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Networking: Gender differences and the association with firm performance John Watson International Small Business Journal 2012 30: 536 originally published online 14 February 2011 DOI: 10.1177/0266242610384888 The online version of this article can be found at: http://isb.sagepub.com/content/30/5/536

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Article

Networking: Gender differences and the association with firm performance

International Small Business Journal 30(5) 536–558 © The Author(s) 2011 Reprints and permission: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0266242610384888 isb.sagepub.com

John Watson

The University of  Western  Australia,  Australia

Abstract This study had two primary objectives. First, to determine whether there are any systematic networking diffesrences between male and female SME owners. Second, to determine if there is an association between networking and firm performance, for both male- and female-controlled SMEs. The results of examining 2,919 male- and 181 female-controlled SMEs (with at least one employee) over a three-year period suggest little difference in the networks accessed by male and female SME owners after controlling for education, experience, industry, age and size. The results also indicate that several formal and informal networks are positively associated with firm survival but only formal networks appear to be associated with growth. In particular, accessing an external accountant is associated with survival and growth for both male- and female-controlled SMEs. Keywords networking, gender, survival, growth

Introduction Given an increasing awareness in the broader community of the significant contribution that small and medium-sized enterprises (SMEs) make to job and wealth creation, examining the antecedent factors associated with successful SME performance has become an important focus for policymakers and researchers (Low and MacMillan, 1988; Rosa et al., 1996). While previous research indicates a link between SME success and various owner characteristics (such as education, experience, planning and hours dedicated to the business), only recently have researchers begun to examine the association between the owner-manager’s personal networks (social capital) and rates of business formation, survival and growth (Aldrich, 1989; Cromie and Birley, 1992; Donckels and Lambrecht, 1995; Reese and Aldrich, 1995; De Clercq and Voronov, 2009). Social capital theory suggests that owners’ ability to gain access to resources not under their control cost-effectively through networking can influence the success of their ventures (Zhao and Aram, 1995). Florin et al. (2003) note that networking provides value to members by allowing them access to social resources embedded within a network: that is, networking can provide the Corresponding author: John Watson, Department of Accounting and Finance,  The University of  Western Australia, 35 Stirling Highway, Crawley WA 6009, Australia Email: [email protected]

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means by which SME owners can tap into needed resources ‘external’ to the firm (Jarillo, 1989). Julien observed that this form of cooperation can facilitate the achievement of economies of scale in small firms ‘without producing the diseconomies caused by large size’ (1993: 161). Similarly, innovation theory suggests that networks (particularly those comprised of many weak ties; Granovetter, 1973) are important in diffusing innovations and, therefore, SMEs whose owners are heavily involved in networking should outperform SMEs whose owners make limited (or no) use of networks (Havnes and Senneseth, 2001). In short, both social capital theory and innovation theory suggest that networking can potentially lower a firm’s risk of ‘failure’ (increase a firm’s chances of ‘success’). In addition, it has been suggested there might be significant differences between males and females in terms of their network use (Hanson and Blake, 2009). For example, Cromie and Birley (1992) argue that networks are the product of personal drive and historical experiences, and the social structure and domestic duties of many women might result in female entrepreneurs having (and therefore using) fewer networks than their male counterparts. Aldrich (1989) noted that these differences in network use could have a significant impact on the rate at which women (compared to men) start new ventures and the performance of those ventures. However, although there has been considerable conjecture about the possible networking differences between men and women, few empirical studies exist that examine the gender differences in networking and, more importantly, the association between networking and firm performance (Hanson and Blake, 2009). Following Ibarra’s (1992) call for further empirical evidence to clarify how men’s and women’s networks differ, the extent of these differences and the potential consequences of any such differences, this study sought to identify any systematic networking differences between male and female SME owners and to determine whether there is an association between networking and firm performance (for both male and female-controlled SMEs). It is hoped that the findings presented and discussed within this paper will assist SME advisers and policymakers to understand better the potential differences in the use of networks by male and female SME owners, and the association between networking and firm performance. This article begins with a summary of the literature that was central to the development of the models examined in this study. Next, there is a discussion of the models proposed to test for gender differences in networking, the relationship between networking and firm performance, and a description of the methodology adopted. The results of the analysis and discussion of those results are given, and the article concludes with the limitations of the study and suggestions for future research.

Literature review Coleman (1988) notes that while information is important to decision-making, it is costly to obtain, hence networks provide a means by which important information can be potentially acquired in a cost-effective manner. Similarly, Hanson and Blake argue that networking can help SME owners ‘reduce transaction costs’ and ‘provide access to resources’ (2009: 144). Therefore, networking can enhance an SME owner’s social capital by providing access to information and ‘[j]ust as physical capital and human capital facilitate productive activity, social capital does as well’ (Coleman, 1988: S101). Seibert et al. (2001) provide a useful summary and discussion of the three conceptualizations of social capital found in the literature. First, there is the weak tie theory proposed by Granovetter (1973). Here, the focus is on the strength of social ties and it is argued that networks comprising strong ties (such as family and friends) are more likely to be a source of redundant information than would be the case where networks comprise weak ties (such as acquaintances). Second, there is Burt’s (1992) notion of structural holes. A structural hole is deemed to exist where two individuals are not connected in any way. Here the focus is not on the direct ties between SME owners and

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individual members of their network, but rather on the relationships between the various members in an SME owner’s network. An SME owner whose network comprises many structural holes (that is, few of the other members of the network are connected) is likely to have ‘more unique and timely access to information’ (Seibert et al., 2001: 221). Third, there is social resource theory (Lin et al., 1981), which focuses on the nature of the resources embedded within a network rather than on the strength of ties or the existence of structural holes. While weak tie theory and structural hole theory examine the links between the members of a network, social resource theory is concerned with the nature of information (i.e. the social resources) held by individual members of the network. A variety of terms can be found in the network literature to describe the important properties of personal networks. For example, Munch et al. (1997) refer to network size, contact volume and composition; Moore (1990) refers to network range, volume of contacts and diversity of alters; Zhao and Aram (1995) refer to network range and intensity; and Ibarra (1992) refers to network composition, homophily, tie strength, range, density and the distinction between formal and informal networks. The focus of this study is on the number of networks that SME owners use to access advice, and the frequency (volume) of their use. In addition, network composition will be examined using Ibarra’s (1992) classification of networks as either formal or informal, with formal networks likely to comprise more weak ties and structural holes (and therefore to be more beneficial) than informal networks. Littunen (2000) suggests that formal networks include the likes of accountants, banks, lawyers and trade associations, while informal networks comprise groups such as business contacts, family and personal relationships. Turning to the possible differences between the networks of male and female SME owners, Cromie and Birley (1992) argue that because the majority of women enter self-employment from a domestic and/or non-managerial background, it is likely that their personal network contacts will not be as extensive or well-developed as their male counterparts. As Munch et al. (1997) note, housework and childrearing are extremely lonely forms of work, and this isolation results in many women having limited network contacts compared to men. Even where women move directly from paid employment into self-employment, it is likely that they will have fewer network contacts because females typically occupy lower level positions within the organizations that they leave, compared to the typical male (Cromie and Birley, 1992). Aldrich (1989) argues that past research indicates that female entrepreneurs might not only have fewer networks than their male counterparts, but are likely to be embedded in different types of networks. Similarly, Munch et al. (1997) suggest that as a result of their childrearing respon­ sibilities, women will typically rearrange their network composition to favour kin (family and friends) over other forms of network contacts. Consistent with this argument, Orhan (2001) notes that the first source of advice for male entrepreneurs is usually professional experts (such as accountants and lawyers), and second is their spouse; whereas the first source of advice for female entrepreneurs is their spouse, second, their friends, and third, professional experts. Similarly, Moore (1990) found that women were more likely to include family members in their networks than men. This suggests that male SME owners are more likely to access formal networks, while female SME owners are more likely to access informal networks (particularly family and friends). In summary, it would seem past research suggests that, compared to men, women are likely to have fewer networks, less time available for networking and networks that favour family and friends (strong ties with few structural holes) over professional advisers (weak ties with many structural holes). This gives rise to the first four hypotheses examined in this study. H1: Female SME owners will have a smaller number of networks than male SME owners. H2: Female SME owners will make less frequent use of networks than male SME owners.

