Factors of Human Capital towards Business Performance of WomenOwned Small Enterprises: A Study in Matara District, Sri Lanka
Ganewatta G. K.H. and Rathnayake R.M.
Department of Management & Entrepreneurship, University of Ruhuna.
[email protected], [email protected]
Abstract
In today’s context, managing business successfully is not an easy task. Many people who
begin the process of starting a new business fail to achieve their goals, although others are
quite successful . It has been recognized that women owned firms were more likely to fail,
and had lower levels of sales, profits, and employment than those owned by men .
Human Capital is arguably the most valuable asset held by any organization today. Prior
research showed that human capital attributes in particular those of the business owner,
have been argued to be a critical resource that affects small business performance. However
the amount of investigations linking performance of women entrepreneurs and human capital
are very limited in Sri Lanka. Therefore this research is is an empirical investigation of the
impact of factors of human capital on the business performance of small enterprises
managed by women entrepreneurs.
The main objective of the study was to determine the impact of human capital and to what
extent factors of human capital contribute to performance changes of small enterprises
managed by women. A secondary objective was to find the most influencing human capital
factor in formulating a composite index of human capital. This study considered seven
factors of human capital as independent variables and business performance as dependent
variable. The strength of the relationship between variables and the level of statistical
significance were assessed using Correlation coefficient and the Multiple regression
procedure .
The findings of the correlation analysis showed that human capital is highly correlated with
performance.
With regard to regression analysis, only two human capital variables i.e
training, and previous entrepreneurial experience, were significantly positively influencing
for performance.
Other five factors; education level, education area and previous
occupation, entrepreneurial skills and parents or husband owns a business were not
significantly affecting for performance. Thus, this study reveals that level of education, area
of education as not influential factors to enhance performance of women entrepreneurs in
small scale enterprises, but training and experience as more important factors. It suggests
that, even without formal education women can be directed to do businesses and their
performance can be improved by giving proper training. Therefore the results of this study
have practical implications for managerial practice and small business development.
The regression model which explains the influence of factors of human capital towards the
human capital index showed that education area as the most influential factor and education
level as the least influential factor in formulating a composite index of human capital.
Key words: Human capital, Performance, Women entrepreneurs
Introduction
Organizations today operate in an environment which is characterized by rapid change and
uncertainty.
In order to thrive in such an environment, organizations must use their
knowledge to improve performance and innovate at a rate that will outstrip their competitors.
Running an enterprise successfully in this dynamic setting is a very challenging task. It
requires not only substantial tangible resources, such as physical or financial capital, but also
more intangible resources embedded in the enterprise, such as the entrepreneur‘s human
capital. This is an immutable fact of the knowledge economy. People and their assets have
come to the forefront. Organizations rely on the brainpower of employees. Organizations
need employees capable of thinking, performing, and adapting. Organizations depend on this
human capital in order to succeed in today‘s economy (Nakamura, 2003). The Human
Capital can be defined as ―the knowledge, skills, competencies, and attributes embodied in
individuals that facilitate the creation of personal, social and economic well-being‖
(Organization for Economic Co-Operation and Development or OECD, 2001) According to
Becker(1993), traditional human capital theory focus on employees‘ human capital and its
effect on earnings. Later the theory has been applied to small-scale businesses as well, where
human capital is usually conceptualized as a characteristic of the business owner (Brueder et
al, 1992).
Small and Medium scale Enterprises are the main sector in which many women earn their
livelihood. In Sri Lankan economy too, Small and Medium scale Enterprises (SMEs) are of
increasing significance in terms of both numbers of enterprises and numbers of employees.
With the socio-cultural and economic changes taking place in Sri Lanka, women are slowly
entering the field of entrepreneurship. Many women support themselves and their families
through income they receive from their entrepreneurial activities. Women entrepreneurship is
slowly gaining credibility as an important activity in contributing to national economy,
helping to foster economic independence of women by allowing them to hold the reins of
their destiny, thus leading to empowerment.
The southern province of Sri Lanka is famous for entrepreneurship. It is popular belief that
people in the province are generally creative, innovative and having entrepreneurial sprit. Of
the total population about 2.34 million in the province, 2.7% account for Entrepreneurs (ILO
SIYB project, 2000). However women's participation in entrepreneurship remains at low
level even in the province. According to the Southern Province Rural Economic
Advancement Project report on bench mark survey in 2003, male entrepreneurs are accounted
for 92% and female are accounted for only 8 % across the province. Further, Dias (1989)
revealed that Sri Lanka women‗s participation in entrepreneurial activities is relatively low .
compared to other developing countries. Although a considerable array of difficulties are a
normal aspect of entrepreneurial activity, knowledge on factors affecting for performance is
quite limited.
Therefore this study has designed to study the effect of Factors of Human Capital towards
business performance of women-owned small enterprises.
Problem Identification
In today‘s context, managing business successfully is not an easy task. The business world is
becoming increasingly complex and dynamic due to globalization and technological
advancement. Many people who begin the process of starting a new business fail to achieve
their goals, although others are quite successful ( Buddhadasa, 1992 as quoted in Perera and
Alwis, 2005). Research from other countries found that women owned firms were more
likely to fail, and had lower levels of sales, profits, and employment than those owned by
men (Kalleberg and Leicht, 1991) because factors affected by women are of different
dimensions and magnitudes, due to many reasons (Hisrich and Peters, 2002) .
The one key factor to achieve higher performance is investing in human capital. The findings
from prior research demonstrated that human capital as a necessary component for small
business success and firm performance (Coleman, 2007). Human Capital attributes such as
education, experience, skills, in particular those of the business owner, have been argued to
be a critical resource in small firms that affects small business performance (Rauch and
Frese, 2000). Although empirical research has obtained a range of results regarding the
relationship between human capital and performance, those results are not in conformity.
