Research Article: 2019 Vol: 25 Issue: 2
Albert Tchey Agbenyegah, Durban University of Technology
Small businesses are widely recognized as creators of job opportunities with high potentials to sustain rural growth and generate economic prosperities. However, more need to be done in terms of empirical study to further understand the contributory variables to rural small businesses. This empirical study seeks to determine the significant relationships between demographic variables and rural owner-managers small business success (ROMSBUS) in specific rural municipalities of South Africa. This empirical study was quantitatively designed to gather a cross-sectional data using snowball and purposive sample techniques to select rural owner- managers (ROMs) of small businesses for primary data to be analysed. In total 268 ROMs who at the time of this study operates small businesses in the two municipalities for over 5 years took part in the survey. Descriptive and inferential statistical tools of cross-tabulation aided by Pearson chi-square test were employed to test the formulated hypotheses. The study revealed mixed findings about the selected variables of age, gender and educational qualifications and ROMSBUS as perceived by ROMS. Out of the two age cohorts, the younger age group of ROMs ranging from 26 to 36 managed to attained moderate to high ROMSBUS. Findings based on formulated hypotheses, it came to light that the age group of ROMs differ in terms of ROMSBUS. However, ROMs level of education differs significantly in terms of ROMSBUS. Further revelations by the study add that gender of ROMs does not differ in terms of the levels of ROMSBUS. Steaming from the findings, the author suggest that in future triangulation approaches could be utilized to reveal deeper understanding of the significant relationship between the selected demographic variables of age, gender and educational qualifications and ROMSBUS.
Small Businesses, Rural Owner-Managers, Demographic Variables, Rural Owner-Managers Small Business Success.
The critical role of entrepreneurial activities in South Africa in the midst of growing unemployment suggests adequate interventions by authorities to stimulate successful operations of small businesses in South Africa. For the purposes of this rural study, entrepreneurial activities represents small businesses. Throughout this study, the author define successful small businesses and entrepreneurial activities as entities and activities own and operated by lone-rural individual with the sole ownership powers to oversee businesses with immediate family members’ support in the proximity of rural communities over five years (Maharati, 2010). For decades, literature points to the rampant failures of small businesses across most emerging countries. Reasons for such failures were documented by researchers and academics. Pockets of suggestions and practical solutions were proposed in several rural small business literature world-wide including few South African entrepreneurship literature (Agbenyegah, 2013). Being known as the hub for rural socio-economic activities, small businesses and entrepreneurial activities cannot be left unnoticed (Sanda, et al., 2011). Hence, for decades the South African government shifted its strategic focus to develop the small business sectors to create job opportunities as part of its core government mandate (Hand, 2009). To-date, there have been steady emergence of varying contributory variables that contributes to business success. Recent study commissioned by Ucbasaran, et al. (2010) stated that knowledge, personal experience and individual competencies are regarded as business success. Business success in another study by Remana, et al. (2008) was said to be realized by other environmental variables such as shift in government regulations, capital market variables, competitors’ plans and existing economic climate.
According to Timm (2011) & Thema (2012), by the turn of 2020 it is expected that over 2.5 million job opportunities could be made available through small businesses country-wide with a spin-off of roughly a decrease of 500,000 dependents on government social grant programs (GSGPs). Given the potentials of small businesses contributions in decreasing socio-economic ills, it is vital to determine the relationships between the selected demographic variables on the level of ROMSBUS in rural localities where the bulk of South African working class suffer from employment opportunities.
Large volumes of scientific evidence echoed varying sentiments on human capital theory which outlines some critical demographic variables namely the level of personal experience and knowledge among others as contributors to business success. Through these demographic variables, ROMSBUSs who are knowledgeable and skillful are able to alter the present negatives perceptions that engulfed the small business sectors and entrepreneurial activities for decades into positives. Further literature states that highly educated people with enormous skills can easily translate the negatives into business success (Hayton & Zahra, 2002). Recent survey by De Groot, Nijkamp & Stough (2004) affirm that acquiring high level management skills enable educated workers to add value to business success in the long-run. Though academics argued that the ROMs of small businesses need to be linked with the environment to succeed (Cronje, et al., 2001), it is also important to determine the relationships between demographic variables and the business success. Drawing from this argument, this empirical study is designed to assess whether there is significant relationships between the selected demographic variables and ROMSBUS. The selected demographic variables are gender, age and educational qualifications of ROMs of small businesses from the research settings.
In order to attain the aim of this study, the study is underpinned by the following research question and supported by the null and alternative hypotheses.
What are the significant relationships between demographic variables (gender, age and educational qualifications) and ROMSBUS?
To ensure that this empirical study emerged with appropriate answers to the above question, the relevant hypotheses were formulated through existing literature search. Non-parametric techniques of Pearson chi-square test was applied during the course of testing the null hypotheses (Cooper & Schindler, 2011).
H10: There is no significant relationship between ROMSBUS and the age group of ROMs.
H1A: There is a significant relationship between ROMSBUS and the age groups of ROMs.
H20: There is no significant relationship between ROMSBUS and ROMs educational qualifications.
H2A: There is a significant relationship between ROMSBUS and ROMs educational qualifications.
H40: There is no significant relationship between ROMSBUS and ROMs gender.