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Watson H3: Female SME owners will make less frequent use of formal networks than male SME owners.

H4: Female SME owners will make more frequent use of informal networks (particularly family and friends) than male SME owners.

As discussed earlier, social capital theory predicts a positive association between networking and firm performance, with formal networks (weak ties with more structural holes) likely to have a greater impact than informal networks (strong ties with fewer structural holes). Support for this proposition can be found in Renzulli et al. (2000: 538), who report that ‘network heterogeneity significantly increased the odds of starting a business’ (see also Burt, 2000: 357 for a detailed review of the evidence supporting ‘the argument that social capital is a function of brokerage across structural holes’). This gives rise to the last hypothesis examined in this study. H5: For both female and male-owned SMEs, firm performance is positively associated with networking – with formal networks having a greater impact than informal networks.

Method Model development Much of the previous work on networking and firm performance has ignored important intervening variables. For example, Hoang and Antoncic (2003) indicate that an owner’s age, experience and level of education are all related to network use. Similarly, the liability of newness (adolescence) literature (Bruderl and Schussler, 1990; Stuart and Sorenson, 2003) suggests that networking is likely to be particularly critical for young (adolescent) firms whose owners have limited experience in, and knowledge of, the industry in which they are operating. As Brüderl and Preisendörfer (1998: 216) note, entrepreneurs endowed ‘with lower stocks of human capital’ are likely to make more effort to develop their social resources. Cooper et al. (1989) found that owners or managers of larger ventures were more likely to access formal networks (such as professional advisers), while the owners or managers of smaller ventures were more likely to access informal sources (such as family and friends). In terms of firm performance, Lussier and Pfeifer (2001) reported that owner-managers of successful firms were more educated than those of unsuccessful firms, Robinson and Sexton (1994) noted that education and experience were positively related to self-employment success, and Becchetti and Trovato (2002) found that firm growth was significantly affected by the industry, size and age of the firm. Therefore, in order to assess properly the relationship between networking and firm performance, it is important to control for such potentially confounding variables. For example if, compared to older firms, younger firms are more likely to fail (Jovanovic, 1982) and their owners are also more likely to have fewer networks, including the age of the firm in the analysis allows the effects of networking on firm performance to be assessed separately from the effects of firm age. Therefore, in order to test for gender differences in networking (number and frequency), the following networking model is proposed: Networking = f(gender, education, experience, industry, age, size)

The dependent variable in this model will take a variety of forms, including the number of networks used to access advice, frequency of network use, frequency of formal network use, and frequency of informal network use. Given that the independent variables in this model include a mix of ordinal, nominal and

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numeric data, categorical regression will be used to test the model. How each of the variables is measured will be discussed in the following section, which describes the data available for this study. For a variety of reasons (such as access to information, new innovations and finance) social capital theory implies that networking should have a significant influence on the success of SMEs (Chell and Baines, 2000; Greene et al., 2001; Kristiansen et al., 2005; Madill et al., 2004; Renzulli et al., 2000). The belief that networking is positively associated with firm performance has been supported by various empirical studies. For example, Florin et al. found that using social networks could provide a venture with a ‘durable source of competitive advantage’ (2003: 374), and Brüderl and Preisendörfer (1998) found that network support increased the probability of survival and growth for new businesses. In terms of professional advisory services, Davidsson and Honig (2003) found that being a member of a business network (such as Chambers of Commerce, Rotary Clubs or Lions) had a significant positive effect on firm performance. Duchesneau and Gartner (1990) found that successful firms were more likely to have used professional advice, and Larsson et al. (2003) found that a lack of contact with outside expert advisers was an obstacle to the expansion of small businesses. Kent (1994) found that the financial performance of a group of small pharmacy businesses was positively related to using external management advisory services. Finally, Zhao and Aram (1995) found that managers of three high-growth firms reported a greater range and intensity of business networking than managers of three low-growth firms. Therefore, in order to test the potential association between networking and firm performance, the following firm performance model is proposed: Firm performance = f(gender, education, experience, industry, age, size, networking)

Given that the two measures of firm performance used in this study are dichotomous (as discussed in the following section), logistic regression is used to test this second model. Firm performance will be examined separately for the male and female-controlled SMEs to specifically examine gender differences in the possible association between networking and firm performance. It should be noted that, consistent with most prior empirical studies on networking, this study focuses on the personal networks of the SME owner rather than the organizational networks of the business (Brüderl and Preisendörfer, 1998). As argued by Bratkovic et al., the personal networks of SME owners and their organization’s networks ‘are almost synonymous since network ties exist at the interpersonal level’ (2009: 487).

Data Low and MacMillan (1988) note that, despite significant resource implications, it is important for SME researchers to have access to large-scale longitudinal data in order to improve confidence in research outcomes and as a basis for theoretical model building. This view was echoed by Reese and Aldrich (1995), specifically in relation to the association between networking and firm per­ formance. Therefore, a major strength of this study is its use of a large longitudinal database. The construction of this database was funded by the Australian federal government and was designed to provide information on the growth and performance of Australian employing businesses. The Australian Bureau of Statistics’ (ABS) Business Register was used as the population frame for the surveys. All employing businesses in the Australian economy were included in the scope of the survey, except for businesses in the nature of: • government enterprises; • libraries;

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Watson • • • • • • • • • •

museums; parks and gardens; private households; employing staff; agriculture, forestry and fishing; electricity, gas and water supply; communication services; government administration and defence; education; and health and community services.

The ABS also employed a stratified random sampling framework in which larger businesses and certain types of businesses (particularly manufacturing concerns) were overrepresented. In all other respects (such as geographical location) the sample was representative of the population of Australian SMEs at the time of the surveys.1 Data collection was through self-administered questionnaires distributed by the ABS for the periods 1994–5, 1995–6, 1996–7 and 1997–8.2 Because the ABS can legally enforce compliance with its data requests (under the Census and Statistics Act 1905), response rates were very high (typically in excess of 90%). A non-response normally meant that the ABS was unable to locate the business proprietor (or the business) and, therefore, these were treated as business closures. Some questions (such as those relating to items on the income statement and balance sheet) were repeated in each survey, while other questions (such as the networking question) were only asked once. For confidentiality reasons, information on all large businesses was excluded from the dataset made available to researchers and, therefore, the dataset examined in this study relates to Australian SMEs. The second ABS survey (1995–6) contained a question relating to the frequency with which SME owners had accessed a number of networks during the past year for advice. Specifically, the question asked respondents to indicate how often (never, between 1 and 3 times or more than 3 times) they had accessed advice during the past year from each of seven formal network sources (banks, business consultants, external accountants, industry associations, the Small Business Development Corporation (SBDC) solicitors and the Australian tax office) and three informal sources (family and friends, local businesses and others in the industry). The question did not ask respondents how often they accessed each listed source for other networking purposes and, therefore, the data provided might not be representative of the full extent of networking by SME owners. However, as accessing advice is one of the major purposes of networking (Hoang and Antoncic, 2003; Kristiansen et al., 2005), the data should provide a useful indication of how widely networks are used, and allow a comparison between male and female-controlled SMEs. As this question was asked in the second survey (1995–6), the analysis in this article is limited to the data contained in the second, third and fourth surveys. It seems reasonable to argue that there will always be a lag between a stimulus (in this case, some form of networking activity) and anticipated benefits (Havnes and Senneseth, 2001); therefore, to properly assess the benefits of networking, it is necessary to examine performance over an extended period. There were 5027 responses to the 1995–6 survey. On examining the data it was found that 13 businesses had no income (sales or other income), so these were excluded from the analysis on the assumption that they were not active businesses. As this study is interested in examining the association between networking and firm performance for male and female-controlled SMEs, a further 1914 firms had to be excluded because they did not have a single major decision-maker, or the gender of that person was not reported. This left 3100 firms (2919 male-controlled and 181 femalecontrolled) that could be examined over the three-year period from 1 July 1995 to 30 June 1998.

