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The following sub-sections will discuss the target population, sampling procedures and intended respondents of this study.

4.6.1 Population Definition

Sampling is the most important procedure of a research activity, as it determines the population to be targeted. The population chosen for this study are those organisations that meet the following criteria:

i. Registered as a manufacturing firm by the Federation of Malaysian Manufacturers (FMM);

ii. For the SMEs, the definition as a small or medium-sized enterprise is according to the definition approved by the National SMEs Development Council (NSDC), Malaysia (see Table 2.2 in section 2.6.1)

Meanwhile, another population chosen for this study are those MNCs located at any six identified MSC Zones for the electrical and electronics industries in Malaysia. These industries were selected to represent the manufacturing industry in the current study, due to their contributions to the Malaysian development and economic growth (NSDC, 2010).

4.6.2 Sampling Design

This section further clarifies the determination of the sampling frame, sampling method and sample size used in the study.

A. Sampling Frame

Sampling frames can be defined as “a (physical) representative of all the elements in the population from which the sample is drawn” (Sekaran & Bougie, 2010, p. 267); for

example, a company database, random-digit dialling or a membership roster (Hair et al., 2009). The sampling frame for this study was the Federation of Malaysian Manufacturers (FMM) directory published in 2010. The directory was chosen as it updates its information in every publication year, and provides the most accurate data about manufacturing companies in Malaysia. The 2010 publication version included a list of 2,225 manufacturing firms of varying sizes, including micro, small, medium and large organisations. In addition, the directory provides detailed information on the manufacturing organisations in Malaysia, inclusive of name, company specialization, postal address, website, contact persons with the respective email addresses and number of employees. The SMEs were chosen for the sample on the basis of number of employees; 1,402 companies were considered to be SMEs.

B. Sampling Methods and Sample Size

Identifying and categorizing SMEs from the FMM directory required a great deal of time. The researcher required to identify and select appropriate firms through manual searching from the overall listed firms. One-by-one selection was done based on the

The selected SMEs have employees between 5 and 150 while MNCs have more than 150 employees.

This study employed the unrestricted probability sampling design, known as simple random sampling method to determine the sample to be studied (Sekaran & Bougie, 2010). Simple random sampling was chosen because it reduces bias by giving equal and independent chance to every member of the population (Kumar, 2005; Lohr, 2009). This method offers the most generalisability for the findings (Sekaran & Bougie, 2010). For this study, 300 SMEs and 300 MNCs were selected to receive the questionnaires.

SEM is based on covariance, and covariance and correlations are unstable when evaluated from small sample size (Tabachnick & Fidell, 2007). There are no clear cut rules or definitive recommendations when it comes to the required sample size to obtain reliable solutions and parameter estimates in SEM. However, while utilising large sample sizes with latent variables to estimate in structural equation models will lead to a degree of confidence about such statistics, the asymptotic statistical theory underlying parameter estimations provides clues as to how large the sample size should be (Holmes-Smith, 2000). The minimum requirements for SEM are presented in Table 4-4.

Table 4-4: Sample Size for Structural Equation Modelling Statistical Analysis Minimum Sample Size

Structural Equation Modelling (SEM)

 Sample size as small as 50 found to provide valid results

 Recommended minimum sample sizes of 100 – 150 to ensure the stable Maximum Likelihood Estimation (MLE) solution

 Suggested sample sizes in a range of 150 - 400 Source: Hair et al., (2003)

Since this study will employ SEM as the main analytical method, it is important to take into account that the Maximum Likelihood Estimation (MLE) method in SEM requires a sufficient sample size. To obtain reliable results, it has been recommended that the sample should include at least 100 observations, and that the sample size should at least be 5 to 20 times the number of parameters being estimated (Hair et al., 1998). McQuitty (2004) suggested that it is important to determine the minimum sample size required in order to achieve a desired level of statistical power with a given model prior to data collection. Schreiber et al. (2006) mentioned that although sample size needed is affected by the normality of the data and estimation method that researchers use, the generally accepted value is 10 participants for every free parameter estimated.

Although there is little consensus on the recommended sample size for SEM, Yuksel et al. (2010), Hoe (2008), Sivo et al,(2006), and Garver and Mentzer (1999) proposed a ‘critical sample size’ of 200. In other words, as a rule of thumb, any number above 200

the number of free parameters should be at least 5:1 to get reliable parameter estimates. Sample size can affect chi-square statistic and measures of goodness-of-fit (Bearden et al., 1982; Yadama & Pandey, 1995). Small sample sizes create problems for maximum likelihood-based estimation procedures like AMOS, and consequently unstable results may occur (Fornell & Larcker, 1981; Gerbing & Anderson, 1988).

4.6.3 Unit of Analysis

The respondent is “the person who answers an interview’s questions or provides

answers to written questions in a self-administered survey”(Zikmund, 2003, p. 175). This study focuses on analysis at the organisational level, implemented through the involvement of production, supply chain, operations, and procurement managers of electrical and electronics MNCs and SMEs in Malaysia. Managers of operations, procurement, production and supply chain management were pre-identified to be the target population for this research. They play a significant role in the decision-making process in their organisation. This approach is intended to validate the applicability of the conceptual model in a working situation.

The rationale for taking this approach relates to the fact that the supply chain relationship model as the main component of the research model was developed for a workplace context where substantial knowledge is applicable on the research topic. The study explored the antecedents of supply chain relationship in agile environment in the context of the electrical and electronics industry in Malaysia. This requires respondents with experience in supply chain management, particularly production, supply chain,

operations and procurement and have significant role in the decision making process in the organization.