Studies examining this relationship have not yielded consistently solid results. For example,
Davidsson and Honig (2003) suggest that the association between human capital and
entrepreneurial performance may be confounded by number of factors, such as persistence
and education. Therefore, it is important to study human capital further, as different types of
human capital may be more important at different stages of entrepreneurial decision making.
(Davidsson & Honig, 2003)
Moreover, prior research mentioned that there is no substantial evidence about Sri Lankan on
women entrepreneur‘s performance (Dias, 1989; Jayawera 1996). Therefore there is a
significant research gap in the existing knowledge pertaining to performance variations of Sri
Lankan women entrepreneurs in relation to human capital. It is notable that researching those
aspects are very crucial and timely need in developing women entrepreneurship in the region
and country as well. Considering the above trends and issues, this research is an attempt to
address the following research question

What is the nature and extent of the impact of human capital on performance of
women entrepreneurs in managing small business ?
Objectives of the Study

To examine the impact of human capital on the performance of enterprises managed
by women entrepreneurs in Matara district , Sri Lanka

To identify impact of factors of human capital towards the performance changes
of enterprises managed by women

To identify most influencing factor in formulating composite index of human capital
Review of Literature
The Concept of Human Capital
The definition of human capital is referred to as ―the knowledge, skills, competencies, and
attributes embodied in individuals that facilitate the creation of personal, social and economic
well-being‖ (Organization for Economic Co-Operation and Development or OECD, 2001:
18). American economist Gary S. Becker (1993) is credited with completing extensive work
and formulating the theory of human capital through the publication of his work. According
to Becker ―The human capital is similar to ‗physical means of production‘, e.g., factories and
machines: one can invest in human capital (via education, training, medical treatment) and
one's income depends partly on the rate of return on the human capital one owns. Thus,
human capital is a stock of assets one owns, which allows one to receive a flow of income,
which is like interest earned. Human capital is substitutable: it will not replace land, labour,
or capital totally, but it can be substituted for them to various degrees and be included as a
separate variable in a production function‖. The term "human capital" has traditionally been
applied to educational attainment and includes the knowledge and skills that the labor force
accumulates through formal instruction, training and experience (Becker, 1993). It has also
been referred to in terms of the time, experience, knowledge and abilities of an individual
household or a generation, which can be used in the production process (Heckman, 2000).
Human capital is multi-faceted in its nature. According to the journal of Human Capital ,
human capital defines and categorizes a person‘s embodied knowledge, health, skills, and
abilities as they affect production, exchange, and entrepreneurship, as well as is embodied
human knowledge, as reflected in publications, patents, and other forms of intellectual capital
that contribute to the formation and transfer of new knowledge and innovation. While
paralleling physical capital—including buildings, factories and machines—as a means of
production, human capital also has a special role in promoting productivity growth and
economic development. Individuals, families, firms, and societies invest in human capital via
education, health care, and organized research. Individual and family incomes depend in
large part on human capital attainments. Thus, human capital is a major input affecting
production in both the marketplace and in the household sector, as well as a key determinant
of wealth creation and social mobility. Studying the role of human capital in the modern
information economy is critical for understanding the continuing transformation and
expansion of individual and societal well-being in our increasingly global economy
((http://www.journals.uchicago.edu/loi/jhc ).
Performance measurement and Performance in Small & Medium Enterprises
"Performance" is operationalized differently in different studies, making cross-comparison
difficult. A number of prior studies cite the firm‘s ability to generate profits as an important
indicator of success (Haber and Reichel 2005; Kelleberg and Leicht 1991). According to
Srinivasan, Woo & Cooper, (1994), the most frequently used operationalizations are survival,
growth in employees, and profitability. Firm growth has also been cited as key measure of
performance in prior research (Haber and Reichel 2005). It does not seem to be an accepted
method of measuring Small & Medium Enterprises‘ performance similar to the economic
measures routinely used for large firms. Murphy, Trailer and Hill (1996) suggested that
‗Accurate performance measurement is critical to understanding new venture and small
business success and failure . When considering performance of small firms, the lack of
separation of ownership and management is believed to allow the goals of the owner to
become the goals of the firm (Naffziger et al. 1994).
The various measures of business performance include, longevity of survival or more
popularly ‗age of the enterprise,‘ sales growth, growth in market share, growth in market
scope (local, national or international), growth in investment (in the same unit), additional
units created via acquisition & diversification growth in number of employees, profits and so
on. Most of these are physical growth and financial growth parameters and have been the
traditional measures of entrepreneurial performance. Of late, other measures
of
performance such as customers‘ satisfaction, employee satisfaction, image, credit rating, etc.
are also becoming increasingly relevant.
(http://www.du.ac.in/course/material/ug/ba/esb/Lesson_3.pdf)
Human capital and Firm performance
Prior research indicates that human capital plays a role in the profitability and growth of
entrepreneurial ventures and firm growth and has also been cited as a key measure of
performance. (Haber and Reichel 2005). It has been suggested that it also enhances venture
performance (Bosma et al. 2004). Human capital attributes (education, experience, skills), in
particular those of the business owner, have been argued to be a critical resource in small
firms (Pfeffer, 1994) that affects small business performance (Rauch & Frese, 2000).
Human capital relates to the human resources people bring to the firm (Wright et al, 2001). It
includes attributes as education, prior business experience, age and maturity, the presence of
partners who can provide additional expertise, and a family history of firm ownership etc. All
of these elements serve to equip the entrepreneur for the challenges of business ownership.
Thus, it stands to reason that a business owner who has the benefit of higher levels of human
capital should be expected to manage a firm to higher levels of firm performance (Coleman,
2007).
Gimeno et al. (1997) found a positive association between the overall level of human capital,
as measured by education level and work experience, and economic performance at both the
entrepreneur‘s level and the firm‘s level.
