Human capabilities and firm growth; an investigation of women-owned established micro and small firms in Ireland

ABSTRACT

Firm growth has been studied extensively, yet limited attention has been devoted specifically to growth in women-owned firms. Women entrepreneurship studies to date has focused largely on the start-up rather than the growth stage. Given the dearth of research with this focus and the recognized prime role of the entrepreneur in driving firm growth, this study focuses on the specific human capabilities driving firm growth, through a survey of 172 micro and small women-owned established firms in Ireland. Regression analysis highlights that specific human capabilities (education, managerial experiences and being team-led) strongly influence firm-financial growth. These human capabilities are leveraged to maximize financial growth, but not employment growth. Therefore, employment as a measure of growth may be a barrier to access policy supports and initiatives for this cohort of entrepreneurs. We contribute a more nuanced understanding of growth from a resource-based view, in women-owned established micro and small firms.

KEYWORDS:

• Women entrepreneurs
• human capabilities
• established micro and small firms
• leveraging growth

Introduction

The extensive study of entrepreneurial firm growth has exemplified the conspicuous role of the entrepreneur, and the firm, as key determinants of firm growth. The entrepreneur has been identified as a key resource in developing and growing a firm (Helfat et al., 2007) with various human (i.e. education, age, gender, previous employment and managerial experience, and owner teams) and firm features (i.e. firm age, size, sector), and their relationship to growth investigated, but with mixed and inconsistent findings (Marlow & McAdam, 2013). More particularly, the study of firm growth in women-owned firms’ remains understudied. While the field has generally witnessed significant development over the past 30 years (Henry et al., 2016), women entrepreneurship research has not given equal attention to the increasing body of research on small firm growth, despite the necessity to do so (Ahl, 2006; Brush & Cooper, 2012; De Bruin et al., 2007; Minniti & Naude, 2010). The call for the ‘rectification' (Marlow & McAdam, 2013) of women’s entrepreneurial performance presents an opportunity to add to the development of a robust empirical base for research on women entrepreneurship, and more specifically, firm growth to advance this field. This study makes a contribution to this lacuna, employing a Resource-Based View (RBV) (Barney, 1991, 1995, 1997, 2001) perspective, to enable a deeper understanding of specific human capabilities driving firm growth, especially where women entrepreneurs, play dominant roles.

Responding to a dual call, this study, firstly, employs a more formal, structured theoretical framework investigating the linkage between human capabilities, and firm growth. Secondly, this study shifts the attention to the question of what human capabilities drive firm growth in women-owned firms. This research responds to contemporary calls to embed normative knowledge and perspectives within women entrepreneurship research (Ahl, 2006; Henry et al., 2016). Such perspectives, in this instance the RBV perspective, provides a platform for the exploration of new research questions and a broader agenda for future women entrepreneurship research (Dean et al., 2019).

The contributions of this study are twofold. Firstly, the application of the resource-based view (RBV) lens advances the empirical knowledge and insights into women entrepreneurship and firm growth, linking theoretical insights from both mainstream strategic management and entrepreneurship domains. Secondly, the study provides much needed robust empirical testing on the relationship and effects of human (i.e. women entrepreneur) capabilities as predictors of firm growth, demonstrating their pivotal role.

Context of the study

The small but very open economy of Ireland, along with the focus on micro, small and medium sized firms provides the locale for this study. Growing Micro, Small and Medium Enterprises (MSMEs),¹ continue to account for a significant proportion (approximately 99.8%) of Irelands population of enterprises (OECD, 2019), delivering strong economic growth and indeed social benefits to the economy (Fitzsimons & O’Gorman, 2020; Ireland, Department of Jobs, Enterprise and Innovation, 2013) which by corollary, has influenced an increasing policy interest in promoting sustainable growth amongst women entrepreneurs (Cooney, 2012; Fitzsimons & O’Gorman, 2017). Specifically, this research examines growth in women-owned firms over a three-year period (2007–2009) immediately following Ireland’s ‘Celtic Tiger' period (1991–2007). This interesting time period captures the impact of the global financial crises (GFC) on this small, open European economy and features women owned MSME growth at a time when the GFC depressed overall economic growth, precipitating a prolonged recession in Ireland. It is a valuable and interesting time period which provides insights into what human capabilities are leveraged internally by women entrepreneurs to achieve growth at this challenging time.

The relationship between human capabilities and firm growth

This study is underpinned by the resource-based view of the firm (RBV) (Barney, 1986; Wernerfelt, 1984). The RBV has at its premise that firm performance is dependent on its use of resources, both tangible and intangible, and that the utilization and configuration of these resources determines the firm’s performance (Barney, 1991; Penrose, 1959; Wernerfelt, 1984). Significantly, the role of the manager, and in this instance the entrepreneur, has been centre stage in the ability of the firm to leverage and orchestrate resources and capabilities for growth (Drucker, 1954; Helfat et al., 2007). Utilizing a RBV perspective enables a focus on those resources and capabilities which provide unique advantages for the firm in driving firm growth. Significantly, within this approach, the role of the entrepreneur/manager has been centre stage in the ability of the firm to leverage and orchestrate resources and capabilities for growth (Helfat et al., 2007). Indeed, underscoring this argument is the pioneering work of Penrose (1959) which focused on the role of the manager and the ‘imagination of managers’ as a key resource for firm growth. It is accepted that although various definitions of ‘resources' and ‘capabilities’ have been used synonymously throughout the literature, the same logic applies to both perspectives and so for consistency purposes, this paper adopts the term ‘capability'.

