*Student Number S3555380
supervised by Dr Michael Wyrwich and co-assessed by Dr Samuele Murtinu
I’m grateful to Dr Michael Wyrwich for his insightful feedback and guidance throughout the thesis writing process. Additionally, I’d like to thank Linda, Leif, and Nikolay for proofreading the document. And finally, I’m grateful to God for His blessings and to my family for their support, prayers & love.
The Role of Fear of Failure in the Entrepreneurial Process:
A Case of Six Sub-Saharan African Countries
Rehema Mussanzi*
MSc Business Administration, Small Business and Entrepreneurship
Faculty of Economics and Business, University of Groningen, The Netherlands
Abstract
Purpose – This article aims to study the moderating role of fear of failure in the relationship between entrepreneurial opportunity discovery and exploitation in the context of Sub-Saharan Africa. Design/methodology/approach – Binary logistic regression is employed to test the formulated hypotheses using data from the 2014 GEM Adult Population Survey deriving from Angola, Botswana, Burkina Faso, Cameroon, South Africa, and Uganda.
Findings – Results confirm that opportunity perception positively relates to entrepreneurial activity in the six countries studied. Although fear of failure does negatively relate to entrepreneurial activity, it does not negatively moderate the relationship between opportunity perception and entrepreneurship.
Originality/value – This study contributes to the debate on the role of entrepreneurship in bringing about economic development in SSA.
Keywords – Entrepreneurship, Fear of Failure, Opportunity Perception, Entrepreneurial Activity, Sub-Saharan Africa, Unemployment, Economic Development
Paper Type – Master Thesis
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1. Introduction
Despite being home to six out of the top ten fastest growing economies in the world over the last 15 years (Fox et al., 2016), Sub-Saharan Africa (or ‘SSA’) is facing many pressing issues for which urgent solutions are required. Youth unemployment, for example, is one of the most pressing issues facing SSA today (Hilson and Osei, 2014). According to Kew (2015), 62% of the African population is under the age of 25, making up approximately 37% of the total labour force (Ncube et al., 2011). This youth population, standing at just over 200 million people, offers a lot of potential for labour productivity but currently constitutes around three-fifths of the continent’s unemployed (Page, 2013). According to Oppenheimer and Spicer (2011), countries such as Mozambique and Ghana have unemployment rates as high as 80 per cent among the youth. In addition to the current unemployment crisis, the Population Division of the UN (2017) has forecasted that out of the circa 2.2 billion people who may be added to the global population between 2017 and 2050, more than half of the population growth is expected to take place in Africa. Africa’s population is expected to increase by 1.3 billion from 1.1 to 2.4 billion in this time period (ibid.). The implications of this anticipated rise in population are going to be far-reaching in SSA. One of the most pertinent implications, which is also equally applicable today, will be the integration of this burgeoning population into the labour force. Hitherto however, policymakers on the continent have been accused of not doing enough to engage the youth, and the response of African governments has been deemed as inadequate thus far (Ackah-Baidoo, 2016).
One of the most recommended remedies to tackle the unemployment problem in SSA, lauded by both scholars and practitioners, has been the stimulation of entrepreneurship through effective government policy (Kiggundu, 2002; Sriram and Mersha, 2010; Awogbenle and Iwuamadi, 2010; Adusei, 2016; Babah Daouda et al., 2016; Chigunta, 2017). The relationship between entrepreneurship and unemployment has been studied in many different contexts but results have thus far been shrouded with ambiguities (Carree and Thurik, 2010). On one hand, unemployment has been found to be a reflection of the macro-economic conditions of an economy and thus the demand side of entrepreneurship (Nyström, 2008). And on the other hand, it has been found to be the supply side of entrepreneurship where unemployed individuals see entrepreneurship as a genuine alternative to unemployment (ibid). Carree (2002) found empirical evidence for both instances. Despite the contradicting results, Acs et al. (2008, p.219) found that entrepreneurship is “an important
mechanism for economic development through employment, innovation and welfare effects.” In the same study, Acs
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that “opportunity entrepreneurship has a positive significant effect on economic development, whereas necessity
entrepreneurship has no effect” (Acs et al., 2008, p.219).
In order to assess the nature of opportunity entrepreneurship, given its positive significant effect on economic development, this paper builds on the economic theory of entrepreneurship within which there are two schools of thought: the neoclassical theory and the Austrian theory. The neoclassical assumes that, due to market equilibrium conditions, everyone can recognise all entrepreneurial opportunities and act accordingly to close the gap in market equilibrium (Baumol, 1993). Other proponents of this theory go a step further by adding that it is an individual’s risk-propensity that determines who becomes an entrepreneur (Kihlstrom and Laffont, 1979; Murphy et al, 2006). On the other hand, the Austrian economic theory refutes this claim and assumes that the discovery of entrepreneurial opportunities depends on the distribution of information in society (Kirzner, 1973). Everyone cannot recognise all entrepreneurial opportunities except a subset of people who are either alert, possess prior knowledge, and/or purposefully search for or accidentally discover opportunities that others cannot perceive (Hayek, 1945; Ray and Cardozo, 1996; Kirzner, 1997; Shane, 2000). Building on the latter economic school of thought, this paper hypothesises that only those who discover opportunities exploit them.
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within the context of SSA. Building on the appraisal theory of emotions, this paper therefore hypothesises that fear of failure negatively relates to entrepreneurial activity, and negatively moderates the relationship between opportunity discovery and exploitation.
