educational aspirations and shapes career preferences through observational learning and modeling but not the Business Performance of women entrepreneurs. The findings of this study too prove that there is a positive relationship between the Social Learning Theory and Business Performance of women entrepreneurs although not significant Scherer et al., (1989) found that the presence of a parent in an entrepreneurial role was associated with increased education and training aspirations, task self-efficacy, and expectancy for an entrepreneurial career. This study also found that individuals with a parent performing an entrepreneurial role were often high performers and significantly different from individuals without role models, who were low performers.
A Canadian study found that 33% of the women entrepreneurs in the sample reported their fathers were entrepreneurs (Belcourt et al., 1991). Considerable evidence related to the findings of the above study was not evident in this study.
One Israeli study did find a clear link between role models (entrepreneurial fathers) and choice of an entrepreneurial career by offspring (Lerner, 1992) but not with the Business Performance of women entrepreneurs. The result of this study almost depicts a correlation with the findings of Lerner (1992). Lerner, Brush and Hisrich (1997) found in the Israeli context that family socialization variable (self-employed father) had no influence on business performance women entrepreneurs. This study also found that there is no strong relationship between these two variables.

Discussion on the relationship between Business Performance and Network Affiliation
The results of the Pearson's correlation analysis give evidence that there is a positive correlation between Business Performance (BUSIPER) and the Network Affiliation (NETAF). But, that relationship is not much strong according to the coefficient of correlation obtained by doing the 1-tailed test. Here the coefficient of correlation is 0.359; which is less than 0.5 and the significant level is 0.000. The multiple linier regression too excludes the (NETAF) from the best fit regression model because its ability to predict the (BUSIPER) is negligible.
The descriptive statistics related to the Network Affiliation of the selected sample of women entrepreneurs show an average level as its Mean is 2.04 and the Standard Deviation is 0.968. That means the average value of Network Affiliation of women entrepreneurs vary between 1.072 (2.04-0.968) and 3.008 (2.04 +0.968). This deviates highly from the mid point 2.

Comparison of the findings of the research with the previous researchs on the same variable
According to Aldrich and Zimmer (1986), entrepreneurship is embedded in a complex network of social relationships.
The research conducted in the Sri Lankan context does not support this finding as the most of the women entrepreneurs of Sri Lanka seemed reluctant to join with social networks mainly due to lack of time. But this research agrees with the finding of the said research that within these networks, entrepreneurship is facilitated or constrained by linkages between aspiring entrepreneurs, resources and opportunities. As they view, the presence or absence of networks such as access or membership in associations has an impact on performance.
According to Aldrich (1989) the women entrepreneurs are more embedded in different personal and social networks than men and that division and barriers limit the research and diversity of their networks, might have far reaching consequences for business performances. How ever with this study it was found that this was not true in the Sri Lankan context.
Importance of support systems, mentors and advisors have been identified by the research conducted by Hisrich and Brush in the United States. The business associates and friends were identified as important to moral support, whereas participation in trade associations and women's groups were identified as related to business guidance (Hisrich and Brush, 1987). Sri Lankan women Entrepreneurs too agree with this finding though they face many constraints when joining in networks. Johannisson (1993) argues that the personal network of the entrepreneurs is the most important resource of the firm. But, this research on Sri Lankan women entrepreneurs does not support that argument. Aldrich et al., (1987) found that personal contacts are helpful to business development and Social networks facilitate the business start-ups. In fact, they suggest that the composition of women is different from men's. They do not discuss about the importance of Social Networks in determining business performance. This research too, concludes that Network Affiliation does not have a significant influence on Business performance of women entrepreneurs in Sri Lanka.
One Canadian study that attempted to assess whether women entrepreneurs were creating an old girl's network through business associations found that although the respondents felt that belonging to business groups was important, they did not often join due to lack of time (Belcourt et al., 1991). The findings of this research done in the Sri Lankan context too confirm the findings of the above research.
A research conducted in the Israeli context by Baum et al., (1993) shows that in collectivist and informal societies, success is dependent on personal contacts and relationships with key individuals who facilitate the start-ups. But, this research of Sri Lankan women entrepreneurs does not agree with the argument of Baum and others. Lerner et al., (1997) also did a research in the Israeli context and came up with the following important conclusions. Membership in a women's organization had a highly significant effect on the profitability of Israeli women owned businesses. Women belonging to such organizations reported higher profitability than women who did not. In fact, use of a number of advisors was linked to better performance in terms of higher revenues. But, use of a number of network affiliations was negatively linked to revenues of the business, personal income and size in terms of number of employees. However, this research in the Sri Lankan context strongly denies these findings of Lerner, Brush and Hisrich (1997).