The entrepreneurship literature has in general recognized the pioneering work of Penrose (1959) in understanding and driving firm growth, and therein recognizes the dominant role of the entrepreneur, viewing them as a critical resource essential for growth. Indeed, some researchers strongly advocate that the entrepreneur and the firm are inextricably linked, and it may be solely the entrepreneur that determines firm growth due to their high level of autonomy of individual choice (Child, 1972; Penrose, 1959). Critically, it is recognized that it is the entrepreneurs’ subjective perceptions, human capabilities, and knowledge and experience (Polanyi, 1966), which is responsible for shaping and driving firm growth in unique ways. This is echoed in the conceptual and seminal study by Storey (1994) who classified key internal capabilities that influence firm growth, namely the entrepreneur, the firm, and strategic activities. Each capability offers indicators in the alchemy required to achieve a growing firm (Cooney, 2012). Thus, it is important to investigate which human capabilities are significant to achieve some specific performance dimension, in this case financial and non-financial growth- an important discussion within the empirical literature on RBV.

Human capabilities

Human capabilities and therein knowledge resources has often been recognized as the most significant factor explaining the effectiveness of capabilities (Arend & Bromiley, 2009; Colbert, 2004; Kok & Ligthart, 2014), which once again, places a distinctive emphasis on human capability, specifically relevant in the context of the entrepreneurial firm. For the most part, the entrepreneur themselves represents the key human resource, responsible for configuration and deployment of capabilities. Specifically, prior studies in both management and entrepreneurship agree, and recognize, the importance of the following factors: educational qualifications, managerial experience, previous work experience and firm/sector specific experience as key human capabilities (see, e.g. Barney & Wright, 1998; Beugelsdijk, 2008; Chadwick & Dabu, 2009; Colbert, 2004; Wright, McMahan & McWilliams, 1994), and as positively influencing firm growth. Furthermore, studies have shown that highly educated individual workers show greater ability to sense changes and monitor environmental variables (Nijssen & Paauwe, 2012; Wei & Lau, 2010; Wright et al., 1994). Such capabilities are echoed in the entrepreneurship literature where a clear, distinct and positive relationship between education, previous work experience, and managerial experience and firm growth is commonly reported (Dobbs & Hamilton, 2007; Storey, 1994, 2011).

Subjective managerial knowledge and perception is also shaped by managerial experiences within a specific industry. Industry-specific experience involves interactions with buyers, suppliers, distributors, and other stakeholders, which produce knowledge about the opportunities, threats, competitive conditions, and governmental regulations that are unique to each industry (Kor, 2003; Mosakowski, 1993; Spender, 1996). Developments in technology alongside competitive, and regulatory conditions in an industry, follow a path-dependent pattern (Arthur, 1994); thus, historical and experience-based knowledge of the industry can be useful for perception and evaluation of new entrepreneurial opportunities. Industry experience can embed goodwill with certain customers, suppliers, and industry stakeholders. Experienced individuals can capitalize on this goodwill by initiating and securing new business relationships for their current firm. Thus, experience in a specific industry not only provides knowledge concerning how the industry works, but such knowledge may also contribute to the economic success of a new business venture when the experienced entrepreneur can more easily secure resources and business orders for the firm through previous industry connections.

In extending the research on small firm growth and performance, there has also been investigations of the nature of ownership as another human capability, whereby firms owed by teams of entrepreneurs, rather than solely-owned are associated with higher firm growth (Birley & Stockley, 2017; Brinckmann & Hoegl, 2011; Schjoedt & Kraus, 2009). This is due to the increased human and social capital of a team, relative to individuals dealing with complexities and challenges of growing firms (West, 2007). Moreover, this factor is derived as a natural consequence of prior employment experience and education of entrepreneurs. According to Storey (1994, 2011), the combined skills of multiple entrepreneurs provide the firm with extra resources, skills, and access to a broader set of external networks facilitating firm growth that is not held by one entrepreneur. Experience in the team involves’ decision-making and implementation experience as a particular team (Priem & Butler, 2001). Individuals’ experience in functioning together as a team includes discussions and debates on strategic decisions, during which individuals learn each other’s strengths, weaknesses, and idiosyncratic habits (Kor, 2003; Penrose, 1959). While it is accepted that the ideal scenario for micro and small firms is to have a breadth of expertise to accommodate the functional requirements of the firm, it is equally argued that this is not the case in many of these firms. It is frequently reported of women-owned firms that they are solely owned and managed rather than owned and managed by teams (Ndemo & Maina, 2007; Roomi et al., 2009; Ruane & Sutherland, 2007). This may be attributed to women entrepreneurs’ access to human capital, which takes many forms including education, prior employment experience and managerial experience and industry experience. Given the insufficient attention to the relationship between ownership structure and firm growth in women-owned firms, this relationship is also investigated in this study.