H4A: There is a no significant relationship between ROMSBUS and ROMs gender.
Characteristics of Rural areas in South Africa
This survey was conducted in one of the largest rural areas in South Africa. Similar to other emerging economy, South Africa is housed to a wider rural communities where the rate of poverty and unemployment is increasing at an alarming rate. In view of this, it is therefore significant to understand what rural communities entails in this survey through demographic lenses. Rural communities throughout this study consists of dwellers on communal lands, traditional farmland, settlements of informal nature as well as small rural townships where communities are permitted to reside and use the land as their only source of revenue and livelihood (Makgetla, 2010).
Available literature points to roughly 70% of the world’s poorest communities that resides in rural areas including 72% of the overall rural communities who are found in the least developed countries (LDCs) (Avila, et al., 2005). In South Africa, the entire rural landscape is primarily characterized by rich resources that are entrenched in highly climatic conditions of underdevelopment. According to Allan & Heese (2008), South Africa rural areas continue to experience ongoing infrastructure deficiency that is further worsen by growing chronic backlog of communication networks and challenges of sub-standard level of service delivery. Besides, these rural communities are confronted with issues of unemployment, persistent rural poverty that is deeply rooted in the socio-cultural as well as the sluggish economic activities that influences the entire population in negative ways (Perez, 2003). Given these complex problems, most rural communities rely primarily on government social grants in the form of pensions and child support initiatives for survival (Vink & Van Rooyen, 2009). Existing government social grants programs are perceived as add-ons to the general farming activities as the primary source of income across the broader communities of the research areas. These social ills according to Dono Van & Mather (2004) are further shortfalls to rural communities that require adequate health care facilities which result from poor problems of sanitation.
Nature of Small Businesses in South Africa
Four categories of the small business sector are recognized in South Africa. These are the microenterprises with employment volume of not more than 5 employees; a very small enterprises which employs from 5 to 20; the small enterprises with the capacity to employ between 21 to 49 and finally, the medium enterprises which employ 50 to 200 (The National Small Business Amendment Act of 2003). Small businesses as defined employ roughly 200 individuals and account for approximately 6 million job opportunities countrywide (Finscope, 2010). Recent Report by Ntsika Annual Review states that small business account for 97.5 of all the businesses in South Africa. These businesses in South Africa generates 34.8% to the GDP, contributes an average of 42.7% of the wages and salaries to the South African workforce as well as providing 54.5% jobs to the private sector (Nieman, et al., 2003).
Through various entrepreneurial activities, small businesses are established to offer opportunities to the general population of South Africans. FinScope Report (2010) states that small businesses are very energetic across the socio-economic landscape of South African in particular, the rural areas. By their nature, small businesses are characterized as labour intensive in contrast to large organizations which are known to be capital intensive. Hence, in most communities of South Africa, small businesses are the preferred choice for of job creation (Peyper, 2012). For example, the bulk of job opportunities are in the retail, wholesale, and the manufacturing sectors of the rural economy. Others include small tourism attractions in the form of bread and breakfast (B & B), beauty salons and other forms of petty trading activities. However, it is unfortunate that the small business sector is marred by declining entrepreneurial activities which can be associated to factors such as skills shortages and resource deficiency. Thus, it is not surprising that in South Africa entrepreneurial activities continue to show poor results in contrast to other emerging countries. Drawing from these empirical sentiments, it is of utmost concern to understand contributions of demographic variables to rural small business success.
Operationalisation of Business Success
Defining business success is very complex especially across rural areas. At the same time it is rather difficult to use a single terminology to define what success entails. Business success according to literature depends on the aim of the study and its background. Different thoughts by academics underpins what constitutes “Success” (Islam et al. 2011). According to Pasanen (2003), business success can be assessed in terms of age, rate of turnover, personal valuation by owners and the level of satisfaction. Over the years several empirical findings have been applied in defining business success (Arslan & Kivrak, 2009). Contributing factors to small business success are documented and form part of the existing literature on large scale. Stefanovic, et al. (2010) concurred that the success of business entities depend on its survival rates, ongoing viability as well as its longevity. Frese, et al. (2002) mentioned few factors such as innovation, ROMs attitudes towards risks and the drivers of competitions as contributors to business success.
Success indicators such as growth and human capital stimulates business success. Extant literature postulates that through growth, the small business sector is able to increase in size, create job opportunities and augment economic prosperities (Davidsson, et al. 2000). According to previous scientific studies, firm growth in generally is perceived to contribute to the overall business success (Dyllick & Hockerts, 2002). Davidsson, et al. (2009) agreed that the firm’s steady growth is a positive sign that the business in on successful path. Hussain & WindSperger (2010) in a similar study stated that human competencies such as business knowledge supported by bounteous training in management intelligence are more likely to lead to high business successes.
More often concepts such as “Success, growth and survival” are associated and used interchangeably in the field of management to denote business success. According to Reijonen & Komppula (2007), any form of ongoing business activities over the years is referred to as successful. Other school of thought aligned business success with firm growth (Parren, 2000). As Storey (1994) put it, growth is applied as the fundamental measurement tool of business success. Other researchers argued that for business to become successful, there must be an increase in the profit margin (Bruderl & Preisendorffer, 1998). Others are of the views that for any form of business to be successful variables including non-financial indicators such as personal growth and satisfaction, adequate customer retention policies, personal career progression, rate of business survival are paramount (Walker & Brown, 2007). Given these descriptions of small businesses above, this empirical study defines rural small business success (RSBUS) as business ventures that are operated in specific rural areas with much reliance on available local resources to achieve success. The focus is therefore on ROMs to utilize untapped “Crude rural resources” for applications to business growth with the view to maximize rural wealth (Petrin, 1999).