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There are three reasons for the relatively low number of female-controlled SMEs in the dataset. First, only 18 percent of Australian businesses are run by an individual female or predominantly by females (Australian Bureau of Statistics, 2001). Second, non-employing businesses were not included in the database, and this further reduced the proportion of female-controlled firms in the study because a relatively greater percentage of predominantly female-run businesses do not employ staff (i.e. 70% of predominantly female-run businesses compared to only 59% of predominantly male-run businesses do not employ staff). Third, there was a sampling bias in favour of large businesses and manufacturing concerns, and these are sectors where females tend to be relatively underrepresented (Marlow et al. 2009).

Measurement of variables Networking variables. Each of the 10 potential networks (seven formal and three informal) was treated as a categorical variable: if a particular network had not been used (to access advice) during the past year it was coded ‘0’; where a network had been used between one and three times during the past year, it was coded ‘1’; and where a network had been used more than three times, it was coded ‘2’. Therefore, in terms of the number of network used, an SME owner could score from 0 (if no networks had been used during the past year) to 10 (if all 10 networks had been used). In terms of the frequency of network use, an SME owner could score from 0 (if no networks had been used during the past year) to 20 (if all 10 networks had been used more than three times). Similarly, for formal (informal) networks, the maximum score for the number and frequency of network use during the past year is 7 (3) and 14 (6) respectively. Given that frequency of network use is a combination of the number of networks used and the frequency of use for each individual network, most of the analysis and tables that follow will focus on frequency of network use. Performance variables. Brüderl and Preisendörfer (1998) argue that survival is the minimum criterion for success. However, policymakers are also interested in firm growth, as growing firms are likely to contribute the most to a country’s economy and job creation. Delmar et al. note there ‘seems to be an emerging consensus that if only one indicator is to be chosen as a measure of firm growth, the most preferred measure should be sales’ (2001: 194). Therefore, in this study firm performance is measured in terms of both firm survival and sales growth (measured as the percentage increase in total income over the three-year period of this study). Surviving firms are coded ‘1’, while discontinued firms are coded ‘0’. In terms of growth, this study focuses on those firms in the top 25 percent (upper quartile, coded ‘1’) for sales growth compared to those in the bottom 25 percent (lower quartile, coded ‘0’). Although it is unusual to discard data, if there is a relationship between networking and firm performance it will most likely be evident at the extreme ends of the performance spectrum. That being the case, focusing on those firms in the tails of the performance distribution (rather than including all firms) is more likely to find such a relationship.3 Control variables. As noted earlier, prior research indicates that potentially significant associations exist between various owner or business characteristics (such as education, experience, industry, age of business and size of business) and both networking and firm performance, with these characteristics also likely to vary by gender. Therefore, as far as possible, these variables need to be controlled in order to understand and assess properly gender differences in networking and the relationship between networking and firm performance. In this study, education, industry and age of business were treated as categorical variables with four categories for education (school, trade, tertiary non-business degree and tertiary business degree), 11 for industry (mining, manufacturing, construction, wholesale trade, retail trade, accommodation, cafes and restaurants, transport and storage, finance and insurance, property and business services, cultural and recreational services, and personal and other services) and five for age (less than 2 years, 2 to less than 5 years, 5 to less than 10 years, 10 to less than 20 years, and 20 or

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Watson Table 1.  Number of Networks used by SME Owners Number of Networks

10 9 8 7 6 5 4 3 2 1 0

All SME owners N = 3100

Male N = 2919

Female N = 181

%

Cum %

%

Cum %

%

Cum %

 3%  5%  8% 12% 12% 13% 12% 11%  8%  6% 12%

  3%   8%  15%  27%  39%  52%  64%  75%  82%  88% 100%

 3%  5%  8% 12% 11% 13% 12% 11%  7%  6% 12%

  3%   8%  16%  28%  39%  52%  64%  75%  82%  88% 100%

 2%  4%  5%  8% 14% 12% 12%  9% 11%  7% 16%

 2%  6%  11%  19%  34%  46%  58%  67%  77%  84% 100%

Note: Chi-square test comparing males and females not significant at 5%.

more years old). The owner’s years of experience and the size of the business (measured in terms of the number of employees) were treated as continuous variables.

Results Before examining the results of the networking and performance models developed earlier, some key descriptive and demographic details are presented in Tables 1, 2, 3 and 4. Table 1 shows the number of networks used during the past year (to obtain advice) by the male and female SME owners. The results show that most of the SME owners (88% of males and 84% of females) used at least one network during the past year, with approximately 50% of all SME owners (52% of males and 46% of females) using five or more networks. This finding is consistent with Cooper et al. (1989) and Robson and Bennett (2000), who reported that entrepreneurs sought information from a variety of sources. However, the results also indicate no significant differences between the male and female owners in terms of the number of networks used to access advice. This result is at odds with H1 (and most of the literature on gender and networking), but supports Cromie and Birley’s (1992) finding that the personal networks of women are just as diverse as those of men. A separate analysis of the subset of SMEs which had used three or fewer networks also failed to find any gender difference, and the same applied to the subset of SMEs which had used seven or more networks. Table 2 provides a summary of the frequency with which the male and female SME owners used a variety of individual formal and informal networks. Contrary to H2, there was no difference in the overall frequency with which male and female owners used all networks (formal and informal). This result, although inconsistent with the majority of the literature, again confirms Cromie and Birley’s (1992) finding that women are just as active in their networking relationships as men. Similarly, Diaz Garcia and Carter (2009) found that male and female business owners devoted a similar amount of time to networking. As noted by Cromie and Birley (1992), once in business, women might well recognize the need to have appropriate network contacts and ‘proceed to develop them vigorously’. Alternatively, compared to men, women might have less entrepreneurial self-efficacy (Wilson et al., 2007) and might feel a stronger need to develop a range of network ties from which they can access advice. As noted by Wilson et al.:

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given the complex tasks involved for an individual to locate an opportunity, assemble the resources, set up a business, and build it into a successful entity, self-efficacy or the belief in one’s ability to succeed as an entrepreneur would seem to be especially important. (2007: 390)

Although there was no difference between males and females in terms of the overall frequency of their network use, consistent with H3, the male SME owners made significantly more use of formal networks, particularly with banks, business consultants, industry associations and solicitors. However, contrary to H4, the female SME owners did not make significantly more use of informal networks, although they did make significantly more use of family and friends. These findings are consistent with Robson et al. (2008), who reported that male Scottish business owners were significantly more likely to seek advice from consultants and Chambers of Commerce, while female Scottish business owners were significantly more likely to turn to friends and relatives. Shaw et al. (2008) also reported that female owners were significantly more likely (than male owners) to identify a family member as their prime network contact. Interestingly, Table 2 shows the network most often used (for accessing advice) by both male and female SME owners (with no significant difference between the two groups) is external accountants (a formal network): 47% of males and 44% of females sought advice from an external accountant more than three times a year. This finding is consistent with Robson and Bennett (2000) who reported that, from the private sector, accountants are the most widely used source of advice. The result is also consistent with Robson et al. (2008), who found that accountants were the most widely used source of advice for both male and female Scottish business owners (with no significant difference by gender). Similarly, both male and female SME owners frequently used others in the industry, with 27% of males and females using this informal network more than three times a year. In summary, unlike Birley (1985), who found that entrepreneurs relied heavily on informal networks but seldom tapped Table 2.  Frequency of Formal and Informal Network use for Male and Female SME Owners Networks

Frequency of use (per year) Nil

Formal   External accountant   Bank   Solicitor   Industry association   Business consultant   Tax office   SBDC    Average formal networks Informal    Others in the industry    Family and friends   Local businesses    Average informal networks    Average all networks

>3 times

1–3 times

Male

Female

Male

Female

Male

Female

19% 36% 41% 57% 71% 58% 84% 52%

20% 44% 48% 75% 82% 65% 87% 60%

34% 36% 35% 23% 19% 32% 13% 27%

36% 39% 40% 15% 13% 30% 12% 26%

47% 28% 24% 20% 10% 10%  3% 20%

44% 18% ** 12% ** 10% **   5% **  6% 1% 13% *

44% 63% 73% 60% 55%

48% 52% 75% 58% 60%

30% 20% 17% 22% 26%

26% 23% 15% 21% 25%

27% 17% 10% 18% 20%

27% 25% **  9% 20% 16%

*, ** Chi-square test significantly different for males and females at 5% and 1% respectively.