The review to date has brought to the fore various human capabilities, with a view to evaluating the following hypotheses in relation to women entrepreneurs:

H₁: There is a positive association between growth (measured by the percentage change in turnover, turnover per employee or employment) and women entrepreneurs’ third level education.

H₂: There is a positive association between growth (measured by the percentage change in turnover, turnover per employee or employment) and previous sector employment experience of women entrepreneurs.

H₃: There is a positive association between growth (measured by the percentage change in turnover, turnover per employee or employment) and women entrepreneur’s previous managerial experience.

H₄: There is a positive association between growth (measured by the percentage change in turnover, employment and turnover per employee growth) and women-owned firms owned and managed by a team.

Growth factors

Other growth-related factors for consideration are firm age, entrepreneur’s age, firm size and sector. Firm age is widely investigated in firm growth (Blackburn et al., 2013; Glancey, 1998), with numerous studies (Barkham et al., 2012; Fadahunsi, 2012; Kinsella et al., 1994; Lotti et al., 2001; Minniti & Naude, 2010; Storey, 1994) suggesting that younger firms (less than 10 years) display faster rates of growth compared to older firms, growing more rapidly particularly in employment and turnover. As such, firm age will be included as a control variable in this study.

The age of women entrepreneurs is included as a control variable given the varied findings highlighted in the literature relating to the influence of age on firm growth. Previous studies (Bosma et al., 2004; Coleman, 2007; Mata, 1996; Persson, 2004; Storey, 1994, 2011; Storey & Greene, 2010) have positively linked younger, middle aged and older owner-managers with firm growth. Thus it is apparent that age is treated in a generic manner (Carter & Allen, 1997; Cliff, 1998; Roomi et al., 2009; Rosa et al., 1996). The evidence on the relationship between age and firm growth to date is general in nature and does not address whether it is younger, middle-aged or older women entrepreneurs that achieve higher firm growth, and so is included as a control variable in this study.

Firm size, which is generally measured by employment numbers (Blackburn & Kovalainen, 2009; Hansen & Hamilton, 2011; Roomi et al., 2009) is also used as a control variable. Essentially, smaller firms like younger firms grow more rapidly, where they endeavour to achieve efficiencies in production and markets as quickly as possible. Barkham et al. (2012) found smaller firms achieved faster growth due to their flexibility in decision making as a result of the dominance of the entrepreneur as the primary decision maker. Additionally, Barkham et al. (2012) suggested that since small firms operate in niche markets, this is another competitive advantage that allows them to achieve firm higher growth. Given that women-owned firms in general tend to be smaller, (Cliff, 1998; Coleman & Robb, 2012; Fairlie & Robb, 2009; Jennings & Brush, 2013), it could be assumed these distinct advantages are features of women-owned firms. Research also highlights that although women-owned firms are smaller in terms of employee numbers, they may well outperform male-owned firms in terms of turnover and profit (Watson, 2002). Thus, this study includes firm size as a control variable when studying growth (Kalleberg & Leicht, 1991).

Industry sector is also included as a control variable given the changes taking place in sectors where women entrepreneurs are currently operating. Women entrepreneurs are setting up firms and achieving high growth in non-feminized sectors (Davis & Shaver, 2012) e.g. pharma, biomedical devices, technology, sectors where women were traditionally underrepresented. In addition, women entrepreneurs are also achieving high growth in feminized sectors (i.e. education, professional services, retail) and so these sectors must be considered. Firstly, these sectors are where the vast number of women entrepreneurs traditionally, and currently operate in (Aylward, 2007; Diaz-Garcia & Brush, 2012; Henry & Johnston, 2003), and, secondly, the evidence of high levels of turnover and employment growth in these sectors. Thus, industry sector (feminized or non-feminized) is deemed important for inclusion in this study as a control variable.

The discussion now turns its attention to the research approach, measures, sample and analysis.

The research method

Quantitative research is the methodological choice for this study based on an evaluation of its appropriateness in previous studies on firm growth (Blackburn & Kovalainen, 2009; Kinsella et al., 1994; Storey, 1994). The research employed a cross-sectional, quantitative research design; applying a self-report survey methodology. Respondents were invited via email to participate in the study by filling out the survey, hosted through an online link at Survey Monkey (www.surveymonkey.com). The survey consisted of 18 closed questions with Likert scales etc. The validity and reliability of the survey instrument were considered to satisfy the criterion of internal consistency (Edmondson & McManus, 2007, p. 1155). While limitations of the self-report survey method include the possible effect of social desirability, every effort was made to remedy such a problem by adopting the ‘sensible approach' as advocated by Podsakoff & Organ (1986) and Podsakoff, MacKenzie, Lee, and Podsakoff (2003) and as much information as possible was provided to the respondents on the research purpose.