Discussion on the relationship between Business Performance and Human Capital (HUMCAP)
According to the evidence given by the data analysis, there is a strong positive correlation between Business Performance and Human Capital of Women entrepreneurs. In this case the coefficient of correlation is 0.626 at a 0.000 significant level. These values were obtained by doing the 1-tailed test.
It is noticeable that the Human Capital (HUMCAP) of the selected sample of women entrepreneurs shows an above average level as its Mean is 2.52 and the Standard Deviation is 0.804 That means the average value of Human Capital of women entrepreneurs vary between 1.716 (2.52 -0.804) and 3.324(2.52 + 0.804).the values which are somewhat close value to 2 and above three. As the corresponding answers to the value 2 is neither agree nor disagree and value 3 is agree, the individuals of the sample show somewhat high level of education and experience. This sample takes the upper side regarding women's educational background and experience. Therefore they are averagely educated and experienced women entrepreneurs.
Comparison of the findings of the research with the previous researchs on the same variable Cooper (1989) proposed that experience and education were "antecedents" to the decision to start the business and ultimately affected the business performance. This Sri Lankan research too, came up with a similar conclusion in this regard.
Several studies showed that, years of formal education of the entrepreneur before establishing a new firm were related to eventual performance of the firm (Box et al., 1993;Brush and Hisrich, 1991). Box et al., (1993) also found a relationship between higher levels of education and increased performance among manufacturing firms in Oklahoma. The findings of the Sri Lankan study also are as same as the findings of the above researches. Hisrich (1986) stated that the high number of service-oriented businesses reflected the educational focus of women entrepreneurs, which generally consisted of a liberal arts college education. This research does not support the finding that there is a correlation between the area of education and the business performance.
The influence of previous entrepreneurial experience upon the success of the small businesses was tested in several studies. Ronstadt (1988) found that longer, more successful entrepreneurial careers were a function of earlier career starts and involvement in multiple ventures. Prior start-ups and years of entrepreneurial experience were significantly correlated with performance in a study of 300 manufacturing firms in Tulsa (Box et al., 1993). This research, to a greater extent agrees with all the above research findings.
40% percent of women entrepreneurs in a Canadian study reported they had not worked in a related field prior to venture creation. This lack of experience in a related field was correlated with significantly lower profits (Belcourt et al., 1991). On the other hand, Hisrich and Brush (1984) found that a majority of their sample of 468 women entrepreneurs (64%) tended to start businesses in fields where they had related experience. In a longitudinal study on this sample 5 years later, Brush and Hisrich (1991) found that among the 172 women business owners responding to the second survey, stated that related experience was associated with business growth. They concluded that the "antecedent influences" of the women entrepreneurs did in fact affect business survival and growth. This research agrees to the above findings to a considerable degree.
The importance of business skills, particularly strength in idea generation and dealing with people, was found to be related to performance in Brush and Hisrich's (1991) longitudinal study. Since business skills are related to stage of business development, it is argued that women face challenges of learning business skills early in the life cycle of their business and are therefore less profitable than in later stages when they are more competent in business skills (Miskin and Rose, 1990). Findings of this Sri Lankan study tally with the above research findings.

Discussion on the relationship between Business Performance and Environmental Influence (ENVINF)
Out of all the independent variables the highest positive correlation with the dependent variable exists between Business performance and Environmental influences (ENVINF). According to the evidence given by the data analysis, there is a strong positive correlation between Business Performance and Environmental Influences. In this case the coefficient of correlation is 0.721 at a 0.000 significant level. These values were obtained by doing the 1-tailed test.
It is noticeable that the Environmental influences of the selected sample of women entrepreneurs shows an above average level as its Mean is 2.75 and the Standard Deviation is 0.715. That means the average value of Environmental influences for the women owned businesses in the selected sample vary between 2.035(2.75 -0.715) and 3.465(2.75 + 0.715), which is a very much close value to 3. As the corresponding answer to the value 3 is Agree, it is evident to conclude that the women owned businesses in the selected sample are favorably affected by the existing environmental factors like capital market, consumer market, socio political variables, and resource availability.

Comparison of the findings of the research with the previous researchs on the same variable
Economic measures of venture profitability, revenues and number of employees are related to environmental economic conditions such as the market structure, regional opportunities, investment climate, availability of labor, and other features (Gibb, 1988). Similarly resource availability, including venture capital, technical labor force, loans, support services and a favorable entrepreneurial subculture are also a major influence on performance (Bruno and Tybjee 1982). The findings of this research in the Sri Lankan context supports the conclusions of the above two researches.