Frontiers of Entrepreneurship Research 1995

Frontiers of Entrepreneurship Research
1995 Edition

1995 Abstracts

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    Miri Lerner, Tel-Aviv University
    Candida G. Brush, Boston University
    Robert D. Hisrich, Case Western Reserve University


    This investigation analyzes the factors influencing performance in businesses owned by women in Israel, examining the applicability of five theoretical explanations for entrepreneurs' business performance. These five perspectives are: a - influence of motivations and goals; b - social learning theory; c - networks; d - human capital; e - environmental. The study was conducted in Israel in 1994 on a sample of 220 women business-owners. The research instrument was a comprehensive questionnaire originally composed in the US and adapted to the Israeli market. The analyses examine the influence of different variables on the following four performance measures: profitability, revenues, income, and number of employees. The findings reflect that there are different correlates to different performance measures. This supports other work that performance cannot be adequately determined with a single measure - rather multiple measures are important when assessing factors influencing success of a venture. The research findings show theories of networks, human capital (regarding the influence of business skills) and motivations to have greater explanatory power than social learning theory.


    Variations in performance (measured by such variables as number of employees, sales, income, profitability, etc.) in entrepreneurial companies has inspired a large quantity of empirical research. Of the factors affecting performance, gender has consistently appeared as a significant demographic variable. Yet, only a relatively small number of studies have investigated the factors influencing entrepreneurial performance exclusively among women. Of these, almost all were carried out in the USA, Canada and Europe. This paper aims to expand the arena of that research by presenting the first systematic study of variance in performance conducted among Israeli women entrepreneurs.

    The purpose of this study is to analyze the factors contributing to variance in performance of women-owned businesses by addressing the following research questions:
    Which factors influence the entrepreneurial performance of Israeli women entrepreneurs?
    Which factors explain any variance in performance among businesses of women entrepreneurs ?
    Are these explanatory factors similar to or different from those found in research conducted in other countries?


    Statistics on employment status and gender in Israel show a great deal of difference between the proportion of self-employment and business-ownership among men and women. Although in 1991 17.6% of the men in the workplace were either self-employed or employers (10.4% and 7.2%, respectively), among women in the workplace only 5.3% were either self-employed or employers - 4.1% and 1.2%, respectively (Statistical Abstract of Israel 1992). This gender gap in the proportion of business ownership among men and women is accompanied by the common trends which are typical of women in business in other countries, such as concentration in services and retail and underrepresentation in manufacturing and large businesses. The emergence of women in entrepreneurship has created interest and greater awareness in Israel, as evidenced by the establishment of several associations of businesswomen. However, projections on the proportion of women-owned businesses in the current decade suggest that the population will remain stable. This is in sharp contrast to the US statistics, which project a great increase in the numbers of women who will start new ventures. The present research sheds some light on possible reasons for this disparity by presenting information about Israeli women entrepreneurs, their businesses and their performance. It also contributes to a better understanding of the determinants of business performance of women entrepreneurs in general and in the Israeli context in particular.


    "Performance" is operationalized differently in different studies, making cross-comparison difficult. Among the most frequently used operationalizations are survival, growth in employees, and profitability (Srinivasan, Woo & Cooper, 1994). While early research investigated samples of women only, some more recent work is comparative, using samples of men and women. Kalleberg and Leicht (1991) found that women's businesses were no more likely to fail and were just as successful as those of the men (contrary to the conventional wisdom that women are inferior in entrepreneurship). The study 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 the entrepreneur's gender" (Kalleberg & Leicht, 1991).

    Studies on performance of women entrepreneurs are few in number, but some of the same variables explaining performance of male-owned businesses have been found significant (Brush & Hisrich, 1991): previous occupational experience, education, business skills, and personal factors (motivations, having a mentor).

    The variance in performance among entrepreneurial ventures has prompted the development of several theoretical explanations. These explanations can be categorized according to five perspectives, each of which has a corresponding body of empirical research on women business owners.