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into formal networks, the results presented in Table 2 suggest that Australian SME owners (male and female) make extensive use of both formal and informal networks. Table 3 provides key demographic details for the male and female owners (education and experience) and their firms (industry, age and size). The results indicate significant gender differences for all variables. Table 4 provides the same demographic details (plus gender) for surviving and non-surviving firms and for high and low-growth firms. With the exception of the owner’s level of education, all variables are again significantly associated with at least one performance measure (survival or growth). For example, the owners of firms that discontinued prior to the final year of the study typically had fewer years of experience than the owners of firms that survived. Similarly, younger firms were less likely to survive and more likely to be in the high-growth group. The finding that younger firms are both less likely to survive and more likely to grow is consistent with Jovanovic’s argument that ‘[f]irms learn about their efficiency as they operate in the industry. The efficient grow and survive; the inefficient decline and fail’ (1982: 649). The result is also consistent with Evans (1987) and Glancey (1998), who found that younger firms grow faster than older firms. The findings reported in Table 4 highlight the importance of controlling for potentially confounding variables, particularly if – as expected – a significant relationship also exists between Table 3.  Descriptive Statistics: Gender  

Male N = 2919

Education of owner   School   Trade   Non-business degree   Business degree Experience of owner    Number of years (median) Industry   Mining   Manufacturing   Construction   Wholesale trade   Retail trade    Accommodation, cafes and restaurants   Transport and storage    Finance and insurance    Property and business services    Cultural and recreational services    Personal and other services Age of business    Less than 2 years old    2 years to less than 5    5 years to less than 10    10 years to less than 20    20 or more years old Size of business    Number of employees (median)

** 35% 24% 20% 21% ** 13 **  1% 40%  7% 15% 10%  2%  4%  4% 14%  2%  1% ** 13% 15% 24% 27% 22% ** 10

Female N = 181 49% 14% 27% 10%  8  1% 24%  1% 10% 18%  6%  6%  2% 19%  4% 10% 22% 19% 28% 22% 10%  4

*, ** Significantly different at 5% and 1%, respectively, using the Chi-square test for categorical variables and the MannWhitney U test for continuous variables.

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Table 4.  Descriptive Statistics: Survival and Growth Variables

Survived

Education of owner   School   Trade   Non-business degree   Business degree Experience of owner    No. of years (median) Industry   Mining   Manufacturing   Construction   Wholesale trade   Retail trade    Accommodation, cafes and restaurants   Transport and storage    Finance and insurance    Property and bus services    Cultural and recreational services    Personal and other services Age of business    Less than 2 years old    2 years to less than 5    5 years to less than 10    10 years to less than 20    20 or more years old Size of business    No. of employees (median) Gender of owner   Male   Female

Growth

Yes N = 2653

No N = 447

High N = 663

Low N = 663

85% 88% 85% 85%

15% 12% 15% 15%

51% 51% 48% 49%

49% 49% 52% 51%

* 13

10

12

13

** 80% 86% 87% 89% 85% 77% 88% 78% 86% 79% 84%

20% 14% 13% 11% 15% 23% 12% 22% 14% 21% 16%

** 50% 46% 66% 52% 49% 36% 40% 52% 52% 52% 62%

50% 54% 34% 48% 51% 64% 60% 48% 48% 48% 38%

* 41% 90% 92% 94% 93%

59% 10%  8%  6%  7%

* 67% 51% 51% 49% 43%

33% 49% 49% 51% 57%

 7

11

10

14% 20%

50% 48%

50% 52%

* 13 ** 86% 80%

*, ** Significantly different at 5% and 1%, respectively, using the Chi-Square test for categorical variables and the MannWhitney U test for continuous variables.

these variables and an SME owner’s level of networking. The importance of this issue is highlighted in Table 5, which provides the results of the networking model proposed earlier. Table 5 reports the results where the dependent variable is the overall number of networks used to access advice, overall frequency of network use, frequency of formal network use and frequency of informal network use. Consistent with the findings reported in Tables 1 and 2, there is no significant gender difference in terms of number of networks, frequency of use and frequency of informal network use. With respect to the frequency of formal network use, the significant gender difference reported in Table 2 disappears when other owner and firm characteristics are controlled. Apart from the last dependent variable (frequency of informal network use, where the adjusted R2 is extremely low), the results reported in Table 5 are very consistent, with education, industry, age and size all being significantly associated with networking. The results suggest a positive

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Watson Table 5.  Modelling Network Use: Number and Frequency Model

Standardized Coefficients B

Number of networks used (H1)   Gender -0.01   Education 0.06   Experience -0.03   Industry -0.09   Age 0.05   Size 0.28   Adj R2 0.10 Frequency of network use (H2)   Gender -0.01   Education 0.04   Experience -0.03   Industry -0.06   Age 0.07   Size 0.33   Adj R2 0.14 Frequency of formal network use (H3)   Gender -0.03   Education 0.05   Experience -0.01   Industry -0.08   Age 0.08   Size 0.39   Adj R2 0.19 Frequency of informal network use (H4)   Gender 0.02   Education -0.01   Experience -0.04   Industry -0.09   Age 0.04   Size 0.07   Adj R2 0.01

SE

d.f.

F

Sig.

0.02 0.02 0.02 0.02 0.02 0.02

 1  3  1 10  4  1

0.51 10.04 2.03 20.51 5.99 196.28

0.47 0.00 0.15 0.00 0.00 0.00

0.02 0.02 0.02 0.02 0.02 0.02

 1  3  1 10  4  1

0.28 5.31 1.85 8.45 11.06 289.13

0.60 0.01 0.17 0.00 0.00 0.00

0.02 0.02 0.02 0.02 0.02 0.02

 1  3  1 10  4  1

1.89 7.24 0.19 18.56 15.95 419.10

0.17 0.01 0.66 0.00 0.00 0.00

0.02 0.02 0.03 0.02 0.03 0.02  

 1  3  1 10  4  1  

0.72 0.22 2.01 13.34 2.59 7.88  

0.40 0.81 0.16 0.00 0.08 0.01  

association between the level of education and networking, which is consistent with Shaw et al. (2008), who noted that individuals with high levels of human capital (such as education) are also likely to possess high levels of social capital (such as network contacts). Similarly, owners of older and larger firms appear to be more involved in networking: a finding consistent with Robson et al., who report that size of the business ‘is the main variable that explains the use of formal external advice’ (2008: 305). With respect to industry, it appears (from further examination of the data, not reported) that the manufacturing and wholesale trade sectors are associated with higher levels of networking, while networking is less prevalent in the service sectors. Interestingly, there appears to be no relationship between experience and networking. The results for modelling the frequency of network use with each of the individual formal and informal networks are not presented; however, two notable findings from the individual network

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analysis are worth noting. First, although not associated with networking at the aggregate level, experience was found to be negatively associated with frequency of network use for some individual formal and informal networks (business consultants, tax office, SBDC, others in the industry and local businesses). It seems that as SME owners gain more experience they feel less inclined (i.e. have less need) to access advice from such groups. Second, and contrary to the findings reported in Table 2, the results of a separate analysis of the use of family and friends as a source of advice found no gender difference, but instead it appears that owners of smaller businesses (where women are typically overrepresented) make greater use of this source of advice. This again highlights the importance of controlling for potentially confounding variables when looking at gender differences. Interestingly, Robson et al. (2008) also reported that while women appeared more likely than men to use family and friends as a source of advice (based on a bivariate analysis), this difference disappeared when owner and firm characteristics were included in a multivariate analysis. Table 6 presents the results of modelling the relationship between network frequency and firm survival and growth, incorporating gender and the various control variables from Table 4. Table 6.  Modelling Firm Survival and Growth against Frequency of Network use Variables in the final models

Survival

Gender Education   School   Trade   Non-business degree Experience Industry   Mining   Manufacturing   Construction   Wholesale trade   Retail trade    Accommodation, cafes and restaurants   Transport and storage    Finance and insurance    Property and business services    Cultural and recreational services Age    Less than 2 years old    2 years to less than 5    5 years to less than 10    10 years to less than 20 Size Frequency of network use Constant    Percentage predicted correctly   Survived/discontinued/overall    Low growth/high growth/overall Nagelkerke R2

Growth

Wald

Sig.

