Questionnaire design

Data collection is a very important part of any research project. Without the collection of the data, there is no way that the research questions could be fully answered (Aldridge and Levine, 2001). The questionnaire design is an important element in the success of data collection. This section discusses the designing of the questionnaire used in this study.

Past experiences have shown that it is very unusual to go through a research project process without having some type of data collection error (Polonsky and Waller, 2004). Therefore, attempts should be made to minimise the total error from any source that can affect the research findings when planning the research.

Brace (2008) claims that there are two generally recognised types of errors in all surveys: Sampling error and Non-sampling errors.

A sample is a relatively small subset of the population that is selected to be representative of the population‟s characteristics. The larger the sample, the more precisely it reflects the target group. Sampling errors result from the random variation in the selection of respondents. By increasing the size of the sample, the effects of sampling errors can be reduced. However, this method is often limited by factors such as: time available, budget and human resource available. Because the rate of improvement in the precision decreases as the sample size increases.

In this study, we are interested in investigating the logistics capabilities of the regions in Britain. Therefore, the sampling population need to be with knowledge of the GB regional economy and the logistics industry. The sample chosen in the study has to be representative of all the regions, and stands for all the stakeholders in the logistics industry. Hence, a list of 40 UK academics, industrial practitioners, and people working in the UK regional development agencies and government offices are selected as the “decision makers” in this

determining the RLC.

The following Table 5-1 lists the basic information of the informants participated in this research, including their names, working fields and based regions.

Experts Field Region Experts Field Region Exp 01 Industrial Yks Exp 21 Industrial London

Table 5-1. Expert list for eliciting weights.

The following Table 5-2 and Table 5-3 list the breakdown of experts contributed to the RLC weighting data of this study by their field and the region they are based in. A good balance among experts in the academia, industry and government agencies is shown in Table 5-2. Since the pilot study stage is carried out in the Yorkshire and Humber region, a higher percentage of

respondents are from this region. The participants were asked to give responses based on all the regions in the UK. Therefore, it is possible the results are slightly biased to the “Yorkshire and Humber opinion”. However, this bias is not considered to be significant to the overall RLC weighting since the percentage is not overwhelming (27%) comparing with the other regions and 7%

of the experts do not have a base region, therefore are representing all the regions.

Field No. of participants %

Academic 9 23%

CILT 5 13%

Consultant 7 18%

Government 5 13%

Industrial 12 30%

Port 1 3%

UKTI 1 3%

Table 5-2. Expert list by field

Region No. of participants %

EA 2 5%

EM 4 10%

London 3 7%

NE 4 10%

NW 3 7%

Scot 5 12%

SE 2 5%

SW 1 2%

UK 3 7%

Wal 2 5%

WM 1 2%

Yks 11 27%

Non-sampling errors arise from mistakes made in areas such as the coding and data entry processes of the survey. Such mistakes can be fatal to the success of the survey. Therefore, it is crucial to properly design the questionnaire questions and analysis methods to collect the right information to address the objectives of the study and to minimise non-sampling errors. To do so, the following four points were followed in designing the questionnaire.

Firstly, the questions need to be closely related to the research objectives. The questionnaires used with all the informants are designed with the standard including brief introduction of the research objectives upfront. Also the questionnaire lists clear definitions of the terminologies used to avoid misunderstanding.

Secondly, the layout of the questions and format of the information required need to be easily understood by the informants and compatible with the data analysis method. In this study, the direct rating method (MAX100) is adopted, where logistics experts are asked to give their weights in two steps: First rank the dimensional and sub-dimensional indicators from most important to least.

Then assign 100 points to the most important indicator, before score the remaining indicators from 100 downwards relative to the 100 starting point benchmark. As discussed earlier, this direct rating method is more reliable and preferable by the subjects than the point allocation method.

Thirdly, interviewer-administrated survey is adopt to collect accurate data in this study, where the completing of the questionnaire is guided by the interviewer

over telephone or face to face interview. In this way, queries about the meaning of a question can be dealt with immediately without jeopardising the quality of the data collected under misunderstanding (Bryman and Bell, 2003). In addition, the respondents can be encouraged to provide deeper responses and further comments beyond the design of the questionnaire, which is especially important in the questionnaire piloting stage.

Fourth, the questionnaire design was first tested within the Yorkshire and Humber region. As a pilot study, twelve experts were interviewed and asked to give weighting estimates of the five criteria that determine RLC, and give insights of any other factors that influence the RLC in GB. The data collected in this pilot study were presented on the LRN2009 conference to illustrate the methodology employed to elicit feedback and verification of its applicability (Song et al., 2009). As a result, the questionnaire and the research method design were verified to be applicable with some minor changes to the indicator list and definition phrasing.