    Influence of Motivations and Goals on Performance

    Psychological motivations such as achievement, independence, and locus of control have received significant research attention with regard to their influence on business start-up (Brockhaus & Horwitz, 1986), but less attention has been paid to their relationship to business performance. Individual motivations and goals have been found to be related to performance in women-owned businesses. US studies found that "push" and "pull" forces operate together, with women typically being drawn to business ownership through a combination of job frustration and market opportunity motivations (Brush, 1990; Hisrich & Brush, 1984). Hisrich and Brush (1987), in their longitudinal study examining growth in women business owners, five years after opening their businesses, found that while the opportunity motive was related to survival, the independence motive was related to "no growth". Women reported lower levels of performance-related self-confidence than did males (Miskin & Rose, 1990).

    The Influence of Social Learning on Performance

    A second approach, emphasizing entrepreneurial socialization (Cooper, 1989), is anchored in social learning theory (Bandura, 1977) as an explanation of entrepreneurial behavior and career development. Social learning theory proposes that learning can occur through the observation of behavior in others, often referred to as models. The individual's socialization process, which occurs in the family setting, transmits social norms, language, and educational aspirations and shapes career preferences through observational learning and modeling (Bandura, 1977). Scherer et al. (1989) found that the presence of a parent entrepreneurial role model was associated with increased education and training aspirations, task self-efficacy, and expectancy of an entrepreneurial career. Further, individuals with a parent entrepreneurial role model were perceived to be high performers and were significantly different from individuals without entrepreneurial role models, who were perceived to be low performers. Relatedly, a Canadian study found that 33% of women entrepreneurs reported that their fathers were entrepreneurs (Belcourt, Burke & Lee-Gosselin, 1991). Taken together, these results indicate the importance of entrepreneurial role models in the backgrounds of practicing entrepreneurs, identified in earlier studies (Brockhaus & Horwitz, 1986; Hisrich & Brush, 1984).

    Influence of Networks on Performance

    A third approach concentrates on the influence of networks on performance, viewing entrepreneurship as being facilitated or constrained by linkages between aspiring entrepreneurs, resources and opportunities (Aldrich & Zimmer, 1986). According to this approach, the presence or absence of networks, such as gender-related entrepreneurial associations, plays a role in influencing performance. Because women entrepreneurs are embedded in different personal and social networks than men, and divisions and barriers limit the reach and diversity of their networks, it is hypothesized that networks may have far-reaching consequences for business performance (Aldrich, 1989). Empirical studies show that access to help and information depends on "know-who" (Belcourt, Burke & Lee-Gosslin, 1991), raising speculations about the effects of "old boys" networks on performance. Women operating or starting up a small business are often excluded from informal networks of information such as male-only clubs, old boys networks, and business lunches (Aldrich, 1989). This presents a significant barrier, as research shows that strong ties in social networks facilitate the start-up process (Brush, 1990). Research in Northern Ireland found women's networks to be as diverse as men's (Cromie and Birely, 1992). A Canadian study emphasized that the infrequent use of women entrepreneurs of sources of assistance (such as business associations and governments) represents "a lost opportunity to break through the isolation of the glass box" (Belcourt, Burke & Lee-Gosslin, 1991). Limited use of mentors by women entrepreneurs was found to be a significant inhibitor to successful new venture development (Carsrud, Gaglio & Olm, 1986). While findings on networks are not equivocal, it appears that having a network appears to be an important factor in performance.

    Influence of Human Capital Factors on Performance

    The human capital perspective focuses on the influence of resources, in particular the level of education, and managerial skills in affecting entrepreneurial performance.

    Influence of Education: Several studies have shown that years of formal education of the entrepreneur before founding a new firm were related to the eventual performance of the firm (Box, White & Barr, 1993; Brush & Hisrich, 1991). Testing the effects of education on business ownership in a longitudinal study, Dolinski et al. (1993) found that the levels of staying in and reentering business increased with increasing education. They argue that less-educated women may face financial or human capital constraints which limit their business pursuits. The social sciences and/or arts educational background typical of women business owners can restrict or discourage them from turning to start-up ventures in manufacturing, finance or technology (Brush, 1990; Birley, Moss & Saunders, 1987). This educational background also partly explains the high concentration of women-owned businesses in the service sectors found in OECD countries (Brush, 1990).