Brüderl et al. (1991) argue that greater entrepreneurial human capital enhances the
productivity of the founder, which results in higher profits and, therefore, lower probability of
early exit. Higher productivity of the founder means the business owner is more efficient in
organizing and managing operations or is able to attract more customers, negotiate better
contracts with suppliers and raise more capital from investors. It can then be argued that
entrepreneurial human capital increases efficiency and plays an important role in the market
selection process
Education and firm performance
Formal education is a component of general human capital that leads to increased levels of
productivity (Becker, 1993) and may assist in the accumulation of explicit knowledge and
skills useful to entrepreneurs (Davidsson & Honig, 2003), thus increasing belief in the
achievement of desired performance levels. Cooper et al. (1994) found that education had a
significant effect on both firm survival and growth. Previous studies showed that years of
formal education of the entrepreneur before establishing a new firm were related to eventual
performance of the firm (Brush and Hisrich 1991). In terms of educational level, Bates
(1990) found that entrepreneurs who had a college education were dramatically less likely to
fail than those who did not. College-educated entrepreneurs also had greater access to loans
from commercial banks. Bosma et al (2004) note that higher education significantly
influences the performance of entrepreneurial ventures as measured by profits. According to
a study of Spanish firms by Pena (2002) found that growth companies were more likely to be
managed by entrepreneurs with college degrees. Robinson and Sexton (1994) found that there
were positive relationships between level of education and venture performance. However
evidence concerning the effect of education on venture performance is not conclusive. (Lee
and Tsang, 2001). The study of Dyke et al (1992) reported both positive and negative
relationships between the level of education and venture performance.
Experience and firm performance
Prior studies have shown that entrepreneurial success is often influenced and shaped by the
experiences which entrepreneurs have gained during their prior employment (Moore and
Buttner 1997). Kim et al. (2006) utilize a range of variables that define prior work
experience. These include years of managerial experience, years of other full-time
experience, prior startup experience, and current self-employment. They argue that these
experiences contribute in important ways to the likelihood of entrepreneurial entry. Of the
four variables, they find that full-time work experience and previous start-up experience were
not positively associated with entry into self-employment.
Relevant experience can also be gained in the same industry as the entrepreneur later operates
his or her business. Such specific prior experience seems to influence business performance
and survival (Pennings, Lee et al. 1998; Pena 2002), probably because of the type of
knowledge one gains about markets, customers, and products.
Loscocco et al. (1991) found that a major determinant of small business success was
industry-specific experience. They noted that women were at a disadvantage in that regard,
because they tended to have fewer years of industry specific experience than men. These
findings were echoed in a study by Bosma et al. (2004), who studied over 1,000 firms in the
Netherlands to find that prior experience in an industry substantially improved small firms‘
prospects for survival, profitability, and growth.
Caputo and Dolinsky (1998) found that having a self-employed husband was the single most
important determinant of a woman being self-employed. Entrepreneur husbands are a source
of knowledge and experience and can also serve as role models. In a study of entrepreneurs in
two Midwestern states, Carter et al. (1997) found that prior experience in other businesses,
experience in the industry, and starting the business with partners decreased the odds of
discontinuance, although women-owned firms were still significantly more likely to
discontinue than men.
Human capital, skills and performance
Skill is itself a rather ambiguous term (Green et al., 1996). It means the ability to perform
given tasks or to master various techniques, or, more broadly, it can refer the range of
behavioural attributes such as reliability, ability to work without supervision, and stability of
employment. Skills can be acquired through education and (formal) training but also (and
mainly) through the course of people‘s activities at work (i.e., learning-by-doing) (Teixeira,
2002). However, given operationalisation difficulties, human capital and skills often appear
in the literature as interchangeable concepts (Teixeira, 2002).
According to the prior research by Lichtenstein and Lyon (2002) as cited in the website
article on ―entrepreneurs vs. experts: Which skills are critical to success?‖ a set of seventeen
entrepreneurial skills were identified as critical to enterprise success. It was described under
four major categories as follows: 1) technical skills, 2) managerial skills, 3) entrepreneurial
skills, and 4) personal maturity skills
Technical Skills
Lyons (2002) described these technical skills as the ―skills necessary to be successful in one‘s
line of business‖
1. Operational – the skills necessary to produce the product or service
2. Supplies/Raw Materials – the skills to obtain them, as necessary
3. Office or Production Space – the skills to match needs and availability
4. Equipment/Plant/Technology – the skills to identify and obtain them
Managerial Skills
Lyons (2002) described managerial skills as ―the skills needed to organize the work on a dayto-day basis‖
1. Management – planning, organizing, supervising, directing, networking
2. Marketing/Sales – identifying customers, distribution channels, supply chain
3. Financial – managing financial resources, accounting, budgeting
4. Legal – organization form, risk management, privacy and security
5. Administrative – people relations, advisory board relations
6. Higher-order – learning, problem-solving
Entrepreneurial Skills
Lyons (2002) described entrepreneurial skills as ―the skills needed to develop innovative
products and services and to generate solutions to emerging needs in the marketplace‖
1. Business Concept – business plan, presentation skills
2. Environmental Scanning - recognize market gap, exploit market opportunity
3. Advisory Board and Networking – balance independence with seeking assistance
Personal Maturity Skills
Lyons (2002) described personal maturity skill as ―the skill needed to attain self awareness,
emotional maturity, ability and willingness to accept responsibility and creativity.‖
1. Self-Awareness – ability to reflect and be introspective
2. Accountability – ability to take responsibility for resolving a problem
3. Emotional Coping – emotional ability to cope with a problem
4. Creativity – ability to produce a creative solution to a problem
Training and Performance
According to Becker (1993) (as cited in Greer, 2001),
training is an investment in human
capital. He has written about two types of training as general and specific training. These
training are: general training—those skills which are ―useful in many firms besides those
providing it‖ and specific training—―training that has no effect on the productivity of trainees
that would be useful in other firms‖. Some literature shows the relationship between training
and performance. Green (1993) pointed out that the workforce‘s lack of training is related to
low competitiveness. In turn, a greater human capital stock is associated with greater
productivity and higher salaries (Mincer, 1997). Likewise, training is linked to the longevity
of companies (Bates, 1990) and greater tendency to business and economic growth (Goetz
and Hu, 1996).