The survey was distributed to a database of women-owned firms operating in Ireland, whereby contact details were gathered from public sources such as Enterprise Ireland, Local Enterprise Offices, and Network Ireland. A total of 1200 firms were identified, representative of the population of established women owner-managers as identified by the GEM report (2010) and reflected the population of established women entrepreneurs. A review of 176 women-owned firms that responded to the survey highlighted that 4 of the 176 were medium-sized firms (with over 50 employees each). Once these 4 firms were eliminated, the remaining 172 micro and small firms in operation for over 3 years represented the final sample, representing a response rate of 14.3%.

Measures

The independent variables utilized in this study are human capabilities, while the dependent variables are the measures of growth, namely turnover, turnover per employee and employment growth over a three-year period from 2007 to 2009. Turnover is the annual revenue of the MSME and growth in turnover is calculated as the average percentage change in turnover over the period of the study, for the cohort of firms. The effect of inflation² was removed from turnover data by deflating it in the base year rates. Growth in employment numbers and growth in turnover per employee are also calculated as the average percentage change over the period of the study. Multiple measures are adopted to provide a more holistic view of small firm growth.

Turnover is the most frequently used growth measure (Gilbert et al., 2006; Roomi et al., 2009) as it reflects the inflationary pressures of the time under study and is familiar to entrepreneurs (Roomi et al., 2009). Furthermore, it reflects both short and long-term changes in the firm, and the environment in which it operates. Turnover data is generally more easily obtained relative to profits and can indicate trends in the financial performance with respect to aggregate sales and market share (Roomi et al., 2009; Senderovitz et al., 2016). The relative measure of the rate of change of turnover from 2007 to 2009 is applied here.

Employment numbers are the most frequently used non-financial measure of firm growth (Blackburn & Kovalainen, 2009; Roomi et al., 2009). They are easily collected and categorized, are not affected by inflation, are beneficial for cross-comparative purposes; are readily measurable; and reflect more accurately growth change over time compared to other variables (profit or turnover) which are more sensitive to external changes in demand or changes in the cost of business (Blackburn & Kovalainen, 2009; Gilbert et al., 2006). It is only when employment is used in conjunction with other financial measures that a more inclusive insight into firm growth is obtained (Du Rietz & Henrekson, 2000; Roomi et al., 2009; Storey, 1994). For example, the turnover per employee ratio captures the level of productivity in a business, essentially how much money each employee generates for the business. Higher productivity is also associated with higher profits. Thus, for this study multiple measures of growth are applied, financial growth (turnover and turnover by employee) and non-financial growth (employee numbers).

In calculating the data for three growth measures, a base year (2007) was selected on which the other years figures were calculated. Employment growth was calculated by taking employment figures for 2009 minus those for 2007, then computing the change in employment performance for the period of the study.³ Table 1 demonstrates the measures for growth including the average growth rate in turnover (12.94%), employment (22.04%) and turnover per employee (5.83%) over the three-year period of the study. This sample shows that the rank of growth measures from largest to smallest in these women-owned micro and small businesses is employment, turnover and productivity.

Table 1. Measures of growth: average turnover, employment, turnover per employee growth (2007–2009) (n = 172).

Growth measure	Average growth (%)
Average growth rate in turnover	12.94
Average growth rate in employment	22.04
Average growth rate in turnover per employee	5.83

Sample description

Preliminary analysis involved analysis of variance (ANOVA) examining the mean differences of the independent variables (human capabilities) in relation to the measures of firm growth. Table 2 outlines the profile of women entrepreneurs and their firms represented across all industry sectors. The majority are aged 18–44, having completed third level education, with managerial and same sector previous employment experience. Most firms were micro firms (less than ten employees), in existence for 3–5 years.

Table 2. Sample description of women owner-managers and their micro and small firms (n = 172).

Description of the women-entrepreneurs in sample		Description of the firms in sample
Age	Percent (%)	Industry sector	Percent (%)
18–34	25.6	Professional services	37.8
35–44	43.6	Manufacturing	15.1
45–54	25.0	ICT	13.4
55+	5.8	Health	11.64
Education		Education and training	9.3
Second level	12.8	Retail	7
Third level	53.5	Food and drink	5.8
Postgraduate level	33.7	Firm age
Work experience		3–5 years	61
Same sector experience	61.6	6–10 years	22.1
Managerial position	63.4	Over 10 years	16.9
Involvement in business start-up	36.6	Firm size
		0–10 (micro)	86
		11–50 (small)	14