    Previous Experience: The influence of previous entrepreneurial experience upon the success of small businesses has been 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, years of formal education, years of entrepreneurial experience and years of industrial experience correlate significantly with increase in employment (Box, White & Barr, 1993). Studies have found that many women start businesses in sectors where they have no experience. Forty percent of women entrepreneurs in a Canadian study reported that they had not worked in a related field before starting their ventures, and this lack of experience in a related field correlated with significantly lower profits among the members of their sample (Belcourt, Burke & Lee-Gosselin, 1990). On the other hand, Hisrich & Brush (1984) found that most of their sample of 468 women entrepreneurs (64%) tended to start businesses in fields where they had job experience. In examining 172 women business owners five years after initiating their ventures, Brush & Hisrich (1987) found that the women entrepreneur who had previous experience in the field of their venture had a better chance of successfully expanding than the women who did not have experience. They concluded that the antecedent influences of the woman entrepreneur do in fact affect business survival and growth.

    Effects of Management Skills on Performance: Brush and Hisrich (1991) found that business skills and particularly strengths in generating ideas and dealing with people were important in establishing a business. Relatedly, there is a growing body of research examining the effects of planning on performance, finding that strategic planing has a significant, positive association with performance in small firms (Schwenk & Shrader, 1993). No studies have examined the relationship between planning and performance in women-owned businesses.

    Environmental Influences on Performance

    The environmental influences perspective presumes factors such as the differential structure of opportunity, location, and sectoral activities, as well as socio-political variables (i.e., availability of government assistance) are critical determinants of performance. Research shows that economic measures of venture profitability, revenues and number of employees are related to environmental economic conditions such as the market structure and the regional opportunities, the investment climate, availability of skilled manpower, the regional labor market and other features that influence economic development in general (Gibb, 1988). Availability of resources such as venture capital, the technical labor force, and incubator organizations, as well as the proximity of universities, the regional loans policy, supporting services and the entrepreneurial subculture are also important dimensions that may affect performance outcomes, according to Bruno & Tybjee's (1982) classification of environmental factors that affect entrepreneurial activity. Availability of sufficient start-up capital is reported to be one of the most important factors influencing the future success and profitability level of the new business venture (Brophy, 1989). However, "availability of financing" was not deemed an important factor in projecting differences in future profitability levels of male and female businessmen (Miskin & Rose, 1990). Studies of women's access to bank loans show that they face perpetual barriers that they are less capable than their male counterparts of overcoming (Fay & Williams, 1993).

    Type of Business: Type of business is also related to profitability (Brophy, 1989). Self-employed women, like their counterparts who work for someone else, are segregated into certain business sectors. Businesses owned by women are concentrated in retail sales and in personal and educational service industries, the so-called female ghetto (Kalleberg & Leicht, 1991). US SBA data (1986) show that in 1982, about half of all women entrepreneurs were in service industries, and another 30% were in retail trade. A Canadian survey found that the retail sector tended to be over-represented in its sample of women entrepreneurs, with the second being the service sector. Both are notorious for their meager returns, long hours and easy entry, because large capital investments are seldom required, especially in the service sector (Belcourt, Burke & Lee-Gosselin, 1991). Companies in the service and trade industries generally have lower growth rates and less success than businesses in other industries, in part because services and trades are highly labor intensive and there is a lot of competition among sellers in their product markets (Kalleberg & Leicht, 1991).

    Based on these five theoretical perspectives, an integrated model of factors that may influence the female entrepreneurs' business performance was developed (see Exhibit 1). The factors examined are predominantly at the individual level, though it is recognized that firm-level factors, such as business characteristics and strategy, impact performance. However, these are beyond the scope of the present investigation.