In addition to the aforementioned gap, there is still considerable scope for the African entrepreneurial process to receive greater attention in entrepreneurial research as well given the vast majority of existing studies have concentrated on North America and Europe (Khavul et al. 2009; Mersha et al, 2010; Sheriff and Muffatto, 2015). In their review of research conducted on African entrepreneurship, Naudé and Havenga (2005) found that just over 520 scientific and academic research papers were published between 1963 and 2001 on the topic. Out of the publications, about 61.2% focused on South Africa followed by general research. Zimbabwe, Nigeria, and Kenya were far behind with under 5% of the attention each (ibid.). As pointed out by Dana and Ratten (2017, p.418), “the field of international entrepreneurship—particularly in the African context—remains void of research
about the processes around recognizing opportunities.” Furthermore, it can be argued that more research is
also needed around the exploitation of entrepreneurial opportunities within the context of SSA. This paper therefore attempts to address these gaps in research by conducting an empirical study of the entrepreneurial process in SSA using secondary data collected by the Global Entrepreneurship Monitor (or ‘GEM’) from Angola, Botswana, Burkina Faso, Cameroon, Uganda, and South Africa. GEM is a research project that was set up with the aim of studying the complex relationship between entrepreneurship and economic growth (Bergmann et al., 2014). As such, by focusing on these six SSA countries that have previously received little attention, except South Africa as previously mentioned (Naudé and Havenga, 2005), the author hopes to contribute to the debate on the role of entrepreneurship in bringing about economic development in SSA (Spring and McDade, 1998; Acs et al., 2008; Brixiova, 2010; Carree and Thurik, 2010). This study employs binary logistic regression to address the following research questions: what is the relationship between opportunity recognition, fear of
failure, and entrepreneurial activity in sub-Saharan Africa? And does fear of failure have a moderating effect on the relationship between opportunity recognition and entrepreneurial activity?
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2. Literature Review
Entrepreneurship is widely considered as one of the drivers of economic development and job creation globally (Reynolds and White, 1997). Anokhin et al. (2008, p. 117) even go as far as claiming that entrepreneurship “is the main vehicle of economic development.” As a result of these claims, the field of entrepreneurship has attracted a lot of attention in academia over the last decades leading to multiple definitions as well as schools of thought ranging from psychology, strategy, management, sociology to economics, among others (Knight, 1921; Schumpeter, 1934, 1936; Kirzner 1960; McClelland, 1961; Shane and Venkataraman, 2000). For the purposes of this study, two definitions of entrepreneurship are adopted. The first adopted definition is in line with GEM’s definition of entrepreneurship as “any attempt at new business or new venture creation, such as self-employment, a new business
organization, or the expansion of an existing business, by an individual, a team of individuals, or an established business” (Reynolds et al., 1999, p.3). Given this study uses secondary data from GEM, any
discussions focusing on entrepreneurial activities in this article will be based on this definition. The second adopted definition is the seminal one posited by Shane and Venkataraman (2000, p.218) in which entrepreneurship is the “examination of how, by whom, and with what effects opportunities to create future
goods and services are discovered, evaluated, and exploited.” As this study focuses on the entrepreneurial
process of discovering, evaluating, and exploiting opportunities in SSA, any discussions on the entrepreneurial process will be based on the second definition. Overlaps in definitions will be clarified where and when necessary. However, prior to delving into the theoretical underpinnings of the study, the next sections of the literature review contextualise youth unemployment, entrepreneurship, and necessity versus opportunity entrepreneurship from an SSA perspective.
2.1. Youth Unemployment in Sub-Saharan Africa
Sub-Saharan Africa, interchangeably abbreviated as SSA in this article, is a term used to describe countries geographically located below the Sahara dessert. Policymakers, academics, and newspapers, among others, often use the term routinely to describe the region. There is however an issue that arises from such denomination. As correctly pointed out by The Economist (2019), “some
countries, like Mauritania, are mostly in the desert itself […] Somalia and Djibouti, both in the Horn of Africa […] are south of the Sahara, but the IMF oversees them from its Middle East and Central Asia department. The World Bank used to include both countries in sub-Saharan Africa, before moving Djibouti to the Middle East and North Africa in 2000. Meanwhile Eritrea, to the north of both of them, is considered sub-Saharan.” Despite the
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Regardless of the association with poor economic performance over the years, SSA has been home to six out of the top ten fastest growing economies in the world over the last 15 years (Fox et al., 2016). This consistent growth in GDP, largely attributed to the exploitation and export of natural resources (Altenburg and Melia, 2014), has translated into some improvement in welfare. According to the World Bank (2014, p.30), “the share of people living on less than $1.25 a day declined from an estimated
58 percent to 48.5 percent between 1996 and 2010. If recent trends of a 1 percent per year decline are sustained, poverty rates will fall below 30 percent by 2030.” In spite of the poverty reduction rates, it is generally
accepted that a large segment of the population in the region has been left unaffected by the economic growth (Fox et al., 2016).
One of the segments left out of the growth process has been the youth. At this point, it is worth noting that youth is a term defined and viewed differently in different parts of SSA. For example, in Uganda anyone between the ages of 12 and 30 is considered as youth, whereas in Nigeria the age range is between 18 and 35 (ILO, 2005). For the purposes of this article, youth is defined as individuals aged between 15 and 24 as per the UN’s definition (Awogbenle & Iwuamadi, 2010).
In line with this definition, over 200 million people living in SSA are considered as youth, making up close to 40% of the total labour force (Garcia and Fares, 2008; Ncube et al., 2011). This is arguably the most abundant asset that SSA possesses, in terms of labour productivity, from which the continent could greatly benefit if more investments were made in the youth (ibid.). Unfortunately, this young population currently constitutes around three-fifths of the continent’s unemployed, making youth unemployment one of the most pressing problems the continent is facing today (Page, 2013; Hilson and Osei, 2014). This problem is exacerbated by the fact that SSA’s youth is projected to grow faster than any other region of the world as shown on figure 1 below.
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Failure to properly engage this growing young population could lead to catastrophic consequences as exemplified by war-torn countries in SSA. In the case of Sierra Leone, for example, Paintin (2007, p.229-230) provides the following explication of youth involvement in the civil war that took place between 1991 to 2002:
“The grievances of young people stoked the fires of Sierra Leone’s brutal civil war (1991–2002), with youth forming the backbone of the Revolutionary United Front (RUF). Indeed, the conflict has been termed by some as a ‘crisis of youth’. . .For many disaffected young people living on the margins of society during that period, joining fighting forces appeared to offer more than the daily exclusion they faced. The decision to fight was very much rooted in their social, economic and political consciousnesses.”
Policymakers and governments in SSA have to therefore find and effectively implement appropriate solutions to the youth bulge in order to better engage the youth and in the process prevent the worst-case scenario similar to Sierra Leone from happening again. As pointed out by Ighobor (2013), SSA’s youth could either be viewed as a ticking time-bomb or an opportunity.