To test the causal relationship between firm growth and women-owned firm’s human capabilities, the model employed in the multiple regression analysis includes the three measure of growth as the dependent variables and is expressed as: $\mathrm{G}\mathrm{r}\mathrm{o}\mathrm{w}\mathrm{t}{\mathrm{h}}_{1-3}={\alpha }_0+{\alpha }_1\mathrm{S}\mathrm{a}\mathrm{m}\mathrm{e}\mathrm{S}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{E}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{o}\mathrm{y}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{E}\mathrm{x}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{e}+{\alpha }_2\mathrm{M}\mathrm{a}\mathrm{n}\mathrm{a}\mathrm{g}\mathrm{e}\mathrm{r}\mathrm{i}\mathrm{a}\mathrm{l}\mathrm{P}\mathrm{o}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}+{\alpha }_3\mathrm{T}\mathrm{h}\mathrm{i}\mathrm{r}\mathrm{d}\mathrm{L}\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{l}\mathrm{E}\mathrm{d}\mathrm{u}\mathrm{c}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}+{\alpha }_4\mathrm{N}\mathrm{a}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{e}\mathrm{o}\mathrm{f}\mathrm{O}\mathrm{w}\mathrm{n}\mathrm{e}\mathrm{r}\mathrm{s}\mathrm{h}\mathrm{i}\mathrm{p}+{\alpha }_5\mathrm{F}\mathrm{i}\mathrm{r}\mathrm{m}\mathrm{S}\mathrm{i}\mathrm{z}\mathrm{e}+{\alpha }_6\mathrm{A}\mathrm{g}\mathrm{e}+{\alpha }_7\mathrm{F}\mathrm{i}\mathrm{r}\mathrm{m}\mathrm{A}\mathrm{g}\mathrm{e}+{\alpha }_8\mathrm{S}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r}+\mathrm{e}\mathrm{r}\mathrm{r}\mathrm{o}\mathrm{r}$

Where the statistical description of the dependent variables of growth are shown in Table 1, and the independent variables are explained as:

• Growth is measured by three dependent variables, both financial and non-financial, firstly the percentage change in turnover from 2007 to 2009; secondly, the percentage change in employment numbers from 2007 to 2009; and finally, the percentage change in the ratio of turnover to employment numbers from 2007 to 2009.

• Same Sector Experience is the women entrepreneur’s previous sector experience.

• Managerial Position is the women entrepreneur’s previous managerial position.

• Third Level Education captures the women entrepreneur’s education level (with or without a third level education).

• Nature of Ownership indicates that the firm is owned by a team.

Control variables:

• Firm Size is a firm of more than 10 employees (to distinguish between micro and small firms).

• Firm Age captures the number of years the firm is in operation.

• Age is the age of the women entrepreneur.

• Sectors include ICT, Manufacturing, Food and Drink, Professional Services Education and Training, Health, and Retail.

Research findings

Table 3 depicts the analysis of variance regarding the relationship between human capabilities and firm growth (measured by the percentage change in turnover, turnover per employee, and employment) in women-owned firms.

Table 3. Analysis of variance between human capabilities and growth (turnover, employment and turnover per employee, n = 172).

Growth metrics/human and organizational capabilities	Turnover growth F Stat	Employment growth F Stat	Turnover per employee growth F Stat
Same sector work experience	1.781	1.745	1.541
p value	.184	.188	.261
Managerial experience	26.575***	.451	5.457**
p value	.000	.503	.021
Third level education	16.445***	.036	5.262**
p value	.000	.850	.023
Nature of ownership	17.842***	1.597	7.986***
p value	.000	.208	.005
Firm size	.112	.033	.782
p value	.738	.856	.378
Age of women entrepreneur	.222	13.156***	1.887
p value	.638	.000	.171
Firm age	.113	4.315***	4.231***
p value	.940	.006	.007
Industry sector	1.460	1.052	3.399***
p value	.195	.394	.003

Note: ANOVA F Statistic **significant at the 5% level, ***significant at the 1% level.

Growth is measured by three dependent variables, both financial and non-financial, firstly the percentage change in turnover from 2007 to 2009; secondly, the percentage change in employment numbers from 2007 to 2009; and finally, the percentage change in the ratio of turnover to employment numbers from 2007 to 2009. Same Sector Experience is the Women entrepreneur’s previous sector experience. Managerial Position is the Women entrepreneur’s previous managerial position. The Age variable is the age of the Women entrepreneur. Third Level Education captures the Women entrepreneur’s education level (with or without a third level education). Firm Size is a firm of more than 10 employees. Nature of Ownership indicates that the firm owned by a team of entrepreneurs. Firm Age is a variable that captures the number of years a firm is in operation. Sectors include ICT, Manufacturing, Food and Drink, Professional Services Education and Training, Health, and Retail.

Regarding human capabilities, attention is drawn in particular to the relationship between education, previous employment sector experience and managerial experience, and firm growth. Turnover and turnover per employee, rather than employment growth are more significantly related to three of the four human capabilities (managerial experience and third level education). The human capability represented by same sector experience fails to be significantly related to any form of growth measure: therefore, this is early evidence that H₂ could be rejected.

Very significant relationships exist between turnover and specific human capabilities of education (F Statistic = 16.445, and p = .000) and managerial experience (F Statistic = 26.575, and p = .000). Turnover per employee growth (productivity) is also significantly related to education (F Statistic = 5.262, and p = .023) and managerial experience (F Statistic = 5.457, and p = .021), and the significance is at the 5% level. These results are preliminary support for H₁ and H₃ for financial growth, but not for non-financial growth i.e. employment numbers.

