Data Collection Methods

Once the sampling frame and participating respondents have been identified, the next key task is to select a suitable data collection method (Aaker et al., 2004) (see Stage Two of Figure 4.1). As previously discussed, the positivistic and descriptive nature of the current study, and the need to test the hypotheses highlighted in Chapter Three, requires the use of primary data. Within the marketing domain, four common approaches – namely surveys, observation,

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interview, and experiments – can be identified (Burn & Bush, 2000; Groves et al. 2011). Given the popularity and extensive use of a surveys among researchers in the areas being studied (e.g., Gong et al., 2009; Gumusluoglu & Ilsev, 2009; Baumgarth & Schmidt, 2010; Lai et al., 2010; Ngo & O’Cass 2012; Troilo et al., 2014), a survey approach was adopted in this study.

The use of surveys as a vehicle to gather primary data can be undertaken using three approaches: computer-administered, person-administered, and self- administered (see also Neuman, 1997; Burns & Bush, 2006; Groves et al., 2011). For the computer-administered and self-administered approaches, the presence of the researcher(s) is not required; while the person-administered approach requires the presence of both the researcher(s) and respondent(s) (Groves et al., 2011). Each approach has its advantages and disadvantages. For example, the computer- administered approach allows the researcher to improve the speed of administration and reduce interview bias; yet it causes issues such as high set-up costs and confidentiality problems (Neuman, 1997; Moutinho & Chien, 2007). In a similar vein, the person-administered approach allows the researcher to achieve a high response rate and obtain rich feedback; yet it can be very costly and time consuming as well as increase the possibility of interviewer bias (Kaplan et al., 1997; Hair et al., 2003). Similarly, the self-administered approach allows the researcher to obtain large amounts of data without introducing interviewer bias and with minimal costs compared with computer-administered and person- administered approaches; yet it can affect the response rate, especially for long surveys (McBurney, 1994; Kaplan et al., 1997).

Having carefully considered the identified benefits and challenges of the three approaches outlined above, a self-administered approach was adopted. This

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approach was adopted because of its identified benefits, including (1) accommodating long surveys; (2) having the potential to gain large samples; (3) having the potential to reduce the interviewer bias, and (4) cost effectiveness. Further, this approach has also been extensively adopted in the literature relevant to this study (e.g., Gong et al., 2009; Gumusluoglu & Ilsev 2009; Baumgarth & Schmidt, 2010; Lai et al., 2010; Ngo & O’Cass, 2012; Troilo et al., 2014).

The self-administered approach can be undertaken in different ways, including drop-and-collect and mail, the commonly used methods in the literature (e.g., Baumgarth & Schmidt, 2010; Lai et al., 2010; Sok & O’Cass, 2011; Vorhies et al., 2011; Ngo & O’Cass, 2012; Troilo et al., 2014). Drop-and-collect approach, in particular, is argued to achieve a higher response rate compared to mail given the interpersonal interaction between the research assistants or researchers and the respondents during the distribution and collection of the survey (Ibeh et al., 2004; Sok & O’Cass, 2011; O’Cass & Sok, 2013). The mail survey, on the other hand, administers data collection through the use of postal systems which allows a wider reach at a relatively low cost (Ibeh et al., 2004; Malhotra, 2006) compared to drop-and-collect approach.

Given its research context, this study adopted the drop-and-collect approach instead of mail approach, for many reasons. First of all, this approach is deemed suitable for research conducted in developing countries (Ibeh et al., 2004) such as Cambodia (Sok & O’Cass, 2011; O’Cass & Sok, 2013) where this study was conducted. In addition, issues related to respondents’ lack of experience with mail surveys, and the unreliable nature of postal systems in developing countries such as Cambodia have been identified (Sok & O’Cass, 2011; O’Cass & Sok, 2013). Consequently, the drop-and-collect approach was deemed appropriate.

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Importantly, the drop-and-collect approach is also argued to be suitable in contexts where personal interaction is critical for information exchange (Hofstede, 1980) such as Cambodia (Sok & O’Cass, 2011; O’Cass & Sok, 2013). The drop-and-collect approach is also argued to yield a higher response rate than other impersonal delivery systems (Ibeh et al., 2004); where many indicate a 40% to 90% response rate can be achieved through this approach (Balabanis & Diamantopoulos, 2004).

As mentioned in Section 4.4.2.1.1, 390 firms agreed to participate. Given that the drop-and-collect approach is labour intensive, a number of research assistants were employed. The researcher and/or trained research assistant dropped the survey package containing Survey A, B, and C to the firm site. The senior executive who was responsible for responding to survey A forwarded survey B and C to the marketing and R&D manager within the firm. A telephone number was provided to the respondents so that the researcher and/or trained research assistant could come to collect the completed questionnaire.

Using the employee and customer lists obtained from the firms, the researcher also randomly selected the employees and customers. The same procedure used to seek participation from the senior executive was used to seek participation from the employees and customers. Upon receiving their consent, the researcher and/or trained research assistant made an appointment with them to drop off and then collect the completed surveys. In total, the researcher managed to gather 130 completed sets in which each set consists of one chief executive, one marketing manager, one R&D manager, seven employees, and five customers, resulting in a 33% response rate.

95 4.4.2.3 Development of Measures of Constructs

Once the data collection method and the sampling plan were determined, as shown in Figure 4.1 Stage Two, and discussed in Sections 4.4.2.1 and 4.4.2.2, the next key task is to develop sound measurements that capture the focal constructs of the study (e.g., Rogelberg et al., 2001; Maholtra, 2006). As presented in Figure 4.2, measurement development procedures involve two key stages – item generation and item refinement. Stage one – items generation – consists of three steps: (step 1) generating items from the literature; (step 2) selecting the scale poles and formation, and (step 3) producing a draft survey. Stage two – item refinement – consists of three specific steps: (step 4) conducting expert judgement of face validity that allows the researcher to refine and delete some items from the item pool; (step 5) conducting pre-tests that allow the researcher to make any adjustments needed to improve the readability and the content of the questionnaire, and (step 6) producing the final questionnaire.

4.4.2.3.1 Stage One - Item Generation Step 1: Generating Items

The procedure recommended by Churchill (1979), which involves either researcher expertise-based item generation and/or literature-based item generation, was adopted in this study as the means to generate the survey items. The discussion and review of the literature undertaken in Chapter Two, and the definitions undertaken in Chapter Three, provides a foundation to develop measures of the focal constructs of the study (market orientation, brand orientation, product innovation capability, brand equity, transformational leadership, customer value creation, and firm value appropriation).

96 Figure 4.2 Measurement Development Procedures

Source: Adapted from Churchill (1979) Step 1

Generating items

Step 2