2.2. Is Entrepreneurship Necessarily Good?
One of the most advocated remedies to tackle the unemployment problem in SSA has been entrepreneurship (Sriram and Mersha, 2010; Hilson and Osei, 2014; Brixiová et al., 2015; Chinguta, 2017). Notwithstanding the contradicting findings from prior studies (Carree, 2002; Nyström, 2008), entrepreneurship is widely “considered to be an important mechanism for economic development through
employment, innovation and welfare effects” (Acs et al., 2008, p.219). Studies researching the relationship
between entrepreneurship and job creation, among others, have generally found a positive effect. Decker et al. (2014), for example, found a positive relationship between entrepreneurship and job creation in the US. In a meta-analysis of 57 studies as well as 87 observations from 12 years of researching the value of entrepreneurship, Van Praag and Versloot (2007) found that entrepreneurship leads to relatively high levels of employment creation in addition to productivity growth and innovation. Other studies providing empirical evidence of the effect of entrepreneurship on economic growth include Audretsch and Keilbach (2004, 2005), Bjørnskov and Foss (2013), and Liñán and Fernandez-Serrano (2014).
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workforce accordingly. In the final phase, countries are innovation-driven as they compete through the introduction of innovative production practices, solutions, products and services (ibid.). GEM, which follows this typology of economic development, considered all countries used in this study as factor-driven economies with the exception of South Africa, which was considered as an efficiency-driven economy (Singer et al., 2015). In the same line of study, Wennekers et al. (2010) found that the relationship between entrepreneurship and a country’s level of economic development is U-shaped. The U-shaped relationship implies that low-income countries have a higher rate of entrepreneurship than middle income countries (ibid.).
Sub-Saharan Africa, for example, is known for having an entrepreneurial landscape that is heavily concentrated in the informal sector (Hilson et al., 2018). According to the ILO (2016), over 70% of the labour force in SSA works in the informal sector, which the ILO considers as ‘vulnerable employment’. The global average is 46.3% (ibid.). To clarify, the informal sector usually includes unregistered, unregulated, and untaxed businesses, whereas the formal sector includes regulated, registered, and taxed businesses (Spring, 2009). Although the informal and formal sectors are different, they can be viewed as dual economies of SSA given their interdependence, as exemplified by Adam’s (1999) study of Nigerian firms. This essentially means that most of the entrepreneurial activities taking place in SSA are based in the informal sector. In line with the ILO’s description of the type of employment, this type of entrepreneurship can be deemed as ‘vulnerable entrepreneurship’ or necessity entrepreneurship, which is further expanded upon in the next section.
Taking into consideration the variety of entrepreneurial paths globally, in his article entitled ‘Is
Entrepreneurship Necessarily Good? Microeconomic Evidence from Developed and Developing Countries’, Vivarelli
(2013) finds that not all entrepreneurship is good. He remarks that:
“From a microeconomic point of view…any set of entrepreneurial ventures can be seen as a rather heterogeneous aggregate where real and innovative entrepreneurs are to be found together with passive followers, overoptimistic gamblers, and even escapees from unemployment. From a macroeconomic point of view, progressive new firm formation can generate permanent economic growth, while defensive and regressive start-ups originate only temporary positive effects, and ultimately market turbulence.” Vivarelli (2013, p.1475 & 1476)
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2.3. Necessity versus Opportunity Entrepreneurship
The Global Entrepreneurship Monitor has been categorising entrepreneurship into two different types: necessity entrepreneurship and opportunity entrepreneurship (Reynold et al., 2002). Necessity entrepreneurship refers to starting a venture out of a need, also referred to as “push” entrepreneurship (Block and Sandner, 2009; Dawson and Henley, 2012). And opportunity entrepreneurship refers to starting a venture in order to pursue an entrepreneurial opportunity, also referred to as “pull” entrepreneurship (ibid.). In their study of 11 countries, Acs and Varga (2005) found that necessity entrepreneurship has no effect on economic development, whereas opportunity entrepreneurship has significant positive effects on economic development. Necessity entrepreneurship may help to alleviate unemployment in the short-term but in the long-term sustainable employment, among others, is provided through opportunity entrepreneurship, which is associated with ventures that produce high growth and more jobs long-term (ibid.). In addition, opportunity entrepreneurs tend to remain in self-employment longer and are more successful than necessity entrepreneurs (Acs, 2006; Block and Sandner, 2009). Given the highlighted importance of opportunity entrepreneurship, the next section delves into the theoretical underpinnings of opportunity entrepreneurship and the role of fear of failure in the entrepreneurial process, which, to the best knowledge of the author, has hitherto received no attention in the context of Sub-Saharan Africa.
3. Theoretical Underpinnings
3.1. Economic Theory of Opportunity Entrepreneurship
In economics theory, there are two schools of thought on discovering and exploiting entrepreneurial opportunities: the neoclassical theory and the Austrian theory. The neoclassical economic theory is said to provide the most basic principles for a theory of entrepreneurship (Renko and Shrader, 2012). The theory assumes equilibrium conditions of demand and supply by the ‘invisible hand’ of market forces (Baumol, 1993). Price and quality are assumed to be facts and thus
“rational market participants all have perfect knowledge of relevant facts and anticipate perfectly the future moves of other market participants” (Renko and Shrader, 2012, p.1235). In this theory, everyone can recognise
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is generally agreed that neoclassical equilibrium theory fails to explain the existence of entrepreneurial opportunities (Eckhardt and Shane, 2003).
The Austrian economic theory, on the other hand, emerged in response to the neoclassical assumption of perpetual market equilibrium which Austrian economists deemed to be unrealistic (Wang et al, 2013). In Austrian theory, markets are in a constant state of disequilibrium and they are
“driven toward equilibrium by entrepreneurs eager to profit from disequilibrium” (Renko and Shrader, 2012,
p.1236). The market equilibrium however is considered to be elusive as a result of which information asymmetries exist (Hayek, 1945). Information asymmetries in turn create ‘knowledge corridors’ that allow only a subset of people within a given society, possessing certain prior knowledge, to recognise entrepreneurial opportunities given they do not appear in well-packaged informational forms (Hayek, 1945; Venkataraman, 1997; Shane, 2000). Based on this assertion, (potential) entrepreneurs can therefore only discover opportunities related to their prior knowledge (Hayek, 1945; Kirzner, 1997). In addition to prior knowledge, Kirzner (1997) contended that entrepreneurial alertness is also necessary to recognise entrepreneurial opportunities. He defined entrepreneurial alertness as
“an attitude of receptiveness to available (but hitherto overlooked) opportunities” (Kirzner, 1997, p.72).