Nature of ownership as a human capability emerges as highly significantly related to growth, more particularly turnover (nature of ownership- F Statistic = 17.842 and p = .000) and turnover per employee growth (nature of ownership- F Statistic = 7.986, and p = .005). Employment growth was not significantly related to the nature of ownership (F Statistic = 1.597, and p = .208). These results are preliminary support for H₄ for financial growth and productivity, but not for non-financial growth i.e. employment numbers.

The control variables of firm age, firm size, age of women entrepreneurs and industry sector were not significant for turnover growth. Firm age and the age of women entrepreneurs were significantly related to employee numbers growth. Therefore, we can conclude that younger women entrepreneurs and younger established firms are most likely to be associated with employment growth. For the final measure of growth, turnover per employee is associated with the Firm Age and Industry Sector control variables. Again, younger established firms are associated with growing turnover per employee, a measure of productivity. To summarize, preliminary statistical results highlight significant differences between the human capabilities of education, managerial experience, and the nature of ownership, controlling for firm age and so warrant further investigation. It is noteworthy that growth measures are not influenced by firm size – this may be as a consequence of the research design to focus on micro and small firms only. A caveat here is that the analysis of variance does not imply causality and does not include the influence of other factors in the relationships tested. In order to gain a more comprehensive view of the relationship between human capabilities and growth, regression analysis is now applied to the growth model (1) explained above.

To test the causal relationship between firm growth and women’s human capabilities, multiple regression analyses were conducted, controlling for firm age, women entrepreneur’s age, firm size and sector. Such analysis highlighted findings that are more specific to individual human capabilities and their relationship to firm growth (as measured by percentage change in turnover, employees and turnover per employee), depicted in Table 4.

Table 4. A multiple regression examining the link between growth (turnover, employment, turnover per employee,) and human capabilities in women-owned Firms (n = 172).

Growth metrics/human and organizational capabilities	Turnover coefficients	Employment coefficients	Turnover by employees coefficients
(Constant)	4.086	69.152**	−37.127**
Same sector experience	−6.585	13.224	−11.326
Managerial position	14.852***	2.564	12.173
Third level education	15.095***	−10.452	23.518**
Nature of ownership	10.447***	13.840	18.773**
Firm size	−2.143	8.086	−26.933**
Age of women entrepreneur	−3.424**	−17.912***	4.668
Firm age	3.101	−17.998***	12.101***
ICT sector	5.736	−30.428	38.498***
Manufacturing sector	−6.625	2.171	6.431
Food and drink sector	−8.485	16.152	−1.191
Education and training sector	−4.547	16.213	4.220
Health sector	−6.794	28.336	−10.655
Retail sector	−2.160	−313.409	3.649

Note: **significant at the 5% level, ***significant at the 1% level.

Growth is measured by three dependent variables, both financial and non-financial, firstly the percentage change in turnover from 2007 to 2009; secondly, the percentage change in employment numbers from 2007 to 2009; and finally, the percentage change in the ratio of turnover to employment numbers from 2007 to 2009. Same Sector Experience is the Women entrepreneur’s previous sector experience. Managerial Position is the Women entrepreneur’s previous managerial position. The Age variable is the age of the Women entrepreneur. Third Level Education captures the Women entrepreneur’s education level (with or without a third level education). Firm Size is a firm of more than 10 employees. Nature of Ownership indicates that the firm owned by a team of entrepreneurs. Firm Age is a variable that captures the number of years a firm is in operation. Sectors include ICT, Manufacturing, Food and Drink, Professional Services Education and Training, Health, and Retail.

A positive and highly significant relationship exists between education as a human capability and micro and small firm financial growth as previously demonstrated in Table 3. Table 4 highlights that the coefficient for the human capability of third level education (coefficients 15.095 and 23.518) is large, positive and highly significantly associated with financial growth (turnover and turnover per employee). This demonstrates that women having completed third level education achieved higher financial growth when compared to those who did not, emphasizing third level education as a strong predictor for financial growth and productivity (turnover and turnover per employee) and supporting H₁ for financial growth. It is noteworthy, however, that this relationship is not replicated for the employment growth measure suggesting that women entrepreneurs with higher-level education profiles increase revenue without necessarily increasing employee numbers. Highly educated women entrepreneurs of micro and small businesses manage to grow their businesses through higher employee productivity rather than higher employee numbers. This finding highlight that women entrepreneurs are capable of developing their staff to maximize their potential.

As depicted in Table 4, the findings regarding previous sector employment experience were not surprising. The preliminary analysis confirmed an insignificant relationship between all measures of growth and same sector employment experience, and the regression analysis outlined in Table 4 confirms that the association is not statistically significant. Therefore, H₂ is rejected for all measures of firm growth. We suggest that micro and small firm growth of nature is not dependent on women entrepreneurs having same sector employment experience.