Demographic variables have significant impact on the successful operations of small businesses (Islam et al., 2011). Others perceived demographic variables as the primary contributors to job creation through small businesses (Stangler & Kauffman, 2013). Extant scientific evidence expounds that personal characteristics including age, education, gender and individuals’ experiences form the basis of small business success (Johnson, 1996). At the heart of existing literature scientists including Parker (2009) explains that individuals’ age and academic status are known to positively influence entrepreneurial activities. Similarly past empirical studies in further proposed that some demographic variables affect family members such as spouses to demonstrate team spirits during business operations (Kelly, et al., 2012; Mhlanga & Vallabh, 2015). Further studies revealed that demographic variables such as education, gender, age, the number of children in the family as well as the nature of business operations. Researchers and academics were of the views that individuals’ ages and gender orientations largely impact on respective personalities and the level of treatment received (Frankenberg, 2003; Corey, 1996).
According to Poza, et al. (1997) the ages of individuals play significant roles in understanding various level of practices within the family businesses. However, Venter (2003) argued that there are no relationships between individuals’ ages of ROMs; others who were successful during business operations are seen to stand at the entry phases of the entity’s profit margins. Farrington (2009) points to the linkages between age and business growth level and financial performance of the business. According to Braaksma, et al. (2012), age barely impact on ROMs levels of performance. The study further add that younger individuals contribute more to business growth in terms of good financial standing and performance. This level of success, the study revealed can be ascribed to younger age groups because of their abilities to be risk-averse, highly driven and more enthusiastic than the older age groups (Farrrington, 2009). Another researcher hypothesized that the general level of quality that is displayed in the working climate by fathers are broadly affected by their individual life styles (Davis, 1982). On educational front, individual educational achievements are accredited to play significant role in successful business operations.
Recent study by Cowie (2007) indicates that management qualifications does not only enable business team managers to become productive but it also prepare them in giving efficient feedback to subordinates. Subsequently, the study further add that individuals who were unable to attain such academic heights lack the potential capabilities in several areas of management. Similar study commissioned by Fritzgerald & Muske (2002) affirmed that team members who lack the requisite level of qualifications are incapacitated to operate and manage successful businesses. Similar empirical study by Fritzgerald & Muske (2002) concluded that old age and high level of education enables individuals to operate successful businesses.
Gender Composition of Rural Owner-Managers (ROMs)
Gender impact on entrepreneurship. Factors such as household conflicts, unequal education system as well as lack of enough experiences contribute negatively to businesses owned by females (Brush, 1997; Carter & Allen, 1997). Countless academic literature has revealed that males are keen to pursue entrepreneurial activities that their female peers (Veciana, et al., 2005; Delmar & Davidsson, 2000; Matthews & Moser, 1995). It further emerged from other empirical studies that female entrepreneurs are known to record minimal success rates of entrepreneurship (Zhao, et al., 2005). Women according to past studies are at the forefront of economic growth in any nation (Arenius & Minniti, 2005). However, drawing from a number of empirical evidence, Klapper & Parker (2011) argued that both male and female embark on the journey to pursue entrepreneurial activities with diverse industry background due to limitations of capital, growing levels of discrimination and education. Regarding industry background, Diaz-Garcia & Jimenez-Morene (2009) stated that female entrepreneurs dominate the retail and service sectors in contrast to high-technology sector where men are known to be efficient. Yet some of the reasons for lower performances by female entrepreneurs despite their industry experiences, female entrepreneurs were cited to lack access to business opportunities, funding difficulties, and suffer from severe restrictions to bank loans because of cultural and social barriers (Gupta, et al., 2009).
For decades gender disparities between male and female have narrowed to a large extend. Yet female entrepreneurs in most countries continue to try and close existing cracks that have consumed the entire gender systems (Parker, 2009; Bosma & Harding, 2007; Arenius & Minniti, 2005; Reynolds, et al., 2004). Extant literature have shown that gender differences regarding entrepreneurship continue to seek redress in growth, innovation and the level of performance in relation to success (Alos, et al., 2006; Coleman, 2007; Kalleberg, et al., 1991). Despite the large volume of literature, gender gaps in terms of human capital and the level of performance impacts on human capital, business operations in regard to gender gaps still remains unclear (Brush, et al., 2004). The reasons for widening gender disparities as stated by literature could be tied to inadequate resources as stated in female entrepreneurship literature in comparison to their male counterparts (Lerner, et al., 1997). Female entrepreneurs struggle to access different types of resources as a result of access to capital and inadequate skills. Over the years numerous evidence suggests that female entrepreneurs are unable to operate successfully due to lack of resources (Nxopo & Iwu, 2016; FinScope, 2016; Laetitia, 2015; World Bank, 2011; Kock, 2008; Maas & Herrington, 2006). According to Parker (2009), Rosa, Carter & Hamilton (1996), females entrepreneurs are highly incapacitated to operate satisfactory entrepreneurship. In most developing countries gender bears no relationships with business success (Storey, 1994).