0.00 0.49 0.03 0.19 0.02 0.10 14.67 0.53 0.38 0.01 0.26 0.01 0.44 0.03 1.81 0.00 0.16 483.34 219.28 2.80 0.27 0.91 0.88 100.85 12.36

0.97 0.92 0.87 0.67 0.90 0.76 0.14 0.47 0.54 0.92 0.61 0.94 0.51 0.87 0.18 0.98 0.69 0.00 0.00 0.09 0.60 0.34 0.35 0.00 0.00

42.7 0.36 

96  

Exp(B)

Wald

1.01

0.11 0.80 0.29 0.45 0.00 0.15 19.42 0.62 2.23 0.05 0.84 1.80 3.37 2.72 0.72 0.80 0.41 16.05 15.81 3.29 4.02 2.25 0.00 5.96 0.09

0.97 1.09 0.98 1.00 1.79 0.76 1.05 1.26 1.04 0.70 1.09 0.51 1.01 0.79 0.05 0.67 0.89 1.24 1.00 1.18 6.14

Sig. 0.74 0.85 0.59 0.50 0.98 0.70 0.04 0.43 0.14 0.82 0.36 0.18 0.07 0.10 0.40 0.37 0.52 0.00 0.00 0.07 0.05 0.13 0.97 0.02 0.76

Exp(B) 1.09 1.09 1.13 1.00 1.00 0.54 0.50 1.12 0.64 0.52 0.32 0.41 0.65 0.65 0.67 2.79 1.43 1.43 1.29 1.00 1.03 0.85

88.4 63.2  0.04

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48.3  

55.7

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Table 7.  Modelling Firm Survival and Frequency of Individual Network use for Male and Female-controlled SMEsa Variables in the final models

Male-controlled SMEs Wald

Age    Less than 2 years old    2 years to less than 5    5 years to less than 10    10 years to less than 20 Formal networks External accountant   Never   1–3 times Industry association   Never   1–3 times Informal networks Others in the industry   Never   1–3 times Family and friends   Never   1–3 times Constant Percentage predicted correctly Survived/discontinued/overall Chi-square significance -2 Log likelihood Nagelkerke R2 Cox and Snell R2

Sig.

434.74 212.94 3.10 0.07 0.69

0.00 0.00 0.08 0.80 0.41

77.07 68.23 2.77 13.27 10.86 1.49

0.00 0.00 0.10 0.00 0.00 0.22

9.48 0.06 5.42

0.01 0.81 0.02

0.00

201.84 96.6  

39.4  

Female-controlled SMEs Exp(B)

Wald

Sig.

Exp(B)

0.05 0.66 0.95 1.21

37.84 14.55 0.15 0.48 0.33

0.00 0.00 0.70 0.49 0.56

0.01 0.59 0.44 2.37

0.24 0.76

10.18 9.40 0.78

0.01 0.00 0.38

0.09 0.53

5.52 0.47 2.91 11.24

0.06 0.49 0.09 0.00

0.60 4.87 77.25

0.50 0.74 0.96 1.58

32.25 88.5 0.00 1716 0.36 0.20

94.5

66.7

 

 

89.0 0.00 91 0.62 0.39

a Note that the variables reported in this table are those that were significant, and therefore ‘in the equation’ as reported by SPSS using the forward stepwise (conditional) logistic regression method.Variables that were not significant, and therefore ‘not in the equation’, are not reported.

Consistent with H5, the results in Table 6 indicate a significant positive relationship between frequency of network use and firm survival (and a negative relationship between age of business and firm survival). Similarly, the results in Table 6 indicate a significant positive relationship between frequency of network use and firm growth (with younger businesses also more likely to achieve high growth), although the explanatory power of this model is low. Note that, consistent with previous studies which have incorporated appropriate controls (see for example, Watson, 2003), there is no relationship between gender and firm performance. In addition, note that when firms were classified as high or low growth based on whether their growth rate was above or below the median result (rather than being based on the upper and lower quartiles), the findings were qualitatively the same as those reported in Table 6; however, the explanatory power of the model was substantially reduced. Finally, Tables 7 and 8 present the results of modelling (separately for male and female-controlled SMEs) the relationship between an owner’s use of specific formal and informal networks (together

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with the control variables discussed previously), and both firm survival and growth, respectively. Given the relatively large number of control variables and networks that could be used to access advice, the forward stepwise (conditional) logistic regression method was used, adopting the SPSS default cut-off of 5 percent for variables entering the model and 10 percent for removal. To check the robustness of the results, the stepwise logistic regressions were run backwards, with no significant differences found. Note that when using stepwise logistic regression, SPSS highlights those variables that are significant and ‘in the equation’. Given space limitations, those variables that are not significant and, therefore, ‘not in the equation’ are excluded from the tables and discussion. Table 7 shows that the only network significantly related to the survival of both male and female-controlled SMEs is external accountants (a formal network). Firms which had never accessed advice from an external accountant during the past year were significantly less likely to survive compared to firms that accessed advice from this source more than three times. Interestingly, there was no advantage to accessing an external accountant more than three times a year compared to accessing this source one to three times a year. This finding suggests that there might be some optimal level of networking with external accountants beyond which no additional benefit is gained (however, there is no evidence that more frequent contact does any harm). The only other formal network that showed up in the model was industry associations, although only for male-controlled SMEs. As was the case with external accountants, it seems that provided male SME owners access industry associations for advice between one and three times a year, there is no additional benefit to accessing this network more frequently. The results with respect to the use of informal networks were also quite interesting, with the males apparently benefiting from networking with others in the industry and the females from family and friends. However, in this case the results strongly suggest that excessive networking might be counterproductive. For male- (female-)controlled SMEs it appears that accessing advice from others in the industry (family and friends) between one and three times a year is significantly more likely to be associated with firm survival than accessing advice from such networks more frequently (or not at all). This finding suggests that the association between firm survival and accessing informal networks for advice might resemble an inverted U-shaped function (Watson, 2007) for both male and female-controlled SMEs. In summary, the final model for predicting survival for male-controlled SMEs incorporates, along with the age of the business, both formal (external accountants and industry associations) and informal (others in the industry) networks. Accessing other networks (Australian tax office, banks, business consultants, family and friends, local businesses, the SBDC and solicitors) does not add significantly to the explanatory power of the model. Similarly, the final model for predicting survival for female-controlled SMEs incorporates, along with the age of the business, both formal (external accountants) and informal (family and friends) networks. Consistent with Granovetter’s (1973) weak tie theory and Burt’s (1992) notion of structural holes (and H5), for both the male and female-controlled SMEs there was a stronger relationship between survival and formal networks than between survival and informal networks; although clearly both types of networks were important. This result is contrary to Brüderl and Preisendörfer’s (1998) finding that strong ties are more important than weak ties in explaining firm survival. However, the results support the suggestion by Uzzi (1996) that networks consisting of a balance of both weak and strong ties ultimately might be more valuable than networks focused on only weak (or only strong) ties. Table 8 provides the results of undertaking a similar analysis using sales growth (rather than firm survival) as the dependent variable. In terms of formal networks, male-controlled high-growth SMEs appeared to gain some advantage from accessing advice from both external accountants and

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industry associations. However, the results again indicate with respect to external accountants that there is an optimum level of networking beyond which no further benefit is gained and, in the case of industry associations, excessive networking (more than three times a year) might be counterproductive. That is, there is no difference (in terms of firm growth) in accessing advice from an external accountant between one and three times a year and accessing this network more often. However, accessing advice from an industry association between one and three times a year appears to be significantly more beneficial than accessing this network more often (or not at all). This suggests that for high-growth male-controlled SMEs, obtaining advice from both external accountants and industry associations up to three times a year might be an optimal strategy; any further interaction with these formal networks is likely to be counterproductive (particularly with respect to networking with industry associations).