    The database for this research was created from a study conducted in Israel in 1994. The research instrument was a comprehensive questionnaire originally composed by Hisrich and Brush (1985), translated into Hebrew, and adapted to the particular conditions of the Israeli population. Most of the questionnaires were distributed at professional meetings of women entrepreneurs associations and returned by mail. The remainder were mailed to the respondents, who subsequently returned them. The sample was identified from the memberships of four major women entrepreneurs associations. There were 220 usable responses, comprising about 40% of the total respondents approached. Since sampling of women who are members of business associations may be biased, the study included women who were not members of women's organizations as well; these respondents comprised nearly 1/4 of the sample. Four different associations were sampled in order to provide greater heterogeneity and decrease against any possible bias.


    The dependent variables in the research were the following four measures of business performance:
    1. Size of business: number of employees.
    2. Profitability: three categories of answers - profitable=1; not profitable and not losing=2; losing=3, during the previous year.
    3. Gross revenues: 11 categories from 30,000 IS up to more than 4 mm IS during the previous year.
    4. Income: open-ended question regarding the entrepreneur's monthly income at present. The Independent variables were divided into six groups, five categories representing the theoretical perspectives and a sixth, demographic variables.

    1.Motivations and goals variables:

    Motives Index: 12 items of motivations to enter into business. Each item had five categories from 1=not important to 5= very important. Reliability analysis showed a high reliability coefficient of the 12 items ((=.77). A factor analysis of the 12 questions loaded on three factors as follows. The first factor was labeled "Achievement Motivation" and consisted of five items with a factor loading of greater than .55 (achievement; status-prestige; career; money; power). The second factor was labeled "Independence Motivation" and consisted of three items with a factor loading of greater than .66 (independence; job satisfaction; opportunity). The third factor was labeled "Necessity Motivation" and consisted of two items with a factor loading of greater than .71 (economic necessity; security).

    Goals Index: 8 items of importance of possible business goals ((=60). Each item had five categories from 1= not important to 5= very important. The eight items were: profitability, revenues and sales growth, product/service excellence, survival of the business, image of the business with customers, personal satisfaction of the owner-manager, contribution to society, and provide high living standard for the owner.

    2.Social Learning: father in business, and economic status during childhood.

    3. Networks variables:

    Number of networks used Index was developed, based upon number of types of networks mentioned as used by the respondent (professional associations; trade associations; women's professional groups; community organizations; college alumni groups; social groups-close friends/family; political groups). Other measures included membership in women's business associations. The Advisers Index was developed on the basis of number and type of advisers mentioned as used by the respondent. The Advisers used belonged to the following seven professional areas: production and/or purchasing; market research and sales, bookkeeping and accounting; budgeting and planning; personnel/labor management; legal personnel; computer specialist.

    4.Human Capital:

    Education level, education areas, previous occupation, previous status at work (employed/self employed), previous experience in starting-up business, previous experience in industry, and involvement in starting up the current business. Business skills variables, including management, planning and areas of strength:

    Management Skills Index: 6 items ((=.74). Each item had five categories from 1= not important to 5=very important. The 6 items were: finance-securing capital, forecasting, budgeting, dealing with people- management, marketing/sales - market research, idea generation, product innovation, business operations, production, daily operation organizing and planning, business strategy, policies.

    Planning Ahead Index: 6 items ( (=.83). Each item had five categories ranging from 1= less than months to 5=more than 2 years. (The six items were: planning sales, cash flows, add or drop products, enter new/exit markets, hiring and other staff decision, expansion of firm's operations, changes in plant, building and equipment).

    Areas of Strength in business Index: 12 items ((=.79). Each item had five categories, ranging from 1=not an area of strength to 5= area of major strength. The 12 items were: location of firm, type of plant, equipment, product/service quality, pricing, customer service, innovation in products, cost control, employee productivity, marketing and selling, cash and financial management, overall quality of management and human resource management.

    5.Environmental variables were composed of industry variables: services/retailing/ manufacturing and sources of finance.

    6.Demographic variables: age, marital status, number and ages of children.