For instance, other studies argued that no disparities exist in terms of gender and educational qualification of ROMs and ROMSBUS (Cowling & Taylor, 2001; Birley, et al., 1987). The study add that between genders, female are more qualified than men (Cowling & Taylor, 2001). The study continue to state that whether females businesses grow faster or slower still remains a matter of concern. Kallerberg & Leicht (1991) in another but related study found that female businesses are less successful than men. Watson (2003) argues and supported by scientific evidence that women businesses failed at higher rates in contrast to men. Some of the reasons given in a study for females’ business failures include the declining human capital levels that persists among women population (Ucbasaran, et al., 2004; Martinez, et al., 2007). Men business according to a study are able at a much faster success rates due to high levels of entrepreneurial desires among men (Kolvereid, 1996). Further study by Downing and Daniels (1992) mentioned issues of females’ household responsibilities, issues of asymmetrical, limitations due to labour mobility.
Educational Qualifications of Rural Owner-Managers (ROMs)
It is assumed that individuals who attained higher educational status turns to be more efficient in managing business operations than others without the requisite qualifications (Trulsson, 2000; Mead & Liedholm, 1998; Rutashobya, 1995; Storey, 1994). As stated by Becker (1993), acquiring high level of education has been the vital cornerstone that emerges as part of the human capital theory (HCT). Several scientific works arose from HCT to measure the impact of education on the performances of small businesses (Unger, et al., 2009a; Schrader & Siegel, 2007). Empirical studies add that the influence of education over the years yielded mixed outcomes. Thus, it is yet not clear to determine the level of relationship between firm growth and education. However, recent academic works have revealed positive findings in terms of firm growth and level of education (Kim, et al., 2006; Barringer, et al., 2005; Watson, et al., 2003). Similar research argued against the findings that relationship exist between firm growth and education (Blackburn, et al., 2013; Silva & Santos, 2012). According to others there is negative relationship between firm growth and level of education (Bartlett & Bukvi-I, 2001; Johnson, et al., 1999).
The significance of education or otherwise depends primarily on the level of education reached (Dimov & Shepherd, 2005). Across the disadvantaged communities, education is perceived as the only means thorough which one can access the job market instead of being self-employed (Kazela, 2009). This implies that the general level of education that is reached by entrepreneurs in a specific industry sector within the firm impacts on its success. Education according to scientific evidence increases the thinking ability of individuals as well as assist entrepreneurs to know of their needs (Mueller & Anderson, 2014). However, researchers argued that ROMs level of education impact on business profit margin than other demographic variables (Jo & Lee, 1996). The next section outlines the methods that were applied to gather dataset in order to ascertain the objectives of the survey.
Age of Rural Owner-Managers (ROMs)
Age characteristics of individuals has positive influence on self-employment. Research indicates that older generation is more likely to pursue self-employment initiatives since they are more knowledgeable, accumulate financial gains over the years (Parker, 2009). Miller (1987) further add that older people are known to be risk averse; as such the idea of entrepreneurship always turn out to very risky; thus not the best option to them. According to Levesque & Minniti (2006), entrepreneur’s age commences during the period of the growing search in entrepreneurial opportunities. The survey further states that increase in opportunities grows with individuals’ ages. This means at old age, individuals become less interested in engaging into activities that is associated with high risks factor and to earn future dividend. This sentiment further echoed the fact that older individuals expect instant yields for investment at no risk (Levesque & Minniti, 2006).
Several scientific evidence has established significant linkages between entrepreneur’s age and positive firm operations. Research indicates that individuals between the ages of 25-45 years are able to establish successful start-ups (Reynolds, et al., 1999). According to Parker (2004), age serves as the purest factor that impacts on decision to embark on business venture. The study further point that at the age of 40 years, individuals are deemed to own their small businesses. Bönte et al. (2009) add that by the age of 40 years, it is believed that individuals have acquired the necessary skills for successful business operations. The implication stems from the fact that at a very old age individuals are able to become successful. It is doubtful whether younger individuals are committed to pursuing entrepreneurial activities (Peters, et al., 1999).
Supporting evidence concurs that individuals venture into entrepreneurial ventures at an average age of more than 40 years to become a potential entrepreneur. Hence, it is hypothesized that entrepreneur’s age bears relationship with the success of the venture (Staw, 1991). The level of experience acquired in operating businesses closely relates with entrepreneurs’ age. Simply put, older entrepreneurs are better experienced to engage in successful entrepreneurial activities (Henry, et al., 2003). In contrast, entrepreneurs in their prime are known to be risk-takers in creating the necessary business growth-path. Hatch & Zweig (2000) argued that anyone could pursue entrepreneurial activities no matter the age. Supporting this claim, Moore, et al. (2008) agreed that age was not the sole pointer of starting entrepreneurial activities; anyone can pursue entrepreneurial venture. In line with this sentiment, Hatch & Zweig (2000) state that the best age to operate entrepreneurial venture is between the ages of 20 and 40 years.