Rauch et al (2005) found that training and development of employees in Small & Medium
Enterprises has positive effects on employment growth. According to Peng (2001),
entrepreneurs with greater training can expect that their ventures would grow because of the
better technology, enhanced professionalism, and legitimacy they bring to their
entrepreneurial initiatives. According to Forbes (2005), entrepreneurs with domain-relevant
training are likely to spend less time seeking, gathering, or analyzing information as they are
familiar with the industry and institutional infrastructure which would increase their
confidence in the efficiency with which entrepreneurial efforts will translate in higher
performance.
Research Methodology
The conceptual model
The conceptual framework depicted in Figure 1 explains the relevant concepts in this study
and the type of relationship between the concepts. It is assumed that human capital factors
which directly influence the enterprise performance. According to the model, independent
variable is human capital and dependent variable is business Performance.
Human Capital








Education level
Education area
Business degree / diploma
training
Previous occupation
Previous entrepreneurial experience
(similar/ other business )
Parents or husband owns a business
Entrepreneurial skills
H1
Business Performance



Sales Income
Profits
Change in the number of
workers since start up
Figure1. : The conceptual model
Each variable is conceptualized as described below.
Human Capital
The variable on human capital consists of eight sub variables including education level,
education area,
business
degree/diploma,
training,
previous
occupation,
previous
entrepreneurial experience (similar/ other business), parents or husband s a business and
entrepreneurial skills.
Business performance
Three main indicators such as Sales income, Profits and Change in the number of workers
since start up were used as measures of business performance.
The Operationalization of the main and sub Variables
Operationalization of the of the main and sub variables are shown in Table1
Table 1: The Operationalization of the Main and Sub Variables.
Concept
Variable
Measurement criteria
Performance
Sales income
Percentage of income increase since start up
Human
Capital
Profit
Increases in employment
Knowledge
Knowledge
Percentage of profit increase since start up
Change in the number of workers since start
up
Education level
Education area ; science, commerce or arts
Knowledge, skill
Having Business degree / diploma
Training, skill
Number of training courses followed
Experience, skill
Experience
Experience
Number of years spent for previous
occupation
Time
spent to acquire entrepreneurial
experience
Parents
or Husband owns a business
Yes or No answers
Yes or No answers for Knowledge on
business concept, Environment scanning,
balance independence with seeking assistance
Entrepreneurial skill
Hypotheses Formulation
Human capital relates to the human resources people bring to the firm (Wright, Dunford, &
Snell, 2001). Human capital theory suggests that education or training raises the productivity
of workers by imparting useful knowledge and skills, hence raising workers‘ future income
by increasing their lifetime earnings (Becker, 1964). Schultz defined human capital theory as
―the knowledge and skills that people acquire through education and training as being a form
of capital, and this capital is a product of deliberate investment that yields returns‖ (Nafukho
et al., 2004). Thus, it stands to reason that a business owner who has the benefit of higher
levels of human capital should be expected to manage a firm to higher levels of firm
performance. The prior research cited in the previous chapter strongly suggests a relationship
between human capital, and firm performance. Hence, following hypotheses can be
formulated with regard to entrepreneurial human capital as follows.
Hypothesis 1: Women entrepreneurs‘ human capital is positively related to performance
Research Approaches
This research is an empirical study aimed at investigating impact of human capital and
networking towards performance. Quantitative and qualitative data were collected through
survey method and interviews. In the beginning an extensive review of articles selected by
the researchers in the field of human capital and performance. Online search of secondary
data was facilitated by search engines like Google and Yahoo. Basically, the purposes of
collecting secondary data were to formulate the research question and develop research
objectives. Reading relevant literature was helped to build the conceptual framework which is
graphically illustrated in Figure 1.1. This study was heavily depending upon the primary data
since research problem is answered based on its analysis.
Population
The population of this study is all the women entrepreneurs who undertake to organize, own
and run an enterprise with less than fifty employees and having capital investment of less
than Rs. 5 million and registered with the Chamber of Commerce of Matara district Sri
Lanka by January 2008.
Sampling Procedure
The Chambers of Commerce of Matara District maintained a register of women owned
entrepreneurs. There were around total of 120 women entrepreneurs registered at the
Chambers of Commerce, Matara by September 2008.
The entire registered list was
considered for the research and the participants were selected by using four criteria.
Firstly it was considered the definition of small scale enterprises. The Department of Small
Industries defines Small and Medium Enterprise as those with capital investment of less than
Rs. 5 million and which employ less than 50 employees.
Accordingly the sample was
selected to represent women owned small scale enterprises in the province having capital
investment of less than Rs. 5 million and less than fifty employees. Secondly, the enterprise
has to be in operation for at least two years. This criterion is necessary to ensure availability
of data about business performance. Third, the participant has to be the founder and owner of
the enterprise and fourth, the enterprise has to be a privately owned business. Fulfilling the
above criteria 92 women entrepreneurs (77 percent) were selected for the survey.
Data Collection Method
After performing literature review and formulating research objectives a pilot study was
carried out to asses the practicability of the study. The interviewing method was used to
conduct the pilot study. Using personal network of the author, ten women entrepreneurs in
the Matara District were selected. They were asked to point out any item that was either
ambiguous or otherwise difficult to answer. Based on their comments, some items were
modified and others were eliminated.
A structured questionnaire was used to collect the primary data from the selected sample of
women entrepreneurs. Each question consists of many factors based on literature review.
Some questions were formulated using the Lickert scale and some were formulated including
multiple choices. After pretest and modifying the questionnaire, the researcher herself went to
Chamber of Commerce, Matara several times to do the survey. Questionnaires were
distributed among the selected 92 participants personally by the researcher and collected data.