    Description of the Sample

    The average age of the Israeli female entrepreneur in the study was 50.5 (SD=8.5). The range of ages was between 32 and 74 with 53% between 32-51. 92.4% of all the subjects were mothers, mostly of two or three children (33% and 36% respectively). 76% of the entrepreneurs in the study were married (only 9% of them never have been married). 12% were divorced or widowed (3%). The average ages of the entrepreneurs' children were 19 for the first child, 16 for the second child, and 14 for the third and fourth. In addition, 55% of the entrepreneurs in the study were exposed to business socialization during their childhood: 55% of them were daughters to business owners.

    Education and occupation: Most of the female entrepreneurs in the study had completed higher education: 70% of them were academics, of whom 27% held a BA and 25% an MA. The remaining 30% had high school education or studied at an educational college. Of the university-educated entrepreneurs, 33% studied economics, management or engineering. The rest studied the humanities and/or social sciences, fine arts, etc. This social science and/or arts educational background is typical of women business owners in the United States and OECD countries (Brush, 1990). While 43.6% of the sample had academic occupations before starting up their businesses and 18% were in managerial positions, 15.7% worked in technical, professional, and secretarial positions, and 18.7% worked in other occupations. Only 4% served as housewives before launching their ventures. For most of the entrepreneurs in the sample (77.7%) the current business was their first venture; only 22.3% had previous experience in starting a business. At the same time, however, 51.6% had previous experience in their economic sector, the percentage of those with previous experience being significantly higher in the services. Most of the entrepreneurs in the sample (85%) were employed before launching their businesses, and 91% were involved in the start-up of their businesses.

    Industry: 58% of the entrepreneurs in the study were in services, 19% in the retail and 23% in manufacturing.

    Sources of finance: The majority of the women entrepreneurs studied (76.4%) used only their personal savings as sources of financing their ventures. Only 23.6% used other sources, such as bank loans.

    Geographical location: The women in the sample owned businesses in 34 different locations in Israel, most in Tel-Aviv area (28.7%), Jerusalem (11.4%), Haifa (10%) and many other places in the urban centers. Thus the sample population is an urban one and does not include the rural parts of the country, nor its southern parts.


    To address the question of factors most influencing performance Pearson correlations were performed on 27 different variables, pertaining to the following areas: demography, background socialization, human capital, networks, motivations, and environment. The analyses examined the influence of each of these variables on the following four performance measures: (1) business profitability, (2) revenues, (3) the entrepreneur's income, (4) the number of persons employed by the business.

    The correlations between each of the previous variables with the performance measures were reviewed separately, then multiple-regression analysis were used with the same variables on the performance measures. Table 1 presents both the Pearson correlation coefficients and the Beta scores of the regression-analysis of the variables that showed a significant impact on performance, in descending order of their influence on the performance measures. As mentioned earlier, this research focuses on individual factors rather than firm level factors.

    The five theoretical perspectives had varying degrees of correlation with performance measures. Generally, the entrepreneur' management skills, areas of strength and planning ahead indices are highly correlated with the performance variables, particularly with revenues. (R=.47, R=.36, R= .32P<.01, respectively). These group of variables seem to have the central impact on performance among the other variables. Of the management skills included in the index, obtaining finance and budgeting skills on the one hand and labor management and organizing and planning skills on the other hand, were highly correlated with revenues. All the areas of planning included in the planning index were significantly correlated with revenues and with number of employees. The entrepreneur' involvement in establishing the business was significantly correlated with most of the performance variables: gross revenues (R= .36P<.01), and withnumber ofemployees (R= .26P<.01). Also, previous experience in current industry was significantly correlated with revenues (R= .28P<.01), supporting previous work (Hisrich & Brush, 1987).

    The number of network connections was negatively correlated with performance measures, particularly with revenues (R= --.23P<.01). In contrast, membership in women's business associations was positively correlated with profitability but negatively correlated with revenues.

    Examination of the correlations of the three main motivations - achievement, independence and economic necessity - with the performance measures showed that economic necessity motives were significantly correlated with profitability. All other relationships were negative and not significant.