On a more positive note, preliminary findings highlighted that financial growth (turnover and turnover per employee) are positively related to women entrepreneurs’ managerial experience. When investigated further in Table 4, turnover growth (coefficient 14.852) is positively and highly significantly associated with previously held managerial positions, confirming H₃ for that measure of financial growth. This finding emphasizes that managerial experience has a positive impact on financial growth but not on non-financial growth and that previous managerial experience does not have to be sector specific. The human capability of previous managerial skills and experience is a driver of financial growth in micro and small businesses owned by women.

Being team-led, rather than solely owned and operated firms, is highly significant and is positively associated with financial growth (turnover coefficient 10.447 and turnover per employment coefficient 18.773). Ownership teams as a human capability is definitely linked to firm growth and confirms H₄ for both measures of financial growth. From Table 4, the non-financial measure of growth (employment) is not significant and demonstrates that human capability of being team-led is a key predictor for growth and productivity, more especially for financial growth (i.e. turnover), clearly supporting H₄.

In terms of the control variables in Table 4, the age of the entrepreneur is an important feature for two of the three measures of growth. Younger women entrepreneurs appear to achieve growth in their firms in terms of turnover and employee numbers without evidence of productivity. The only sector that is linked to this type of growth is the ICT sector, where there are highly significant levels of productivity (turnover per employee), highlighting the case that ‘jobless growth' is occurring in this particular sector. Otherwise, the relationships that exist between human capabilities and growth are stable irrespective of sector.

Prior studies frequently report that smaller firms achieve higher firm growth; however, this effect was not reflected in this study for the financial growth metric (turnover). Turnover is not related to firm size, (coefficient −2.143). Naturally, one expects that there will be a relationship between firm size (based on employment numbers) and growth measures utilizing employee figures. Firm size is negatively related to turnover by employees (productivity) (coefficient −26.933), suggesting that smaller firms are more productive than larger firms in the small.

The control variable of firm age is negatively related to micro and small firm non-financial growth of employee numbers (coefficient −17.998), suggesting that hiring staff and growing the team is a younger firm’s priority. When examining the growth metric of turnover per employee (productivity), the firm age is positively and highly significantly (coefficient 12.101) associated with this metric. In other words, the older the firm, the more likely that productivity increases. Taken together, these findings suggest that younger firms grow their employee numbers in the earlier years and then turnover growth overtakes employee growth as the firm becomes more established and productivity is enhanced (turnover per employee) – the older micro firm is more productive. This could be a reflection of the changing nature of work, as automation and digitization reduce reliance on human resources.

Discussion

This research sought to address three key topics emerging from the literature. Firstly, it sought to extend and contribute to the lacuna of research on the relationship between human capabilities and growth, in women-owned micro and small firms. The application of the RBV perspective to the field of women entrepreneurship and firm growth, links theoretical insights from both strategic management and entrepreneurship domains and helps develop much needed theory in this area. Additionally, the study provides empirical testing on the relationship and effects of specific human capabilities of women entrepreneurs as predictors of various measures of firm growth, demonstrating their pivotal role thereby providing a more holistic and integrated perspective of growth in these firms. In addition, this study demonstrates firm growth is not determined by one single capability but is influenced by numerous interlinked human capabilities.

The evidence highlights that specific women’s human capabilities have a positive and statistically significant influence on firm growth, most notably for turnover and turnover per employee growth. It is noteworthy that employment growth is not related to any specific human capability. Of significance, when growth is measured as the percentage change in turnover, three of four women entrepreneur’s human capabilities, namely education and managerial experiences and team ownership structure dominantly drive financial growth and productivity within women-owned firms. For turnover per employee, two of the four human capabilities (education and team ownership) drive productivity within women-owned firms.

The results demonstrate that highly educated women entrepreneurs achieve higher financial growth, confirming higher level education as a key human capability for driving growth. This outcome highlights the need to target and support younger women in understanding the role of education in achieving their firms financial growth and productivity potential, mirroring Storey (2011) and Fadahunsi (2012) and so brings to the fore the need to promote entrepreneurship as a career choice to women at third level. More particularly, women students studying STEM should be targeted given the high growth potential of these sectors. Having managerial experience as a human capability demonstrated statistically significant results for turnover growth. However, same sector experience did not influence either financial or non-financial growth, suggesting that skills developed by women in any prior employment at a managerial level are fully transferable across sectors and can be harnessed to drive financial growth. Being team-led emerges as having a positive influence on financial growth, highlighting that working in a team of entrepreneurs is a key driver for success. Women entrepreneurs collaborating, with different transferrable skills in a team are more likely to achieve firm financial growth and productivity.

The results for the control variable of firm age is negatively related to employment, and positively related to turnover per employee growth (productivity), which supports the notion that younger firms invest in their human capital to a point where the optimal employee numbers are reached to maximize productivity. Firm size (as measured by employee numbers) confirms this by highlighting the inverse relationship between size and productivity. Firm size did not influence firm growth in this study (except for turnover by employees), diverging from previous studies reporting this as a determinant of firm growth where firms with fewer than 10 employees achieve higher firm growth (Barkham, Gudgin, Hart, & Hanvey, 1996; Glancey, 1998; Kinsella et al., 1994; Storey, 1994).