Entrepreneurial opportunities, according to this definition, are exogenous phenomena that exist objectively in markets ready to be discovered by (potential) entrepreneurs. Shane and Venkataraman (2000, p.220) adopted this view when they defined entrepreneurial opportunities as “those situations
in which new goods, services, raw materials, and organizing methods can be introduced and sold at greater than their cost of production.” Borrowing from Drucker (1985, as referenced by Shane and Venkataraman, 2000),
they recognised that entrepreneurial opportunities and situations take various forms ranging from the creation of new information, the exploitation of market inefficiencies, to the reaction to shifts in the costs and benefits of alternative uses for resources.
In order to identify and discover those situations, empirical studies in line with the Austrian economic theory have confirmed that (potential) entrepreneurs need to either possess alertness (Ray and Cardozo, 1996; Buzenitz, 1996; Ardichvili et al., 2003), prior knowledge (Sigrist, 1999; Shane, 2000), and/or the ability to perceive entrepreneurial opportunities either through purposeful search or accidental discovery (Koller, 1988; Teach et al., 1989; Kirzner, 1997). Based on these findings, it can be deduced that only those individuals who are either alert, with prior knowledge, and/or who are able to perceive entrepreneurial opportunities end up discovering and exploiting them. The first hypothesis of this study is therefore as follows:
Hypothesis 1 [H1]: Perceiving an entrepreneurial opportunity positively relates to engaging in entrepreneurial
11 3.2. Appraisal Theory of Fear of Failure
Before or after discovering an entrepreneurial opportunity, a decision has to be made on whether or not to engage in entrepreneurial activity. During this decision-making phase, there are various factors at play that determine whether or not the (potential) entrepreneur will engage in entrepreneurial activity (Shane and Venkataraman, 2000). The factors at play include the expected value of the discovered opportunity and individual differences in the (potential) entrepreneurs, among others (ibid.). As a result, not all discovered opportunities are always exploited following the evaluation phase. One of the factors that plays a role in the entrepreneurial process but which has not been fully elaborated upon by proponents of the economic theory of entrepreneurship is fear of failure (Foo, 2011).
As a construct, fear of failure was initially conceptualised in the psychology literature as the motive for failure avoidance (McClelland et al., 1953). This conceptualisation led to Atkinson (1957, p.360) defining fear of failure as a “disposition to avoid failure and/or the capacity for experiencing shame and
humiliation as a consequence of failure.” According to this definition, emotions such as shame and
humiliation play a role in driving fear of failure in individuals. The emergence of fear of failure in the entrepreneurship literature is thus not surprising given the pervasiveness of failure in entrepreneurship. Knott and Posen (2005), for example, found in their study that most new ventures end in failure. Entrepreneurial fear of failure has therefore been defined as a “feeling that leaves a person
discouraged and afraid that he or she will not succeed even before making an attempt” (Ekore and Okekeocha,
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Lerner, 2008; Morgan and Sisak, 2016). In their review of fear of failure within the entrepreneurship literature, Cacciotti and Hayton (2014, p.24) concluded that based on existing literature “the emotional
experience of fear of failure decreases an individual’s probability to start a venture.” The second hypothesis of
this article is thus as follows:
Hypothesis 2 [H2]: Fear of failure negatively relates to engaging in entrepreneurial activity
Given the process of evaluating an opportunity entails deciding whether or not to exploit the opportunity, it can be argued that fear of failure also moderates the relationship between opportunity discover and exploitation. In their study, Mitchell and Shepherd (2011) focused on the moderating role of fear of failure on the relationship between human capital, self-efficacy and the propensity to entrepreneurial action. They found that fear of failure moderates those relationships but that the effects of the moderation differ across different dimensions. In one dimension, fear of devaluing one’s self-esteem and fear of uncertainty impede influence on behaviour. In another dimension, fear of upsetting others positively influences the decision to pursue an opportunity (ibid.). Apart from Mitchell and Shepherd (2011), studies focusing on the moderating role of fear of failure have been deemed by Cacciotti and Hayton (2014) as inconsistent, or in the case of SSA non-existent. To the best knowledge of the author, no prior studies have focused specifically on the moderating role of fear of failure in the relationship between opportunity discovery and exploitation. In an attempt to address this gap, and based on the appraisal tendency framework, it can therefore be deduced that the negative valence of fear associated with the aversive consequence of failure would lead to a negative moderating effect on opportunity discover and exploitation. The final hypothesis of this study is therefore as follows:
Hypothesis 3 [H3]: Fear of failure negatively moderates the relationship between perceived opportunity and
entrepreneurial activity
(-) H3 (-) H2
(+) H1
Figure 2: Conceptual Model
Perceived Opportunity Entrepreneurial Activity
Fear of Failure
13 3.3. Conceptual Model
As a result of the literature review, theoretical underpinnings, and hypotheses formulation, figure 2 shows the conceptual model for this study. The next section outlines the research methodology adopted to test the formulated hypotheses.
4. Methodology
The research methodology adopted for this study is quantitative. Quantitative research methodologies assess relationships between variables, which are measured numerically and analysed using a range of statistical and graphical techniques (Saunders et al., 2016). This methodology is associated with a deductive approach, where the focus is on using data to test theory and often uses probability sampling techniques to ensure generalisability (Ibid).