    On the other hand, the entrepreneur's level of education was not significantly correlated with the performance of the business. This may reflect the fact that most of the respondents in the sample were well-educated. No significant correlations were found between relevant areas of academic studies (economics, business, engineering) with performance variables. Even the previous entrepreneurial experience was not significantly correlated with performance variables. This lack of correlation may be due to the fact that for most of the respondents (78%) the current venture is their first one. Previous experience before the current business was significantly correlated with profitability (R= .19P<.05), with 87.5% of the sample being previously employed.

    The results of the multiple-regression analyses shown in Table 1 indicate that each of the four measures of performance is influenced by a different cluster of factors. In other words, there is no identity among the clusters of factors having a dominant influence upon profitability, revenues, income or number of employees. The tables also show that the factors selected for this study only partially explain the variance in each of the performance measures, suggesting that firm-level variables, such as products or strategies, contribute more to this unexplained variance.

    Two performance measures - revenues and number of employees - are better explained by the independent variables in the multiple regression analyses (39% of the variance in revenues and 31% of the variance in number of employees, in comparison to only 15% in profitability and 7% of the variance in income). The factors having the most influence upon the revenues of the women entrepreneurs' businesses were their involvement in the start-up of their businesses, their management skills, use of networks and professional advisers, and the areas in which their business strength lies (see Table 1). Next in order of influence were previous experience in industry and planning of the business activities. The impact of the motivational variables (independence motives and economic necessity) are less influential than the previously-mentioned variables.

    Surprisingly, membership in women's business associations is the variable that is the first to influence profitability (see Table 1). First child's age, sources of finance of the business, and business skills index are also factors that influence profitability. Less influential are economic necessity motives, previous experience as an employed person, the industry sector and marital status. The variables dominant in explaining the variance in the entrepreneur's income are achievement motives, and the areas in which the entrepreneur's business strength lies.

    As expected, the number of employees is primarily influenced by the industry; with services and manufacturing being the first to appear in the multiple-regression analysis (see Table 1). The significant differences in size of the average businesses in the services and retail businesses are four and eight employees, in comparison to 36 employees in the manufacturing firms examined in the study (the standard deviations are 12, 16 and 37 employees respectively). Beyond industry, the dominant influential variables are the assisting networks and the entrepreneur's involvement in the start-up of their business.

    Business performance also was influenced by the family situation and constraints of the women entrepreneurs. Most of the Israeli women entrepreneurs in the study were married mothers who became business owners after their children grew up, enabling them more flexibility to devote in their careers. Among the demographic variables, the age of the entrepreneur's children was working mother model as long as she gives priority to her family. In selecting employment, women attribute great importance to "convenience" - namely, the extent to which the job can be found to influence business profitability significantly (Beta= .21P<.001). This finding reflects the high level of family orientation in Israeli culture and the institutional arrangements that support the accommodated to family life (Azmon & Izraeli, 1993:10). The fact that the average age of the women business owners in this study is fifty (S.D.=8.5) and the average ages of their children are high (19, 16, 14) also reflects this family-oriented emphasis.


    Motivation had a significant effect on the business performance of women entrepreneurs. Factor analysis identified three basic groups of motivations: the achievement motives, the independence motives, and the economic necessity motives. The results of this study show economic necessity motives are most highly correlated with the profitability (R = .22 P<.01); the achievement motives appear first in the regression of income (Beta= .22 P < .001). The overall picture of the influence of motivations on performance measures is impressive, particularly the effect of achievement motives on the income measure.

    In contrast, the findings indicate that Bandura's (1977) Social Learning Theory has no power in explaining the business performance of women entrepreneurs in Israel. Multiple-regression analyses showed that the family socialization variables (the father owing a business) had no influence on the performance measures of the women entrepreneurs sampled. It should be remembered that studies based on Bandura's Social Learning Theory focused on the influence of exposure to an entrepreneurial role model in the family on the formation of entrepreneurial preferences and behavior (Scherer, Adams and Wiebe, 1989). The present study investigated the influence of the factors affecting business performance rather than the choice of an entrepreneurial career itself. Many studies have focused on the clear link between an entrepreneur father and the tendency of the offspring to follow the path of entrepreneurship. This link was also demonstrated in a study carried out in Israel (Lerner, 1992). In contrast, the present study shows that the Social Learning Theory does not explain the variance of the performance of businesses owned by women in Israel.

