The Sampling Processes

Based on the available information provided in the supply chain, the processing palm and pineapple businesses, farmers, distributors and supporting institutions were the main source of the data for the study.

Processing businesses in the palm and pineapple study areas were the main sample frame from which survey data was extracted for the study. The reason for choosing the processing firms is that they are registered and details of their address and telephone numbers are therefore available. The pilot study shows that processing businesses apparently play a central role in the clustering and internationalisation process and may offer a better source of information in explaining the linkages within the supply chain of these business clusterings. The sample frame for the quantitative survey was 243, which comprises 225 and 18 exporting and non-exporting palm and pineapple processing enterprises, respectively.

In all, 99 processing businesses were surveyed. Eighty-two of these respondents were from the palm processing study area and the remaining 17 respondents were from the pineapple processing study area. Sample participants from the various groups of actors were interviewed to complement the information from the sampled surveys. In sum, 24 participants

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were interviewed from their respective sample frames. The determination of the sample sizes is justified for the surveys and interviews in the next sub-sections.

4.6.1 Sample size determination in the palm cluster

The research adapted Yamane’s 1967 formula to determine the representative sample size for processing businesses within the palm clusters. The researcher assumed a normal distribution in the responses of firms in relation to their operational activities in the cluster. Based on the formula, there are 225 processing firms in the palm cluster (N = 225). At an acceptable 95% level of confidence, we have a statistical z value of 2 (z = 2) and an error limit of 10%. Based on Yamane’s formula, the required sample for the palm cluster is determined as:

Where,

n = required response e2 = limit of error N = sample size

This means that the lowest acceptable response must be 69 at a 95% level of confidence with level of error at 10%.

The researcher, as part of the administered survey, had 82 responses from firms in the palm cluster, which is above Yamane’s required response. Therefore, to determine the confidence level and limit of error at the actual received response, Yamane’s formula had to be adopted.

-

=

= 0.007144

= 0.084

n1 = actual response received

e = error limit

N = population surveyed p = actual response as a percentage of

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The results indicate that a 34% response rate at a 95% confidence level has approximately an 8% error limit. Gaur and Gaur (2009) noted that in social science research a 95% confidence level with an error limit of 10% is acceptable.

The researcher ensured that the sample was fairly distributed among the various business categories in the palm cluster. This was achieved by the adoption of a stratified sampling technique which ensured that each group in the cluster was represented. Figure 4.3 below shows the sample distribution of firms in the palm cluster.

Figure 4.3: Sample distribution in the palm cluster

4.6.2 Sample size determination in the pineapple cluster

In the case of the processing firms in the pineapple cluster, since it has a relatively small population size, the researcher sampled all 18 firms. By choosing the entire population, the survey does not conform to the definition of probability sampling and there is no room for estimating sampling error and confidence interval, which resonates with scholarly work by Yen (2009). Similarly, the adoption of the entire population of 18 firms, made up of large, medium and small enterprises, ensured that the various categories of firms were equally represented in the survey. The distribution of the firms in the pineapple cluster is illustrated in Figure 4.4 below.

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Figure 4.4: Sample distribution in the pineapple cluster

4.6.3 Determining qualitative sample size

Unlike quantitative research that has an accepted sample size required for conducting research, qualitative research appears not to have a defined sample size designated for this purpose (Morse, 1995, 2000; Curtis et al, 2000; Hill et al, 2005; Guest et al, 2006; Koerber and McMichael, 2008; Trotter, 2012; Creswell, 2014). Several scholars have used different sample sizes in arriving at their findings based on certain factors. Sample size determination could be influenced by several factors. These include the nature of the research; that is, whether it is exploratory or explanatory research (Morse, 1995; Guest et al, 2006); the structure of the population, whether they are homogeneous or heterogeneous (Guest et al, 2006; Trotter, 2012); and the qualitative research technique adopted, whether it is semi- structured or unstructured interviews – all influence the qualitative researcher’s choice of sample size for any given study (Curtis et al, 2000; Hill et al, 2005; Creswell, 2014). According Morse (2000, p. 3), “The number of participants required in a study is one area in which it is clear that too many factors are involved and conditions of each study vary too greatly to produce tight recommendations’”

However, researchers agree that qualitative research does not require an infinite number of participants and that saturation in the data acquired is the signpost to determine the sample size needed. The point at which all data has been thoroughly optimised such that no new information emerges from participants may also depend on several factors. These factors

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have been summed up by Morse (2000) to include the nature of the topic, quality of data, study design and the use of shadowed data. As a result, several scholars have proposed different sample sizes for saturation (Morse, 2000; Hill et al, 2005; Guest et al, 2006).

Therefore, the researcher’s choice of 24 interviews (Table 4.1) and observations are based on the fact that there are some similarities in the study areas. There is commonality in language and data, which shows that there are similarities in the economic activities undertaken in the study areas. The pilot study revealed that there exist similar actors along the supply chain – farmers, processors, exporters and supporting institutions – in the study area. As a result, the researcher has purposefully selected participants, as shown in Table 4.1 below, along the value chain whose activities are similar. This is to strengthen the quality of information and reach data saturation for the relatively narrow objective of this study (Curtis, 2000; Yin, 2009; Trotter, 2012).

Table 4.1. List of interviewed participants in the two clusters

Number of interviews Palm cluster Pineapple cluster Total By Firm Size

Small-scale processing firms 2 1 3

Medium-scale processing firms 3 2 5

Large-scale processing firms 1 4 5

By Sub-sector Farmers 1 1 2 Artisans 1 - 1 Distributors 2 - 2 By Institution Public institutions/Agencies 3 2 4 Research institutions 1 - 2 Total 14 10 24