Extensive literature search have revealed that not much first-hand scientific work was performed to get an insight in to the significant relationship between selected demographic variables of gender, age and educational qualifications of ROMS business success. However, scientific literature has shown increasingly many studies on the challenges that contributes to small business failures in rural locations (Olawale & Garwe, 2010; Marshall & Oliver, 2005; Abor & Quartey, 2010; Kim, 2010; Morara & Mureithi, 2009). Other literature review centered on business structures, diverse perception about entrepreneurship myths, different characteristics and individual personality traits (Brockhaus, 1987; Olson, 2000; Deamer & Earle, 2004). Similar research by Merrett & Gruidl (2000) focused on business success factors and demographic variables.
Despite the fact that more have been achieved to understand rural small business failures, not much was researched in rural areas to determine significant relationship between selected variables. Given such minimal research output on selected demographic variables and rural business success, ROMs are most likely to doubt successful operations of small businesses in rural areas. Drawing from these views, the current empirical rural study is designed to assist ROMs to understand demographic variables and business success. Providing requisite knowledge through this survey could positioned policymakers in the local municipal authorities (LMAs) to play critical roles in assisting rural small businesses. What emerged from this final will immensely contribute to rural entrepreneurial activities and small businesses in rural areas.
Research Design and Approach
This empirical study was quantitatively designed as a non-experimental and cross-sectional to solicit primary data for approximately two months (Cohen, et al., 2013). Being non-experimental, the author applied different methods, intended to determine the significant relationships between selected variables (Tredoux & Durrheim, 2013). Much difficulties occurred during the data gathering periods for over two months. However, for purposes of data verification and to determine the level of accuracy, the quantitative design was the best option (Poni, 2014).
Empirical data for this study was collected during the author’s PhD study in 2013 from two rural local municipalities (RLMs) in the Northern Cape Province (Agbenyegah, 2013). Due to lack of credible database, two data collection approaches; the snowball and purposive techniques were utilized during the data gathering processes (Hays & Wood, 2013). Justification for using these sampling techniques were twofold. Firstly, due to the chronic lack of credible and reliable database. Besides, by adopting the purposive tools, the author could gather insightful information from individuals on rural businesses. The design of this study include participants sample size of 268 from the two research settings. The author approached two trained informants who assisted in identifying ROMs based on two set criteria for participation in the study (Perez, et al., 2013; Kent, 2007; Wegner, 2007). First, participants had to own active small businesses in one of the research areas (Stewart, et al., 2001). Second, participants of all age groups had to operate small businesses in the study areas for over five years. Besides these small businesses must only be selected provided the employees are five. A target population of 500 ROMs were identified as participants consisting of small businesses as defined and classified into different sectors namely the retail, wholesale, construction, manufacturing, accommodation, transport, food, agricultural, garden services, internet services, home appliances repairers and beauty salons among others. The author administered a seven-point Likert scale questionnaire to 282 ROMs of small businesses but only 268 questionnaires were returned free of errors and ready to be analysed.
The current survey was structured to gather quantitative data from ROMs of small businesses by means of a seven point Likert-scale questionnaire. To ascertain the study objective, the general design of a seven point Likert-scale consisted of various but related sections. These include sections 12 items labelled as business success business success indicators (BSIs) and the selected demographic variables of gender, age and educational qualifications. For reliability modifications of the seven scales were conducted based on previous empirical studies (Lau, et al., 2007; Olson, 2000; Goodman, 1994). Dataset for the study were drawn on demographic variables such as the age, educational qualifications and gender of participants. In total, a 4-part questionnaire was administered and completed by ROMs. Responses from the ROMs were based on variables as indicated and described on the seven point Likert-scales earlier in the study. Prior to distributing the questionnaire, experts were approached to participate in piloting the survey instrument (Saunders, et al., 2003).
The errors that were identified during the pilot two-week processes were adjusted for clarity. Every scientific study according to researchers must address the twin issues of validity and reliability of the research instrument (Hair, et al., 2010). Thus, validity and reliability of the survey instrument was performed by means of statistical tool of the Cronbach’s coefficient alpha. The seven-point Likert scale questionnaire and the research objectives were carefully compared for validity. The Cronbach’s coefficient alpha was performed to assess the reliability of the survey instrument in line with Cronbach’s alpha coefficients with cut-off points in the range of 0.60-0.80 (Nunnally & Bernstein, 2010; Cohen, et al., 2013). All the items used in constructing the questionnaire achieved internal consistency as well as coefficient values of 0.74-0.86. Thus, the data collection instrument applied was reliable and acceptable for scientific interpretations.
This survey was designed in search of quantitative primary data. The data were drawn from demographic variables of participants’ age, gender, and educational qualifications. A seven-point Likert-scale questionnaires anchored as (7) strongly disagree to (1) strongly agree was adopted as the statistical tool to collect data. In addition to the demographic variables, a total of 12 items were applied as business success indexes (BSIs) in the questionnaires to which the ROMs of small businesses were asked to respond. These indicators were further used to prepare related indexes of business successes. The indexes emerged as part of ROMs responses to questions E1 to E12. These Responses focused on questions E1 to E12 and thereafter added together to determine the indexes of ROMSBUS. The various index values were converted into categories namely low, moderate and high. For the purpose of this study, the original racial groups of five were collapsed into four variables as well as the demographic variables to suit the present empirical data assessments. For instance, the age group of ROMs have changed to only two categories; namely young (24-36 years) and old (37-60+). For clarity, educational qualifications of ROMs were restructured into three variables on the questionnaires namely matric and certificates, trade skills and diploma in addition to other degree holders.