For some women entrepreneurs face-to-face interviews were held. Survey was conducted at
the three meetings held at the Chamber during the 05 th of October 2008 to 18 th November
2008. In total, 88 filled questionnaires were returned. However, 08 questionnaires were
disqualified as the respondents had not completed all the questions in questionnaires. Among
the selected sample 86 percent was properly responded.
Data Analysis
Basically only quantitative analysis was employed for analyzing the collected data. The
demographic information was analyzed using
simple percentages and frequency analysis.
For statistical analysis, the responses in each questionnaire were coded and these scores were
captured in a Microsoft excel spreadsheet. Then the scores captured onto a Microsoft excel
spreadsheet were imported into Soft Ware Package for Social Sciences (SPSS). Using the
SPSS, the multiple regression procedure and correlation coefficient was employed to find the
significant impact of the factors identified in the model. The correlation analysis helped in
determining both the form and degree of the relationship between the Human Capital, and
Performance. Thus, both the strength of the relationship between variables and the level of
statistical significance were assessed.
Measurements
There are one dependent variable and one independent variable. Composite indicators were
developed for each variable as follows.
Human Capital
Human capital of women entrepreneurs were measured using eight sub variables related to
human capital in the questionnaire as follows, Education level, Education area, Business
degree/diploma, training, Previous occupation, Previous entrepreneurial experience (similar/
other business ), Parents or Husband owns a business, Entrepreneurial skills .
However among the sample any of the respondents were not having a business degree or
diploma. Therefore other seven sub variables were considered to formulate composite index
for human capital and they were coded and assessed as follows.
1. The level of education
The level of education is coded as follows
A/L Passed
= 13
OL Passed
= 11
Between grade 5 to O/L
=8
no schooling
=0
2. The Education area
The Education area is coded as follows
Studying in commerce area in the A/L = 1
Studying in science or arts in the A/L = 0
3. Training
Training is assessed based on the number of training programmes followed and each
programme was coded as follows.
Diploma courses = 3 for each
Certificate courses=2 for each
Other training courses=1 for each
Then total rating for each respondent is calculated by adding the coded values received for
each course
4. Previous occupation
Duration of previous occupation in years considered as the total rating
One year period =1
5. Previous entrepreneurial experience prior to owning business as corded as follows
one year working experience in similar type of business = 2
one year working in different type of business = 1
Then total rating for each respondent is calculated by adding the coded values received for
each experienced year in similar or different type of business.
6. Parents or Husband owns a business,
Parents or husband not owns a business = 0
Parents or husband owns a business = 1
Both parent and husband owns a business= 3
7. Entrepreneurial skills
Entrepreneurial skills was measured using three items and each item carries 1 mark as
follows
Business concept = 1
Environmental scanning = 1
Balance independence with seeking assistance = 1
By adding the coded values, a total rating for each respondent under the each sub variables
was calculated. Then percentage value for each respondent under the each sub variable was
assessed as follows.
= Total Rating received for each respondent under the each sub variable
X100
Maximum rating received among the respondent under the each sub variable
Then average value for each respondent was calculated after summing up the seven
percentage values received for seven sub variables. This value was taken as the composite
index for human capital for each respondent
Performance
Five performance indicators were developed based on the respondent‘s data of sales income,
profits and change in the number of workers since start up.
Those indicators are relative
income, annual relative growth of income, relative profit, annual relative growth of profit and
annual employment growth rate. After summing up all values received for the above five
performance indicators, the average was calculated. This value was taken as the composite
index for performance.
Each performance indicator was measured as follows.
Relative Income
=
Income in 2008
* 100
Maximum income among the respondents in 2008
Annual relative growth of Income
= (Income 2008 - income at the starting year)
* 100
Maximum value received for income difference among the respondents* Firm Age
Income growth is a more valid indicator rather than income figures because of firms from
different industries were considered.
Relative Profit
=
Profit in 2008
* 100
Maximum Profit among the respondents in 2008
Annual relative growth of profit
= (Profit 2008 – Profit at the starting year)
* 100
Maximum value received for profit difference among the respondents* Firm Age
Annual Employment Growth rate
= Change in the number of workers since start-up * 100
Firm Age* Maximum rating among the respondents
Results & Discussion
Respondents’ profile
This section initially intends to provide a general illustration of the women entrepreneurs who
represented the sample. The demographics of the sample are described by business
background, firm age, women entrepreneurs‘ age, and marital status.
Distribution of respondents across the sectors
According to the Table 2, maximum number (25%) of women entrepreneurs were engaged in
the enterprises pertaining to textile apparel and leather and the second highest number (20%)
were in the production of food and beverages. Approximately 14% were engaged in the
fishing sector and 11% were in the trading. The other studied enterprises were spread over
agriculture, beauty culture, coir related products, construction, wood and wooden product,
and service sectors.
Table 2: Distribution of respondents across the sectors
Sector
Number of
Percentages
respondents
Enterprises pertaining to textile, apparel and leather
20
25
Production of Food and Beverages
16
20
Trading
15
18.75
Fishing
11
13.75
Agriculture
7
8.75
Beauty culture
4
5
Coir related Products
2
2.5
Miscellaneous
5
6.25
Total
80
100
Source: survey data 2008
Distribution of respondents across the firm age
Most of the women entrepreneurs in the sample (50%) are having firm age between 2-5
years. Approximately 32% of the enterprises have been operating for 6-10 years and 17%
have been operating for 10-30 years. The women entrepreneurs responding to the survey have
been in business for an average period of eight years (mean= 7.6, SD=5.8). This result
implies that there is a trend to commence enterprises by women.
Number of employees working in a single business
Numbers of employees working in an average single business are given in Table 3. It shows
that more than 60% of sample employed two or less than two employees and over 87 %
employed less than five employees. The surveyed sample showed that single enterprise has
employed an average of three employees. (Mean= 2.55, SD=1.67).