4.1. Data Collection
To test the formulated hypotheses, this study uses secondary data from the Global Entrepreneurship Monitor, which is a global standardised survey on entrepreneurial activities. The data derives from the 2014 representative Adult Population Surveys (or ‘APS’) of the GEM project as this is the most recent dataset at the time of writing this article. In 2014, a total of 70 countries took part in the APS survey with a total representative sample of 201,841 participating adults aged between 17 and 100 years old (Singer et al., 2015). In each represented country, data was collected by professional firms either via face-to-face, online, telephone interviews or a combination of means. These firms were in turn supervised by an academic or research institution and the project was coordinated by the Global Entrepreneurship Research Association (ibid.). For a critical review of the use of GEM data in academic research, see Bergmann et al. (2014).
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countries in SSA, data from GEM is widely used in academic researches as it provides a “major
database for internationally comparative entrepreneurship” (Bergmann et al., 2014, p1).
The initial total number of participants from the SSA sub-sample was 15,131 (Singer et al., 2015). Using listwise deletion, the dataset was sorted to only retain records of participants in Angola (n=1,287), Botswana (n=1,258), Burkina Faso (n=1,857), Cameroon (n=1,713), South Africa (n=3,448), and Uganda (n=2,047) who provided answers to all the questions posed for the variables considered for this study, which totalled 11,610. Listwise deletion is a method in which all cases with missing values are deleted and is considered the easiest way to obtain a complete dataset (Von Hippel, 2004). This method is known to produce biased estimates but it is worth noting that the software used to run regressions for this study uses a complete case analysis method, which as a default ignores missing observations (Bennett, 2001). The reason for using listwise deletion was to therefore start with a complete dataset.
For the analysis, although most studies using GEM samples restrict the maximum age of respondents to 64 because of the unlikelihood of older individuals entering entrepreneurship, this study takes into consideration all participants given the different context used. Older individuals in SSA may choose to enter entrepreneurship for necessity reasons or to exploit opportunities temporarily with the intention of perhaps handing the business over to a family member.
4.2. Measurements
All the measures for this study, excluding the control variables of age and age^2, are categorical and yield binary answers. Although the single item measurement is a limitation of this study, other studies have found that single item measures can be used to assess constructs in reliable and valid ways (Fisher et al., 2016). Another limitation of this study is the snapshot nature of the collected data, which provides little scope for further analysis other than based on the responses given at the time of the survey. Despites these limitations, the GEM variables selected for this study have successfully been used in previous studies (Langowitz and Minniti, 2007; Ho and Wong, 2007).
4.2.1. Dependent Variable
The dependent variable for this study is entrepreneurial activity, which has been found to play different roles depending on a country’s economic development (Porter et al., 2002; Acs et al, 2008; Wennekers et al., 2010). GEM’s operational definition of entrepreneurial activity is the “percentage of
individuals aged 18-64 who are either a nascent entrepreneur or owner-manager of a new business” (Singer et al.,
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(=0) or yes (=1). Using GEM data, van Stel et al. (2005) found that entrepreneurial activity by nascent entrepreneurs and owner/managers of new firms affects economic growth. The effect however depended on the level of per capita income (ibid.).
4.2.2. Independent Variables
The first independent variable of this study is perceived opportunity. GEM’s operational definition of perceived opportunities is the “percentage of individuals aged 18-64 involved in any stage of
entrepreneurial activity excluded who see good opportunities to start a business in the area where they live” (Singer et
al., 2015, p.24). To assess this variable, respondents were asked if there would be good opportunities for starting a business in the next six months around where they lived. They had to answer either no (=0) or yes (=1). Among the many studies focusing on entrepreneurial opportunity discovery, Ardichvili et al. (2003) found that entrepreneurial alertness is a necessary condition for the identification of opportunities.
The second independent variable is fear of failure. GEM’s operational definition of this variable is the “percentage of individuals aged 18-64 involved in any stage of entrepreneurial activity excluded who report that
fear of failure would prevent them from setting up a business” (Singer et al., 2015, p.24). Respondents were
asked if fear of failure would prevent them from starting a business: no (=0) or yes (=1). The main effects of both independent variables on TEA are first tested separately and directly. Given H3 focuses on the moderating role of fear of failure in the relationship between opportunity perception and entrepreneurial activity, the moderating effects are measured through the creation of four dummy variables as recommended by Wooldridge (2013). See section 4.3 below for more details.
4.2.3. Control Variables
16 4.3. Logit and Moderation Models
In order to address the research questions, this study adopts the binary (or ‘binomial’) logistic regression data analysis technique. According to Peng et al. (2002, p.4), “logistic regression is well suited
for describing and testing hypotheses about relationships between a categorical outcome variable and one or more categorical or continuous predictor variables.” A logistic regression essentially predicts the logit of Y from X. Thus, a logit is a natural logarithm (ln) of odds of Y, whereas odds are ratios of probabilities (π)
of Y happening or not happening (ibid.). The logit model used for this study is therefore:
ln(ODDS) = ln(π/1-π) =α + β1X1 + β2X2 (1) For this logistic regression, π is the predicted probability of engaging in total entrepreneurial activity (or ‘TEA’), α is the Y intercept, βs are the regression coefficients, and Xs are perceived opportunity and fear of failure (ibid.). The odds ratios are calculated as (e) exponentiated βs.
In order to assess the moderation effect of fear of failure on the relationship between perceived opportunity and entrepreneurial activity, a moderation analysis is performed. According to Wu and Zumbo (2008, p.379), “a moderator’s job is to explain the strength and direction of the causal effect of the focal
independent variable (e.g., treatment) on the dependent variable.” Statistically, a moderator variable represents
the effect of two variables working together, apart from their separate effects, on the dependent variable (ibid.). The moderation model used in studies with continuous-continuous/dummy variables is as follow:
Y = α + β1X1 + β2X2 + β3(X1*X2) (2)
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5. Analysis
This section outlines the descriptive characteristics of the data including the correlation analysis of the variables, and the regression outputs.
5.1. Descriptive Statistics
The SSA sample used for this study consisted of 6,091 (52.5%) men and 5,519 (47.5%) women. The age of the participants ranged between 18 and 91 with the overall average of 36 (M=35.99,
SD=14.22). When asked if in the next six months there will be good opportunities for starting a
business in the area where they live, 6,888 (59.3%) participants answered ‘yes’ and 4,722 (40.7%) answered ‘no’. When asked if fear of failure would prevent them from starting a business, 2,878 (24.8%) participants answered ‘yes’ and 8,732 (75.2%) answered ‘no’. When asked if they were involved in total early-stage entrepreneurial activity, 2,929 (25.2%) participants answered ‘yes’ and 8,681 (74.8%) answered ‘no’. Table 1 shows the rest of the descriptive statistics. These responses already point to the discrepancy between the number of people perceiving opportunities and those involved in TEA to be discussed in the next sections.