Earlier in the survey, ROMs responses were determined through descriptive statistics of frequencies as indicated by the questions according to set categories and in line with the survey questionnaire. In order to arrive at credible inferences, the Pearson chi-square test and a one-way ANOVA supported by cross-tabulation were used to ascertain the significant relationships between gender, age and educational qualifications of ROMs.
As stated in the last section, the author established new indexes to ensure accurate empirical interpretations. Regarding participants’ age cohorts, the younger participants ranges from 26 to 36 years while the older participants’ ranges from 37 to over 60 years. These changes were made known to the participants. Hence, their responses to questions were not only truthful and accurate but also reliable taking into account the variations in participants’ ages. Given these explanations, the researcher applied cross tabulations to determine significant relationships between variables while formulated hypotheses were tested by means of Pearson chi-square tests. Initially, frequencies were applied to describe the level of ROMSBUS as shown on the following pages.
Table 1 above depicts low (50.4%) level of ROMSBUS. This was followed by moderate (28.4%) and high (21.3%) level of ROMSBUS. These results does not differ from other previous scientific literature which disclosed lack of small business successes; in otherwise, growing small business failures (Olawale & Garwe, 2010; Marshal & Oliver, 2005). These revelations corroborate with a study by Martin & Staines (2008), Lather (2009); Bowen, et al. (2009); Abor & Quartey, (2010) as well as Kim (2011) that small businesses experienced ongoing challenges contributing to high failures rather than success. This implies that there are series of inhibiting factors that impedes small business successes in general.
|Table 1: Frequency Distribution Of The Level Of Business Success|
|Level of ROMSBUS||High||57||21.3|
Table 2 shows the older aged group of ROMs have reached (60.7%) low level of ROMSBUS than the younger ROMs (39.3%). Younger aged ROMs (61.3%) have achieved moderate level of ROMSBUS as compared to older aged ROMs (38.7%). Besides, a greater proportion of younger ROMs (59.6%) reached a high level of ROMSBUS than older aged ROMs (40.4%). These findings concur with past survey that older ROMs avoid risks instead they expect immediate yields on investments (Levesque & Minniti, 2006). Entrepreneurial activities entails risky operations; thus it could be said that younger ROMs take risks at early age which may contribute to their level of business success (Hatch & Zweig, 2000).
|Table 2: Cross Tabulation Of Level Of Romsbus And Age Group|
|Age group of ROMs|
|Younger aged group 26 to 36 years||Older aged group 37 to 60 years||Total|
|Level of ROMSBUS||Low||53||39,3||82||60.7||135||100|
Testing Formulated Hypotheses
A non-parametric Pearson chi-square test was conducted (see table 3) to test for the significant relationship between the level of ROMSBUS and age group at 5% level of significance. From the table above there was a significant relationship between level of ROMSBUS and age group of ROMs at p-value less than 0.05, χ2 (2)=12.202, p-value=0.002. Therefore, H10 the null hypothesis is rejected at 5% level of significance. Thus, in this sample age group of ROMs differ significantly as expressed according to the opinion regarding level of ROMSBUS.
|Table 3: Pearson Chi-Square Test Of Level Of Romsbus And Age Group|
|Age group of ROMs|
|Level of ROMSBUS||χ2||12,202|
Table 4 above, illustrates (49.6%) of ROMs obtained qualifications in matric and certificate ascertained low level of ROMSBUS as compared to (32.3%) ROMs who obtained diplomas and degrees are few (18.0%) of ROMs who completed trade skills. The level of ROMSBUS is moderate for ROMs who obtained (74.3%) matric and certificate qualifications, the ROMs (23.0%) qualifications in diploma and degree as well as ROMs who qualified (2.7%) in trade skills. The level of ROMSBUS was high (86.0%) ROMs with qualifications in matric and certificates while s (12.3%) obtained diploma and degrees in contrast to ROMs (1.8%) in trade skills.
|Table 4: Cross Tabulation Of Level Of Romsbus And Level Of Education|
|ROMs level of education|
|Matric and Certificates||Trade Skills||Diploma and degrees||Total|
|Level of ROMSBUS||Low||66||49,6||24||18,0||43||32,3||133||100|
Testing Formulated Hypotheses
A non-parametric chi-squared test (Table 5) was conducted to test for significant relationship between level of ROMSBUS and the ROMs’ level of education at 5% level of significance. Table 5 depicts a significant relationship between level of ROMSBUS and ROMs’ level of education at p-value less than 0.05, χ2 (4)=32,358 p-value=0.000. Therefore, H20 the null hypothesis was rejected at 5% level of significance. Thus, in this sample ROMs’ level of education differs significantly in the likelihood regarding the opinion of the level of ROMSBUS.
|Table 5: Pearson Chi-Square Test Of Level Of Business Success And Level Of Education|
|ROMs’ level of education|
|Level of ROMSBUS||χ2||32,358|
Table 6 above illustrates low level (66.9%) of ROMSBUS for females followed by (33.1%) of their male counterparts. Moderate level of (53.9%) ROMSBUS for males in contrast to (46.1%) for females. The level of ROMSBUS was high (64.9%) for males and (35.1%) level of ROMSBUS for females.