Table 3: Number of employees working in a single business
Number of employees
Frequency
Percentage
1-2
48
60
3-4
22
27.5
5-6
8
10
7-10
2
2.5
Total
80
100
Source: survey data 2008
Distribution of women entrepreneurs across age groups
The women entrepreneurs in this study ranged in age from 20 to 60+ years. Most of the
women entrepreneurs (35 percent) were in the age category of 40-49, while 28 percent were
in the age category of 30-39. Women entrepreneurs in the sample between the age category
of 50-59 and 20-29 were 16.25 percent respectively. The average age of the women
entrepreneur of the sample was 41 years (SD=8.10).
Marital Status of women entrepreneurs
Majority of the women entrepreneurs (95 percent) are married while 5 percent are single.
Among the married women entrepreneurs, the majority (78%) had dependents.
Factors of Human Capital among the respondents
Educational level among the respondents
Educational levels of women entrepreneurs in the sample revealed three levels of education
categories among the respondents. All of them indicated they had attained a minimum of
grade 5. The highest level of education gained by the majority of women was recorded as
GCE Ordinary Level (53%). This was followed by General Certificate of Education (G.C.E)
Advance level (36%) and grades 5-9 classes (11%). Any of the women entrepreneurs in the
sample is not having a university degree. Among the G. C. E Advance level completed
women entrepreneurs, 48% had studied their education in commerce while 41% had studied
in Arts and 10% in science.
Training among the respondents
Survey findings showed that 74% of the sample has received training and 26 % has not
received any training.
Previous occupations among the respondents
Most of women entrepreneurs (78%) in the sample had not engaged in occupation before
starting up their enterprises. Only 21% had previous occupations.
Entrepreneurial Experience in similar or different business
Table 4 shows that 51% are having previous entrepreneurial experience and 49% are not
having. Among the respondents, 26% are having experience only in similar type of business
and 13% are having experience only in different type of business. A total of 13% of the
sample is having experience in both similar and different type of businesses.
Table 4: Entrepreneurial Experience in similar or different business
Experience
Frequency Percentage
Previous Experience
Both similar and different businesses
41
10
51%
13%
Similar business only
21
26%
Different business only
10
13%
No Previous Experience
39
49%
Total
80
100
Source: survey data 2008
Husband or parents having business among the respondents
Table 5: Husband or parent owns a business
Frequency Percentage
Parent or husband owns a business
39
49%
Both parent and husband owns a business
17
21
Only parent owns a business
10
12
Only husband owns a business
12
15
Parent or husband not owns a business
41
51%
Total
80
100
Source: survey data 2008
Table 5 indicates that 49% percent of the sample is having a husband or parent who owns a
business and 51% is not having. Only parent owns businesses can be found in 21% and only
husband owns businesses in15%. A total of 21% is having both parent and husband who own
businesses.
Performance among the respondents
Figure 2 shows the performance data of women entrepreneurs and three types of
performance categories have been identified as follows. Performance below than 30 as low,
between 30-60 as medium and above 60 as high. It indicates that majority of respondents
(58%) are having low performance and minority of respondents (9%) are having high
performance. The 34% of the sample is having medium performance
Categorization of women entrepreneurs
according to their performance
Percentage of respondents
70
60
50
40
30
20
10
0
low
medium
high
Performance
Figure 2: Performance among the respondents
Source: survey data 2008
Relationship between Human capital and Performance
As both variables were measured in interval scale the Pearson correlation coefficient was
used to find the relationship between variables
Table 6: Summary of correlation results between factors of human capital and performance
R
Education level and performance
-0.004
Education area and performance
0.018
Training and performance
0.656**
Previous occupation and performance
-0.044
Entrepreneurial experience in similar or different type of business
0.515**
and performance
Parent or Husbands owns a business and performance
0.283*
Entrepreneurial skills and performance
0.349**
Human Capital and performance
0.533**
Source: survey data 2008
**Correlation is significant at the 0.01 level (2-tailed)
* Correlation is significant at the 0.05 level (2-tailed)
The correlation analysis results for seven dimensions of human capital and the business
performance is given in Table 6.
Correlation was run on performance with the seven
dimensions of human capital (education level, education area, training, previous occupation,
experience, Parent or Husband owns a business, entrepreneurial skill.). It shows that four of
the seven variables of human capital are having positive relationships with performance.
Among the all variables, the highest correlation is found between training and performance
(training=0.656) and it is significant at the 1% level. It suggests that training received by
women entrepreneurs is strongly linked to their performance.
A significantly higher
correlation coefficient (rexp=0.515) is found between experience and performance at 1% level.
Here experience is calculated in both similar and different type of business. Thus it can be
stated that performance is highly related to the entrepreneurial experience in similar or
different type of business.
There is a significant positive correlation between entrepreneurial skills and performance.
(renskill= 0.349). It should be noted that the correlation coefficient is statistically significant at
1% level. The correlation coefficient for parent or husbands owns a business and performance
gives a weak positive relationship (rparehus=0.283) and that value is significant at 5 % level.
Entrepreneurs with parents who owned business may have the opportunity to learn and
acquire skills from the parents from a young age and also can understand the requirements of
being an entrepreneur to get them ready for what to anticipate in owning business ventures.
Also, entrepreneurs can easily get the experience and learn about the skills needed to run a
business by having a husband who owns a business.
A very weak insignificant correlation is shown between the education area and performance
(reduarea=0.018). It was expected that studying commerce might influence the performance of
women entrepreneur‘s. However this finding suggests that whether it is arts, science or
commerce have no impact on women entrepreneur‘s performance. This result is also unable
to demonstrate that the level of education and previous occupation are positively related to
performance giving negative correlation coefficient value for each item as r = -0.004 .