Table 1: Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
TEA 11,610 0 1 .25 .434 Opportunity 11,610 0 1 .59 .491 Fear of Failure 11,610 0 1 .25 .432 Gender 11,610 0 1 .52 .499 Age 11,610 18 91 35.99 14.226 Unemployment 11,610 0 1 .31 .461
Table 2: Pearson’s Correlation
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19 Table 3: The Main Effects of Opportunity Perception & Fear of Failure on TEA in SSA
Model 1 Model 2
Variable B SE Exp(B) B SE Exp(B)
Constant -1.750 *** 0.044 0.174 -2.814 *** 0.223 0.060 Main Effects Opportunity = 1 1.133 *** 0.050 3.105 0.741 *** 0.053 2.098 Fear of Failure = 1 -0.383 *** 0.054 0.682 -0.336 *** 0.058 0.715 Controls Male = 1 - - - 0.050 0.047 1.051 Unemployed = 1 - - - -0.616 *** 0.061 0.540 Age - - - 0.108 *** 0.012 1.114 Age^2 - - - -0.002 *** 0.000 0.998
Uganda=6 - - - Ref Ref Ref
Angola=1 - - - -0.084 0.083 0.920 Botswana=2 - - - 0.443 *** 0.086 1.557 South Africa=3 - - - -1.638 *** 0.091 0.194 Burkina Faso=4 - - - -0.177 * 0.072 0.838 Cameroon=5 - - - 0.400 *** 0.072 1.492 Observations 11,610 11,610 -2 Log likelihood 12,467.125*** 11,330.508*** Nagelkerke Pseudo-R2 0.080 0.211 Omnibus Chi2 648.2, df=2, p<001 1784.8, df=11, p<001
Notes: B = Beta coefficients; SE = Standard Errors; Exp (B)= Exponentiated Beta Coefficient
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Table 4.1: Country-Specific Main Effects of Opportunity Perception & Fear of Failure on TEA
Angola Botswana Burkina Faso
Variable B SE Exp(B) B SE Exp(B) B SE Exp(B)
Constant -3.865 *** 0.595 0.021 -4.850 *** 0.622 0.008 -2.185 *** 0.606 0.112 Main Effects Opportunity = 1 0.969 *** 0.162 2.634 0.665 *** 0.132 1.945 0.531 *** 0.117 1.701 Fear of Failure = 1 -0.035 0.130 0.965 -0.529 ** 0.178 0.589 -0.620 *** 0.134 0.538 Controls Male = 1 -0.018 0.129 0.983 0.153 0.127 1.165 0.265 * 0.115 1.304 Unemployed = 1 -0.170 0.145 0.844 -0.617 *** 0.134 0.539 -0.774 *** 0.222 0.461 Age 0.131 *** 0.032 1.140 0.246 *** 0.035 1.279 0.068 * 0.032 1.070 Age^2 -0.002 *** 0.000 0.998 -0.003 *** 0.000 0.997 -0.001 * 0.000 0.999 Observations 1287 1258 1857 -2 Log likelihood 1440.395*** 1467.847*** 2182.079*** Nagelkerke Pseudo-R2 0.076 0.136 0.069 Omnibus Chi2 69.8, df=6, p<001 128.9, df=6, p<001 93.2, df=6, p<001
Notes: B = Beta coefficients; SE = Standard Errors; Exp (B)= Exponentiated Beta Coefficient
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Table 4.2: Country-Specific Main Effects of Opportunity Perception & Fear of Failure on TEA
Cameroon South Africa Uganda
Variable B SE Exp(B) B SE Exp(B) B SE Exp(B)
Constant -3.830 *** 0.536 0.022 -4.698 *** 0.643 0.009 -0.682 0.466 0.506 Main Effects Opportunity = 1 0.875 *** 0.120 2.400 1.158 *** 0.155 3.184 0.400 ** 0.119 1.491 Fear of Failure = 1 -0.265 * 0.120 0.767 -0.588 ** 0.198 0.555 -0.170 0.145 0.844 Controls Male = 1 0.114 0.104 1.120 0.187 0.153 1.206 -0.256 ** 0.096 0.774 Unemployed = 1 -0.379 ** 0.113 0.685 -0.963 *** 0.183 0.382 -1.517 *** 0.248 0.219 Age 0.164 *** 0.030 1.179 0.106 ** 0.032 1.111 0.034 0.027 1.034 Age^2 -0.002 *** 0.000 0.998 -0.001 *** 0.000 0.999 -0.001 *** 0.000 0.999 Observations 1713 3448 2047 -2 Log likelihood 2164.162*** 1369.647*** 2535.431*** Nagelkerke Pseudo-R2 0.099 0.113 0.087 Omnibus Chi2 129.6, df=6, p<001 140.6, df=6, p<001 133.9, df=6, p<001
Notes: B = Beta coefficients; SE = Standard Errors; Exp (B)= Exponentiated Beta Coefficient
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Table 5: The Moderating Effects of Fear of Failure on Opportunity Perception & TEA in SSA
Model 1 Model 2
Variable B SE OR B SE OR
Constant -1.754 *** 0.048 0.173 -2.716 *** 0.658 0.066
Moderating Effects
Opport = 0 and Fearfail = 0 Ref Ref Ref Ref Ref Ref
Opport = 1 and Fearfail = 0 1.140 *** 0.056 3.126 0.727 *** 0.060 2.069
Opport = 0 and Fearfail = 1 -0.361 *** 0.103 0.697 -0.381 *** 0.107 0.683
Opport = 1 and Fearfail = 1 0.748 *** 0.074 2.113 0.409 *** 0.079 1.289
Controls
Male = 1 - - - 0.050 0.045 1.051
Unemployed = 1 - - - -0.617 *** 0.061 0.540
Age - - - 0.108 *** 0.012 1.114
Age^2 - - - -0.002 *** 0.000 0.998
Uganda = 6 - - - Ref Ref Ref
Angola = 1 - - - -0.087 0.083 0.917 Botswana = 2 - - - 0.443 *** 0.086 1.558 South Africa = 3 - - - -1.639 *** 0.091 0.194 Burkina Faso = 4 - - - -0.178 * 0.072 0.837 Cameroon = 5 - - - 0.400 *** 0.072 1.492 Observations 11610 11610 -2 Log likelihood 12467.060 11330.260 Nagelkerke Pseudo-R2 0.080 0.211 Omnibus Chi2 648.3, df=3, p<001 1785.1, df=12, p<001
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Table 6.1: Country-Specific Moderating Effects of Fear of Failure on Opportunity Perception & TEA
Angola Botswana Burkina Faso
Variable B SE OR B SE OR B SE OR
Constant -4.058 *** 0.606 0.017 -4.837 *** 0.623 0.008 -2.189 *** 0.606 0.112
