Control Variables

Even though FNGOs provide MC and ET to MSEs, the impact of these activities on their performance may be influenced by MSE characteristics and the way in which

138

these inputs are used. There are several factors which influence the performance of MSEs. One of the significant research in this direction has been conducted by Cooper et. al (1994) where it was concluded that factors such as ‘education, gender, management know-how, specific industry knowledge, access to the market as well as industry category are critical to the growth of MSEs. Drawing on the concept of competitiveness and the competency approach, Man et al. (2002) indicates that, internal firm factors, the external environment, and the influence of the entrepreneur are the three main factors which affect the performance of an MSE. Building on the role of the entrepreneur in the success of the MSE, Miller (2014) argues that, individual characteristics such as the willingness to take a risk with personal resources, the drive to achieve autonomy, power and independence, plays a major role in contributing to the performance of MSEs. According to Park and Bae (2004), the growth patterns of a successful MSE vary according to three main factors namely; the initial conditions facing the firm at the time of the founding, the entrepreneur's characteristics and management abilities, and finally the strategy to develop and accumulate technological capability. The researchers again emphasised that technological capability of a firm determines to a larger extent its growth and performance.

In terms of ownership structure, Wu et al. (2007) indicate that MSEs which are family owned and are sole proprietorships have access to equity financing from personal resources than their counterparts with shareholding structures. This is because sole proprietorships would want to avoid agency conflict and excessive control by public shareholders. Morse so, owner-managers of MSEs can drive MSEs to higher performance than non-owner managers. This happens because; founding family

139

leadership (CEO or Chair) can regulate the relationship between ownership structure and firm performance as well as reducing agency cost (Randøy & Goel, 2003).

Therefore, based on the evidence in the literature and the conceptual model discussed in chapter 5, the following are used as control variables for this study:

i. Age of business ii. Industry category

iii. Manager’s level of education iv. Gender

These variables were selected because, it has been observed that gender, industry category, the level of education of the MSE manager as well as the age of the MSEs could influence how both MC and ET are used to achieve performance goals (Cooper et. al,1994). These variables are further discussed below.

Age of Business

The age of an MSE is noted to influence its performance. Both current and strategic needs of an MSE depend on its age (Cooper et al.,1994). It has also been argued that both start-up and old firms face different categories of challenges which need to be addressed (Scott & Bruce, 1987; Anderson & Eshima, 2013). Therefore, in extending MC and ET to MSEs, FNGOs need to understand their peculiar needs based on the age of the MSE. For instance, the training needs of new MSEs might differ in terms of content and delivery from those MSEs which are old (De Mel et al., 2014). Business age was measured on a scale of 4 indicating the number of years the business had existed (2-5, 6-10, 11-15, and more than 15 years).

140

Industry Category

The industry in which an MSE find itself determines its resource needs regulation and growth (Cooper et al., 1994). For instance, MSEs in the service industry are noted to use a fewer staff than those in the production-based category (Man, Lau, & Chan, 2002). This is the case because, in developing countries, MSEs scarcely use technology in their operations hence the high dependence on a lot of employees to undertake business activities. It has also been noted that, the industry in which an MSE finds itself can also affect its profitability (Parker & Praag, 2012).

Industry category was measured by asking respondents to classify their businesses according to eight (8) categories namely agriculture (1), manufacturing (2) construction (3), general services (4) general trading(5), hotels and restaurants(6), education(7) transport and distribution (8). General services represent business activities such as barber shops, hair salons, shoe repairs, communication services and such likes. General trading represents the sale of items such as foodstuffs, water, and firewood, construction category represents manufacturing of building blocks, the sale of cement and sale of other building materials. Transport and distribution category represents taxi owners and commercial drivers. Hotels and restaurant category represent guest houses and, food services. The education category represents private basic schools only.

Manager’s Level of Education

Both general and specific educational background of an MSE manager could have an influence on the performance of the MSE (Cooper et al.,1994). Managers who have broad educational background with specific skills and knowledge such as in accounting, numeracy and marketing are known to have a better impact on their MSEs than those without (Newman et al, 2014). Miller (2014) argues that the

141

entrepreneur’s characteristics which include his educational background have an important ramification on the future and success of the MSE. Therefore, the educational background of the MSE manager could influence MSEs’ overall performance. Manager’s level of education was measured using six categories of educational levels namely no formal education (1), primary school (2), secondary education(3), an undergraduate degree (4) and postgraduate degree(5).

Gender

There is some agreement in the microfinance literature that, MC works better with women than men (Chowdhury & Chowdhury, 2011). This is because, in developing countries, women are seen to be more responsible for delivering welfare activities to the household than men (Guérin, Kumar, & Agier, 2013). Secondly, women are also noted to be more creditworthy in terms of paying back their loans than men (D'Espallier, Guérin, & Mersland, 2011). Therefore, in the delivery of MC and ET to MSEs by FNGOs, gender could influence the impact of such resources. Gender was πmeasured as a dichotomous variable where 0 represents male and 1 represents female. Based on the constructs and variables discussed in chapter 5 and the various constructs defined above, a hypothesised conceptual model as shown in Figure 6.3 is developed to underpin this study.

142

Figure 6.3: The Hypothesised Model for the impact of FNGO services on the Performance of MSEs in the Volta Region of Ghana

(3) Control Variables • Age of business • Industry Category • Managers level of education • Gender (4) Performance of MSEs (P) • Employment Growth • Sales Growth • Profitability Growth (1) Microcredit Construct • Loan Cost • Flexibility of Loan Repayment Method • Loan Amount • Loan Accessibility (2) ET Construct • Training Content • Training Efficiency • Training Frequency • Training Accessibility H1 H2

143