Table 8.  Modelling firm Growth and Frequency of Individual Network use for Male and Female-controlled SMEsa Variables in the final models

Age    Less than 2 years old    2 years to less than 5    5 years to less than 10    10 years to less than 20 Industry   Mining   Manufacturing   Construction   Wholesale trade   Retail trade    Accommodation, cafes and restaurants   Transport and storage    Finance and insurance    Property and business services    Cultural and recreational services External accountant   Never   1–3 times Industry Association   Never   1–3 times Constant Percentage predicted correctly High/low/overall Chi-square significance -2 Log likelihood Nagelkerke R2

Male-controlled SMEs

Female-controlled SMEs

Wald

Wald

17.06 15.71 2.92 5.03 1.49 21.65 0.93 2.47 0.00 1.55 2.62 4.03 3.26 1.16 1.22 0.60 6.71 6.36 0.08 6.46 0.09 3.07 0.69

Sig. 0.00 0.00 0.09 0.03 0.22 0.02 0.33 0.12 0.97 0.21 0.11 0.05 0.07 0.28 0.27 0.44 0.04 0.01 0.78 0.04 0.76 0.08 0.41

53.2

63.0

 

 

Exp(B)

Sig.

Exp(B)

2.78 1.39 1.49 1.23 0.44 0.42 0.98 0.49 0.39 0.22 0.32 0.52 0.53 0.57 0.64 0.96

8.10 3.02 7.25

0.02 0.08 0.01

0.25 0.24

0.95 1.37 1.62

3.52

0.06

2.00

58.1 0.00 1683 0.06

a

62.9  

71.1  

67.1 0.01 92 0.15

Note that the variables reported in this table are those that were significant and, therefore, ‘in the equation’ as reported by SPSS using the forward stepwise (conditional) logistic regression method.Variables that were not significant and, therefore, ‘not in the equation’ are not reported.

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For the female-controlled SMEs, the results presented in Table 8 indicate that using an external accountant for advice more than three times a year is significantly more likely to be associated with high growth compared to never using this network, or only using this network one to three times a year. Beyond noting that using an external accountant for advice more than three times a year appears beneficial, it is not possible (due to data limitations) to indicate what the optimum level of contact with external accountants might be for high-growth female-controlled SMEs. These results suggest that while male SME owners make effective use of both external accountants and industry associations, female SME owners tend to rely more heavily on external accountants (possibly because of problems associated with accessing industry associations which typically meet after hours). Interestingly, no informal networks (which typically consist of stronger ties and fewer structural holes) appear to be related to firm growth for either the male or female-controlled SMEs. This result is consistent with Bratkovic et al.’s (2009) finding that strong ties can negatively affect firm growth. In summary, the final model for predicting high-growth male-controlled SMEs incorporates, along with the age and industry of the business, two formal networks (external accountants and industry associations) but no informal networks. The final model for predicting high-growth female-controlled SMEs incorporates only one formal network (external accountants) and no informal networks. This finding is consistent with Granovetter’s (1973) weak tie theory and Burt’s (1992) notion of structural holes, because both theories suggest that SME owners are likely to derive more benefit in terms of accessing new products and markets from formal rather than informal networks. The results are also consistent with Brüderl and Preisendörfer’s (1998) finding that strong ties are more important to firm survival than to firm growth.

Discussion Several interesting observations arise from the results presented in the previous section. First, while male and female SME owners appear to use a similar number of networks, male SME owners appear to make more frequent use of formal networks (in particular banks, solicitors, industry associations and business consultants). However, once appropriate controls are introduced only one gender difference remains: men appear to make more use of industry associations. Further, with the exception of the relationship between industry associations and survival, the formal networks used significantly more frequently by male (compared to female) SME owners (banks, solicitors and business consultants) have no apparent association with firm performance. Therefore, it would appear that female-controlled SMEs are not disadvantaged by their owners devoting fewer resources to networking with these groups. Second, external accountants are the only formal network source significantly related to firm survival and growth for both male and female-controlled SMEs. Therefore, given limited time for networking, it would seem that SME owners would be well advised to ensure they maintain regular contact with an external accountant; this would appear to be particularly relevant for female SME owners. While this finding is consistent with Potts, who found that ‘successful companies rely more heavily on accountants’ information and advice than do unsuccessful companies’ (1977: 93), it contrasts with the results of Robson and Bennett (2000) and Cooper et al. (1994). Robson and Bennett (2000) found no statistically significant relationship between accessing advice from accountants and any of their measures of firm performance. Similarly, Cooper et al. (1994) found that the use of professional advisers had no significant effect on firm performance. Third, with respect to informal networks, there does not appear to be any significant difference in the overall frequency with which male and female SME owners sought advice from these groups, although female owners appear to make significantly more use of family and friends. However,

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this apparent gender difference with respect to accessing family and friends for advice disappears when appropriate controls are introduced. Further, while SME owners appear to make frequent use of a variety of informal networks, none of these networks appear to be related to firm growth, and only two appear to be related to firm survival (others in the industry for male-controlled SMEs, and family and friends for female-controlled SMEs). The finding that no informal networks were related to firm growth (for either the male or female-controlled SMEs) is contrary to Fischer and Reuber’s (2003) observation, that owners of high-growth firms see the owners of other highgrowth firms as an invaluable source of relevant and useful advice. However, this finding supports Nelson’s (1989) argument, that owners who want to grow their firms are best advised to make more frequent use of a limited number of networks where they can access the particular expertise (i.e. advice) that they require. The finding also supports the argument that weak ties are more important than strong ties for business growth and development (Granovetter, 1973). Fourth, there were fewer networks associated with firm growth than was the case for firm survival. This, again, suggests that owners seeking rapid growth for their firms might be best advised to access more frequent help from a smaller number of networks that have the specific expertise required (Nelson, 1989; Zhao and Aram, 1995). This result might also help to explain the finding by Bates that heavy use of social support networks typified ‘the less profitable, more failure-prone businesses’ (1994: 671). Therefore, it might be important for SME owners to regularly assess their networking activities, in order to ensure that they are accessing appropriate networks without devoting too many resources to networking relative to the benefits they receive. Through a process of expanding and culling their networks, SME owners can identify those relationships that merit ‘continued development and future investment’ (Larson and Starr, 1993: 6). Fifth, while there are some notable differences between the male and female-controlled SMEs in terms of the network sources that were significant in the models developed to predict firm performance, these differences do not appear to impact negatively the performances of femalecontrolled SMEs relative to their male counterparts. Indeed, there was no significant gender difference in the performances (survival or growth) of the male and female-controlled SMEs in this study. This result is consistent with a social feminist theory perspective (Fischer et al., 1993), in that although there might be differences in the networks accessed by male and female SME owners, both groups appear equally effective in terms of the overall economic benefits that they derive from their networking activities. Finally, for the relatively few networks that are significantly related to firm performance, there is some evidence to suggest that excessive networking (more than three times a year) might be counter-productive. This was particularly true of the association between firm survival and the use of certain informal networks (others in the industry for male-controlled SMEs and family and friends for female-controlled SMEs). In summary, although SME owners appear to use a number of different networks, few of these networks appear to be associated with firm performance (survival or growth). The only networks to show up as being significantly associated with firm performance are: external accountants (for firm survival and growth, for both male and female-controlled SMEs); industry associations (for the survival and growth of male-controlled SMEs); others in the industry (for the survival of male-controlled SMEs); and family and friends (for the survival of femalecontrolled SMEs).