Moderating Effects
Opport = 0 and Fearfail = 0 Ref Ref Ref Ref Ref Ref Ref Ref Ref
Opport = 1 and Fearfail = 0 1.213 *** 0.213 3.363 0.640 *** 0.145 1.897 0.540 *** 0.131 1.715
Opport = 0 and Fearfail = 1 0.465 0.292 0.111 -0.614 * 0.269 0.541 -0.591 * 0.246 0.554
Opport = 1 and Fearfail = 1 1.059 *** 0.215 2.883 0.179 0.251 1.196 -0.093 0.183 0.911
Controls Male = 1 -0.015 0.129 0.998 0.154 0.127 1.166 0.265 * 0.115 1.303 Unemployed = 1 -0.183 0.145 0.832 -0.616 *** 0.134 0.540 -0.773 *** 0.222 0.462 Age 0.132 *** 0.032 1.141 0.246 *** 0.035 1.279 0.068 * 0.032 1.070 Age^2 -0.002 *** 0.000 0.998 -0.003 *** 0.000 0.997 -0.001 ** 0.000 0.999 Observations 1287 1258 1857 -2 Log likelihood 1436.845*** 1467.666*** 2182.059*** Nagelkerke Pseudo-R2 0.080 0.136 0.069 Omnibus Chi2 73.3, df=7, p<001 127.1, df=7, p<001 93.3, df=7, p<001
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Table 6.2: Country-Specific Moderating Effects of Fear of Failure on Opportunity Perception & TEA
Cameroon South Africa Uganda
Variable B SE OR B SE OR B SE OR
Constant -3.779 *** 0.539 0.023 -4.701 *** 0.645 0.009 -0.708 0.467 0.493
Moderating Effects
Opport = 0 and Fearfail = 0 Ref Ref Ref Ref Ref Ref Ref Ref Ref
Opport = 1 and Fearfail = 0 0.814 *** 0.140 2.258 1.164 *** 0.170 3.202 0.442 ** 0.128 1.556
Opport = 0 and Fearfail = 1 -0.421 ^ 0.228 0.656 -0.567 ^ 0.322 0.567 0.088 0.309 1.092
Opport = 1 and Fearfail = 1 0.612 *** 0.175 1.844 0.563 * 0.264 1.756 0.203 0.192 1.225
Controls Male = 1 0.114 0.104 1.120 0.188 0.153 1.206 -0.254 ** 0.096 0.775 Unemployed = 1 -0.384 ** 0.113 0.681 -0.963 *** 0.183 0.382 -1.522 *** 0.249 0.218 Age 0.164 *** 0.030 1.178 0.106 ** 0.032 1.111 0.033 0.027 1.034 Age^2 -0.002 *** 0.000 0.998 -0.001 *** 0.000 0.999 -0.001 * 0.000 0.999 Observations 1713 3448 2047 -2 Log likelihood 2163.495*** 1369.641*** 2534.570*** Nagelkerke Pseudo-R2 0.099 0.113 0.087 Omnibus Chi2 130.2, df=7, p<001 140.6, df=7, p<001 134.8, df=7, p<001
25 5.2. Logistic Regression Results
Tables 3 and 5 show the results of the logistic regressions for the main and moderating effects respectively using the full sample from Angola, Botswana, Burkina Faso, Cameroon, South Africa, and Uganda (n = 11,610). To assess the goodness-of-fit of the variables, the -2 LOG Likelihood (or ‘-2LL’) test was used for models 1 and 2 in both cases. The -2LL measures whether variables are significant according to the Chi-square distribution and always yield negative numbers (Wooldridge, 2013). The closer to 0 the better the model fit (ibid.). Model 1 in table 3, for example, only included the main effects, and model 2 added the control variables. The results of the -2LL starting at 12,467.125 (p < .001) for model 1 and ending at 11,330.508 (p < .001) for model 2 indicated that as control variables were added to the model, the fit of the model improved. The improvement is evident in table 5 as well. The Nagelkerke Pseudo-R2 and Omnibus Chi2 were also used to assess the overall model fit. An increase in the Pseudo-R2 and Omnibus Chi2 statistics indicates an improvement over the previous model. Tables 3 and 5 show the increasing test statistics for the models. The improved model 2, containing all the necessary variables for both the main and moderating effects, was therefore adopted as the reference model to check for robustness using the country-specific sub-samples.
26
H2 of this study predicted that fear of failure would negatively relate to engaging in entrepreneurial activity in SSA. In order to test this hypothesis, the same approach applied for H1 was adopted. The result of the full sample, as presented in table 3, confirmed that fear of failure (B= -0.383, p < .001) negatively relates to engaging in entrepreneurial activity. In other words, the odds of fear of failure preventing individuals from engaging in TEA were 0.682 greater than the odds for individuals who did not fear failure. This result was robust when control variables were added to model 2. To further check for robustness, the logistic regressions from the country specific sub-samples, as presented in tables 4.1 and 4.2, provided some interesting outcomes. Individuals who fear failure in Botswana (B= 0.529, p < .01), Burkina Faso (B= 0.620, p < .001), Cameroon (B= -0.265, p < .05) and South Africa (B= -0.588, p < .05) are less likely to start businesses. However, results from Angola and Uganda were insignificant meaning that fear of failure does not influence engagement in TEA in those countries. These results partly confirm H2 but also point to the lack of influence of fear of failure on TEA in certain contexts to be explored further in the discussion section.