|Table 6: Cross Tabulation Of Level Of Romsbus And Gender|
|Level of ROMSBUS||Low||89||66,9||44||33,1||133||100|
Testing Formulated Hypotheses
A non-parametric chi-squared test (Table 7) was conducted to test for the significant relationship between level of ROMSBUS and ROMs’ gender at 5% level of significance. From the table above there is no significant relationship between level of ROMSBUS and ROMs’ gender at p-value greater than 0.05, χ2 (2)=3.623, p-value=0.163. Therefore H40 the null hypothesis was not rejected at 5% level of significance. Thus in this sample, ROMs’ gender does not differ significantly in the likelihood on opinion of the level of ROMSBUS. The finding further support previous studies that more males participates in entrepreneurship that females (Veciana, et al., 2005; Delmar & Davidsson, 2000).
|Table 7: Pearson Chi-Square Test Of Level Of Romsbus And Gender|
|Level of ROMSBUS||χ2||3,623|
This empirical study was designed to explore the significant relationship between demographic variables and ROMSBUS. The literature study commenced with discussions of the rural small businesses success and demographic variables. Chief propositions that surfaced throughout the context points to continuous decline in small businesses operations in general and in particular across rural settings. In otherwise, instead of growth in the sector, there is increasingly degeneration of operations in rural areas where there is growing concern of infrastructure deficiencies among other severe challenges. Further evaluations of empirical data (refer table 1) depicted that across the research areas there is low to moderate ROMSBUS of 50.4% and 28.4% respectively. These results confirmed prior scientific evidence that small businesses in general lack sustainability (Marshal & Oliver, 2005).
The frequency table (Table 2) indicates that younger age groups between the ages of 26 to 36 years (59.6%) operates successful small businesses in contrast to the older age groups who are aged between 37 to 60 years (40%). The results further revealed that the older age groups are incapable of operating successful small businesses. These results could be due to a number of variables including lack of technological skills, managerial and financial acumen as well as education and training (Gibb, 1993a; Dana, 2001; Bosma & Levie, 2010). Similarly, while the older participants’ could be more experienced and be able to quickly solicit business funding, the general approach to the business is key to success provided more training and education are provided to the older groups (Weber & Schaper, 2004). This finding about older age groups support recent argument by Levesque and Minniti (2006) that older entrepreneurs are hesitant to invest sufficient time into entrepreneurial activities. Similarly, the success attained by the youth could be linked to varying variables namely youth technological skills through “new found” education, active economic participation, ability to pursue high risks business activities and willingness to seek information over social media platforms and informal networking (Surin & Wahab, 2013; Machirori & Fatoki, 2013).
In terms of gender, the males have ascertained high (64.9%) to moderate (53.9%) of ROMSBUS in comparison to their female ROMs who were able to achieve a very low (33.1%) business success. The present findings resonates with similar studies by Watson (2003) and Kallerberg & Leicht (1991) who found that businesses owned by females failed at alarming rates in developing countries than males. Some of the attributes to these setbacks could be due to too much family responsibilities by females, lower level of human capital, and immobility of labour by women as well as other asymmetrical issues (Downing & Daniels, 1992). As indicated by Kolvereid, 1996), males are able to operate successful businesses because of their growing entrepreneurial desires. In an environment such as rural both genders lack the know-how of the financial sector. For instance, others argued that businesses own by females and male are likely to fail due to lack of awareness of existing state funding initiatives and related networks (Thusi & Zondo, 2016).
The study further revealed that education plays critical role in small business success. Empirical finding have shown that ROMs who obtained only matric and other forms of certificates are able to achieve higher to very moderate small business success rates. ROMs with diplomas and degrees according to the study attained very low business success. This revelation is consistent with a study in Morocco by Gray, Foster and Howard (2006) that any form of education by ROMs of small businesses creates “The growth path” towards successful businesses. One of the major reasons for high business failure are the results from sub-standard system of education (Soderbom & Teal, 2001; El Hamzaoui, 2006).
In order to ascertain the primary objectives of this empirical study, three null hypotheses were designed aided by Pearson chi-square test tool to determine the significant relationships between the demographic variables of age, educational qualifications and gender and ROMSBUS. Table 3 above inferred significant relationships between ROMSBUS and the age groups of ROMs. The result indicates that the null hypothesis was rejected (the p-value was lesser than 0.05 level of significance). This implies that there was no significant relationship between ROMs age and ROMSBUS. Given this finding it means that business success does not influence or bear relationship with individuals’ age. As such, age as a variable cannot be used to explain the relationship between ROMSBUS. Whether ROMs is young or old, it can therefore be stated that business success does not dependent on age; there are other variables such as the ROMs competencies, technology and various forms of skills that could be applied to assess business success. For instance, the frequency table (Table 2) revealed that between the ages of 26 to 36 years the younger ROMs are able to operate successful businesses than the older group of ROMs. This could result from various knowledge by the younger generation in technology and other forms of knowledge which the older generation are unable to acquire. The finding further point to the fact that ROMs age is not the sole determinant of business success; therefore, no relationship exist between ROMs age and business success. Scientific evidence concurred that it is rather the middle age owner-managers of businesses that in most instance achieves significant business success not the younger or the older owner-mangers (Storey, 1994; Kinsella, et al., 1993). Recent study by Stuart & Abetti (1990) add that demographic variable such as age bears no relationship to business success.
