In this section, the Spearman’s correlation was used to investigate whether there were correlations between the factor scores and the RPA. The results shown in Table 9.9 reveals that there was one factor only in each model that was statistically significantly correlated with RPA namely factor 3 in EB, factor 4 in NEB and factor 5 in PE model as pointed in bold number with a star symbol explaining its significance level. The coefficients of the factor that was significantly correlated in these analyses are higher than in those presented earlier in Section 9.4, however, the correlations are not strong enough (lower than absolute 0.50). The improvement of the coefficient correlation suggests that the correlation existed but other factors may have a stronger influence on the relationship. In order to assess whether the socio-demographic cluster influenced the relationship between the factor of attitude/opinion and feeling and the perception of travel time, further analysis was carried out and presented in Section 9.4.2.
Table 9.9 Spearman’s correlations between RPA and factors for EB, NEB and PE.
Factor Label Main Activity
EB NEB PE
Factor was represented by component with highest factor loading
Factor 1 Personal feeling -0.13 0.08 -0.02 Factor 2 Multitasking ability -0.05 0.00 0.11 Factor 3 Technology effect 0.20 ** 0.04 0.20 Factor 4 Train comfort potential -0.07 -0.10 -0.06
Factor 5 Productivity -0.07 0.05 0.34 *
Factor 6 Journey duration 0.13 0.10 0.20 Factor was represented by sum of components score
Factor 1 Personal feeling 0.04 0.03 -0.08 Factor 2 Multitasking ability -0.03 -0.01 0.17 Factor 3 Technology effect -0.27 * 0.05 0.08
Factor 4 Train comfort potential 0.02 -0.15 ** -0.07
Factor 5 Productivity -0.05 0.04 0.32 **
Factor 6 Journey duration 0.13 0.10 0.20
Factor was represented by factor score
Factor 1 Personal feeling -0.06 0.05 -0.12 Factor 2 Multitasking ability -0.05 -0.03 0.16 Factor 3 Technology effect 0.29 * 0.04 0.09 Factor 4 Train comfort potential -0.02 -0.16 ** -0.16
Factor 5 Productivity -0.08 0.10 0.34 *
Factor 6 Journey duration 0.05 0.03 0.18
* Statistically significant data at the 95% level of confidence ** Statistically significant data at the 90% level of confidence
9.4.2 Relationship between attitude and perception of travel time by clusters
In order to investigate whether the factors and the RPA by groups of respondents were significantly correlated, a Spearman’s correlation test was conducted for each of the clusters discussed in Chapter 8. The evaluation test results shown in Table 9.10 revealed that the correlations were not statistically significance between the factors and RPA for all clusters except those which were written in bold numbers and given a star symbol to explain their level of significance. However, similar to the results of analyses presented earlier in Section 9.4.1, none of these correlations were strong (>0.50).
Table 9.10 Spearman’s correlations between RPA and factors for clustered data
Factor Label Cluster
1 2 3 4
Factor was represented by component with highest factor loading
Factor 1 Personal feeling 0.06 -0.17 0.25 -0.02 Factor 2 Multitasking ability 0.00 0.07 -0.21 0.12 Factor 3 Technology effect -0.01 0.18 0.19 0.10 Factor 4 Train comfort potential -0.03 -0.04 -0.05 -0.20
Factor 5 Productivity 0.07 0.07 0.09 -0.01
Factor 6 Journey duration 0.07 0.20 ** -0.05 0.10 Factor was represented by sum of components score
Factor 1 Personal feeling 0.06 -0.17 0.20 -0.07 Factor 2 Multitasking ability -0.06 0.07 -0.16 0.11 Factor 3 Technology effect 0.05 0.15 0.18 0.11 Factor 4 Train comfort potential -0.23 * -0.03 -0.05 -0.15
Factor 5 Productivity 0.04 0.07 0.14 0.13
Factor 6 Journey duration 0.07 0.20 ** -0.05 0.10 Factor was represented by factor score
Factor 1 Personal feeling 0.07 -0.22 * 0.26 ** -0.09 Factor 2 Multitasking ability -0.09 0.08 -0.19 0.08 Factor 3 Technology effect 0.04 0.15 0.20 0.08 Factor 4 Train comfort potential -0.19 -0.09 -0.11 -0.19
Factor 5 Productivity 0.04 0.03 0.15 0.14
Factor 6 Journey duration 0.07 0.13 -0.06 0.00 * Statistically significant data at the 95% level of confidence
** Statistically significant data at the 90% level of confidence
9.5 Summary
An analysis of the relationship between the attitude to train services and the perception of time was reported in this chapter. In the regression, the 6 factors resulted from factor analysis and the ratio of the perceived and actual travel time (RPA) were used as variables.
Before conducting the regression, a descriptive analysis was conducted to examine the data distribution. Most of the respondents either strongly agreed or strongly disagreed with the statements presented to them. The data showed that most of respondents agreed that they have an opportunity to work on train. Respondents also agreed that journey time seemed to pass more quickly when they are using electronic devices. However, more than half of respondents were not prepared to pay the additional cost for the Wi-Fi
services on board. This finding suggests that the Wi-Fi service provided on the train was not a major influence on passengers, either because they have their own connection to the Internet on a smart phone or a mobile broadband device.
Most of respondents did not exhibit negative feelings about train services. Furthermore, most of respondents suggested that they would encourage people to use train services and felt comfortable in a public environment surrounded by unfamiliar people.
Aggregation of variables using factor analysis extracted 6 factors which were labelled personal feeling, multitasking ability, technology effect, train comfort potential, productivity, and journey duration. The regression analysis of the factors solutions revealed that most of the factors were found not to be correlated with the perception of time. When all data of the respondents were used, despite not being so strong, the factor of the effect of technology was positively correlated with the ratio of the perceived and actual travel time. This infers that the use of technology increases the perception of time.
As discussed earlier in Chapter 7, the perception of time was higher when using technology was associated with productivity. When productivity was high, the perceived travel time was high. On the other hand, the factor related to train comfort potential was negatively correlated with the ratio of the perceived and actual travel time. The negative correlation inferred that when the comfort of the train was high, the perception of time was lower. Travel time passing more quickly when the train is comfortable has emerged from previous studies including Lyons et al. (2007) and Ettema and Verschuren (2007).
When the respondents were disaggregated by clusters and by main activity groups, the correlation between the majority of factor scores and the RPA were not significant for all clusters and activity groups. However as expected significant correlations between particular factors with some selected clusters and/or main activity groups were clearly evident, for example, the effect of technology on EB, train comfort in NEB, and productivity in PE. It should be noted that a lack of statistical significant at the cluster level of analysis is due in part to the lower sample size in this disaggregated level.