Conclusion The key findings from this study indicate that SME owners make extensive use of both formal and informal networks, with females making more frequent use of family and friends, and males

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making more frequent use of banks, solicitors, industry associations and business consultants. However, although the types of networks used by men and women appear to differ, most differences disappear when appropriate controls are included in the analysis. Further, despite the extensive use made by SME owners (male and female) of formal and informal networks, the majority of these networks – with the exception of external accountants, industry associations, others in the industry and family and friends – do not appear to be associated with firm performance (i.e. survival or growth). Zhao and Aram (1995) note that broad-ranging networks cost more financially, and in terms of the owner’s time and effort to develop and maintain; Starr and MacMillan argue that individuals ‘invest their time and energy in social transactions based on their expectations of future profits and rewards’(1990: 80); and Uzzi suggests that an ‘organization’s network position, network structure, and distribution of embedded exchange relationships shape performance such that performance reaches a threshold as embeddedness in a network increases’ (1996: 675). Consistent with these comments, and the notion of ‘parsimony’, the results in this study suggest that too many resources devoted to networking might not be helpful to SME performance. This finding provides support for Lerner et al. (1997) and Bates (1994), who found that participation in multiple networks was negatively related to firm performance. As noted by Low and MacMillan: ‘Aspiring entrepreneurs are advised to evaluate and map their current networks. Doing so is the first step toward building an effective network, an activity that is too important to be left to chance’ (1988: 155). In summary, the results from this study suggest that, given limited time for networking, SME owners should ensure, at a minimum, that they maintain regular contact with an external accountant. This might be particularly important for female SME owners with family commitments and limited time available for networking (particularly after hours). The results also suggest that, in terms of firm survival, accessing advice from a mix of formal and informal networks is likely to be preferable to only accessing either formal or informal networks. However, this does not apply to firm growth where only formal networks appear to have a positive impact. Finally, and contrary to the findings of some prior research, the results suggest that female-controlled SMEs are not failing to make appropriate use of networks.

Limitations of the study This study has a number of potential limitations that should be acknowledged. First, the SME owners were asked to indicate, within three categories, how often they accessed advice from a variety of sources. As this question only relates to accessing advice, it might not provide a true indication of the total networking involvement by SME owners. Further, the question did not ask respondents to indicate the nature of their network contact: that is, a simple phone call or a more in-depth meeting. It has been argued that, due to its dynamic and fluid nature, it is difficult to fully appreciate networking behaviour based simply on a count of the number of contacts made (Chell and Baines, 2000). Second, another potential limitation of the study (as noted in the data section) is the relatively low number of female compared to male-controlled SMEs in the sample. Third, a further potential limitation relates to the classification of firms as being either female or male-controlled. Where there was more than one owner of the business, this classification was based on the major decision-maker. If the major decision-maker was male (or female), the firm was classified as a male (or female)-controlled firm for the purposes of this study, even though the major decisionmaker might not have been a majority owner. Fourth, this study was conducted at a time when the use of social media networks (such as Facebook and Twitter) was limited and, therefore, this is an area that future research could usefully explore.

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Finally, it should be noted that it is not possible to conclude causality from a study such as this; all we can do is draw inferences based on an apparent association between networking and firm performance. For example, it might be that rapidly growing firms have a greater need to consult with external accountants, rather than regular contact with external accountants necessarily leading to rapid growth. This limitation is not as critical when firm survival is the dependent variable under consideration. The reader should be cautioned against interpreting the results of this study as indicating that networking with those groups not featured in the various models has no benefit. SME owners might receive other benefits from networking beyond the purely economic benefits that were the focus of this study. For example, through networking owners might draw more comfort (i.e. reassurance) from their future plans, and might gain the reassurance needed to continue in difficult times (Birley, 1985). In addition, networks can help SME owners integrate into the social life of a community (Donckels and Lambrecht, 1995). Further, the benefits from some networking sources might be firm and/or situation-specific and might not show up in a large-scale study looking at average outcomes. For example, using management consultants might be of substantial benefit in a few very specific cases. An analysis of a large data set might mask, or make it difficult to detect, these benefits. This is an area that future research could investigate further. From a government policy perspective, it should be noted that while the lack of association between accessing the SBDC and firm performance could be viewed as disappointing, the SBDC’s main objective is to help with new firm formations rather than to provide assistance to established firms (which were the focus of this study). This is reflected in Table 2, which shows that the SBDC was the least used network for the SMEs in this study. Therefore, the results should not be seen as conflicting with the findings of Chrisman and McMullan (2004), who reported a positive association between survival and an outsider assistance programme. So, while the results indicate that a variety of formal and informal networks are associated with SME performance (particularly external accountants and, to a lesser extent, industry associations, others in the industry and family and friends), they also indicate that SME owners need to monitor the resources that they devote to networking in order to ensure that the benefits they receive from networking exceed the costs. That is, the results do not support the widespread involvement of SME owners in multiple networks. This finding appears to be consistent for male and femalecontrolled SMEs. Finally, it should be noted that the results from this study indicate that women do not appear to be disadvantaged (relative to men) by potential differences in their networking activities, calling into question the suggestion by Aldrich (1989) that female entrepreneurs should attempt to break into the ‘Old Boys’ network whenever possible. Notes 1. To maintain a representative sample, businesses that ceased operations were replaced with similar businesses. 2. Copies of the questionnaires can be obtained from the ABS. Also note that the Australian tax year runs from July 1 to June 30. 3. Note that the findings were not improved by examining firms above and below the median.

References Aldrich H (1989) Networking among women entrepreneurs. In: Hagan O, Rivchun C and Sexton D (eds) Women-owned Businesses. New York: Praeger, 103–132. Australian Bureau of Statistics (2001) Characteristics of Small Business, Australia (8127.0). Canberra: Australian Bureau of Statistics.

Downloaded from isb.sagepub.com at University of Western Australia on July 25, 2012

556

International Small Business Journal 30(5)