Table 5 shows the finding relating to ROMs educational qualifications and rural small business success. The null hypothesis was rejected (the p-value was lesser than 0.05 level of significance). Aside other demographic variables, educational qualifications attained a p-value below 0.05 (5% level of significance). The implication is that there is no significant relationship in terms of ROMSBUS as compared to gender. Education in general being under or post-graduate achievements does not lead to operating successful businesses. The outcomes of this study in inconsistent with a study by Unger et al. (2009a) which revealed that there is relationship between education and business success. Furthermore, the present research outcomes differ from several prior studies by Cooper, Gimeno-Gascón, et al. (1997); Storey, (1994); and Kangasharju & Pekkala (2002) in which it was evident that there was significant relationship between higher level of educational status and business success.
Table 7 shows the empirical outcome relating to the significant relationship between ROMs age and ROMSBUS as hypothesized. The null hypothesis was not rejected (the p-value was greater than 0.05 level of significance). In simple terms age does not matter to any form of business success. The result further demonstrates that younger and older business owners can be successful not based on age but rather on other variables. Earlier in the study, the cross tabulation shows the younger population (Table 2) are more successful in contrast to the older ones. This finding is in sharp contrast to a recent study which showed that the older owner-managers are more successful in operating small businesses (Kristiansen, et al., 2003). Further examination of prior scientific evidence add that due to the risky nature of younger entrepreneurs, there is negative relationship between business growth and the age of entrepreneurs (Munoz, et al., 2014; Papadaki, et al., 2002).
There is growing interests in operating small businesses in South Africa. Some of the underlining reasons for these interests stem from the fact that small businesses are known to create employment opportunities and economic prosperities. This empirical study has some significant implications for RPCs and in particular, ROMs of small businesses. Regarding RPCs, this empirical results made contributions to the relationships between demographic variables and small business success in rural areas. Much scientific values are therefore added to the knowledge of existing perceived advantages of operating rural small business activities with the view of becoming successful. Business success can be possible once RPCs comprehend the implications of relationships at various levels within rural small businesses. Aside these relationships, the findings also depicts the levels of success in terms of ROMs’ ages.
Given these results, RPCs are better equipped to focus on specific age groups as interventions for small businesses are crafted and earmarked for rural business success. Regarding ROMs, there are countless scientific evidence that suggests reasons for lack of small business success. Nonetheless, insights into some demographic variables might require ROMs to redesign their operational plans for rural small businesses. Redesigning operational plans means taking deeper look into the age variations, personal educational status and the gender of individuals to determine the level of these variables and business success. It is imperative that ROMs redesign for example, individual skills and training programs to accommodate the variables of small rural business success.
Furthermore, it is essential that ROMs take into account the age and gender issues that relates to small rural business success. The fact that small businesses in rural areas contribute to decline in unemployment figures and sustain household incomes, ROMs who have not yet consider these variables are likely to comprehend positive shift towards rural business success. This could be attained through integration of all the variables as stimulants to small business success despite the challenges. In the end, this empirical study submits how ROMs can increase small business success through the exploitation of demographic variables such as gender, age and educational qualifications.
Limitations and Recommendations for Future Research
Similar to other studies, this empirical work was without setbacks (Marshall & Rossman, 1999); since it was conducted in a precise rural contexts. Hence, its outcomes cannot be easily generalized to include other rural settings country-wide. Another major limitation was that the sample used represented rural municipalities where demographic variables formed the core primary data collection processes. Based on these limitations, future empirical studies could measure more demographic and environmental variables to pave the ways for more generalization. Future empirical studies should also focus on adopting longitudinal data to illuminate all the antecedent variables that impact on the success of rural small businesses. Finally, the author suggests that in future, both the case and empirical survey research procedures be employed for in-depth results. The author is of the opinion that findings that emerged from such studies should provide guidance to ROMs as they endeavor to achieve rural small business success.
The primary aim of this study was to determine the significant relationships between demographic variables and ROMSBUS. By means of descriptive and inferential statistical tools, a null hypotheses were formulated and tested through the Pearson chi-square test. Earlier the study revealed that older ROMs are less successful in operating small businesses. This revelation contradict the fact that younger ROMs are able to attain high and moderate business satisfaction. Through the descriptive cross-tabulation and Pearson chi-square test, the final outcome of this study revealed mixed results in terms of ROMSBUS. About educational status, empirical findings showed that ROMs who obtained matric and other forms of certificates were able to operate high and moderate small business successfully. ROMs who achieved diplomas and other forms of degrees were unable to successfully operate rural small businesses. This result implies that for ROMs to be successful in operating rural small businesses, dos not only depend on the level of qualifications; success may be linked to other variables. The Pearson chi-square test indicated significant relationships between ROMSBUS and educational achievements of ROMs. In terms of gender, the result showed no significant relationships between ROMSBUS and ROMs. Further suggestions based on the findings proved previous evidence that males are more entrepreneurial and attained ROMSBUS than females. The author concluded that information provided through statistical tools of Pearson chi-square test emerged as diverse results. This finding was confirmed through the formulated hypotheses as two of the null hypotheses were rejected out of the three.
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