Finally considering the contribution of all these elements, correlation for human capital index
and performance is calculated. It gives the value of 0.533 and it is significant at the 1% level.
According to these findings, it can be stated that there is a positive significant relationship
between the human capital of women entrepreneurs and their performance. Hypothesis one
proposed that the human capital and performance would be positively correlated. Therefore
hypothesis one (H1) is strongly supported.
Impact of human capital on performance of the women enterprises
Multiple regression analysis was used to assess the impact human capital on business
performance . A number of multivariate models were developed using a measure of firm
performance as the dependent variable and a series of independent variables representing
various aspects of human capital, considered in the conceptual framework.
Human Capital Index and Performance
The first model is developed including composite index of human capital, and composite
index of performance as follows
Y= a+b1X1+e
Where
Y=composite index of performance
X1=composite index of human capital
b1= coefficient of human capital
e = error
Performance= a+ b1 (Human Capital) + +error
As shown in Table 7, the coefficient of human capital is found statistically significant (ß
=0.533, ∞ =.0.01).
Thus H1 is supported. Hypothesis one proposes a positive relationship
between human capital and firm performance. Therefore, this study establishes that some
basic theoretical premises on relationship between performance and human capital, which
were developed in other countries (Becker 1993; Cooper et al., 1994) and hold true for the Sri
Lankan context as well.
Table 7: Regression findings of firm performance with Human capital index
.533a
R
R2
.284
Adjusted R2
.275
Standard Error
Analysis
16.12997
DF
Variance
Regression
1
Residual
78
Sum of
Mean
Square
Square
8050.086
8050.086
20293.714
260.176
F
30.941
Sig
.000a
Variable in the
B
Standard Error
Beta
T
Sig
equation
Constant
3.475
4.922
Human
.710
.128
.533
.706
.482
5.562
.000
Capital
Source: survey data 2008
Performance= 3.475+ 0.533 ** (Human Capital) +error
All Human capital variables and performance
Model two check the effects of the all human capital variables on performance to find which
factors of human capital are more effective on performance of the enterprises.
Y= a+b1X1+b2 X2 + b3 X3 + b4 X4+ b5 X5+ b6 X6+ b7 X7 +e
Where
Y=composite index of performance
X1=education level
X2=education area
X3=training
X4=previous occupation
X5=experience
X6=parent or husband owns a business
X7=entrepreneurial skill
b1= coefficient of education level
b2= coefficient of education area
b3= coefficient of training
b4= coefficient of previous occupation
b5= coefficient of experience
b6= parent husband owns a business
b7= entrepreneurial skill
e = error
Table 8: Regression findings of firm performance with factors of human capital
R
.746
R2
556
Adjusted R2
.513
Standard Error
13.21370
Analysis
DF
Variance
Sum of
Mean
F
Square
Square
Regression
7
15772.468
2253.210
Residual
72
12571.332
174.602
Variable in the
B
Standard Error
Sig
.000a
12.905
Beta
T
Sig
equation
13.494
12.421
1.086
.281
Education level
.007
.138
.004
.048
.961
Education Area
-.026
.046
-.049
-.565
.574
.345
.068
.477
5.103
.000
-.235
.085
-.239
-2.752
.007
Experience
.219
.055
.384
3.999
.000
Parent/Husband
.004
.042
.009
.106
.916
Entrepre. Skill
.056
.050
.097
1.117
.268
Constant
Training
Previous Occupation
Source: survey data 2008
The result of the regression analysis depicted in the model 2 includes human capital factors
that check the impact on performance. The analysis gives mixed results. As shown in table
8, only two variables have a significant relationship with firm performance in the direction
predicted. Those are training and experience and having the beta (ß) coefficient values of
0.477 and 0.384 respectively. The highest beta value is received for training (ß =0.477, ∞
=.0.00) and this implies that training is the most influencing factor for performance among
the tested human capital variables. These findings consistent with Peng (2001) who found
that training and experience of entrepreneurs impact on growth of business enterprise.
According to Becker (1993), education and prior experience are the critical components of an
entrepreneur‘s human capital, which reflects the degree of development of managerial know-
how and capability. In support to that, this research categorize experience as the second
influencing factor (ß =0.384, ∞ =.0.00) for performance among the studied human capital
variables. However, in contrast to many earlier findings (Box, White, and Barr 1993; Brush
and Hisrich 1991; Cooper et al. 1994), this study demonstrates that the level of education of
women entrepreneurs is not related to performance. A possible explanation for this might be
that none of the women entrepreneurs in the considered sample are included degree holders
or having education above advanced level. Moreover the sample consisted 62% of
entrepreneurs having education below G. C. E Advance level. This might lead to give non
significant results.
Another unanticipated finding was that coefficient of education area is not significant with
performance (ß =-0.049, ∞ =.0.574). Though it is expected that education in commerce may
have an impact on performance other than education in science or arts, findings show that it
is not so.
However, these results support earlier research by Lerner et al (2007), who found
that level of education and area of education was not associated with business performance of
women entrepreneurs in Israel.
What is surprising in this study is that previous occupation and performance shows
significant negative relationship (ß =-0.239, ∞ =.0.007). It is difficult to explain this result,
but it might be related to most of women entrepreneurs (78%) in the sample had not engaged
in occupation before starting up their enterprises. The result indicates that parent or husbands
owns a business (ß =0.009, ∞ =.0.916) and entrepreneurial skill (ß =0.097, ∞ =.0.268) are
having positive relationship with performance, but these values are not significant.
