Building on the survey results reported in Sections 4.2 and 4.3, this section is dedicated to identifying the possible existence of association and interrelationship between the LSPs and LSUs groups, and linkage between certain logistics operations‘
attributes and their sustainable practices through statistical testing. The majority of the data collected in the survey are categorical, of which most are nominal and a minority ordinal; hence non-parametric statistical methods have formed the main part of the analysis in this section.
4.4.1 Sustainable Practices of Different Stakeholders
Starting with an analysis of the top solution selected by the respondents – diesel fuel taxation on road transport (‗fuel taxation’ in the following text), this section summarises the Pearson‘s chi-square test results for each of the solutions by logistics role that the respondents are playing in the industry (i.e. LSPs or LSUs).
Fuel taxation stood out as the sustainable solution with the greatest impact on the respondent companies‘ logistics operations. This is a result from both number of times it was selected as one of the top five influential measures, and the relatively high ranking it was assigned by the respondents. According to the result of weighted impact score shown in Table 4.5, fuel taxation consistently achieved the top rank within each group, and the variation in preference was unapparent. However, the difference was more evident in terms of frequency when the measure topped the rank in the LSPs group but only came fifth in the LSUs group (see Table 4.4). In order to further examine the significance of this variation, Table 4.6 was compiled to give an overview of the distribution of responses within each group.
Table 4.6: Summary of ranking responses on impact of diesel fuel taxation by group
Source: author’s questionnaire survey, stage 1; total number of respondents N=115 Of the LSP respondents, 70% identified fuel taxation as one of the top five solutions in terms of impact on business, while nearly half of the LSUs did so. Of those who selected the solution, over three quarters of the LSPs gave it the first or second priority, and two thirds of the LSUs did the same. By combining the lower three ranks (i.e. 3, 4 and 5) into one category, a chi-square test was then conducted, and the contingency table with observed and expected cell values, along with the test results, is shown in Table 4.7.
Fuel Taxation 1 2 3 4 5 Total Weighted Impact
LSPs (n=60) 24 8 3 3 4 42 171
LSUs (n=55) 6 12 3 3 3 27 96
Combined 31 22 9 10 12 69 267
Table 4.7: SPSS Chi-square test results for diesel fuel taxation by group Part (a): Diesel fuel taxation cross-tabulation by group
* 3 is the category combining the respondents who assigned the lower three ranks (i.e. 3, 4 or 5) to fuel taxation.
** 4 is the category for unselected options, where the respondents did not select fuel taxation as one of the five solutions with most impact (hence no ranking).
Part (b):Chi-square test for rankings by LSP and LSU groups on diesel fuel taxation
Value Df
Asymp. Sig.
(2-sided)
Pearson Chi-Square 13.635(a) 3 .003
(0 cells (.0%) have expected count less than 5. The minimum expected count is 9.09.)
Source: author’s questionnaire survey, stage 1; total number of respondents N=115 The p-value gained from the two-sided Pearson‘s chi-square test is 0.003, indicating that the probability of having the observed difference by chance is less than 0.3%.
There is, therefore, a statistically highly significant difference between the LSPs and LSUs groups in their perceptions of fuel taxation. The major factor that contributed to this difference lies in the extent to which fuel taxation impacts on the logistics operation. For LSPs, most votes on this solution were given the first rank, indicating that the service providers are most concerned and affected by the implementation of, or changes in, diesel fuel tax. While it is still perceived as a major concern for LSUs, it is so only to a lesser extent. As service users are generally involved in logistics
Ranking N=115 Degree of Freedom* Test Statistics Value p-Value
operations in a less direct manner, particularly in the case of those outsourcing their logistics functions or simply hiring services on demand, the cost implication of fuel taxation may not be as visible and critical as it is for the logistics operators.
Applying the same analysis to the remaining sustainable solutions individually, the test results are collectively summarised into Table 4.8. (Note: Using the same re-coding methods, the grouping of different ranks varies for various solutions according to the specific distribution of the assigned priorities.)
Table 4.8: Summary of Chi-square test results for rankings on sustainable solutions by group
* Degree of freedom is 2 or 3 depending on the grouping of different ranks.
Source: author’s questionnaire survey, stage 1; total number of respondents N=115 As highlighted in the table, the LSPs and LSUs groups have shown significant differences in their perceived importance of four sustainable solutions. Diesel fuel taxation and reverse logistics are significant at the 1% level; while driver training and supply chain optimisation are significant at the 5% level. The bottom three sustainable solutions have not been tested by this approach due to the small number of responses received within each category. The results obtained would be invalid, as there would be a large proportion of cells with an expected value of less than 5. For these three cases it is better to examine any variation by simply comparing the data.