While the industry sector did not emerge as significant for any growth measure, this suggests that growth is not influenced by industry choice for this sample; however, this may be a consequence of the sample composition. Therefore any conclusions about sectors are limited by the potential for these findings to be generalized. Accordingly, micro and small firms operating in feminized (e.g. retail, health, education services) and non-feminized (e.g. ICT, manufacturing) industry sectors may be equally recognized in terms of potential for financial growth and productivity. This echoes the discussion relating to the education of women recommending that a concerted effort is required to actively pave the way for women to enter non-feminized industry sectors.

Conclusions, recommendations and implications

This research answers in the affirmative that the mainstream entrepreneurship literature on firm growth non-gender specific in nature – is applicable in the women context. In fact, this research demonstrates little or no deviation from the mainstream literature and indeed, it shows the interconnectedness of the mainstream firm growth literature with that of growth in women-owned firms, providing a more comprehensive understanding of the key role of human capabilities, in determining micro and small firm growth in women-owned firms. Such findings add value in terms of their contributions and applications for further study in single and dual gender studies.

This research has used quantitative measures to test relationships between human capabilities and firm growth, in response to the demand for such an investigation of firm growth. Empirical evidence highlights the necessity to use quantitative measures to capture the multi-dimensional nature of firm growth, reinforcing the mainstream literature, and highlighting the importance of using multiple measures of growth in further studies in the women context. The study has also established that firm growth is dependent on a range of human capabilities and that measures of growth (turnover, employee numbers and productivity) are not interchangeable, highlighting that not all measures should be equally recognized and valued.

A key finding from this study is that by leveraging women’s human capabilities, growth is achieved in turnover and productivity, without necessarily increasing employment numbers in micro and small firms; perhaps for women entrepreneurs ‘small is beautiful'. This suggests that micro and small size is a choice rather than a consequence of the market opportunities. From a managerial perspective, the study suggests that aspiring women entrepreneurs should concentrate on developing the human capabilities of education and transferrable managerial experiences and skills, to achieve financial growth success. Another implication is that ambitious women entrepreneurs should work in teams to maximize the potential for success in their own businesses and optimize their productivity. Established women entrepreneurs should consider exploiting the significance of their managerial experience and their team-led capabilities when driving firm growth and productivity.

This research also has implications for policy. This study highlights that as these businesses become more established, growth is primarily financially driven and not related to growing employee numbers. This pattern of micro and small firm growth should be an important policy consideration in determining firm growth criteria for policy development in the women entrepreneurial context. The predominance of employee numbers as a significant requirement to qualify for grants and governments supports is excluding micro, small sized firms, which account for the majority of SMEs, valuably contributing to the economy of society. The women entrepreneur and their role in the growth of the economy has policy implications for government funding and training supports in education for women entrepreneurs, and in the STEM context, in post-recessionary environments.

Limitations of the study and suggestions for future research

This study sheds light on the human capabilities that drive financial growth in micro and small firms. As this study was focused on the Ireland context, the findings of this study may only be relevant to the context or similar contexts researched, and future research could therefore investigate general capabilities for growth, as well as of course human capabilities for growth, in other small open economy contexts. Comparison studies could be conducted with for example emerging economies, developing nations and large developed economies. Another limitation of the study is that the interactions of these human capabilities is not captured in the data and study design. Future research could concentrate on assessing the combinative capabilities of individual human capabilities, using quantitative measures and qualitative measures. Further, more in-depth research is required to examine more specifically the role of the entrepreneur in combining such capabilities, and to further investigate if their combined, rather than individualistic effect, have a more significant influence on firm growth. This may assist in providing further insights in the dominant role of women entrepreneur and indeed their absorptive capacity, a research topic that is gaining momentum.

To conclude, this study sheds lights on the role of human capabilities in driving firm growth in women-owned established MSMEs in Ireland, with an interesting overall finding that such firms can achieve growth without requiring growth in employee numbers, through the leveraging of resources such as education, previous employment sector experience, team experience, and managerial experience to drive firm growth.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 European Commission (2019) definition of Micro, Small and Medium Enterprises (MSMEs): A micro enterprise has fewer than 10 employees and either an annual turnover and/or an annual balance sheet total not exceeding €2m; a small enterprise has fewer than 50 employees and either an annual turnover and/or an annual Balance Sheet total not exceeding €10m; a medium enterprise has between 50 and 249 employees and has either an annual turnover not exceeding €50m or an annual Balance Sheet total not exceeding €43m.

2 This real value of the turnover data was obtained by adjusting it for price movements and to allow for inflation using the Consumer Price Index (CPI) which was differentiated separately between goods and services.

3 The percentage change was calculated as follows: (value at the end of year-value at the end of base year)/ value at base year*100 and averaged for the three years of the period of the study.