Performance=13.494 +0.004
ns
(education level) -0.049
ns
(education area) +0.477**
(training) -0. 239* (previous occupation) + 0.384** (experience) + 0.009 ns (parent husband
owns a business) + 0.097 ns (entrepreneurial skill) + error
Human capital index and all human capital variables
Model three check the effects of the seven human capital variables on human capital index to
find out which factors of human capital are more influencing for composite human capital
index
Y= a+b1X1+b2 X2 + b3 X3 + b4 X4+ b5 X5+ b6 X6+ b7 X7 +e
Where
Y=composite index of human capital
X1=education level
X2=education area
X3=training
X4=previous occupation
X5=experience
X6=parent or husband owns a business
X7=entrepreneurial skill
b1= coefficient of education level
b2= coefficient of education area
b3= coefficient of training
b4= coefficient of previous occupation
b5= coefficient of experience
b6= parent husband owns a business
b7= entrepreneurial skill
e = error
Table 9 indicates that all factors of human capital are having highly significant relationship
with composite index of human capital (∞ =.0.000). Among the seven
human capital variables, education area shows the highest beta value ((ß =-0.379). followed
by parent or husband owns a business (ß =-0.359). It indicate that these two factors can be
considered as the most important factors among the above mentioned seven factors in
formulating composite index of human capital. Education level is the least influencing factor
for the composite index of human capital showing the least beta value (ß =-0.117)
Human Capital index=1.454 +0.117** (education level) +0.379** (education area) +0.252**
(training)+0.206** (previous occupation) + 0.307** (experience) + 0.359**
(parent husband owns a business) + 0.311** (entrepreneurial skill) + error
Table 9: Regression findings of Human capital index and all human capital variables
R
.991
R2
.983
Adjusted R2
.981
Standard Error
1.96997
Analysis
DF
Variance
Sum of
Mean
F
Square
Square
Regression
7
15696.570
2242.367
Residual
72
279.417
3.881
Variable in the
B
Standard Error
Sig
.000a
577.811
Beta
T
Sig
equation
1.454
1.852
.785
.435
Education level
.139
.021
.117
6.776
.000
Education Area
.150
.007
.379
22.116
.000
Training
.137
.010
.252
13.561
.000
Previous Occupation
.152
.013
.206
11.954
.000
Experience
.131
.008
.307
16.109
.000
Parent/Husband
.129
.006
.359
20.538
.000
Entrepre. Skill
.135
.007
.311
18.067
.000
Constant
Source: survey data 2008
Conclusion and Recommendations
The primary purpose of this research was to examine the impact of factors of human capital
towards the business performance of enterprises managed by women entrepreneurs.
The
Pearson correlation coefficient was used to test the significance of the relationship between
variables and the multiple regression procedure was used to test the impact of human capital
on performance.
The findings of the Pearson correlation analysis showed that human capital is highly
correlated (r=0.533) with performance at 1%level. Accordingly, hypothesis one (H1) is
strongly supported.
The one specific objective of this study was to identify impact of factors of human capital
towards the performance changes. This study used
seven factors of human capital as;
education level, education area, training, previous occupation, previous entrepreneurial
experience (similar/ other business), parents or husband owns a business and entrepreneurial
skills. The results of Pearson correlation analysis indicated that four of the seven factors of
human capital are positively related to performance. The training (r =0.656, ∞ = 0.01)
indicated the greatest relationship with performance and others are previous entrepreneurial
experience(r =0.515, ∞ = 0.01), entrepreneurial skills (r =0.349, ∞ = 0.01) and parents or
husband owns a business(r =0.283, ∞ = 0.05) respectively. Education level, education area
and previous occupation are the three factors of human capital which are not significantly
relating to performance changes of enterprises managed by women.
With regard to multiple regression analysis, only two factors of human capital i.e training (ß
=0.477, ∞ = 0.01), and previous entrepreneurial experience (ß =0.384, ∞ = 0.01), positively
significantly influencing for performance. Other five factors i.e. education level, education
area and previous occupation, entrepreneurial skills and parents or husband owns a business
are not significantly affecting performance changes of enterprises managed by women.
Considering both findings it is very clear that training as the most important factor of human
capital which influence performance changes of women enterprises. The next important
factor of human capital was previous entrepreneurial experience. Another important finding
was that that level of education and area of education was not affecting for performance
changes of small firms managed by women entrepreneurs. Thus it can be concluded that
training and experiences as the most influencing factors in deciding small business
performance of women entrepreneurs among the seven factors of human capital studied.
In formulating a composite index of human capital, findings showed that education area ((ß
=0.379 ∞ =.0.01) as the most influential factor and education level as the least influential
factor among the seven factors studied.
Implications
Human resources are essentially important and an optimal utilization of skills and knowledge
increases small business performance. Thus, one can improve the probability of success by
increasing human capital and by developing and utilizing human resources. Among the
considered factors of human capital, it is revealed that level of education is not an important
factor to enhance performance of women entrepreneurs in small scale enterprises, but training
and experience as more important factors. Thus, even without formal education women can
be directed to do businesses and their performance can be improved by giving proper
training.
Therefore the results of this study have practical implications for managerial
practice, entrepreneurship theory and public policy.
Suggestions for further research
The data analyzed in this study were obtained from the survey of eighty women entrepreneurs
registered at the Chambers of Commerce, Matara district. Therefore the conclusions of the
study may not be applicable to generalization at the country level. Therefore future research
can be conducted by taking samples representing each district of the country.
Limitation of the study
Though this study obtained valuable results, it has number of limitations. Mainly, the data
analyzed in this study are obtained from the survey of eighty
women entrepreneurs
registered at the Matara District Chamber of Commerce. Therefore, it makes it difficult to
generalize the findings to the total population. Another potential problem is that respondents‘
views would only represent a particular set of industries and will not be representative of all
categories of small and medium scale industries. Sometimes findings of this study may vary
according to the views of unattended firms.
Next, a limited number of items were measured for each variable. In future studies, more
items could be added to assess the important construct, which could increase the validity of
variable measurement.
Another potential problem relates to the research questionnaire is that people are not
providing true responses when obtaining income and profit data. Further, the questionnaire
has many items and it reduces the willingness of filling out the complete questionnaire which
may leads to reduce the quality of responses.
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