Sampling

According to Churchill and Iacobucci (2002) there are six major steps for drawing a sample:

1. Definition of the target population 2. Identification of the sample frame 3. Selection of the sample procedure 4. Determination of the sample size 5. Selection of the sample elements

5.4.1 Target Population

Firstly, it is necessary to describe which elements the target population consists of, for example, individuals, households, business companies etc. The results of the current research are going to be generalised to UK export manufacturing firms who represent the target population for the study. Based on a report by the British Chambers of Commerce titled ‘Exporting Britain’ and published in June 2009, the following information is known about the target population:

• The British Chambers of Commerce have 100,000 registered businesses that together employ more than 5 million employees. ‘31% of Chamber members have exported goods or services from the UK in the last 12 months; 14% of those not currently exporting have previously done so and 11% are considering doing so in the future’ (‘Exporting Britain’, 2009 p.4). Over half of exporting comes from the manufacturing sector – 51%.

• ‘87% of Chamber members, who are currently exporters, are exporting to Europe. Asia is served by 56% of businesses; 39% export to the Middle and Near East, 23% to China, 21% to India, and 20% to South East Asia; 49% are exporting to the Americas; 40% to the USA, 22% to Canada, and 16% to South America’ (‘Exporting Britain’, 2009 p. 4).

• Among companies exporting goods or services from the UK, 81% indicated that they have less than 50 employees; 12% have between 50 and 249 and 6% have 250 employees and more (see Table 5.1 below):

Table 5.1: Number of Employees

Exported goods or services from UK in the last 12 months (according to ‘Exporting Britain’ 2009)

% 0 – 4 employees 34 5 – 49 employees 47 50 – 249 employees 12 250 + employees 6 5.4.2 Sample Frame

The second step in a sample selection is to identify the sample frame. A sample frame is a list of the elements from which the actual sample is drawn (Malhotra and Birks, 2003). Unfortunately, ‘there is rarely a perfect correspondence between the

sampling frame and the target population of interest’ (Churchill and

Iacobucci, 2002 p. 451). There are different lists which can be used as a sample frame, including telephone books, lists of registered voters, databases etc. If lists are not readily available, they can be created by the researchers themselves (c.f.

Cowan, 1991).

Despite the fact that for the exploratory research Kompass database was used, the decision was made to use a different database for the quantitative study, as the information in Kompass database was severely outdated (information on export activity of the company, telephone number, name of the manager, email address). For the current study the ‘Database of British Exporters’ was used. The database was first complied in 1989 by the company ‘UK Exporters Ltd’ and since then it is updated on a daily basis (according to the information on the company website: http://www.exportuk.co.uk/index.php). Initially the database consisted of 8000 companies. However, the same database was used by another study undertaken at Loughborough University. As a result, only half of the database (4000 companies) was available as a sample frame for the study. The split was made on a random sampling basis to eliminate bias.

5.4.3 Sample Procedure

The third step is related to the selection of the sample procedure. There are two broad sampling procedures identified: drawing on probability samples or non- probability samples (Kinnear and Taylor, 1991).

Non-probability samples are drawn based on the personal judgement of the

researcher, the probability of selecting any particular element is not known and as a result the findings cannot be generalised to the target population (c.f. Yeager et al., 2011).

There are four main types of non-probability sample techniques: convenience sampling, judgemental sampling, quota sampling and snowball sampling (Churchill and Iacobucci, 2002). Convenience sample is based on the elements convenient for the researcher to obtain the information from. Judgemental sample is a special type

researcher. Quota sample is a two-stage non-probability sample technique. ‘Sample is chosen in such a way that the proportion of sample elements possessing certain characteristics is approximately the same as the proportion of the elements with the characteristics in the population’ (Churchill, 2001 p. 455). Thus, initially, the control quotas of the population elements are developed (e.g. age groups). Secondly, the sample is drawn based on convenience or judgement. In snowball sampling, firstly, a small group of respondents is chosen to obtain the information from. After they have participated in the study, they are asked to recommend other potential participants of their knowledge who belong to the target population (Malhotra and Birks, 2003). ‘Probability samples are distinguished by the fact that each population element has a known, nonzero chance of being included in the sample’ (Churchill and

Iacobucci 2002, p.453). The probabilities of the selection do not have to be equal, but have to be known. That allows the probability sample to be representative of the population. Representative sample is the one which accurately reflects the target population. The representativeness of the sample enables the findings of the research to be generalised to the target population (Aaker and Day, 1990). There are four basic probability sampling techniques: simple random sampling, systematic sampling, stratified sampling and cluster sampling (Cohran, 1977). Simple random sampling is based on random and independent criterion of the sample elements selection. Each element has a known and equal probability to be selected. In systematic sampling, initially, a random starting point is chosen and then each ith element is chosen from the sample frame. ‘The sampling interval i, is

determined by dividing the population size N by the sample size n and rounding to the nearest whole number’ (Malhotra and Birks, 2003 p.368). Similarly to simple random sampling, in systematic sampling each element has a known and equal probability to be selected. In stratified sampling, first, strata or sub-groups of the population are identified from the target population, and then the elements are randomly selected from each stratum. In cluster sampling the target population is ‘divided into mutually exclusive and collectively exhaustive sub-populations’

(Malhotra and Birks, 2003 p.370). Then a random sample is drawn from the clusters available. Either all elements from the clusters selected are included in the final

sample or the elements are chosen from the clusters based on one of the probability sample techniques.

Probability sampling was used for the current study, allowing for greater

representativeness of the sample, and therefore better grounds for the generalisation of the results to the population of UK manufacturing firms involved in exporting. More specifically, systematic probability sampling was employed, ‘24’ was randomly

chosen as a starting point and then every second element was selected from the database to form the sample.

5.4.4 Sample Size

The results of the pre-test indicated the anticipated response rate for the main survey is about 10-12% (see section 5.7 for more details). In order to test for

reliability and validity of the measures, it is recommended to obtain information from between 100 to 200 respondents (Spector, 1992). Taking into account the initial response rate of 10-12%, in order to obtain 100-200 responses a sample of 1000- 2000 elements should be drawn (see section 5.8.2 for more details).

5.4.5 Selection of Sample Elements

The data was collected from a single respondent. The use of a single informant was considered to be appropriate for the current study. First, the use of a single informant is appropriate if a respondent is knowledgeable about the subject (Wilson and

Lilien, 1992). Secondly, the results of the exploratory study highlighted that often only one person in the export function is a key decision-maker for the export matters. Furthermore, sometimes the whole export function could consist of one person (especially in SMEs). According to Sousa, Martinez-Lopez and Coelho (2008 p.349) ‘[G]enerating information from multiple informants on export marketing issues may lead to the generation of data from individuals who are not very knowledgeable about the firm’s export operations, and thereby decrease the accuracy of the information provided’. As a result, use of multiple respondents can create biases, if the respondents are not knowledgeable about the export decisions. The exact job title was not specified as different types of managers can be in charge of export duties. The most important criterion was the job role of the respondent: he or she