events in public projects;
• Section 3 involves the use of a 5-point Likert scale (1-strongly disagree, 2-disagree, 3- neither agree nor disagree, 4-agree, 5-strongly agree) to determine the degree of agreement or disagreement of the respondents on statements relating to the supply chain vulnerability factors;
• Section 4 involves the use of the same 5-point Likert scale to determine the degree of agreement or disagreement of the respondents on statements relating to the supply chain capability factors;
• Section 5 involves a 5-point Likert scale on the relative level of importance (range from not very important, moderately important, to critical) of the main vulnerability and capability factors.
3.5.1.3 Method of Analysis for Questionnaire
The Statistical Package for the Social Science (SPSS Version 24) software was used to compute the data collected, conduct rigorous statistical data analysis and compare and analyse the relationships between the variables in the study. The results are presented using visual tools such as diagrams, frequency tables, bar charts and scatterplots computed by SPSS. The internal consistency and reliability of the survey measures were tested during the initial pilot study using Cronbach’s Alpha Coefficient. According to Cavan et al. (2001), well-developed scales will have a Cronbach’s Alpha value of 0.70 or greater; above this value indicates a good correlation between the item and the true scores (Churchill, 1979).
For the overall questionnaire results, the analysis was divided into five main sections corresponding to the sections in the questionnaire. Data from Sections 1 and 2 was subjected to descriptive statistics. According to Trochim (2005), the simplest distribution involves the list of every value of a variable and the number or percentage of the persons who selected each value. In this case, the respondents’ background (Section 1) is presented as frequency and percentage in a table, and past experience of disruptive events (Section 2) as percentages through visual tools such as bar graphs and pie charts.
measurement of mean for central tendency and standard deviations for variability, computed through SPSS. The use of mode was also taken into consideration in assessing the likert scale of the vulnerability and capability factors. An example of analysis of the mean and mode of the main vulnerability factors of the public organisations are presented in Table 3.5 below to compare the results between the two analysis. Table 3.5 shows that both analysis of the mean and mode yield quite similar results. However, in this case, as the composite scores of the sub-factors are considered in ranking the main vulnerability and capability factors, the mean score were ultimately used in the final analysis (see analysis in Section 4.4).
Table 3.5: Example of the results of mean and mode of the public organisations’ vulnerability factors Rank Main Vulnerability Factors Factor
Label Mean Mode
1 Political/Legal Pressures V6 3.66 4.00 2 Management Vulnerability V2 3.58 4.00 3 Liquidity/Credit Vulnerability V10 3.40 4.00 4 Strategic Vulnerability V1 3.36 3.25 5 Market Pressures V9 3.24 3.25 6 Process Vulnerability V4 2.98 3.00 7 Environmental Factors V7 2.96 3.00 8 Supplier/Customer Disruptions V5 2.87 3.00 9 Personnel Vulnerability V3 2.81 3.00
10 Physical Damage Disruptions V8 2.62 2.40
Overall, the mean allows the researcher to compute the average score of the vulnerability and capability factors of the respondents. The computed standard deviation establishes the dispersion of the results through the assessment of the common trends running through the responses. Factors with a highly dispersed distribution of data will obtain a higher standard deviation than those with low distribution. This measurement of central tendency was used by previous researchers, such as Pettit (2008) and Stephenson (2010), in assessing the vulnerability and capability factors of respondents. Furthermore, scatterplots were used to assess the critical vulnerability and capability of the respondents by comparing their current vulnerability and capability scores (from Sections 3 and 4 of the questionnaire respectively) against the rated importance of the variables in Section 5 of the questionnaire. The scatterplots (see Section 4.5) present a two-dimensional coordinate graph, showing the relationship between the two abovementioned quantitative variables, with each observation in a data set plotted as a point in the graph (Lewis-Beck et al., 2003).
Additional non-parametric data analysis such as the Mann-Whitney U and Kruskal-Wallis tests were conducted to make judgments of the probability of observed difference between two or more groups of respondents being dependable or having happened by chance (Field, 2009). The Mann-Whitney U test is suitable here instead of the t-test, due to the expected skewed distribution in the data obtained from the Likert-scales construct measuring vulnerability and capability. For instance, when the majority of respondents select the positive anchor of ‘agree’ or ‘strongly agree’ in the construct, the distribution is expected to be negatively skewed; conversely, when the majority select the negative anchor of ‘disagree’ or ‘strongly disagree’, the distribution is expected to be positively skewed. Pallant (2013) agreed that many scales and measures used in the social sciences are not normally distributed and have scores that are skewed, either positively or negatively. It is worth noting here, however, that this does not necessarily indicate a problem with the scale, but rather reflects the underlying nature of the construct being measured. In this case, instead of violating the assumption of a normally distributed data in a parametric analysis, the used of non-parametric analysis, such as the Mann-Whitney U test, is preferred (Field, 2009; Pallant, 2013).
Correlational analysis using Spearman rho was also conducted to identify significant relationships among the vulnerability and capability factors. Correlation is a relationship measure among different factors or parties indicating the level of strength and direction of the relationship (Assaf and Al-Hejji, 2006). The Spearman rho correlation was also used to test the level of agreement or disagreement among the different groups of respondents (public organisations, consultants and contractors) on these factors. Spearman’s correlation results range between the value of 1 and −1, whereby values closer to 1 indicate a perfect positive relationship (or high degree of agreement) and -1 implies a perfect negative relationship (or disagreement) (Assaf and Al-Hejji, 2006).
3.5.2 Semi-structured Interviews
While questionnaires can provide evidence of patterns amongst large populations, qualitative interview data often produces more in-depth insights into participants’ attitudes, thoughts and actions (Kendall, 2008). Semi-structured interviews are conducted after the questionnaire