    The findings regarding the influence of networks on performance corroborate the findings of a cross-cultural study which focused on the orientations of US versus Israeli entrepreneurs and managers (Baum et al., 1993). In explaining their finding that Israeli entrepreneurs had stronger needs for affiliation than the US entrepreneurs, the researchers argued that in a collectivist and informal society like Israel's, success in entrepreneurial role depends, to a great extent, on social networks and personal contacts that facilitate start-ups (e.g., through personal loans). They argued that Israeli with high needs for affiliation are more likely to develop the necessary support networks critical to the start-up (Baum et al., 1993, p. 510). Let us then turn to network theory. An examination of the multiple-regression results indicated the explanatory power of the network theory with regard to the performance of businesses owned by women in Israel. Membership in women's entrepreneurial organization had a significant effect on the profitability of the businesses (Beta= .25P<.001). Similarly, women belonging to such organizations reported higher profitability than women who did not. Making use of a number of advisers was also clearly linked to business performance, specifically the revenues volume measure. In contrast, the availability and use of a number of supportive networks was found to be negatively linked to the revenues of the businesses, to personal income, and to size of the business in terms of number of employees (Beta= -.17, -.14P<.001).

    The effect of human capital factors on performance was less clear. Education in itself, that is the level and areas of education, had no direct effect on business performance, nor did previous entrepreneurial experience show any significant effect. However, previous salaried employment was strongly correlated with business profitability (R = .18 P <.01), and previous experience in the industry also showed a direct and significant correlation with revenues (R = .28 P < .001). Business skills were also highly correlated with revenues, obtaining finance, budget, and planning ahead being most influential. Overall, these results offer mixed support for human capital theory, in that previous education and entrepreneurial experience were not as important, but business skills and employment were related to performance.

    A comparison of the women entrepreneurs in the different industry sectors reveals several interesting differences. The level of education of the women entrepreneurs in the services is significantly higher than that of their colleagues in other sectors(P<.001). Moreover, women entrepreneurs in the services showed a higher level of previous experience in the relevant economic sector on average than their colleagues in other sectors (P< .001). Beyond the sectoral affiliation of the women entrepreneurs, it appears that involvement in establishing the business has a direct and significant influence on the performance variables of sales and number of employees (though not of profitability and/or income). In summary, this analysis showed network theory offered the greatest explanation for differential performance of Israeli women entrepreneurs, with motivation theory a strong secondary explanation.


    This systematic investigation of factors influencing performance in businesses owned by Israeli women examined the applicability of five theoretical explanations, finding that motivational and networking factors were most explanatory in this context. While this research was deliberately limited to Israel women entrepreneurs, future studies should compare samples of men in order to determine if these findings are generally true of all entrepreneurs.

    This research also showed different correlations to various performance measures, supporting previous work indicating that performance cannot be adequately determined with a single measure. Rather, multiple measures are important when assessing the success of a venture. In particular, profitability was related to economic necessity motives, membership in organizations, previous salaried employment, and competence in business skills. Similarly, revenues were associated with use of advisers, previous experience in the industry, and competence in business skills, while income was related to achievement motivation. This findings imply that Israeli women entrepreneurs should be aware of depending upon their desired performance outcome, it may be necessary to develop different personal dimensions. For example, if profitability is the priority, the woman entrepreneur should endeavor to gain experience in a salaried position, develop business skills, and become a member of professional organization. Or, if revenues are most important, the woman entrepreneur should utilize outside advisers. However, it is important to note that these factors affecting performance are necessary but not sufficient in and of themselves, because other factors such as product/market strategies, competitive aspects, organizational competencies, and strategic decisions may also affect performance.

    Finally, this research should provide a useful starting point for comparative studies on women entrepreneurs in other countries. In particular, the explanatory power of the five theoretical perspectives should be tested in order to determine whether these findings can be corroborated.


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