27
perception (=1) positively related to entrepreneurial activity in Angola (B= 1.059, p<.001), Cameroon (B= 0.612, p<.001), and South Africa (B= 0.563, p<.05). Results for this interaction were statistically insignificant in Botswana, Burkina Faso, and Uganda meaning that the combination of opportunity perception and fear of failure did not influence engagement in TEA in those countries. These results refute H3.
With regards to the control variables, gender was mainly insignificant suggesting that there was not much of a difference between men and women when it came to starting businesses in these SSA countries. The only cases where gender was significant were in Burkina Faso and Uganda. In the former, men were more likely to start businesses and in the latter men were less likely to start businesses than women. This finding is in line with Bardasi et al. (2009). On the other hand, unemployed individuals were mostly less likely to engage in TEA than employed individuals. The only context where this variable was statistically insignificant was only in Angola. Finally, apart from Uganda, age was mostly positive and significant suggesting that engagement in entrepreneurship increases with age in relatively younger years, but slows down in pace in latter parts of life. This finding is in line with Lévesque and Minniti (2006) who found that age has an inverted U-shape relationship with entrepreneurship. The next section discusses these findings in more details including the limitations of this study before concluding with some implications for scholars and practitioners.
6. Discussion and conclusion
The purpose of this study was to address the following research questions: what is the relationship
between opportunity recognition, fear of failure, and entrepreneurial activity in sub-Saharan Africa? And does fear of failure have a moderating effect on the relationship between opportunity recognition and entrepreneurial activity? The
reason for selecting SSA was twofold: (1) the region has hitherto received little attention in entrepreneurship research; and (2) the region is facing multiple pressing issues, including unemployment, for which entrepreneurship has been lauded as a solution by both practitioners and scholars alike (Sriram and Mersha, 2010; Hilson and Osei, 2014; Brixiová et al., 2015; Chinguta, 2017). Additionally, and to the best knowledge of the author, no prior studies in the field of entrepreneurship had focused on the moderating role of fear of failure in the entrepreneurial process within the context of SSA.
28
perceive opportunities, in the countries selected for this study, are more likely to engage in entrepreneurial activity than those who do not perceive opportunities. In other words, the likelihood of becoming an entrepreneur increases when individuals perceive opportunities. The SSA countries studied in this paper seem to however be supplying individuals with an abundance of good opportunities given most people are able to detect them. When asked whether there will be good opportunities to start a business in the area where they lived, almost 60% of the participants answered ‘yes’ (see table 1). From the country-specific samples, the percentage of respondents recognising good opportunities ranged from 78% (Uganda), 70% (Angola and Cameroon), 67% (Burkina Faso), 58% (Botswana), to 38% (South Africa).
In order to explain these mostly high opportunity perception rates, it is worth considering the current economic development phase of the countries selected for this study. As presented in section 2.2 of the literature review, Porter et al. (2002) argued that the economic development of countries follows three phases: factor-driven, efficiency-driven, and innovation-driven. GEM, adopting this typology of economic development, considered all countries used in this study as factor-driven economies with the exception of South Africa, which was considered as an efficiency-driven economy (Singer et al., 2015). Therefore, it can be argued that the abundance of opportunities in these SSA countries may be related to the fact that they are mostly still at the early stage of economic development. The nascent nature of the factor-driven phase may thus be providing opportunities to exploit market inefficiencies, to introduce new products or services, to improve production practices, and to close gaps in markets, among others. For this reason, it can be argued that entrepreneurial alertness (Ray and Cardozo, 1996), prior knowledge (Shane, 2000), and/or opportunity detection through purposeful search or accidental discovery (Kirzner, 1997) may not necessarily be as prominent at this economic development phase as in others. As a result, most people are able to perceive the readily available opportunities although, in line with prior research, the majority are likely concentrated in the informal sector (ILO, 2016; Hilson et al., 2018).
29
30
a business, which may make opportunities attractive enough to pursue, regardless of fear of failure, in the context of SSA where poverty and unemployment, among others, are pressing issues. The individual benefits most likely include financial gain (Reynolds et al., 2004), need for achievement (McClelland, 1961), desire for control (Minztberg, 1973), autonomy, and independence (Davidsson, 2004), among others. The culmination of the benefits of pursuing entrepreneurial opportunities may therefore be the reason people are able to make positive evaluations of entrepreneurship, which are in turn enabling them to overcome the stumbling block of fear of failure by starting businesses.
31
entrepreneurship but most importantly to reduce switching costs for employed individuals who may be interested in becoming self-employed. This recommendation aligns Sriram and Mersha (2006; 2010) and Gohmann (2012).
Although the GEM dataset has been instrumental in the completion of this study, there are some limitations related to the survey design and the use in this study. First, the use of single-item measures for all variables yielding dichotomous responses is a limitation of this study. Single-item measures have been widely asserted to lack construct validity because the questions indicate that the only outcome of questions are related to the wording of the questions (Fuchs and Diamantopoulos, 2009). The results of this study, for example, confirmed that fear of failure actually positively relates to entrepreneurial activity when moderated with opportunity perception. The wording of the question, however, assumes that fear of failure should only deter people from starting businesses. As previously mentioned however, data from GEM is widely used in academic researches and the variables used in this study have also successfully been used in prior studies (Langowitz and Minniti, 2007; Ho and Wong, 2007; Bergmann et al., 2014). Second, the static nature of the survey does not appreciate the dynamism of entrepreneurial intentions and activities. Third, the inconsistency of African countries participating in the APS survey limited this study from performing longitudinal studies of countries over time to measure changes in entrepreneurial intentions or activities. And fourth, this paper assumed that all entrepreneurial activity was opportunity-driven whereas the dependent variable did not specify. It would have been interesting to control for whether the type of entrepreneurial engagement was necessity or opportunity to determine whether one was more popular than the other and to seek applicable implications for opportunity entrepreneurship.
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