Prior to the questionnaire distribution, prospective respondents were informed that, for them to be eligible to participate in the study, they were supposed to meet the requirements set out below:
The organisation must be an SME employing not more than 75 people with a fixed asset base not exceeding US$500 000 (to adhere to SEDCO‟s definition of SMEs as stipulated in 2014).
The organisation must be exporting goods, be based in Harare and exporting processed food, textiles, and leather products; must have been exporting for at least two years, excluding experience from domestic market; should be indigenous to Zimbabwe and not a subsidiary of a large company or partly owned by a foreign company, so as to reduce the influence of strategy orientation from these large organisations or access to resources and export decision-making.
4.5.1. Sample and Sampling 4.5.1.1. The research sample
Although according to Makanyeza and Ndlovu (2016:28), “there are approximately 1 500 registered SMEs in Harare which are exporters,” however, this study was only interested in sampling SMEs in the leather, food processing and textile sector only. Gibbs, Shafer and Dufur (2015) note that research cannot be carried out by interviewing all the elements of the population; therefore a sample should be established. Creswell (2013) asserts that a sample is a proportion from a total number of an element in a population. Sampling is very important as
“the choice of the sample influences the research results” (Ibrahim, 2014:85). A sampling frame was obtained from SEDCO, which is a list of all the population elements from which a sample is drawn (Cooper & Schindler 2011). In this study, respondents were stratified based on their products, with group A consisting of leather processing, group B consisting of food
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processing, and group C consisting of textiles. The sampling frame was the telephone directory of owner-managers, from which participants were randomly selected, until the desired sample size was reached, namely 44 from group, 132 from group B 132 and 169 from group C, resulting in a total sample size of 345 SME owner-managers. On the other hand, four policy-makers and four export business analysts were selected as guided by Saruchera (2014:80), who highlights that “when the population is low, the researcher will collect data from the entire population as the influence of a single extreme case on subsequent statistical analyses is more pronounced than for larger samples.” The total sample size for the study is reflected in Table 4.1, which conforms to Wilson‟s (2010:202) recommendation that “the sample size should consist of at least 30 cases so as to make inferences to the wider population.”
Table 4.1: Sample size for SMEs, Policymakers and Exports Analysts
Population groups Sampling
Frame
Sample Size
GROUP A: Leather processing 50 44 GROUP B: Food processing 200 132
GROUP C: Textiles 300 169
TOTAL 550 345
Source: researcher‟s compilation
This sample size determination reflected in Table 4.1 is also supported by Krejice and Morgan‟s (1970) model (Table 4.2), since the aforementioned argues that when the population size is 10, the sample size will be 10 and when the population size is 20 the sample size will be 19, and so on.
71 Table 4.2: Sample size determination
Source: Krejice and Morgan (1970)
When the above (Table 4.2) model was used, the same population of 550 produced a sample size of 345, which was also similar to the method used to determine sample size for the study.
Having determined the sample size, there was need to ascertain the sampling technique and this is explained in the section below.
4.5.1.2. Sampling techniques and procedures
According to Creswell (2013:12), “a sampling method refers to the way the sample elements are to be selected.” Given the mixed methodology used in this study, probability and non- probability sampling techniques were used to select the respondents. Probability sampling methods include, stratified sampling, systematic sampling, cluster sampling and simple random sampling and non-probability sampling methods are convenience sampling, quota sampling, snow-ball sampling and purposive sampling (Bryman & Bell, 2015). For the purpose of this research, stratified and purposive sampling were selected due to the nature of the respondents. The respondents were stratified based on their production processes, with group A consisting of respondents in leather processing, group B food processing and group C textiles. Having stratified the firms into groups, respondents were then randomly telephoned to avoid bias while following the chosen criteria highlighted in Section 4.4 above, until the desired sample sizes were reached. This definition of SEDCO (2014) helped to place SMEs in their respective appropriate strata using stratified sampling method, again since the
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study targeted SMEs in the leather, food processing and textile industries. Placing these respondents into various strata enabled inferences to be made on each stratum. A sample size also supported by Krejcie and Morgan‟s (1970) formula was stratified as follows: leather 44, food processing 132, textiles 169, while on the other hand four policy-makers and four export business analysts were purposively sampled and interviewed, given the fact that they involved a small number of informative respondents that gave insightful information to meet the needs of the research (Ibrahim, 2014). It is argued that “the appropriateness of a sample design is largely influenced by the degree of accuracy, the availability of resources, time and the advanced knowledge of the population under consideration” (Creswell, 2013:14).
Furthermore, probability sampling techniques also enabled to statistically estimate the population characteristics from the sample, while non-probability sampling technique enabled the study to make some statistical inferences of the characteristics of the population (Saunders, Lewis & Thornhill, 2009).