As analysed before, the perceived difference in the fuel taxation solution can be interpreted by the various core businesses conducted by LSPs and LSUs. And probably for the same reason, reverse logistics has shown considerably contrasting patterns between the two groups (see Table 4.9). Nearly half of the LSUs regarded reverse logistics as of great impact on their logistics operations, even though most of them gave it relatively lower ranks among their top five. On the contrary, only 17%
of LSPs selected it, and the ranks are distributed quite evenly. This may largely be a result of the very nature of the businesses concerned: whereas LSUs are committed to and managing reverse logistics as a sometimes obligatory and integrated part of the business, for LSPs it is a business segment which generates revenues and has a positive effect on their balance sheets. Moreover, as is the case for some LSUs, the importance of reverse logistics is often stipulated by regulations and attached to the return flow of the products, where the complexity and cost-effectiveness of this operation disproportionately affects bottom-line performance; on the other hand, when it comes to specialised LSPs, efficiently managing the flow of goods in both directions and eliminating waste are more a part of normal practice and do not constitute so much of a pressing issue of concern.
Table 4.9: Summary of ranking responses on impact of reverse logistics by group
Source: author’s questionnaire survey, stage 1; total number of respondents N=115 Likewise, driver training and supply chain optimisation have received different levels of attention from LSPs and LSUs, mainly due to the nature of their core business. In general, driver training is deemed to be of greater influence on logistics operation by LSPs, while supply chain optimisation is so highly valued by LSUs that it tops the rank, both in absolute and weighted frequencies. Based on the contrasting
Reverse Logistics 1 2 3 4 5 Total Weighted Impact
LSPs (n=60) 2 2 2 1 3 10 29
LSUs (n=55) 1 3 6 4 11 25 54
Combined 4 7 11 9 19 35 83
results from the LSPs and LSUs groups, it seems that LSUs are taking the more proactive and leading role in supply chain optimisation, implying a recognition of the need for collaborating and interacting with other parties within the logistics network, and a willingness to do so. Surprisingly, this is not so much the case for LSPs, whose overall rank for supply chain optimisation was only 7th, suggesting that they attached less importance to interaction with other parties along the supply chain. That said, how strong and active the interactions are between LSPs and LSUs, and the nature of their influence on each other with regard to sustainable practice, were both still quite vague at this stage; the issue will be further explored in more detail in Section 4.5.4.
For the remainder of the seven solutions, the results from chi-square test do not show sufficient evidence to reject the initial hypotheses – in other words, no significant difference can be detected between the LSPs and LSUs groups. This implies that these sustainable measures would have similar impacts on the businesses of both LSPs and LSUs, and therefore do not require adaptation to the parties involved.
Since the chi-square test is not suitable for the three sustainable categories with lowest ranks, the distributions of frequencies have been listed in Table 4.10 by group for comparison. There is no evident distinction between the two groups for the categories product/packaging design and inclusion of transport in the EU ETS.
However, the urban logistics category does signal an unequivocal distinction between them. Out of all respondent LSPs, only one identified urban logistics as an influential solution, and even then this was with the lowest rank; while the percentage is considerably higher among LSUs, with priorities spread more or less evenly across the five ranks. However, it is worth noting that the lowest rank of urban logistics among the 14 solutions might be a result of the vagueness of the term used in the survey. As reviewed in Section 2.5.3.13, ‗urban logistics‘ covers a comprehensive range of sustainable policies and practices, which might have led to confusion or
misinterpretation by the respondents. Given this consideration, the survey results regarding urban logistics have not been read too much into the final conclusion in this research.
Table 4.10: Summary of responses on the three lowest-ranked measures by group
Source: author’s questionnaire survey, stage 1; total number of respondents N=115
4.4.2 Sustainable Practices Related to Other Operational Characteristics
The same statistical testing method has been employed to find out whether the attributes of logistics operations, such as geographic scope and fleet size, have significant impacts on their sustainable practice. At aggregate level, none of the 14 sustainable solutions turned out to be evaluated differently by the respondents with different geographic scopes or operational scales. The attempt to explore the interrelationship within each logistics group produces the same result for LSUs, while the LSPs group shows some minor divisions for three solutions. Supply chain optimisation and alternative fuels are significant at the 5% and 10% level respectively by geographic scope, and reverse logistics is significant at the 5% level by fleet size. The patterns observed indicate that logistics operators operating at local or national level place a higher priority on supply chain optimisation; while the international operators seem to pay more attention to alternative fuels. As far as fleet size is concerned, larger operators tend to evaluate reverse logistics as of more influence than is the case with their smaller competitors.
1 2 3 4 5 Total Weighted Impact
Based on the survey data and the testing results, the observed discrepancies are not significant enough compared with the overall consistent patterns which appeared in operators with different sizes and geographic scopes, not to mention the fact that the credibility of the test is impaired by the small sample size when the data set was broken into LSPs and LSUs groups. For the reasons given above, it appears that the companies‘ attributes such as operational scale and geographic scope are not critical factors that significantly influence their strategy and practice in sustainable logistics.