Bates T (1994) Social resources generated by group support networks may not be beneficial to Asian immigrant-owned small businesses. Social Forces 72(3): 671–689. Becchetti L and Trovato G (2002) The determinants of growth for small and medium-sized firms. The role of the availability of external finance. Small Business Economics 19(4): 291–306. Birley S (1985) The role of networks in the entrepreneurial process. Journal of Business Venturing 1(1): 107–117. Bratkovic T, Antoncic B and Ruzzier M (2009) Strategic utilization of entrepreneur’s resource-based social capital and small firm growth. Journal of Management and Organization 15(4): 486–499. Brüderl J and Preisendörfer P (1998) Network support and the success of newly-founded business. Small Business Economics 10(3): 213–225. Bruderl J and Schussler R (1990) Organizational mortality: The liability of newness and adolescence. Administrative Science Quarterly 35: 530–547. Burt RS (1992) Structural Holes. Cambridge, MA: Harvard University Press. Burt RS (2000) The network structure of social capital. Research in Organizational Behavior 22(1): 345–423. Chell E and Baines S (2000) Networking, entrepreneurship and microbusiness behaviour. Entrepreneurship & Regional Development 12(3): 195–215. Chrisman JJ and McMullan EW (2004) Outsider assistance as a knowledge resource for new venture survival. Journal of Small Business Management 42(3): 229–244. Coleman JS (1988) Social capital in the creation of human capital. American Journal of Sociology 94(supp.): S95–S120. Cooper AC, Gimeno–Gascon JF and Woo C (1994) Initial human and financial capital as predictors of new venture performance. Journal of Business Venturing 9(5): 371–395. Cooper AC, Woo CY and Dunkelberg WC (1989) Entrepreneurship and the initial size of firms. Journal of Business Venturing 4(5): 317–332. Cromie S and Birley S (1992) Networking by female business owners in Northern Ireland. Journal of Business Venturing 7(3): 237–251. Davidsson P and Honig B (2003) The role of social and human capital among nascent entrepreneurs. Journal of Business Venturing 18(3): 301–331. De Clercq D and Voronov M (2009) Towards a practice perspective of entrepreneurship: Entrepreneurial legitimacy as habitus. International Small Business Journal, 27(4) 395–419. Diaz Garcia CM and Carter S (2009) Resource mobilization through business owners’ networks: Is gender an issue? International Journal of Gender and Entrepreneurship 1(3): 226–252. Donckels R and Lambrecht J (1995) Networks and small business growth: An explanatory model. Small Business Economics 7(4): 273–289. Duchesneau DA and Gartner WB (1990) A profile of new venture success and failure in an emerging industry. Journal of Business Venturing 5(5): 297–312. Evans DS (1987) Tests of alternative theories of firm growth. Journal of Political Economy 95(4): 657–674. Fischer E and Reuber RA (2003) Support for rapid-growth firms: A comparison of the views of founders, government policymakers, and private sector resource providers. Journal of Small Business Management 41(4): 346–365. Fischer EM, Reuber RA and Dyke LS (1993) A theoretical overview and extension of research on sex, gender, and entrepreneurship. Journal of Business Venturing 8(2): 151–168. Florin J, Lubatkin M and Schulze W (2003) A social capital model of high-growth ventures. Academy of Management Journal 46(3): 374–384. Glancey K (1998) Determinants of growth and profitability in small entrepreneurial firms. International Journal of Entrepreneurial Behaviour & Research 4(1): 18–27. Granovetter MS (1973) The strength of weak ties. American Journal of Sociology 78(6): 1360–1380.

Downloaded from isb.sagepub.com at University of Western Australia on July 25, 2012

557

Watson

Greene PG, Brush CG, Hart MM, et al. (2001) Patterns of venture capital funding: Is gender a factor? Venture Capital 3(1): 63–83. Hanson S and Blake M (2009) Gender and entrepreneurial networks. Regional Studies 43(1): 135–149. Havnes P and Senneseth K (2001) A panel study of firm growth among SMEs in networks. Small Business Economics 16(4): 293–302. Hoang H and Antoncic B (2003) Network-based research in entrepreneurship. Journal of Business Venturing 18(2): 165–187. Ibarra H (1992) Homophily and differential returns: Sex differences in network structure and access in an advertising firm. Administrative Science Quarterly 37(3): 422–447. Jarillo CJ (1989) Entrepreneurship and growth: The strategic use of external resources. Journal of Business Venturing 4(2): 133–147. Jovanovic B (1982) Selection and the evolution of industry. Econometrica 50(3): 649–670. Julien PA (1993) Small business as a research subject: Some reflections on knowledge of small businesses and its effects on economic theory. Small Business Economics 5(2): 157–166. Kent P (1994) Management advisory services and the financial performance of clients. International Small Business Journal 12(4): 45–58 Kristiansen S, Kimeme J, Mbwambo A, et al. (2005) Information flows and adaptation in tanzanian cottage industries. Entrepreneurship & Regional Development, 17(5): 365–388. Larson A and Starr JA (1993) A network model of organization formation. Entrepreneurship Theory and Practice 17(2): 5–15. Larsson E, Hedelin L and Garling T (2003) Influence of expert advice on expansion goals of small businesses in rural Sweden. Journal of Small Business Management 41(2): 205–212. Lerner M, Brush C and Hisrich R (1997) Israeli women entrepreneurs: An examination of factors affecting performance. Journal of Business Venturing 12(4): 315–339. Lin N, Ensel WM and Vaughn JC (1981) Social resources and strength of ties: Structural factors in occupational status attainment. American Sociological Review 46(4): 393–405. Littunen H (2000) Networks and local environmental characteristics in the survival of new firms. Small Business Economics 15(1): 59–71. Low MB and MacMillan IC (1988) Entrepreneurship: Past research and future challenges. Journal of Management 14(2): 139–162. Lussier RN and Pfeifer S (2001) A cross-national prediction model for business success. Journal of Small Business Management 39(3): 228–239. Madill JJ, Haines GH and Riding AL (2004) Networks and linkages among firms and organizations in the Ottawa region technology cluster. Entrepreneurship & Regional Development 16(5): 351–368. Marlow S, Henry C and Carter S (2009) Exploring the impact of gender upon women’s business ownership. International Small Business Journal, 27(2) 139–149. Moore G (1990) Structural determinants of men’s and women’s personal networks. American Sociological Review 55(5): 726–735. Munch A, McPherson JM and Smith-Lovin L (1997) Gender, children, and social contact: The effects of childrearing for men and women. American Sociological Review 62(4): 509–520. Nelson GW (1989) Factors of friendship: Relevance of significant others to female business owners. Entrepreneurship Theory and Practice 13(4): 7–18. Orhan M (2001) Women business owners in France: The issue of financing discrimination. Journal of Small Business Management 39(1): 95–102. Potts AJ (1977) A study of the success and failure rates of small businesses and the use or non-use of accounting information. Doctoral thesis, George Washington University, Washington, DC.

Downloaded from isb.sagepub.com at University of Western Australia on July 25, 2012

558

International Small Business Journal 30(5)

Reese PR and Aldrich HE (1995) Entrepreneurial networks and business performance: A panel study of small and medium-sized firms in the research triangle. In: Birley S and MacMillan IC (eds) International Entrepreneurship. London: Routledge, 124–144. Renzulli LA, Aldrich H and Moody J (2000) Family matters: Gender, networks, and entrepreneurial outcomes. Social Forces 79(2): 523–546. Robinson PB and Sexton EA (1994) The effect of education and experience on self-employment success. Journal of Business Venturing 9(2): 141–156. Robson P, Jack SL and Freel MS (2008) Gender and the use of business advice: Evidence from firms in the scottish service sector. Environment and Planning C: Government and Policy 26(2): 292–314. Robson PJA and Bennett RJ (2000) SME growth: The relationship with business advice and external collaboration. Small Business Economics 15(3): 193–208. Rosa P, Carter S and Hamilton D (1996) Gender as a determinant of small business performance: Insights from a British study. Small Business Economics 8(4): 463–478. Seibert SE, Kraimer ML and Liden RC (2001) A social capital theory of career success. Academy of Management Journal 44(2): 219–237. Shaw E, Lam W and Carter S (2008) The role of entrepreneurial capital in building service reputation. Service Industries Journal 28(7): 899–917. Starr JA and MacMillan IC (1990) Resource cooptation via social contracting: Resource acquisition strategies for new ventures. Strategic Management Journal 11(5): 79–92. Stuart T and Sorenson O (2003) The geography of opportunity: Spatial heterogeneity in founding rates and the performance of biotechnology firms. Research Policy 32(2): 229–253. Uzzi B (1996) The sources and consequences of embeddedness for the economic performance of organizations: The network effect. American Sociological Review 61(4): 674–698. Watson J (2003) Failure rates for female controlled businesses: Are they any different? Journal of Small Business Management 41(3): 262–277. Watson J (2007) Modeling the relationship between networking and firm performance. Journal of Business Venturing 22(6): 852–874. Wilson F, Kickul J and Marlino D (2007) Gender, entrepreneurial self-efficacy, and entrepreneurial career intentions: Implications for entrepreneurship education. Entrepreneurship: Theory and Practice 31(3): 387–406. Zhao L and Aram JD (1995) Networking and growth of young technology-intensive ventures in China. Journal of Business Venturing 10(5): 349–370. John Watson is a professor in the Department of Accounting and Finance, The University of Western Australia. His research interests lie in performance evaluation and measurement, and particularly the definition of SME failure, SME failure rates, the effect of macro-economic variables on failure rates and comparing the performances of male and female-controlled SMEs.

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